Miracle-dz/UniFAR-FTUs huggingface dataset:
Knockdown of CD-74 in the Proliferative and Apoptotic Activity of Breast Cancer Cells

BACKGROUND: The cluster of differentiation (CD) 74 is known for its immunological functions and its elevated level was reported in various cancer cells. AIM: The aim of the present study was to investigate the expression and potential roles of CD74 in the proliferative and apoptotic activity of breast cancer. METHODS: Expression of CD74, macrophage migration inhibitory factor (MIF) and CD44 was assayed in CAMA-1 and MDA-MB-231 cell lines using flow cytometry. CD74 was knocked down using CD74 siRNA-transfection in CAMA-1, and MDA-MB-231 cells and proliferation and apoptosis were determined in the transfected breast cancer cells. RESULTS: The data showed that CD74, MIF and CD44 were expressed in breast cancer cell lines and were associated with cell proliferation and apoptosis. Correlation analysis revealed that CD74 was positively correlated and colocalised with MIF on the cell-surface of CAMA-1 and MDA-MB-231. The knockdown of CD74 significantly reduced CAMA-1 and MDA-MB-231 cell proliferation and increased the level of apoptotic cells. CONCLUSION: We concluded that the interactions of CD74 with MIF and CD74 with CD44 could be a potential tumour marker for breast cancer cells. Moreover, the level of co-expression of MIF and CD74 or CD44 could be a surrogate marker for the efficacy of anti-angiogenic drugs, particularly in breast cancer tumours. In short, the study revealed the potential roles of CD74 in the proliferation and apoptosis of breast cancer which may serve as a potential therapeutic target for breast cancer.
Ensemble based discriminative models for Visual Dialog Challenge 2018

This manuscript describes our approach for the Visual Dialog Challenge 2018. We use an ensemble of three discriminative models with different encoders and decoders for our final submission. Our best performing model on 'test-std' split achieves the NDCG score of 55.46 and the MRR value of 63.77, securing third position in the challenge.
PRESENT 2020: Text Expanding on the Checklist for Proper Reporting of Evidence in Sport and Exercise Nutrition Trials.

© 2020 Human Kinetics, Inc. Some readers may not have access to the full paper, so a properly formatted and well-written abstract is imperative. Authors should give priority to information about the current study rather than using the abstract for an extensive background or rationale. 2a Methods: Key information regarding the study design, methods, and population should be summarized to enable broad understanding of the study from the abstract. 2b Results: Readers are interested in extracting key data that reflect the main findings of the study. The abstract should present data (e.g., the absolute magnitude of values and the size/precision of effects—specifying which measures of central tendency and variability are stated) rather than simply stating the presence, absence, or direction of effects. The presentation of p values or similar inferential statistics is no substitute for reporting actual data (Maughan, 2004). 2c Conclusion: Priority should be given to the reporting of results as per the previous section, with only a brief concluding statement thereafter. A concise conclusion based on what was actually measured in the study is preferred to speculative interpretations, with cautious use of language to avoid hyperbole or improper inference of causality (Brown et al., 2013). It is not appropriate or necessary to identify further research priorities here.
Adaptive Bipartite Event-Triggered Output Consensus of Heterogeneous Linear Multiagent Systems Under Fixed and Switching Topologies

This article addresses the adaptive bipartite event-triggered output consensus issue for heterogeneous linear multiagent systems. We consider both cooperative interaction and antagonistic interaction between neighbor agents in both fixed and switching topologies. An adaptive bipartite compensator consisting of time-varying coupling weights and dynamic event-triggered mechanism is first proposed to estimate the leader’s state in a fully distributed manner. Different from the existing methods, the proposed compensator has three advantages: 1) it does not depend on any global information of the network graph; 2) it avoids the continuous communication between neighbor agents; and 3) it is applicable for the signed communication topology. Assume that the system states are unmeasurable, and we thus design the state observer. Based on the devised compensator and observer, the distributed control law is developed such that the bipartite event-triggered output consensus problem can be achieved. Moreover, we extend the results in fixed topology to switching topology, which is more challenging in that state estimation is updated in two cases: 1) the interaction graph is switched or 2) the event-triggered mechanism is satisfied. It is proven that no agent exhibits Zeno behavior in both fixed and switching interaction topologies. Finally, two examples are provided to illustrate the feasibility of the theoretical results.
Play&Go, an Urban Game Promoting Behaviour Change for Sustainable Mobility

