Miracle-dz/UniFAR-FTUs huggingface dataset:
Lactobacillus reuteri in digestive system diseases: focus on clinical trials and mechanisms

Objectives Digestive system diseases have evolved into a growing global burden without sufficient therapeutic measures. Lactobacillus reuteri (L. reuteri) is considered as a new potential economical therapy for its probiotic effects in the gastrointestinal system. We have provided an overview of the researches supporting various L. reuteri strains’ application in treating common digestive system diseases, including infantile colic, diarrhea, constipation, functional abdominal pain, Helicobacter pylori infection, inflammatory bowel disease, diverticulitis, colorectal cancer and liver diseases. Methods The summarized literature in this review was derived from databases including PubMed, Web of Science, and Google Scholar. Results The therapeutic effects of L. reuteri in digestive system diseases may depend on various direct and indirect mechanisms, including metabolite production as well as modulation of the intestinal microbiome, preservation of the gut barrier function, and regulation of the host immune system. These actions are largely strain-specific and depend on the activation or inhibition of various certain signal pathways. It is well evidenced that L. reuteri can be effective both as a prophylactic measure and as a preferred therapy for infantile colic, and it can also be recommended as an adjuvant strategy to diarrhea, constipation, Helicobacter pylori infection in therapeutic settings. While preclinical studies have shown the probiotic potential of L. reuteri in the management of functional abdominal pain, inflammatory bowel disease, diverticulitis, colorectal cancer and liver diseases, its application in these disease settings still needs further study. Conclusion This review focuses on the probiotic effects of L. reuteri on gut homeostasis via certain signaling pathways, and emphasizes the importance of these probiotics as a prospective treatment against several digestive system diseases.
Improving students' mathematical critical thinking ability using learning start with a questions

This study compares Learning Start with a Question (LSQ) with traditional learning to investigate how to improve mathematical critical thinking skills. Quantitative research methods are used in a quasi-experimental way. The sample of this study was 83 junior high school students in Ternate City, Indonesia. An essay test was used, with questions based on mathematical critical thinking skills. Inferential statistics and an independent sample t-test were used to analyze the data. Based on the study's results, with the application of LSQ learning, most students can solve problems in critical mathematical thinking correctly, assisted by steps and indicators of completeness of mathematical critical thinking skills. Students who use LSQ learning have a higher average test score than traditional learning students. The results of the t-test calculation show that students who learn to use LSQ learning have differences in increasing mathematical critical thinking skills with students who follow traditional learning.
A Low-Cost Multibeam Switching Antenna Using Reconfigurable Hybrid Metasurface for Beamforming Applications

In this paper, we proposed a multibeam switching antenna based on a low-cost reconfigurable hybrid metasurface applied for beamforming systems. The antenna consists of two parts: a microstrip feed antenna and a transmission hybrid metasurface. The latter is composed of three types of units with different amplitude and phase responses to electromagnetic waves so as to control the beams of the feed antenna. Sixteen PIN diodes are arranged in the metasurface with a simple bias network. When two different direct-current voltages are applied to the PIN diodes, the antenna can dynamically switch between two beams and four beams. For demonstration, the proposed antenna is fabricated, and the measured results show that the antenna operates at 9.07–9.42 GHz (−10 dB bandwidth) with a total size of 1. 80λ0 × 1. 52λ0 × 0. 22λ0 (λ0 corresponds to the wavelength of 9.28 GHz in free space). With the merits of a compact structure, low cost and good radiation performance, the proposed design is suitable for beamforming applications.
The Impact of Weather Conditions on Capital Bikeshare Trips

