ALBERT

Description: Seismic facies analysis can generate a map to describe the spatial distribution characteristics of reservoirs, and therefore plays a critical role in seismic interpretation. To analyse the characteristics of the horizon of interest, it is usually necessary to extract seismic waveforms along the target horizon using a selected time window. The inaccuracy of horizon interpretation often produces some inconsistent phases and leads to inaccurate classification. Therefore, the developed adaptive phase K-means algorithm proposed a sliding time window to extract seismic waveforms. However, setting the maximum offset of the sliding window is difficult in a real data application. A value that is too large may cause the cross-layer problem, whereas a value that is too small reduces the flexibility of the algorithm. To address this disadvantage, this paper proposes a robust K-means (R-K-means) algorithm with a Gaussian-weighted sliding window for seismic waveform classification. The used weights punish those windows distant from the interpretation horizon in the objective function, consequently producing a smaller range of horizon adjustments even when using relatively large maximum offsets and benefitting the generation of stable and reliable seismic facies maps. The application of real seismic data from the F3 block proves the effectiveness of the proposed algorithm.

Description: Motivation Accurate automatic annotation of protein function relies on both innovative models and robust data sets. Due to their importance in biological processes, the identification of DNA-binding proteins directly from protein sequence has been the focus of many studies. However, the data sets used to train and evaluate these methods have suffered from substantial flaws. We describe some of the weaknesses of the data sets used in previous DNA-binding protein literature and provide several new data sets addressing these problems. We suggest new evaluative benchmark tasks that more realistically assess real-world performance for protein annotation models. We propose a simple new model for the prediction of DNA-binding proteins and compare its performance on the improved data sets to two previously published models. Additionally, we provide extensive tests showing how the best models predict across taxonomies. Results Our new gradient boosting model, which uses features derived from a published protein language model, outperforms the earlier models. Perhaps surprisingly, so does a baseline nearest neighbor model using BLAST percent identity. We evaluate the sensitivity of these models to perturbations of DNA-binding regions and control regions of protein sequences. The successful data-driven models learn to focus on DNA-binding regions. When predicting across taxonomies, the best models are highly accurate across species in the same kingdom and can provide some information when predicting across kingdoms. Code and Data Availability The data and results for this paper can be found at https://doi.org/10.5281/zenodo.5153906. The code for this paper can be found at https://doi.org/10.5281/zenodo.5153683. The code, data and results can also be found at https://github.com/AZaitzeff/tools_for_dna_binding_proteins.

Description: As the robustness for the wave equation-based inversion methods, wave equation migration velocity analysis (WEMVA) is stable for overcoming the multipathing problem and has become popular in recent years. As a rapidly developed method, differential semblance optimisation (DSO) is convenient to implement and can automatically detect the moveout existing in common image gathers (CIGs). However, by implementing in the image domain with the target of minimising moveouts and improving coherence of the CIGs, the DSO method often suffers from imaging artefacts caused by uneven illumination and irregular observation geometry, which may produce poor velocity updates with artefact contamination. To deal with this issue, in this paper, by introducing Wiener-like filters, we modify the conventional image matching-based objective function to a new one by introducing the quadratic Wasserstein metric technique. The new misfit function measures the distance of two distributions obtained by the convolutional filters and target functions. With the new misfit function, the adjoint sources and the corresponding gradients are improved. We apply the new method to two numerical examples and one field dataset. The corresponding results indicate that the new method is robust to compensate low frequency components of velocity models.

Description: Atlantic salmon (Salmo salar) populations throughout the North Atlantic have declined in recent decades largely due to reduced marine survival, yet our understanding of marine distribution patterns and migratory routes remains limited. Here, we assigned archived individual samples (n = 3891) collected over a half century (1968–2018) throughout the North Atlantic to region of origin using range-wide genetic assignment. In the Northwest Atlantic, the distribution of assignments reinforced the importance of the Labrador Sea as an aggregation area, with 73% of all reporting groups detected. Moreover, individuals from six European reporting groups were identified in the Northwest Atlantic, and detections decreased with decreasing latitude spanning an area from Greenland to southern Newfoundland. In the Northeast Atlantic, six North American reporting groups were detected in samples from around the Faroe Islands. Based on the distribution of samples, estimates of trans-Atlantic migration distance averaged 3861 and 2889 km for North American and European salmon respectively. Our analysis highlights the widespread importance of the Labrador Sea and Faroe Islands to the species marine distribution patterns, and the prevalence of long-distance trans-Atlantic migration. Ultimately, the results suggest that environmental conditions experienced by many Atlantic salmon populations span much of the North Atlantic Ocean.

Description: Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computational solutions in relation to established techniques. To this end, we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high-throughput screening studies, comprising 64 200 unique combinations of 4153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular representations and quantify their similarity by adapting the Centered Kernel Alignment metric. Our work demonstrates that to identify an optimal molecular representation type, it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects.

Description: Motivation The investigation of quantitative trait loci (QTL) is an essential component in our understanding of how organisms vary phenotypically. However, many important crop species are polyploid (carrying more than two copies of each chromosome), requiring specialized tools for such analyses. Moreover, deciphering meiotic processes at higher ploidy levels is not straightforward, but is necessary to understand the reproductive dynamics of these species, or uncover potential barriers to their genetic improvement. Results Here, we present polyqtlR, a novel software tool to facilitate such analyses in (auto)polyploid crops. It performs QTL interval mapping in F1 populations of outcrossing polyploids of any ploidy level using identity-by-descent probabilities. The allelic composition of discovered QTL can be explored, enabling favourable alleles to be identified and tracked in the population. Visualization tools within the package facilitate this process, and options to include genetic co-factors and experimental factors are included. Detailed information on polyploid meiosis including prediction of multivalent pairing structures, detection of preferential chromosomal pairing and location of double reduction events can be performed. Availabilityand implementation polyqtlR is freely available from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/package=polyqtlR. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Circular RNAs (circRNAs) are widely expressed in highly diverged eukaryotes. Although circRNAs have been known for many years, their function remains unclear. Interaction with RNA-binding protein (RBP) to influence post-transcriptional regulation is considered to be an important pathway for circRNA function, such as acting as an oncogenic RBP sponge to inhibit cancer. In this study, we design a deep learning framework, CRPBsites, to predict the binding sites of RBPs on circRNAs. In this model, the sequences of variable-length binding sites are transformed into embedding vectors by word2vec model. Bidirectional LSTM is used to encode the embedding vectors of binding sites, and then they are fed into another LSTM decoder for decoding and classification tasks. To train and test the model, we construct four datasets that contain sequences of variable-length binding sites on circRNAs, and each set corresponds to an RBP, which is overexpressed in bladder cancer tissues. Experimental results on four datasets and comparison with other existing models show that CRPBsites has superior performance. Afterwards, we found that there were highly similar binding motifs in the four binding site datasets. Finally, we applied well-trained CRPBsites to identify the binding sites of IGF2BP1 on circCDYL, and the results proved the effectiveness of this method. In conclusion, CRPBsites is an effective prediction model for circRNA-RBP interaction site identification. We hope that CRPBsites can provide valuable guidance for experimental studies on the influence of circRNA on post-transcriptional regulation.

Description: Intratumoral heterogeneity is a well-documented feature of human cancers and is associated with outcome and treatment resistance. However, a heterogeneous tumor transcriptome contributes an unknown level of variability to analyses of differentially expressed genes (DEGs) that may contribute to phenotypes of interest, including treatment response. Although current clinical practice and the vast majority of research studies use a single sample from each patient, decreasing costs of sequencing technologies and computing power have made repeated-measures analyses increasingly economical. Repeatedly sampling the same tumor increases the statistical power of DEG analysis, which is indispensable toward downstream analysis and also increases one’s understanding of within-tumor variance, which may affect conclusions. Here, we compared five different methods for analyzing gene expression profiles derived from repeated sampling of human prostate tumors in two separate cohorts of patients. We also benchmarked the sensitivity of generalized linear models to linear mixed models for identifying DEGs contributing to relevant prostate cancer pathways based on a ground-truth model.

Description: Efforts to elucidate protein–DNA interactions at the molecular level rely in part on accurate predictions of DNA-binding residues in protein sequences. While there are over a dozen computational predictors of the DNA-binding residues, they are DNA-type agnostic and significantly cross-predict residues that interact with other ligands as DNA binding. We leverage a custom-designed machine learning architecture to introduce DNAgenie, first-of-its-kind predictor of residues that interact with A-DNA, B-DNA and single-stranded DNA. DNAgenie uses a comprehensive physiochemical profile extracted from an input protein sequence and implements a two-step refinement process to provide accurate predictions and to minimize the cross-predictions. Comparative tests on an independent test dataset demonstrate that DNAgenie outperforms the current methods that we adapt to predict residue-level interactions with the three DNA types. Further analysis finds that the use of the second (refinement) step leads to a substantial reduction in the cross predictions. Empirical tests show that DNAgenie’s outputs that are converted to coarse-grained protein-level predictions compare favorably against recent tools that predict which DNA-binding proteins interact with double-stranded versus single-stranded DNAs. Moreover, predictions from the sequences of the whole human proteome reveal that the results produced by DNAgenie substantially overlap with the known DNA-binding proteins while also including promising leads for several hundred previously unknown putative DNA binders. These results suggest that DNAgenie is a valuable tool for the sequence-based characterization of protein functions. The DNAgenie’s webserver is available at http://biomine.cs.vcu.edu/servers/DNAgenie/.

Description: Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

Description: Heterotrophic protists are essential components of the marine ecosystem, yet they are often excluded from monitoring programmes. With limited resources, monitoring strategies need to be optimised considering both scientific knowledge and available resources. In doing so, it is crucial to understand how sampling frequency affects the value of the data. We analysed 11 years of weekly heterotrophic protist time-series data from Station L4 in the Western English Channel to explore how different sampling intervals impact data quality. In the L4 dataset, comprising 55 protist taxa, the reduction of sampling frequency from weekly to four times a year at specific seasons decreased the number of taxa encountered by 38% for ciliates and 29% for heterotrophic dinoflagellates while the mean annual biomass or its mean variation were not affected. Furthermore, when samples were taken only four times a year, biomass peaks of the ten most important taxa were often missed. The primary motivator for this study was furthering the development of the heterotrophic protist monitoring in temperate and subarctic marine areas, e.g. the Baltic Sea. Based on our findings, we give recommendations on sampling frequency to optimise the value of heterotrophic protist monitoring.

Description: The Lafaye orbicular body was emplaced in the Villatange tonalite-granodiorite unit of the Guéret magmatic complex (Massif Central, France). It consists of plagioclasic orbicules (4–35 cm diameter) embedded in homogeneous cordierite granodiorite. Orbicule cores consist mostly of residual metasedimentary xenoliths or autolithic plagioclasic cumulates. Rims (0.7–8 cm thickness) are single- or multi-layered; layers, mostly comb-textured, comprise alternating sheets dominated by cordierite (XFe = 0.32–0.37) or plagioclase (mostly An25–30). Additional mineral phases are minor biotite (XFe = 0.52; AlVI = 0.58–0.92 atoms per formula unit) and interstitial quartz. Plagioclase and cordierite morphologies (needle-like, skeletal, branching or fan-shaped) indicate growth under high initial supersaturation. However, the final polyhedral shapes and primary zoning of many individual plagioclase crystals, as well as evidence of partial recrystallization, imply significant textural maturation. Whole-rock major and trace element data (A/CNK = 1.12–1.46) and Sr and Nd isotopic compositions (εNd(355 Ma)  = −8.6 to −7.4; 87Sr/86Sr(355 Ma) = 0.7110–0.7147) suggest that the parental magma of the orbicules resulted from bulk assimilation of aluminous metasediments by a Villatange-type granodioritic magma. Heterogeneous nucleation and growth of plagioclase and cordierite around xenoliths/autoliths are interpreted in terms of (1) adiabatic decompression of magma pulses ascending in dykes leading to superheating and resorption of early solids, and (2) volatile exsolution, inducing undercooling, supersaturation, and rim crystallization. The variability of layers (number, thickness, mineral distribution, and texture) is considered to result from oscillatory crystallization combined with variable plagioclase growth rates linked to changes in the degree of supersaturation as a function of the extent of melt degassing, itself linked to magma transfer dynamics.

