Miracle-dz/UniFAR-FTUs huggingface dataset:
Big Data Processing using Machine Learning algorithms: MLlib and Mahout Use Case

Machine learning is a field within artificial intelligence that allows machines to learn on their own from existing information to make predictions or/and decisions. There are three main categories of machine learning techniques: Collaborative filtering (for making recommendations), Clustering (for discovering structure in collections of data) and Classification (form of supervised learning). Machine learning helps users to make better decisions, Machine learning algorithms create patterns based on previous information and use them to design predictive models, then, use this models to obtain predictions about future data. A huge amount of data from several sources need methods and techniques to be processed correctly, in order to exploit this data efficiently, machine learning is a great technology for exploiting the needs in big data analysis. This paper describes the implementation of Apache Spark MLlib and Apache Mahout in order to process Big Data using Machine Learning algorithms. Furthermore, we conduct experimental simulations to show the difference between this two Machine Learning frameworks. Subsequently, we discuss the most striking observations that emerge from the comparison of these technologies through several experimental studies.
Aged Sanroque Mice Spontaneously Develop Sjögren's Syndrome-like Disease

Sjögren's syndrome (SS) is a chronic inflammatory autoimmune disorder that affects mainly salivary and lacrimal glands, but its cause remains largely unknown. Clinical data indicating that SS occurs in a substantial proportion of patients with lupus points to common pathogenic mechanisms underlying the two diseases. To address this idea, we asked whether SS develops in the lupus-prone mouse strain sanroque (SAN). Owing to hyper-activation of follicular helper T (Tfh) cells, female SAN mice developed lupus-like symptoms at approximately 20 wk of age but there were no signs of SS at that time. However, symptoms typical of SS were evident at approximately 40 wk of age, as judged by reduced saliva flow rate, sialadenitis, and IgG deposits in the salivary glands. Increases in serum titers of SS-related autoantibodies and numbers of autoantibody-secreting cells in cervical lymph nodes (LNs) preceded the pathologic manifestations of SS and were accompanied by expansion of Tfh cells and their downstream effector cells. Thus, our results suggest that chronic dysregulation of Tfh cells in salivary gland-draining LNs is sufficient to drive the development of SS in lupus-prone mice.
Comparison of Candida Albicans Fatty Acid Amide Hydrolase Structure with Homologous Amidase Signature Family Enzymes

Fatty acid amide hydrolase (FAAH) is a well-characterized member of the amidase signature (AS) family of serine hydrolases. The membrane-bound FAAH protein is responsible for the catabolism of neuromodulatory fatty acid amides, including anandamide and oleamide, that regulate a wide range of mammalian behaviors, including pain perception, inflammation, sleep, and cognitive/emotional state. To date, limited crystal structures of FAAH and non-mammalian AS family proteins have been determined and used for structure-based inhibitor design. In order to provide broader structural information, the crystal structure of FAAH from the pathogenic fungus Candida albicans was determined at a resolution of 2.2 Å. A structural comparison with a brown rat Rattus norvegicus FAAH as well as with other bacterial AS family members, MAE2 and PAM, showed overall similarities but there were several discriminative regions found: the transmembrane domain and the hydrophobic cap of the brown rat FAAH were completely absent in the fungal FAAH structure. Along with these results, a phylogenetic analysis of 19 species within the AS family showed that fungal FAAHs diverged from a common ancestor before the separation of eukarya and prokarya. Taken together, this study provides insights into developing more potent inhibitors of FAAH as well as expanding our knowledge of the relationships between AS family members.
Pregnancy-Associated Plasma Protein A Induces Inflammatory Cytokine Expression by Activating IGF-I/PI3K/Akt Pathways

