Miracle-dz/UniFAR-FTUs huggingface dataset:
Nearshore ecosystem predictors of a bacterial infection in juvenile Chinook salmon

Disease epidemiology requires information about ecological and environmental condi- tions to identify factors that can influence disease progression. Bacterial kidney disease (BKD) is an endemic disease among Pacific Northwest salmonids that causes significant morbidity and mortality in artificially propagated stocks, but risk factors for infection among free-living salmon are unknown. We evaluated infection by the causative agent of BKD, Renibacterium salmoninarum, in 1752 fish across 52 sampling sites monthly from May to November 2003 as a component of a broader study of neritic habitat use in Puget Sound by juvenile Chinook salmon Oncorhynchus tshawytscha. Infection intensity was ≤10 cells per slide for 77% of the fish. Correlations between the density of Chinook salmon with infection prevalence and with infection intensity were observed across multiple spatial scales. Capture location was a stronger predictor of infection than fish origin (based on coded wire tags) or genetic stock. Influential risk factors by logistic regression were temperature, densities of marked and unmarked Chinook salmon, and density of river lamprey Lampetra ayresis. Renibac- terium salmoninarum were found in gut contents and kidney of river lamprey, suggesting this species may be a transmission vector. The low infection intensity, lack of an effect of fish origin, effect of capture bay, and strong associations with Chinook salmon density are consistent with horizontal transmission of R. salmoninarum during the juvenile neritic phase, posing a potential for infectious interaction between sympatric hatchery and wild fish.
Structural Bioinformatics Analysis of Disease-related Mutations

Abstract In order to understand the protein functions that are re-lated to disease, it is important to detect the correlation between amino acid mutations and disease. Many mu-tation studies about disease-related proteins have been carried out through molecular biology techniques, such as vector design, protein engineering, and protein cry-stallization. However, experimental protein mutation studies are time-consuming, be it in vivo or in vitro. We therefore performed a bioinformatic analysis of known disease-related mutations and their protein structure changes in order to analyze the correlation between mu-tation and disease. For this study, we selected 111 dis-eases that were related to 175 proteins from the PDB database and 710 mutations that were found in the pro-tein structures. The mutations were acquired from the Human Gene Mutation Database (HGMD). We selected point mutations, excluding only insertions or deletions, for detecting structural changes. To detect a structural change by mutation, we analyzed not only the structural properties (distance of pocket and mutation, pocket size, surface size, and stability), but also the phys-ico-chemical properties (weight, instability, isoelectric point (IEP), and GRAVY score) for the 710 mutations. We detected that the distance between the pocket and disease-related mutation lay within 20 A (98.5%, 700 proteins). We found that there was no significant corre-lation between structural stability and disease-causing mutations or between hydrophobicity changes and crit-ical mutations. For large-scale mutational analysis of disease-causing mutations, our bioinformatics approach, using 710 structural mutations, called “Structural Mutatomics,” can help researchers to detect dis-ease-specific mutations and to understand the biological functions of disease-related proteins. Keywords: human gene mutation database (HGMD), structural property, physico-chemical property, structural mutatomics
First Experiments of Deep Learning on LiDAR Point Clouds for Classification of Urban Objects

Large urban agglomerations nowadays are facing some major issues such as economic restrictions, environmental challenges, global and systemic approaches in city management [8]. One of them is to precisely monitor urban objects which can be natural (trees), artificial (traffic lights or poles), static or moving (cars). This is essential to analyze their mutual interaction (for example branches of a tree which are close to an electric pole) and to prevent risks associated with them (for example, dead parts of a tree which may fall on a street). Thus being able to localize urban objects and provide informations about their status is essential. One way of achieving this task is to mount LiDAR scanners on vehicles and analyze acquisitions performed on a regular time basis. However this requires to automatically process the 3D point clouds given by the LiDAR. Automatic object detection has seen an increase of popularity in the recent years due to the rise of Deep-Learning methods [3] being able to achieve human-like performances for object classification in 2D images. This increase of performance is starting to have a huge impact on research in 3D shape recognition (see papers presented on the Web site). Given a complete scene obtained with a LiDAR scanner, i.e. a cloud of 3D points, the ultimate goal is then to localize and identify 3D urban objects. In this paper, we focus on the classification of small part of the 3D scene reduced to a single object. This requires to use a new Deep Learning method dedicated to 3D points. In the following, we briefly review existing methods and we present some preliminary results obtained with the PointNet network.
The correlation threshold as a strategy for gene filtering, with application to irritable bowel syndrome and breast cancer microarray data.

