Miracle-dz/UniFAR-FTUs huggingface dataset:
High Red Cell Distribution Width and Low Absolute Lymphocyte Count Associate With Subsequent Mortality in HCV Infection

Background: Hepatitis-C virus (HCV) chronic infection can lead to cirrhosis, hepatocellular carcinoma (HCC), end-stage liver disease, cardiovascular disease (CVD), and mortality. Transient Elastography (TE) is used to non-invasively assess fibrosis. Whether immune monitoring provides additive prognostic value is not established. Increased red-cell distribution width (RDW) and decreased absolute lymphocyte count (ALC) predict mortality in those without liver disease. Whether these relationships remain during HCV infection is unknown. Materials and Methods: A retrospective cohort of 1,715 single-site VA Liver Clinic patients receiving Transient Elastography (TE) 2014-2019 to evaluate HCV-associated liver damage were evaluated for RDW and ALC in relation to traditional parameters of cardiovascular risk, liver health, development of HCC, and mortality. Results: The cohort was 97% male, 55% African American, 26% with diabetes mellitus, 67% with hypertension, and 66% with tobacco use. After TE, 3% were subsequently diagnosed with HCC, and 12% (n=208) died. Most deaths (n=189) were due to non-liver causes. The TE score associated with prevalent CVD, positively correlated with atherosclerotic cardiovascular disease (ASCVD) 10-Year Risk Score, age, RDW, and negatively correlated with ALC. Patients with anisocytosis (RDW above 14%) or lymphopenia (ALC level under 1.2×109/L) had greater subsequent all-cause mortality, even after adjusting for age, TE score, and comorbidities. TE score, and to a modest degree RDW, were associated with subsequent liver-associated mortality, while TE score, RDW, and ALC were each independently associated with non-liver cause of death. Conclusion: Widely available mortality calculators generally require multiple pieces of clinical information. RDW and ALC, parameters collected on a single laboratory test that is commonly performed, prior to HCV therapy may be pragmatic markers of long-term risk of mortality.
24rd International Meshing Roundtable (IMR24) Fast generation of large-size adapted meshes

We devise a strategy in order to generate large-size adapted anisotropic meshes O (10 8 − 10 9 ) as required in many ﬁelds of application in scientiﬁc computing. We target moderate scale parallel computational resources as typically found in R&D units where the number of cores ranges in O (10 2 − 10 3 ). Both distributed and shared memory architectures are handled. Our strategy is based on typical domain splitting algorithm to remesh the partitions in parallel. Both the volume and the surface mesh are adapted simultaneously and the e ﬃ ciency of the method is independent of the complexity of the geometry. The originality of the method relies on (i) a metric-based static load-balancing, (ii) dedicated mesh partitioning techniques to (re)split the (complex) interfaces meshes, (iii) anisotropic Delaunay cavity to deﬁne the interface meshes, (iv) a fast, robust and generic sequential cavity-based mesh modiﬁcation kernel, and (v) out-of-core storing of completing parts to reduce the memory footprint. We show that we are able to generate (uniform, isotropic and anisotropic) meshes with more than 1 billion tetrahedra in less than 20 minutes on 120 cores. Examples from Computational Fluid Dynamics (CFD) simulations are also discussed.
Online Teaching in an Un-prepared Country during COVID-19: An Interview Study on Final Year Medical and Dental Students

