Word correlation matrices for protein sequence analysis and remote homology detection

<p>Abstract</p> <p>Background</p> <p>Classification of protein sequences is a central problem in computational biology. Currently, among computational methods discriminative kernel-based approaches provide the most accurate results. However, kernel-based methods often lack an interpretable model for analysis of discriminative sequence features, and predictions on new sequences usually are computationally expensive.</p> <p>Results</p> <p>In this work we present a novel kernel for protein sequences based on average word similarity between two sequences. We show that this kernel gives rise to a feature space that allows analysis of discriminative features and fast classification of new sequences. We demonstrate the performance of our approach on a widely-used benchmark setup for protein remote homology detection.</p> <p>Conclusion</p> <p>Our word correlation approach provides highly competitive performance as compared with state-of-the-art methods for protein remote homology detection. The learned model is interpretable in terms of biologically meaningful features. In particular, analysis of discriminative words allows the identification of characteristic regions in biological sequences. Because of its high computational efficiency, our method can be applied to ranking of potential homologs in large databases.</p

Structure of the silicon vacancy in 6H-SiC after annealing identified as the carbon vacancy–carbon antisite pair

We investigated radiation-induced defects in neutron-irradiated and subsequently annealed 6H-silicon carbide (SiC) with electron paramagnetic resonance (EPR), the magnetic circular dichroism of the absorption (MCDA), and MCDA-detected EPR (MCDA-EPR). In samples annealed beyond the annealing temperature of the isolated silicon vacancy we observed photoinduced EPR spectra of spin S=1 centers that occur in orientations expected for nearest neighbor pair defects. EPR spectra of the defect on the three inequivalent lattice sites were resolved and attributed to optical transitions between photon energies of 999 and 1075 meV by MCDA-EPR. The resolved hyperfine structure indicates the presence of one single carbon nucleus and several silicon ligand nuclei. These experimental findings are interpreted with help of total energy and spin density data obtained from the standard local-spin density approximation of the density-functional theory, using relaxed defect geometries obtained from the self-consistent charge density-functional theory based tight binding scheme. We have checked several defect models of which only the photoexcited spin triplet state of the carbon antisite–carbon vacancy pair (CSi-VC) in the doubly positive charge state can explain all experimental findings. We propose that the (CSi-VC) defect is formed from the isolated silicon vacancy as an annealing product by the movement of a carbon neighbor into the vacancy

Disclosing conflicts of interest in German publications concerning health services research

<p>Abstract</p> <p>Background</p> <p>The influence of the pharmaceutical industry and other stakeholders on medical science has been increasingly criticised. When dealing with conflicts of interest in scientific publications it is important to ensure the best possible transparency. The objective of this work is to examine the disclosure practice of financial and non-financial conflicts of interest in German language publications concerning health services research for the first time.</p> <p>Methods</p> <p>We performed a systematic literature search in the PubMed data base using the MeSH term "health services research". The review was conducted on July 10, 2006, setting the limits "dates: published in the last 2 years" and "languages: German" (only articles with abstracts). 124 articles in 31 magazines were found. In the magazines the instructions for authors were examined as to whether a statement on conflicts of interest is expected – and if, in which form. Regarding the articles in the journals which require a statement, we examined whether the statement is explicitly published. The results are descriptively represented.</p> <p>Results</p> <p>13 magazines (42%) do not require any statement on conflicts of interest, whereas 18 journals (58%) expect a statement. Two of these 18 magazines refer explicitly to the uniform requirements of the <it>International Committee of the Medical Journal Editors </it>(ICMJE); the remaining 16 magazines give differently accentuated instructions on how to disclose conflicts of interest, whereby the focus is primarily on financial issues. A statement on conflicts of interest is explicitly published in 11 of the 71 articles (15%) which are found in the magazines that require a statement with the submission of a manuscript. Related to the total number of included articles, this means that the reader explicitly receives information on potential conflicts of interest in 9% of the cases (11 of 124 articles). Statements of others that are involved in the publication process (reviewers, editors) are not available in any of the articles examined.</p> <p>Conclusion</p> <p>A better sensitization for possible conflicts of interest in German publications concerning health services research is necessary. We suggest tightening the criteria for disclosure in the instructions for authors in the scientific journals. Among other things the equivalent consideration of financial and non-financial conflicts of interest as well as the obligatory publication of the statements should be part of good practice.</p

Digital maturity and its determinants in General Practice: a cross- sectional study in 20 countries

