Miracle-dz/UniFAR-FTUs huggingface dataset:
First Choice Lipid-Lowering Treatment of Hypercholesterolemias

Several recent studies with lipid-lowering substances have shown a significant reduction in complications of this disease with drastic reduction of LDL-cholesterol levels. However, a huge proportion of patients in secondary prevention remain with inefficient treatment. Vastatins, also called statins, are considered worldwide as the firstline pharmacotherapy for the treatment of hypercholesterolemia, being the most efficient drugs to lower LDLcholesterol, still presenting excellent tolerability. The main mechanism of action of vastatins is inhibition of the enzyme hydroxymethylglutaril-coenzyme A (HMG-CoA) reductase. Currently, the therapeutic arsenal for cases of family hypercholesterolemia and statin intolerance incorporated PCSK9 inhibitors (proprotein convertase subtilisin/kexin type 9) which is an enzyme that is involved in the degradation of the LDLcholesterol receptor in the lysosome, preventing its recycling to the cell surface, thus reducing the amount of LDLcholesterol receptors available, with consequent elevation of this lipid. Mutations with gain of function that lead to an increase in the expression of this enzyme cause, therefore, an increase in LDLcholesterol, while mutations that lead to a decrease in its expression decrease LDL-cholesterol.
The relationship between peripheral arterial disease severity and allostatic load: A national health and nutrition examination survey study

Department of Health Policy and Management, School of Public Health, University of Maryland, 4200 Valley Dr, College Park, MD 20742, United States. Department of Surgery, Howard University College of Medicine, 2041 Georgia Ave NW, Washington, DC 20060, United States. Louis Stokes Health Sciences Library, College of Medicine, Howard University, 501 W Street NW, Washington, DC 20060, United States. Department of Behavioral and Community Health, School of Public Health, University of Maryland, 4200 Valley Dr, College Park, MD 20742, United States. University of Maryland Capital Region Health, 3001 Hospital Dr, Cheverly, MD 20785, United States.
Bibliometric analysis of manuscript characteristics that influence citations: A comparison of four major neurology journals

Objective: To inspect 28 data characteristics among the top neurology journals with the highest impact factor and their influence on citation rate. Methods: Consecutive articles from January 2004 to June 2004 were collected from four major neurology journals with the highest impact factor: The Lancet Neurology (impact factor, 11.964), Acta Neuropathologica (7.589), Brain (5.858) and Annals of Neurology (5.706). Web of Science was used to extract the citation count for these articles, and 28 article characteristics were tabulated manually. Univariate analysis and a multiple regression model were performed to predict citation number from the collected variables. Results: A total of 288 manuscripts i.e. 24 in The Lancet Neurology, 70 in Acta Neuropathologica, 117 in Brain and 77 in Annals of Neurology. Univariate analysis revealed the following variables to have a significant positive correlation with increased citations: journal (1; p<0.0001), country of origin (15; p<0.0001), number of tables (28; p=0.0007), words per title (7; p=0.0006), design of study (17; p=0.001), open access (22; p<0.0001), total words (24; p<0.0001), total references (25; p<0.0001) and total number of pages (26; p<0.0001). In a multivariate regression model the following variables predicted increased citation count (p < 0.0001, R2 = 0.4377): number of pages, open access status, multicenter studies and journal origin. Conclusion: The results of our bibliometric study may be used by authors while compiling their manuscript to increase recognition and improve the impact of their articles over what they would normally experience.
Enhanced Late Miocene Chemical Weathering and Altered Precipitation Patterns in the Watersheds of the Bay of Bengal Recorded by Detrital Clay Radiogenic Isotopes