Aims: To assess medical students’ perception of online teaching to suggest transforming the future curriculum in low-economic countries. Study Design: Cross-sectional online interview study. Place and Duration of Study: A team of collaborators interviewed final year medical and dental students of Pakistan from 07/08/2020 till 17/09/2020. Methodology: A questionnaire was developed based on open and close-ended questions in Google forms; focusing on institutional preparedness, views on online education, the institute’s closure and COVID-19, and long-term effects of closure of the institute. Independent fellow researchers systematically analyzed the unaltered transcripts of the responses, and themes were then identified and coded to conclude the results. SPSS version 23 used for analysis. As this study was based on final year students. Results: In response to an invitation email, 2442/2661 (91.77%) students voluntarily participate in this qualitative study. Most participants were females (1614, 66.10%). Closing down institutes was directly linked to a lack of motivation and feel of helplessness. As most showed dissatisfaction with online teaching compounded by psychological effects, students feared losing clinical skills and life during the pandemic. Conclusion: The psychological impact of the crisis led to resistance to accepting the change for a better outcome. Incorporating telemedicine, different interactive learning style to online teaching, and resilience training would result in fruitful outcomes. Developed countries may also guide build infrastructure in developing countries to develop a more robust online teaching methodology in the long-run.
PMSG BASED WIND POWER SYSTEM USING ANFIS AND MODIFY SEPIC CONVERTER

Wind energy is becoming a very useful energy source at present. With the increasing wind power penetration, improvements are required in order to comply with the grid interconnection requirements. The focus of this paper is to maximize the output power and address the output control of a utility-connected Permanent Magnet Synchronous Generator (PMSG) for wind power generation systems. PMSG has a back-to-back converter to control the output of the PMSG driven by the wind turbine. To supply commercially the power of WPGS to the grid without any problems related to power quality, the real and reactive powers of PMSG are strictly controlled at the required level. In this paper it is realized with the Adaptive Neuro Fuzzy Inference System (ANFIS) controller based on the field orientation control. The DC voltage of the DC link capacitor is also controlled at a certain level with the conventional Proportion-Integral controller of the real power. Studies demonstrate that the performance of the system with the IS controller parameters permits an improvement of the converter capability and system performance.
A Deep Learning Ensemble to Classify Anxiety, Depression, and their Comorbidity from Texts of Social Networks

The use of social networks to expose personal difficulties has enabled works on the automatic identification of specific mental conditions, particularly depression. Depression is the most incapacitating disease worldwide, and it has an alarming comorbidity rate with anxiety. In this paper, we explore deep learning techniques to develop a stacking ensemble to automatically identify depression, anxiety, and comorbidity, using data extracted from Reddit. The stacking is composed of specialized single-label binary classifiers that distinguish between specific disorders and control users. A meta-learner explores these base classifiers as a context for reaching a multi-label, multi-class decision. We developed extensive experiments using alternative architectures (LSTM, CNN, and their combination), word embeddings, and ensemble topologies. All base classifiers and ensembles outperformed the baselines. The CNN-based binary classifiers achieved the best performance, with f-measures of 0.79 for depression, 0.78 for anxiety, and 0.78 for comorbidity. The ensemble topology with best performance (Hamming Loss of 0.29 and Exact Match Ratio of 0.47) combines base classifiers according to three architectures, and do not include comorbidity classifiers. Using SHAP, we confirmed the influential features are related to symptoms of these disorders.
Desafiando la Validez de Constructo de Soborno en la Economía Experimental

The bribery construct employed by experimental studies of corruption faces three interrelated challenges. First, the notion of trust used in the bribery construct reduces it to a mere calculation of risk. Second, and as a consequence of the above, the appropriate context of interaction is undetermined. Finally, the experiments show an insufficient clarification of the normative framework. In short, researchers on corruption should be mindful of the challenges of the construct they employ in the lab, and they should tackle the aforementioned issues in order not to compromise the validity of their results.
Milk Traits of Damascus Goats at Different Lactation Stages: 2. Fatty Acid Profiles and Related Lipogenic Genes Expression Levels*