Designing more sustainable cities is increasingly pressing, and mobility behaviour plays an important role in how much cities are socially, economically and environmentally sustainable. We report our experience in deploying the third edition of an urban game that exploits gamification for promoting a positive behavioural change of mobility habits. This edition of Play&Go ran for 6 months and involved 635 active players who tracked their trips on sustainable transportation means such as by bike, bus, train and walking trips. Players tracked 54293 trips in total, corresponding to 244.394 sustainable kilometres. We evaluated the user experience of Play&Go and its impact through questionnaires, interviews and game log analysis, and we report on players’ participation and engagement, reported behaviour change and impact of different gamification motivational elements.
Assessment of the relationship between knee ultrasound and clinical symptoms in patients with thyroid dysfunction

Objective To determine whether musculoskeletal ultrasound (MSUS) abnormalities were associated with thyroid dysfunction. Methods This study included 109 patients with thyroid diseases, including thyroid dysfunction or autoimmune thyroid disease. Patients were categorized as euthyroid, hypothyroid, or hyperthyroid based on their recent thyroid function tests. To evaluate MSUS, an experienced rheumatologist examined the presence of synovial fluid, synovial hypertrophy, and grade of inflammation in both gray-scale and power Doppler scans of the knee joint. Associations between MSUS abnormalities, thyroid status, visual analog scale (VAS) score for knee pain, and presence of thyroid autoantibodies were investigated. Results MSUS abnormalities were more frequently associated with hyperthyroid or hypothyroid states than with a euthyroid state. High knee VAS score was significantly associated with overall MSUS severity score regardless of knee osteoarthritis. However, there was no difference in MSUS abnormalities based on the presence of thyroid autoantibodies. Conclusions Both hypothyroid and hyperthyroid states were associated with MSUS abnormalities and knee arthralgia. MSUS examination may be helpful in uncontrolled thyroid dysfunction and knee arthralgia.
Real-Time Dense Semantic Labeling with Dual-Path Framework for High-Resolution Remote Sensing Image

Dense semantic labeling plays a pivotal role in high-resolution remote sensing image research. It provides pixel-level classification which is crucial in land cover mapping and urban planning. With the recent success of the convolutional neural network (CNN), accuracy has been greatly improved by previous works. However, most networks boost performance by involving too many parameters and computational overheads, which results in more inference time and hardware resources, while some attempts with light-weight networks do not achieve satisfactory results due to the insufficient feature extraction ability. In this work, we propose an efficient light-weight CNN based on dual-path architecture to address this issue. Our model utilizes three convolution layers as the spatial path to enhance the extraction of spatial information. Meanwhile, we develop the context path with the multi-fiber network (MFNet) followed by the pyramid pooling module (PPM) to obtain a sufficient receptive field. On top of these two paths, we adopt the channel attention block to refine the features from the context path and apply a feature fusion module to combine spatial information with context information. Moreover, a weighted cascade loss function is employed to enhance the learning procedure. With all these components, the performance can be significantly improved. Experiments on the Potsdam and Vaihingen datasets demonstrate that our network performs better than other light-weight networks, even some classic networks. Compared to the state-of-the-art U-Net, our model achieves higher accuracy on the two datasets with 2.5 times less network parameters and 22 times less computational floating point operations (FLOPs).
Health-Promoting Effects of a Concurrent Workplace Training Program in Inactive Office Workers (HealPWorkers): A Randomized Controlled Study

