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Motivation: Computational techniques for drug-disease prediction are essential in enhancing drug discovery and repositioning. While many methods utilize multimodal networks from various biological databases, few integrate comprehensive multi-omics data, including transcriptomes, proteomes, and metabolomes. We introduce STRGNN, a novel graph deep learning approach that predicts drug-disease relationships using extensive multimodal networks comprising proteins, RNAs, metabolites, and compounds. We have constructed a detailed dataset incorporating multi-omics data and developed a learning algorithm with topological regularization. This algorithm selectively leverages informative modalities while filtering out redundancies. Results: STRGNN demonstrates superior accuracy compared to existing methods and has identified several novel drug effects, corroborating existing literature. STRGNN emerges as a powerful tool for drug prediction and discovery. The source code for STRGNN, along with the dataset for performance evaluation, is available at https://github.com/yuto-ohnuki/STRGNN.git. [ABSTRACT FROM AUTHOR]

Cannabis has gained popularity in recent years as a substitute treatment for pain following the risks of typical treatments uncovered by the opioid crisis. The active ingredients frequently associated with pain-relieving effects are the phytocannabinoids Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD), but their effectiveness and mechanisms of action are still under research. In this study, we used Caenorhabditis elegans, an ideal model organism for the study of nociception that expresses mammal ortholog cannabinoid (NPR-19 and NPR-32) and vanilloid (OSM-9 and OCR-2) receptors. Here, we evaluated the antinociceptive activity of THC and CBD, identifying receptor targets and several metabolic pathways activated following exposure to these molecules. The thermal avoidance index was used to phenotype each tested C. elegans experimental group. The data revealed for the first time that THC and CBD decreases the nocifensive response of C. elegans to noxious heat (32–35 °C). The effect was reversed 6 h post- CBD exposure but not for THC. Further investigations using specific mutants revealed CBD and THC are targeting different systems, namely the vanilloid and cannabinoid systems, respectively. Proteomic analysis revealed differences following Reactome pathways and gene ontology biological process database enrichment analyses between CBD or THC-treated nematodes and provided insights into potential targets for future drug development. [ABSTRACT FROM AUTHOR]

LncRNAs are non-coding RNAs with a length of more than 200 nucleotides. More and more evidence shows that lncRNAs are inextricably linked with diseases. To make up for the shortcomings of traditional methods, researchers began to collect relevant biological data in the database and used bioinformatics prediction tools to predict the associations between lncRNAs and diseases, which greatly improved the efficiency of the study. To improve the prediction accuracy of current methods, we propose a new lncRNA-disease associations prediction method with attention mechanism, called ResGCN-A. Firstly, we integrated lncRNA functional similarity, lncRNA Gaussian interaction profile kernel similarity, disease semantic similarity, and disease Gaussian interaction profile kernel similarity to obtain lncRNA comprehensive similarity and disease comprehensive similarity. Secondly, the residual graph convolutional network was used to extract the local features of lncRNAs and diseases. Thirdly, the new attention mechanism was used to assign the weight of the above features to further obtain the potential features of lncRNAs and diseases. Finally, the training set required by the Extra-Trees classifier was obtained by concatenating potential features, and the potential associations between lncRNAs and diseases were obtained by the trained Extra-Trees classifier. ResGCN-A combines the residual graph convolutional network with the attention mechanism to realize the local and global features fusion of lncRNA and diseases, which is beneficial to obtain more accurate features and improve the prediction accuracy. In the experiment, ResGCN-A was compared with five other methods through 5-fold cross-validation. The results show that the AUC value and AUPR value obtained by ResGCN-A are 0.9916 and 0.9951, which are superior to the other five methods. In addition, case studies and robustness evaluation have shown that ResGCN-A is an effective method for predicting lncRNA-disease associations. The source code for ResGCN-A will be available at https://github.com/Wangxiuxiun/ResGCN-A. [ABSTRACT FROM AUTHOR]

Neosalanx taihuensis is widely distributed in freshwater and brackish water areas in China. Due to its high commercial value, it has been artificially introduced into many lakes and reservoirs, showing strong ecological adaptability. Here, a gap-free chromosome-level reference genome was constructed by combining short reads, PacBio HiFi long reads, Nanopore ultralong reads and Hi-C data. The reference genome of N. taihuensis was 397.29 Mb with a contig N50 of 15.61 Mb. The assembled sequences were anchored to 28 chromosomes. Furthermore, 20,024 protein-coding genes and 98.16% of the predicted genes were annotated in publicly available biological databases. This high-quality gap-free assembled genome will provide an essential reference for studying the evolution and ecological adaptability of N. taihuensis. [ABSTRACT FROM AUTHOR]

Current biological and chemical research is increasingly dependent on the reusability of previously acquired data, which typically come from various sources. Consequently, there is a growing need for database systems and databases stored in them to be interoperable with each other. One of the possible solutions to address this issue is to use systems based on Semantic Web technologies, namely on the Resource Description Framework (RDF) to express data and on the SPARQL query language to retrieve the data. Many existing biological and chemical databases are stored in the form of a relational database (RDB). Converting a relational database into the RDF form and storing it in a native RDF database system may not be desirable in many cases. It may be necessary to preserve the original database form, and having two versions of the same data may not be convenient. A solution may be to use a system mapping the relational database to the RDF form. Such a system keeps data in their original relational form and translates incoming SPARQL queries to equivalent SQL queries, which are evaluated by a relational-database system. This review compares different RDB-to-RDF mapping systems with a primary focus on those that can be used free of charge. In addition, it compares different approaches to expressing RDB-to-RDF mappings. The review shows that these systems represent a viable method providing sufficient performance. Their real-life performance is demonstrated on data and queries coming from the neXtProt project. [ABSTRACT FROM AUTHOR]

For decades, query processing over uncertain databases has received much attention from the database community due to the pervasive data uncertainty in many real-world applications such as location-based services (LBS), sensor networks, business planning, biological databases, and so on. In this paper, we will study a novel query type, namely range-constrained probabilistic mutual furthest neighbor query (PMFN), over uncertain databases. PMFN retrieves a set of object pairs, (o i , o j) , within a given query range Q, such that uncertain objects o i and o j are furthest neighbors of each other with high probabilities. In order to efficiently tackle the PMFN problem, we propose effective pruning methods, range, convex hull, and hypersphere pruning, for filtering out uncertain objects that can never appear in the PMFN answer set. Then, we also design spatial and probabilistic pruning methods to rule out false alarms of PMFN candidate pairs. Finally, we utilize a variant of the R ∗ -tree to integrate our proposed pruning methods and efficiently process ad hoc PMFN queries. Extensive experiments show the efficiency and effectiveness of our pruning techniques and PMFN query processing algorithms over real and synthetic data sets. [ABSTRACT FROM AUTHOR]

