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Objectives: Predictive models for critical events in the intensive care unit (ICU) might help providers anticipate patient deterioration. At the heart of predictive model development lies the ability to accurately label significant events, thereby facilitating the use of machine learning and similar strategies. We conducted this study to establish the validity of an automated system for tagging respiratory and hemodynamic deterioration by comparing automatic tags to tagging by expert reviewers.Methods: This retrospective cohort study included 72,650 unique patient stays collected from Electronic Medical Records of the University of Massachusetts’ eICU. An enriched subgroup of stays was manually tagged by expert reviewers. The tags generated by the reviewers were compared to those generated by an automated system.Results: The automated system was able to rapidly and efficiently tag the complete database utilizing available clinical data. The overall agreement rate between the automated system and the clinicians for respiratory and hemodynamic deterioration tags was 89.4% and 87.1%, respectively. The automatic system did not add substantial variability beyond that seen among the reviewers.Conclusions: We demonstrated that a simple rule-based tagging system could provide a rapid and accurate tool for mass tagging of a compound database. These types of tagging systems may replace human reviewers and save considerable resources when trying to create a validated, labeled database used to train artificial intelligence algorithms. The ability to harness the power of artificial intelligence depends on efficient clinical validation of targeted conditions; hence, these systems and the methodology used to validate them are crucial.

Recent academic studies have demonstrated the possibility of inferring user actions performed in mobile apps by analyzing the resulting encrypted network traffic. Due to the multitude of app versions, mobile operating systems, and device models (collectively referred to in this paper as configurations) previous approaches are not applicable to real life settings. In this work, we ex-tend the ability of these approaches to generalize across different configurations. We treat the different configurations as a case for transfer learning, and adapt the co-training method to sup-port the transfer learning process. Our approach leverages a small number of labeled instances of encrypted traffic from a source configuration, in order to construct a classifier capable of identi-fying a users actions in a different (target) configuration which is completely unlabeled. Experi-ments on real datasets collected from different applications on Android devices show that the proposed method achieves F1 measures over 0.8 for most of the considered user actions.

BACKGROUND AnWj is a high-incidence blood group antigen associated with three clinical disorders: lymphoid malignancies, immunologic disorders, and autoimmune hemolytic anemia. The aim of this study was to determine the genetic basis of an inherited AnWj-negative phenotype. METHODS We identified a consanguineous family with two AnWj-negative siblings and 4 additional AnWj-negative individuals without known familial relationship to the index family. We performed exome sequencing in search for rare homozygous variants shared by the two AnWj-negative siblings of the index family and searched for these variants in the four non-related AnWj-negative individuals. RESULTS Exome sequencing revealed seven candidate genes that showed complete segregation in the index family and for which the two AnWj-negative siblings were homozygous. However, the four additional non-related AnWj-negative subjects were homozygous for only one of these variants, rs114851602 (R320Q) in the SMYD1 gene. Considering the frequency of the minor allele, the chance of randomly finding 4 consecutive such individuals is 2.56 × 10-18 . CONCLUSION We present genetic and statistical evidence that the R320Q substitution in SMYD1 underlies an inherited form of the AnWj-negative blood group phenotype. The mechanism by which the mutation leads to this phenotype remains to be determined.

Purpose To explore the molecular basis of familial, early onset, age-related macular degeneration (AMD) with diverse phenotypes, using whole exome sequencing (WES). Methods We performed WES on four patients (two sibs from two families) manifesting early-onset AMD and searched for disease-causing genetic variants in previously identified macular degeneration related genes. Validation studies of the variants included bioinformatics tools, segregation analysis of mutations within the families and mutation screening in an AMD cohort of patients. Results The index patients were in their 50s when diagnosed and displayed a wide variety of clinical AMD presentations: from limited drusen in the posterior pole to multiple basal-laminar drusen extending peripherally. Severe visual impairment due to extensive geographic atrophy and/or choroidal-neovascularisation was common by the age of 75 years. Approximately, 400 000 genomic variants for each DNA sample were included in the downstream bioinformatics analysis, which ended in the discovery of two novel variants; in one family a single bp deletion was identified in the Hemicentin ( HMCN1 ) gene (c.4162delC), whereas in the other, a missense variant (p.V412M) in the Complement Factor-I ( CFI ) gene was found. Screening for these variants in a cohort of patients with AMD identified another family with the CFI variant. Conclusions This report uses WES to uncover rare genetic variants in AMD. A null-variant in HMCN1 has been identified in one AMD family, and a missense variant in CFI was discovered in two other families. These variants confirm the genetic complexity and significance of rare genetic variants in the pathogenesis of AMD.

Many mutations disappear from the population because they impair protein function and/or stability. Thus, amino acid positions that are essential for proper function evolve more slowly than others, or in other words, the slow evolutionary rate of a position reflects its importance. Con- Surf (http://consurf.tau.ac.il), reviewed in this manuscript, exploits this to reveal key amino acid positions that are im- portant for maintaining the native conformation(s) of the protein and its function, be it binding, catalysis, transport, etc. Given the sequence or 3D structure of the query protein as input, a search for similar sequences is conducted and the sequences are aligned. The multiple sequence alignment is subsequently used to calculate the evolutionary rates of each amino acid site, using Bayesian or maximum-likelihood algorithms. Both algorithms take into account the evolution- ary relationships between the sequences, reflected in phylo- genetic trees, to alleviate problems due to uneven (biased) sampling in sequence space. This is particularly important when the number of sequences is low. The ConSurf-DB, a new release of which is presented here, provides precalcu- lated ConSurf conservation analysis of nearly all available structures in the Protein DataBank (PDB). The usefulness of ConSurf for the study of individual proteins and mutations, as well as a range of large-scale, genome-wide applications, is reviewed.

Positively charged electrostatic patches on protein surfaces are usually indicative of nucleic acid binding interfaces. Interestingly, many proteins which are not involved in nucleic acid binding possess large positive patches on their surface as well. In some cases, the positive patches on the protein are related to other functional properties of the protein family. PatchFinderPlus (PFplus) http://pfp.technion.ac.il is a web-based tool for extracting and displaying continuous electrostatic positive patches on protein surfaces. The input required for PFplus is either a four letter PDB code or a protein coordinate file in PDB format, provided by the user. PFplus computes the continuum electrostatics potential and extracts the largest positive patch for each protein chain in the PDB file. The server provides an output file in PDB format including a list of the patch residues. In addition, the largest positive patch is displayed on the server by a graphical viewer (Jmol), using a simple color coding.

Motivation: The study of RNA virus populations is a challenging task. Each population of RNA virus is composed of a collection of different, yet related genomes often referred to as mutant spectra or quasispecies. Virologists using deep sequencing technologies face major obstacles when studying virus population dynamics, both experimentally and in natural settings due to the relatively high error rates of these technologies and the lack of high performance pipelines. In order to overcome these hurdles we developed a computational pipeline, termed ViVan (Viral Variance Analysis). ViVan is a complete pipeline facilitating the identification, characterization and comparison of sequence variance in deep sequenced virus populations. Results: Applying ViVan on deep sequenced data obtained from samples that were previously characterized by more classical approaches, we uncovered novel and potentially crucial aspects of virus populations. With our experimental work, we illustrate how ViVan can be used for studies ranging from the more practical, detection of resistant mutations and effects of antiviral treatments, to the more theoretical temporal characterization of the population in evolutionary studies. Availability and implementation: Freely available on the web at http://www.vivanbioinfo.org Contact: nshomron@post.tau.ac.il Supplementary information: Supplementary data are available at Bioinformatics online.