Machine Learning S-Wave Scattering Phase Shifts Bypassing the Radial Schr\"odinger Equation

We present a machine learning model resting on convolutional neural network (CNN) capable to yield accurate scattering phase shifts in s-wave caused by different three-dimensional spherically symmetric potentials at fixed collision energy thereby bypassing the radial Schr\"odinger equation. We obtain good performance even in the presence of potential instances supporting bound states

GenColors: Annotation and comparative genomics made easy

GenColors is a web-based software/database system initially aimed at an improved and accelerated annotation of prokaryotic genomes making extensive use of genome comparison (Romualdi et al., _Bioinformatics_ 2005; Romualdi et al., _Methods Mol. Biol._ 2007). It offers a seamless integration of data from ongoing sequencing projects and annotated genomic sequences obtained from GenBank. With GenColors dedicated genome browsers containing a group of related genomes can be easily set up and maintained. The tool has been efficiently used for sequenceing and annotating the Borrelia garinii genome and is currently applied to a number of other ongoing genome projects on _Legionella_, _Pseudomonas_ and _E. coli_ genomes. Examples for freely accessible GenColors-based dedicated genome browsers are the Spirochetes Genome Browser SGB ("sgb.fli-leibniz.de":http://sgb.fli-leibniz.de), the Photogenome Browser CGB ("cgb.fli-leibniz.de":http://cgb.fli-leibniz.de) and the Enterobacter Genome Browser ENGENE ("engene.fli-leibniz.de":http://engene.fli-leibniz.de). The system has now been adapted to handle also eukaryotic genomes. A first application of this feature is the annotation and analysis of two fungal species (unpublished). Another GenColors-based tool is the Jena Prokaryotic Genome Viewer - JPGV ("jpgv.fli-leibniz.de":http://jpgv.fli-leibniz.de). Contrary to the dedicated browsers it offers information on almost all finished bacterial genomes. Currently, it includes 1140 genomic elements of 293 species

Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach

Acute appendicitis is one of the major causes for emergency surgery in childhood and adolescence. Appendectomy is still the therapy of choice, but conservative strategies are increasingly being studied for uncomplicated inflammation. Diagnosis of acute appendicitis remains challenging, especially due to the frequently unspecific clinical picture. Inflammatory blood markers and imaging methods like ultrasound are limited as they have to be interpreted by experts and still do not offer sufficient diagnostic certainty. This study presents a method for automatic diagnosis of appendicitis as well as the differentiation between complicated and uncomplicated inflammation using values/parameters which are routinely and unbiasedly obtained for each patient with suspected appendicitis. We analyzed full blood counts, c-reactive protein (CRP) and appendiceal diameters in ultrasound investigations corresponding to children and adolescents aged 0–17 years from a hospital based population in Berlin, Germany. A total of 590 patients (473 patients with appendicitis in histopathology and 117 with negative histopathological findings) were analyzed retrospectively with modern algorithms from machine learning (ML) and artificial intelligence (AI). The discovery of informative parameters (biomarker signatures) and training of the classification model were done with a maximum of 35% of the patients. The remaining minimum 65% of patients were used for validation. At clinical relevant cut-off points the accuracy of the biomarker signature for diagnosis of appendicitis was 90% (93% sensitivity, 67% specificity), while the accuracy to correctly identify complicated inflammation was 51% (95% sensitivity, 33% specificity) on validation data. Such a test would be capable to prevent two out of three patients without appendicitis from useless surgery as well as one out of three patients with uncomplicated appendicitis. The presented method has the potential to change today’s therapeutic approach for appendicitis and demonstrates the capability of algorithms from AI and ML to significantly improve diagnostics even based on routine diagnostic parameters

A global gene evolution analysis on Vibrionaceae family using phylogenetic profile