More than 61,000 proteins have up-to-date correspondence between their amino acid sequence (UniProtKB) and their 3D structures (PDB), enabled by the Structure Integration with Function, Taxonomy and Sequences (SIFTS) resource. SIFTS incorporates residue-level annotations from many other biological resources. SIFTS data is available in various formats like XML, CSV and TSV format or also accessible via the PDBe REST API but always maintained separately from the structure data (PDBx/mmCIF file) in the PDB archive. Here, we extended the wwPDB PDBx/mmCIF data dictionary with additional categories to accommodate SIFTS data and added the UniProtKB, Pfam, SCOP2, and CATH residue-level annotations directly into the PDBx/mmCIF files from the PDB archive. With the integrated UniProtKB annotations, these files now provide consistent numbering of residues in different PDB entries allowing easy comparison of structure models. The extended dictionary yields a more consistent, standardised metadata description without altering the core PDB information. This development enables up-to-date cross-reference information at the residue level resulting in better data interoperability, supporting improved data analysis and visualisation. [ABSTRACT FROM AUTHOR]

The rapid growth of biological databases has resulted in the vast development of many state-of-the-art molecular analysis tools for accurate disease diagnosis and drug discovery. Protein Data Bank (PDB) is a leading molecular database consisting of three-dimensional (3D) experimental structures of macromolecules and small molecules. The most significant role of PDB in Bioinformatics includes molecular modelling and computer-aided drug design (CADD). PDBms is a web tool for splitting PDB file and interactive visualization of molecules. It parses coordinate section records in the PDB file and categorizes them into a group of molecules. Moreover, it supports 3D graphic visualization in the various model for polymers and target-ligand interactions of complex structures. The web interface of PDBms is designed using NGL Viewer/WebGL JavaScript package and programming languages such as PHP, HTML, CSS, JavaScript, AJAX, and jQuery. PDBms is freely accessible at https://www.biogem.org/tool/pdbms/. [ABSTRACT FROM AUTHOR]

Herein, we constructed the first sepsis biomarker knowledge database, MetaSepsisBase (http://sysbio.org.cn/sbd/), which contains biomedical information on 320 biomarkers collected from PubMed. Despite numerous studies published and many biomarkers identified, there exists an urgent need for a comprehensive database to collect, annotate, and display the information on human sepsis biomarkers [[3]]. Our goal is to collect and integrate all the reported sepsis biomarkers to provide users with comprehensive biomarker information, and assist critical care physicians in finding specific biomarkers or combined biomarkers to improve the rapid diagnosis and treatment effectiveness of human sepsis. [Extracted from the article]

At numerous phases of the computational process, pattern matching is essential. It enables users to search for specific DNA subsequences or DNA sequences in a database. In addition, some of these rapidly expanding biological databases are updated on a regular basis. Pattern searches can be improved by using high-speed pattern matching algorithms. Researchers are striving to improve solutions in numerous areas of computational bioinformatics as biological data grows exponentially. Faster algorithms with a low error rate are needed in real-world applications. As a result, this study offers two pattern matching algorithms that were created to help speed up DNA sequence pattern searches. The strategies recommended improve performance by utilizing word-level processing rather than character-level processing, which has been used in previous research studies. In terms of time cost, the proposed algorithms (EFLPM and EPAPM) increased performance by leveraging word-level processing with large pattern size. The experimental results show that the proposed methods are faster than other algorithms for short and long patterns. As a result, the EFLPM algorithm is 54% faster than the FLPM method, while the EPAPM algorithm is 39% faster than the PAPM method. [ABSTRACT FROM AUTHOR]

Medical concept normalization aims to construct a semantic mapping between mentions and concepts and to uniformly represent mentions that belong to the same concept. In the large-scale biological literature database, a fast concept normalization method is essential to process a large number of requests and literature. To this end, we propose a hierarchical concept normalization method, named FastMCN, with much lower computational cost and a variant of transformer encoder, named stack and index optimized self-attention (SISA), to improve the efficiency and performance. In training, FastMCN uses SISA as a word encoder to encode word representations from character sequences and uses a mention encoder which summarizes the word representations to represent a mention. In inference, FastMCN indexes and summarizes word representations to represent a query mention and output the concept of the mention which with the maximum cosine similarity. To further improve the performance, SISA was pre-trained using the continuous bag-of-words architecture with 18.6 million PubMed abstracts. All experiments were evaluated on two publicly available datasets: NCBI disease and BC5CDR disease. The results showed that SISA was three times faster than the transform encoder for encoding word representations and had better performance. Benefiting from SISA, FastMCN was efficient in both training and inference, i.e. it achieved the peak performance of most of the baseline methods within 30 s and was 3000–5600 times faster than the state-of-the-art method in inference. [ABSTRACT FROM AUTHOR]

Biodiversity related observational data are collected in a variety of ways and for a variety of purposes, mostly in the form of some sort of organised data collection action. Data management solutions are often developed to manage the data collection processes and organise the data, which may work well on their own but are less compatible with other data management tools. In a continuous development process, we have created the OpenBioMaps (OBM) biodiversity data management platform, which can be used as a self-hosted data management platform and as a free service, hosted by several institutions for biological database projects. OBM has the ability to integrate biological databases without any structural or functional constraints, allowing a high degree of flexibility in data management and development; it provides interfaces to facilitate communication between different end-user communities, including scientists, citizens, conservationists and educational staff. We have also established a network of OBM services based on collaboration between government, educational and scientific institutions and NGOs to provide a public service to those who lack the capacity or knowledge to set up or manage their own self-hosted servers. OpenBioMaps uniquely focuses on the entire data management process, from building the data structure to data collection, visualisation, sharing and processing. [ABSTRACT FROM AUTHOR]

Delayed cerebral ischemia (DCI) is a devastating complication of aneurysmal subarachnoid hemorrhage (aSAH). We aim to investigate the efficacy of early CT perfusion (CTP) parameters for predicting DCI in patients with aSAH. The search was conducted in five databases (PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, and China Biology Medicine database). Studies were reviewed by two independent authors, and the included studies were assessed for methodological quality. Fifteen studies with 882 participants were included for the final analysis. The meta-analysis of quantitative parameters showed that mean transit time represented the most valuable predictor when the calculation of the mean value was uniformed (MD 0.30 s, 95% CI: 0.10 to 0.49 s, P = 0.003). Semi-quantitative parameters using relative values or index scores were also widely used to minimize undue variations derived from patients, operators, machines, and software. Studies also demonstrated that these relative parameters had better predictive accuracy than corresponding absolute parameters. Perfusion thresholds in each study were incomparable, and the results warranted further validation. The best threshold for the prediction was 0.9 using the relative cerebral blood flow parameter (sensitivity 97% and specificity 89%). We conclude that CTP in the early phase is a promising tool for predicting DCI in aSAH patients. However, the parameters require standardization. Future studies with prospective, multi-centered design and large sample size are needed to validate the thresholds and optimize the parameters. [ABSTRACT FROM AUTHOR]

