Miracle-dz/UniFAR-FTUs huggingface dataset:
Dyspepsia in Primary Care: Acid Suppression as Effective as Prokinetic Therapy. A Randomized Clinical Trial

Background: While dyspeptic patients in primary care often receive empirical treatment with antisecretory drugs, a substantial number suffer from motility disturbances which may be associated with their complaints. We aimed to compare the effectiveness of treatment with antisecretory treatment with a prokinetic agent in uninvestigated dyspepsia. Methods: 563 patients presenting dyspeptic complaints to the general practitioner with a low likelihood of organic (ulcer, reflux or malignant) disease, i.e. absence of alarm symptoms or a history of peptic ulcer disease or gastro-oesophageal reflux disease were included. They entered a randomized, double-blind trial of 4 weeks of ranitidine 150 mg bid compared with 4 weeks of cisapride 10 mg bid, with 3 months follow-up. Treatment failure was defined as no response to treatment or a relapse of symptoms within the follow-up period. Also studied were the effect on dyspepsia severity, response to treatment after 4 weeks, and time to relapse. Results: For all randomized patients, the incidence of overall treatment success after 3 months follow-up with antisecretory treatment was 107/271 (39.5%) and with a prokinetic agent 122/282 (43.3%); the risk difference was 3.8% (95% CI-4.4% to 12.0%); the difference in symptom severity score after 4 weeks of treatment was 0.3; 95% CI-0.4% to 1.0%. For patients responding to 4 weeks of treatment, relapsefree time was 86 days in the prokinetic group and 79 days in the acid suppression group (P = 0.005). Conclusions: Antisecretory and prokinetic therapies are equally effective in primary care patients with uninvestigated dyspeptic complaints, though relapse rates are lower in patients treated with prokinetic treatment.
Redescription of Lernaeopoda centroscyllii Hansen, 1923 (Copepoda: Lernaeopodidae)

Lernaeopoda centroscyllii, a lernaeopodid copepod parasitic on Centroscyllium fabricii, and briefly described by Hansen on the basis of one damaged female specimen in 1923, has not been recorded since that date. This paper gives a detailed account of both the male and the female of the species, recently taken in Newfoundland waters.
Serum Dipeptidyl Peptidase IV (DPP IV, CD26) Activity in Chronic Hepatitis C

Background: DPP IV is a cell surface ectoenzyme widely distributed in the human body. It has been implicated in T-cell activation, hepatocyte-extracellular-matrix interactions and fibroblast proliferation. Furthermore, upregulated CD26 expression has been found on the surface of human hepatoma cells transfected with hepatitis C virus (HCV) c-DNA. We examined the serum DPP IV activity in a large number of patients with chronic HCV infection in a cross-sectional study. We also investigated whether the activity differs from that in controls and depends upon the response to interferon (IFN) therapy. Methods: Serum DPP IV activity was measured by microplate-based (Multiskan-Plus-MKII, Labsystem) kinetic assay in 144 patients with chronic HCV infection. Seventy-four out of 144 patients (46 nonresponders, 28 responders) were formerly treated with interferon. Sixty healthy blood donors served as controls. Gly-Pro-PNA (Bachem, Torrance, USA) was used as substrate. Results are expressed in nmol/ml/min (U/l). Shapiro-Wilk's test, Mann-Whitney U test and Spearman rank order correlation were used for statistical analysis. Results: Serum DPP IV activity was significantly higher (mean = 20.89 [s 9.6]) in patients with chronic HCV infection than in healthy controls (12.39 [2.76, P < 10-5]). The enzyme activities significantly differed in naive HCV-positive patients (22.2 [9.89, P < 10-5]) and nonresponders (23.28 [9.57, P < 10-5]) from that in the healthy controls and also from that in responders (13.69 [4.21]). Correlation was found between DPP IV activity and AST (r = 0.44, P < 10-5), ALT (r = 0.44, P < 10-5), GGT (r = 0.41, P < 10-5) levels. Conclusion: Serum DPP IV activity seems to be an indicator of HCV induced liver injury. The activity may reflect the efficacy of interferon therapy.
VAC: Vertex-Centric Attributed Community Search

