ALBERT

Description: This paper concerns the numerical modelling of time-domain mechanical waves in viscoelastic media based on a generalized Zener model. To do so, classically in the literature relaxation mechanisms are introduced, resulting in a set of the so-called memory variables and thus in large computational arrays that need to be stored. A challenge is thus to accurately mimic a given attenuation law using a minimal set of relaxation mechanisms. For this purpose, we replace the classical linear approach of Emmerich & Korn with a nonlinear optimization approach with constraints of positivity. We show that this technique is more accurate than the linear approach. Moreover, it ensures that physically meaningful relaxation times that always honour the constraint of decay of total energy with time are obtained. As a result, these relaxation times can always be used in a stable way in a modelling algorithm, even in the case of very strong attenuation for which the classical linear approach may provide some negative and thus unusable coefficients.

Description: Groundwater flow plays an important role in affecting the thermal performance of borehole heat exchangers (BHEs). In the present work, a few field tests are conducted in Shouguang, China, to compare the thermal performance of three BHEs in different aquifers. The results show that there is a good linear correlation between the heat transfer rate of BHEs at steady state and the average fluid temperature. A larger slope usually means a higher heat transfer rate of a BHE, under the same borehole conditions. The thermal performance of BHEs is extremely enhanced, especially in those regions with multiple aquifers, and even the thermal response test results can be enlarged to a great extent by groundwater flow. The enhanced effect of groundwater flow depends mainly on the amount, thickness and depth of aquifers. It is found that when the depth of aquifers covers the middle or lower part of BHEs, at least exceeding the depth of the constant temperature ground layer, the enhancement on the thermal performance of BHEs becomes more intense due to the increase of the heat transfer temperature difference and the decrease of the total thermal resistance from the inner fluid to the surrounding ground. Groundwater flow in aquifers is helpful to reduce the required length of BHEs and the construction cost of ground source heat pump systems.

Description: Mitogen/extracellular signal-regulated kinase-5 ( MEK5 )/extracellular signal-regulated protein kinase-5 ( ERK5 ) pathway plays a pro-oncogenic role in tumourigenesis by anticell apoptosis, promoting cell proliferation and differentiation in response to extracellular stimuli. As overexpressed MEK5 / ERK5 is involved in the development of lung cancer, we hypothesised that the single nucleotide polymorphisms (SNPs) in MEK5 and ERK5 genes may influence gene expression and thus be associated with lung cancer risk. Five putative functional polymorphisms (rs3743353T〉C, rs7172582C〉T and rs2278076A〉G of MEK5 and rs3866958G〉T and rs2233083C〉T of ERK5 ) were genotyped in two independent case–control studies with a total of 1559 lung cancer patients and 1679 controls in southern and eastern Chinese population. We found the rs3866958G〉T of ERK5 was significantly associated with lung cancer risk, while other SNPs were not. Compared with the rs3866958TG/TT genotypes, the GG genotype conferred an increased risk of lung cancer (odds ratio = 1.30, 95% confidence interval = 1.12–1.51, P = 5.0 x 10 –4 ), and this effect was more pronounced in smokers, accompanying with a significant interaction with smoking ( P interaction = 0.013). The GG genotype also had significant higher mRNA levels of ERK5 in lung cancer tissues than TG/TT genotypes ( P = 1.0 x 10 –4 ); the luciferase reporter with the G allele showed significant higher transcription activities than the T allele, especially after the treatment with tobacco extract in vitro . Our data indicated that the functional polymorphism rs3866958G〉T in ERK5 was associated with an increased lung cancer risk in smokers by virtue of the positive interaction with smoking on promoting the ERK5 expression, which might be a valuable indicator for predicting lung cancer risk in smokers.