Description: La Réunion Island includes two major volcanic systems. About 0·5 Myr ago, Piton des Neiges volcano declined, while Piton de la Fournaise volcano grew on its flank. Since then the Piton de la Fournaise shield volcano has produced homogeneous lavas with chemical compositions transitional between alkali and tholeiitic basalts. In April 2007, the volcano emitted a very small volume of trachytic pumice during its largest historical eruption. We conducted a comprehensive petrological and geochemical study of the pumice to understand the occurrence of such silicic melt in the feeding system of this highly active basaltic volcano. Isotopes of Sr, Nd, Pb and O, together with trace elements, indicate that the trachyte is genetically related to the La Réunion mantle plume and derives from crystallization of a typical basalt. The trachyte chemistry records a long and complex history of differentiation and outgassing. The extensive depletion of moderately volatile elements (F, Cl, B, Cs, Cu, Li) and less volatile uranium is consistent with exsolution of dense fluids at depths of several kilometres. Lithium isotopes point to closed-system degassing during the very late stages of crystallization. U-series isotopes and radiogenic 208Pb*/206Pb* constrain the age of U loss to between 0·4 and 2·1 Ma. This age is as old as or older than the Piton de la Fournaise shield edifice. The 2007 trachyte could thus be a liquid remnant of an extinct volcano, such as Piton des Neiges or Les Alizés (Piton de la Fournaise proto-volcano). It could also result from partial melting of an old syenite intrusion or remobilization of interstitial melts not fully solidified. Thermal modelling indicates that the sustained heat flux from hot basaltic magmas rising from the mantle can maintain temperatures above 800 °C in the central feeding system, and prevent total solidification of magmas trapped in this hot core.

Description: Motivation The investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geometry can lead to more adequate predictions of their interactions. In the present work, we assess the performance of reference shape retrieval methods from the computer vision community on protein shapes. Results Shape retrieval methods are efficient in identifying orthologous proteins and tracking large conformational changes. This work illustrates the interest for the protein surface shape as a higher-level representation of the protein structure that (i) abstracts the underlying protein sequence, structure or fold, (ii) allows the use of shape retrieval methods to screen large databases of protein structures to identify surficial homologs and possible interacting partners and (iii) opens an extension of the protein structure–function paradigm toward a protein structure-surface(s)-function paradigm. Availabilityand implementation All data are available online at http://datasetmachat.drugdesign.fr. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Fisheries management is usually supported by technical and financial measurements (i.e. logbooks and market data), which are helpful for ecological or economic assessments. Yet this information is not able to address social heterogeneity and fisher motivations, which are key to understanding fisher behaviour. This case study of the demersal segment in the Netherlands shows that combining quantitative analysis of logbooks with qualitative data collected by engaging with fishers can capture both fishing activity and its motivations, generating a more social understanding of fisher behaviour. A métier analysis of logbook data describes five dominant fishing practices among the selected segment. Twenty-five in-depth interviews with fishers along with focus groups including other experts identify three social factors that influence fisher behaviour in the Dutch demersal fleet: business structure, working rhythm, and polyvalence. The results show that motivations for fisher behaviour are more complex than complying with regulations or seeking profit: social factors also influence fishing activity. Furthermore, these social factors have real implications for the impacts of management measures on both the fishing communities and the environment, especially in times of change. These results are useful for management strategy development or evaluation because they are feasibly observable through existing data collection protocols.

Description: Harbour seals are surveyed aerially when they haul-out to moult in August. The proportion of the population hauled out throughout the year is related to temporal, environmental, and meteorological variables. Thus, monitoring is conducted under predefined ranges of conditions. Effects of variation within these ranges are rarely reviewed. We used linear models to assess effects of time, date and weather on the difference between counts predicted by a population growth model and observed counts, based on a 30-year time-series. Our top-ranked model explained 34.4% of the variance. Survey date and its interaction with survey year were the most important variables, with higher counts earlier in August, particularly early in the time series, where surveys may not have been timed optimally to capture the peak in the moult. Cloud cover, wind speed, temperature, and interactions between these were of lesser importance; there were fewer seals on land during cloudy, windy days and on clear, warm days. These effects of weather are likely related to temperature regulation. Power analyses suggested that correction for survey conditions would allow detection of a one percentage point annual change in population growth rate with 80% power 4 years sooner than without taking survey conditions into account.

Description: Sargassum forests play an important role in coastal waters as habitats for marine organisms, including commercial species. However, human activities have negatively affected their distribution causing a worldwide decline of Sargassum forests. Mapping and monitoring the distribution and biomass of these habitats using acoustic remote sensing techniques is key for their conservation. Nonetheless, most researches based on acoustic remote sensing methods focus on estimations of macrophyte area and its canopy height, and less researches reporting 3D visualization of these habitats. This study demonstrates the use of high-resolution multibeam echosounder (MBES) bathymetric data to visualize the 3D structure of Sargassum forests. Comparing acoustic data and underwater camera photos collected in field surveys, we identified Sargassum individuals as vertical clusters of contiguous sounding points with a base close to the sea bottom in the sounding data of the MBES. Using this criterion, we could distinguish Sargassum echoes, visualize the 3D structure of Sargassum forests and estimate the number of Sargassum individuals in the survey area. Using the relation between thallus length and dry weight of sampled Sargassum plants, standing stock and biomass could be estimated assuming the thallus length was the height of Sargassum plants identified with the MBES.

Description: This paper reviews application of the Daily Egg Production Method (DEPM) to sardine (Sardinops sagax) off southern Australia between 1995 and 2019. Coefficients of variation (CVs) of estimates of spawning biomass (SB) were reduced from 23–59% to 8–12% by: (i) estimating mean daily egg production (P0), spawning fraction (S), and sex ratio (R) from all historical data rather than annually; and (ii) combining batch fecundity (F) and female weight (W) into a single parameter, relative fecundity (F′ = F^/W). Total daily egg production was estimated most precisely from annual estimates of spawning area (A) and estimates of P0 obtained from historical data. Both S and R were estimated most precisely from historical data. Estimating W and F from historical data did not increase precision. F′ had lower CVs than both W and F, and was stable across years and a wide range of W. Findings demonstrate that A can be converted into a precise estimates of SB using estimates of P0, S, R, and F′ obtained from historical data. However, the possibility that DEPM parameters may change in the future cannot be discounted. Future monitoring should include annual estimation of P0 and periodic (e.g. 3–5 years) re-estimation of adult parameters.

Description: In his 1953 paper, Harald Sverdrup argued that the development of a spring bloom in the ocean depends on the juxtaposition of two depth horizons: the mixed-layer depth and the critical depth. Mixed-layer depth shallower than the critical depth favours phytoplankton growth in the layer and vice versa. However, mathematically, Sverdrup left the problem unsolved in the form of a transcendental equation. In spite of the high number of citations that this paper has garnered, the solution to this equation has not been found, until now. In this work, we present an analytical solution for the critical depth, as originally defined by Sverdrup. The paper opens with the definition of the critical depth and the description of the Lambert W function. The analytical solution for critical depth follows. Sverdrup’s original model is extended to include the effect of light attenuation by phytoplankton and the analytical solution for steady-state biomass in the mixed layer is derived. The expression for mixed-layer production at steady state is also presented. Two novel variants of the critical depth are defined: the optically uncoupled critical depth and the optically coupled critical depth. It is demonstrated that at steady state the optically coupled critical depth equals the mixed-layer depth and that the irradiance at the base of the mixed layer equals the irradiance at the optically uncoupled critical depth. Competitive exclusion is demonstrated to hold and the optically uncoupled critical depth is linked to the critical light intensity in multi-species competition. Finally, a conservation principle for the critical depth is found.

Description: Fishery bycatch poses a serious threat to seabird populations globally. Traditional haul-only post-capture observations are inadequate and inefficient to document seabird bycatch due to the substantial bycatch loss known to occur. Pre-capture observations offer an alternative by documenting seabird interactions leading up to captures. Based on the long-term large-scale dedicated field observations, this study revealed significant risk factors for the pre-capture stages of the seabird bycatch process in pelagic longline fisheries using Bayesian methods. Rough sea conditions were found to correlate with more seabirds following fishing vessels. Species identity, density effect, inter-species effect, and sea condition were found to significantly affect how frequently seabirds aggregated around a fishing vessel engage in bait-taking interactions. Intra-species competition was found to be the dominant type of density effect. Moreover, a web of inter-species interactions was identified to facilitate the bait-taking of superior competitors at the expense of inferior ones. The findings of this study are relevant to fishery managers in updating current data collection protocols to alleviate data issues caused by bycatch loss, to conservation biologists in quantifying bycatch risks for susceptible seabird populations, and in aiding the design and evaluation of bycatch mitigation measures.

Description: Fishing and translocation of marine species for use in aquaculture is widespread. Corkwing, goldsinny, and ballan wrasse (Symphodus melops, Ctenolabrus rupestris, and Labrus bergylta) are fished on the Swedish west coast for use as cleaner-fish in Norwegian salmon farms. Here, we aim to provide knowledge and recommendations to support ecosystem-based management for wrasse fisheries in Sweden. We compared fished and non-fished areas to test if current fishery levels have led to stock depletion. To gain insight on the role of wrasse in the algal belt trophic chain, we analysed the gut contents of goldsinny and corkwing using metabarcoding. Finally, we analysed the trophic interactions of wrasse and potential prey in a mesocosm study. We could not detect any signs of stock depletion or altered size structure in fished areas compared to the protected control area. Gut analyses confirmed both goldsinny and corkwing as non-specialized, omnivorous opportunists and revealed, with 189 prey taxa detected, a broader spectrum of prey than previously known. Common prey items included mesoherbivores such as small gastropods and crustaceans, but also insects and algae. We conclude that there are no visible signs of stock depletion at the current removal level of wrasses by the fishery. However, this emerging fishery should be closely monitored for potential cascading effects on the algal belt ecosystem, and our study could provide a baseline for future monitoring.

Description: Over the past decade, genome-wide assays for chromatin interactions in single cells have enabled the study of individual nuclei at unprecedented resolution and throughput. Current chromosome conformation capture techniques survey contacts for up to tens of thousands of individual cells, improving our understanding of genome function in 3D. However, these methods recover a small fraction of all contacts in single cells, requiring specialised processing of sparse interactome data. In this review, we highlight recent advances in methods for the interpretation of single-cell genomic contacts. After discussing the strengths and limitations of these methods, we outline frontiers for future development in this rapidly moving field.