Automated bicycle renting systems have seen tremendous growth over the last few years, with many cities implementing systems. Washington, DC, has one of the largest systems of bikesharing in the US and recently made all their usage data publically available for analysis. One issue with promoting bicycling as a reliable alternative mode of travel is the impact of unfavorable weather conditions on usage. In theory, usage can be affected by colder weather, precipitation, and excessive heat. The research presented here analyzes the effect of weather on the use of the Washington, DC, bikeshare system. Hourly weather data, including temperature, rainfall, snow, wind, fog, and humidity levels are linked to hourly usage data and statistical models linking both number of users and duration of use are estimated. Further, the authors evaluate trips from bikeshare stations within one quarter mile of Metro (subway) stations at times when Metro is operating. This allows them to determine whether Metro serves as a back-up option when weather conditions are unfavorable for bicycling. Results show that cold temperatures, rain, and high humidity levels reduce both the likelihood of using bikeshare and the duration of trips. Trips taken from bikeshare stations proximate to Metro stations are affected more by rain than trips not proximate to Metro stations and less likely when it is dark. This information is useful for understanding bicycling behavior and also for those planning bikeshare systems in other cities.
PHYTOCHEMICAL ANALYSIS AND EVALUATION OF ANTIBACTERIAL ACTIVITY OF TERMINALIA CHEBULA, MOMORDICA CHARANTIA, DREGEA VOLUBILIS PLANT EXTRACTS.

Thupurani Murali Krishna 1 , Urmila Bonkuri 2 , Racha Srikanth 3 , Challa Surekha 3 , Peddoju Pranay 3 and Venkalapally Thirupathaiah 2 . 1. Department of Biotechnology, Chaitanya Postgraduate College (Autonomous), Kishanpura, Hanamkonda506001 TS-India. 2. Natural Products Laboratory, Chaitanya Postgraduate College (Autonomous), Kishanpura, Hanamkonda506001-TS-India. 3. Department of Biochemistry and Bioinformatics, Gitam University,Rushikonda, Visakhapatnam, AP-India. Manuscript Info Abstract . Manuscript History Received: 20 October 2018 Final Accepted: 22 November 2018 Published: December 2018
Cognitive impairment in essential tremor assessed by the cerebellar cognitive affective syndrome scale

Background Essential tremor (ET) is a movement disorder characterized by cerebellar neurodegenerative changes. ET is also associated with non-motor symptoms including cognitive impairment. The neuropsychologic profile of a patient with ET could relate to cerebellar cognitive affective syndrome (CCAS). Objective This study aimed to assess the prevalence of cognitive impairment in patients with ET and identify whether the cognitive impairment in ET corresponds to a CCAS. Methods Cognitive functions were evaluated with the CCAS-Scale (CCAS-S) in 20 patients with ET and 20 controls matched for age, sex, and level of education. The results of the CCAS-S were compared between patients and controls. The underlying determinant of CCAS inpatients with ET was identified through the correlation between the results of the CCAS-S and age at onset of symptoms, disease duration, and the Essential Tremor Rating Assessment Scale (TETRAS). Results On a group level, ET patients performed significantly worse than matched controls. In total, 13 individuals with ET had a definite CCAS (CCAS-S failed items ≥ 3). ASO and TETRAS scores significantly correlated with CCAS-S performances in ET patients. Conclusion CCAS is highly prevalent in patients with ET which supports the cerebellar pathophysiology of associated cognitive impairment and supports a more systematic use of the CCAS-S to cognitively assessed patients with ET.
Convolutional neural networks with approximation of Shapley values for the classification and interpretation of pneumonia in X-ray images

Pneumonia is a lung disease responsible for the highest number of deaths from infection in children and adults. Its diagnosis must be fast and accurate so that procedures are taken as soon as possible to combat the disease. In this work, Convolutional Neural Networks were explored for the classification of chest radiography images in the context of pneumonia diagnosis. Although these models are highly effective, their predictions are difficult to interpret. Therefore, the proposed method additionally aims at presenting an explainable model based on Shapley approximation values to perform the diagnosis of pneumonia with higher robustness. Results show that the model achieves competitive accuracy when compared to other architectures, and overcome them with respect to interpretation abilities.
Would Emotionally Intelligent Service Employees Stay Longer? The Moderating Role of Internal Service Climate