The late Miocene was a period of declining CO2 levels and extensive environmental changes, which likely had a large impact on monsoon strength as well as on the weathering and erosion intensity in the South Asian Monsoon domain. To improve our understanding of these feedback systems, detrital clays from the southern Bay of Bengal (International Ocean Discovery Program Site U1443) were analyzed for the radiogenic isotope compositions of Sr, Nd, and Pb to reconstruct changes in sediment provenance and weathering regime related to South Asian Monsoon rainfall from 9 to 5 Ma. The 100 kyr resolution late Miocene to earliest Pliocene record suggests overall low variability in the provenance of clays deposited on the Ninetyeast Ridge. However, at 7.3 Ma, Nd and Pb isotope compositions indicate a switch to an increased relative contribution from the Irrawaddy River (by ∼10%). This shift occurred during the global benthic δ13C decline, and we suggest that global cooling and increasing aridity resulted in an eastward shift of precipitation patterns leading to a more focused erosion of the Indo‐Burman Ranges. Sr isotope compositions were decoupled from Nd and Pb isotope signatures and became more radiogenic between 6 and 5 Ma. Grassland expansion generating thick, easily weatherable soils may have led to an environment supporting intense chemical weathering, which is likely responsible for the elevated detrital clay 87Sr/86Sr ratios during this time. This change in Sr isotope signatures may also have contributed to the late Miocene increase of the global seawater Sr isotope composition.
Experience of Video Classes Related to Mobile Development Produced by Multidisciplinary Students Who Used the Challenge Based Learning Methodology

— In Brazil, many people cannot speak English and at the same time there is increasing interest in learning mobile development. Depending on the subject, it is easy to find good video classes online in English in the form of tutorials and even more traditional lectures from top universities. However, the same is not true for videos in Portuguese, which are scarce and often times poorer in quality. From that scenario, there is a great opportunity to create video classes in Portuguese with high quality that can impact the community interested to learn mobile development. Aiming to help the community, this paper presents the experience of an activity, which requested the creation of video classes covering topics related to mobile development from two multidisciplinary groups of students coming from different courses at a university in South America. These videos aimed to be short and offer direct explanations using practical examples. That group of students learned to develop mobile applications using the Challenge Based Learning (CBL) methodology, which provides an efficient and effective framework for learning while solving real-world challenges. This experience shows how different approaches used to present CBL impacted the creation of videos, and how the offered activity contributed positively to the learning of the students and the mobile community in Brazil.
THE IMPORTANCE OF EMOTIONAL INTELLIGENCE OF LEADERS IN CRISIS SITUATIONS

In the modern world, today, due to rapid changes, development of technology, market demands, we can conclude that more than ever there is a danger of various types of crises. In this regard, more than ever before, it is necessary to understand what resources companies need to be more resilient to crises. This paper provides an overview of the theoretical framework, as well as research conducted in the field of emotional intelligence and crisis management. Many studies show that the importance of emotional intelligence of leaders in a crisis is great. Leaders who have a higher level of emotional intelligence possess certain skills that can be crucial to the organization in crisis situations. This paper aims to present the correlation that exists between emotionally intelligent leaders and crisis situations, taking into account several studies conducted in this area. All studies conducted have shown that there is a positive relationship between the emotional intelligence of leaders and crisis management. The conclusion is that examining the impact of the relationship between the emotional intelligence of leaders and the crisis is an exceptional framework that provides an opportunity for human resources to operate within organizations, as management of organizations themselves, seeking and shaping leaders with emotional intelligence skills.
A Narrative Review on Student- Athletes in Higher Education: Barriers, Solutions and Satisfactory Post-Sport Career Transition

This study aimed to analyse the conciliation between sports and academic education of student-athletes in higher education. We searched the Capes Journal Platform (Portal Capes) and selected 14 articles for analysis after refinement. We noted that student-athletes encounter different barriers related to the stereotype of intellectual inferiority attributed to them, their training routine and to the preparation for life after sport. The encouragement of academic goals and the educational institutions’ understanding of the difficulties encountered by student-athletes were pointed out as means of overcoming the barriers related to the negative stereotype. The participation in activities that promote vocational development in conjunction with the provision of online courses during preparation periods for competitions is indicated as a strategy for a satisfactory post-sport career transition. We raise questions and suggestions so that the dual career can be understood as an effective opportunity for the formation of elite athletes.
Clinical Evaluation of the Biting Force of the Mandibular Implant Supported Overdenture by Two Conventional and Two Mini Implants

