Critical assessment of protein intrinsic disorder prediction.

Intrinsically disordered proteins, defying the traditional protein structure-function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude

Building capacity for HIV/AIDS program leadership and management in Uganda through mentored Fellowships

Background: Around the world, health professionals and program managers are leading and managing public and private health organizations with little or no formal management and leadership training and experience. Objective: To describe an innovative 2-year, long-term apprenticeship Fellowship training program implemented by Makerere University School of Public Health (MakSPH) to strengthen capacity for leadership and management of HIV/AIDS programs in Uganda. Implementation process: The program, which began in 2002, is a 2-year, full-time, non-degree Fellowship. It is open to Ugandan nationals with postgraduate training in health-related disciplines. Enrolled Fellows are attached to host institutions implementing HIV/AIDS programs and placed under the supervision of host institution and academic mentors. Fellows spend 75% of their apprenticeship at the host institutions while the remaining 25% is dedicated to didactic short courses conducted at MakSPH to enhance their knowledge base. Achievements: Overall, 77 Fellows have been enrolled since 2002. Of the 57 Fellows who were admitted between 2002 and 2008, 94.7% (54) completed the Fellowship successfully and 50 (92.3%) are employed in senior leadership and management positions in Uganda and internationally. Eighty-eight percent of those employed (44/54) work in institutions registered in Uganda, indicating a high level of in-country retention. Nineteen of the 20 Fellows who were admitted between 2009 and 2010 are still undergoing training. A total of 67 institutions have hosted Fellows since 2002. The host institutions have benefited through staff training and technical expertise from the Fellows as well as through grant support to Fellows to develop and implement innovative pilot projects. The success of the program hinges on support from mentors, stakeholder involvement, and the hands-on approach employed in training. Conclusion: The Fellowship Program offers a unique opportunity for hands-on training in HIV/AIDS program leadership and management for both Fellows and host institutions

The politicisation of evaluation: constructing and contesting EU policy performance

Although systematic policy evaluation has been conducted for decades and has been growing strongly within the European Union (EU) institutions and in the member states, it remains largely underexplored in political science literatures. Extant work in political science and public policy typically focuses on elements such as agenda setting, policy shaping, decision making, or implementation rather than evaluation. Although individual pieces of research on evaluation in the EU have started to emerge, most often regarding policy “effectiveness” (one criterion among many in evaluation), a more structured approach is currently missing. This special issue aims to address this gap in political science by focusing on four key focal points: evaluation institutions (including rules and cultures), evaluation actors and interests (including competencies, power, roles and tasks), evaluation design (including research methods and theories, and their impact on policy design and legislation), and finally, evaluation purpose and use (including the relationships between discourse and scientific evidence, political attitudes and strategic use). The special issue considers how each of these elements contributes to an evolving governance system in the EU, where evaluation is playing an increasingly important role in decision making

Introductory clifford analysis

In this chapter an introduction is given to Clifford analysis and the underlying Clifford algebras. The functions under consideration are defined on Euclidean space and take values in the universal real or complex Clifford algebra, the structure and properties of which are also recalled in detail. The function theory is centered around the notion of a monogenic function, which is a null solution of a generalized Cauchy–Riemann operator, which is rotation invariant and factorizes the Laplace operator. In this way, Clifford analysis may be considered as both a generalization to higher dimension of the theory of holomorphic functions in the complex plane and a refinement of classical harmonic analysis. A notion of monogenicity may also be associated with the vectorial part of the Cauchy–Riemann operator, which is called the Dirac operator; some attention is paid to the intimate relation between both notions. Since a product of monogenic functions is, in general, no longer monogenic, it is crucial to possess some tools for generating monogenic functions: such tools are provided by Fueter’s theorem on one hand and the Cauchy–Kovalevskaya extension theorem on the other hand. A corner stone in this function theory is the Cauchy integral formula for representation of a monogenic function in the interior of its domain of monogenicity. Starting from this representation formula and related integral formulae, it is possible to consider integral transforms such as Cauchy, Hilbert, and Radon transforms, which are important both within the theoretical framework and in view of possible applications

