Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating compliance with guidelines for the prevention of thromboembolic complications in hospitalised patients with atrial fibrillation

Introduction. Atrial fibrillation (AF) is the most common supraventricular arrhythmia. It results in an increased frequency of thromboembolic complications and a higher death rate. The frequency of arrhythmia is steadily increasing, and a sustained growth in the number of patients with AF can be expected in the coming years. Thromboembolic episodes are the most serious complications of AF. Anticoagulant treatment, based on recommendations, is considered to be the priority action in patients with AF. The aim of this paper is to evaluate compliance with guidelines for the prevention of thrombosis, recommended on discharge, in patients with non-valvular AF, who were hospitalised in a cardiology ward. Methods and materials. 4,099 patients with non-valvular AF, who had been hospitalised and discharged from a cardiology ward between 2004 and 2012, were subject to a retrospective analysis. We assessed the risk of thromboembolic (via CHADS2 score) and haemorrhagic (via HAS-BLED score) complications, as well as data on comorbid conditions and the recommended anticoagulant prevention. The compliance of the prevention of thromboembolic complications with the guidelines was assessed according to the applied anticoagulant and antiplatelet treatment in particular groups of thromboembolism risk. Results. The average age of the examined group was 70.6 (± 10.9) years. AF co-existed most frequently with the following diseases: hypertension (74.8%), ischaemic heart disease (56.7%), and heart failure (54.8%). A low risk of thromboembolic complications was reported in 7.2% of patients, whereas 25.4% were rated as moderate risk, and 67.4% as high thromboembolic risk. A high risk of bleeding was reported in 34.6% of patients. In the prevention of thromboembolic complications, an oral anticoagulant, in monotherapy or in combination with antiplatelet drugs, was recommended for 64% of patients on discharge. According to the guidelines, 66.9% of patients from the examined group qualified for the prevention of thromboembolic complications: 62.4% of those with a high risk of stroke, 86% of those with a moderate risk, and 41.1% of those without stroke risk factors. The highest percentage of patients treated pursuant to the guidelines in the chosen clinical situations was to be seen among patients after a thromboembolic episode (70.8%). We found that in 73.8% of patients aged 65–74, and in 55.2% of patients over the age of 80, anticoagulant prevention was applied in accordance with the guidelines.
Computed Tomography Coronary Angiography: is There Any Progress?

Significant improvements in technical capabilities of multislice computed tomography (MSCT) scanners over the recent years have resulted in better temporal and spatial resolution of computed tomography coronary angiography (CTCA) and a decrease of the acquisition time and reduction of the radiation dose. CTCA has been validated as having an excellent negative predictive value for ruling-out coronary artery disease (CAD) in populations with low-to-intermediate pretest probability and a high accuracy for detecting CAD in patients with atypical chest pain. It can, also, aid in decision-making for the clinical management of patients found to have significant coronary artery stenoses and in the follow-up these patients. The recent improvements of this technology are herein briefly overviewed.
hrough the lens of justice : Just world beliefs mediate elationships between perceived discrimination and ubjective well-being

Previous research has found that experiences with discrimination are often associated with lower levels of well-being among ethnic minority members, but hardly any attention has been given to the processes underlying this relationship. Two studies were conducted among ethnic Turks and Moroccans in the Netherlands to examine the hypothesis that the belief in a just world for self mediates the association between perceived discrimination and subjective well-being. In both studies, negative relationships were found between perceived blatant and subtle discrimination and subjective well-being. These relationships were, however, fully mediated by people’s belief in a just world for self. The reversed possibility that perceptions of discrimination mediate the relationship between the belief in a just world for self and well-being, was not supported. These findings lend support to the idea that discrimination has a negative effect on ethnic minority members’ well-being because it undermines their belief that the world is just to them. © 2013 Elsevier Ltd. All rights reserved.
Improving Candidate to Job Matching with Machine Learning