It is well established in the literature that certain disease-associated gene signatures can be identified as a source for predicting the classification of samples or cell lines into diagnostic groups – for example, healthy and diseased. Using standard techniques for the selection of significant genes may lead to many highly correlated genes to be chosen, which may be an issue if we are limited in the number of genes we can select. This article therefore aims to investigate methods for selecting genes with the application of a correlation threshold. The methods are applied to two high-dimensional microarray datasets, one to aid the prediction of the presence or absence of Irritable Bowel Syndrome, and one to predict whether the oestrogen-receptor class of a given breast cancer cell line is positive or negative. Our results suggest that the effectiveness of the correlation threshold as a gene selection parameter depends on the particular microarray dataset and classification problem. While the correlation threshold may be beneficial in some specific scenarios where the number of required genes is restrictively small, it may also have no or even detrimental effect on the classification accuracy.
Human Factors In Computing Systems

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MicroRNA-372 functions as a tumor suppressor in cell invasion, migration and epithelial-mesenchymal transition by targeting ATAD2 in renal cell carcinoma

In recent years, renal cell carcinoma (RCC) has exhibited an increasing incidence and mortality rate worldwide. Accumulating evidence has identified that microRNAs (miRNAs) function as negative or positive regulators of many malignant tumors; however, the roles of miR-372 in RCC remain unclear. The focus of the present study was the functions of miR-372 in RCC metastasis and EMT. Data revealed that miR-372 expression levels were significantly downregulated in RCC tissue samples and cells. Moreover, the decreased expression levels were strongly associated with the poor survival rates and adverse clinical characteristics of RCC patients. Accordingly, miR-372 overexpression markedly inhibited RCC cell invasion, migration and EMT. In terms of the potential mechanisms, ATAD2, the expression of which was inversely correlated with miR-372 expression in RCC, was identified as a direct functional target of miR-372. Notably, ATAD2 silence exerted suppressive functions in RCC cells, being similar to the effects of miR-372 overexpression. In conclusion, findings of this study indicate that miR-372 repressed RCC EMT and metastasis via targeting ATAD2, suggesting that the miR-372/ATAD2 axis may be therapeutic biomarkers for RCC.
Effectiveness of enhanced external counter pulsation on clinical profile and health-related quality of life in patients with coronary heart disease: a systematic review

Introduction. Enhanced External Counter Pulsation (EECP) is a non-invasive United States Food and Drug Administration (FDA) approved outpatient treatment option for the complex problem of angina, a common symptom of coronary heart disease. A systematic review of the literature searched to assess the effect of EECP on the clinical profile that comprised physiological measurements, biochemical assessments, cardiac clinical symptoms, physical functional status, and Health-Related Quality of Life (HRQoL) in Coronary Heart Disease (CHD) patients. Material and methods. Total 258 EECP research articles from the early stage of EECP development to till date screened. Out of 258 EECP articles, total 60 articles (53 EECP articles for clinical profile and 7 article for HRQoL matched the inclusion criteria and other (n = 198) articles excluded due to irrelevant to study objectives. Results. All enrolled studies showed a significant improvement in angina pectoris and HRQoL with reduction of nitroglycerine use and exercise tolerance. There are several gaps in research has been found for further research to evaluate the EECP effectiveness on Body Mass Index (BMI), Heart Rate, Cholesterol, Triglyceride, High Density Lipoprotein (HDL), Low Density Lipoprotein (LDL), HbA1C, SpO2, Vo2max level with a comparative assessment of cardiac and non-cardiac metabolic markers including blood glucose. Conclusions. There is further need of multi-centric randomize controlled trial studies to evaluate the effect of EECP on obese, diabetic, hypertension and other metabolic disease patients and more research required for further modifications in EECP device to cure, prevent and treat chronic diseases by the non-invasive procedure.
Study of the transport mechanism of the melibiose permease from Escherichia coli