Biological interaction databases accommodate information about interacted proteins or genes. Clustering on the networks formed by the interaction information for finding regions highly connected could reveal the functional affinities or structural similarities between protein or gene entities. With the ever-increasing amounts of information in these databases, the runtime of a clustering task is more and more unaffordable. In this paper, we propose a heterogeneous parallel algorithm focusing on accelerating clustering tasks using distributed CPU–GPU clusters. Our parallel implementation is based on the original serial algorithm of the Markov clustering (MCL). In our parallel implementation, we utilize both the CPUs and GPUs to exploit the power of heterogeneous platforms. With the BioGRID biological interaction database, we have tested the proposed algorithm on a computer cluster equipped with NVIDIA Tesla P100 GPU accelerators. The result shows that, the algorithm is efficient in GPU memory usage and inter-node data transmission, and it can complete the clustering task in 3.2 minutes with the best speedup of 70.02 times compared to the serial counterpart.We believe our work can provide key insights for realizing fast MCL analyses on large-scale biological data, with distributed CPU–GPU computer clusters. [ABSTRACT FROM AUTHOR]

Background: To identify the association between diabetes mellitus (DM) and the risk of chemotherapy-induced peripheral neuropathy (CIPN) through a systematic review and meta-analysis. Methods: An electronic literature search was conducted in PubMed, Embase, Web of Science, the Wanfang database, the VIP Journals database (CQVIP), the China National Knowledge Infrastructure (CNKI) database, and the China Biology Medicine database (Sinomed) between January 2010 and January 2021. Articles were included if they investigated CIPN and DM. Stata 15.1 was used to analyze the data. Results: We examined 8923 cancer patients from 25 studies comprising 9 cohort studies and 16 case–control studies. Meta-analysis showed that there was a statistically significant positive correlation between DM and CIPN (odds ratio [OR] = 1.60, 95% confidence interval [CI] = 1.38–1.85, P < 0.001). Egger's test (P = 0.824) showed no evidence of publication bias. The positive associations did not significant differ by study type, study quality, evaluation instrument, and type of antineoplastic drug. Omission of any single study had little effect on the combined risk estimate. Little evidence of heterogeneity was observed. Conclusion: This meta-analysis provides evidence of a significant positive association between DM and risk of CIPN. Furthermore, a more detailed evaluation is warranted for cancer patients with diabetes when they are treated with antineoplastic drugs that have the potential to cause peripheral neuropathy. [ABSTRACT FROM AUTHOR]

With the rapid development of biotechnology and computer technology, increasing studies have shown that the occurrence of many diseases in the human body is closely related to the dysfunction of miRNA, and the relationship between them has become a new research hotspot. Exploring disease-related miRNAs information provides a new perspective for understanding the etiology and pathogenesis of diseases. In this study, we proposed a new method based on similarity constrained learning (RSCMDA) to infer disease-associated miRNAs. Considering the problems of noise and incomplete information in current biological datasets, we designed a new framework RSCMDA, which can learn a new disease similarity network and miRNA similarity network based on the existing biological information, and then update the predicted miRNA–disease associations using robust similarity constraint learning method. Consequently, the AUC scores obtained in the global and local cross-validation of RSCMDA are 0.9465 and 0.8494, respectively, which are superior to the other methods. Besides, the prediction performance of RSCMDA is further confirmed by the case study on lung Neoplasms, because 94% of the top 50 miRNAs predicted by the RSCMDA method are confirmed from the existing biological databases or research results. All the results show that RSCMDA is a reliable and effective framework, which can be used as new technology to explore the relationship between miRNA and disease. [ABSTRACT FROM AUTHOR]

Discovering genes involved in complex human genetic disorders is a major challenge. Many have suggested that machine learning (ML) algorithms using gene networks can be used to supplement traditional genetic association-based approaches to predict or prioritize disease genes. However, questions have been raised about the utility of ML methods for this type of task due to biases within the data, and poor real-world performance. Using autism spectrum disorder (ASD) as a test case, we sought to investigate the question: can machine learning aid in the discovery of disease genes? We collected 13 published ASD gene prioritization studies and evaluated their performance using known and novel high-confidence ASD genes. We also investigated their biases towards generic gene annotations, like number of association publications. We found that ML methods which do not incorporate genetics information have limited utility for prioritization of ASD risk genes. These studies perform at a comparable level to generic measures of likelihood for the involvement of genes in any condition, and do not out-perform genetic association studies. Future efforts to discover disease genes should be focused on developing and validating statistical models for genetic association, specifically for association between rare variants and disease, rather than developing complex machine learning methods using complex heterogeneous biological data with unknown reliability. [ABSTRACT FROM AUTHOR]

Identifying knowledge gaps and the potential biases and limitations of biological databases is essential for biogeographical research, to efficiently plan biodiversity surveys, and to accurately design conservation efforts. Here we describe the taxonomic, temporal, and spatial coverage of a comprehensive database mainly composed of data collected between 1963 and 2003 of the largest clade of Neotropical lianas, the tribe Bignonieae (Bignoniaceae). We assess the level of database completeness and propose new survey areas to fill knowledge gaps and optimize sampling coverage. The Bignonieae database includes 28,763 records representing 98% of the known species. The database covers 72% of the Neotropical region and includes data collected mainly during the last 40 years of the 20th century. Members of the tribe are conspicuous components of lowland forests, with most species showing narrow range sizes. The Amazon rainforest is the most under-sampled region and the area with the lowest sampling rate. On the other hand, the best sampled areas are scattered across Central America, the Peruvian and Bolivian Amazon, and selected Brazilian cities. Sampling rate across the geographical extent of Bignonieae was best predicted by the distance from cities. Collection effort is needed across the Neotropics so that a higher number of localities can be sampled, especially in the Amazon, where Bignonieae is centered. New surveys are urgently needed to maximize new species discoveries and to effectively design conservation plans that maximize biodiversity-rich regions facing increased threat. [ABSTRACT FROM AUTHOR]

Discovering relations of cross-type biomedical entities is crucial for biology research. A large amount of potential or indirect connected biological relations is hidden in millions of biomedical literatures and biological databases. The previous rules-based and deep learning approaches rely on plenty of manual annotations, which is laborious, time-consuming and unsatisfactory. It is necessary to be able to combine available annotated gene databases, chemical, genomic, clinical and other types of data repositories as domain knowledge to assist the extraction of biological entity relations from numerous literatures. Under this scenario, this paper proposes BioGraphSAGE model, a Siamese graph neural network with structured databases as domain knowledge to extract biological entity relations from literatures. Our model combines both biological semantic features and positional features to improve the recognition of relations between distant entities in the same literature. The experiment results show that BioGraphSAGE achieves the best F1 score among other relation extraction models on smaller annotated samples. Moreover, the proposed model can still maintain a F1 score of 0.526 without using annotated training samples. [ABSTRACT FROM AUTHOR]