Description: Good knowledge of a peptide’s tertiary structure is important for understanding its function and its interactions with its biological targets. APPTEST is a novel computational protocol that employs a neural network architecture and simulated annealing methods for the prediction of peptide tertiary structure from the primary sequence. APPTEST works for both linear and cyclic peptides of 5–40 natural amino acids. APPTEST is computationally efficient, returning predicted structures within a number of minutes. APPTEST performance was evaluated on a set of 356 test peptides; the best structure predicted for each peptide deviated by an average of 1.9Å from its experimentally determined backbone conformation, and a native or near-native structure was predicted for 97% of the target sequences. A comparison of APPTEST performance with PEP-FOLD, PEPstrMOD and PepLook across benchmark datasets of short, long and cyclic peptides shows that on average APPTEST produces structures more native than the existing methods in all three categories. This innovative, cutting-edge peptide structure prediction method is available as an online web server at https://research.timmons.eu/apptest, facilitating in silico study and design of peptides by the wider research community.

Description: Target strength model inputs including morphometry, material properties, lipid composition, and in situ orientations were measured for sub-Arctic krill (Euphausia pacifica, Thysanoessa spinifera, T. inermis, and T. raschii) in the eastern Bering Sea (EBS, 2016) and Gulf of Alaska (GOA, 2017). Inter-species and -regional animal lengths were significantly different (F1,680 = 114.10, p 

Description: Atlantic cod (Gadus morhua) is one of the most commercially important fish species in the North Atlantic. Environmental factors, such as water temperatures, influence growth of individuals over time, thus forming population-specific growth patterns across climatic regions. Here we develop an integrative approach to investigate the role of temperature in shaping geographic differences of cod growth in the Celtic Sea, North Sea, Iceland, and Barents Sea. We combine a physiology-based growth model and 50-years observational temperature data of 0.5 × 0.5° spatial resolution to simulate continuous growth of cod. The model generated weight-at-age data for the period 1959–2007 which we compared to observational data from fishery-independent scientific surveys. In the Celtic and the northern North Sea, simulated growth matches well observational data. We also show that relatively warm temperatures in the Celtic Sea facilitate maximum growth rates; future warming is likely to have a negative impact on growth of these cod stocks. Growth simulations in Icelandic waters and the Barents Sea are less consistent with local observational data. More complex growth patterns in these regions are probably shaped by ontogenetic shifts in temperature regimes, feeding conditions and physiological adaptations. These findings should stimulate further research on critical processes to be considered in population-specific projections of growth of cod and productivity.

Description: In designing and performing surveys of animal abundance, monitoring programs often struggle to determine the sampling intensity and design required to achieve their objectives, and this problem greatly increases in complexity for multispecies surveys with inherent trade-offs among species. To address these issues, we conducted a multispecies stratified random survey design optimization using a spatiotemporal operating model and a genetic algorithm that optimizes both the stratification (defined by depth and longitude) and the minimum optimal allocation of samples across strata subject to prespecified precision limits. Surveys were then simulated under those optimized designs and performance was evaluated by calculating the precision and accuracy of a resulting design-based abundance index. We applied this framework to a multispecies fishery-independent bottom trawl survey in the Gulf of Alaska, USA. Incorporating only spatial variation in the optimization failed to produce population estimates within the prespecified precision constraints, whereas including additional spatiotemporal variation ensured that estimates were both unbiased and within prespecified precision constraints. In general, results were not sensitive to the number of strata in the optimized solutions. This optimization approach provides an objective quantitative framework for designing new, or improving existing, survey designs for many different ecosystems.

Description: Changes in animal movement and behaviour at fine scales (tens of metres) in immediate proximity to tidal stream turbine structures are largely unknown and have implications for risks of animal collision with turbine blades. This study used upward-facing multibeam echosounder data to detect and track animal movement comprising fish, diving seabirds, and marine mammals. Measurements over spring-neap tidal cycles at a turbine structure (no blades present) are compared to a neighbouring reference area with no structure and comparable conditions, with measurements consecutive in time to maximize comparability. The majority of tracked animals (93.4% around turbine structure and 99.1% without turbine structure) were observed swimming against the flow, with 87.5% and 97.8%, respectively, making ground and showing capability of manoeuvring in tidal stream flow speeds. Track tortuosity increased around the turbine structure compared to the reference site, particularly in the wake and at low flow speeds, indicating animal station-holding or milling behaviour. These data also evidence the benefits of multibeam echosounders to measure animal movement through larger measurement volumes rather than relying on single-beam echosounders to measure animal presence alone, including to avoid large biases overestimating the size of schools swimming against the flow measured by time-in-beam.

Description: The Antarctic Peninsula is one of the most rapidly warming regions on earth, and it is likely that the abundance and distribution of marine predators will change as a result.Procellariiform seabirds are highly mobile predators, which target specific habitat characteristics associated with underlying distributions of prey and areas of increased prey availability. We use ship surveys and hurdle models, to estimate the summer distribution and relative density of 11 seabird species within the northern Antarctic Peninsula marine ecosystem. Models differed among species; however, sea surface temperature and depth were frequently associated with seabird occurrence and had the greatest explanatory power across many species. Null models based on observation data were better at predicting seabird density than models that included environmental covariates. This suggests that the main driver of distribution patterns is the broad-scale habitat features, and fine-scale aggregations within these ranges are harder to predict. Our seabird distribution models reflect known habitat associations, species hotspots, and community organization relative to oceanic and coastal marine processes. Application of species distribution models will benefit the assessments of critical habitat and potential responses to climate change and anthropogenic disturbance, which will provide insight into how species may change in polar ecosystems.

Description: Many data-limited fish stocks worldwide require management advice. Simple empirical management procedures have been used to manage data-limited fisheries but do not necessarily ensure compliance with maximum sustainable yield objectives and precautionary principles. Genetic algorithms are efficient optimization procedures for which the objectives are formalized as a fitness function. This optimization can be included when testing management procedures in a management strategy evaluation. This study explored the application of a genetic algorithm to an empirical catch rule and found that this approach could substantially improve the performance of the catch rule. The optimized parameterization and the magnitude of the improvement were dependent on the specific stock, stock status, and definition of the fitness function. The genetic algorithm proved to be an efficient and automated method for tuning the catch rule and removed the need for manual intervention during the optimization process. Therefore, we conclude that the approach could also be applied to other management procedures, case-specific tuning, and even data-rich stocks. Finally, we recommend the phasing out of the current generic ICES “2 over 3” advice rule in favour of case-specific catch rules of the form tested here, although we caution that neither works well for fast-growing stocks.

Description: Ocean grabbing occurs when traditional users, such as small-scale fishers, are pushed aside by new development activities. This grabbing must be prevented to avoid sea uses that maintain or increase social inequity. In this paper, we show that in tropical Atlantic countries, such as Brazil and Senegal, examples of ocean grabbing already occur. In this context, we analyse if Maritime Spatial Planning (MSP) may be an opportunity to limit ocean grabbing or, to the contrary, poses a risk to increase it. MSP calls for an ecosystem approach that requires integrated coastal and marine management and involves stakeholders in developing a shared vision of the future, where society and environment are preserved. However, recent studies have shown that MSP is a process to be used cautiously to ensure equitable decisions. Meanwhile, the concept is spreading worldwide including in tropical Atlantic countries. We highlight that context matters and the specificities of the tropical Atlantic must be taken into account when deploying MSP processes. In the tropical Atlantic context, there is increased imbalances of stakeholder power, traps from decision support tools, and a need for adaptive management. These specific features must be addressed when deploying MSP in a way to avoid ocean grabbing.

Description: The influence of oceanographic variables on assemblages of meso- and bathypelagic fish was investigated along a Northeastern Atlantic Ocean transect (Cape Verde to the Bay of Biscay) during May 2019. Fish were collected using a mrozooplankton trawl during daylight hours at ten stations. Along the transect, 17 hydrographic stations were also performed with a CTD (Conductivity, Temperature, and Depth). A total of 130 fish taxa were identified. The dominant family was Gonostomatidae, with four species (Cyclothone braueri, Cyclothone microdon, Cyclothone pseudopallida, and Cyclothone pallida) being responsible of more than 78% of the total density. The most frequent species that appeared to be ubiquitous were C. braueri and C. pseudopallida, while Myctophidae was the most diverse family. Multivariate analyses revealed two clusters related with the latitudinal gradient. The fish community in the southern stations (25–37°N) was more diverse than in the northern stations (42–48°N). Temperature from 300 to 700 m depth explained 65% of variation in terms of density and 58% in terms of biomass, both statistically significant. The investigated variation in the deep-pelagic ecosystems on a large spatial scale gives essential information to ecosystem management approhes and marine spatial planning.

Description: In the European Union (EU), the ecosystem approach to fisheries is implemented through several directives and polices, which are overarched by the Marine Strategy Framework Directive (MSFD). The MSFD requires the assessment of the environmental status of exploited fish and shellfish stocks, among others, to advise the EU Common Fisheries Policy (CFP) on sustainable catch options. The stock assessments for the CFP and the MSFD are supposed to be congruent, yet they differ in several substantial subjects. While the annual CFP assessment is based on two indicators, fishing mortality (F) and spawning stock biomass (SSB), in its Descriptor 3 (D3) the MSFD requires the assessment of three criteria (F, SSB, and age or size structure) within a six year period. Assessing exploited fish and shellfish stocks under the MSFD by using existing CFP assessments has therefore been a half-completed task, which had to be fulfilled by the member states of the EU. This paper suggests six easy steps, referred to as “quick wins” (QW), which are based on existing information from CFP stock assessments. The implementation of these six QW would allow for assessments of exploited fish and shellfish stocks that are compliant to D3. These QW are to (i) assess length/age structure within a stock, (ii) analyse and assess selectivity by fisheries, (iii) use all available information to assess a stock, (iv) use response indicators to assess environmental targets, (v) provide integrated stock-specific advice, and (vi) provide assessments with a mid-term perspective over a-period of six years. International Council for the Exploration of the Sea (ICES) combines the infrastructure and expertise to produce stock-specific assessment products tailored to the requirements of MSFD D3. Thereby, ICES could provide a valuable service to EU member states in the north-east Atlantic region by providing scientifically validated, quality-assured, and MSFD-compliant single-stock assessment products. This would be a big advancement towards implementing the ecosystem approach to fisheries management within Europe.

Description: Deep generative models have been an upsurge in the deep learning community since they were proposed. These models are designed for generating new synthetic data including images, videos and texts by fitting the data approximate distributions. In the last few years, deep generative models have shown superior performance in drug discovery especially de novo molecular design. In this study, deep generative models are reviewed to witness the recent advances of de novo molecular design for drug discovery. In addition, we divide those models into two categories based on molecular representations in silico. Then these two classical types of models are reported in detail and discussed about both pros and cons. We also indicate the current challenges in deep generative models for de novo molecular design. De novo molecular design automatically is promising but a long road to be explored.

Description: DNA methylation may be regulated by genetic variants within a genomic region, referred to as methylation quantitative trait loci (mQTLs). The changes of methylation levels can further lead to alterations of gene expression, and influence the risk of various complex human diseases. Detecting mQTLs may provide insights into the underlying mechanism of how genotypic variations may influence the disease risk. In this article, we propose a methylation random field (MRF) method to detect mQTLs by testing the association between the methylation level of a CpG site and a set of genetic variants within a genomic region. The proposed MRF has two major advantages over existing approaches. First, it uses a beta distribution to characterize the bimodal and interval properties of the methylation trait at a CpG site. Second, it considers multiple common and rare genetic variants within a genomic region to identify mQTLs. Through simulations, we demonstrated that the MRF had improved power over other existing methods in detecting rare variants of relatively large effect, especially when the sample size is small. We further applied our method to a study of congenital heart defects with 83 cardiac tissue samples and identified two mQTL regions, MRPS10 and PSORS1C1, which were colocalized with expression QTL in cardiac tissue. In conclusion, the proposed MRF is a useful tool to identify novel mQTLs, especially for studies with limited sample sizes.