In the present study, goat milk fatty acid profile and some of the lipogenic genes activities in milk somatic cells were evaluated during the lactation periods in Damascus goats. Relationship between fatty acid profile and lipogenic genes during the lactation were also investigated. The lactation periods of goats were divided to three stages as early, mid and late lactation. Beside fatty acid profile, FASN, SCD, ACACA, SREBPF1, and PPARγ genes expression levels were determined in milk. It was determined that fatty acid contents of milk samples changed during the lactation (P<0.05). FASN and SCD gene expression levels were downregulated in mid and late lactation stages (P<0.05). While ACACA was almost 3 folds upregulated in mid lactation stage (P<0.05), SREBPF1 was approximately 9 folds downregulated in late lactation stage (P<0.01). Also, significant correlations were found between fatty acids and lipogenic genes in different lactation stages (P<0.05). In conclusion, it was determined that Damascus goats showed different metabolic activities in different stages of lactation in terms of both fatty acid profile and some lipogenic genes. It was thought that more studies at the molecular level is needed for understanding the complex milk fatty acid profile mechanism of goats.
Increased Survival of Lactococcus lactis Strains Subjected to Freeze-Drying after Cultivation in an Acid Medium: Involvement of Membrane Fluidity Cultivation in Acid Medium to Improve Bacterial Survival of Freeze-Drying

SUMMARY Research background Freeze-drying is the most widely used dehydration process in the food industry for the stabilization of bacteria. Studies have shown the effectiveness of an acid prestress in increasing the resistance of lactic acid bacteria to freeze-drying. Adaptation of bacteria to an acid stress is based on maintaining the properties of the plasma membrane. Indeed, the fatty acid composition of the membrane of lactic acid bacteria is often changed after an acid prestress. However, few studies have measured membrane fluidity after an acid stress during lactic acid bacterial strain cultivation. Experimental approach In order to use two pH profiles, the strains Lactococcus lactis NCDO 712 and NZ9000 were cultivated in two media, without any pH control. The two pH profiles obtained were representative of the initial medium composition, medium buffering properties and strain metabolism. Absorbance at 600 nm and pH were measured during bacterial cultivation. Then, the two strains were freeze-dried and their survival rates determined. Membrane fluidity was evaluated by fluorescence anisotropy measurements using a spectrofluorometer. Results and conclusions Cultivation under more acidic conditions significantly increased the survival during freeze-drying (p<0.05, ANOVA) of both strains. Moreover, in both strains of L. lactis, a more acidic condition during cultivation significantly increased membrane fluidity (p<0.05, ANOVA). Our results revealed that cultivation under such conditions, fluidifies the membrane and allows a better survival during freeze-drying of the two L. lactis strains. A more fluid membrane can facilitate membrane deformation and lateral reorganization of membrane components, critical for the maintenance of cellular integrity during dehydration and rehydration. Novelty and scientific contribution A better understanding of the involvement of membrane properties, especially of membrane fluidity, in bacterial resistance to dehydration is provided in this study.
Ultrafiltration Volume: Surrogate Marker of the Extraction Ratio, Determinants, Clinical Correlates and Relationship with the Dialysis Dose

Citation: Uduagbamen PK, Ogunkoya JO, Nwogbe CI, Eigbe SO, Timothy OR (2021) Ultrafiltration Volume: Surrogate Marker of the Extraction Ratio, Determinants, Clinical Correlates and Relationship with the Dialysis Dose. J Clin Nephrol Ren Care 7:068. doi.org/10.23937/2572-3286.1510068 Accepted: September 21, 2021: Published: September 23, 2021 Copyright: © 2021 Uduagbamen PK, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Open Access ISSN: 2572-3286
Coralline Hydroxyapatite Bone Graft in Non-Contained Defects: Case Report

Substitute bone grafts are gaining popularity in orthopaedics and faciomaxillary surgery. There are numerous products that are available to clinicians currently on the market. Here, we present a case report of a large non-contained mandibular defect that was successfully treated with a Coralline Hydroxyapatite (CHA). This paper will discuss the need for CHA in dental bone defect repair, and it’s effectiveness in the specific case study.
A SYSTEMATIC ANALYSIS OF COMPOUNDS PRESENT IN