Attributed community search aims to find the community with strong structure and attribute cohesiveness from attributed graphs. However, existing works suffer from two major limitations: (i) it is not easy to set the conditions on query attributes; (ii) the queries support only a single type of attributes. To make up for these deficiencies, in this paper, we study a novel attributed community search called vertex-centric attributed community (VAC) search. Given an attributed graph and a query vertex set, the VAC search returns the community which is densely connected (ensured by the k-truss model) and has the best attribute score. We show that the problem is NP-hard. To answer the VAC search, we develop both exact and approximate algorithms. Specifically, we develop two exact algorithms. One searches the community in a depth-first manner and the other is in a best-first manner. We also propose a set of heuristic strategies to prune the unqualified search space by exploiting the structure and attribute properties. In addition, to further improve the search efficiency, we propose a 2-approximation algorithm. Comprehensive experimental studies on various realworld attributed graphs demonstrate the effectiveness of the proposed model and the efficiency of the developed algorithms.
A Rapid Deployment Indoor Positioning Architecture based on Image Recognition

With the rapid development of information, the immediacy, interoperability, and portability of information long been essential elements in present society. Technology has also continued to develop from these elements and creating more and more information equipment. In recent years, as new technologies continue to be developed, they have been mainly divided into two major technologies, namely “deep learning” and “augmented reality. These are including face recognition, image recognition, and augmented reality (AR), virtual reality (VR) and so on which are the key development trends now whether on mobile devices or web pages. In this study, the emerging mainstream “deep learning” and “augmented reality” are used to solve the problem of Google Map that cannot perform indoor navigation. The image recognition technology in deep learning is used to achieve the positioning function and the algorithm is used to calculate the best route to your destination, then indoor navigation presented. Finally, augmented reality (AR) technology is used to set up an exclusive AR floating electronic bulletin board at a specific location to display the relevant information and content of the specific location point for users. This research has the characteristics of rapid deployment which can be launched quickly in an indoor environment.
The Dietary Combination of Germinated Barley Foodstuff plus Clostridium butyricum Suppresses the Dextran Sulfate Sodium-induced Experimental Colitis in Rats

Background: Recent studies have suggested that dietary fiber exerts a therapeutic effect on IBD patients. The aim of this study was to evaluate the effects of a dietary combination of germinated barley foodstuff (GBF), derived from the aleurone and scutellum fraction of germinated barley, plus Clostridium butyricum against dextran sulfate sodium (DSS)-induced experimental colitis in rats. Methods: Sprague-Dawley rats were fed a 3% DSS diet containing GBF only, GBF plus C. butyricum, cellulose only (control) or cellulose plus C. butyricum for 8 days. The mucosal damage (macroscopic and microscopic inflammation) and fecal short-chain fatty acid (SCFA) levels were then determined. Results: The combination of GBF plus C. butyricum most effectively prevented bloody diarrhea and mucosal damage. The GBF-only diet also showed some preventive effects. With respect to fecal SCFAs, the combination of GBF plus C. butyricum most effectively increased the fecal SCFA level. Conclusion: The dietary combination of GBF plus C. butyricum most effectively suppressed DSS-induced experimental colitis in rats. These effects may be closely associated with its high activity to increase SCFA levels in the gut lumen. The potential clinical efficacy of GBF in IBD patients is also discussed.
Parâmetros clínicos, hematológicos, bioquímicos e hormonais de bovinos submetidos ao estresse calórico