Background: The extent to which digital technologies are employed to promote the delivery of high-quality healthcare is known as Digital Maturity. Individual and systemic digital maturity are both necessary to ensure a successful, scalable and sustainable digital transformation in healthcare. However, digital maturity in primary care has been scarcely evaluated. Objectives: This study assessed the digital maturity in General Practice (GP) globally and evaluated its association with participants' demographic characteristics, practice characteristics and features of Electronic Health Records (EHRs) use. Methods: GPs across 20 countries completed an online questionnaire between June and September 2020. Demographic data, practice characteristics, and features of EHRs use were collected. Digital maturity was evaluated through a framework based on usage, resources and abilities (divided in this study in its collective and individual components), interoperability, general evaluation methods and impact of digital technologies. Each dimension was rated as 1 or 0. The digital maturity score was calculated as the sum of the six dimensions and ranged between 0 to 6 (maximum digital maturity). Multivariable linear regression was used to model the total score, while multivariable logistic regression was used to model the probability of meeting each dimension of the score. Results: One thousand six hundred GPs (61% female, 68% Europeans) participated. GPs had a median digital maturity of 4 (P25–P75: 3–5). Positive associations with digital maturity were found with: male gender [B = 0.18 (95% CI 0.01; 0.36)], use of EHRs for longer periods [B = 0.45 (95% CI 0.35; 0.54)] and higher frequencies of access to EHRs [B = 0.33 (95% CI 0.17; 0.48)]. Practicing in a rural setting was negatively associated with digital maturity [B = −0.25 (95%CI −0.43; −0.08)]. Usage (90%) was the most acknowledged dimension while interoperability (47%) and use of best practice general evaluation methods (28%) were the least. Shorter durations of EHRs use were negatively associated with all digital maturity dimensions (aOR from 0.09 to 0.77). Conclusion: Our study demonstrated notable factors that impact digital maturity and exposed discrepancies in digital transformation across healthcare settings. It provides guidance for policymakers to develop more efficacious interventions to hasten the digital transformation of General Practice

End-joining long nucleic acid polymers

Many experiments involving nucleic acids require the hybridization and ligation of multiple DNA or RNA molecules to form a compound molecule. When one of the constituents is single stranded, however, the efficiency of ligation can be very low and requires significant individually tailored optimization. Also, when the molecules involved are very long (>10 kb), the reaction efficiency typically reduces dramatically. Here, we present a simple procedure to efficiently and specifically end-join two different nucleic acids using the well-known biotin–streptavidin linkage. We introduce a two-step approach, in which we initially bind only one molecule to streptavidin (STV). The second molecule is added only after complete removal of the unbound STV. This primarily forms heterodimers and nearly completely suppresses formation of unwanted homodimers. We demonstrate that the joining efficiency is 50 ± 25% and is insensitive to molecule length (up to at least 20 kb). Furthermore, our method eliminates the requirement for specific complementary overhangs and can therefore be applied to both DNA and RNA. Demonstrated examples of the method include the efficient end-joining of DNA to single-stranded and double-stranded RNA, and the joining of two double-stranded RNA molecules. End-joining of long nucleic acids using this procedure may find applications in bionanotechnology and in single-molecule experiments

What research agenda could be generated from the European General Practice Research Network concept of Multimorbidity in Family Practice?

This is the final version of the article. Available from the publisher via the DOI in this record.BACKGROUND: Multimorbidity is an intuitively appealing, yet challenging, concept for Family Medicine (FM). An EGPRN working group has published a comprehensive definition of the concept based on a systematic review of the literature which is closely linked to patient complexity and to the biopsychosocial model. This concept was identified by European Family Physicians (FPs) throughout Europe using 13 qualitative surveys. To further our understanding of the issues around multimorbidity, we needed to do innovative research to clarify this concept. The research question for this survey was: what research agenda could be generated for Family Medicine from the EGPRN concept of Multimorbidity? METHODS: Nominal group design with a purposive panel of experts in the field of multimorbidity. The nominal group worked through four phases: ideas generation phase, ideas recording phase, evaluation and analysis phase and a prioritization phase. RESULTS: Fifteen international experts participated. A research agenda was established, featuring 6 topics and 11 themes with their corresponding study designs. The highest priorities were given to the following topics: measuring multimorbidity and the impact of multimorbidity. In addition the experts stressed that the concept should be simplified. This would be best achieved by working in reverse: starting with the outcomes and working back to find the useful variables within the concept. CONCLUSION: The highest priority for future research on multimorbidity should be given to measuring multimorbidity and to simplifying the EGPRN model, using a pragmatic approach to determine the useful variables within the concept from its outcomes.The study had a Grant of 8000 Euros from the EGPRN

Which DSM validated tools for diagnosing depression are usable in primary care research? A systematic literature review