El transportador de melibiosa d'Escherichia coli (MelB) pot utilitzar l'energia electroquimica tant de H+, Na+ o Li+ per transportar la melibiosa a l'interior de les cel·lules en contra del seu gradient de concentracio. La MelB es una proteina de 473 aminoacids disposats en 12 helices transmembrana, amb els extrems N- i C-terminal situats al costat citoplasmic. Mitjancant l'us de metodes espectroscopics i bioquimics, hem analitzat el paper d'alguns aminoacids en el bucle 7-8/final d'helix VII, que conte diversos aminoacids aromatics altament conservats, aixi com dos residus carregats negativament. Aplicant tecniques de mutagenesi, es van obtenir mutants individuals en que cada aminoacid s'ha canviat a cisteina excepte Ser-259, que tambe es va canviar a alanina. L'espectroscopia de fluorescencia va mostrar que els mutants dels aminoacids conservats Tyr-256, Tyr-257, Phe-258 i Tyr-260 no uneixen substrats, i els espectres de diferencia d'infrarojos de Y256C i Y260C van confirmar l’absencia d’unio al substrat. Simulacions de dinamica molecular van mostrar que aquests residus aromatics formen part d'un bloc d’interaccions hidrofobiques que jugaria un paper destacat en el mecanisme de transport. D'altra banda, els espectres de fluorescencia i de diferencia d’infraroig van demostrar que el mutant Cys del residu Asp-266 i del restant aminoacid aromatic del bucle 7-8 Phe-268 son capacos d'unir sodi i melibiosa en una forma semblant a la MelB nativa (Cless). Altres mutants de cisteina (S259C, S259A, V261C, G263C, D264C, A265C i L267C) del bucle 7-8/final d'helix VII mostren una capacitat d'unio similar a Cless. Aquests resultats suggereixen que els aminoacids conservats Tyr-256, Tyr-257, Phe-Tyr-258 i 260 tenen un paper estructural important en el mecanisme de transport de la MelB.
Differential Equation Model on the Prevalence of Diabetic Neuropathy

Accepted 25/Aug/2018, Online 30/Aug/2018 Abstract— Diabetic neuropathy is the most common complication of diabetes mellitus, and can be related to Type1 as well as Type2 diabetes. This neurological impairment is not known or understood; hence the treatments are still empirical and not efficient as stated by the physicians. The purpose of this study is to discuss the population based Mathematical model of diabetic neuropathy patients. The study adopted in Ordinary Differential Equations which contains the percentage of diabetic patients with and without diabetic neuropathy. By appropriate definition of a parameter, the solution discussed the critical nature of percentage of diabetic patients with and without diabetic neuropathy is discussed for stability.
Genetically Targeted Fractionated Chemotherapy

Complex and late stage cancer patients are in need of novel methods of selecting and administering chemotherapy particularly for those patients who are refractory to current treatment methods. The use of biomarkers to enhance decision making with regard to the molecular profile of a person’s cancer is becoming more important in the practice of oncology. The standard for the last several decades is to elect chemotherapeutic agents based on staging and histological identification of the primary cancer site alone versus utilizing the genetic and molecular profile information along with histological primary cancer site and staging to select chemotherapy regimens. Cancers are caused by mutations that occur within cells and therefore selecting treatment based on mutations and not primary cancer site alone can provide advantages that may have gone overlooked. As time progresses, more biomarkers continue to be discovered which can lead to more targets for drugs either currently on the market or clinical trials. In addition to advancements made in the progression of cancer treatment with utilizing molecular profiles effectively, there are other therapeutic strategies that have been postulated as advanced effective ways to administer chemotherapy. These strategies provide chemotherapy to patients while fasting, giving insulin or other biological response modifiers adjunctively prior to chemotherapy for enhanced targeting, and giving chemotherapy in micro-doses to allow for increased frequency of administration and the utilization of multiple targeted chemotherapeutic agents concurrently. In this paper we will discuss these topics and explain their benefits in addition to the evidence that supports these treatments. A review on biomarkers and cancer cell metabolism is discussed as it relates to providing a framework for what constitutes a biomarker in addition to what metabolic processes are related to fasting and administering insulin with chemotherapy. The information provided in this document is designed to illuminate and provide evidence for various methodologies that are underserved in the treatment of cancer.
Developing a patient-led cancer care website, ‘CanCare': what do end users want?

The role of the internet, and in particular e-health, in chronic illness is a burgeoning phenomenon but its use to date in cancer care is somewhat limited. In this study an iterative consultative process was undertaken with the aim to develop a cancer care website that was patient-led, facilitated cancer self-management and had the potential to improve communication between patients, family and friends and health care professionals. The process involved multiple demonstrations of an already established base platform (SeeCare©) to potential end users (patients, family members and friends and health professionals) to identify required functions of a cancer care specific platform. An exhaustive list of specifications was prioritised and operationalised resulting in a web platform for people affected by cancer that was ready for testing in the clinical setting: CanCare.
Genetic Algorithm Based Optimization of Space Vector Modulation Employed in STATCOM to Reduce Harmonics in Power System