Objective: The objective of this in vivo study was done to evaluate the biting force when restoring the edentulous mandible with an implant supported overdenture using two conventional implants or two mini implants. Subjects and Methods: Sixteen patients were selected for this study free from any systemic disease or local factor that contraindicate implant placement. Patients were divided into two equal groups: In group I (control group) each patient had received a two conventional implant in the mandible and in group II (test group) each patient had received two mini implant in the mandible. Implants were placed using flapless surgery and immediately loaded by picking up the metal house into the denture. Assessment of bite force was done by force sensor. The evaluation was done at 1 week, and at 3, 6, 9, 12 months. Results: The results showed that the control group had statistically significant higher values of the bite force than the test group. Conclusions: Mandibular implant supported overdenture by two mini implants have shown less retention, biting force, and masticatory efficacy than the overdenture supported by two conventional implants.
Learning-Augmented Energy-Aware Scheduling of Precedence-Constrained Tasks

We study the scheduling problem of precedence-constrained tasks to balance between performance and energy consumption. To this point, scheduling to balance performance and energy has been limited to settings without dependencies between jobs. In this extended abstract, we consider a system with multiple servers capable of speed scaling and seek to schedule precedence constrained jobs to minimize a linear combination of performance and energy consumption. Inspired by the single server setting, we propose the concept of pseudo-size for individual tasks, which is a measure of the importance of a task in the precedence graph that is learned from workload data. We then propose a two-stage learningaugmented list scheduling algorithm which uses the learned pseudo-size approximation and achieves a provable approximation bound on the linear combination of performance and energy consumption, where the quality of the bound depends on that of the approximation of task pseudo-sizes, for both makespan and total weighted completion time.
Adding Context Information to Deep Neural Network based Audio Source Separation

This thesis deals with deep learning-based music source separation. Based on a convolutional time-frequency masking architecture, a number of unique strategies are developed that enhance the receptive field of the network to a more relevant and music-specific context. This leads to a better exploitation of the data, as well as a more stable training convergence. Firstly, the exponentially scaled harmonic relationships inside a spectrogram are made accessible to the convolutional neural network by restructuring the input data and aligning harmonically related frequency bins over the feature dimension. Secondly, a post-processing network is proposed to re-align the contaminated mixture phase, which is commonly used for the reconstruction of the source signals. Finally, a novel self-attention mechanism is proposed, which is able to filter out unwanted interferences and distortions by utilizing the repetitive nature of music. All proposed methods are evaluated on the MusDB18 dataset and improve the instrument separation. The final separation method outperforms the current State-of-the-Art results on MusDB18. Additionally, an ablation study on different source separation network architectures and training methods is provided with this thesis.
NDT_A_240542 433..439

Alemayehu Molla 1 Birhanie Mekuriaw 1 Endashaw Habtamu Moges Mareg 1Department of Psychiatry, College of Medicine and Health Science, Dilla University, Dilla, Ethiopia; 2Departments of Reproductive Health, College of Medicine and Medical Sciences, Dilla University, Dilla, Ethiopia Background: Even though a large number of patients live with epilepsy, few of them receive effective treatment. Most people with epilepsy, particularly those from rural communities, do not seek medical care as they are convinced that solutions lie only with traditional healers (traditional leaders, prophets and community elders). Therefore, studying treatment-seeking behavior regarding this major neurological problem would provide additional knowledge and help to identify a gap which needs to be addressed when tackling related problems. Participants and Methods: This rural community-based cross-sectional study was conducted in Gedeo zone, Ethiopia, among 755 participants. Data were collected using face-toface interview-based questionnaires. Collected data were entered into EpiData version 3.01 and analyzed using SPSS version 20. Bivariate and multivariate logistic regression analysis was conducted to identify associated factors. The strengths of associations were presented as adjusted odds ratios with 95% confidence intervals. Results: The prevalence of poor treatment-seeking behavior was 54.6% (95% CI 51.4, 58.2). Poor knowledge (adjusted odds ratio [AOR]=3.21, 95% CI 2.14, 4.81), poor social support (AOR=2.48, 95% CI 1.12, 5.53), unfavorable attitude (AOR=1.84, 95% CI 1.34, 2.54) and having no history of experiencing others' seizures (AOR=2.17, 95% CI 1.47, 3.2) were variables strongly associated with poor help-seeking behavior towards epilepsy. Conclusion: The study showed that more than half of the participants had poor treatmentseeking behavior towards epilepsy. This indicates the need to implement measures to raise community awareness regarding treatment options for epilepsy.
Multipath Transmission in IoT using Hybrid Salp Swarm-Differential Evolution Algorithm