Description: Motivation The mathematically optimal solution in computational protein folding simulations does not always correspond to the native structure, due to the imperfection of the energy force fields. There is therefore a need to search for more diverse suboptimal solutions in order to identify the states close to the native. We propose a novel multimodal optimization protocol to improve the conformation sampling efficiency and modeling accuracy of de novo protein structure folding simulations. Results A distance-assisted multimodal optimization sampling algorithm, MMpred, is proposed for de novo protein structure prediction. The protocol consists of three stages: The first is a modal exploration stage, in which a structural similarity evaluation model DMscore is designed to control the diversity of conformations, generating a population of diverse structures in different low-energy basins. The second is a modal maintaining stage, where an adaptive clustering algorithm MNDcluster is proposed to divide the populations and merge the modal by adjusting the annealing temperature to locate the promising basins. In the last stage of modal exploitation, a greedy search strategy is used to accelerate the convergence of the modal. Distance constraint information is used to construct the conformation scoring model to guide sampling. MMpred is tested on a large set of 320 non-redundant proteins, where MMpred obtains models with TM-score≥0.5 on 291 cases, which is 28% higher than that of Rosetta guided with the same set of distance constraints. In addition, on 320 benchmark proteins, the enhanced version of MMpred (E-MMpred) has 167 targets better than trRosetta when the best of five models are evaluated. The average TM-score of the best model of E-MMpred is 0.732, which is comparable to trRosetta (0.730). Availability and implementation The source code and executable are freely available at https://github.com/iobio-zjut/MMpred. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Managers, stakeholders, and scientists recognize the need for collaborative, transparent, integrated approaches to complex resource management issues, and frameworks to address these complex issues are developing. Through the course of 2019, the Mid-Atlantic Fishery Management Council developed a conceptual model of ecosystem linkages and risks for summer flounder, a species of recreational and commercial fisheries importance. The proximal aim of the model was to develop a list of integrated management questions that could be refined and addressed through a future quantitative management strategy evaluation. As such, this conceptual model served as a scoping tool. However, the true value of the conceptual model lays elsewhere: familiarizing resource managers historically focused on single-species management with the potential utility of an ecosystem approach to management. This paper details the goals and development of the conceptual model and situates this process in the broader context of best practices for collaborative open science and scientific reproducibility. Further, it highlights a successful path by which the shift towards ecosystem-based management can be actuated.

Description: Understanding the competitive interactions of ecological similar species is essential to determine their role and niche in the ecosystem. Using both conventional dietary methods and stable isotope analysis, we examined the feeding ecology, trophic position and possible niche partitioning of Northeast Atlantic mackerel (Scomber scombrus), Icelandic summer spawning (ISS) herring (Clupea harengus), and Norwegian spring spawning (NSS) herring from Icelandic waters during the spring and summer 2012 and 2014. The stomach analysis showed differences in prey preferences among the species during summer, where mackerel diets were almost exclusively calanoid copepods, while herring ate larger zooplankton, i.e. euphausiids and amphipods. Analysis of isotopic diet contribution of mackerel and herring using Bayesian mixing models, representing mainly the spring consumption, revealed that euphausiids were the main dietary contributor of both mackerel and herring. Fish prey were also an important dietary source for ISS herring, and amphipods were important to the diet of NSS herring. In addition, mackerel encompassed a broad isotopic niche, whereas the two herring stocks had narrower and separated niches from each other. The results from this analysis reveal new and holistic information into the diet of these species, which have various ecological implications.

Description: Theoretical size at the onset of maturity (TSOM) for female Norway lobster was estimated by a new methodology based on the probability distributions of mature individuals built on physiological maturity measures. Onset of maturity using TSOM varied from 18.4- to 33.7-mm carapace length for the Irish functional management units (FUs). These estimates showed a significant negative linear relationship (R2 = 0.60) with population density and a significant positive linear relationship with average size in females (R2 = 0.84). The size class at which 50% of the females are sexually mature (L50) was linked to the new TSOM metric by a significant positive linear relationship (R2 = 0.40). This set of linear relationships ultimately allowed TSOM and L50 to be estimated without a requirement for maturity stages to be distinguished. As well as contributing to the stock assessment and management of Nephrops (e.g. in data-limited FUs) and its potential for the calibration of more routinely used estimates, TSOM might be applied in new species and meta-analyses where size of maturity data are scarce. This new metric also better-defines the maturity process since, taken together, TSOM, L50, and smallest berried female represent sequential maturity events: (i) onset of maturity, (ii) 50% mature (from gonad staging), and (iii) berried females.

Description: Although gelatinous zooplankton are an important component of marine ecosystems, gelatinous mesozooplankton that are

Description: The carapace length (CL) at which American lobster (Homarus americanus) females reach maturity can be used to evaluate egg production, growth patterns, and the overall health of lobster stocks. The female maturity datasets used to represent Gulf of Maine (GOM) lobsters in the 2015 Atlantic States Marine Fisheries Commission American Lobster Stock Assessment were collected in the 1990s by the Maine Department of Marine Resources at two coastal sites. Many studies have demonstrated an inverse relationship between temperature and the size at maturity in female lobsters, and GOM waters have warmed significantly over this period. To update these GOM maturity datasets, we used ovarian staging to determine the maturity status of over 1200 females from fives sites over 3 years. Broad application of this methodology in tandem with key growth measurements on females 50–120 mm CL allowed us to characterize reproductive development and generate maturity ogives (proportion mature at a given CL). We observed a latitudinal gradient in the size at maturity across this coastal region of the GOM and quantified a decrease in this size over 25 years. These findings have implications for future stock assessment approaches and management measures implemented to sustain this valuable fishery.

Description: The carbonate chemistry in coastal waters is more variable compared with that of open oceans, both in magnitude and time scale of its fluctuations. However, knowledge of the responses of coastal phytoplankton to dynamic changes in pH/pCO2 has been scarcely documented. Hence, we investigated the physiological performance of a coastal isolate of the coccolithophore Emiliania huxleyi (PML B92/11) under fluctuating and stable pCO2 regimes (steady ambient pCO2, 400 μatm; steady elevated pCO2, 1200 μatm; diurnally fluctuating elevated pCO2, 600–1800 μatm). Elevated pCO2 inhibited the calcification rate in both the steady and fluctuating regimes. However, higher specific growth rates and lower ratios of calcification to photosynthesis were detected in the cells grown under diurnally fluctuating elevated pCO2 conditions. The fluctuating pCO2 regime alleviated the negative effects of elevated pCO2 on effective photochemical quantum yield and relative photosynthetic electron transport rate compared with the steady elevated pCO2 treatment. Our results suggest that growth of E. huxleyi could benefit from diel fluctuations of pH/pCO2 under future-projected ocean acidification, but its calcification was reduced by the fluctuation and the increased concentration of CO2, reflecting a necessity to consider the influences of dynamic pH fluctuations on coastal carbon cycles associated with ocean global changes.

Description: Millions of people’s livelihoods rely on artisanal fisheries. However, in many regions fishers are increasingly facing ciguatera poisoning, a seafood-borne illness. The toxin, produced by benthic dinoflagellates, can spread through marine food webs and to humans by direct consumption. Ciguatera risk can play a major role in fisher’s activities but has never been considered in any marine spatial plans thus far. To fill this gap, we examined if integrating ciguatera in systematic conservation plans could affect these decisions. We developed through map-based interviews, a novel seven-step framework to collect and map local knowledge on ciguatera risk and fisheries activities with two innovations: (i) better mapping of fishing grounds by combining geomorphological habitat and fishing gear information, and (ii) integrating ciguatera risk directly into systematic spatial planning designs and scenarios conceived to maximize benthic habitat conservation while minimizing impacts to fishers. The approach is illustrated for Raivavae Island, in French Polynesia, Pacific Ocean. We found that integrating ciguatera significantly improved prioritization solutions with a 24–38% decrease of costs to fishers compared with scenarios based solely on fishery data. This framework was designed for scientists and managers to optimize the implementation of conservation plans and could be generalized to ciguatera-prone areas.

Description: Deep-sea marine fishes support important fisheries but estimates of their distributions are often incomplete as the data behind them may reflect fishing practices, access rights, or political boundaries, rather than actual geographic distributions. We use a simple suitable habitat model based on bottom depth, temperature, and salinity to estimate the potential distribution of Greenland halibut (Reinhardtius hippoglossoides). A large presence-only dataset is examined using multivariate kernel densities to define environmental envelopes, which we link to spatial distribution using a pan-Arctic oceanographic model. Occurrences generally fit the model well, although there were gaps in the predicted circum-Arctic distribution likely due to limited survey activity in many of the ice-covered seas around the Arctic Ocean. Bottom temperature and depth were major factors defining model fit to observations, but other factors, such as ecosystem interactions and larval drift could also influence distribution. Model predictions can be tested by increasing sampling effort in poorly explored regions and by studying the connectivity of putative populations. While abundances of Greenland halibut in the High Arctic are currently low, some areas are predicted to be suitable habitat for this species, suggesting that on-going sea-ice melt may lead to fisheries expansion into new areas.

Description: Antimicrobial resistance (AMR) poses a threat to global public health. To mitigate the impacts of AMR, it is important to identify the molecular mechanisms of AMR and thereby determine optimal therapy as early as possible. Conventional machine learning-based drug-resistance analyses assume genetic variations to be homogeneous, thus not distinguishing between coding and intergenic sequences. In this study, we represent genetic data from Mycobacterium tuberculosis as a graph, and then adopt a deep graph learning method—heterogeneous graph attention network (‘HGAT–AMR’)—to predict anti-tuberculosis (TB) drug resistance. The HGAT–AMR model is able to accommodate incomplete phenotypic profiles, as well as provide ‘attention scores’ of genes and single nucleotide polymorphisms (SNPs) both at a population level and for individual samples. These scores encode the inputs, which the model is ‘paying attention to’ in making its drug resistance predictions. The results show that the proposed model generated the best area under the receiver operating characteristic (AUROC) for isoniazid and rifampicin (98.53 and 99.10%), the best sensitivity for three first-line drugs (94.91% for isoniazid, 96.60% for ethambutol and 90.63% for pyrazinamide), and maintained performance when the data were associated with incomplete phenotypes (i.e. for those isolates for which phenotypic data for some drugs were missing). We also demonstrate that the model successfully identifies genes and SNPs associated with drug resistance, mitigating the impact of resistance profile while considering particular drug resistance, which is consistent with domain knowledge.