<p>Abstract</p> <p>Background</p> <p><it>Vibrionaceae </it>represent a significant portion of the cultivable heterotrophic sea bacteria; they strongly affect nutrient cycling and some species are devastating pathogens.</p> <p>In this work we propose an improved phylogenetic profile analysis on 14 <it>Vibrionaceae </it>genomes, to study the evolution of this family on the basis of gene content.</p> <p>The phylogenetic profile is based on the observation that genes involved in the same process (e.g. metabolic pathway or structural complex) tend to be concurrently present or absent within different genomes. This allows the prediction of hypothetical functions on the basis of a shared phylogenetic profiles. Moreover this approach is useful to identify putative laterally transferred elements on the basis of their presence on distantly phylogenetically related bacteria.</p> <p>Results</p> <p><it>Vibrionaceae </it>ORFs were aligned against all the available bacterial proteomes. Phylogenetic profile is defined as an array of distances, based on aminoacid substitution matrixes, from single genes to all their orthologues. Final phylogenetic profiles, derived from non-redundant list of all ORFs, was defined as the median of all the profiles belonging to the cluster. The resulting phylogenetic profiles matrix contains gene clusters on the rows and organisms on the columns.</p> <p>Cluster analysis identified groups of "core genes" with a widespread high similarity across all the organisms and several clusters that contain genes homologous only to a limited set of organisms. On each of these clusters, COG class enrichment has been calculated. The analysis reveals that clusters of core genes have the highest number of enriched classes, while the others are enriched just for few of them like DNA replication, recombination and repair.</p> <p>Conclusion</p> <p>We found that mobile elements have heterogeneous profiles not only across the entire set of organisms, but also within <it>Vibrionaceae</it>; this confirms their great influence on bacteria evolution even inside the same family. Furthermore, several hypothetical proteins highly correlate with mobile elements profiles suggesting a possible horizontal transfer mechanism for the evolution of these genes. Finally, we suggested the putative role of some ORFs having an unknown function on the basis of their phylogenetic profile similarity to well characterized genes.</p

Single cell analysis reveals the involvement of the long non-coding RNA Pvt1 in the modulation of muscle atrophy and mitochondrial network

Long non-coding RNAs (lncRNAs) are emerging as important players in the regulation of several aspects of cellular biology. For a better comprehension of their function, it is fundamental to determine their tissue or cell specificity and to identify their subcellular localization. In fact, the activity of lncRNAs may vary according to cell and tissue specificity and subcellular compartmentalization. Myofibers are the smallest complete contractile system of skeletal muscle influencing its contraction velocity and metabolism. How lncRNAs are expressed in different myofibers, participate in metabolism regulation and muscle atrophy or how they are compartmentalized within a single myofiber is still unknown. We compiled a comprehensive catalog of lncRNAs expressed in skeletal muscle, associating the fiber-type specificity and subcellular location to each of them, and demonstrating that many lncRNAs can be involved in the biological processes de-regulated during muscle atrophy. We demonstrated that the lncRNA Pvt1, activated early during muscle atrophy, impacts mitochondrial respiration and morphology and affects mito/autophagy, apoptosis and myofiber size in vivo. This work corroborates the importance of lncRNAs in the regulation of metabolism and neuromuscular pathologies and offers a valuable resource to study the metabolism in single cells characterized by pronounced plasticity

Increased NK Cell Count in Multiple Sclerosis Patients Treated With Dimethyl Fumarate: A 2-Year Longitudinal Study