Recruiting candidates to fit a certain job is a task crucial to many companies in the field of human resources. This usually starts with a labor-intensive process of manually searching through the available applicants in a certain collection, reviewing their CVs, and eventually producing a shortlist of suitable candidates to be interviewed. We researched how to use user feedback (annotations) to improve a software product for matching vacancies to candidates. We learned a ranking algorithm to execute on top of a standard search engine to improve candidate shortlist generation given a vacancy. Here we describe the user instruction procedure we conducted, the evaluation procedure we used, and the challenges we faced when applying techniques from academia in a production system, along with their solutions. In particular, we dealt with ambiguous and incomplete assessment data by combining unassessed and assessed documents. Our assumption is unassessed documents carry some implicit feedback. In the end we achieved around 20 percent improvement in relevant results on our evaluation data. A qualitative analysis showed us that because of the relatively small final filtered dataset this did not generalize well enough to be used in the production system. We conclude with some further research and recommendations for future projects in the same area.
Algorithm Designing for Edges and Surfaces Extraction from Mesh Models

ISSN: 2394-2231 http://www.ijctjournal. org Page 141 Algorithm Designing for Edges and Surfaces Extraction from Mesh Models AbdAlrazzakDabbour*, RabieHabib**, Mariam Saii*** *(Department of Mechatronics Engineering, Tishreen University, Syria) ** (Department of Mechatronics Engineering, Tishreen University, Syria) *** (Department of Computer and Automatic Control Engineering, Tishreen University, Syria)
Suplemento de oxígeno peri-operatorio e infección del sitio operatorio: meta-análisis de ensayos clínicos controlados

Objetivo: revisar sistematicamente la evidencia acerca de la eficacia del suplemento de dosis altas de oxigeno perioperatorio y el riesgo de infeccion del sitio operatorio. Fuentes de datos: Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, LILACS, CINAHL, contacto con investigadores, libros. Seleccion estudios: ensayos clinicos aleatorios o cuasialeatorios que evaluaran suplemento con oxigeno a dosis altas FIO2 " 80% durante el transoperatorio y al menos dos horas postoperatorias con dosis estandares de oxigeno FIO2 #35% y evaluaran la infeccion del sitio operatorio superficial al menos a los 15 dias postoperatorio. Extraccion datos: los autores extrajeron los datos en forma independiente y evaluaron la calidad de los estudios con la escala de Jadad. Sintesis datos: Se incluyeron cuatro estudios con 989 participantes. La infeccion del sitio operatorio con la administracion de FIO2 alta tuvo un OR 0,79 IC 95% (0,34, 1,84) valor p = 0,58 y con evidencia de heterogeneidad X2 11,77 gl 3, p = 0.008, I2 74,5%. Sobre la mortalidad el OR 0,17 IC 95% (0,03, 0,98) Valor p = 0,05 y sin evidencia de heterogeneidad X2 0,01 gl 1, p = 0,94, I2 0%. La duracion de la hospitalizacion tuvo una WMD 0,75 IC 95% (0,05, 1,45)) Valor p = 0,03 y sin evidencia de heterogeneidad X2 2,74 gl 2, p = 0,25, I2 26,9%. Conclusiones: los resultados no apoyan el uso de FIO2 altas en el manejo de pacientes electivos con cirugia abdominal para reducir la infeccion del sitio operatorio y sugiere que puede disminuir la mortalidad global.
High Multiplicity Strip Packing

In the two-dimensional high multiplicity strip packing problem (HMSPP), we are given k distinct rectangle types, where each rectangle type Ti has ni rectangles each with width 0 < wi ≤ 1 and height 0 < hi ≤ 1. The goal is to pack these rectangles into a strip of width 1, without rotating or overlapping the rectangles, such that the total height of the packing is minimized. Let OPT(I) be the optimal height of HMSPP on input I. In this thesis, we consider HMSPP for the case when k = 3 and present an OPT(I) + 3 polynomial time approximation algorithm for it. Additionally, we consider HMSPP for the case when k = 4 and present an OPT(I) + 2 polynomial time approximation algorithm for it.
Why WAIT Program: A Novel Model for Diabetes Weight Management in Routine Clinical Practice