Description: Protein engineering and design principles employing the 20 standard amino acids have been extensively used to achieve stable protein scaffolds and deliver their specific activities. Although this confers some advantages, it often restricts the sequence, chemical space, and ultimately the functional diversity of proteins. Moreover, although site-specific incorporation of non-natural amino acids (nnAAs) has been proven to be a valuable strategy in protein engineering and therapeutics development, its utility in the affinity-maturation of nanobodies is not fully explored. Besides, current experimental methods do not routinely employ nnAAs due to their enormous library size and infinite combinations. To address this, we have developed an integrated computational pipeline employing structure-based protein design methodologies, molecular dynamics simulations and free energy calculations, for the binding affinity prediction of an nnAA-incorporated nanobody toward its target and selection of potent binders. We show that by incorporating halogenated tyrosines, the affinity of 9G8 nanobody can be improved toward epidermal growth factor receptor (EGFR), a crucial cancer target. Surface plasmon resonance (SPR) assays showed that the binding of several 3-chloro-l-tyrosine (3MY)-incorporated nanobodies were improved up to 6-fold into a picomolar range, and the computationally estimated binding affinities shared a Pearson’s r of 0.87 with SPR results. The improved affinity was found to be due to enhanced van der Waals interactions of key 3MY-proximate nanobody residues with EGFR, and an overall increase in the nanobody’s structural stability. In conclusion, we show that our method can facilitate screening large libraries and predict potent site-specific nnAA-incorporated nanobody binders against crucial disease-targets.

Description: Over the past few years, meta-analysis has become popular among biomedical researchers for detecting biomarkers across multiple cohort studies with increased predictive power. Combining datasets from different sources increases sample size, thus overcoming the issue related to limited sample size from each individual study and boosting the predictive power. This leads to an increased likelihood of more accurately predicting differentially expressed genes/proteins or significant biomarkers underlying the biological condition of interest. Currently, several meta-analysis methods and tools exist, each having its own strengths and limitations. In this paper, we survey existing meta-analysis methods, and assess the performance of different methods based on results from different datasets as well as assessment from prior knowledge of each method. This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools.

Description: On the continental shelf, New Zealand bryozoans dominate ecologically-important three-dimensional benthic habitat providing structural complexity which hosts a wide variety of fauna, including economically valuable species (e.g. oysters and blue cod). The association between these species and bryozoan-dominated biogenic habitat commonly results in trawling damage to the benthos; eliminating pressure from destructive fishing practices could support bryozoan regrowth. In 2002, a voluntary fishing restriction was designated over part of one such bryozoan-dominated biogenic habitat, but the efficacy of this ban has not been assessed. Statutory marine protected areas have been proposed in the same area; they aim to achieve bryozoan protection and recovery. A comparison of the bryozoan fauna using benthic images from 2003 and 2019 from the Otago shelf, South Island, New Zealand/Te Waipounamu, Aotearoa shows that a 17-year voluntary fishing ban has been ineffective at increasing the cover and richness of bryozoan-dominated biogenic habitat on the Otago shelf. This study provides a baseline for future monitoring and develops useful metrics for recovery, while calling for further research on the biology and ecology of habitat-forming bryozoans.

Description: Ecosystem-based management is generally viewed as one of the most promising avenues for addressing the various anthropogenic pressures facing the world’s marine ecosystems. These approaches have been developed to varying degrees by individual countries or international organisations, but there remain a large proportion of marine ecosystems, particularly in developing regions, that have not yet been the subject of such research. In these areas, lack of effective regulation and the often high importance of the marine environment in providing food and economic opportunities, together create conditions where marine resources and habitats come under unsustainable levels of pressure. Here, we present a data-limited assessment approach to discern marine ecological patterns, in this case for the exclusive economic zone of Vietnam. By combining data from environmental and biological surveys from the Vietnamese national survey dataset and local oceanographic models, we have identified a series of 12 candidate ecological production units, delineated by their environmental characteristics, and the key commercial species that exist within them. These units are suggested as a possible foundation for a spatial management structure in the Vietnamese exclusive economic zone including considerations such as placement of marine protected areas, or ecological boundaries of key areas of socio-economic importance.

Description: This paper explores the possibility of using the ensemble modelling paradigm to fully capture assessment uncertainty and improve the robustness of advice provision. We identify and discuss advantages and challenges of ensemble modelling approaches in the context of scientific advice. There are uncertainties associated with every phase in the stock assessment process: data collection, assessment model choice, model assumptions, interpretation of risk, up to the implementation of management advice. Additionally, the dynamics of fish populations are complex, and our incomplete understanding of those dynamics and limited observations of important mechanisms, necessitate that models are simpler than nature. The aim is for the model to capture enough of the dynamics to accurately estimate trends and abundance, and provide the basis for robust advice about sustainable harvests. The status quo approach to assessment modelling has been to identify the “best” model and generate advice from that model, mostly ignoring advice from other model configurations regardless of how closely they performed relative to the chosen model. We discuss and make suggestions about the utility of ensemble models, including revisions to the formal process of providing advice to management bodies, and recommend further research to evaluate potential gains in modelling and advice performance.

Description: Motivation Co-evolution analysis can be used to accurately predict residue–residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predicting binary contacts to predict distances between pairs of residues. These developments have significantly improved the accuracy of de novo prediction of static protein structures. With AlphaFold2 lifting the accuracy of some predicted protein models close to experimental levels, structure prediction research will move on to other challenges. One of those areas is the prediction of more than one conformation of a protein. Here, we examine the potential of residue–residue distance predictions to be informative of protein flexibility rather than simply static structure. Results We used DMPfold to predict distance distributions for every residue pair in a set of proteins that showed both rigid and flexible behaviour. Residue pairs that were in contact in at least one reference structure were classified as rigid, flexible or neither. The predicted distance distribution of each residue pair was analysed for local maxima of probability indicating the most likely distance or distances between a pair of residues. We found that rigid residue pairs tended to have only a single local maximum in their predicted distance distributions while flexible residue pairs more often had multiple local maxima. These results suggest that the shape of predicted distance distributions contains information on the rigidity or flexibility of a protein and its constituent residues. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Climate effects on marine fish depend on life stage, particularly when life stages differ in habitat utilization. In the present study, we investigated life stage-dependent responses of lesser sandeel (Ammodytes marinus) to temperature at contrasting geographical scales. We related population density and individual growth to temperature and found different temperature responses between the first and the second years of life. During the first year of life, fish size was the single most important factor influencing sandeel abundances, indicating a positive relationship between growth and survival. In contrast, during the second year of life, autumn bottom temperature was negatively correlated with sandeel abundance, suggesting elevated mortality in warm years. Southerly areas, experiencing higher temperatures in general, were also the areas showing the strongest response to temperature. This study sheds light on how warming impacts population dynamics of one of the most important forage fishes in the North Sea and supports the discussion of underlying mechanisms.

Description: Motivation The well-known fact that protein structures are more conserved than their sequences forms the basis of several areas of computational structural biology. Methods based on the structure analysis provide more complete information on residue conservation in evolutionary processes. This is crucial for the determination of evolutionary relationships between proteins and for the identification of recurrent structural patterns present in biomolecules involved in similar functions. However, algorithmic structural alignment is much more difficult than multiple sequence alignment. This study is devoted to the development and applications of DAMA—a novel effective environment capable to compute and analyze multiple structure alignments. Results DAMA is based on local structural similarities, using local 3D structure descriptors and thus accounts for nearest-neighbor molecular environments of aligned residues. It is constrained neither by protein topology nor by its global structure. DAMA is an extension of our previous study (DEDAL) which demonstrated the applicability of local descriptors to pairwise alignment problems. Since the multiple alignment problem is NP-complete, an effective heuristic approach has been developed without imposing any artificial constraints. The alignment algorithm searches for the largest, consistent ensemble of similar descriptors. The new method is capable to capture most of the biologically significant similarities present in canonical test sets and is discriminatory enough to prevent the emergence of larger, but meaningless, solutions. Tests performed on the test sets, including protein kinases, demonstrate DAMA’s capability of identifying equivalent residues, which should be very useful in discovering the biological nature of proteins similarity. Performance profiles show the advantage of DAMA over other methods, in particular when using a strict similarity measure QC, which is the ratio of correctly aligned columns, and when applying the methods to more difficult cases. Availability and implementation DAMA is available online at http://dworkowa.imdik.pan.pl/EP/DAMA. Linux binaries of the software are available upon request. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary Landslides can cause devastating damage. In particular, heavy rainfall-triggered landslides pose a chain of natural hazards. However, such events are often difficult to detect, leaving the physical processes poorly understood. Here we apply a novel surface-wave detector to detect and locate landslides during the transit of Typhoon Talas 2011. We identify multiple landslides triggered by Typhoon Talas, including a landslide in the Tenryu Ward, Shizuoka prefecture, Japan, ∼400 km east from the typhoon track. The Tenryu landslide displaced a total volume of 1.2 − −1.5 × 106 m. The landslide is much smaller than those detected by using globally recorded surface waves, yet the event generated coherent seismic signals propagating up to 3000 km away. Our observations show that attributes of small and large landslides may follow the same empirical scaling relationships, indicating possible invariant failure mechanisms. Our results also suggest an alerting technology to detect and locate landslides with a sparse seismic network.

Description: Motivation Assigning new sequences to known protein families and subfamilies is a prerequisite for many functional, comparative and evolutionary genomics analyses. Such assignment is commonly achieved by looking for the closest sequence in a reference database, using a method such as BLAST. However, ignoring the gene phylogeny can be misleading because a query sequence does not necessarily belong to the same subfamily as its closest sequence. For example, a hemoglobin which branched out prior to the hemoglobin alpha/beta duplication could be closest to a hemoglobin alpha or beta sequence, whereas it is neither. To overcome this problem, phylogeny-driven tools have emerged but rely on gene trees, whose inference is computationally expensive. Results Here, we first show that in multiple animal and plant datasets, 18 to 62% of assignments by closest sequence are misassigned, typically to an over-specific subfamily. Then, we introduce OMAmer, a novel alignment-free protein subfamily assignment method, which limits over-specific subfamily assignments and is suited to phylogenomic databases with thousands of genomes. OMAmer is based on an innovative method using evolutionarily-informed k-mers for alignment-free mapping to ancestral protein subfamilies. Whilst able to reject non-homologous family-level assignments, we show that OMAmer provides better and quicker subfamily-level assignments than approaches relying on the closest sequence, whether inferred exactly by Smith-Waterman or by the fast heuristic DIAMOND. Availability OMAmer is available from the Python Package Index (as omamer), with the source code and a precomputed database available at https://github.com/DessimozLab/omamer. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary We studied the broad-band spectra of the 8 largest earthquakes that have occurred in Chile in the last 25 years using strong-motion records and 1-Hz high-rate GNSS (cGNSS) data. To avoid the numerical instability problem with the double integration of the accelerograms, we computed velocity spectra integrating the acceleration time series in the spectral domain and compared them to time-differentiated the cGNSS displacement records. To compute the velocity spectrum, we used a multitaper algorithm so as to provide stability over the entire spectral band. We found that the velocity spectra of records obtained close to the main rupture of the earthquakes are different from classical Aki and Brune spectra. The velocity spectrum of large events in Chile presents a flat trend at low frequencies produced by the near-field waves. This trend converges at low frequencies to the static displacement as determined from GNSS data. For different magnitude earthquakes, we observe a transition in the ground-velocity spectrum from a decay of ${f^{ - 1}}$ at high frequencies and a flat trend at low frequencies to a more classical model with a peak at the corner frequency. The source-station distance influences the shape of the velocity spectrum at low frequencies, but there is no simple rule for the records available at present. At intermediate frequencies, the spectra are controlled by surface waves and S waves. We found a transition in the velocity spectrum for the 2014 Iquique earthquake, which indicates a change in the decay of the spectrum for stations at distances greater than ∼200 km. Finally, we show that the flat low-frequency trend of the velocity spectra determined from accelerograms, and the peak ground-displacement (PGD) determined from GNSS data scales with the moment to the power 2/3.