✵ ABSTRACT The objective of this study was to gather data, create a database of the compounds present in Ocimum tenuiflorum (O. tenuiflorum), and gather related literature on the compounds found. A thorough literature search was performed to gather information on compounds present in O. tenuiflorum, including chemical structures, relative abundance, presence in different plant parts, and availability from chemical supply vendors. The compounds’ chemical structures were refined using Discovery Studio Visualizer and Chimera software for future insilico docking studies. The structures with cleaned structural geometry were obtained through D.S. Visualizer for docking in the future. From the literature search of previously presented articles, it was found that methyl eugenol had the greatest percent composition in O. tenuiflorum. After searching the Protein Data Bank, COX-1, COX-2, and NF Kappa B were found to be the main protein targets of O. tenuiflorum compounds in the arachidonic acid inflame matory pathway. Thus, the anti-inflammatory properties of O. tenuiflorum have been analyzed in this article for future in silico docking.
Chebyshev Polynomial Based Rayleigh-Ritz method and Navier’s Technique for Vibration Analysis of a Functionally Graded Porous Beam Embedded in Kerr

Present study is dealt with the applicability of shifted Chebyshev polynomial based RayleighRitz method and Navier’s technique on free vibration of Functionally Graded (FG) beam with uniformly distributed porosity along the thickness of the beam. The material properties such as Young’s modulus, mass density, and Poisson’s ratio are also considered to vary along the thickness of the FG beam as per the power-law exponent model. The porous FG beam is embedded in an elastic substrate; namely, the Kerr elastic foundation and the displacement field of the beam is governed by a Refined Higher Order Shear Deformation Theory (RHSDT). The effectiveness of the Rayleigh-Ritz method is due to the use of the shifted Chebyshev polynomials This is a post-peer-review, pre-copyedit version of an article published in ENGINEERING WITH COMPUTERS. The final authenticated version is available online at: http://dx.doi. org/10.1007/s00366-020-01018-7
Left Ventricular Hypertrophy (LVH) in Patients with Advanced Stages of Chronic Kidney Disease (CKD)

Objective: To determine the left ventricular hypertrophy (LVH) prevalence in patients admitted with advanced stage of Chronic kidney disease at Ziauddin hospital. Methodology: This was a cross-sectional study conducted in department of Nephrology of Ziauddin University Hospital, Karachi from January to July 2016. The inclusion criteria involved patients with CKD stages 3-5 undergoing two-dimensional M mode Doppler echocardiography. The sample size of the study was 147. LVH was considered as positive when Inter-ventricular-septal-wall-thickness in diastole (IVSd) >11 mm, Left-Ventricular-Septal-Wall-Thickness in diastole (LVPWd) >11 mm and Left-Ventricular-Mass-Index (LVMi) >131 g/m2 for men and > 100 g/m2 for women. The exclusion criteria included patients with terminal illness, on mechanical ventilator support, valvular heart diseases and congenital heart diseases, liver diseases and patients with acute kidney injury on chronic kidney disease. Results: 88 male and 59 female patients were included. The mean duration of CKD was 7.02±1.60 years. 94(63.9%) study subjects were observed with left ventricular hypertrophy. A significant association of LVH was observed with gender and CKD Stages. Conclusion: LVH can be easily diagnosed and assessed by M-mode or 2D echocardiography. The prevalence was high (60.5%) in stage 3–5 CKD patients.
Haplodontiumaltunense (Bryaceae, Bryopsida), a new moss species from Northwest China

Abstract Haplodontiumaltunense X.R. Wang & S.Mamtimin, a new moss species of the family Bryaceae from Xinjiang Uygur Autonomous Region, China is described and illustrated. Genetic analysis based on ITS sequences shows that this species is a member of the Bryaceae and in the same clade as Anomobryum. Particularly distinctive features of the new species include: double peristome; the exostome has raised and membranous chomata with united lamellae between two teeth proximally; the endostome is poorly developed and all the endostomial material tightly adherent to the exostome.
Corporate social responsibility in the time of COVID-19 pandemic: An exploratory study of developing country corporates

