The PhysioNet/Computing in Cardiology (CinC) Challenge 2017 focused on differentiating AF from noise, normal or other rhythms in short term (from 9–61 s) ECG recordings performed by patients. A total of 12,186 ECGs were used: 8,528 in the public training set and 3,658 in the private hidden test set. Due to the high degree of inter-expert disagreement between a significant fraction of the expert labels we implemented a mid-competition bootstrap approach to expert relabeling of the data, levering the best performing Challenge entrants’ algorithms to identify contentious labels. A total of 75 independent teams entered the Challenge using a variety of traditional and novel methods, ranging from random forests to a deep learning approach applied to the raw data in the spectral domain. Four teams won the Challenge with an equal high F1 score (averaged across all classes) of 0.83, although the top 11 algorithms scored within 2% of this. A combination of 45 algorithms identified using LASSO achieved an F1 of 0.87, indicating that a voting approach can boost performance.

Peroxisome proliferators such as clofibric acid, nafenopin, and WY-14,643 have been shown to activate PPAR (peroxisome proliferator-activated receptor), a member of the steroid nuclear receptor superfamily. We have cloned the cDNA from the rat that is homologous to that from the mouse [Issemann, I. & Green, S. (1990) Unsaturated fatty acids induce peroxisomal proliferation and lower blood triglyceride levels (1-3). Similar effects are evoked by a number of man-made compounds which are either considered for therapy of hyperlipidemia-e.g., clofibric acid, nafenopin, WY-14,643, or sulfur-substituted fatty acids-or are in use as industrial plasticizers (4-6). The steroid dehydroepiandrosterone (DHEA) also induces peroxisomal proliferation but increases blood triglyceride and cholesterol levels (7,8). Two main hypotheses have been developed to explain the complex response of peroxisomal proliferation to this wide variety of inducers. According to one theory the intracellular accumulation of fatty acids is the key stimulus for triggering peroxisomal proliferation (4, 9). The other theory postulates the involvement of a receptor protein (4, 10) and an as-yet-unknown intracellular messenger-e.g., the ligand for this receptor.The latter idea gained substantial support from the discovery of mouse peroxisome proliferator-activated receptor (mPPAR), a member of the steroid nuclear receptor superfamily (11,12). The gene encoding PPAR belongs to a number of genes cloned in the last few years by means of their homology with steroid receptors. In general, ligands or physiologically occurring activators have been identified for only a few of these so-called orphan receptors (13-15). In the case of mPPAR, transactivation studies using chimeric proteins composed of the putative ligand-binding domain of the novel receptor and DNA-binding domains of known steroid receptors showed that mPPAR could be activated by peroxisome proliferators (11). However, the identity ofthe ultimate ligand of the receptor protein, the nature of physiological activators, and how the receptor might relate to the concept of fatty acids as inducers of peroxisomal proliferation remain unclear.We now describe the cloning from rat liver of a gene homologous to that encoding mPPAR.

In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.

Here, the hybrid of NiCo2S4 nanoparticles grown on graphene in situ is first described as an effective bifunctional nonprecious electrocatalyst for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) in the alkaline medium. NiCo2S4@N/S-rGO was synthesized by a one-pot solvothermal strategy using Co(OAc)2, Ni(OAc)2, thiourea, and graphene oxide as precursors and ethylene glycol as the dispersing agent; simultaneously, traces of nitrogen and sulfur were double-doped into the reduced graphene oxide (rGO) in the forms of pyrrolic-N, pyridinic-N, and thiophenic-S, which are often desirable for metal-free ORR catalysts. In comparison with commercial Pt/C catalyst, NiCo2S4@N/S-rGO shows less reduction activity, much better durability, and superior methanol tolerance toward ORR in 0.1 M KOH; it reveals higher activity toward OER in both KOH electrolyte and phosphate buffer at pH 7.0. NiCo2S4@graphene demonstrated excellent overall bicatalytic performance, and importantly, it suggests a novel kind of promising nonprecious bifunctional catalyst in the related renewable energy devices.

Rhizoctonia solani is a major fungal pathogen of rice (Oryza sativa L.) that causes great yield losses in all rice-growing regions of the world. Here we report the draft genome sequence of the rice sheath blight disease pathogen, R. solani AG1 IA, assembled using next-generation Illumina Genome Analyser sequencing technologies. The genome encodes a large and diverse set of secreted proteins, enzymes of primary and secondary metabolism, carbohydrate-active enzymes, and transporters, which probably reflect an exclusive necrotrophic lifestyle. We find few repetitive elements, a closer relationship to Agaricomycotina among Basidiomycetes, and expand protein domains and families. Among the 25 candidate pathogen effectors identified according to their functionality and evolution, we validate 3 that trigger crop defence responses; hence we reveal the exclusive expression patterns of the pathogenic determinants during host infection.