The Resource Description Framework (RDF), together with well-defined ontologies, significantly increases data interoperability and usability. The SPARQL query language was introduced to retrieve requested RDF data and to explore links between them. Among other useful features, SPARQL supports federated queries that combine multiple independent data source endpoints. This allows users to obtain insights that are not possible using only a single data source. Owing to all of these useful features, many biological and chemical databases present their data in RDF, and support SPARQL querying. In our project, we primary focused on PubChem, ChEMBL and ChEBI small-molecule datasets. These datasets are already being exported to RDF by their creators. However, none of them has an official and currently supported SPARQL endpoint. This omission makes it difficult to construct complex or federated queries that could access all of the datasets, thus underutilising the main advantage of the availability of RDF data. Our goal is to address this gap by integrating the datasets into one database called the Integrated Database of Small Molecules (IDSM) that will be accessible through a SPARQL endpoint. Beyond that, we will also focus on increasing mutual interoperability of the datasets. To realise the endpoint, we decided to implement an in-house developed SPARQL engine based on the PostgreSQL relational database for data storage. In our approach, data are stored in the traditional relational form, and the SPARQL engine translates incoming SPARQL queries into equivalent SQL queries. An important feature of the engine is that it optimises the resulting SQL queries. Together with optimisations performed by PostgreSQL, this allows efficient evaluations of SPARQL queries. The endpoint provides not only querying in the dataset, but also the compound substructure and similarity search supported by our Sachem project. Although the endpoint is accessible from an internet browser, it is mainly intended to be used for programmatic access by other services, for example as a part of federated queries. For regular users, we offer a rich web application called ChemWebRDF using the endpoint. The application is publicly available at https://idsm.elixir-czech.cz/chemweb/. [ABSTRACT FROM AUTHOR]

Objectives: The Turkish population is vaccinated with Bacille Calmette-Guérin (BCG), and the BCG vaccination decreases the specificity of the tuberculin skin test (TST). The purpose of this study was to investigate the incidence of active tuberculosis (TBC) among rheumatic patients who were screened only with the QuantiFERON®-TB Gold In-Tube (QFT-GIT) test for latent TBC prior to biological treatment. Methods: The Hacettepe University Biological Database (HUR-BIO) was used for latent TBC assessment. Consecutive patients were evaluated from July 2015 to October 2016 by a questionnaire that included the patients' demographic characteristics, treatment history, and symptoms of active TBC. A total of 664 patients were interviewed by physicians. TBC statuses of the 671 non-interviewed patients were checked from the Turkish National Tuberculosis Registry records. Mean TBC incidence per year was calculated for anti-tumor necrosis factor-alpha (TNF-α) agents. Results: A total of 1335 (58.2% female) patients with the mean age of 44.2 ± 12.9 years were included. Of the patients, 836 (62.6%) had spondyloarthropathy, 432 (32.4%) had rheumatoid arthritis, and 67 (5%) had other rheumatologic diseases. The total biological drug exposure was 2292 patient-years (2043 patient-years for anti-TNF-α, 249 patient-years for non-TNF-α inhibitors). Positive and indeterminate QFT-GIT results were found in 258 (19.3%) and 23 (1.7%) patients, respectively. The median follow-up time after the onset of biological agent was 19.4 months (IQR = 29.5). Pulmonary TBC was found in 3 (0.2%) of the 1335 patients. The annual incidence of TBC was 147/100,000 patient-years for all TNF-α inhibitors (249/100,000 and 123/100,000 patient-years for QFT-GIT-positive and negative patients, respectively). Conclusions: TBC incidence increased by nearly seven times the Turkish national TBC incidence. The QFT-GIT Test appears acceptable to determine latent TBC before biological agent use. Consequently, the QFT-GIT Test can be appropriately used in BCG-vaccinated countries. Key Points • Our study contributes to filling the gap in the literature by reflecting real-life data about TBC frequency after QFT-GIT use in patients receiving biological agents. • The frequency of active TBC will remain within acceptable limits when only QFT-GIT is used in the screening of latent TBC prior to the use of biological agents in a population where the majority are vaccinated with BCG. • Using the QFT-GIT alone for latent TBC screening prior to biologic treatment in countries with high BCG vaccination rates reduces the number of patients needing isoniazid (INH) treatment. [ABSTRACT FROM AUTHOR]

Two-photon imaging is a major recording technique used in neuroscience. However, it suffers from several limitations, including a low sampling rate, the nonlinearity of calcium responses, the slow dynamics of calcium dyes and a low SNR, all of which severely limit the potential of two-photon imaging to elucidate neuronal dynamics with high temporal resolution. We developed a hyperacuity algorithm (HA_time) based on an approach that combines a generative model and machine learning to improve spike detection and the precision of spike time inference. Bayesian inference was performed to estimate the calcium spike model, assuming constant spike shape and size. A support vector machine using this information and a jittering method maximizing the likelihood of estimated spike times enhanced spike time estimation precision approximately fourfold (range, 2–7; mean, 3.5–4.0; 2SEM, 0.1–0.25) compared to the sampling interval. Benchmark scores of HA_time for biological data from three different brain regions were among the best of the benchmark algorithms. Simulation of broader data conditions indicated that our algorithm performed better than others with high firing rate conditions. Furthermore, HA_time exhibited comparable performance for conditions with and without ground truths. Thus HA_time is a useful tool for spike reconstruction from two-photon imaging. [ABSTRACT FROM AUTHOR]

Stroke is a leading cause of long-term disability in adults and a leading cause of death in developed nations. The cascade of cellular events and signalling that occur after cerebral ischemia are complex, however, analyzing global element markers of metabolic state affords the means to monitor stroke severity, status of injury, and recovery. These markers provide a multi-parameter method for assessing changes through the post-stroke time course. We employ synchrotron-based elemental mapping to follow elemental changes in the brain at 1 h, 1-, 2-, and 3-days, and at 1-, 2-, 3-, and 4-weeks post-stroke in a photothrombotic stroke model in mice. Our analysis reveals a highly consistent metabolic penumbra that can be readily identified based on the level of dysregulated potassium and other key elements. Maps of elemental distributions are also useful to demarcate events in the cellular response to the inflammatory cascade, including ion dysregulation, recruitment of cells to the lesion, and glial scar formation. [ABSTRACT FROM AUTHOR]