Clinical, haemathological, biochemical, and hormonal parameters were characterized in 39 males and 32 females crossbred cattle (½ Holstein x ½ Gir), averaging 14 to 20 months of age, submitted to thermoneutrality (22oC and 70% of RH) and heat stress (42oC and 60% RH) in summer and winter in climatic chamber. At clinical examination, all animals presented salivation and ocular globe retraction. Under heat stress, animals increased erythrocytes count, hemoglobin total concentration, hematocrit, concentraction of total proteins, urea, creatinin, sodium, potassium, chlorides, cortisol, urinary pH and density, and dry matter of feces (P 0.05). It was concluded that hematology, biochemical, and hormonal parameters are important to characterize the heat stress in cattle.
Effect of structural parameters on functional performance of CMUT: a simulation study and analysis

Capacitive micromachined ultrasonic transducer (CMUT) is getting broader attention nowadays, its frequency and the deflection are important parameters because they are closely related to CMUT performance including the resolution, the penetration depth, and the acoustic impedance. Current CMUTs are mainly based on silicon nitride and polysilicon as the membrane materials, understand the frequency versus CMUT structure and the deflection versus the structure and the applied bias voltage are important. Authors have developed a finite element simulation method to investigate the optimal parameters in CMUT design by studying the effects of different structures on the frequency and the deflection. The simulation studied the effects of membrane thickness and top electrode size on the frequency, as well as the effect of voltage on the membrane bending. Results have indicated that smaller radius, thin membrane and thin top electrode as well as large top electrode size are preferred for CMUT design.
Evaluation of Projects Risks for Dairy Industry Using Best-Worst Multi-criteria Decision-Making

In today's world, the transformation of qualitative language somewhat is extremely helpful, using from fuzzy trapezoidal numbers are sensible for this reason, on the other hand, the most current strategy for decision making in industrial management (BWM) can have many applications for experts, the fuzzy weights of criteria and alternatives with respect to different criteria. With respect to different criteria multiplied, the fuzzy ranking scores of alternatives can be derived from the fuzzy weights of alternatives by fuzzy weights of the corresponding criteria, and then the crisp ranking score of alternatives can be calculated by diffuzzification. Fuzzy BWM organize decision-making options with the utilization of merg fuzzy information of trapezoidal numbers and discover the exact answer for options than Triangular numbers. By solving this approach in risk management as case study, it has shown Fuzzy BWM is extremely sensitive method for decisions with trapezoidal numbers.
Small-scale demographic compensation in a high-mountain endemic: the low edge stands still

Background: Under the current warming process, with its implications of higher temperatures, less rainfall, snowfall and snow cover, low-edge populations of high-mountain plants are expected to decline. Demographic studies are useful to foresee the future dynamics of species ranges. Adaptation, phenotypic plasticity, and demographic compensation have been proposed as mechanisms to cope with climate change. Aim: We studied the population dynamics of the high-mountain narrow endemic Armeria caespitosa at the extremes of its altitude distribution (low versus high edge), the high edge representing colonised summits that provide optimal growing conditions. The focus of the study was to establish if the species is retracting at its lower distribution range. Methods: We used 4 years of population censuses and soil seed bank data. Population matrix models allowed us to study the stochastic population growth rates, the species long-term viability under higher frequency of extreme years, and the life cycle transitions responsible for the differences in population dynamics between elevation range edges. Results: The low edge of the A. caespitosa elevation range appeared stable, with positive population growth rates under current environmental conditions, and a null quasi-extinction probability in the long-term under scenarios of high frequency of extremely dry years. Conclusions: The species will likely withstand the current climate-warming scenario along its elevation range. Results supported the role of so-called demographic compensation at a small spatial scale, which we argue might be common in the Mediterranean mountains.
A Decentralized Autonomous Ecosystem for Peer-To-Peer Clean Energy Trade

