SalmoNet, an integrated network of ten Salmonella enterica strains reveals common and distinct pathways to host adaptation

Salmonella enterica is a prominent bacterial pathogen with implications on human and animal health. Salmonella serovars could be classified as gastro-intestinal or extra-intestinal. Genome-wide comparisons revealed that extra-intestinal strains are closer relatives of gastro-intestinal strains than to each other indicating a parallel evolution of this trait. Given the complexity of the differences, a systems-level comparison could reveal key mechanisms enabling extra-intestinal serovars to cause systemic infections. Accordingly, in this work, we introduce a unique resource, SalmoNet, which combines manual curation, high-throughput data and computational predictions to provide an integrated network for Salmonella at the metabolic, transcriptional regulatory and protein-protein interaction levels. SalmoNet provides the networks separately for five gastro-intestinal and five extra-intestinal strains. As a multi-layered, multi-strain database containing experimental data, SalmoNet is the first dedicated network resource for Salmonella. It comprehensively contains interactions between proteins encoded in Salmonella pathogenicity islands, as well as regulatory mechanisms of metabolic processes with the option to zoom-in and analyze the interactions at specific loci in more detail. Application of SalmoNet is not limited to strain comparisons as it also provides a Salmonella resource for biochemical network modeling, host-pathogen interaction studies, drug discovery, experimental validation of novel interactions, uncovering new pathological mechanisms from emergent properties and epidemiological studies. SalmoNet is available at http://salmonet.org

Surgical Skill Assessment on In-Vivo Clinical Data via the Clearness of Operating Field

Surgical skill assessment is important for surgery training and quality control. Prior works on this task largely focus on basic surgical tasks such as suturing and knot tying performed in simulation settings. In contrast, surgical skill assessment is studied in this paper on a real clinical dataset, which consists of fifty-seven in-vivo laparoscopic surgeries and corresponding skill scores annotated by six surgeons. From analyses on this dataset, the clearness of operating field (COF) is identified as a good proxy for overall surgical skills, given its strong correlation with overall skills and high inter-annotator consistency. Then an objective and automated framework based on neural network is proposed to predict surgical skills through the proxy of COF. The neural network is jointly trained with a supervised regression loss and an unsupervised rank loss. In experiments, the proposed method achieves 0.55 Spearman's correlation with the ground truth of overall technical skill, which is even comparable with the human performance of junior surgeons.Comment: MICCAI 201

Sprouty4 Is an Endogenous Negative Modulator of TrkA Signaling and Neuronal Differentiation Induced by NGF

The Sprouty (Spry) family of proteins represents endogenous regulators of downstream signaling pathways induced by receptor tyrosine kinases (RTKs). Using real time PCR, we detect a significant increase in the expression of Spry4 mRNA in response to NGF, indicating that Spry4 could modulate intracellular signaling pathways and biological processes induced by NGF and its receptor TrkA. In this work, we demonstrate that overexpression of wild-type Spry4 causes a significant reduction in MAPK and Rac1 activation and neurite outgrowth induced by NGF. At molecular level, our findings indicate that ectopic expression of a mutated form of Spry4 (Y53A), in which a conserved tyrosine residue was replaced, fail to block both TrkA-mediated Erk/MAPK activation and neurite outgrowth induced by NGF, suggesting that an intact tyrosine 53 site is required for the inhibitory effect of Spry4 on NGF signaling. Downregulation of Spry4 using small interference RNA knockdown experiments potentiates PC12 cell differentiation and MAPK activation in response to NGF. Together, these findings establish a new physiological mechanism through which Spry4 regulates neurite outgrowth reducing not only the MAPK pathway but also restricting Rac1 activation in response to NGF

Thermal modelling of gas generation and retention in the Jurassic organic-rich intervals in the Darquain field, Abadan Plain, SW Iran

The petroleum system with Jurassic source rocks is an important part of the hydrocarbons discovered in the Middle East. Limited studies have been done on the Jurassic intervals in the 26,500 km2 Abadan Plain in south-west Iran, mainly due to the deep burial and a limited number of wells that reach the basal Jurassic successions. The goal of this study was to evaluate the Jurassic organic-rich intervals and shale gas play in the Darquain field using organic geochemistry, organic petrography, biomarker analysis, and basin modelling methods. This study showed that organic-rich zones present in the Jurassic intervals of Darquain field could be sources of conventional and unconventional gas reserves. The organic matter content of samples from the organic-rich zones corresponds to medium-to-high-sulphur kerogen Type II-S marine origin. The biomarker characteristics of organic-rich zones indicate carbonate source rocks that contain marine organic matter. The biomarker results also suggest a marine environment with reducing conditions for the source rocks. The constructed thermal model for four pseudo-wells indicates that, in the kitchen area of the Jurassic gas reserve, methane has been generated in the Sargelu and Neyriz source rocks from Early Cretaceous to recent times and the transformation ratio of organic matter is more than 97%. These organic-rich zones with high initial total organic carbon (TOC) are in the gas maturity stage [1.5–2.2% vitrinite reflectance in oil (Ro)] and could be good unconventional gas reserves and gas source rocks. The model also indicates that there is a huge quantity of retained gas within the Jurassic organic-rich intervals

Targeting Huntington’s disease through histone deacetylases

Huntington’s disease (HD) is a debilitating neurodegenerative condition with significant burdens on both patient and healthcare costs. Despite extensive research, treatment options for patients with this condition remain limited. Aberrant post-translational modification (PTM) of proteins is emerging as an important element in the pathogenesis of HD. These PTMs include acetylation, phosphorylation, methylation, sumoylation and ubiquitination. Several families of proteins are involved with the regulation of these PTMs. In this review, I discuss the current evidence linking aberrant PTMs and/or aberrant regulation of the cellular machinery regulating these PTMs to HD pathogenesis. Finally, I discuss the evidence suggesting that pharmacologically targeting one of these protein families the histone deacetylases may be of potential therapeutic benefit in the treatment of HD

Existence, uniqueness and stability of fuzzy fractional differential equations with local Lipschitz and linear growth conditions

Abstract Fuzzy fractional differential equations (FFDEs) driven by Liu’s process are a type of fractional differential equations. In this paper, we intend to provide and prove a novel existence and uniqueness theorem for the solutions of FFDEs under local Lipschitz and linear growth conditions. We also investigate the stability of solutions to FFDEs by a theorem. Finally, some examples are provided

Application of machine learning in thermal comfort studies: A review of methods, performance and challenges

This paper provides a systematic review on the application of Machine Learning (ML) in thermal comfort studies to highlight the latest methods and findings and provide an agenda for future studies. Reviewed studies were investigated to highlight ML applications, parameters, methods, performance and challenges. The results show that 62% of reviewed studies focused on developing group-based comfort models, while 35% focused on personal comfort models (PCMs) which account for individual differences and present high prediction accuracy. ML models could outperform PMV and adaptive models with up to 35.9% and 31% higher accuracy and PCMs could outperform PMV models with up to 74% higher accuracy. Applying ML-based control schemas reduced thermal comfort-related energy consumption in buildings up to 58.5%, while improving indoor quality up to 90% and reducing CO2 levels up to 24%. Using physiological parameters improved the prediction accuracy of PCMs up to 97%. Future studies are recommended to further investigate PCMs, determine the optimum sample size and consider both fitting and error metrics for model evaluation. This study introduces data collection, thermal comfort indices, time scale, sample size, feature selection, model selection, and real world application as the remaining challenges in the application of ML in thermal comfort studies