To determine whether lower performance on executive function tests in subjective cognitive decline (SCD) individuals are associated with higher levels of brain amyloid beta (Aβ) deposition and regional volumetric reduction in areas of interest for Alzheimer's disease (AD). 195 individuals with SCD from the FACEHBI study were assessed with a neuropsychological battery that included the following nine executive function tests: Trail Making Test A and B (TMTA, TMTB), the Rule Shift Cards subtest of BADS, the Automatic Inhibition subtest of the Syndrom Kurz Test (AI-SKT), Digit Span Backwards and Similarities from WAIS-III, and the letter, semantic, and verb fluency tests. All subjects underwent an 18F-Florbetaben positron emission tomography (FBB-PET) scan to measure global standard uptake value ratio (SUVR), and a magnetic resonance imaging (MRI). A multiple regression analysis, adjusted for age, was carried out to explore the association between global SUVR and performance on executive tests. Then, on those tests significantly associated with amyloid burden, a voxel-based morphometry (VBM) analysis was carried out to explore their correlates with grey matter volume. Multiple regression analysis revealed a statistically significant association between Aβ deposition and performance on one of the executive tests (the AI-SKT). Moreover, VBM analysis showed worse AI-SKT scores were related to lower volume in bilateral hippocampus and left inferior frontal regions. In conclusion, in SCD individuals, worse automatic inhibition ability has been found related to higher cerebral Aβ deposition and lower volume in the hippocampus and frontal regions. Thus, our results may contribute to the early detection of AD in individuals with SCD. [ABSTRACT FROM AUTHOR]

Staphylococcus aureus (S. aureus) commonly colonizes the human skin and nostrils. However, it is also associated with a wide variety of diseases. S. aureus is frequently isolated from the skin of patients with atopic dermatitis (AD), and is linked to increased disease severity. S. aureus impairs the skin barrier and triggers inflammation through the secretion of various virulence factors. S. aureus secretes phosphatidylinositol-specific phospholipase C (PI-PLC), which hydrolyses phosphatidylinositol and cleaves glycosylphosphatidylinositol-anchored proteins. However, the role of S. aureus PI-PLC in the pathogenesis of skin diseases, including AD, remains unclear. In this study, we sought to determine the role of S. aureus PI-PLC in the pathogenesis of skin diseases. PI-PLC was observed to enhance the invasion and persistence of S. aureus in keratinocytes. Besides, PI-PLC promoted the penetration of S. aureus through the epidermal barrier in a mouse model of AD and the human organotypic epidermal equivalent. Furthermore, the loss of PI-PLC attenuated epidermal hyperplasia and the infiltration of Gr-1+ cells and CD4+ cells induced by S. aureus infection in the mouse model of AD. Collectively, these results indicate that PI-PLC eases the entry of S. aureus into the dermis and aggravates acanthosis and immune cell infiltration in infected skin. [ABSTRACT FROM AUTHOR]

The shape of the cell is connected to its function; however, we do not fully understand underlying mechanisms by which global shape regulates a cell's functional capabilities. Using theory, experiments and simulation, we investigated how physiologically relevant cell shape changes affect subcellular organization, and consequently intracellular signaling, to control information flow needed for phenotypic function. Vascular smooth muscle cells going from a proliferative and motile circular shape to a contractile fusiform shape show changes in the location of the sarcoplasmic reticulum, inter-organelle distances, and differential distribution of receptors in the plasma membrane. These factors together lead to the modulation of signals transduced by the M3 muscarinic receptor/Gq/PLCβ pathway at the plasma membrane, amplifying Ca2+ dynamics in the cytoplasm, and the nucleus resulting in phenotypic changes, as determined by increased activity of myosin light chain kinase in the cytoplasm and enhanced nuclear localization of the transcription factor NFAT. Taken together, our observations show a systems level phenomenon whereby global cell shape affects subcellular organization to modulate signaling that enables phenotypic changes. [ABSTRACT FROM AUTHOR]

Bone marrow mesenchymal stromal cells (BM-MSCs) have a critical role in tissue regeneration and in the hematopoietic niche due to their differentiation and self-renewal capacities. These mechanisms are finely tuned partly by small non-coding microRNA implicated in post-transcriptional regulation. The easiest way to quantify them is RT-qPCR followed by normalization on validated reference genes (RGs). This study identified appropriate RG for normalization of miRNA expression in BM-MSCs and HS27a and HS5 cell lines in various conditions including normoxia, hypoxia, co-culture, as model for the hematopoietic niche and after induced differentiation as model for regenerative medicine. Six candidates, namely miR-16-5p, miR-34b-3p, miR-103a-3p, miR-191-5p, let-7a-5p and RNU6A were selected and their expression verified by RT-qPCR. Next, a ranking on stability of the RG candidates were performed with two algorithms geNorm and RefFinder and the optimal number of RGs needed to normalize was determined. Our results indicate miR-191-5p as the most stable miRNA in all conditions but also that RNU6a, usually used as RG is the less stable gene. This study demonstrates the interest of rigorously evaluating candidate miRNAs as reference genes and the importance of the normalization process to study the expression of miRNAs in BM-MSCs or derived cell lines. [ABSTRACT FROM AUTHOR]

CytoMegaloVirus (CMV) is known to cause infection in humans and may remain dormant throughout the life span of an individual. CMV infection has been reported to be fatal in patients with weak immunity. It is transmitted through blood, saliva, urine, semen and breast milk. Although medications are available to treat the infected patients, there is no cure for CMV. This concern prompted us to construct a comprehensive database having exhaustive information regarding CMV, its infections and therapies to be available on a single platform. Thus, we propose a newly designed database that includes all the information from various public resources such as biological databases, virus taxonomy databanks, viral databases, and drug bank, integrated into this database, named as cytomegalovirus database (CMVdb). It features all the relevant data regarding the strains of CMV, genes, expressed proteins, the genomic sequence of CMV and drugs used in the treatment of cytomegalovirus infection. CMVdb has a unique feature of in-house data analysis, so all the data obtained from various resources are processed within the system. The user interface is more responsive because of the integrated platform that will highly facilitate the researchers. Based on CMVdb functionality and quality of the data, it will accelerate the research and development in the field of infectious diseases and immunology with a special focus on CMV. The obtained data would be useful in designing better therapeutic strategies and agents for the treatment of CMV infections. The proposed database (CMVdb) is freely accessible at http://shaktisahislab.com/include/CMV/ or http://weislab.com/WeiDOCK/include/content/CMV/. [ABSTRACT FROM AUTHOR]

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Modern nanotechnology research has generated numerous experimental data for various nanomaterials. However, the few nanomaterial databases available are not suitable for modeling studies due to the way they are curated. Here, we report the construction of a large nanomaterial database containing annotated nanostructures suited for modeling research. The database, which is publicly available through http://www.pubvinas.com/, contains 705 unique nanomaterials covering 11 material types. Each nanomaterial has up to six physicochemical properties and/or bioactivities, resulting in more than ten endpoints in the database. All the nanostructures are annotated and transformed into protein data bank files, which are downloadable by researchers worldwide. Furthermore, the nanostructure annotation procedure generates 2142 nanodescriptors for all nanomaterials for machine learning purposes, which are also available through the portal. This database provides a public resource for data-driven nanoinformatics modeling research aimed at rational nanomaterial design and other areas of modern computational nanotechnology. The low curation of existing nanomaterials's databases is limiting their application in modeling studies. Here the authors report a publicly available nanomaterial database that contains annotated nanostructures of diverse nanomaterials immediately available for modeling research studies. [ABSTRACT FROM AUTHOR]

