Miracle-dz/UniFAR-FTUs huggingface dataset:
Hepatoprotective effect of beetroot juice on liver injury in male Sprague–Dawley rats

Background: The ability of plant extract to improve injury in the liver has gained interest in recent times. This could be due to the side effects and expense of modern medicines that are used to manage hepatic diseases; hence beetroot juice as a potential hepatoprotective agent was evaluated. Materials and Methods: Thirty Sprague–Dawley rats were assigned by weight into six groups average body weight (160.20 ± 2.54 g). Group I: rats plus distilled water (Normal control) 2 ml/kg; Group II: rats received olive oil (2 ml/kg); Group III: rats received carbon tetrachloride (CCl4) suspended in olive oil (2 ml/kg) and 250 mg/kg of beetroot extract. Group IV: rats received CCl4suspended in olive oil (2 ml/kg) and 500 mg/kg of beetroot extract. Group V: rats received olive oil (2 ml/kg) and 100 mg/kg of silymarin; Group VI: rats received (2 ml/kg)CCl4suspended in olive oil. Liver injury was induced by oral administration of CCl4using gastric gavage at 2 ml/kg every 48 h for 14 days, followed by treatment with beetroot extract and silymarin. Animals were euthanized by decapitation, blood and liver tissue harvested for biochemical and histopathological evaluations. Results: Alanine aminotransferase, aspartate aminotransferase, and alkaline phosphatase levels were significantly (P < 0.05) increased in the groups treated with extract and silymarin when compared to the animals administered only CCl4, whereas malondialdehyde level was significantly (P < 0.05) increased in the group administered only CCl4when compared to other groups. Histopathologically, the animals treated with 500 mg/kg of extract showed a normal hepatocyte and mild portal congestion. Conclusion: Beetroot juice has potential hepatoprotective effects on the liver in a dose-dependent manner.
A diffusion-weighted MRI study of acute ischemic distal arm paresis

Objective: To study the site of the ischemic lesion, the underlying cause, and the prognosis of acute stroke with distal arm paresis. Method: The authors investigated 14 consecutive patients with acute distal arm paresis with a diagnostic stroke protocol and early MRI, including T2-weighted images, diffusion-weighted images (DWI), and perfusion-weighted images (PWI). Acute DWI lesions were shown on coregistered T2-weighted images for analysis of the exact anatomic lesion location. Results: Patients showed a uniform (7/14), radial (3/14), or ulnar (4/14) distribution of hand paresis. In all cases, DWI identified small lesions located in the motor cortex. Topographic lesion analysis, which was correlated with the clinical deficit, showed lesions centered in the hand knob area (2/14), involving the lateral (6/14), medial (4/14), or both (2/14) borders of the hand knob. PWI (calculated time-to-peak maps) did not show a mismatch between the DWI lesion and the PWI lesion. In six patients, DWI and PWI lesions were identical in size and location; no definite perfusion deficit was seen in eight patients. In agreement with PWI, no patient showed clinical worsening, and six patients recovered completely within a week. Further investigations showed a potential source of embolus in 11 cases. Conclusions: Acute ischemic distal arm paresis is usually caused by a small cortical lesion in the motor hand cortex attributable to distal Rolandic artery obstruction without additional tissue at risk. These findings confirm the observed benign clinical course and its apparent main cause (artery-to-artery or cardiac embolism).
Cost based Model for Big Data Processing with Hadoop Architecture

-With fast pace growth in technology, we are getting more options for making better and optimized systems. For handling huge amount of data, scalable resources are required. In order to move data for computation, measurable amount of time is taken by the systems. Here comes the technology of Hadoop, which works on distributed file system. In this, huge amount of data is stored in distributed manner for computation. Many racks save data in blocks with characteristic of fault tolerance, having at least three copies of a block. Map Reduce framework use to handle all computation and produce result. Jobtracker and Tasktracker work with MapReduce and processed current as well as historical data that’s cost is calculated in this paper
Transcriptional responses to IFN-γ require Mediator kinase-dependent pause release and mechanistically distinct functions of CDK8 and CDK19