Description: Motivation: Research interests in microRNAs have increased rapidly in the past decade. Many studies have showed that microRNAs have close relationships with various human cancers, and they potentially could be used as cancer indicators in diagnosis or as a suppressor for treatment purposes. There are several databases that contain microRNA–cancer associations predicted by computational methods but few from empirical results. Despite the fact that abundant experiments investigating microRNA expressions in cancer cells have been carried out, the results have remain scattered in the literature. We propose to extract microRNA–cancer associations by text mining and store them in a database called miRCancer. Results: The text mining is based on 75 rules we have constructed, which represent the common sentence structures typically used to state microRNA expressions in cancers. The microRNA–cancer association database, miRCancer, is updated regularly by running the text mining algorithm against PubMed. All miRNA–cancer associations are confirmed manually after automatic extraction. miRCancer currently documents 878 relationships between 236 microRNAs and 79 human cancers through the processing of 〉26 000 published articles. Availability: miRCancer is freely available on the web at http://mircancer.ecu.edu/ Contact: dingq@ecu.edu

Description: Deep sequencing has become a popular tool for novel miRNA detection but its data must be viewed carefully as the state of the field is still undeveloped. Using three different programs, miRDeep (v1, 2), miRanalyzer and DSAP, we have analyzed seven data sets (six biological and one simulated) to provide a critical evaluation of the programs performance. We selected these software based on their popularity and overall approach toward the detection of novel and known miRNAs using deep-sequencing data. The program comparisons suggest that, despite differing stringency levels they all identify a similar set of known and novel predictions. Comparisons between the first and second version of miRDeep suggest that the stringency level of each of these programs may, in fact, be a result of the algorithm used to map the reads to the target. Different stringency levels are likely to affect the number of possible novel candidates for functional verification, causing undue strain on resources and time. With that in mind, we propose that an intersection across multiple programs be taken, especially if considering novel candidates that will be targeted for additional analysis. Using this approach, we identify and performed initial validation of 12 novel predictions in our in-house data with real-time PCR, six of which have been previously unreported.

Description: Author(s): H. J. Liao, Z. Y. Xie, J. Chen, X. J. Han, H. D. Xie, B. Normand, and T. Xiang We perform a systematic study of the antiferromagnetic Heisenberg model on the Husimi lattice using numerical tensor-network methods based on projected entangled simplex states. The nature of the ground state varies strongly with the spin quantum number S . For S = 1 2 , it is an algebraic (gapless) quan… [Phys. Rev. B 93, 075154] Published Mon Feb 29, 2016

Description: Motivation: Polyadenylation is the addition of a poly(A) tail to an RNA molecule. Identifying DNA sequence motifs that signal the addition of poly(A) tails is essential to improved genome annotation and better understanding of the regulatory mechanisms and stability of mRNA. Existing poly(A) motif predictors demonstrate that information extracted from the surrounding nucleotide sequences of candidate poly(A) motifs can differentiate true motifs from the false ones to a great extent. A variety of sophisticated features has been explored, including sequential, structural, statistical, thermodynamic and evolutionary properties. However, most of these methods involve extensive manual feature engineering, which can be time-consuming and can require in-depth domain knowledge. Results: We propose a novel machine-learning method for poly(A) motif prediction by marrying generative learning (hidden Markov models) and discriminative learning (support vector machines). Generative learning provides a rich palette on which the uncertainty and diversity of sequence information can be handled, while discriminative learning allows the performance of the classification task to be directly optimized. Here, we used hidden Markov models for fitting the DNA sequence dynamics, and developed an efficient spectral algorithm for extracting latent variable information from these models. These spectral latent features were then fed into support vector machines to fine-tune the classification performance. We evaluated our proposed method on a comprehensive human poly(A) dataset that consists of 14 740 samples from 12 of the most abundant variants of human poly(A) motifs. Compared with one of the previous state-of-the-art methods in the literature (the random forest model with expert-crafted features), our method reduces the average error rate, false-negative rate and false-positive rate by 26, 15 and 35%, respectively. Meanwhile, our method makes ~30% fewer error predictions relative to the other string kernels. Furthermore, our method can be used to visualize the importance of oligomers and positions in predicting poly(A) motifs, from which we can observe a number of characteristics in the surrounding regions of true and false motifs that have not been reported before. Availability: http://sfb.kaust.edu.sa/Pages/Software.aspx Contact: lsong@cc.gatech.edu or xin.gao@kaust.edu.sa Supplementary information: Supplementary data are available at Bioinformatics online.

Description: We have developed Lynx ( http://lynx.ci.uchicago.edu )—a web-based database and a knowledge extraction engine, supporting annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Its underlying knowledge base (LynxKB) integrates various classes of information from 〉35 public databases and private collections, as well as manually curated data from our group and collaborators. Lynx provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization to assist the user in extracting meaningful knowledge from LynxKB and experimental data, whereas its service-oriented architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.