ALBERT

Description: SUMMARY In this study, we investigated correlations between electromagnetic and seismic signals of the 2017 February 15 Veracruz, Mexico, earthquake (Mw = 4.8). We carried out a time–frequency misfit analysis based on the continuous wavelet transform in order to compare electric, magnetic and seismic records accurately. This analysis was performed for horizontal and vertical components separately. Our results from time–frequency misfit and goodness-of-fit criteria confirm the general similarity between seismic and electromagnetic signals both in frequency and time. Additionally, we studied the behaviour of peak amplitudes of seismoelectromagenetic records as a function of magnitude and distance. Our observations are in good agreement with previous studies, confirming scaling with magnitude and attenuation with distance. Radiated seismic energy estimations were performed with two methods: integration of velocity records and empirical Green function, respectively. Estimated energy magnitudes (4.35 

Description: We analyzed the seismicity of oceanic earthquakes in the Pacific oceanic regime of Mexico. We used data from the earthquake catalogues of the Mexican National Service (SSN) and the International Seismological Centre (ISC) from 1967 to 2017. Events were classified into two different categories: intraplate oceanic (INT) and transform fault zone and mid-ocean ridges (TF-MOR) events, respectively. For each category, we determined statistical characteristics such as magnitude frequency distributions, the aftershocks decay rate, the nonextensivity parameters, and the regional stress field. We obtained b values of 1.17 and 0.82 for the INT and TF-MOR events, respectively. TF-MOR events also exhibit local b-value variations in the range of 0.72–1.30. TF-MOR events follow a tapered Gutenberg–Richter distribution. We also obtained a p value of 0.67 for the 1 May 1997 (Mw=6.9) earthquake. By analyzing the nonextensivity parameters, we obtained similar q values in the range of 1.39–1.60 for both types of earthquakes. On the other hand, the parameter a showed a clear differentiation, being higher for TF-MOR events than for INT events. An important implication is that more energy is released for TF-MOR events than for INT events. Stress orientations are in agreement with geodynamical models for transform fault zone and mid-ocean ridge zones. In the case of intraplate seismicity, stresses are mostly related to a normal fault regime.

Description: Summary Single-cell RNA-sequencing (scRNA-seq) technology enables studying gene expression programs from individual cells. However, these data are subject to diverse sources of variation, including ‘unwanted’ variation that needs to be removed in downstream analyses (e.g. batch effects) and ‘wanted’ or biological sources of variation (e.g. variation associated with a cell type) that needs to be precisely described. Surrogate variable analysis (SVA)-based algorithms, are commonly used for batch correction and more recently for studying ‘wanted’ variation in scRNA-seq data. However, interpreting whether these variables are biologically meaningful or stemming from technical reasons remains a challenge. To facilitate the interpretation of surrogate variables detected by algorithms including IA-SVA, SVA or ZINB-WaVE, we developed an R Shiny application [Visual Surrogate Variable Analysis (V-SVA)] that provides a web-browser interface for the identification and annotation of hidden sources of variation in scRNA-seq data. This interactive framework includes tools for discovery of genes associated with detected sources of variation, gene annotation using publicly available databases and gene sets, and data visualization using dimension reduction methods. Availability and implementation The V-SVA Shiny application is publicly hosted at https://vsva.jax.org/ and the source code is freely available at https://github.com/nlawlor/V-SVA. Contact leed13@miamioh.edu or duygu.ucar@jax.org Supplementary information Supplementary data are available at Bioinformatics online.

Description: Genetic variation is the fuel of evolution, with standing genetic variation especially important for short-term evolution and local adaptation. To date, studies of spatiotemporal patterns of genetic variation in natural populations have been challenging, as comprehensive sampling is logistically difficult, and sequencing of entire populations costly. Here, we address these issues using a collaborative approach, sequencing 48 pooled population samples from 32 locations, and perform the first continent-wide genomic analysis of genetic variation in European Drosophila melanogaster. Our analyses uncover longitudinal population structure, provide evidence for continent-wide selective sweeps, identify candidate genes for local climate adaptation, and document clines in chromosomal inversion and transposable element frequencies. We also characterize variation among populations in the composition of the fly microbiome, and identify five new DNA viruses in our samples.

Description: Gram-negative bacteria utilize secretion systems to export substrates into their surrounding environment or directly into neighboring cells. These substrates are proteins that function to promote bacterial survival: by facilitating nutrient collection, disabling competitor species or, for pathogens, to disable host defenses. Following a rapid development of computational techniques, a growing number of substrates have been discovered and subsequently validated by wet lab experiments. To date, several online databases have been developed to catalogue these substrates but they have limited user options for in-depth analysis, and typically focus on a single type of secreted substrate. We therefore developed a universal platform, BastionHub, that incorporates extensive functional modules to facilitate substrate analysis and integrates the five major Gram-negative secreted substrate types (i.e. from types I–IV and VI secretion systems). To our knowledge, BastionHub is not only the most comprehensive online database available, it is also the first to incorporate substrates secreted by type I or type II secretion systems. By providing the most up-to-date details of secreted substrates and state-of-the-art prediction and visualized relationship analysis tools, BastionHub will be an important platform that can assist biologists in uncovering novel substrates and formulating new hypotheses. BastionHub is freely available at http://bastionhub.erc.monash.edu/.