Employee turnover intention is huge challenge for employers, especially in the highly stressful service industry. This study surveyed 1,236 front line casino employees across 29 organizations in Macau, the Casino paradise. Findings reveal that emotional intelligence (EI) plays a crucial role in allowing employ-ees to cope with environmental demands and pressures successfully. Our results demonstrate that job sat-isfaction and occupational commitment can relieve the turnover intention of employees. Additionally, we investigated the moderating effect of an internal service climate (ISC) that is an organizational contextual factor (including psychological environment and re-sources available for employees). Our findings suggest that when the ISC is high, satisfied and committed employees have lower turnover intentions. ISC offers a well being and supportive environment that helps bring down the turnover intention in the very stressful service industry. Our study recommends that companies should provide a positive ISC for employees to reduce turnover intension among employees.
Becoming the Mentors We Needed: Formative First-Year Practices and Motivating Factors of Peer Mentorship in College

Abstract The purpose of this qualitative study was to examine formative first-year practices and motivating factors that encouraged first-year college students to pursue peer mentorship roles. The examination of peer mentorship within undergraduate populations owes much to Jacobi’s (1991) study on mentoring and academic success, which more recently has been continued by Nora and Crisp (2007), Crisp and Cruz (2009), and Crisp et al. (2017). Peer mentorship bolsters both individual student development and undergraduate enrollment (Crisp et al., 2017; Yomtov et al., 2017), and assuming a peer mentor role contributes in meaningful ways to a college learning environment (Kuh et al., 2011). Although a significant amount of research has been conducted on peer mentorship, there has been less focus on motivating factors during the first year of college that prompt peer mentors to seek such roles in their second year of undergraduate education. The results of this study indicated students were motivated to pursue peer mentorship roles for four reasons: the desire for community, their sense of self-efficacy, a vocational connection, and the opportunity to impact incoming students’ experiences. Implications of these results are discussed in terms of implementing formative best practices into first-year experiences as a means of encouraging and recruiting second-year peer mentors.
Metabolic Syndrome in Affective Disorders: Associations with Dark Triad Personality Traits

Previous research has focused on the relationship between affective disorders (AD) and metabolic syndrome (MetS). Aside from biological and lifestyle factors, personality traits were identified as influencing aspects. In particular, the Dark Triad personality traits (DT; Machiavellianism, narcissism, psychopathy) were connected to both AD and worse somatic health, thus possibly resulting in MetS. This observational study aimed to investigate the associations between DT and anthropometric parameters and differences in the DT traits concerning the presence of MetS in individuals with AD. A total of 112 individuals (females = 59, males = 51, diverse = 2, Mage = 47.5, SDage = 11.5) with AD filled out the Short Dark Triad questionnaire. Body Mass Index (BMI) and MetS criteria, including blood pressure, waist circumference, lipid, and glucose levels, were assessed. For Machiavellianism, a positive association with BMI (r = 0.29, p < 0.05) and a negative association with systolic blood pressure (r = −0.23, p < 0.05) were found. No relationship between the overall MetS and DT score (r = 0.08, p = 0.409) was observed. The results were limited by the lack of a control group and the cross-sectional study design, which does not allow for the determination of causality. Machiavellianism was associated with a higher BMI and lower systolic blood pressure, indicating a deteriorating health effect of this trait. Possibly, the higher prevalence of MetS in AD stems from aspects such as lifestyle or medication intake, which might also be influenced by DT. Further research is needed to disentangle underlying mechanisms.
End-to-End Human Instance Matting

Human instance matting aims to estimate an alpha matte for each human instance in an image, which is extremely challenging and has rarely been studied so far. Despite some efforts to use instance segmentation to generate a trimap for each instance and apply trimap-based matting methods, the resulting alpha mattes are often inaccurate due to inaccurate segmentation. In addition, this approach is computationally inefficient due to multiple executions of the matting method. To address these problems, this paper proposes a novel End-to-End Human Instance Matting (E2E-HIM) framework for simultaneous multiple instance matting in a more efficient manner. Specifically, a general perception network first extracts image features and decodes instance contexts into latent codes. Then, a united guidance network exploits spatial attention and semantics embedding to generate united semantics guidance, which encodes the locations and semantic correspondences of all instances. Finally, an instance matting network decodes the image features and united semantics guidance to predict all instance-level alpha mattes. In addition, we construct a large-scale human instance matting dataset (HIM-100K) comprising over 100,000 human images with instance alpha matte labels. Experiments on HIM-100K demonstrate the proposed E2E-HIM outperforms the existing methods on human instance matting with 50% lower errors and 5X faster speed (6 instances in a 640X640 image). Experiments on the PPM-100, RWP-636, and P3M datasets demonstrate that E2E-HIM also achieves competitive performance on traditional human matting.
FIRST INTERACTION REGION LOCAL COUPLING CORRECTIONS IN THE LHC RUN 3 ∗