The InterPro protein families and domains database: 20 years on

The InterPro database (https://www.ebi.ac.uk/interpro/) provides an integrative classification of protein sequences into families, and identifies functionally important domains and conserved sites. InterProScan is the underlying software that allows protein and nucleic acid sequences to be searched against InterPro's signatures. Signatures are predictive models which describe protein families, domains or sites, and are provided by multiple databases. InterPro combines signatures representing equivalent families, domains or sites, and provides additional information such as descriptions, literature references and Gene Ontology (GO) terms, to produce a comprehensive resource for protein classification. Founded in 1999, InterPro has become one of the most widely used resources for protein family annotation. Here, we report the status of InterPro (version 81.0) in its 20th year of operation, and its associated software, including updates to database content, the release of a new website and REST API, and performance improvements in InterProScan

Using neural networks and evolutionary information in decoy discrimination for protein tertiary structure prediction

Background: We present a novel method of protein fold decoy discrimination using machine learning, more specifically using neural networks. Here, decoy discrimination is represented as a machine learning problem, where neural networks are used to learn the native-like features of protein structures using a set of positive and negative training examples. A set of native protein structures provides the positive training examples, while negative training examples are simulated decoy structures obtained by reversing the sequences of native structures. Various features are extracted from the training dataset of positive and negative examples and used as inputs to the neural networks.Results: Results have shown that the best performing neural network is the one that uses input information comprising of PSI-BLAST [1] profiles of residue pairs, pairwise distance and the relative solvent accessibilities of the residues. This neural network is the best among all methods tested in discriminating the native structure from a set of decoys for all decoy datasets tested. Conclusion: This method is demonstrated to be viable, and furthermore evolutionary information is successfully used in the neural networks to improve decoy discrimination

Cultural Aspects of Attachment Anxiety, Avoidance, and Life Satisfaction: Comparing the US and Turkey

Attachment insecurity can interfere with the experience, expression, and benefits of positive emotions, including happiness and life satisfaction (LS). However, both the pattern and effects of insecure attachment orientations on LS vary across cultures. Considering that attachment anxiety is higher in collectivist cultures and attachment avoidance is relatively high in individualistic cultures, the present chapter elaborates on the idea that anxious and avoidant attachment would have varying effects on LS in individualistic and collectivistic cultural contexts. Study 1 (N = 2456) involved a community sample of married couples in Turkey and demonstrated that attachment avoidance was a stronger predictor of LS than attachment anxiety in Turkish collectivist context. Study 2 tested the hypothesis that the roles of attachment anxiety and avoidance in predicting LS would vary between collectivistic and individualistic cultures. Mothers’ adult attachment dimensions and LS in Turkey (N = 89) and the United States (N = 91) were measured. As expected, results indicated that LS was predicted only by attachment avoidance in Turkey and by attachment anxiety in the United States. These findings are in line with the cultural fit hypothesis, suggesting that culturally incongruent attachment orientations have a stronger negative impact on individuals’ LS

Lessons from the CAGI-4 Hopkins clinical panel challenge

The CAGI-4 Hopkins clinical panel challenge was an attempt to assess state of the art methods for clinical phenotype prediction from DNA sequence. Participants were provided with exonic sequences of 83 genes for 106 patients from the Johns Hopkins DNA Diagnostic Laboratory. Five groups participated in the challenge, predicting both the probability that each patient had each of fourteen possible classes of disease, as well as one or more causal variants. In cases where the Hopkins laboratory reported a variant, at least one predictor correctly identified the disease class in 36 of 43 patients (84%). Even in cases where the Hopkins laboratory did not find a variant, at least one predictor correctly identified the class in 39 of 63 patients (62%). Each prediction group correctly diagnosed at least one patient that was not successfully diagnosed by any other groups. We discuss the causal variant predictions by the different groups and their implications for further development of methods to assess variants of unknown significance. Our results suggest that clinically relevant variants may be missed when physicians order small panels targeted on a specific phenotype. We also quantify the false positive rate of DNA-guided analysis in the absence of prior phenotypic indication. This article is protected by copyright. All rights reserved