The redfish species (genus Sebastes) are widely distributed across the North Atlantic and adjacent seas of the Arctic Ocean. It is also known that redfish sporadically occur in the White Sea as by-catches in targeted fisheries. However, to date, the information about species affinity, biology and ecology of the White Sea redfish is scarce and contradictory. Some researchers argued that redfish sporadically captured in the White Sea might belong to both Sebastes viviparus and Sebastes norvegicus. In this paper, we presented for the first time genetic analysis and new biological data of the White Sea redfish. We studied nucleotide sequences of the mitochondrial control region (D-loop) and the second intron of the nuclear S7 ribosomal protein gene (RP2 S7) to clarify their species affinity. Results clearly demonstrate that the White Sea redfish belongs to S. norvegicus. [ABSTRACT FROM AUTHOR]

A new, conformal and flexible slot microstrip antenna for treatment and ablation of cancerous tumors is proposed in this paper. The main purpose of the proposed antenna is using a silicon layer on the surface of the skin, with/instead of water bolus to reduce the percentage of burns on the skin surface and resolve problems caused by the layer of water bolus. The performance of this design for achieving the maximum local SAR is evaluated carefully and step by step. The performance of this antenna is investigated in multi-stage biological tissue in various positions, including a three-layer tissue (skin, fat and muscle) and 5 layers (skin, fat, muscle, and two cortical and cancellous bone layers). The main feature in this antenna is centralization of SAR at the muscle tissue that it reduces side damages of hyperthermia. The dimensions of the studied antenna are 1.93 mm × 124 mm × 124 mm. Finally constructed antenna and its measured results have been shown and a good consistency between simulated and measured results has been reported. [ABSTRACT FROM AUTHOR]

Networks are largely used for modelling and analysing a wide range of biological data. As a consequence, many different research efforts have resulted in the introduction of a large number of algorithms for analysis and comparison of networks. Many of these algorithms can deal with networks with a single class of nodes and edges, also referred to as homogeneous networks. Recently, many different approaches tried to integrate into a single model the interplay of different molecules. A possible formalism to model such a scenario comes from node/edge coloured networks (also known as heterogeneous networks) implemented as node/ edge-coloured graphs. Therefore, the need for the introduction of algorithms able to compare heterogeneous networks arises. We here focus on the local comparison of heterogeneous networks, and we formulate it as a network alignment problem. To the best of our knowledge, the local alignment of heterogeneous networks has not been explored in the past. We here propose L-HetNetAligner a novel algorithm that receives as input two heterogeneous networks (node-coloured graphs) and builds a local alignment of them. We also implemented and tested our algorithm. Our results confirm that our method builds high-quality alignments. The following website *contains Supplementary File 1 material and the code. [ABSTRACT FROM AUTHOR]

Introduction: Several software packages containing diverse algorithms are available for processing Liquid Chromatography-Mass Spectrometry (LC–MS) chromatographic data and within these deconvolution packages different parameters settings can lead to different outcomes. XCMS is the most widely used peak picking and deconvolution software for metabolomics, but the parameter selection can be hard for inexpert users. To solve this issue, the automatic optimization tools such as Isotopologue Parameters Optimization (IPO) can be extremely helpful. Objectives: To evaluate the suitability of IPO as a tool for XCMS parameters optimization and compare the results with those manually obtained by an exhaustive examination of the LC–MS characteristics and performance. Methods: Raw HPLC-TOF–MS data from two types of biological samples (liver and plasma) analysed in both positive and negative electrospray ionization modes from three groups of piglets were processed with XCMS using parameters optimized following two different approaches: IPO and Manual. The outcomes were compared to determine the advantages and disadvantages of using each method. Results: IPO processing produced the higher number of repeatable (%RSD < 20) and significant features for all data sets and allowed the different piglet groups to be distinguished. Nevertheless, on multivariate level, similar clustering results were obtained by Principal Component Analysis (PCA) when applied to IPO and manual matrices. Conclusion: IPO is a useful optimization tool that helps in choosing the appropriate parameters. It works well on data with a good LC–MS performance but the lack of such adequate data can result in unrealistic parameter settings, which might require further investigation and manual tuning. On the contrary, manual selection criteria requires deeper knowledge on LC–MS, programming language and XCMS parameter interpretation, but allows a better fine-tuning of the parameters, and thus more robust deconvolution. [ABSTRACT FROM AUTHOR]

We investigated the interconnection on knowledge of biological molecules, biological phenomena, and diseases to efficiently collect information regarding the functions of chemical compounds and gene products, roles, applications, and involvements in diseases using knowledge graphs (KGs) developed from Resource Description Framework (RDF) data and ontologies. NikkajiRDF linked open data provide information on approximately 3.5 million chemical compounds and 694 application examples. We integrated NikkajiRDF with Interlinking Ontology for Biological Concepts (IOBC), including approximately 80,000 concepts, information on gene products, drugs, and diseases. Using IOBC's ontological structure, we confirmed that this integration enabled us to infer new information regarding biological and chemical functions, applications, and involvements in diseases for 5038 chemical compounds. Furthermore, we developed KGs from IOBC and added protein, biological phenomena, and disease identifiers used in major biological databases: UniProt, Gene Ontology, and MeSH to the KGs. Using the extended KGs and federated search to the DisGeNET, we discovered more than 60 chemicals and 700 gene products, involved in 32 diseases. [ABSTRACT FROM AUTHOR]

Purpose: A systematic review and meta-analysis was performed to assess the risk of congenital heart defects (CHDs) and its specific phenotypes associated with maternal diabetes mellitus (DM) including pregestational diabetes mellitus (PGDM) and gestational diabetes mellitus (GDM).Methods: PubMed, Embase, Medline, Google Scholar, Cochrane Libraries, China National Knowledge Infrastructure, Wanfang Database, Chinese Scientific Journals Fulltext Database and China Biology Medicine disc were searched from the inception dates to 15 December 2018, to identify case-control or cohort studies assessing the association between maternal DM and risk of CHDs. The exposure of interest was maternal DM; the outcomes of interest were CHDs and its specific phenotypes. Either a fixed- or a random-effects model was used to calculate the overall combined risk estimates. Subgroup analyses were performed to explore potential heterogeneity moderators.Results: Total 52 studies, which involved 259,917 patients with CHDs among 16,929,835 participants, were included for analysis. Overall, mothers with DM compared with those without DM had a significantly higher risk of CHDs in offspring [odds ratios (OR) = 2.71, 95% confidence intervals (CI) 2.28-3.23]. When data were restricted to different types of DM, a significantly increased risk of CHDs was observed among mothers with PGDM (OR = 3.18, 95% CI 2.77-3.65) and GDM (OR = 1.98, 95% CI 1.66-2.36). Our study suggested the risk of CHDs was significantly higher among mothers with PGDM than those with GDM. Additionally, this study suggested maternal DM was significantly associated with most phenotypes of CHDs; of these, double outlet of the right ventricle (OR = 10.89; 95% CI 8.77-13.53), atrioventricular septal defect (OR = 5.74; 95% CI 3.20-10.27) and truncus arteriosus (OR = 5.06; 95% CI 2.65-9.65) were identified as the first three of the most common phenotypes of CHDs associated with maternal DM.Conclusions: The maternal DM including PGDM and GDM are significantly associated with risk of CHDs and its most phenotypes. The PGDM seems to be more likely to cause CHDs in offspring than GDM. Further studies are needed to clarify the underlying mechanisms. [ABSTRACT FROM AUTHOR]