Description: The numerical climate simulations from the Brazilian Earth System Model (BESM) are used here to investigate the response of the polar regions to a forced increase in CO2 (Abrupt-4×CO2) and compared with Coupled Model Intercomparison Project phase 5 (CMIP5) and 6 (CMIP6) simulations. The main objective here is to investigate the seasonality of the surface and vertical warming as well as the coupled processes underlying the polar amplification, such as changes in sea ice cover. Polar regions are described as the most climatically sensitive areas of the globe, with an enhanced warming occurring during the cold seasons. The asymmetry between the two poles is related to the thermal inertia and the coupled ocean–atmosphere processes involved. While at the northern high latitudes the amplified warming signal is associated with a positive snow– and sea ice–albedo feedback, for southern high latitudes the warming is related to a combination of ozone depletion and changes in the wind pattern. The numerical experiments conducted here demonstrated very clear evidence of seasonality in the polar amplification response as well as linkage with sea ice changes. In winter, for the northern high latitudes (southern high latitudes), the range of simulated polar warming varied from 10 to 39 K (−0.5 to 13 K). In summer, for northern high latitudes (southern high latitudes), the simulated warming varies from 0 to 23 K (0.5 to 14 K). The vertical profiles of air temperature indicated stronger warming at the surface, particularly for the Arctic region, suggesting that the albedo–sea ice feedback overlaps with the warming caused by meridional transport of heat in the atmosphere. The latitude of the maximum warming was inversely correlated with changes in the sea ice within the model's control run. Three climate models were identified as having high polar amplification for the Arctic cold season (DJF): IPSL-CM6A-LR (CMIP6), HadGEM2-ES (CMIP5) and CanESM5 (CMIP6). For the Antarctic, in the cold season (JJA), the climate models identified as having high polar amplification were IPSL-CM6A-LR (CMIP6), CanESM5(CMIP6) and FGOALS-s2 (CMIP5). The large decrease in sea ice concentration is more evident in models with great polar amplification and for the same range of latitude (75–90∘ N). Also, we found, for models with enhanced warming, expressive changes in the sea ice annual amplitude with outstanding ice-free conditions from May to December (EC-Earth3-Veg) and June to December (HadGEM2-ES). We suggest that the large bias found among models can be related to the differences in each model to represent the feedback process and also as a consequence of each distinct sea ice initial condition. The polar amplification phenomenon has been observed previously and is expected to become stronger in the coming decades. The consequences for the atmospheric and ocean circulation are still subject to intense debate in the scientific community.

Description: We report the discovery of BOSS-EUVLG1 at z = 2.469, by far the most luminous, almost un-obscured star-forming galaxy known at any redshift. First classified as a QSO within the Baryon Oscillation Spectroscopic Survey, follow-up observations with the Gran Telescopio Canarias reveal that its large luminosity, MUV ≃ −24.40 and log(LLyα/erg s–1) ≃ 44.0, is due to an intense burst of star formation, and not to an active galactic nucleus or gravitational lensing. BOSS-EUVLG1 is a compact (reff ≃ 1.2 kpc), young (4–5 Myr) starburst with a stellar mass log(M*/M⊙) = 10.0 ± 0.1 and a prodigious star formation rate of ≃1000 M⊙ yr−1. However, it is metal- and dust-poor [12 + log(O/H) = 8.13 ± 0.19, E(B – V) ≃ 0.07, log(LIR/LUV) 〈 −1.2], indicating that we are witnessing the very early phase of an intense starburst that has had no time to enrich the ISM. BOSS-EUVLG1 might represent a short-lived (

