Miracle-dz/UniFAR-FTUs huggingface dataset:
Serum Metabolites Associated with Blood Pressure in Chronic Kidney Disease Patients

Blood pressure is one of the most basic health screenings and it has a complex relationship with chronic kidney disease (CKD). Controlling blood pressure for CKD patients is crucial for curbing kidney function decline and reducing the risk of cardiovascular disease. Two independent CKD cohorts, including matched controls (discovery n = 824; validation n = 552), were recruited. High-throughput metabolomics was conducted with the patients’ serum samples using mass spectrometry. After controlling for CKD severity and other clinical hypertension risk factors, we identified ten metabolites that have significant associations with blood pressure. The quantitative importance of these metabolites was verified in a fully connected neural network model. Of the ten metabolites, seven have not previously been associated with blood pressure. The metabolites that had the strongest positive association with blood pressure were aspartylglycosamine (p = 4.58 × 10−5), fructose-1,6-diphosphate (p = 1.19 × 10−4) and N-Acetylserine (p = 3.27 × 10−4). Three metabolites that were negatively associated with blood pressure (phosphocreatine, p = 6.39 × 10−3; dodecanedioic acid, p = 0.01; phosphate, p = 0.04) have been reported previously to have beneficial effects on hypertension. These results suggest that intake of metabolites as supplements may help to control blood pressure in CKD patients.
Epigenome-Wide Association Study Reveals Differential Methylation Sites and Association of Gene Expression Regulation with Ischemic Moyamoya Disease in Adults

Background The association of DNA methylation with the pathogenesis of adult ischemic moyamoya disease (MMD) is unknown. Here, we investigated the genome-wide DNA methylation profiles in patients with MMD and identified the genes related to the pathogenesis of MMD. Methods Whole blood samples were collected from 20 individuals, including 10 patients with ischemic moyamoya disease without any underlying disease and 10 healthy individuals. Genome-wide DNA methylation analysis was performed using Illumina 850K microarrays. Transcriptional correlation was verified using quantitative reverse transcription-polymerase chain reaction. In vitro experiments were used to analyze the association of functional defects with candidate epigenetic markers. Results The genome-wide methylation level in the whole blood of adults with ischemic MMD was higher than that in the healthy individuals. In total, 759 methylation probes differed significantly between the case and control. The hypermethylated regions were mostly concentrated in the gene spacer regions. Among genes with the highest degree of the differential expression, KCNMA1 and GALNT2 were upregulated, whereas SOX6 and RBM33 were downregulated. Conclusions This is the first study showing that the low expression of genes associated with epigenetic regulation, such as SOX6 and RBM33, may be related to vascular occlusion in MMD, whereas the overexpression of KCNMA1 and GALNT2 may be related to the vascular hyperplasia. The results suggest that DNA methylation was involved in the pathogenesis of MMD, and new pathogenic genes were proposed as biological markers.
Tetramethylpyrazine Improves Monocrotaline-Induced Pulmonary Hypertension through the ROS/iNOS/PKG-1 Axis

Background Tetramethylpyrazine (TMP), a potent anti-free radical and anti-inflammations substance, has been demonstrated to possess a direct vessel relaxation property. This study aimed to evaluate the effect of TMP treatment in pulmonary hypertension (PH) and test the hypothesis that TMP prevents or reverses the process of PH. Methods Rats (n = 36) injected with 50 mg/kg of monocrotaline (MCT) subcutaneously 4 weeks to develop PH were then randomized to TMP (5 mg/kg per day) for another 4 weeks. Hemodynamics was evaluated via the right ventricle. Pulmonary vessels structural remodeling and inflammation were examined by histologic and transmission electron microscopy observation. The expression of inducible nitric oxide synthase (iNOS) and cGMP-dependent protein kinases 1 (PKG-1) was detected by immunohistochemical staining and Western blot. Generation of reactive oxygen species (ROS) and antioxidation species was measured by biochemical analyses. Results MCT increased PH and right ventricle hypertrophy. TMP alleviated pulmonary arterial pressure elevation, leukocyte infiltration, and structural remodeling of pulmonary arterials induced by MCT successfully. TMP treatment significantly increased the PKG-1 expression and suppressed the iNOS expression. The activity of superoxide dismutase (SOD), glutathione peroxidase (GSH), and catalase (CAT) was significantly higher than control group, while malondialdehyde (MDA) levels were lower compared with MCT group. Conclusion TMP can suppress established MCT-induced PH through the ROS/iNOS/PKG axis. The underlying mechanisms may be associated with its anti-inflammatory, antioxidant, and antiproliferative properties in pulmonary arterial.
Communication-Efficient Distributed Learning for High-Dimensional Support Vector Machines