Purpose: To the best of our knowledge, no previous study has examined the health-promoting effects of a daily supervised concurrent workplace training program in inactive office workers. The main objective of this study was to examine the effectiveness of a 6-month workplace training program on health indices, musculoskeletal pains, functional capacity, and physical fitness in office workers. Design: Randomized controlled study. Setting: Four workplaces in the Region of Thessaly, Greece. Participants: A total of 36 office workers (≥6 hours/d, 5 days/wk) were randomly assigned to either a training group (TG; n = 18) or a control group (CG; n = 18). Intervention: The TG participated, every working day, in a 6-month supervised concurrent (flexibility, strength, balance, aerobic) training program (120 training sessions, 2 workouts/d of 15-20 minutes) that was implemented, in small groups, at the workplace during the work shift. The CG did not participate in any training. Measures: Health indices (body composition, blood pressure, respiratory function), musculoskeletal pains, functional capacity (flexibility, balance), and physical fitness (maximal strength, cardiorespiratory fitness) were measured before and after the completion of the program. After the completion of the program, participants’ enjoyment was assessed. Analysis: Two-way analysis of variance (group × time) with repeated measures on the “time” factor. Results: The statistical power, for all parameters, ranged from 0.85 to 0.94. Training group significantly increased lean body mass (3.81%); respiratory function (4.20%-4.53%); cervical, handgrip, back, and leg maximal strength (8.75%-26.55%); and functional capacity (19.71%-188.20%; P < .001-.01; Cohen’s effect size: 0.80-7.21), while significant reductions were observed on body fat (7.58%), blood pressure (4.99%-8.05%), heart rate (12.80%), and musculoskeletal pains (33.33%-100%; P < .001; Cohen’s effect size: 0.81-6.21). In CG, all the above variables did not change. Furthermore, a great percentage of workers (94.4%) reported high levels of enjoyment. Conclusion: The program “HealPWorkers” is an enjoyable exercise modality that may be safely and effectively used, to work settings, for the improvement of worker’s health, overall fitness, and functional capacity.
Targeting Non-Oncogene Addiction: Focus on Thyroid Cancer

Thyroid carcinoma (TC) is the most common malignancy of endocrine organs with an increasing incidence in industrialized countries. The majority of TC are characterized by a good prognosis, even though cases with aggressive forms not cured by standard therapies are also present. Moreover, target therapies have led to low rates of partial response and prompted the emergence of resistance, indicating that new therapies are needed. In this review, we summarize current literature about the non-oncogene addiction (NOA) concept, which indicates that cancer cells, at variance with normal cells, rely on the activity of genes, usually not mutated or aberrantly expressed, essential for coping with the transformed phenotype. We highlight the potential of non-oncogenes as a point of intervention for cancer therapy in general, and present evidence for new putative non-oncogenes that are essential for TC survival and that may constitute attractive new therapeutic targets.
Modification of Rigid Polyurethane Foams with the Addition of Nano-SiO2 or Lignocellulosic Biomass

Many achievements have been made on the research of composite polyurethane foams to improve their structure and mechanical properties, and the composite foams have been widely utilized in building insulation and furniture. In this work, rigid polyurethane foams (RPUFs) with the addition of different fillers (nano-SiO2, peanut shell, pine bark) were prepared through the one-step method. The effects of inorganic nano-SiO2 and organic biomass on foam properties were evaluated by means of physical and chemical characterization. The characterization results indicate that the compressive strength values of prepared foams could fully meet the specification requirement for the building insulation materials. The inorganic and organic fillers have no effect on the hydrogen bonding states in composite RPUFs. Furthermore, compared to the biomass fillers, the addition of nano-SiO2 greatly influenced the final residual content of the fabricated foam. All composite foams exhibit closed-cell structure with smaller cell size in comparison with the parent foam. The prepared composite foams have the potential for utilization in building insulation.
Common Variants in IL-20 Gene are Associated with Subclinical Atherosclerosis, Cardiovascular Risk Factors and IL-20 Levels in the Cohort of the Genetics of Atherosclerotic Disease (GEA) Mexican Study