Description: Summary VCF files with results of sequencing projects take a lot of space. We propose the VCFShark, which is able to compress VCF files up to an order of magnitude better than the de facto standards (gzipped VCF and BCF). The advantage over competitors is the greatest when compressing VCF files containing large amounts of genotype data. The processing speeds up to 100 MB/s and main memory requirements lower than 30 GB allow to use our tool at typical workstations even for large datasets. Availability and Implementation https://github.com/refresh-bio/vcfshark Supplementary information Supplementary data are available at publisher’s Web site.

Description: Motivation As the generation of complex single-cell RNA sequencing datasets becomes more commonplace it is the responsibility of researchers to provide access to these data in a way that can be easily explored and shared. Whilst it is often the case that data is deposited for future bioinformatic analysis many studies do not release their data in a way that is easy to explore by non-computational researchers. Results In order to help address this we have developed ShinyCell, an R package that converts single-cell RNA sequencing datasets into explorable and shareable interactive interfaces. These interfaces can be easily customised in order to maximise their usability and can be easily uploaded to online platforms to facilitate wider access to published data. Availability ShinyCell is available at https://github.com/SGDDNB/ShinyCell and https://figshare.com/projects/ShinyCell/100439.

Description: Motivation Genomic selection (GS) is currently deemed the most effective approach to speed up breeding of agricultural varieties. It has been recognized that consideration of multiple traits in GS can improve accuracy of prediction for traits of low heritability. However, since GS forgoes statistical testing with the idea of improving predictions, it does not facilitate mechanistic understanding of the contribution of particular single nucleotide polymorphisms (SNP). Results Here we propose a L2,1-norm regularized multivariate regression model and devise a fast and efficient iterative optimization algorithm, called L2,1-joint, applicable in multi-trait GS. The usage of the L2,1-norm facilitates variable selection in a penalized multivariate regression that considers the relation between individuals, when the number of SNPs is much larger than the number of individuals. The capacity for variable selection allows us to define master regulators that can be used in a multi-trait GS setting to dissect the genetic architecture of the analyzed traits. Our comparative analyses demonstrate that the proposed model is a favorable candidate compared to existing state-of-the-art approaches. Prediction and variable selection with data sets from Brassica napus, wheat and Arabidopsis thaliana diversity panels are conducted to further showcase the performance of the proposed model. Availability and implementation The model is implemented using R programming language and the code is freely available from https://github.com/alainmbebi/L21-norm-GS. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary Finding informative predictive features in high dimensional biological case-control datasets is challenging. The Extreme Pseudo-Sampling (EPS) algorithm offers a solution to the challenge of feature selection via a combination of deep learning and linear regression models. First, using a variational autoencoder, it generates complex latent representations for the samples. Second, it classifies the latent representations of cases and controls via logistic regression. Third, it generates new samples (pseudo-samples) around the extreme cases and controls in the regression model. Finally, it trains a new regression model over the upsampled space. The most significant variables in this regression are selected. We present an open-source implementation of the algorithm that is easy to set up, use, and customize. Our package enhances the original algorithm by providing new features and customizability for data preparation, model training and classification functionalities. We believe the new features will enable the adoption of the algorithm for a diverse range of datasets. Availability The software package for Python is available online at https://github.com/roohy/eps

Description: Summary Seismic imaging techniques such as elastic full waveform inversion (FWI) have their spatial resolution limited by the maximum frequency present in the observed waveforms. Scales smaller than a fraction of the minimum wavelength cannot be resolved, and only a smoothed, effective version of the true underlying medium can be recovered. These finite-frequency effects are revealed by the upscaling or homogenization theory of wave propagation. Homogenization aims at computing larger scale effective properties of a medium containing small-scale heterogeneities. We study how this theory can be used in the context of FWI. The seismic imaging problem is broken down in a two-stage multiscale approach. In the first step, called homogenized full waveform inversion (HFWI), observed waveforms are inverted for a smooth, fully anisotropic effective medium, that does not contain scales smaller than the shortest wavelength present in the wavefield. The solution being an effective medium, it is difficult to directly interpret it. It requires a second step, called downscaling or inverse homogenization, where the smooth image is used as data, and the goal is to recover small-scale parameters. All the information contained in the observed waveforms is extracted in the HFWI step. The solution of the downscaling step is highly non-unique as many small-scale models may share the same long wavelength effective properties. We therefore rely on the introduction of external a priori information, and cast the problem in a Bayesian formulation. The ensemble of potential fine-scale models sharing the same long wavelength effective properties is explored with a Markov chain Monte Carlo algorithm. We illustrate the method with a synthetic cavity detection problem: we search for the position, size and shape of void inclusions in a homogeneous elastic medium, where the size of cavities is smaller than the resolving length of the seismic data. We illustrate the advantages of introducing the homogenization theory at both stages. In HFWI, homogenization acts as a natural regularization helping convergence toward meaningful solution models. Working with fully anisotropic effective media prevents the leakage of anisotropy induced by the fine scales into isotropic macro-parameters estimates. In the downscaling step, the forward theory is the homogenization itself. It is computationally cheap, allowing us to consider geological models with more complexity (e.g. including discontinuities) and use stochastic inversion techniques.

Description: Summary Particle Image Velocimetry (PIV), a method based on image cross-correlation, is widely used for obtaining velocity fields from time series of images of deforming objects. Rather than instantaneous velocities, we are interested in reconstructing cumulative deformation, and use PIV-derived incremental displacements for this purpose. Our focus is on analogue models of tectonic processes, which can accumulate large deformation. Importantly, PIV provides incremental displacements during analogue model evolution in a spatial reference (Eulerian) frame, without the need for explicit markers in a model. We integrate the displacements in a material reference (Lagrangian) frame, such that displacements can be integrated to track the spatial accumulative deformation field as a function of time. To describe cumulative, finite deformation, various strain tensors have been developed, and we discuss what strain measure best describes large shape changes, as standard infinitesimal strain tensors no longer apply for large deformation. PIV or comparable techniques have become a common method to determine strain in analogue models. However, the qualitative interpretation of observed strain has remained problematic for complex settings. Hence, PIV-derived displacements have not been fully exploited before, as methods to qualitatively characterize cumulative, large strain have been lacking. Notably, in tectonic settings, different types of deformation - extension, shortening, strike-slip - can be superimposed. We demonstrate that when shape changes are described in terms of Hencky strains, a logarithmic strain measure, finite deformation can be qualitatively described based on the relative magnitude of the two principal Hencky strains. Thereby, our method introduces a physically meaningful classification of large 2D strains. We show that our strain type classification method allows for accurate mapping of tectonic structures in analogue models of lithospheric deformation, and complements visual inspection of fault geometries. Our method can easily discern complex strike-slip shear zones, thrust faults and extensional structures and its evolution in time. Our newly developed software to compute deformation is freely available and can be used to post-process incremental displacements from PIV or similar autocorrelation methods.

Description: Sue Bowler remembers the Guernsey-born astronomer

Description: Jacqueline Mitton examines one strand in the life and research of the pioneering astronomer

Description: Robert Massey ponders the potential post-pandemic world for those working in astronomy and geophysics, following an online meeting in January

Description: A digest of some of the big stories from the past two months

Description: Motivation There are high demands for joint genotyping of structural variations with short-read sequencing, but efficient and accurate genotyping in population scale is a challenging task. Results We developed muCNV that aggregates per-sample summary pileups for joint genotyping of 〉 100,000 samples. Pilot results show very low Mendelian inconsistencies. Applications to large-scale projects in cloud show the computational efficiencies of muCNV genotyping pipeline. Availability muCNV is publicly available for download at: https://github.com/gjun/muCNV Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary It is generally accepted that melt extraction from the mantle at mid-ocean ridges is concentrated in narrow regions of elevated melt fraction called channels. Two feedback mechanisms have been proposed to explain why these channels grow by linear instability: shear flow of partially molten mantle and reactive flow of the ascending magma. These two mechanisms have been studied extensively, in isolation from each other, through theory and laboratory experiments as well as field and geophysical observations. Here, we develop a consistent theory that accounts for both proposed mechanisms and allows us to weigh their relative contributions. We show that interaction of the two feedback mechanisms is insignificant and that the total linear growth rate of channels is well-approximated by summing their independent growth rates. Furthermore, we explain how their competition is governed by the orientation of channels with respect to gravity and mantle shear. By itself, analysis of the reaction-infiltration instability predicts the formation of tube-shaped channels. We show that with the addition of even a small amount of extension in the horizontal, the combined instability favours tabular channels, consistent with the observed morphology of dunite bodies in ophiolites. We apply the new theory to mid-ocean ridges by calculating the accumulated growth and rotation of channels along streamlines of the solid flow. We show that reactive flow is the dominant instability mechanism deep beneath the ridge axis, where the most unstable orientation of high-porosity channels is sub-vertical. Channels are then rotated by the solid flow away from the vertical. The contribution of the shear-driven instability is confined to the margins of the melting region. Within the limitations of our study, the shear-driven feedback does not appear to be responsible for significant melt focusing or for the shallowly dipping seismic anisotropy that has been obtained by seismic inversions.

Description: Summary To understand earth processes, geoscientists infer subsurface earth properties such as electromagnetic resistivity or seismic velocity from surface observations of electromagnetic or seismic data. These properties are used to populate an earth model vector, and the spatial variation of properties across this vector sheds light on the underlying earth structure or physical phenomenon of interest, from groundwater aquifers to plate tectonics. However, to infer these properties the spatial characteristics of these properties need to be known in advance. Typically, assumptions are made about the length scales of earth properties, which are encoded a priori in a Bayesian probabilistic setting. In an optimisation setting, appeals are made to promote model simplicity together with constraints which keep models close to a preferred model. All of these approaches are valid, though they can lead to unintended features in the resulting inferred geophysical models owing to inappropriate prior assumptions, constraints or even the nature of the solution basis functions. In this work it will be shown that in order to make accurate inferences about earth properties, inferences can first be made about the underlying length scales of these properties in a very general solution basis. From a mathematical point of view, these spatial characteristics of earth properties can be conveniently thought of as “properties” of the earth properties. Thus, the same machinery used to infer earth properties can be used to infer their length scales. This can be thought of as an “infer to infer” paradigm analogous to the “learning to learn” paradigm which is now commonplace in the machine learning literature. However, it must be noted that (geophysical) inference is not the same as (machine) learning, though there are many common elements which allow for cross-pollination of useful ideas from one field to the other, as is shown here. A non-stationary trans-dimensional Gaussian Process (TDGP) is used to parameterise earth properties, and a multi-channel stationary TDGP is used to parameterise the length scales associated with the earth property in question. Using non-stationary kernels, i.e., kernels with spatially variable length scales, models with sharp discontinuities can be represented within this framework. As GPs are multi-dimensional interpolators, the same theory and computer code can be used to solve geophysical problems in 1D, 2D and 3D. This is demonstrated through a combination of 1D and 2D non-linear regression examples and a controlled source electromagnetic (CSEM) field example. The key difference between this and previous work using TDGP is generalised nested inference and the marginalisation of prior length scales for better posterior subsurface property characterisation.