Distributed learning has received increasing attention in recent years and is a special need for the era of big data. For a support vector machine (SVM), a powerful binary classification tool, we proposed a novel efficient distributed sparse learning algorithm, the communication-efficient surrogate likelihood support vector machine (CSLSVM), in high-dimensions with convex or nonconvex penalties, based on a communication-efficient surrogate likelihood (CSL) framework. We extended the CSL for distributed SVMs without the need to smooth the hinge loss or the gradient of the loss. For a CSLSVM with lasso penalty, we proved that its estimator could achieve a near-oracle property for l1 penalized SVM estimators on whole datasets. For a CSLSVM with smoothly clipped absolute deviation penalty, we showed that its estimator enjoyed the oracle property, and that it used local linear approximation (LLA) to solve the optimization problem. Furthermore, we showed that the LLA was guaranteed to converge to the oracle estimator, even in our distributed framework and the ultrahigh-dimensional setting, if an appropriate initial estimator was available. The proposed approach is highly competitive with the centralized method within a few rounds of communications. Numerical experiments provided supportive evidence.
Production Method and Wine Style Influence Metal Profiles in Sparkling Wines

The elemental composition of wine provides important information about origin, authenticity, and sensory considerations. Although various wine regions and varieties of still wines have been extensively studied, limited research has evaluated the metal profiles of sparkling wines, which can be produced via the bottle-fermented traditional method (TM) or the tank-fermented Charmat method (CM) in both rosé and non-rosé styles. In this study, 73 commercial sparkling wines from Canada’s Niagara Peninsula were analyzed by inductively coupled plasma-optical emission spectrometry and inductively coupled plasma-mass spectrometry to quantify 28 metal ions (silver [Ag], aluminum [Al], arsenic [As], boron [B], barium [Ba], beryllium [Be], calcium [Ca], cadmium [Cd], cobalt [Co], chromium [Cr], copper [Cu], iron [Fe], potassium [K], magnesium [Mg], manganese [Mn], molybdenum [Mo], sodium [Na], nickel [Ni], lead [Pb], antimony [Sb], selenium [Se], tin [Sn], strontium [Sr], titanium [Ti], thallium [Tl], uranium [U], vanadium [V], zinc [Zn]). All metal levels were below internationally regulated maximum limits. Higher mean levels of Cr, Ni, and Sr (0.021 ± 0.008 mg/L, 0.018 ± 0.004 mg/L, and 0.32 ± 0.07 mg/L, respectively) and lower mean levels of B (3.0 ± 0.6 mg/L) were identified in CM compared with TM wines. Cr and Ni are of particular interest because of their association with stainless steel contact during CM production. In addition, the results identified higher mean levels of K (613 ± 153 mg/L) and lower mean levels of Cu (0.034 ± 0.036 mg/L) in rosé wines compared with non-rosé style wines. These results represent the first investigation of metal content in Canadian sparkling wines and identify important elemental differences related to production technique that can inform future authenticity assessments.
Aceitação e Uso do Agregador Podcast na Contabilidade no Ensino Superior: Uma Abordagem Simétrica e Assimétrica