Pregnancy-associated plasma protein A (PAPP-A) was previously reported to be an inflammatory biomarker and a prognostic marker of acute coronary syndrome (ACS) and involved in the process of atherosclerosis and plaque rupture. However, the role of PAPP-A in inflammation is poorly understood. In this study, we aimed to investigate the role of PAPP-A in macrophage activation and inflammatory cytokine production. RAW264.7 macrophages were treated with or without PAPP-A. Reverse-transcriptase quantitative real-time PCR (RT-qPCR) and Western blot were performed to detect gene and protein expressions. The concentration of monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6) in culture supernatants was determined by ELISA. Results showed that PAPP-A significantly stimulated the expression of MCP-1, TNF-α, and IL-6 at both transcriptional and translational levels in a dose-dependent and time-dependent manner. The secretion of these inflammatory cytokines by macrophages was also increased after PAPP-A treatment. Moreover, PAPP-A activated the IGF-I/PI3K/Akt signaling pathway in macrophages. The PAPP-A-mediated upregulation of MCP-1, TNF-α, and IL-6 mRNA and protein levels were strongly inhibited by PI3K inhibitors or IGF-IR siRNA, indicating that the upregulation of MCP-1, TNF-α, and IL-6 could involve the IGF-I/PI3K/Akt pathway. Together, this study demonstrates that PAPP-A activates the macrophage signaling pathway (IGF-I/PI3K/Akt), which drives the expression and production of inflammatory cytokines known to contribute to the initiation and progression of ACS. These findings indicate that PAPP-A may play a proinflammatory role in the pathophysiology of ACS and serve as a potential therapeutic target.
Analysis of technology adoption for an online platform for development planning discussion

Currently, development planning in Jakarta has used bottom-up approach in order to cover the whole community needs. This is supported by the development of an Online Platform for Development Planning Discussion (OPDPD) that can be accessed via website for all Jakarta citizens. After five years since its first launch in 2014, OPDPD is currently used by all of the neighborhood chairman in Jakarta. However, this condition does not fit the actual target user of OPDPD which is all Jakarta citizens. Therefore, this research aims to develop adoption strategy which is derived from the analysis of factor that affects technology adoption of OPDPD and its current stage on the Technology Adoption Life Cycle. The combination of Unified Theory of Acceptance and Use of Technology (UTAUT) and Information Systems Success Model (ISSM) is used to be tested by Partial Least Square-Structural Equation Modeling. Results show that there are six factors that significantly affects OPDPD adoption and it is still adopted by the Innovators as analyzed in Technology Adoption Life Cycle.
Reconstructing the geographic and climatic origins of long‐distance bird migrations

Seasonal bird migration is one of the most fascinating global ecological phenomena. Yet, the biogeographic scenarios and climatic drivers that led single species or entire lineages to evolve seasonal migration between disjunct breeding and wintering ranges remain unclear. Based on distribution and phylogenetic data for all birds worldwide, we explored the biogeographic and climatic context of the evolutionary emergence of seasonal geographic migration in birds.
Design and Programming of the Micromanipulator Network to Study Single Cardiac Cell Mechanics

Computer-controlled biomechanical experiments on single cells require high accuracy and synchronicity of signals and stability of experimental set-ups. In this paper, we propose a digital micromanipulator network consisting of two micromanipulators to study single cardiac cell mechanics. The micromanipulation system using Ethernet for this network has a large positioning range (20 mm), covering the experimental bath with the cells and nanometer movement resolution (5 nm) for precise experiments. The software for the micromanipulator network was developed in the LabVIEW to provide sufficient synchronicity of micromanipulator movement. The testing of seven consistent steps of two micromanipulators showed that movement was synchronous and maximum loss of synchrony of coordinates was 170 nm. We believe that studies on the changes in single cardiac cell length and force under various mechanical loads can be carried out using the developed network.
Epidemiology of Extrapulmonary Tuberculosis among Inpatients, China, 2008–2017

We investigated the epidemiology of extrapulmonary tuberculosis (TB) among patients admitted to Beijing Chest Hospital, Beijing, China, during January 2008–December 2017. Of 19,279 hospitalized TB patients, 33.4% (6,433) had extrapulmonary TB and 66.6% (12,846) had pulmonary TB. The most frequent forms of extrapulmonary TB observed were skeletal TB (41.1%) and pleural TB (26.0%). Younger, female patients from rural areas were more likely to have extrapulmonary TB. However, patients with diabetes mellitus were less likely to have extrapulmonary TB compared with patients without diabetes. A higher proportion of multidrug-resistant (MDR) TB was observed among patients with extrapulmonary TB than among patients with pulmonary TB. We observed a large increase in MDR TB, from 17.3% to 35.7%, for pleural TB cases. The increasing rate of drug resistance among extrapulmonary TB cases highlights the need for drug susceptibility testing and the formulation of more effective regimens for extrapulmonary TB treatment.
Comparison of Radioembolization and Sorafenib for the Treatment of Hepatocellular Carcinoma with Portal Vein Tumor Thrombosis: A Systematic Review and Meta-Analysis of Safety and Efficacy