The prevalence of overweight and obesity has increased rapidly among U.S. adults, and in particular, among those diagnosed with type 2 diabetes mellitus (T2DM). Data from the 1999–2002 National Health and Nutrition Examination Survey (NHANES) indicate that the prevalence of overweight and obesity among U.S. adults with diabetes now exceeds 85%. We previously demonstrated that modest weight reduction of approximately 7% over a 6-month period through caloric reduction and increased physical activity improved insulin sensitivity, endothelial function, and several markers of inflammation and coagulation in obese patients with and without diabetes.1,2 The ongoing Look AHEAD (Action for Health in Diabetes) study is exploring the health outcomes associated with modest weight loss maintained over 10 years using an intensive lifestyle intervention (ILI) that combines decreased caloric intake, increased physical activity, and behavior modification versus standard diabetes support and education (DSE) in patients with T2DM. The Look AHEAD study group recently published their first-year results, which are encouraging.3 They found that participants randomized to ILI lost an average of 8.6% of their initial body weight compared with 0.7% in the DSE group. Although both groups experienced blood glucose reductions compared to baseline, A1c improvement in the ILI group was significantly greater than that observed in the DSE group (absolute A1c reduction: −0.64% [ILI] versus −0.14% [DSE], p < 0.001; baseline A1c for both groups: ~7.3%). Notably, A1c lowering was Why WAIT Program: A Novel Model for Diabetes Weight Management in Routine Clinical Practice
DIFFERENT METHODS FOR ACQUISITION AND ANALYSIS OF ECG SIGNAL USING BIOMEDICAL SOFTWARE

: This paper describes the various techniques used for automatic detection and analysis of electrical activity of heart using ECG. Electrocardiogram (ECG) is used for diagnostic purpose. Where, it is used for Diagnosing heart condition, fitness, regular checkup and many other diseases. If heart disease is not treated properly on time it may leads to cardiac arrest, arrhythmia, stroke and many other diseases. There are various method of analyzing ECG signals by detection of QRS complex, PR interval, R-R interval and ST segment.
A Sparse-Coding Approach for Clothing Style Recognition

We propose a sparse-coding approach to explore the clothing elements for style recognition. In this study, 10 kinds of clothing styles are defined and collected on the internet, including baroque, bohemian, bustier style, cute style, office lady clothes, overcoat, preppy style, punk, sporty style and suit. Firstly, the region of interest (ROI) is manually segmented for each clothing image. Then the color-based feature is extracted in the CIE L*a*b* color space. In order to explore the clothing elements for styles, the texture-based feature, local binary patterns (LBP) and histogram of gradients (HOG), are extracted as well. Then the sparsecoding approach is applied to learn the dictionaries for the clothing elements, and each clothing image is represented by sparse codes. Finally, the texturebased sparse codes is concatenated with color feature for clothing style recognition via support vector machine (SVM) model. A series of experiments are performed to analyze feature properties for clothing recognition. We discover that the concatenated features of the sparse codes with LBP feature and color feature can provide the best result.
The effect of bronchoscopy on oxidative and antioxidative status

Hypoxemia often occurs during bronchoscopy. Pulmonologists managed it with supplement oxygen and sometimes stopping the procedure. We suggest that the main source of the reactive oxygen species is hypoxia during bronchoscopy. We investigated the alterations in oxidative and antioxidative status during bronchoscopy using oxidative stress parameters including oxidative stress index (OSI) and total oxidant status (TOS). Twenty two patients included to the study for whom bronchoscopy was performed. Twelve patients were diagnosed with lung cancer. Ten patients with normal bronchoscopy comprised the control group. Blood samples were taken just before and 1 hour after bronchoscopy. For antioxidative status, total antioxidant capacity (TAC) and total free sulfhydryl groups were determined. Indicators of oxidative stress (TOS, lipid hydroperoxides, and OSI) were statistically higher ( p <0.05, p <0.05, p <0.05), whereas indicators of antioxidative status (TAC and free sulfhydryl) were statistically lower in the after bronchoscopy blood samples than before bronchoscopy blood samples in all patients ( p <0.05, p <0.05). Before bronchoscopy, indicators of oxidative stress were higher (p <0.001, p<0.05, and p<0.001 respectively), and indicators of antioxidative status (p<0.05, p<0.001 respectively) were lower in the lung cancer group than control group. After bronchoscopy of lung cancer group, indicators of oxidative stress (TOS, lipid hydroperoxides, and OSI) showed significant increases (p<0.05, p<0.05, and p<0.01 respectively), whereas indicators of antioxidative status (TAC and total free sulfhydryl groups) (p<0.05, p<0.01 respectively) were significantly decreased than control group. We demonstrate that bronchoscopy is associated with increased oxidative stress and decreased antioxidative response through possibly caused hypoxemia.
DETERMINING BODY MASS INDEX OF ADULTS TAKING PHYSICAL EXERCISE AND INDIVIDUALS LEADING A SEDENTARY LIFE