O objetivo do estudo é identificar os fatores que contribuem para a aceitação e utilização de agregador de podcast capaz de fornecer explicações resumidas sobre aulas expositivas, aos discentes da graduação em Ciências Contábeis de uma Universidade Federal do Sul do Brasil. O estudo seguiu a Teoria Unificada de Aceitação e Uso de Tecnologia (UTAUT). Os dados foram coletados por meio da aplicação de questionário fechado in loco. A modelagem de equações estruturais baseada no método de mínimos quadrados parciais (PLS-SEM) foi utilizada para a análise simétrica e a análise qualitativa comparativa fuzzy-set (fsQCA) para a análise assimétrica dos dados do presente estudo. Os resultados demonstram que simetricamente, a influência social influencia a intenção de uso, enquanto as condições facilitadoras e a intenção de uso influenciam o comportamento de uso dos podcasts. Pela ótica assimétrica, são elencadas cinco combinações causais que promovem alta intenção de uso, e outras quatro soluções que promovem alto comportamento de uso. Os achados contribuem para a prática docente, para a elaboração de estratégias de ensino e para a integração de tecnologias no processo de aprendizagem.
Barayin Morphosyntax

This book contains a Lexical-Functional Grammar (LFG) analysis of Barayin morphosyntax, with a particular focus on serial verb constructions. Barayin is a Chadic language spoken by about 5000 people in the Guera region of Chad. The core chapters of the book provide analyses of the basic clause, noun phrases, verb phrases, and serial verb constructions. The version of LFG assumed includes two recent innovations. The first is minimal c-structure which results in simpler phrase structure representations. The second is the assumption that glue semantics accounts for argument selection, rejecting the need for a level of a-structure or for Completeness and Coherence in f-structure. This allows argument sharing in serial verb constructions to be modeled in a connected s-structure. This method of modeling semantic composition in complex predicates is extended to directional and associated motion complex predicates in Choctaw and Wambaya, removing the need to appeal to a special mechanism to unite semantic forms in such constructions.
Using Social Networking Sites as a Platform for E-Learning

This paper presents a web-based elearning system that takes advantages of social networking sites and social software to provide learners with a comprehensive and feature rich environment for building and utilizing applications, and potential for collaborating with the right co-learners. To demonstrate the effectiveness of this approach, we have developed two applications that use MySpace as development platform, and composed of several services available on a cloud computing infrastructure. The first application allows users to query and access learning materials (docs and videos), while the second one generates quizzes. The system has a service oriented architecture that simplifies the management and increase the effective utilization of the underlying web resources.
A Semi-Supervised Semantic Segmentation Method for Blast-Hole Detection

: The goal of blast-hole detection is to help place charge explosives into blast-holes. This process is full of challenges, because it requires the ability to extract sample features in complex environments, and to detect a wide variety of blast-holes. Detection techniques based on deep learning with RGB-D semantic segmentation have emerged in recent years of research and achieved good results. However, implementing semantic segmentation based on deep learning usually requires a large amount of labeled data, which creates a large burden on the production of the dataset. To address the dilemma that there is very little training data available for explosive charging equipment to detect blast-holes, this paper extends the core idea of semi-supervised learning to RGBD semantic segmentation, and devises an ERF-AC-PSPNet model based on a symmetric encoder– decoder structure. The model adds a residual connection layer and a dilated convolution layer for down-sampling, followed by an attention complementary module to acquire the feature maps, and uses a pyramid scene parsing network to achieve hole segmentation during decoding. A new semi-supervised learning method, based on pseudo-labeling and self-training, is proposed, to train the model for intelligent detection of blast-holes. The designed pseudo-labeling is based on the HOG algorithm and depth data, and proved to have good results in experiments. To verify the validity of the method, we carried out experiments on the images of blast-holes collected at a mine site. Compared to the previous segmentation methods, our method is less dependent on the labeled data and achieved IoU of 0.810, 0.867, 0.923, and 0.945, at labeling ratios of 1/8, 1/4, 1/2, and 1.
Detecting Human Emotions Using Smartphone Accelerometer Data

