Predicting risk of hospital readmission for comorbidity patients through a novel deep learning framework

Hospital readmission is widely recognized as indicator of inpatient quality of care which has significant impact on healthcare cost. Thus, early recognition of readmission risk has been of growing interest in various hospitals. Additionally, there has been growing attention to provide better care to patients with more complications, whose care would impact the quality of care in multiple directions. To this regard, this research specifically targets comorbidity patients i.e., the patients with chronic disease. This research proposes a novel deep learning- framework termed SDAE-GAN. The presented approach consists of three phases. Firstly, various groups of variables from heterogeneous sources are collated. These variables mainly include demographic, socioeconomic, some statistics about patient’s frequent admissions and their diagnosis codes. Then, more processing applies dealing missing values, digitization and data balancing. Afterwards, stacked denoising auto-encoders function to learn underlying representation; and technically to forms a latent space. The latent variables then are used by a Generative Adversarial Neural Networks to evaluate the risk of 30- day readmission. The model is fine-tuned and being compared with state-of-the-arts. Experimental results exhibit competitive performance with higher sensitivity

High throughput screening of hydrolytic enzymes from termites using a natural substrate derived from sugarcane bagasse

<p>Abstract</p> <p>Background</p> <p>The description of new hydrolytic enzymes is an important step in the development of techniques which use lignocellulosic materials as a starting point for fuel production. Sugarcane bagasse, which is subjected to pre-treatment, hydrolysis and fermentation for the production of ethanol in several test refineries, is the most promising source of raw material for the production of second generation renewable fuels in Brazil. One problem when screening hydrolytic activities is that the activity against commercial substrates, such as carboxymethylcellulose, does not always correspond to the activity against the natural lignocellulosic material. Besides that, the macroscopic characteristics of the raw material, such as insolubility and heterogeneity, hinder its use for high throughput screenings.</p> <p>Results</p> <p>In this paper, we present the preparation of a colloidal suspension of particles obtained from sugarcane bagasse, with minimal chemical change in the lignocellulosic material, and demonstrate its use for high throughput assays of hydrolases using Brazilian termites as the screened organisms.</p> <p>Conclusions</p> <p>Important differences between the use of the natural substrate and commercial cellulase substrates, such as carboxymethylcellulose or crystalline cellulose, were observed. This suggests that wood feeding termites, in contrast to litter feeding termites, might not be the best source for enzymes that degrade sugarcane biomass.</p

CodingQuarry: Highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts

Background: The impact of gene annotation quality on functional and comparative genomics makes gene prediction an important process, particularly in non-model species, including many fungi. Sets of homologous protein sequences are rarely complete with respect to the fungal species of interest and are often small or unreliable, especially when closely related species have not been sequenced or annotated in detail. In these cases, protein homology-based evidence fails to correctly annotate many genes, or significantly improve ab initio predictions. Generalised hidden Markov models (GHMM) have proven to be invaluable tools in gene annotation and, recently, RNA-seq has emerged as a cost-effective means to significantly improve the quality of automated gene annotation. As these methods do not require sets of homologous proteins, improving gene prediction from these resources is of benefit to fungal researchers. While many pipelines now incorporate RNA-seq data in training GHMMs, there has been relatively little investigation into additionally combining RNA-seq data at the point of prediction, and room for improvement in this area motivates this study. Results: CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts. RNA-seq data informs annotations both during gene-model training and in prediction. Our approach capitalises on the high quality of fungal transcript assemblies by incorporating predictions made directly from transcript sequences. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci. Stringent benchmarking against high-confidence annotation subsets showed CodingQuarry predicted 91.3% of Schizosaccharomyces pombe genes and 90.4% of Saccharomyces cerevisiae genes perfectly. These results are 4-5% better than those of AUGUSTUS, the next best performing RNA-seq driven gene predictor tested. Comparisons against whole genome Sc. pombe and S. cerevisiae annotations further substantiate a 4-5% improvement in the number of correctly predicted genes. Conclusions: We demonstrate the success of a novel method of incorporating RNA-seq data into GHMM fungal gene prediction. This shows that a high quality annotation can be achieved without relying on protein homology or a training set of genes. CodingQuarry is freely available (https://sourceforge.net/projects/codingquarry/), and suitable for incorporation into genome annotation pipelines

Hybridization and adaptive evolution of diverse Saccharomyces species for cellulosic biofuel production

Additional file 15. Summary of whole genome sequencing statistics

Biology and biotechnology of Trichoderma

Fungi of the genus Trichoderma are soilborne, green-spored ascomycetes that can be found all over the world. They have been studied with respect to various characteristics and applications and are known as successful colonizers of their habitats, efficiently fighting their competitors. Once established, they launch their potent degradative machinery for decomposition of the often heterogeneous substrate at hand. Therefore, distribution and phylogeny, defense mechanisms, beneficial as well as deleterious interaction with hosts, enzyme production and secretion, sexual development, and response to environmental conditions such as nutrients and light have been studied in great detail with many species of this genus, thus rendering Trichoderma one of the best studied fungi with the genome of three species currently available. Efficient biocontrol strains of the genus are being developed as promising biological fungicides, and their weaponry for this function also includes secondary metabolites with potential applications as novel antibiotics. The cellulases produced by Trichoderma reesei, the biotechnological workhorse of the genus, are important industrial products, especially with respect to production of second generation biofuels from cellulosic waste. Genetic engineering not only led to significant improvements in industrial processes but also to intriguing insights into the biology of these fungi and is now complemented by the availability of a sexual cycle in T. reesei/Hypocrea jecorina, which significantly facilitates both industrial and basic research. This review aims to give a broad overview on the qualities and versatility of the best studied Trichoderma species and to highlight intriguing findings as well as promising applications