Description: Summary The Z-transform of a complex time signal (or the analytic signal of a real signal) is equal to the Z-transform of a prediction error divided by the Z-transform of the prediction error operator. This inverse is decomposed into a sum of partial fractions, which are used to obtain impulse response operators formed by non-causal filters which complex-conjugate symmetric coefficients. The time-components are obtained by convolving the filters with the original signal, and the peak frequencies, corresponding to the poles of the prediction error operator, are used for mapping the time-components into frequency components. For non-stationary signals, this decomposition is done in sliding time windows, and the signal component values, in the middle of each window, are attributed to the peak value of its frequency response which corresponds to the pole of this partial fraction component. The result is an exact, but non-unique, time-frequency representation of the input signal. A sparse signal decomposition can be obtained by summing along the frequency axis in patches with similar characteristics in the time-frequency domain. The peak amplitude frequency of each new time component is obtained by computing a scalar prediction error operator in sliding time windows, resulting in a sparse time-frequency representation. In both cases, the result is a time-frequency matrix where an estimate of the frequency content of the input signal can be obtained by summation over the time variable. The performance of the new method is demonstrated with excellent results on a synthetic time signal, the LIGO gravitational wave signal, and on seismic field data.

Description: Summary Physical properties of near-surface soil and rock layers play a fundamental role in the seismic site effects analysis, being an essential element of seismic hazard assessment. Site-specific mechanical properties (i.e. shear- and compressional-wave velocities and mass density) can be inferred from surface wave dispersion and horizontal-to-vertical or ellipticity data by non-linear inversion techniques. Nevertheless, results typically exhibit significant inherent non-uniqueness as different models may fit the data equally well. Standard optimization inversion techniques minimize data misfit, resulting in a single representative model, rejecting other models providing similar misfit values. An alternative inversion technique can be formulated in the Bayesian framework, where the posterior probability density on the model space is inferred. This paper introduces an inversion approach of surface wave dispersion and ellipticity data based on a novel multizonal transdimensional Bayesian formulation. In particular, we parameterize one-dimensional layered velocity models by the varying number of Voronoi nuclei, allowing us to treat the number of layers as an unknown parameter of the inverse problem. The chosen parameterization leads to the transdimensional formulation of the model space, sampled by a reversible jump Markov chain Monte Carlo algorithm to provide an ensemble of random samples following the posterior probability density of model parameters. The used type of the sampling algorithm controls a model complexity (i.e. the number of layers) self-adaptively based on the measured data's information content. The method novelty lies in the parsimonious selection of sampling models and in the multizonal formulation of prior assumptions on model parameters, the latter allows including additional site-specific constraints in the inversion. These assumptions may be based on, e.g. stratigraphic logs, standard penetration tests, known water table, and bedrock depth. The multizonal formulation fully preserves the validity of the transdimensional one, as demonstrated analytically. The resultant ensemble of model samples is a discrete approximation of the posterior probability density function of model parameters and associated properties (e.g. VS30, quarter-wavelength average velocity profile, and theoretical SH-wave amplification function). Although the ultimate result is the posterior probability density function, some representative models are selected according to data fit and maximum of the posterior probability density function. We first validate our inversion approach based on synthetic tests and then apply it to field data acquired from the active seismic survey and single-station measurements of ambient vibrations at the SENGL seismic station site in central Switzerland.

Description: Motivation Molecular property prediction is a hot topic in recent years. Existing graph-based models ignore the hierarchical structures of molecules. According to the knowledge of chemistry and pharmacy, the functional groups of molecules are closely related to its physio-chemical properties and binding affinities. So, it should be helpful to represent molecular graphs by fragments that contain functional groups for molecular property prediction. Results In this paper, to boost the performance of molecule property prediction, we first propose a definition of molecule graph fragments that may be or contain functional groups, which are relevant to molecular properties, then develop a fragment-oriented multi-scale graph attention network for molecular property prediction, which is called FraGAT. Experiments on several widely-used benchmarks are conducted to evaluate FraGAT. Experimental results show that FraGAT achieves state-of-the-art predictive performance in most cases. Furthermore, our case studies showthat when the fragments used to represent the molecule graphs contain functional groups, the model can make better predictions. This conforms to our expectation and demonstrates the interpretability of the proposed model. Availability and implementation The code and data underlying this work are available in GitHub, at https://github.com/ZiqiaoZhang/FraGAT. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Volcán Quizapu, Chile, is an under-monitored volcano that was the site of two historical eruptions: an effusive eruption in 1846-1847 and a Plinian eruption in 1932, both of which discharged ∼5 km3 (DRE) of lava and/or tephra. The majority of material erupted in both cases is trachydacite, nearly identical for each event. We present H2O-saturated, phase equilibrium experiments on this end-member dacite magma, using a pumice sample from the 1932 eruption as the main starting material. At an oxygen fugacity (fO2) of ∼NNO+0.2, the phase assemblage of An25-30 plagioclase + amphibole + orthopyroxene, without biotite, is stable at 865±10 °C and 110±20 MPa H2O pressure (PH2O), corresponding to ∼4 km depth. At these conditions, experiments also reproduce the quenched glass composition of the starting pumice. At slightly higher PH2O and below 860 °C, biotite joins the equilibrium assemblage. Because biotite is not part of the observed Quizapu phase assemblage, its presence places an upper limit on PH2O. At the determined storage PH2O of ∼110 MPa, H2O undersaturation of the magma with XH20fluid==0.87 would align Ptotal to mineral-based geobarometry estimates of ∼130 MPa. However, XH20fluid=1〈 1 is not required to reproduce the Quizapu dacite phase assemblage and compositions. A second suite of experiments at lower fO2 shows that the stability fields of the hydrous silicates (amphibole and biotite) are significantly restricted at NNO-2 relative to NNO+0.2. Additional observations of Quizapu lava and pumice samples support the existing hypothesis that rapid pre-eruptive heating drove the effusive 1846-1847 eruption, with important refinements. We demonstrate that microlites in the end-member dacite lavas are consistent with in situ crystallization (during ascent), rather than transfer from an andesite. In one end-member dacite lava, newly identified reverse zoning in orthopyroxene and incipient destabilization of amphibole are consistent with small degrees of heating. Our work articulates a clear direction for future Quizapu studies, which are warranted given the active nature of the Cerro Azul-Descabezado Grande volcanic axis.

Description: Motivation The Anatomical Therapeutic Chemical (ATC) system is an official classification system established by the World Health Organization for medicines. Correctly assigning ATC classes to given compounds is an important research problem in drug discovery, which can not only discover the possible active ingredients of the compounds, but also infer theirs therapeutic, pharmacological, and chemical properties. Results In this paper, we develop an end-to-end multi-label classifier called CGATCPred to predict 14 main ATC classes for given compounds. In order to extract rich features of each compound, we use the deep Convolutional Neural Network (CNN) and shortcut connections to represent and learn the seven association scores between the given compound and others. Moreover, we construct the correlation graph of ATC classes and then apply graph convolutional network (GCN) on the graph for label embedding abstraction. We use all label embedding to guide the learning process of compound representation. As a result, by using the Jackknife test, CGATCPred obtain reliable Aiming of 81.94%, Coverage of 82.88%, Accuracy 80.81%, Absolute True 76.58% and Absolute False 2.75%, yielding significantly improvements compared to exiting multi-label classifiers. Availability The codes of CGATCPred are available at https://github.com/zhc940702/CGATCPred and https://zenodo.org/record/4552917. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary Until now, the polar motion resonance (PMR) complex frequency has been determined in the seasonal and retrograde diurnal band of the polar motion. In this study this resonance is studied in the prograde diurnal band, where polar motion is mainly composed of periodic terms caused by the diurnal oceanic tide. The resonance parameters (period and quality factor) are encompassed in the frequency transfer function between generating tidal potential and polar motion, and can be estimated accordingly. To this aim, we gather three published sets of prograde diurnal terms determined from GNSS and VLBI, to which we append our own estimates based upon a processing of the VLBI delays over the period 1990-2020. Then, by fitting the PMR parameters so that the prograde diurnal terms match the corresponding components of the tide generating potential, we obtained a resonance period of about 401 days and an equivalent quality factor of −22, differing from the ones reigning in the seasonal band (PPMR ≈ 431 days; QPMR ≈ 56 − 255) and in the retrograde diurnal band (PPMR ≈ 380 days; QPMR ≈ −10). Our estimates confirm strikingly the theoretical prediction derived from the tidal ocean angular momentum derived from the FES 2014 ocean tide model.

Description: Motivation Sequence motif discovery algorithms can identify novel sequence patterns that perform biological functions in DNA, RNA and protein sequences—for example, the binding site motifs of DNA-and RNA-binding proteins. Results The STREME algorithm presented here advances the state-of-the-art in ab initio motif discovery in terms of both accuracy and versatility. Using in vivo DNA (ChIP-seq) and RNA (CLIP-seq) data, and validating motifs with reference motifs derived from in vitro data, we show that STREME is more accurate, sensitive and thorough than several widely used algorithms (DREME, HOMER, MEME, Peak-motifs) and two other representative algorithms (ProSampler and Weeder). STREME’s capabilities include the ability to find motifs in datasets with hundreds of thousands of sequences, to find both short and long motifs (from 3 to 30 positions), to perform differential motif discovery in pairs of sequence datasets, and to find motifs in sequences over virtually any alphabet (DNA, RNA, protein and user-defined alphabets). Unlike most motif discovery algorithms, STREME reports a useful estimate of the statistical significance of each motif it discovers. STREME is easy to use individually via its web server or via the command line, and is completely integrated with the widely-used MEME Suite of sequence analysis tools. The name STREME stands for “Simple, Thorough, Rapid, Enriched Motif Elicitation”. Availability The STREME web server and source code are provided freely for non-commercial use at http://meme-suite.org.

Description: Motivation Understanding the mechanisms by which the zebrafish pectoral fin develops is expected to produce insights on how vertebrate limbs grow from a 2D cell layer to a 3D structure. Two mechanisms have been proposed to drive limb morphogenesis in tetrapods: a growth-based morphogenesis with a higher proliferation rate at the distal tip of the limb bud than at the proximal side, and directed cell behaviors that include elongation, division and migration in a nonrandom manner. Based on quantitative experimental biological data at the level of individual cells in the whole developing organ, we test the conditions for the dynamics of pectoral fin early morphogenesis. Results We found that during the development of the zebrafish pectoral fin, cells have a preferential elongation axis that gradually aligns along the proximodistal axis (PD) of the organ. Based on these quantitative observations, we build a center-based cell model enhanced with a polarity term and cell proliferation to simulate fin growth. Our simulations resulted in 3D fins similar in shape to the observed ones, suggesting that the existence of a preferential axis of cell polarization is essential to drive fin morphogenesis in zebrafish, as observed in the development of limbs in the mouse, but distal tip-based expansion is not. Availability Upon publication, biological data will be available at http://bioemergences.eu/modelingFin, and source code at https://github.com/guijoe/MaSoFin. Supplementary information Supplementary data are included in this manuscript.