Melanoblast migration is important for embryogenesis and is a key feature of melanoma metastasis. Many studies have characterized melanoblast movement, focusing on statistical properties and have highlighted basic mechanisms of melanoblast motility. We took a slightly different and complementary approach: we previously developed a mathematical model of melanoblast motion that enables the testing of biological assumptions about the displacement of melanoblasts and we created tests to analyze the geometric features of cell trajectories and the specific issue of trajectory interactions. Within this model, we performed simulations and compared the results with experimental data using geometric tests. In this paper, we developed the associated mathematical model and the main focus is to study the crossings between trajectories with new theoretical results about the variation of number of intersection points with respect to the crossing times. Using these results it is possible to study the random nature of displacements and the interactions between trajectories. This analysis has raised new questions, leading to the generation of strong arguments in favor of a trail left behind each moving melanoblast. [ABSTRACT FROM AUTHOR]

When conducting ecological research on tidal mudflats, understanding how long any given area is underwater is vitally important. This paper describes a method for calculating the percent inundation time for a single tidal cycle (neap to spring tide) within a GIS environment. The primary variable is elevation, thus, having an accurate digital elevation model (DEM) is critical. Output from the model can then be integrated into existing biological databases to help interpret and understand the ecology of tidal mudflats. [ABSTRACT FROM AUTHOR]

Objective: Psoriatic Arthritis Impact of Disease 12-item questionnaire (PsAID-12) has been developed to be used in psoriatic arthritis in daily practice. The aim of the present study was to assess the change values of PsAID-12 in PsA patients continuing or discontinuing anti-TNF treatment.Methods: We recruited patients from the Hacettepe University biological database (HUR-BIO). Overall, 70 PsA patients had PsAID-12 score before the initiation of the first anti-TNF treatment. Stopping or switching the anti-TNF treatment due to inefficacy was definitely considered a negative response. Changes were evaluated by the comparison with the baseline PsAID-12 score in compliance with the favorable and unfavorable responses to anti-TNF treatments. The standardized response mean (SRM) was used for determining the response.Results: Seventy (78.6% female) patients were analyzed and their mean age was 45.5 years (12.0 years). The mean follow-up duration was 18.3 months (12.6 months). At baseline, the mean PsAID-12 score was 6.6 (1.5). Physicians stopped or switched the treatment in 28 patients (40.0%) due to the inefficacy of anti-TNF treatment. The ΔPsAID-12 score was 0.25 (1.71) in the patients discontinuing anti-TNF treatment and 3.52 (2.31) in the patients continuing their anti-TNF treatment (p < 0.001). The SRM scores higher for PsAID-12, particularly in the well response to anti-TNF treatments.Conclusion: A decrease of 3.5 units in PsAID-12 score shows a favorable response to anti-TNF treatment. Changes in PsAID-12 score had well discrimination capacity for anti-TNF treatments. [ABSTRACT FROM AUTHOR]

Exploration of mysterious facts from the sequences and structures of biomolecules of an organism is the essential requirement for understanding their molecular and evolutionary processes. Sequence analysis approach is an exciting choice for exploring those mysterious facts from biological data at genomic, transcriptomic and proteomic level. Development of bioinformatics tools is the most challenging task for analyzing these biological data at above three levels. In this communication, an attempt has been made to develop a bioinformatics program “Biological Data Analysis Program (BDAP)” having the ability to analyze the DNA/RNA/protein sequence data at molecular level. It also includes the links of various online databases, tools, search engines and many of the prestigious journals. The coding of the program has been done in Perl language. BDAP is freely available at https://sites.google.com/site/dwivediplanet/bdap under the terms and conditions of GNU General Public License. [ABSTRACT FROM AUTHOR]

In 2015-2016, 785887 specimens were scanned at the Herbarium of Moscow State University (MW), which comprised 77.7% of the total collection. The metadata of the digital herbarium include the identifier (ID) of each specimen, its species name, and geographical reference (area code). As the largest in Russia database with respect to biodiversity, the Herbarium includes 712925 images of specimens of vascular plants and 72962 specimens of bryophytes. The digital herbarium is interrelated with the Catalogue of Life international database and has enriched it with 160 new species. Images of 781882 specimens have been put online as JPG files (the resolution is 300 dpi) on the web portal of the National Depository Bank of Live Systems (https://plant.depo.msu.ru/). The portal is open for all Russian biological collections. [ABSTRACT FROM AUTHOR]

Pacific abalone, Haliotis discus hannai, has become the predominant farmed abalone species in Fujian, southern China, since 2006. As an introduced abalone species from northern China, mass mortality of the farmed Pacific abalones in Fujian often occurred during summer in recent years. However, this phenomenon of summer mortality still lacks thorough investigation. We conducted a field investigation to monitor biological conditions of the 2-year-old animals, including monthly survival rates; visual gonad index; monthly variation in metabolic parameters including protein, glycogen, and nonesterified fatty acid (NEFA) content; and enzyme activities of lactate dehydrogenase (LDH) and antioxidative capacities including superoxide dismutase (SOD) and total antioxidative capacity (T-AOC) from April to October 2014. A range of environmental parameters were assessed and correlated against biological indicators for abalone conditions, metabolic parameters, and antioxidative capacities. Results showed a total mortality rate of 58.58% over the sampled period. Visual gonad index assessment showed that the sampled animals had similar gonad development levels during each sampling date. Environmental parameters and abalone mortality monitored during the study showed significant positive correlation between seawater temperature and mortality of abalones. All energy resources tended to be depleted at the end of summer. Results also showed that both SOD and T-AOC increased in the first few months and then decreased in September and October. The variations of metabolic parameters and antioxidative capacities indicated an insufficient energy supply and peroxidative damage in the farmed animals during summer. This study provided the biological data of farmed Pacific abalone by field investigation in terms of summer mortality for the first time in southern China. These findings that might be applicable to the management and development of Pacific abalone aquaculture in sea-based farms of southern China were also discussed in the study. [ABSTRACT FROM AUTHOR]