Iris Steinparzer1, Vitaly Sedlyarov2, Jonathan D. Rubin3, Kevin Eislmayr1, Matthew D. Galbraith4,5, Cecilia B. Levandowski3, Terezia Vcelkova1, Lucy Sneezum1, Florian Wascher1, Fabian Amman1,6, Renata Kleinova1, Heather Bender5, Zdenek Andrysik5, Joaquin M. Espinosa4,5, Giulio Superti-Furga2,7, Robin D. Dowell8,9, Dylan J. Taatjes3,*, Pavel Kovarik1,* 1Max F. Perutz Laboratories, University of Vienna, Vienna Biocenter (VBC), Dr. Bohr-Gasse 9, Vienna, Austria
Data Assimilation Algorithm for Remaining Useful Life Prediction of Aircraft Engine

It’s a challenge to predict the remaining useful life of the aeroengine. The performance degradation of aircraft engines involves many parameters, so it is difficult to establish an accurate physical failure model. However, with time running, its performance degradation will be reflected in the change of monitoring parameters. In this paper, a kind of remaining life prediction algorithm based on data assimilation was proposed. The real-time data collected during the operation was used to predict the change trend of engine parameters, and then the remaining life of the engine was further predicted. Experiments show that the prediction method of engine remaining life based on data assimilation has better effects, which provides a new idea and method for engine remaining life prediction.
Runtime Performance Benchmarking for NoSQL Databases

An experimental study regarding the performance of NoSQL database management systems is presented in this paper. In this study, two NoSQL database management systems (MongoDB and Cassandra) were compared, and the following factors were considered: degree of parallelism and workload. Two distinct workloads (mostly read and update heavy) were used, and various numbers of client threads. The measured results refer to total runtime and they confirm that MongoDB outperforms Cassandra in almost all tests considered.
Threats Beyond the Digital Divide in the Post-Digital Ecosystem

Today there are unprecedented changes in technology. Robots replace the humans and take over the jobs continuously and increasingly. Unemployment is inevitably spreading into the world. Despite the developments in technology, digital divide continues its existence especially in developing countries, and beyond the digital divide economical and biological divides take place. Within this new ecosystem, there are numerous threats. The purpose of this article is to draw attention to these emerging threats.
Image Segmentation Approach Using Python OpenCV to Detect Tuberculosis

Tuberculosis (TB) is one of the major disease spreading whole over the world. TB caused by bacteria known as Mycobacterium tuberculosis. Nowadays, TB is increasing widely in the region of Karachi and now it’s becoming a challenging task for all researchers. The process is to partitioning digital image into different segments according to the set of pixels known as image segmentation. It’s used to find segments & extract meaningful information of an image. Image segmentation approaches are providing new ways in the field of medical and it’s exactly suitable for TB images, block-based & layer-based segmentation helps to identify edges, thresholding regional growth, clustering, water shading, erosion & dilation, utilizing histogram for the betterment of TB patients. Chest X-ray is playing a vital role to diagnose TB rapidly. TB image contains binary colors, it’s either black & white but it would have been different level of the color shades. Diagnosing symptoms and intensity of TB in a patients’ x-ray is such a critical problem. The purposed solution is to overcome the problem and reduce the ratio of TB patients in Karachi region by using image segmentation approaches on chest X-ray and calculates the alternative way to detect the intensity level of TB in individual patient’s report with effectively, efficiently & accurately with minimum amount of time by using Python Open CV.
Utility based channel aware MIMO downlink scheduling in IEEE802.16e broadband wireless system