One of the visions of our United Arab Emirates founding father, the late Sheikh Zayed bin Sultan Al Nahyan was to transform UAE into a sustainable country. Sustainability is not only environment related as popularly known, but also the economy and the people of the society. The UAE as a country has been focusing on sustainability from different aspects, mainly clean energy generation. UAE’s ambitious road to sustainability is centralized on clean energy generation. The proposed Decentralized Autonomous Digital Ecosystem is an autonomous Blockchain based application framework that aims to accomplish UAE’s target of reaching up to 25% clean energy generation by 2030. The digital ecosystem is an autonomous Blockchain based peer-to-peer clean energy trading platform between peers, where the excessive energy from small-scale decentralized energy resources including homes, office buildings and factories with capacity to generate clean energy using PV systems.
Task Allocation in Dependency-aware Spatial Crowdsourcing

Ubiquitous smart devices and high-quality wireless networks enable people to participate in spatial crowdsourcing tasks easily, which require workers to physically move to specific locations to conduct their assigned tasks. Spatial crowdsourcing has attracted much attention from both academia and industry. In this paper, we consider a spatial crowdsourcing scenario, where the tasks may have some dependencies among them. Specifically, one task can only be dispatched when its dependent tasks have already been assigned. In fact, task dependencies are quite common in many real-life applications, such as house repairing and holding sports games. We formally define the dependency-aware spatial crowdsourcing (DA-SC), which focuses on finding an optimal worker-and-task assignment under the constraints of dependencies, skills of workers, moving distances and deadlines to maximize the successfully assigned tasks. We prove that the DA-SC problem is NP-hard and thus intractable. Therefore, we propose two approximation algorithms, including a greedy approach and a game-theoretic approach, which can guarantee the approximate bounds of the results in each batch process. Through extensive experiments on both real and synthetic data sets, we demonstrate the efficiency and effectiveness of our DA-SC approaches.
SVkNN: Efficient Secure and Verifiable k-Nearest Neighbor Query on the Cloud Platform*

With the boom in cloud computing, data outsourcing in location-based services is proliferating and has attracted increasing interest from research communities and commercial applications. Nevertheless, since the cloud server is probably both untrusted and malicious, concerns of data security and result integrity have become on the rise sharply. However, there exist little work that can commendably assure the data security and result integrity using a unified way. In this paper, we study the problem of secure and verifiable k nearest neighbor query (SVkNN). To support SVkNN, we first propose a novel unified structure, called verifiable and secure index (VSI). Based on this, we devise a series of secure protocols to facilitate query processing and develop a compact verification strategy. Given an SVkNN query, our proposed solution can not merely answer the query efficiently while can guarantee: 1) preserving the privacy of data, query, result and access patterns; 2) authenticating the correctness and completeness of the results without leaking the confidentiality. Finally, the formal security analysis and complexity analysis are theoretically proven and the performance and feasibility of our proposed approaches are empirically evaluated and demonstrated.
In-hospital Mortality Prediction for ICU Patients on Large Healthcare MIMIC Datasets Using Class Imbalance Learning

The problem of class imbalance in the in-hospital mortality prediction for ICU patients is presented. We propose to build a novel predicting model using the balanced random forest (BRF) algorithm, and tune the hyper parameters using a better performance measure, i.e., adjusted geometric-mean. The performance of the model is evaluated using the data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database. Our results show that the recall rate of the death class of ICU patients was significantly improved compared with the benchmarking model
Fas/Fas Ligand Expression and Characteristics of Primed CD45RO+ T Cells in the Inflamed Mucosa of Ulcerative Colitis