Description: Motivation Knowledge manipulation of Gene Ontology (GO) and Gene Ontology Annotation (GOA) can be done primarily by using vector representation of GO terms and genes. Previous studies have represented GO terms and genes or gene products in Euclidean space to measure their semantic similarity using an embedding method such as the Word2Vec-based method to represent entities as numeric vectors. However, this method has the limitation that embedding large graph-structured data in the Euclidean space cannot prevent a loss of information of latent hierarchies, thus precluding the semantics of GO and GOA from being captured optimally. On the other hand, hyperbolic spaces such as the Poincaré balls are more suitable for modeling hierarchies, as they have a geometric property in which the distance increases exponentially as it nears the boundary because of negative curvature. Results In this paper, we propose hierarchical representations of GO and genes (HiG2Vec) by applying Poincaré embedding specialized in the representation of hierarchy through a two-step procedure: GO embedding and gene embedding. Through experiments, we show that our model represents the hierarchical structure better than other approaches and predicts the interaction of genes or gene products similar to or better than previous studies. The results indicate that HiG2Vec is superior to other methods in capturing the GO and gene semantics and in data utilization as well. It can be robustly applied to manipulate various biological knowledge. Availability https://github.com/JaesikKim/HiG2Vec Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation Facing the increasing gap between high-throughput sequence data and limited functional insights, computational protein function annotation provides a high-throughput alternative to experimental approaches. However, current methods can have limited applicability while relying on protein data besides sequences, or lack generalizability to novel sequences, species and functions. Results To overcome aforementioned barriers in applicability and generalizability, we propose a novel deep learning model using only sequence information for proteins, named Transformer-based protein function Annotation through joint sequence–Label Embedding (TALE). For generalizability to novel sequences we use self attention-based transformers to capture global patterns in sequences. For generalizability to unseen or rarely seen functions (tail labels), we embed protein function labels (hierarchical GO terms on directed graphs) together with inputs/features (1D sequences) in a joint latent space. Combining TALE and a sequence similarity-based method, TALE+ outperformed competing methods when only sequence input is available. It even outperformed a state-of-the-art method using network information besides sequence, in two of the three gene ontologies. Furthermore, TALE and TALE+ showed superior generalizability to proteins of low similarity, new species, or rarely annotated functions compared to training data, revealing deep insights into the protein sequence–function relationship. Ablation studies elucidated contributions of algorithmic components toward the accuracy and the generalizability. Availability The data, source codes and models are available at https://github.com/Shen-Lab/TALE Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation Random sampling of metabolic fluxes can provide a comprehensive description of the capabilities of a metabolic network. However, current sampling approaches do not model thermodynamics explicitly, leading to inaccurate predictions of an organism’s potential or actual metabolic operations. Results We present a probabilistic framework combining thermodynamic quantities with steady-state flux constraints to analyze the properties of a metabolic network. It includes methods for probabilistic metabolic optimization and for joint sampling of thermodynamic and flux spaces. Applied to a model of E. coli, we use the methods to reveal known and novel mechanisms of substrate channeling, and to accurately predict reaction directions and metabolite concentrations. Interestingly, predicted flux distributions are multimodal, leading to discrete hypotheses on E. coli’s metabolic capabilities. Availability Python and MATLAB packages available at https://gitlab.com/csb.ethz/pta. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary Bayesian inversion of electromagnetic data produces crucial uncertainty information on inferred subsurface resistivity. Due to their high computational cost, however, Bayesian inverse methods have largely been restricted to computationally expedient 1D resistivity models. In this study, we successfully demonstrate, for the first time, a fully 2D, trans-dimensional Bayesian inversion of magnetotelluric data. We render this problem tractable from a computational standpoint by using a stochastic interpolation algorithm known as a Gaussian process to achieve a parsimonious parametrization of the model vis-a-vis the dense parameter grids used in numerical forward modeling codes. The Gaussian process links a trans-dimensional, parallel tempered Markov chain Monte Carlo sampler, which explores the parsimonious model space, to MARE2DEM, an adaptive finite element forward solver. MARE2DEM computes the model response using a dense parameter mesh with resistivity assigned via the Gaussian process model. We demonstrate the new trans-dimensional Gaussian process sampler by inverting both synthetic and field magnetotelluric data for 2D models of electrical resistivity, with the field data example converging within 10 days on 148 cores, a non-negligible but tractable computational cost. For a field data inversion, our algorithm achieves a parameter reduction of over 32x compared to the fixed parameter grid used for the MARE2DEM regularized inversion. Resistivity probability distributions computed from the ensemble of models produced by the inversion yield credible intervals and interquartile plots that quantitatively show the non-linear 2D uncertainty in model structure. This uncertainty could then be propagated to other physical properties that impact resistivity including bulk composition, porosity and pore-fluid content.

Description: Summary Ambient wavefield data acquired on existing (so-called “dark fiber”) optical fiber networks using distributed acoustic sensing (DAS) interrogators allow users to conduct a wide range of subsurface imaging and inversion experiments. In particular, recorded low-frequency (

Description: Summary The stability of partial differential equations determines the properties of their solutions. This study focuses on the stability analysis of the equations describing wave propagation in fluids-saturated porous media. We briefly introduce the stability analysis method for the wave propagation equations and discuss the adverse effects on the solutions. In this way, the first part of this paper is mainly devoted to the analysis of the Tuncay and Corapcioglu's (TC) model, which describes the dynamic behaviour of porous media saturated with two immiscible fluids. It is pointed out that the TC model allows spatially bounded but time-exponentially exploding solutions and may yield unstable numerical results. Based on the deduced unstable factors, we construct a stable equivalent fluid (SEF) model. We rigorously analyze the stability of the SEF model using the energy method. For predicting the influence of saturation on wave velocity, the robustness of this model is preserved due to its consistency with the original TC model. Furthermore, the numerical simulations of the wave fields show that the results of the TC model exponentially increase with time after the initial effective wave signal, which does not occur in the SEF model curves. This indicates the necessity of considering the stability from a mathematical point of view during the construction of physical model. It could be useful to merge the mathematical stability theory with the geophysical wave propagation modelling theory.

Description: Motivation Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an open problem how to effectively utilize large and noisy biomedical KG for DDI detection. Due to its sheer size and amount of noise in KGs, it is often less beneficial to directly integrate KGs with other smaller but higher quality data (e.g., experimental data). Most of existing approaches ignore KGs altogether. Some tries to directly integrate KGs with other data via graph neural networks with limited success. Furthermore most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is more meaningful but harder task. Results To fill the gaps, we propose a new method SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module that can efficiently anchor on relevant subgraphs from a KG, a self-attention based subgraph summarization scheme to generate reasoning path within the subgraph, and a multi-channel knowledge and data integration module that utilizes massive external biomedical knowledge for significantly improved multi-typed DDI predictions. SumGNN outperforms the best baseline by up to 5.54%, and performance gain is particularly significant in low data relation types. In addition, SumGNN provides interpretable prediction via the generated reasoning paths for each prediction. Availability The code is available in the supplementary. Supplementary information Supplementary data are available at Bioinformatics online.

Description: In the Southern Ribeira Belt (Brazil), the Itatins Complex, composed of ortho- and para-derived migmatites record granulite-facies metamorphism. Our work focuses on the ortho-derived migmatite, which displays features indicative of partial melting ranging from in-situ melting (cm-thick lenses and veins) to accumulations of melt as leucosomes and patches of diatexite. The unit comprises a biotite felsic granulite (metagranodiorite) with a residual neosome and a leucocratic neosome, generally referred to as leucosome. Phase equilibrium modelling using a modified bulk composition from the geochemical standard JG-1 (granodiorite) was carried out to evaluate whether a protolith of granodiorite bulk composition could produce the mineral assemblages observed in the residual neosome and the compositions of melt and residue produced by the partial melting. Isopleth thermobarometry of plagioclase, biotite and orthopyroxene points to P-T conditions of 0.7-1.0 GPa and 845-872 ºC. The modified granodiorite bulk composition generated just 10 mol % melt at these conditions, far lower than the amount estimated from outcrops (around 32 %). A T-XH2O pseudosection shows that increasing H2O content enhances the fertility of the bulk composition and it can become capable of producing melt volumes similar to that observed in outcrop. The melt compositions determined for partial melting of the modified granodiorite bulk composition in open- and closed-systems using THERMOCALC are granitic, and peraluminous. The melts have SiO2 contents between 52.02 mol % and 77.27 mol. % and show XFe values of 0.68 and 0.86. In K2O vs. Na2O+CaO, K2O vs. XFe and K2O-(FeO+MgO)-(Na2O+CaO) space natural samples (5 from the residual neosome and 2 from leucosome) from the Itatins Complex plot in the direction of the trends for melt and residue compositions obtained from modelling of the modified granodiorite bulk composition. The differences between modeled compositions and real rocks are interpreted to result from the interaction of melt with its residual counterpart. Chondrite-normalized REE patterns from the residual neosome show considerably greater depletion of HREE than geochemical standard JG-1 (granodiorite), and the two analyzed leucosomes show lower HREE contents. Zircon rims have REE patterns interpreted to indicate metamorphic recrystallization. U-Pb zircon Concordia ages indicate that the protolith formed during the Paleoproterozoic (2137 Ma) but that metamorphic recrystallization is Neoproterozoic (between 632 and 601 Ma). The metamorphic age pre-dates the intense granitic magmatism that affected the Southern Ribeira Belt between 600 and 570 Ma.

Description: Summary Uncovering the distribution of magnitudes and arrival times of aftershocks is a key to comprehending the characteristics of earthquake sequences, which enables us to predict seismic activities and conduct hazard assessments. However, identifying the number of aftershocks immediately after the main shock is practically difficult due to contaminations of arriving seismic waves. To overcome this difficulty, we construct a likelihood based on the detected data, incorporating a detection function to which Gaussian process regression (GPR) is applied. The GPR is capable of estimating not only the parameters of the distribution of aftershocks together with the detection function, but also credible intervals for both the parameters and the detection function. The property that the distributions of both the Gaussian process and aftershocks are exponential functions leads to an efficient Bayesian computational algorithm to estimate hyperparameters. After its validation through numerical tests, the proposed method is retrospectively applied to the catalog data related to the 2004 Chuetsu earthquake for the early forecasting of the aftershocks. The results show that the proposed method stably and simultaneously estimates distribution parameters and credible intervals, even within t ≤ 3h after the main shock.

Description: Motivation Most protein-structure superimposition tools consider only Cartesian coordinates. Yet, much of biology happens on the surface of proteins, which is why proteins with shared ancestry and similar function often have comparable surface shapes. Superposition of proteins based on surface shape can enable comparison of highly divergent proteins, identify convergent evolution and enable detailed comparison of surface features and binding sites. Results We present ZEAL, an interactive tool to superpose global and local protein structures based on their shape resemblance using 3D (Zernike-Canterakis) functions to represent the molecular surface. In a benchmark study of structures with the same fold, we show that ZEAL outperforms two other methods for shape-based superposition. In addition, alignments from ZEAL was of comparable quality to the coordinate-based superpositions provided by TM-align. For comparisons of proteins with limited sequence and backbone-fold similarity, where coordinate-based methods typically fail, ZEAL can often find alignments with substantial surface-shape correspondence. In combination with shape-based matching, ZEAL can be used as a general tool to study relationships between shape and protein function. We identify several categories of protein functions where global shape similarity is significantly more likely than expected by random chance, when comparing proteins with little similarity on the fold level. In particular, we find that global surface shape similarity is particular common among DNA binding proteins. Availability ZEAL can be used online at https://andrelab.org/zeal or as a standalone program with command line or graphical user interface. Source files and installers are available at https://github.com/Andre-lab/ZEAL Supplementary information Supplementary data are available at Bioinformatics online.

Description: Ed Cliver, Lyndsay Fletcher and Hugh Hudson are looking for a photograph of Richard Carrington. Can you help?

Description: Richard Ellis recalls the life of the eminent cosmic-ray physicist

Description: Claire Davies recounts a successful outreach project based on Saturn and Jupiter's 2020 conjunction, a celebration of closeness in a year dominated by distancing