One of the most important features of IEEE802.16e is the ability of sophisticated QoS provisioning. The basic approach for providing the QoS guarantees in IEEE 802.16e is that the uplink and downlink scheduling at the base station (BS). To maximize usage of available resources and take advantage of the varying channel condition at the subscriber stations (SSs), adaptive and dynamic scheduling techniques are typically used. After making the scheduling decision, the BS informs all SSs about the scheduling decision at the beginning of each frame using the MAP messages. In this paper, we propose a new adaptive resource allocation and scheduling scheme for RT-downlink traffic in a single cell MIMO-based IEEE802.16e broadband wireless system. Our scheme is based on cross-layer approaches, which improves system performance in terms of increasing system throughput and provide better quality of service for users in terms of reducing Head Of Line (HOL) packet delays. We use a utility based framework to provide the link between physical layer and data link layer. In this architecture a new utility function is used. The subchannelization scheme we used is based on contiguous subcarriers in WiMAX is called band adaptive modulation and coding (AMC). To improve throughput, adaptive modulation and coding (AMC) at the physical layer is used. We provide simulation results comparing our proposal with different schemes. * Higher Technological Institute ** Cairo University *** Military Technical College **** Cairo University Proceedings of the 6 ICEENG Conference, 27-29 May, 2008 EE129 2 The simulation results illustrate the superiority of our scheme for different channel conditions and different numbers of active users in the system.
Measurement and visualization of human active echolocation cues approaching obstacles

Some blind people have the ability to use active echolocation, i.e. they manage to navigate in their environment and detect obstacles distantly by producing oral sounds and listening to their echoes. Using a dummy head with microphones at the ears, a directive loudspeaker near the mouth and a reference microphone, we measured the propagation of sound from the mouth to the ears, contained in the Oral-Binaural Room Impulse Responses (OBRIR), at different distances and orientations in front of obstacles made out of cardboard boxes. We analyse and graphically depict the OBRIRs so as to identify potential auditory cues used in human active echolocation.
Concurrent Programming with Threads

Another important factor in the popularity of threads is the adoption of the standard Pthreads (Posix threads) interface for manipulating threads from C programs. The benefit of threads has been known for some time, but their use was hindered because each computer vendor developed its own incompatible threads package. As a result, threaded programs written for one platform would not run on other platforms. The adoption of Pthreads in 1995 has improved this situation immensely. Posix threads are now available on most systems, including Linux.
Cloud Computing at a Cross Road: Quality and Risks in Higher Education

The key determinants of cloud computing provide a convincing argument for HEIs and its stakeholders to adopt the innovation. These benefits reflect the essential quality characteristics of the cloud, such as Broad network Access; Measured Service; On-demand Self-Service; Rapid Elasticity; and Resource Pooling. However, there are also risks associated with the cloud, leading to non-adoption, such as Confidence, Privacy, Security, Surety and Trust. Understanding the impact of these factors can support multiple stakeholders, such as students, lecturers, senior managers and admins in their adoptive decision of CC in their respected institutions. Using the Multiview 3 (MV3) methodology, a research model was proposed to explore the key qualities and risks that determine the adoption or non-adoption of CC by UK HEIs from multiple perspectives. An exploratory qualitative study was conducted on 32 University stakeholders across 2 UK Universities. The findings suggest that security, privacy and trust are the key determinants to non-adoption as participants felt that the cloud cannot fully guarantee the safeguarding of sensitive information. Determinants to cloud adoption include improving relationships between students and teachers via collaborative tools, in addition to proposing cloud apps for mobile devices for accessing virtual learning materials and email securely off-campus. In conclusion, University stakeholders are still at a cross-road when it comes to cloud adoption, but future advances of the cloud may help to steer their decision to adopt this innovative technology given its overwhelming potential.
An Approach to Control the Positional Accuracy of Point Features in Volunteered Geographic Information Systems

Volunteered geographic information (VGI) is a huge source of user-generated geographic information. There is an enormous potential to use VGI in different mapping activities due to its significant advantages. VGI is found to be richer and more up-to-date than authoritative geographic information. However, VGI quality is an obvious challenge that needs to be addressed in order to get the full potential of VGI. Positional accuracy is one of the important aspects of VGI quality. Although VGI positional accuracy can be high in some contexts, VGI datasets are characterized by a large spatial heterogeneity. This paper proposes an approach for controlling positional accuracy as well as decreasing the spatial heterogeneity of point features in VGI systems. A case study has been conducted in order to ensure the applicability and effectiveness of the proposed approach.
An Automatic Approach for Customization of Teaching Process Based on Learning Styles in Adaptive and Intelligent Learning Systems