Background: Chronic immune activation in the colon is characteristic of ulcerative colitis (UC). Fas/Fas ligand (FasL) system is a mechanism responsible for activation-induced cell death (AICD), which maintains homeostasis within the immune system. Thus, Fas/FasL expression on activated colonic T cells of UC patients, as well as the susceptibility of such T cells to AICD was investigated in order to determine the role of activated colonic T cells in the long lasting inflammation in UC. Methods: Fas, FasL, and CD45RO expression on peripheral blood and colonic T cells of UC patients were assayed by flow cytometry. Apoptosis of colonic T cells induced by anti Fas antibody was assessed using the terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end-labeling (TUNEL) assay. Results: The majority of colonic T cells expressed both CD45RO and Fas in the colonic mucosa, a situation that was quite different from that in the peripheral blood. The number of CD45RO+CD8+ and Fas+CD8+ T cells was significantly lower in UC patients than the controls, unlike the number of Fas+CD4+ T cells. In contrast, the number of both CD45RO+CD4+ and CD45RO+CD8+ T cells in UC mucosa expressing FasL was significantly higher than in the controls. While Fas mediated apoptosis of CD45RO+CD8+ T cells was higher in UC patients than the controls, the number of apoptotic CD45RO+CD4+ T cells from UC mucosa was not. Conclusions: In UC patients, CD45RO+CD4+ T cells are less sensitive to apoptotic signals mediated by Fas. These phenomena may contribute to the pathogenesis of UC.
Dongba classical ancient books image classification method based on ReN-Softplus convolution residual neural network

Dongba culture is a treasure in the world’s historical civilization. In the traditional method of classification and preservation of Dongba ancient books, it is mainly through the translation of the scriptures of Dongba priests, and the artificial identification of damaged books is carried out, and then a large amount of manpower is spent on each The book is compiled and compiled, and finally classified. This paper proposes a Dongba classic ancient book intelligent image classification method based on ReN-Softplus convolution residual neural network: in the structural design of convolutional neural network oriented to Dongba image, by using the principle of residual network, and using 1*1, 3*3 and 5*5, which are relatively small convolution kernels, construct a network suitable for Dongba image recognition and classification, and then form a final network structure by repeated superposition. In addition, combining the smoothing characteristics of the Softplus function with the advantages of the better sparse representation of the ReLUs function, a ReN-Softplus activation function is proposed to improve the performance and classification accuracy. Applying this method to the classification identification of Dongba classic ancient books, the test results show that the accuracy of the AlexNet method is increased by 0.7%, and the time of the VGGNet method is reduced by 18s.
Testing the Dependence between Network Positions and National Attributes in Countries Interaction Network

Exploring the relationship between the positions of countries in the countries interaction network and their development status is of great significance in world system studies. To characterize the complex interactions between countries, network methodologies have been extensively employed. The methods typically used in the field include blockmodel analysis and statistical models, which have the deficiencies of instability, dependency on specific assumptions, and requirement of domain knowledge. Enlightened by the network embedding methods, we propose a novel framework combining network embedding and correlation analysis for addressing the problem in a model-free data-driven way, and provide a quantitative test statistic representing the magnitude of the correlation. We test our method on a real world dataset containing countries’ trade and political relations as well as several indices of development levels. Experiment results derived from our method are in consistency with previous findings via other approaches, and provide extra information about the pattern of the relationship.
An Integer Programming Model for the Deterministic Wave-Based Same-Day Pickup and Delivery Problem

This paper focuses on the development an Integer Programming (IP) model for the Deterministic Wave-based Same-day Pickup and Delivery Problem (D-WSDPD), where a vehicle is allowed to leave the depot at specific time periods, called waves. Moreover, each customer request comprises of a pair of services - namely pickup and delivery - not necessarily be completed in the same wave. If the request had been picked, it must be also be delivered by the end of the day. We explored how numbers of daily dispatch waves affect major service performance metrics, including total rewards from serving customer orders and the overall service level on 20 randomly generated instances. We found that, by increasing the numbers of daily dispatch waves, all service performance metrics could be potentially improved as expected, with greater numbers of dispatched waves. However, having too frequent daily dispatch waves would not significantly improve such metrics due to limited operational interval, i.e. planning horizon. In contrast, the computational times required grow exponentially; and, thence, cautious trade-offs between the desired service performance and operational costs must be made. While our mathematical formulation is only preliminary for the practical WSDPD, with high level of uncertainty, it could be regarded as a prelude to the development of efficient heuristics and policies that will be explored in the next steps.