Fast training of self organizing maps for the visual exploration of molecular compounds

Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen’s Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data. In this paper we present a fast learning algorithm for SOMs that uses a simulated annealing method to adapt the learning parameters. The algorithm has been adopted in a data analysis framework for the generation of similarity maps. Such maps provide an effective tool for the visual exploration of large and multi-dimensional input spaces. The approach has been applied to data generated during the High Throughput Screening of molecular compounds; the generated maps allow a visual exploration of molecules with similar topological properties. The experimental analysis on real world data from the National Cancer Institute shows the speed up of the proposed SOM training process in comparison to a traditional approach. The resulting visual landscape groups molecules with similar chemical properties in densely connected regions

Physiopathologica, epidemiologica, clinical and therapeutic aspects of exercise-associated hyponatriemia

Exercise-associated hyponatremia (EAH) is dilutional hyponatremia, a variant of inappropriate antidiuretic hormone secretion (SIADH), characterized by a plasma concentration of sodium lower than 135 mEq/L. The prevalence of EAH is common in endurance (6 hours in duration), in which both athletes and medical providers need to be aware of risk factors, symptom presentation, and management. The development of EAH is a combination of excessive water intake, inadequate suppression of the secretion of the antidiuretic hormone (ADH) (due to non osmotic stimuli), long race duration, and very high or very low ambient temperatures. Additional risk factors include female gender, slower race times, and use of nonsteroidal anti-inflammatory drugs. Signs and symptoms of EAH include nausea, vomiting, confusion, headache and seizures; it may result in severe clinical conditions associated with pulmonary and cerebral edema, respiratory failure and death. A rapid diagnosis and appropriate treatment with a hypertonic saline solution is essential in the severe form to ensure a positive outcome

Marked elevation of transaminases and pancreatic enzymes in severe malnourished male with eating disorder

We report a case of a 45 year old Caucasian malnourished male with an history of eating disorder who developed severe liver and pancreatic damage and multiorgan disfunction. At admission to our department, his body mass index (BMI) was 11.1. Biochemical evaluation showed elevated serum levels of transaminases (AST= 2291 U/L, ALT= 1792 U/L), amylase (3620 U/L), lipase (4102 U/L), CPK= 1370 U/L, LDH= 2082 U/L. No other cause of acute liver and pancreatic damage was evidenced. Haematological disorders (anemia, thrombocytopenia, leukopenia) found on admission seem related to bone marrow hypoplasia and to gelatinous marrow transformation described in severe state of malnutrition. Although a moderate increase in liver and pancreatic enzymes are a common finding in malnourished patients, only a small number of reports describes severe liver injury and multiorgan dysfunction. After a few days of treatment (hydration and nutritional support) a marked decrease of serum transaminases, lipase, amylase, CPK, LDH occurred, despite a transient increase in these levels secondary to refeeding syndrome. The association of chronic malnutrition and a decrease in systemic perfusion may be responsible for multiorgan dysfunction. In our patient the high levels of transaminases and pancreatic enzymes were the most important biochemical abnormalities normalized after refeeding

Context-aware visual exploration of molecular databases

Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Especially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results. The presented method uses this experimental data to select molecular fragments of the underlying molecules that have interesting properties and uses the resulting space to generate a two dimensional map based on a singular value decomposition algorithm and a self organizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions

A Logical Architecture for Active Network Management

This paper focuses on improving network management by exploiting the potential of “doing” of the Active Networks technology, together with the potential of “planning,” which is typical of the artificial intelligent systems. We propose a distributed multiagent architecture for Active Network management, which exploits the dynamic reasoning capabilities of the Situation Calculus in order to emulate the reactive behavior of a human expert to fault situations. The information related to network events is generated by programmable sensors deployed across the network. A logical entity collects this information, in order to merge it with general domain knowledge, with a view to identifying the root causes of faults, and to deciding on reparative actions. The logical inference system has been devised to carry out automated isolation, diagnosis, and even repair of network anomalies, thus enhancing the reliability, performance, and security of the network. Experimental results illustrate the Reasoner capability of correctly recognizing fault situations and undertaking management actions

GOWDL: gene ontology-driven wide and deep learning model for cell typing of scRNA-seq data

Single-cell RNA-sequencing (scRNA-seq) allows for obtaining genomic and transcriptomic profiles of individual cells. That data make it possible to characterize tissues at the cell level. In this context, one of the main analyses exploiting scRNA-seq data is identifying the cell types within tissue to estimate the quantitative composition of cell populations. Due to the massive amount of available scRNA-seq data, automatic classification approaches for cell typing, based on the most recent deep learning technology, are needed. Here, we present the gene ontology-driven wide and deep learning (GOWDL) model for classifying cell types in several tissues. GOWDL implements a hybrid architecture that considers the functional annotations found in Gene Ontology and the marker genes typical of specific cell types. We performed cross-validation and independent external testing, comparing our algorithm with 12 other state-of-the-art predictors. Classification scores demonstrated that GOWDL reached the best results over five different tissues, except for recall, where we got about 92% versus 97% of the best tool. Finally, we presented a case study on classifying immune cell populations in breast cancer using a hierarchical approach based on GOWDL

High intrinsic activity of the oxygen evolution reaction in low-cost NiO nanowall electrocatalysts

NiO nanowalls grown by low-cost chemical bath deposition and thermal annealing are a high-efficiency and sustainable electrocatalytst for OER

Correction: High intrinsic activity of the oxygen evolution reaction in low-cost NiO nanowall electrocatalysts

Correction for 'High intrinsic activity of the oxygen evolution reaction in low-cost NiO nanowall electrocatalysts' by Salvatore Cosentino et al., Mater. Adv., 2020, DOI: 10.1039/d0ma00467g

Diabetes Mellitus and Ramadan: Physiopathological, Clinical and Therapeutic Aspects

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Relationship between hospital volume and short-term outcomes: A nationwide population-based study including 75,280 rectal cancer surgical procedures

There is growing interest on the potential relationship between hospital volume (HV) and outcomes as it might justify the centralization of care for rectal cancer surgery. From the National Italian Hospital Discharge Dataset, data on 75,280 rectal cancer patients who underwent elective major surgery between 2002 and 2014 were retrieved and analyzed. HV was grouped into tertiles: low-volume performed 1-12, while high-volume hospitals performed 33+ procedures/year. The impact of HV on in-hospital mortality, abdominoperineal resection (APR), 30-day readmission, and length of stay (LOS) was assessed. Risk factors were calculated using multivariate logistic regression. The proportion of procedures performed in low-volume hospitals decreased by 6.7 percent (p<0.001). The rate of in-hospital mortality, APR and 30-day readmission was 1.3%, 16.3%, and 7.2%, respectively, and the median LOS was 13 days. The adjusted risk of in-hospital mortality (OR = 1.49, 95% CI = 1.25-1.78), APR (OR 1.10, 95%CI 1.02-1.19), 30-day readmission (OR 1.49, 95%CI 1.38-1.61), and prolonged LOS (OR 2.29, 95%CI 2.05-2.55) were greater for low-volume hospitals than for high-volume hospitals. This study shows an independent impact of HV procedures on all short-term outcome measures, justifying a policy of centralization for rectal cancer surgery, a process which is underwa