The successful operation of large scale particle accelerators depends on the precise correction of unavoidable magnetic field or magnet alignment errors present in the machine. During the LHC Run 2, local linear coupling in the Interaction Regions (IRs) was shown to have a significant impact on the beam size, making its proper handling a necessity for Run 3 and the High Luminosity LHC (HL-LHC). A new approach to accurately minimise the local IR linear coupling based on correlated external variables such as the |𝐶 − | had been proposed, which relies on the application of a rigid waist shift in order to create an asymmetry in the IR optics. In this contribution, preliminary corrections from the 2021 beam test and the early 2022 commissioning are presented, as well as first results of the new method’s experimental configuration tests in the LHC Run 3 commissioning.
Intelligent Anti-Jamming Decision With Continuous Action and State in Bivariate Frequency Agility Communication System

The conventional frequency hopping (FH) system is susceptible to malicious jamming due to the prearranged hopping frequency table. In this paper, we develop a bivariate frequency agility (BFA) communication system to improve the anti-jamming capability by assigning time-varying characteristics to the communication parameters such as fixed frequency interval and hopping rate in conventional FH. Our goal is to find the optimal frequency interval and hopping rate strategy in jamming environment to maximize the signal-to-noise ratio (SINR). We formulate the parameter decision problem as a Markov decision process (MDP). Then, we propose a deep deterministic policy gradient (DDPG) based algorithm for frequency interval selection and hopping rate setting. In addition, to overcome the shortcomings of DDPG, which is prone to fall into local optimum and unstable convergence, an improved deep deterministic policy gradient algorithm with a weighted dual-prioritized experience replay and periodically updated learning rate (IDDPG) is proposed. In IDDPG, on the one hand, the model is trained by replaying more experiences with high immediate reward and large temporal difference error (TD error) to make it more accurate. On the other hand, the learning rate is periodically decayed so that the update rate of the network model varies periodically, resulting in a richer and more diverse exploration. The simulation results under different electromagnetic jamming environment indicates that the anti-jamming performance of the proposed two algorithms outperforms that of the PPER-DQN algorithm and the RFH algorithm.
Improving Learning in Low- and Lower-Middle-Income Countries

The current challenge of education systems is learning. Across low-income countries (LICs) and lower-middle-income countries (LMCs), 62 % of 10-year-olds could not read at a minimally sufficient level in 2015. This study provides an overview of recent spending on education and its correlation with learning outcomes. We show that the relationship between education spending and learning is historically weak. From 2000 to 2015, LICs and LMCs increased spending on education in primary schools by ~$137 per student, an 80 % inflation-adjusted increase, with no corresponding change on the average learning outcomes. We then conduct a benefit-cost analysis of candidate interventions that could increase learning at low cost. Two interventions – structured pedagogy and, teaching at the right level, with and without a technology component generate large benefit-cost ratios. If deployed uniformly to reach 90 % of the 467 million students in LICs and LMCs, these interventions would cost on average $18 per student per year or $7.6 billion annually, generating $65 in benefits for every $1 spent. The economic logic behind this finding is that the hard and costly work of getting children into primary schools has mostly been accomplished, leaving open the possibility of learning interventions that improve the efficiency of the existing education system at low cost. Our results show that increasing education expenditure by just 6 % could increase learning by 120 % if directed toward these highly cost-effective interventions.
Integrating CUDA memory management mechanisms for domain decomposition of an acoustic wave kernel implemented in OpenMP