The work presented in this thesis concerns the classification of emotions based on smartphone accelerometer data. The data is collected from individuals who have been carrying their phone in a pocket while walking. While other methods of emotion detection generally are more intrusive or only give estimations over longer periods of time, using accelerometer data presents an opportunity for real-time and non-intrusive emotion detection. An Android app was developed in order to monitor the smartphone accelerometer of the individuals who participated in the study and occasionally request them to submit their emotional state. This way, data is collected from a natural environment rather than a laboratory setting. The recorded data is then processed and used to train different classifiers to be compared. The machine learning algorithms decision tree, support vector machine and multilayer perceptron are used for this purpose. Emotions are classified in two dimensions: pleasantness and arousal (activation). While the recognition rate for the arousal dimension is promising at 75%, pleasantness is harder to predict, with a recognition rate of 51%. These findings indicate that by only analyzing accelerometer data recorded from a smartphone, it is possible to make predictions of a person’s state of activation. Such predictions can be used, especially in conjunction with other methods of emotion detection, to adapt services to the user’s emotional state.
APPLICATIONS OF MATHEMATICS CHARGED ISLAMIC VALUES BY USING MACROMEDIA FLASH AND CAMTASIA

Some factors of low interresting of Mathematic learning material for student on Junior High School grade (SMP/MTs) are design of learning material that is less animation and the difficuties of use the learning material. Therefore, the use of learning material should be inovated for increasing the learners’ interrest, to make them easy understanding the subject that has been learnt, and still capable to increase reigion value in their ownselves . One form of this effort is to create learning material in the form of flash mathematics with c onsits Islamic v alue . Flash application is an application that can be developed in accordance animatif developer creativity. The purpose of the research is the developing of learning material in the electric learning form that consi s ts Islamic value in Transformation subject. The method of research is research and development, and the design of the research is using the steps of ADDIE model, there are analysis , design , development , implemetation , and evaluation . Data collection techniques included interviews and observations with research instruments are questionnaires, observation sheets and tests. Further analysis of the data conducted by researchers consists of qualitative and quantitative analysis to determine the feasibility and effectiveness of the teaching materials developed. The results of the expert evaluation consisting of a material expert and a media expert, shows that the average velue obtained from every aspects: content qua l ity and purpose , instructional quality of learning material, aspects of the charge Islamic value, and the technique quality was 4.37 that very good category. The results of t he assessment by the teachers of Junior High School grade (MTs) Nurul Huda Munjul which consist of two Mathematic s teacher and a teacher of Information and Communication Technology (ICT) , shows that the average velue of each aspect was 4.46 with very good category. Than the student q uation n a i re responses result data obtained by the average velue of each aspects: the quality of learning and instructional, technic al aspects of the display, and payload integrit ion aspects of Islamic value in learning material, namely 3.98, with good categories. The data test scores of student learning outcomes gained an average of 82.34 > KKM (75) with the classical completeness of 89.19% > 85%. The conclusion showed that the quality of Mathematic learning material s charged Islamic velues are developed with m acromedia f lash and c atamsia aplications is feasi ble and effective to be used in the learning process. Keywords: Learning Material , Flash Multimedia, Islamic Value , Transfromatio n.
Pengaruh kepuasan kerja dan komitmen organisasional terhadap intensi turnover pegawai pd pasar tohaga bogor

The increasing impact of environmental issues that generated in process of agricultural activities, that make us need a form of approach that emphasizes the importance of environmental aspects in implementation process of agricultural activities. Green productivity is a way to increase productivity and minimize waste generated through a reduction on environmental protection, such as the reduction of resources (materials, energy, etc.), waste minimization and reduction of the pollution generated. This research aims are to calculate the productivity of KUR in one year, counting of Green Productivity Index (GPI) in current states at KUR, analyze problems that occur in the value chain of productivity in natural rubber cultivation and also give the best recommendations of strategy to repair the problems that occur in the value chain process of cultivation of natural rubber with Green Productivity. The method of this research is using a systems approach to identification of number of needs in research by using Green Value Stream Mapping, so that can produce an operating system that are effective and efficient. The systems approach begins with finding all factors contained in the system to get the best solution for resolving the problem, then making a model of AHP to help select the best alternative solution and than the alternative choosed testing by GPI to see its feasibility. The results showed that the productivity of the KUR has decreased every month, cause of using the land that is not optimal. GPI on the current state is 3.6, which means they can be improved further to be even better. The problem occurs in the value chain of natural rubber cultivation at KUR when used supporting material that is not environmentally friendly. The best solution chosed is composting, this solution can reduce the waste generated from the cultivation of natural rubber by 95.7% and the use of materials was 66.6%. This solution can also lower the costs generated, increasing the income of farmers in KUR, and increase GPI in amount of 3,4 points.
Feature Selection and Classifier Development for Radio Frequency Device Identification

