Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data

Use of socially generated “big data” to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society''s reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between “real time monitoring” and “early predicting” remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.     

Early embryogenesis study on a dimethoate containing formulation and Cd-sulphate in chicken embryos

The toxic effects of a widely used organophosphate insecticide (BI 58 EC containing 38% dimethoate as active ingredient) applied alone or in combination with cadmium sulphate modelling the heavy metal load of the environment were studied on chicken embryos in the early phase of development. Solutions and emulsions of different concentrations were made from the test materials and injected in 0.1 ml volume into the air space of eggs on the first day (day 0) of incubation. Subsequently, on days 2 and 3 of incubation permanent preparations were made from the embryos in order to study the early developmental stage. Embryos fixed on slides and stained with osmium tetroxide solution were studied under light microscope. Summarising the findings, it can be established that the embryotoxicity increased after the simultaneous administration of Cd-sulphate and 38% dimethoate containing insecticide formulation compared to the control or the individually treated groups.     

Benchmarking protein classification algorithms via supervised cross-validation

Development and testing of protein classification algorithms are hampered by the fact that the protein universe is characterized by groups vastly different in the number of members, in average protein size, similarity within group, etc. Datasets based on traditional cross-validation (k-fold, leave-one-out, etc.) may not give reliable estimates on how an algorithm will generalize to novel, distantly related subtypes of the known protein classes. Supervised cross-validation, i.e., selection of test and train sets according to the known subtypes within a database has been successfully used earlier in conjunction with the SCOP database. Our goal was to extend this principle to other databases and to design standardized benchmark datasets for protein classification. Hierarchical classification trees of protein categories provide a simple and general framework for designing supervised cross-validation strategies for protein classification. Benchmark datasets can be designed at various levels of the concept hierarchy using a simple graph-theoretic distance. A combination of supervised and random sampling was selected to construct reduced size model datasets, suitable for algorithm comparison. Over 3000 new classification tasks were added to our recently established protein classification benchmark collection that currently includes protein sequence (including protein domains and entire proteins), protein structure and reading frame DNA sequence data. We carried out an extensive evaluation based on various machine-learning algorithms such as nearest neighbor, support vector machines, artificial neural networks, random forests and logistic regression, used in conjunction with comparison algorithms, BLAST, Smith-Waterman, Needleman-Wunsch, as well as 3D comparison methods DALI and PRIDE. The resulting datasets provide lower, and in our opinion more realistic estimates of the classifier performance than do random cross-validation schemes. A combination of supervised and random sampling was used to construct model datasets, suitable for algorithm comparison.

The datasets are available at http://hydra.icgeb.trieste.it/benchmark.     

An integrated approach for automating validation of extracted ion chromatographic peaks

SUMMARY: Accurate determination of extracted ion chromatographic peak areas in isotope-labeled quantitative proteomics is difficult to automate. Manual validation of identified peaks is typically required. We have integrated a peak confidence scoring algorithm into existing tools which are compatible with analysis pipelines based on the standards from the Institute for Systems Biology. This algorithm automatically excludes incorrectly identified peaks, improving the accuracy of the final protein expression ratio calculation. SOURCE AND SUPPLEMENTARY INFORMATION: http://www.chem.uky.edu/research/lynn/Nelson.pdf.     

Double-stranded RNA binding may be a general plant RNA viral strategy to suppress RNA silencing