This paper presents a new approach for automatic personalization of the teaching process in intelligent and adaptive systems for distance education, taking into account students learning styles. Promising results have been obtained through experiments with a method that uses a learning styles model based on probability distributions instead of using fixed types, which resulted in a non-deterministic student modeling and teaching personalization processes, enabling treatment of the uncertainties inherent to learning styles modeling and content adaptation processes.
pH/redox sensitive nanoparticles with platinum(iv) prodrugs and doxorubicin enhance chemotherapy in ovarian cancer

pH/redox sensitive, dual drug loaded nanoparticles were prepared from poly(ethylene glycol)-block-poly(l-lysine) (PEG-b-PLL) for improving cancer therapy. Platinum(iv) and cis-aconitic anhydride-doxorubicin (CAD) were anchored to lysine residual amine groups of PLL to form polymer prodrug conjugates, which then self-assembled into nanoparticles with hydrophobic platinum(iv) prodrugs and CAD as the core. The nanoparticles were stable in neutral environments, but once under acidic and reductive conditions, the drugs were rapidly released. The dual-loaded nanoparticles had comparable intracellular toxicity to the regimen of combined application of free cisplatin and doxorubicin.
Efficacy of antioxidant vitamins (vitamin C, vitamin E, beta-carotene) and selenium supplement on D-galactosamine-induced lung injury

Effects of vitamin combination with selenium on acute lung injury in rats were examined in this study. Four experimental groups of rats were used as follows: group 1, animals administered intraperitoneally physiological saline solution; group 2, rats fed with vitamin C, vitamin E, beta-carotene and sodium selenate for three days; group 3, a single intraperitoneally injection of D-galactosamine (D-GaIN; 500 mg kg-1) into rats; group 4, animals fed with the antioxidant vitamin combination with selenium for three days, and administered D-GaIN. Lung tissues were examined using light microscope, and the following biochemical parameters were measured glutathione (GSH), lipid peroxidation (LPO) levels and tissue factor (TF), lactate dehydrogenase (LDH), catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GPx), paraoxonase (PON), myeloperoxidase (MPO), xanthine oxidase (XO) and sodium potassium ATPase (Na+/K+-ATPase) activities in lung tissues. Extensive edema in peripheral areas, mononuclear cell infiltrations around venules and locally a honeycomb-like structure were observed in the lung of group 3 rats. GSH, GPx and PON activities were decreased, whereas LPO level, TF, LDH, CAT, SOD, MPO and XO activities were increased in rats treated with D-GalN. Administration of the antioxidant combination protected lung tissue against damage by enhancing biochemical chances and pulmonary edema in group 3 animals, while no significant effect on protection of pulmonary inflammation was observed. In conclusion, the antioxidant vitamin supplementation with selenium can be used in the prevention of acute lung injury.
On SDN-Enabled Online and Dynamic Bandwidth Allocation for Stream Analytics