Description: Virulence factors (VFs) enable pathogens to infect their hosts. A wealth of individual, disease-focused studies has identified a wide variety of VFs, and the growing mass of bacterial genome sequence data provides an opportunity for computational methods aimed at predicting VFs. Despite their attractive advantages and performance improvements, the existing methods have some limitations and drawbacks. Firstly, as the characteristics and mechanisms of VFs are continually evolving with the emergence of antibiotic resistance, it is more and more difficult to identify novel VFs using existing tools that were previously developed based on the outdated data sets; secondly, few systematic feature engineering efforts have been made to examine the utility of different types of features for model performances, as the majority of tools only focused on extracting very few types of features. By addressing the aforementioned issues, the accuracy of VF predictors can likely be significantly improved. This, in turn, would be particularly useful in the context of genome wide predictions of VFs. In this work, we present a deep learning (DL)-based hybrid framework (termed DeepVF) that is utilizing the stacking strategy to achieve more accurate identification of VFs. Using an enlarged, up-to-date dataset, DeepVF comprehensively explores a wide range of heterogeneous features with popular machine learning algorithms. Specifically, four classical algorithms, including random forest, support vector machines, extreme gradient boosting and multilayer perceptron, and three DL algorithms, including convolutional neural networks, long short-term memory networks and deep neural networks are employed to train 62 baseline models using these features. In order to integrate their individual strengths, DeepVF effectively combines these baseline models to construct the final meta model using the stacking strategy. Extensive benchmarking experiments demonstrate the effectiveness of DeepVF: it achieves a more accurate and stable performance compared with baseline models on the benchmark dataset and clearly outperforms state-of-the-art VF predictors on the independent test. Using the proposed hybrid ensemble model, a user-friendly online predictor of DeepVF (http://deepvf.erc.monash.edu/) is implemented. Furthermore, its utility, from the user’s viewpoint, is compared with that of existing toolkits. We believe that DeepVF will be exploited as a useful tool for screening and identifying potential VFs from protein-coding gene sequences in bacterial genomes.

Description: Human leukocyte antigen class I (HLA-I) molecules are encoded by major histocompatibility complex (MHC) class I loci in humans. The binding and interaction between HLA-I molecules and intracellular peptides derived from a variety of proteolytic mechanisms play a crucial role in subsequent T-cell recognition of target cells and the specificity of the immune response. In this context, tools that predict the likelihood for a peptide to bind to specific HLA class I allotypes are important for selecting the most promising antigenic targets for immunotherapy. In this article, we comprehensively review a variety of currently available tools for predicting the binding of peptides to a selection of HLA-I allomorphs. Specifically, we compare their calculation methods for the prediction score, employed algorithms, evaluation strategies and software functionalities. In addition, we have evaluated the prediction performance of the reviewed tools based on an independent validation data set, containing 21 101 experimentally verified ligands across 19 HLA-I allotypes. The benchmarking results show that MixMHCpred 2.0.1 achieves the best performance for predicting peptides binding to most of the HLA-I allomorphs studied, while NetMHCpan 4.0 and NetMHCcons 1.1 outperform the other machine learning-based and consensus-based tools, respectively. Importantly, it should be noted that a peptide predicted with a higher binding score for a specific HLA allotype does not necessarily imply it will be immunogenic. That said, peptide-binding predictors are still very useful in that they can help to significantly reduce the large number of epitope candidates that need to be experimentally verified. Several other factors, including susceptibility to proteasome cleavage, peptide transport into the endoplasmic reticulum and T-cell receptor repertoire, also contribute to the immunogenicity of peptide antigens, and some of them can be considered by some predictors. Therefore, integrating features derived from these additional factors together with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy of immunogenic peptides. As such, we anticipate that this review and benchmarking survey will assist researchers in selecting appropriate prediction tools that best suit their purposes and provide useful guidelines for the development of improved antigen predictors in the future.

Description: Diatoms tend to dominate the Arctic spring phytoplankton bloom, a key event in the ecosystem including a rapid decline in surface-water pCO2. While a mass sedimentation event of diatoms at the bloom terminus is commonly observed, there are few reports on the status of diatoms' health during Arctic blooms and its possible role on sedimentary fluxes. Thus, we examine the idea that the major diatom-sinking event which occurs at the end of the regional bloom is driven by physiologically deteriorated cells. Here we quantify, using the Bottle-Net, Arctic diatom stocks below and above the photic zone and assess their cell health status. The communities were sampled around the Svalbard islands and encompassed pre- to post-bloom conditions. A mean of 24.2±6.7 % SE (standard error) of the total water column (max. 415 m) diatom standing stock was found below the photic zone, indicating significant diatom sedimentation. The fraction of living diatom cells in the photic zone averaged 59.4±6.3 % but showed the highest mean percentages (72.0 %) in stations supporting active blooms. In contrast, populations below the photic layer were dominated by dead cells (20.8±4.9 % living cells). The percentage of diatoms' standing stock found below the photic layer was negatively related to the percentage of living diatoms in the surface, indicating that healthy populations remained in the surface layer. Shipboard manipulation experiments demonstrated that (1) dead diatom cells sank faster than living cells, and (2) diatom cell mortality increased in darkness, showing an average half-life among diatom groups of 1.025±0.075 d. The results conform to a conceptual model where diatoms grow during the bloom until resources are depleted and supports a link between diatom cell health status (affected by multiple factors) and sedimentation fluxes in the Arctic. Healthy Arctic phytoplankton communities remained at the photic layer, whereas the physiologically compromised (e.g., dying) communities exported a large fraction of the biomass to the aphotic zone, fueling carbon sequestration to the mesopelagic and material to benthic ecosystems.