In plants, RNA silencing (RNA interference) is an efficient antiviral system, and therefore successful virus infection requires suppression of silencing. Although many viral silencing suppressors have been identified, the molecular basis of silencing suppression is poorly understood. It is proposed that various suppressors inhibit RNA silencing by targeting different steps. However, as double-stranded RNAs (dsRNAs) play key roles in silencing, it was speculated that dsRNA binding might be a general silencing suppression strategy. Indeed, it was shown that the related aureusvirus P14 and tombusvirus P19 suppressors are dsRNA-binding proteins. Interestingly, P14 is a size-independent dsRNA-binding protein, while P19 binds only 21-nucleotide ds-sRNAs (small dsRNAs having 2-nucleotide 3' overhangs), the specificity determinant of the silencing system. Much evidence supports the idea that P19 inhibits silencing by sequestering silencing-generated viral ds-sRNAs. In this study we wanted to test the hypothesis that dsRNA binding is a general silencing suppression strategy. Here we show that many plant viral silencing suppressors bind dsRNAs. Beet yellows virus Peanut P21, clump virus P15, Barley stripe mosaic virus gammaB, and Tobacco etch virus HC-Pro, like P19, bind ds-sRNAs size-selectively, while Turnip crinkle virus CP is a size-independent dsRNA-binding protein, which binds long dsRNAs as well as ds-sRNAs. We propose that size-selective ds-sRNA-binding suppressors inhibit silencing by sequestering viral ds-sRNAs, whereas size-independent dsRNA-binding suppressors inactivate silencing by sequestering long dsRNA precursors of viral sRNAs and/or by binding ds-sRNAs. The findings that many unrelated silencing suppressors bind dsRNA suggest that dsRNA binding is a general silencing suppression strategy which has evolved independently many times.     

Causation at different levels: tracking the commitments of mechanistic explanations

This paper tracks the commitments of mechanistic explanations focusing on the relation between activities at different levels. It is pointed out that the mechanistic approach is inherently committed to identifying causal connections at higher levels with causal connections at lower levels. For the mechanistic approach to succeed a mechanism as a whole must do the very same thing what its parts organised in a particular way do. The mechanistic approach must also utilise bridge principles connecting different causal terms of different theoretical vocabularies in order to make the identities of causal connections transparent. These general commitments get confronted with two claims made by certain proponents of the mechanistic approach: William Bechtel often argues that within the mechanistic framework it is possible to balance between reducing higher levels and maintaining their autonomy at the same time, whereas, in a recent paper, Craver and Bechtel argue that the mechanistic approach is able to make downward causation intelligible. The paper concludes that the mechanistic approach imbued with identity statements is no better candidate for anchoring higher levels to lower ones while maintaining their autonomy at the same time than standard reductive accounts are, and that what mechanistic explanations are able to do at best is showing that downward causation does not exist.     

New Perspectives in the Renin-Angiotensin-Aldosterone System (RAAS) IV: Circulating ACE2 as a Biomarker of Systolic Dysfunction in Human Hypertension and Heart Failure

Background

Growing evidence exists for soluble Angiotensin Converting Enzyme-2 (sACE2) as a biomarker in definitive heart failure (HF), but there is little information about changes in sACE2 activity in hypertension with imminent heart failure and in reverse remodeling.

Methods, Findings

Patients with systolic HF (NYHAII-IV, enrolled for cardiac resynchronisation therapy, CRT, n = 100) were compared to hypertensive patients (n = 239) and to a healthy cohort (n = 45) with preserved ejection fraction (EF>50%) in a single center prospective clinical study. The status of the heart failure patients were checked before and after CRT. Biochemical (ACE and sACE2 activity, ACE concentration) and echocardiographic parameters (EF, left ventricular end-diastolic (EDD) and end-systolic diameter (ESD) and dP/dt) were measured.sACE2 activity negatively correlated with EF and positively with ESD and EDD in all patient''s populations, while it was independent in the healthy cohort. sACE2 activity was already increased in the hypertensive group, where signs for imminent heart failure (slightly decreased EF and barely increased NT-proBNP levels) were detected. sACE2 activities further increased in patients with definitive heart failure (EF<50%), while sACE2 activities decreased with the improvement of the heart failure after CRT (reverse remodeling). Serum angiotensin converting enzyme (ACE) concentrations were lower in the diseased populations, but did not show a strong correlation with the echocardiographic parameters.

Conclusions

Soluble ACE2 activity appears to be biomarker in heart failure, and in hypertension, where heart failure may be imminent. Our data suggest that sACE2 is involved in the pathomechanism of hypertension and HF.