OpenMP is a well-known tool for parallelizing code in a directive-based programming model. While it has been extended to include support for offloading for devices such as GPUs, multi-gpu programming using data map directives requires redundant data allocation and non-intuitive data synchronization. This paper studies an alternative implementation of a CUDA-OpenMP hybrid kernel using native Unified Virtual Addressing memory pointers in an OpenMP target kernel.
Uremic Toxins Induce THP-1 Monocyte Endothelial Adhesion and Migration through Specific miRNA Expression

Atherosclerosis is initiated by the activation of endothelial cells that allows monocyte adhesion and transmigration through the vascular wall. The accumulation of uremic toxins such as indoxyl sulphate (IS) and p-cresol (PC) has been associated with atherosclerosis. Currently, miRNAs play a crucial role in the regulation of monocyte activation, adhesion, and trans-endothelial migration. The aim of the present study is to evaluate the effect of IS and PC on monocyte adhesion and migration processes in monocytes co-cultured with endothelial cells as well as to determine the underlying mechanisms. The incubation of HUVECs and THP-1 cells with both IS and PC toxins resulted in an increased migratory capacity of THP-1 cells. Furthermore, the exposure of THP-1 cells to both uremic toxins resulted in the upregulation of BMP-2 and miRNAs-126-3p, -146b-5p, and -223-3p, as well as the activation of nuclear factor kappa B (NF-κB) and a decrease in its inhibitor IĸB. Uremic toxins, such as IS and PC, enhance the migratory and adhesion capacity of THP-1 cells to the vascular endothelium. These toxins, particularly PC, contribute significantly to uremia-associated vascular disease by increasing in THP-1 cells the expression of BMP-2, NF-κB, and key miRNAs associated with the development of atherosclerotic vascular diseases.
Automatic Tree Height Measurement Based on Three-Dimensional Reconstruction Using Smartphone

Tree height is a crucial structural parameter in forest inventory as it provides a basis for evaluating stock volume and growth status. In recent years, close-range photogrammetry based on smartphone has attracted attention from researchers due to its low cost and non-destructive characteristics. However, such methods have specific requirements for camera angle and distance during shooting, and pre-shooting operations such as camera calibration and placement of calibration boards are necessary, which could be inconvenient to operate in complex natural environments. We propose a tree height measurement method based on three-dimensional (3D) reconstruction. Firstly, an absolute depth map was obtained by combining ARCore and MidasNet. Secondly, Attention-UNet was improved by adding depth maps as network input to obtain tree mask. Thirdly, the color image and depth map were fused to obtain the 3D point cloud of the scene. Then, the tree point cloud was extracted using the tree mask. Finally, the tree height was measured by extracting the axis-aligned bounding box of the tree point cloud. We built the method into an Android app, demonstrating its efficiency and automation. Our approach achieves an average relative error of 3.20% within a shooting distance range of 2–17 m, meeting the accuracy requirements of forest survey.
Organizations Should Maintain Employee’s Work-Life Balance

The scope of the current study is to determine the challenges of employees in between work-life and personal life balance. Organizations should prioritize and maintain employees’ work-life balance. Work-life balance refers to the equilibrium between an employee's work responsibilities and their personal life, including family, hobbies, and personal well-being. When employees have a healthy work-life balance, they experience less stress and improve their working skills to achieve the targets, and this well-being leads to improved mental and physical health as well as work productivity. This descriptive research approach surveyed by 67 responses as simple random technique, provides a significant result toward organizations that they should maintain employee’s work-life balance. The research finds that organizations that prioritize work-life balance are more likely to attract and retain top talent. By the managing time, regular assessment and adjustment in work-life balance initiatives, organizations can enhance their employer brand and improve employee retention rates. Work-life balance allows employees to have time for personal pursuits and interests outside of work which can contribute to their creativity and innovation. When employees have a healthy work-life balance, they are more likely to have positive relationships with their colleagues and supervisors. Improved relationships can lead to better teamwork, collaboration, and a more positive work environment.