AIM/BACKGROUND: The aim of this study to compare Body Mass Index (BMI) with sedentary adults and physical activity adults. METHODS: Totally 412 healthy adult join the study as a volunteered. 204 participation is a regular physical activity twice a week. Participation 208 is a sedentary adults. BMI was categorized as normal weight (
Direct Steering of de novo Molecular Generation using Descriptor Conditional Recurrent Neural Networks (cRNNs)

Deep learning has acquired considerable momentum over the past couple of years in the domain of de-novo drug design. Particularly, transfer and reinforcement learning have demonstrated the capability of steering the generative process towards chemical regions of interest. In this work, we propose a simple approach to the focused generative task by constructing a conditional recurrent neural network (cRNN). For this purpose, we aggregate selected molecular descriptors along with a QSAR-based bioactivity label and transform them into initial LSTM states before starting the generation of SMILES strings that are focused towards the aspired properties. We thus tackle the inverse QSAR problem directly by training on molecular descriptors, instead of iteratively optimizing around a set of candidate molecules. The trained cRNNs are able to generate molecules near multiple specified conditions, while maintaining an output that is more focused than traditional RNNs yet less focused than autoencoders. The method shows promise for applications in both scaffold hoping and ligand series generation, depending on whether the cRNN is trained on calculated scalar molecular properties or structural fingerprints. This also demonstrates that fingerprint-to-molecule decoding is feasible, leading to molecules that are similar – if not identical – to the ones the fingerprints originated from. Additionally, the cRNN is able to generate a larger fraction of predicted active compounds against the DRD2 receptor when compared to an RNN trained with the transfer learning model.
CONTROL OF ACTIVATED SLUDGE FILAMENTOUS BULKING THROUGH OPERATION AT VERY HIGH MLSS CONCENTRATION

Filamentous sludge bulking problems are worldwide common operational problems for activated sludge systems. If not controlled, excessive sludge bulking can lead to a complete failure of the entire wastewater treatment process. This paper presents the control of filamentous sludge bulking at Umm-AlHaiman activated sludge system, in Kuwait, through operation at mixed liquor suspended solids (MLSS) concentration higher than 7000 mg/l. Weekly samples of wastewater and sludge were collected and analyzed for nine months to determine the wastewater quality, operating variables of the activated sludge system, and the dominant filamentous bacteria. Despite the growth of various types of filamentous bacteria, which have been identified and quantified through VIT method (a molecular biology technique), the sludge volume index (SVI) was steadily less than 150 l/mg. Also the effluent was of high quality. That is, the control strategy used (MLSS > 7000 mg/l) had prevented filamentous sludge bulking from occuring. The paper also discusses the advantages and disadvantages of this control strategy.
Multi-resolution Regression, Divide and Conquer Risk Estimation, and the Large-scale Universe