Background: Dimethyl fumarate (DMF) is a disease-modifying drug for relapsing-remitting multiple sclerosis. Among others, DMF impedes immune activation by shifting the balance between inflammatory and regulatory cell types and by inducing apoptosis-triggered lymphopenia. Although the decrease in lymphocyte count is an early effect of the drug in several patients, the long-term impact on lymphocyte subsets is largely unknown.Methods: We performed a 2-years observational study on total lymphocyte count and subsets thereof by flow cytometry of peripheral blood of 38 multiple sclerosis patients in treatment with DMF. Data were collected at the beginning and after 3, 6, 12, and 24 months of therapy.Results: Total lymphocyte count decreased in relation to time of exposure to DMF. Mean absolute B cell count decreased by 34.1%(p &lt; 0.001) within the first 3months of therapy and then remained stable over time. Mean absolute CD3(+) T cells count decrement reached 47.5% after 12 months of treatment ( p &lt; 0.001). NK cells count showed a heterogeneous trend, increasing by 85.9%( p &lt; 0.001) after 2 years of treatment. CD4(+) T cells and CD8(+) T cells substantially decreased, with a significant increase of CD4(+)/CD8(+) ratio during the first year of therapy.Conclusions: NK cells showed a heterogeneous behavior during DMF treatment with a significant increase over time. Since NK cells may also have a regulatory effect on immune system modulation, their increase during DMF treatment might play a role in the efficacy and safety of the drug

Circulating Cell-Free DNA in Dogs with Mammary Tumors: Short and Long Fragments and Integrity Index

Circulating cell-free DNA (cfDNA) has been considered an interesting diagnostic/prognostic plasma biomarker in tumor-bearing subjects. In cancer patients, cfDNA can hypothetically derive from tumor necrosis/apoptosis, lysed circulating cells, and some yet unrevealed mechanisms of active release. This study aimed to preliminarily analyze cfDNA in dogs with canine mammary tumors (CMTs). Forty-four neoplastic, 17 non-neoplastic disease-bearing, and 15 healthy dogs were recruited. Necrosis and apoptosis were also assessed as potential source of cfDNA on 78 CMTs diagnosed from the 44 dogs. The cfDNA fragments and integrity index significantly differentiated neoplastic versus non-neoplastic dogs (P<0.05), and allowed the distinction between benign and malignant lesions (P<0.05). Even if without statistical significance, the amount of cfDNA was also affected by tumor necrosis and correlated with tumor size and apoptotic markers expression. A significant (P<0.01) increase of Bcl-2 in malignant tumors was observed, and in metastatic CMTs the evasion of apoptosis was also suggested. This study, therefore, provides evidence that cfDNA could be a diagnostic marker in dogs carrying mammary nodules suggesting that its potential application in early diagnostic procedures should be further investigated

Impact of Host Genes and Strand Selection on miRNA and miRNA* Expression

Dysregulation of miRNAs expression plays a critical role in the pathogenesis of genetic, multifactorial disorders and in human cancers. We exploited sequence, genomic and expression information to investigate two main aspects of post-transcriptional regulation in miRNA biogenesis, namely strand selection regulation and expression relationships between intragenic miRNAs and host genes. We considered miRNAs expression profiles, measured in five sizeable microarray datasets, including samples from different normal cell types and tissues, as well as different tumours and disease states. First, the study of expression profiles of “sister” miRNA pairs (miRNA/miRNA*, 5′ and 3′ strands of the same hairpin precursor) showed that the strand selection is highly regulated since it shows tissue-/cell-/condition-specific modulation. We used information about the direction and the strength of the strand selection bias to perform an unsupervised cluster analysis for the sample classification evidencing that is able to distinguish among different tissues, and sometimes between normal and malignant cells. Then, considering a minimum expression threshold, in few miRNA pairs only one mature miRNA is always present in all considered cell types, whereas the majority of pairs were concurrently expressed in some cell types and alternatively in others. In a significant fraction of concurrently expressed pairs, the major and the minor forms found at comparable levels may contribute to post-transcriptional gene silencing, possibly in a coordinate way. In the second part of the study, the behaved tendency to co-expression of intragenic miRNAs and their “host” mRNA genes was confuted by expression profiles examination, suggesting that the expression profile of a given host gene can hardly be a good estimator of co-transcribed miRNA(s) for post-transcriptional regulatory networks inference. Our results point out the regulatory importance of post-transcriptional phases of miRNAs biogenesis, reinforcing the role of such layer of miRNA biogenesis in miRNA-based regulation of cell activities