Existing of nonlinear loads and harmonic generation in electric power systems makes it prevalent to use harmonic filters. STATCOM plays an important role in eliminating harmonics and fixing the voltage using power electronics switches. Various control methods have been introduced for switching in a STATCOM. In this paper the Space Vector Modulation (SVM) is described and it is optimized by Genetic Algorithm (GA) to reduce the harmonic contents. At last, some simulations of power system with STATCOM have been done in MATLAB to prove the efficiency of the proposed switching method employed in a STATCOM. Results including voltage weighted THD show the proper performance of STATCOM using optimized SVM by GA.
Open Judicial Data: A Comparative Analysis

Open data gained considerable traction in government, nonprofit, and profit organizations in the last several years. Open judicial data increase transparency of the judiciary and are an integral part of open justice. This article identifies relevant judicial data set types, reviews widely used open government data evaluation methodologies, selects a methodology for evaluating judicial data sets, uses the methodology to evaluate openness of judicial data sets in chosen countries, and suggests actions to improve efficiency and effectiveness of open data initiatives. Our findings show that judicial data sets should at least include court decisions, case registers, filed document records, and statistical data. The Global Open Data Index methodology is the most suitable for the task. We suggest considering actions to enable more effective and efficient opening of judicial data sets, including publishing legal documents and legal data in standardized machine-readable formats, assigning standardized metadata to the published documents and data sets, providing both programmable and bulk access to documents and data, explicitly publishing licenses which apply to them in a machine-readable format, and introducing a centralized portal enabling retrieval and browsing of open data sets from a single source.
Prevalence of Trachoma and Access to Water and Sanitation in Benue State, Nigeria: Results of 23 Population-Based Prevalence Surveys

ABSTRACT Purpose: We sought to determine the prevalence of trachoma in each local government area (LGA) of Benue State, Nigeria. Methods: Two-stage cluster sampling was used to conduct a series of 23 population-based prevalence surveys. LGAs were the evaluation units surveyed. In each LGA, 25 households were selected in each of 25 clusters, and individuals aged 1 year and above resident in those households were invited to be examined for trachoma. Data on access to water and sanitation were also collected at household level. Results: A total of 91,888 people were examined from among 93,636 registered residents across the 23 LGAs. The LGA-level prevalence of trachomatous inflammation—follicular (TF) in 1–9 year olds ranged from 0.3% to 5.3%. Two LGAs had TF prevalences of 5.0–9.9%. The LGA-level prevalence of trichiasis in ≥15-year-olds ranged from 0.0% to 0.35%. Access to improved drinking water sources ranged from 0% in Gwer West to 99% in Tarka, while access to improved sanitation ranged from 1% in Gwer West to 92% in Oturkpo. Conclusion: There is a need for public health-level interventions against trachoma in three LGAs of Benue State.
IMRT beam angle optimization using Tabu search

The number of cancer patients continues to grow worldwide and the World Health Organization has even considered cancer as one of the main threats to human health and development. Depending on the location and specificities of the tumor, there are many treatments that can be used, including surgery, chemotherapy, immunotherapy and radiation therapy. Intensity Modulated Radiation Therapy (IMRT) is one of the most advanced radiation therapy modalities, and optimization can have a key role in the quality of the treatment delivered. In IMRT, the radiation beam can be thought of as being composed by several small beams, through the use of a multileaf collimator, allowing radiation intensity to be modulated. This complex optimization problem can be divided in three related subproblems that can be solved sequentially. For each patient, the angles from which the radiation will be delivered have to be determined (geometric problem — beam angle optimization). Then, for each of these angles, the radiation intensity map is calculated (fluence or intensity optimization). Finally, it is necessary to determine the behavior of the multileaf collimator that guarantees that the desired radiation intensities are, indeed, delivered (realization problem). In each of these optimization problems, the quality of the treatment delivered depends on the models and algorithms used. In this work the attention will be focused in beam angle optimization, a problem known to be highly non–convex, with many local minima and with an objective function that is time expensive to calculate, which, respectively, means that algorithms that are gradient–based or that require many objective function evaluations will not be adequate. Metaheuristics can be the right tool to tackle this problem, since they are capable of escaping local minima and are known to be able to calculate good solutions for complex problems. In this work, an application of Tabu Search to beam angle optimization is described. Computational results considering ten clinical cases of head–and–neck cancer patients are presented, showing that Tabu Search is capable of improving the equidistant solution usually used in clinical practice. “Truth is ever to be found in the simplicity, and not in the multiplicity and confusion of things. ” – Sir Isaac Newton (1643-1727)