: The improvements in technology in the field of communication did Wireless Sensor Network (WSN) on the basis of the IoT desirable and appropriate to several regions. It is encompassed that IoT nodes perform on restricted battery provisions. Therefore, a maximum-routing protocol performance is necessary for routing in such networks to surmount the energy constraint issues. For multipath data transmission, an energy-efficient routing algorithm named Hybrid Salp Swarm and Simulated Algorithm (Hybrid SS and SA) is adopted in this work. The adopted approach enhanced the routing procedure in a dual-phase procedure. Initially, the cluster heads are chosen by exploiting the Salp Swarm Algorithm. Subsequently, multiple paths are produced from the sender to the receiver by exploiting DE. Here, inter and intra-cluster distance, energy, delay and lifetime is considered as the objective function. In the fitness model, an optimal path for the transmission is presented. Finally, the investigational outcomes exhibit that the performance of the proposed model shows maximum performance with respect to the alive nodes and energy in contrast with the conventional approaches
“False ischaemic penumbra” complicates decision making in acute stroke thrombolysis

Reperfusion forms the cornerstone of treatment for acute ischaemic stroke (AIS) aiming to salvage tissue in the ischaemic penumbra by exploiting a short therapeutic time window.1,2 Intravenous thrombolysis with alteplase between 4.5 and 9 hours of stroke onset has recently been shown to be associated with better neurological recovery, provided that a favourable imaging profile can be demonstrated by perfusion imaging.3 This tailored approach to treatment of AIS, where advanced imaging techniques distinguish irreversibly injured tissue (infarct core) from salvageable brain (critically hypoperfused, functionally ischaemic ‘at-risk’ tissue or penumbra) with a wider treatment window4,5 is conceptually appealing. In this paradigm, discrimination of viable and ischaemic tissue using imaging techniques to measure quantitative cerebral perfusion parameters is fundamental in selecting patients for thrombolytic therapy.1,6 Computed tomography perfusion (CTP) is widely preferred in initial AIS assessment, usually in combination with non-contrast CT (NCCT) and “arch-to-vertex” CT angiography (CTA), being fast, increasingly available, safe when performed correctly, and affordable.7,8 The “core” is typically operationally defined as the area with a matched decrease in both cerebral blood flow (CBF) and volume (CBV), and “penumbra,” as the mean transit time (MTT) or CBF lesion volume, with CBV maintained or even elevated compared to contralateral values. However, not all anomalies in the CTP are specifically related to AIS, as CTP is susceptible to unrelated alterations in blood flow. We present two patients who presented with acute neurological symptoms and extensive perfusion abnormalities that were subsequently shown to be due to chronic oligaemia. CASE REPORTS
Performance Analysis of Three Phase Multilevel Inverter with SPWM Techniques used in Railway System 2965

2964 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: A1449109119/2020©BEIESP DOI: 10.35940/ijeat.A1449. 029320  Abstract: The medium voltage & high power converters are used in electrical railway system because of its high efficiency. Due to the use of heavy load in the railway system, conventional inverters causes power quality issues such as harmonic distortion, low power factor, etc. In this paper, new cascaded multilevel inverter (MLI) using cascaded H-bridge is introduced. The advantage of the proposed method is to mitigate power semiconductor switches in number which reduces the cost of the circuit as well as the complexity. The analysis and evaluation of various Sinusoidal Pulse Width Modulation (SPWM) Methods with different levels of an inverter has been modeled and simulated by using MATLAB/Simulink.
Immunocontraceptive potential of a recombinant chimeric protein composed by a single gonadotropin-releasing hormone molecule and B subunit of Escherichia coli heat-labile enterotoxin in a mice model