Background: With the rapid accumulation of biological datasets, machine learning methods designed to automate data analysis are urgently needed. In recent years, so-called topic models that originated from the field of natural language processing have been receiving much attention in bioinformatics because of their interpretability. Our aim was to review the application and development of topic models for bioinformatics. Description: This paper starts with the description of a topic model, with a focus on the understanding of topic modeling. A general outline is provided on how to build an application in a topic model and how to develop a topic model. Meanwhile, the literature on application of topic models to biological data was searched and analyzed in depth. According to the types of models and the analogy between the concept of document-topic-word and a biological object (as well as the tasks of a topic model), we categorized the related studies and provided an outlook on the use of topic models for the development of bioinformatics applications. Conclusion: Topic modeling is a useful method (in contrast to the traditional means of data reduction in bioinformatics) and enhances researchers' ability to interpret biological information. Nevertheless, due to the lack of topic models optimized for specific biological data, the studies on topic modeling in biological data still have a long and challenging road ahead. We believe that topic models are a promising method for various applications in bioinformatics research. [ABSTRACT FROM AUTHOR]

Microorganisms are continuously exposed to numerous stress conditions and had evolved with numerous evolutionary adaptations and physiological acclimation mechanisms against stress effects. Any information related to the microbes responsive to stress conditions will help scientists working in the area of stress biology. Currently, there is lack of information resource on this aspect and for getting information about microbes susceptible or tolerant to different environmental changes, literature searching is the only option. Here, we present a database StressMicrobesInfo that was developed with a mandate to provide information about microbes responding to various biotic and abiotic stress conditions. This database currently contains information about 183 microbes along with a brief detail for each. StressMicrobesInfo will facilitate researchers working on stress-related microbes as a starting point and will facilitate them with the microbes which are susceptible or resistant towards particular stress conditions. [ABSTRACT FROM AUTHOR]

Background: Identification of novel drug-target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. Results: Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17% of these predictions being correct, and it achieves 49.22% correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. Conclusions: DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. [ABSTRACT FROM AUTHOR]

The ability of general scaling models to capture the central tendency or dispersion in biological data has been questioned. In fact, the appropriate domain of such models has never been clearly articulated and they have been supported and challenged using both interspecific and/or intraspecific data. Here, we evaluate several simplifying assumptions and predictions of two prominent scaling models: West, Brown and Enquist's fractal model (WBE) and a null model of geometric similarity (GEOM). Using data for 53 herbaceous angiosperm species from the Songnen Grasslands of Northern China, we compared both the interspecific and intraspecific scaling relationships for plant geometry and biomass partitioning. Specifically, we considered biomass investment in shoots and leaves as well as related several traits not commonly collected in plant allometric analyses: shoot volume, leaf number, and mean leaf mass. At the interspecific level, we find substantial variation in regression slopes, and the simplifying assumptions of WBE and predictions of both the WBE and GEOM models do not hold. In contrast, we find substantial support for the WBE model at the intraspecific level, and to a lesser extent for GEOM. The differences between our results at interspecific and intraspecific levels are due to the fact that leaf size and stem tissue density vary considerably across species in contrast to the simplifying assumptions of WBE. These results highlight the domain within which simplifying model assumptions might be most appropriate, and suggest allometric models may be useful points of departure within some species, growth forms or taxonomic groups. [ABSTRACT FROM AUTHOR]

Globally, ovarian cancer is responsible for approximately 125,000 deaths each year. Identifying the genetic contributions to ovarian cancer will lead to advances in diagnosis and therapy. Here we analyzed SNPs through genotyping in Korean serous ovarian cancer patients at stage IIIc for the purpose of applying a pathway analysis-based approach using candidate loci. The results of statistical analysis based on the Korean HapMap showed that a total of 759 SNPs had significant differences in ovarian cancer. Our biological pathway analysis included the comparison of all SNPs with SNPs of serous ovarian cancer patients in The Cancer Genome Atlas (TCGA) to identify the molecular pathway in Korean serous ovarian cancer. The results suggest that genetic alterations associated with these signaling pathways might contribute to the discovery of unique biomarkers for diagnostic predictor of Korean serous ovarian cancer at stage IIIc. [ABSTRACT FROM AUTHOR]

Compared to ( S)-selective amine transaminase (( S)-AT), the ( R)-selective counterpart (( R)-AT) has been less studied. As such, a simplified 'Motif Sequence Blast' search (Höhne et al. Nat Chem Biol 6:807-813, ) was carried out to identify new ( R)-ATs from the protein databases. The combined conserved sequence motifs of ( R)-ATs based on the previous in silico method of predicting ( R)-selective amine transaminase were used as the template sequence for BLASTP search at default settings in NCBI, and six candidate sequences were identified. These putative ( R)-AT genes were synthesized and overexpressed in Escherichia coli. Among them, five new ( R)-ATs were expressed as soluble protein and showed unusual substrate specificity and high stereoselectivity. Furthermore, several unnatural amino acids, such as d-alanine, d-2-aminobutyric acid, and d-norvaline, were synthesized via the ( R)-AT-catalyzed amino transfer reaction to the corresponding keto acids. Optically pure ( S)-amines were also obtained by kinetic resolution of racemic amines catalyzed with these new ( R)-ATs. Therefore, the Motif Sequence Blast search offers a quick and effective method for in silico identification of new ( R)-ATs, and the newly identified ( R)-ATs are attractive additions to the toolbox of ( R)-ATs for further study and industrial application. [ABSTRACT FROM AUTHOR]

Agriculture provides many ecosystem services such as food, fiber, clean water, and sequestration of carbon. The efficiency of such ecosystem services depends on crop composition and farmer decisions. Current knowledge on landscape changes is focused on crop allocation process at farm scale and rotations at field scale, whereas the impact of farmer decisions on the choice of crop acreages is poorly known. Therefore, we have built a method to assess the evolution of farm crop acreages in time and space and to identify factors ruling agricultural landscape changes. We use a dynamic typology, which is a multi-year classification of farmers. The seven steps of the method include three steps on farm typology, three steps on landscape changes, and then one step on change factors. We applied the method on 3,591 farms in Guadeloupe. Eight farm types were distinguished according to crop acreages. Our results show evidence of a diversification of 111 sugarcane growers toward production of vegetables and fruits. Spatial analysis revealed a relationship between diversification and water availability. Our method could be used to measure ecosystem services or disservices associated with changes in agricultural landscapes. [ABSTRACT FROM AUTHOR]

The mechanisms of immune response to cancer have been studied extensively and great effort has been invested into harnessing the therapeutic potential of the immune system. Immunotherapies have seen significant advances in the past 20 years, but the full potential of protective and therapeutic cancer immunotherapies has yet to be fulfilled. The insufficient efficacy of existing treatments can be attributed to a number of biological and technical issues. In this review, we detail the current limitations of immunotherapy target selection and design, and review computational methods to streamline therapy target discovery in a bioinformatics analysis pipeline. We describe specialized bioinformatics tools and databases for three main bottlenecks in immunotherapy target discovery: the cataloging of potentially antigenic proteins, the identification of potential HLA binders, and the selection epitopes and co-targets for single-epitope and multi-epitope strategies. We provide examples of application to the well-known tumor antigen HER2 and suggest bioinformatics methods to ameliorate therapy resistance and ensure efficient and lasting control of tumors. [ABSTRACT FROM AUTHOR]