COVID-19 pandemic has long-lasting consequences on the health, economic and social life of a country (He & Harris, 2020). In a developing country like India, the socio-economic disruption has led to collaborative action between the central government and state government machinery together with the development sector to curb the impact caused by the virus. Academia substantiates the symbiotic relationship existing between the business and the society (McGuire, 1963; Carroll & Shabana, 2010). The corporates are contributing towards alleviating the pandemic situation through their corporate social responsibility (CSR) activities (Mahmud, Ding, & Hasan, 2021). This article provides insights into the CSR strategies adopted by corporates in India during the COVID-19 pandemic through exploratory research. The study is based on semi-structured interviews of 27 CSR managers involved in strategizing and implementation of CSR activities in their respective organizations. The results outline the commitment shown by corporates towards alleviating the consequence of the virus by multiple CSR strategies. Thus, this research furthers the understanding of CSR and forms a base for future research on COVID-19 and CSR
TE-dependent analysis of multi-echo fMRI with tedana

Functional magnetic resonance imaging (fMRI) is a popular method for in vivo neuroimaging. Modern fMRI sequences are often weighted towards the blood oxygen level dependent (BOLD) signal, which is closely linked to neuronal activity (Logothetis, 2002). This weighting is achieved by tuning several parameters to increase the BOLD-weighted signal contrast. One such parameter is “TE,” or echo time. TE is the amount of time elapsed between when protons are excited (the MRI signal source) and measured. Although the total measured signal magnitude decays with echo time, BOLD sensitivity increases (Silvennoinen et al., 2003). The optimal TE maximizes the BOLD signal weighting based on a number of factors, including several MRI scanner parameters (e.g., field strength), imaged tissue composition (e.g., grey vs. white matter), and proximity to air-tissue boundaries.
Mathematics E-LKPD with Project-Based Learning and HOTS Activities

Less attractive learning media causes low interest in student learning. So, the need for concrete learning media following the stages of student development. This study aimed to create a valid and practical demonstration-based learning video on changing objects in the fifth grade of elementary school. This research is development research using the 4D model. The subjects of this study were two material experts, two media experts, two practitioners, and five students. The method used in data collection in this study is the rating scale method with an instrument in the form of a rating scale ranging from 1-4. The data obtained were analyzed by calculating the validity index of expert judgment and calculating the average response of practitioners and students. The analysis results showed that the material expert's validity index obtained the lowest score of 0.84 and the highest score was 1 with the very good category. The media expert validity index got the lowest score of 0.67 in the good category, and the highest score was 1, which is in the very good category. The overall average score of the practitioner's response was 3.82, and the student response was 3.78, which was in the very good category. Based on the results of this analysis, demonstration-based instructional video media on the topic of changing objects in fifth-grade elementary schools were declared good and suitable for use in the learning process.
Learning Predictors from Multidimensional Data with Tensor Factorizations

Statistical machine learning algorithms often involve learning a linear relationship between dependent and independent variables. This relationship is modeled as a vector of numerical values, commonly referred to as weights or predictors. These weights allow us to make predictions, and the quality of these weights influence the accuracy of our predictions. However, when the dependent variable inherently possesses a more complex, multidimensional structure, it becomes increasingly difficult to model the relationship with a vector. In this paper, we address this issue by investigating machine learning classification algorithms with multidimensional (tensor) structure. By imposing tensor factorizations on the predictors, we can better model the relationship, as the predictors would take the form of the data in question. We empirically show that our approach works more efficiently than the traditional machine learning method when the data possesses both an exact and an approximate tensor structure. Additionally, we show that estimating predictors with these factorizations also allow us to solve for fewer parameters, making computation more feasible for multidimensional data.