Inflammation has been involved in the development of atherosclerosis, type 2 diabetes mellitus, insulin resistance, and obesity. Interleukin 20 is a pro-inflammatory cytokine encoded by a polymorphic gene located in chromosome 1. The aim of the study was to evaluate the association of two IL-20 polymorphisms (rs1400986 and rs1518108) with subclinical atherosclerosis (SA), cardiovascular risk factors and IL-20 levels in a cohort of Mexican individuals. The polymorphisms were determined in 274 individuals with SA and 672 controls. Under different models, rs1400986 (OR = 0.51, Pcodominant1 = 0.0001; OR = 0.36, Pcodominant2 = 0.014; OR = 0.49, Pdominant = 0.0001 and OR = 0.55, Padditive = 0.0001) and rs1518108 (OR = 0.62, Pcodominant2 = 0.048 and OR = 0.79, Padditive = 0.048) were associated with a lower risk of SA. These polymorphisms were associated with cardiovascular risk factors in individuals with SA and controls. Controls with the rs1400986 TT genotype presented high levels of IL-20 (p = 0.031). In individuals with the rs1400986 CC genotype, we observed a negative correlation between IL-20 levels and total abdominal tissue (TAT), visceral abdominal tissue (VAT) and subcutaneous abdominal tissue (SAT). Our results indicate that the IL-20 rs1400986 and rs1518108 polymorphisms were associated with decreased risk of developing SA and with some cardiovascular risk factors in individuals with SA and healthy controls. Negative correlation between BMI and VAT/SAT ratio in individuals with rs1400986 CC genotype and among IL-20 levels and TAT, VAT and SAT was observed.
A Two-Stream Meticulous Processing Network for Retinal Vessel Segmentation

Vessel segmentation in fundus is a key diagnostic capability in ophthalmology, and there are various challenges remained in this essential task. Early approaches indicate that it is often difficult to obtain desirable segmentation performance on thin vessels and boundary areas due to the imbalance of vessel pixels with different thickness levels. In this paper, we propose a novel two-stream Meticulous-Processing Network (MP-Net) for tackling this problem. To pay more attention to the thin vessels and boundary areas, we firstly propose an efficient hierarchical model automatically stratifies the ground-truth masks into different thickness levels. Then a novel two-stream adversarial network is introduced to use the stratification results with a balanced loss function and an integration operation to achieve a better performance, especially in thin vessels and boundary areas detecting. Our model is proved to outperform state-of-the-art methods on DRIVE, STARE, and CHASE_DB1 datasets.
Learning Control Policies of Driverless Vehicles from UAV Video Streams in Complex Urban Environments

The way we drive, and the transport of today are going through radical changes. Intelligent mobility envisions to improve the efficiency of traditional transportation through advanced digital technologies, such as robotics, artificial intelligence and Internet of Things. Central to the development of intelligent mobility technology is the emergence of connected autonomous vehicles (CAVs) where vehicles are capable of navigating environments autonomously. For this to be achieved, autonomous vehicles must be safe, trusted by passengers, and other drivers. However, it is practically impossible to train autonomous vehicles with all the possible traffic conditions that they may encounter. The work in this paper presents an alternative solution of using infrastructure to aid CAVs to learn driving policies, specifically for complex junctions, which require local experience and knowledge to handle. The proposal is to learn safe driving policies through data-driven imitation learning of human-driven vehicles at a junction utilizing data captured from surveillance devices about vehicle movements at the junction. The proposed framework is demonstrated by processing video datasets captured from uncrewed aerial vehicles (UAVs) from three intersections around Europe which contain vehicle trajectories. An imitation learning algorithm based on long short-term memory (LSTM) neural network is proposed to learn and predict safe trajectories of vehicles. The proposed framework can be used for many purposes in intelligent mobility, such as augmenting the intelligent control algorithms in driverless vehicles, benchmarking driver behavior for insurance purposes, and for providing insights to city planning.
Child maltreatment and cognitive vulnerabilities: Examining the link to posttraumatic stress symptoms

Abstract Research attempting to identify pathways from childhood maltreatment to adulthood posttraumatic stress symptoms (PTSS) implicates cognitive vulnerabilities that may increase risk for mental health concerns. The present study examined two cognitive vulnerabilities and their relation to PTSS amongst individuals with and without childhood maltreatment histories: attentional control and repetitive negative thinking (RNT). This research surveyed college students (N = 376) on constructs of interest. Results revealed that attentional control and RNT demonstrated significant indirect effects on PTSS (95% CI =.0003, .0336 and 95% CI =.0643, .1857, respectively). Findings suggest that childhood maltreatment severity was related to decreased attentional control and increased RNT, which were then both related to increased PTSS. Interventions for increasing attentional control and decreasing RNT should be implemented and evaluated for young adults with early adversity currently experiencing PTSS.
What Contributes to a Crowdfunding Campaign's Success? Evidence and Analyses from GoFundMe Data