In this proposal, we extend our previous work on mapping the large-scale intergalactic medium by developing relevant areas of statistical methodology and subsequently applying them to the twelfth data release of the Baryon Oscillation Spectroscopic Survey (BOSS DR12). Specifically, our two main statistical contributions, as outlined below, are the development of a novel multi-resolution spatio-temporal model on the sphere and a highly generalizable, computationally efficient algorithm for predictive risk estimation of nonparametric regression estimators. In the first part of this proposal, we develop a multi-resolution regression model for predicting spatio-temporal fields on the sphere embedded in three-dimensional Euclidean space. The multiresolution feature of the model stems from the organization of wavelet functions on lattices of increasing resolution in S × R. Several potential configurations are discussed, however, the proposed model for mapping the IGM exploits irregular lattices constructed from a combination of a HEALPix hierarchal tessellation and k-centers clustering of the sphere. In particular, this organization is designed to create efficient local adaptivity in the time domain by mimicking the highly irregular redshift distribution of BOSS DR12. In this setting, the temporal dimension of the model is regarded as perpendicular to the surface of the sphere, signifying the lookback time to an observed location in intergalactic space. Furthermore, by defining the wavelet coefficient vector to be the solution of an L1-penalized weighted regression, we allow for further efficient local adaptivity at fixed redshifts, namely in relatively noisy and/or sparsely sampled regions of the two-dimensional sky. In the second part of this proposal, we develop a spatial divide and conquer (SDAC) algorithm for efficient estimation of predictive risk curves for a very general class of nonparametric regression estimators. The generalizability is achieved by sampling compact, disjoint subsets of the data instead of random (non-compact) subsets, as is typically done in divide and conquer algorithms. A predictive risk curve is then estimated by cross validation, or another criterion, for each compact subset and the global risk curve is then taken to be a function of the local risk curves. In particular, we consider both a weighted average of the local risk curves and a weighted geometric median. A natural implication of this algorithm is a procedure for (global) tuning parameter selection. We have used this algorithm as a heuristic but, to this point, a theoretical foundation has not been developed. Our primary goals in laying this foundation
A Multi-Objective Approach for Buffer Allocation in General Queueing Networks

The optimal buffer allocation problem in real-life systems is a difficult optimization problem. Besides being non-linear, different objectives conflict with each other: maximize the throughput and minimize the number of buffers allocated. In this paper, we present a solution methodology for the buffer allocation problem in single server general queueing networks based on a multi-objective approach. The complete set of all best solutions is derived by a genetic algorithm (GA), resulting in the Pareto set. The GA proves to be suitable for multi-objective problems because of the availability of a well-known formulation for dealing with these problems. Preliminary results attest for the efficacy and efficiency of the methodology proposed.
APPLICATION OF TENSOR FACTORISATION TO ANALYSE SIMILARITIES IN CAD ASSEMBLY MODELS

Generating fit-for-purpose CAD models from complex assemblies is time consuming for analysts. Tedious tasks include to identify and isolate the components of interest for the analysis, remove duplicate components, or correct inconsistent components’ interfaces are common for large assemblies during the product development process. In this paper a new approach to help engineers analyse the consistency of CAD assembly models is proposed. The method utilises a tensor factorisation technique developed for relational machine learning and applies it on B-Rep topological and geometrical relations. The generated decomposition is used to identify which entities in the assembly are similar (within a threshold) to a selected input entity. The factorisation model regards globally all input relationships, e.g. the connections between components, to identify similar entities based on their relationships in the relational domain. It is shown that a hierarchical clustering method can group entities based on the similarities of their attributes and relationships.
Dynamics of Magnetized Anisotropic Dark Energy in f ( R , T ) Gravity with Both Deceleration and Acceleration

In this paper we investigate some features of Bianchi type II, VIII & IX universe in the presence of magnetized anisotropic dark energy fluid that has an anisotropic equation of state (EoS) parameter in f(R, T ) gravity.To ensure deterministic solution, we choose the scale factor a(t) = (te) which yields a time dependent deceleration parameter representing a class of models which generate a transition of the universe from the early decelerating phase to the recent accelerating phase. In our investigation, we found that the EoS parameter (ω) for the dark energy is time-dependent and its existing range for derived models is in good agreement with data obtained from recent theoretical observations. The physical and geometric aspects of the universe are also discussed in detail. PACS codes: 04.50. Kd, 95.36. +x, 98.80. Jk