IntroductionDepression occurs frequently in primary care. Its broad clinical variability makes it difficult to diagnose. This makes it essential that family practitioner (FP) researchers have validated tools to minimize bias in studies of everyday practice. Which tools validated against psychiatric examination, according to the major depression criteria of DSM-IV or 5, can be used for research purposes

Irradiation-induced telomerase activity and gastric cancer risk: a case-control analysis in a Chinese Han population

<p>Abstract</p> <p>Background</p> <p>Telomerase expression is one of the characteristics of gastric cancer (GC) cells and telomerase activity is frequently up-regulated by a variety of mechanisms during GC development. Therefore, we hypothesized that elevated levels of activated telomerase might enhance GC risk due to increased propagation of cells with DNA damage, such as induced by γ-radiation.</p> <p>Methods</p> <p>To explore this hypothesis, 246 GC cases and 246 matched controls were recruited in our case-control study. TRAP-ELISA was used to assess the levels of telomerase activity at baseline and after γ-radiation and the γ-radiation-induced telomerase activity (defined as after γ-irradiation/baseline) in cultured peripheral blood lymphocytes (PBLs).</p> <p>Results</p> <p>Our data showed that there was no significant difference for the baseline telomerase activity between GC cases and controls (10.17 ± 7.21 <it>vs. </it>11.02 ± 8.03, <it>p </it>= 0.168). However, after γ-radiation treatment, γ-radiation-induced telomerase activity was significantly higher in the cases than in the controls (1.51 ± 0.93 <it>vs</it>. 1.22 ± 0.66, <it>p </it>< 0.001). Using the median value of γ-radiation-induced telomerase activity in the controls as a cutoff point, we observed that high γ-radiation-induced telomerase activity was associated with a significantly increased GC risk (adjusted odds ratio, 2.45; 95% confidence interval, 1.83-3.18). Moreover, a dose response association was noted between γ-radiation-induced telomerase activity and GC risk. Age, but not sex, smoking and drinking status seem to have a modulating effect on the γ-radiation-induced telomerase activities in both cases and controls.</p> <p>Conclusion</p> <p>Overall, our findings for the first time suggest that the increased γ-radiation-induced telomerase activity in PBLs might be associated with elevated GC risk. Further confirmation of this association using a prospective study design is warranted.</p

Physicochemical property distributions for accurate and rapid pairwise protein homology detection

<p>Abstract</p> <p>Background</p> <p>The challenge of remote homology detection is that many evolutionarily related sequences have very little similarity at the amino acid level. Kernel-based discriminative methods, such as support vector machines (SVMs), that use vector representations of sequences derived from sequence properties have been shown to have superior accuracy when compared to traditional approaches for the task of remote homology detection.</p> <p>Results</p> <p>We introduce a new method for feature vector representation based on the physicochemical properties of the primary protein sequence. A distribution of physicochemical property scores are assembled from 4-mers of the sequence and normalized based on the null distribution of the property over all possible 4-mers. With this approach there is little computational cost associated with the transformation of the protein into feature space, and overall performance in terms of remote homology detection is comparable with current state-of-the-art methods. We demonstrate that the features can be used for the task of pairwise remote homology detection with improved accuracy versus sequence-based methods such as BLAST and other feature-based methods of similar computational cost.</p> <p>Conclusions</p> <p>A protein feature method based on physicochemical properties is a viable approach for extracting features in a computationally inexpensive manner while retaining the sensitivity of SVM protein homology detection. Furthermore, identifying features that can be used for generic pairwise homology detection in lieu of family-based homology detection is important for applications such as large database searches and comparative genomics.</p

A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis

<p>Abstract</p> <p>Background</p> <p>Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences.</p> <p>Results</p> <p>In this paper, a novel building block of proteins called Top-<it>n</it>-grams is presented, which contains the evolutionary information extracted from the protein sequence frequency profiles. The protein sequence frequency profiles are calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into Top-<it>n</it>-grams. The protein sequences are transformed into fixed-dimension feature vectors by the occurrence times of each Top-<it>n</it>-gram. The training vectors are evaluated by SVM to train classifiers which are then used to classify the test protein sequences. We demonstrate that the prediction performance of remote homology detection and fold recognition can be improved by combining Top-<it>n</it>-grams and latent semantic analysis (LSA), which is an efficient feature extraction technique from natural language processing. When tested on superfamily and fold benchmarks, the method combining Top-<it>n</it>-grams and LSA gives significantly better results compared to related methods.</p> <p>Conclusion</p> <p>The method based on Top-<it>n</it>-grams significantly outperforms the methods based on many other building blocks including N-grams, patterns, motifs and binary profiles. Therefore, Top-<it>n</it>-gram is a good building block of the protein sequences and can be widely used in many tasks of the computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the prediction of protein binding sites.</p