Researchers have attempted to measure the success of crowdfunding campaigns using a variety of determinants, such as the descriptions of the crowdfunding campaigns, the amount of funding goals, and crowdfunding project characteristics. Although many success determinants have been reported in the literature, it remains unclear whether the cover photo and the text in the title and description could be combined in a fusion classifier to better predict the crowdfunding campaign's success. In this work, we focus on the performance of the crowdfunding campaigns on GoFundMe over a wide variety of funding categories. We analyze the attributes available at the launch of the campaign and identify attributes that are important for each category of the campaigns. Furthermore, we develop a fusion classifier based on random forest that significantly improves the prediction result, thus suggesting effective ways to make a campaign successful.
Nitric Oxide Gene Polymorphism is a Risk Factor for Diabetic Nephropathy and Atherosclerosis in Type 1 Diabetic Patients

AIM: To assess the risk factor for diabetic atherosclerosis nephropathy and diabetic nephropathy in type 1 diabetic patients. PATIENTS AND METHODS: Thirty healthy volunteers age and sex-matched and Sixty-five type 1 diabetic patient were in rolled in the study. The mean age of patients was 17.99 ± 2.59 years, mean age of onset of diabetes was 7.00 ± 3.28 years, mean duration of diabetes was 10.91 ± 3.54 years. Glycosylated sex-matched (HbA1c) was assessed in blood samples, serum lipid profile was determined, and serum level of oxidised low-density lipoprotein (OxLDL), and nitric oxide was evaluated by enzyme-linked immunosorbent assay (ELISA) technique. Nitric oxide 894G > T genotype was analysed by (PCR-RFLP) method and confirmed by Sequencing. Assessment of the albumin / creatinine ratio was done in urine samples. Renal Doppler and Carotid intima-media thickness (cIMT) via ultrasound was also performed. RESULTS: OxLDL, lipid profile, albumin/creatinine ratio, cIMT and resistivity index were significantly higher in diabetic patients while nitric oxide was significantly lower. Nitric oxide genotype shows no significant difference between diabetic’s patients and controls. Diabetic patients with homozygous NO had a significantly lower serum level of Nitric oxide, a significantly higher OxLDL, albumin / creatinine ratio and lipid profile. CONCLUSION: diabetic patients are liable for the occurrence of early diabetic nephropathy and atherosclerosis as a result of the presence of low level of nitric oxide. Nitric oxide gene polymorphism 894G > T in diabetic patients is a risk factor for diabetic nephropathy and atherosclerosis.
A Genetic Algorithm-Based Approach for Composite Metamorphic Relations Construction

The test oracle problem exists widely in modern complex software testing, and metamorphic testing (MT) has become a promising testing technique to alleviate this problem. The inference of efficient metamorphic relations (MRs) is the core problem of metamorphic testing. Studies have proven that the combination of simple metamorphic relations can construct more efficient metamorphic relations. In most previous studies, metamorphic relations have been mainly manually inferred by experts with professional knowledge, which is an inefficient technique and hinders the application. In this paper, a genetic algorithm-based approach is proposed to construct composite metamorphic relations automatically for the program to be tested. We use a set of relation sequences to represent a particular class of MRs and turn the problem of inferring composite MRs into a problem of searching for suitable sequences. We then dynamically implement multiple executions of the program and use a genetic algorithm to search for the optimal set of relation sequences. We conducted empirical studies to evaluate our approach using scientific functions in the GNU scientific library (abbreviated as GSL). From the empirical results, our approach can automatically infer high-quality composite MRs, on average, five times more than basic MRs. More importantly, the inferred composite MRs can increase the fault detection capabilities by at least 30 % more than the original metamorphic relations.