Abstract : The proliferation of simple and low-cost devices, such as IEEE 802.15. 4 ZigBee and Z-Wave, in Critical Infrastructure (CI) increases security concerns. Radio Frequency Distinct Native Attribute (RF-DNA) Fingerprinting facilitates biometric-like identification of electronic devices emissions from variances in device hardware. Developing reliable classifier models using RF-DNA fingerprints is thus important for device discrimination to enable reliable Device Classification (a one-to-many looks most like assessment) and Device ID Verification (a one-to-one looks how much like assessment). AFITs prior RF-DNA work focused on Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) and Generalized Relevance Learning Vector Quantized Improved (GRLVQI) classifiers. This work 1) introduces a new GRLVQI-Distance (GRLVQI-D) classifier that extends prior GRLVQI work by supporting alternative distance measures, 2) formalizes a framework for selecting competing distance measures for GRLVQI-D, 3) introducing response surface methods for optimizing GRLVQI and GRLVQI-D algorithm settings, 4) develops an MDA-based Loadings Fusion (MLF) Dimensional Reduction Analysis (DRA) method for improved classifier-based feature selection, 5) introduces the F-test as a DRA method for RF-DNA fingerprints, 6) provides a phenomenological understanding of test statistics and p-values, with KS-test and F-test statistic values being superior to p-values for DRA, and 7) introduces quantitative dimensionality assessment methods for DRA subset selection.
Importance of perioperative nutrition and euthyroid function in achieving successful head and neck free tissue reconstruction: editorial comment

Department of Otolaryngology, Virginia Commonwealth Medical Center, Richmond, VA, USA; Department of Anesthesiology, Virginia Commonwealth Medical Center, Richmond, VA, USA Correspondence to: Thomas S. Lee. Department of Otolaryngology, Virginia Commonwealth Medical Center, Richmond, VA, USA. Email: thomas.lee@vcuhealth.org. Comment on: Healy DW, Cloyd BH, Straker T, et al. Expert Consensus Statement on the Perioperative Management of Adult Patients Undergoing Head and Neck Surgery and Free Tissue Reconstruction From the Society for Head and Neck Anesthesia. Anesth Analg 2021;133:274-83.
Physician Compliance with Nutrition Support Team Recommendations: Effects on the Outcome of Treatment for Critically Ill Patients

Purpose: Attending physicians in Korea are aware of the existence of the Nutrition Support Team (NST), but even when the NST are consulted, compliance with their recommendations may be low. This study was performed to identify physicians’ compliance with the NST advice and how this affected the outcome of treatment for critically ill patients.Methods: This study was a retrospective observational study. Critically ill patients who were older than 18 years, younger than 90 years, and had been admitted and managed in the intensive care unit were selected for this study. Patients were assigned to either the compliance group or the non-compliance group according to physician compliance with the NST advice. Each group were compared using variables such as calorie supply, protein supply, laboratory findings, hospital stay, 30-day mortality, and survival rate.Results: The compliance group (81% of cases) was supplied with a significantly higher energy (1,146.36 ± 473.45 kcal vs. 832.45 ± 364.28 kcal, p < 0.01) and a significantly higher protein (55.00 ± 22.30 g/day vs. 42.98 ± 24.46 g/day, p = 0.04) compared with the non-compliance group. There was no significant difference in the basic demographics between groups, although the compliance group had a better outcome in the 30-day mortality rate (8% vs. 26%, p = 0.02), and in survival beyond 1 year (Crude model, hazard ratio: 2.42, CI: 1.11-5.29). Conclusion: Critically ill patients whose attending physician complied with the NST advice, received an increased energy intake and supply of protein which was positively associated with survival.