2452-0721/2452-0721 © 2020 Sociedade Brasileira de Biotecnologia. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial No Derivative License, which permits unrestricted noncommercial use, distribution, and reproduction in any medium provided the original work is properly cited and the work is not changed in any Immunocontraceptive potential of a recombinant chimeric protein composed by a single gonadotropin-releasing hormone molecule and B subunit of Escherichia coli heat-labile enterotoxin in a mice model
A Modular Framework for Unsupervised Graph Representation Learning

Graphs are data structures well suited to model many sources of information in the real world, including social networks, domain knowledge, and molecules. The availability of large datasets motivates the application of machine learning methods in tasks such as detecting clusters, predicting links between entities, and assigning a label to an entity. Several techniques have been proposed in the literature towards solving these problems, some of which involve carefully designing an appropriate representation of the data with enough predictive power that can be used to train a machine learning model. Instead of specifying a representation in advance, more recent approaches seek to learn the representation from the data, so that it preserves the structure of the graph while being useful to solve related tasks, in the absence of a ground truth. This is the problem of unsupervised graph representation learning. We review recently proposed methods, and we identify across them a design pattern composed by a series of components. This turns into a modular framework that can be used to study existing methods and devise novel ones. We validate our framework with experiments on real world graphs, and we find that some changes in the components of existing methods can yield significant improvements. A hyperparameter study allows us to identify a particularly strong method of representation learning for the tasks of link prediction and node classification. Following recent advances in optimal transport for machine learning, we experiment with a method to learn node representations using Wasserstein spaces. Under several conditions, we find that these spaces preserve the structure of the graph well, but in spite of regularization strategies, they do not generalize well.
Appraisal of mammography in Nigerian women in a new teaching hospital

The advent of newer imaging techniques has necessitated the need for us to evaluate the distribution pattern of common and significant imaging findings on mammography at the Lagos state University Teaching Hospital. A retrospective study on all the 300 patients referred to the radiology department of LASUTH from August 2003 to August 2006 for mammography was carried out and the imaging findings were recorded using the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BIRAD) classification method to assess mammographic density. However, ultrasound scans were done in patients less than 35 years of age who had a mass. The mammography images result revealed that majority of the breasts, about 39 (13%) were mainly fatty, 11 (3.7 %) were glandular, whereas, 3 (1%) were mainly fibrous. However, 149 (49.7%) were mixed (fibro-fatty) and 98 (32.7%) dense in nature. Using the BIRAD classification, 41 (13.7 %) patients were classified as Birad 1 whereas the majority of the cases seen, 154 (51.3 %) were classified as Birad 2, while 57 (19%) and 48 (16%) were categorized into Birad 3 and 4, respectively. However, mammographic findings were normal in 164 (54.7%) patients seen. Various types of calcifications were seen in 87 (29%) of patients. Of these, 22 (7.3 %) were microcalcifications. However, two of the confirmed malignant masses had microcalcifications and dilated ducts were seen in 3 (1%) patients. The nipple was tethered and inverted in 3 (1%) patients, skin thickening was seen in 3 (1%) patients, widened subcutaneous tissue in 3 (1%), scar in 2 (%). One (0.3%) patient had postsurgical clips, while skin fold was seen in one (0.3%). Of the six histology reports obtained, 3 (1%) were confirmed malignant, 2 (0.7%) were not malignant and one (0.3 %) was a fibrocystic disease. We found out that most masses seen were benign, although 33.3% of suspected cancer picked up on mammography was confirmed malignant. Also, microcalcifications were a strong indicator of malignancy in this study.