Data communication in cloud-based distributed stream data analytics often involves a collection of parallel and pipelined TCP flows. As the standard TCP congestion control mechanism and its variants are designed for achieving “fairness” among competing flows and are agnostic to the application layer contexts, the bandwidth allocation among a set of TCP flows traversing bottleneck links often leads to sub-optimal application-layer performance measures, e.g., stream processing throughput or average tuple complete latency. Motivated by this and enabled by the rapid development of the software-defined networking (SDN) techniques, in this paper, we re-investigate the design space of the bandwidth allocation problem and propose a cross-layer framework which utilizes the instantaneous information obtained from the application layer and provides on-the-fly and dynamic bandwidth adjustment algorithms for assisting the stream analytics applications achieving better performance during the runtime. We implement a prototype cross-layer bandwidth allocation framework based on a popular open-source distributed stream processing platform, Apache Storm, together with the OpenDaylight controller, and carry out extensive experiments with real-world analytical workloads on top of a local cluster consisting of ten workstations interconnected by a SDN-enabled fat-tree like testbed. The experiment results clearly validate the effectiveness and efficiency of our proposed framework and algorithms. Finally, we leverage the proposed cross-layer SDN framework and introduce an exemplary mechanism for bandwidth sharing and performance reasoning among multiple active applications and show a case of a point solution on how to approximate application-level fairness.
Outage Performance Analysis for SWIPT-Based Cooperative Non-Orthogonal Multiple Access Systems

This letter investigates simultaneous wireless information and power transfer (SWIPT)-based cooperative non-orthogonal multiple access (NOMA) system, where the near user close to the source node serves as a relay to help the far user. The near user adopts the power splitting (PS) protocol for harvesting energy and decoding the signals of both users. To ensure the user fairness, we propose a joint design for the power allocation coefficients and the PS factor to enhance the outage performance of the near user, and thereby to improve the system performance. Analytical expressions for the outage probabilities and diversity orders of both the far user and the near user are derived. The numerical results are exhibited to verify our analysis and further show that the proposed scheme significantly enhances the performance of the near user, and consequently enjoys a larger system throughput compared with the existing scheme.
Annotation for transparent inquiry: Transparent data and analysis for qualitative research

How can authors using many individual pieces of qualitative data throughout a publication make their research transparent? In this paper we introduce Annotation for Transparent Inquiry (ATI), an approach to enhance transparency in qualitative research. ATI allows authors to connect specific passages in their publication with an annotation. These annotations provide additional information relevant to the passage and, when possible, include a link to one or more data sources underlying a claim; data sources are housed in a repository. After describing ATI’s conceptual and technological implementation, we report on its evaluation through a series of workshops conducted by the Qualitative Data Repository (QDR) and present initial results of the evaluation. The article ends with an outlook on next steps for the project.
Evaluation of metallothionein response in Lymnaea stagnalis pond snail exposured to trace metals and thiocarbamate fungicide

© 2019 L. L. Gnatyshyna. Published by the Ivan Franko National University of Lviv on behalf of Біологічні Cтудії / Studia Biologica. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://www.budapestopenaccessinitiative.org/ and Creative Commons Attribution 4.0 License), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. UDC: 577.29:564.38:57.014
CrowDNet: Enabling a Crowdsourced Object Delivery Network Based on Modern Portfolio Theory

In recent years, takeout ordering and delivery (TOD) has become an emerging service due to its convenience and efficiency. However, current online ordering platforms still suffer from some issues, such as limited delivery coverage and delayed delivery. To address these issues, we propose a spatial crowdsourcing (SC)-based system called crowd delivery network (CrowDNet) to have packages take hitchhiking rides with existing taxis. We first tackle passenger riding queries based on an evolutionary algorithm and then insert appropriate food delivery requests into a partial schedule with an improved insertion approach. Finally, we propose a ranking module based on the modern portfolio theory to recommend the delivery path, which can achieve a balance between the delivery cost and timely services. Evaluations based on three real-world datasets demonstrate that our proposed algorithms outperform baseline methods.
Research on Pose Estimation of Mobile Robot Based on Convolutional Neural Network

In this paper, we propose a new learning scheme for generating camera pose to be used for visual odometry. Pose estimation of mobile robot in unknown environment is formulated as a learning problem. We train a convolutional neural network end to end to compute the 6-dof pose of the robot from a series of images. The architecture is a kind of residual network with multiple attention resblocks, which can give features different weights in order to improve the accuracy of pose prediction. The network estimates ego-motion taking monocular image sequences as input instead of separate images. We call this novel network architecture Posenet. Compared to traditional methods, results are more accurate and in which we can see the potential of pose estimation methods with convolutional neural network.