Objective To compare the safety and efficacy of radioembolization with that of sorafenib for the treatment of hepatocellular carcinoma (HCC) with portal vein tumor thrombosis (PVTT). Materials and Methods MEDLINE, EMBASE, and Cochrane databases were searched for studies reporting outcomes in patients with HCC and PVTT treated with radioembolization or sorafenib. Meta-analyses of cumulative overall survival (OS) and Kaplan-Meier survival rates according to the time to progression (TTP) and incidence of adverse events (AEs) were performed. Subgroup analyses were conducted on 1-year OS data. Results Seventeen studies were identified (four involving radioembolization, 10 involving sorafenib, and three comparing both). Pooled OS rates were higher in the radioembolization group, notably at 6 months {76% (95% confidence interval [CI], 64–85%) vs. 54% (95% CI, 45–62%)} and 1 year (47% [95% CI, 38–57%] vs. 24% [95% CI, 18–30%]); TTP was also longer with radioembolization. In patients undergoing radioembolization, the proportion of patients with Eastern Cooperative Oncology Group status 0 (p < 0.0001), Child-Pugh A (p < 0.0001), extrahepatic metastasis (p = 0.0012), and a history of cancer treatment (p = 0.0048) was identified as a significant source of heterogeneity for the 1-year OS. Radioembolization was associated with a lower incidence of grade 3/4 AEs than sorafenib (9% [95% CI, 3–27%] vs. 28% [95% CI, 17–43%]). Conclusion Compared with sorafenib, radioembolization is a safer and more effective treatment for HCC with PVTT and is associated with prolonged survival, delayed tumor progression, and fewer grade 3/4 AEs.
Nowe możliwości leczenia uogólnionego raka piersi

Despite improving screening methods, diagnosis of early breast cancers, new therapies the prognosis in breast cancer remains poor. Advanced, metastatic breast cancer is an incurable, persistent disease. Hormonotherapy is the main treatment of receptor positive breast cancer patients. Till now in case of hormone resistant disease chemotherapy was the only choice nowadays there is possibility to use combination of endocrine and targeted therapy, the mTOR inhibitor everolimus. In HER2 receptor positive breast cancer therapy with trasuzumab improved treatment results. Despite many new treatment methods there is still need for new therapies which could improve the patients outcome. key wordS: breast cancer, mTOR inhibitor, everolimus, trastuzumab, pertuzumab
Survey on Implementation of One Health Approach for MERS-CoV Preparedness and Control in Gulf Cooperation Council and Middle East Countries

In 2015, a One Health Working Group was established in Qatar to conduct a survey in the Gulf Cooperation Council countries, Egypt, and Jordan to monitor preparedness of public health and veterinary health authorities in response to the Middle East respiratory syndrome coronavirus epidemic. All but 1 country indicated they established joint One Health policy teams for investigation and response. However, the response to the questionnaires was largely limited to veterinary authorities. Critical barriers and limitations were identified. National and regional leaders, policy makers, and stakeholders should be prompted to advocate and enhance adoption of the One Health framework to mitigate the risk for Middle East respiratory syndrome and other emerging zoonotic diseases.
Managing Response Time Tails by Sharding

Matrix analytic methods are developed to compute the probability distribution of response times (i.e., data access times) in distributed storage systems protected by erasure coding, which is implemented by sharding a data object into N fragments, only K<; N of which are required to reconstruct the object. This leads to a partial-fork-join model with a choice of canceling policies for the redundant N−K tasks. The accuracy of the analytical model is supported by tests against simulation in a broad range of setups. At increasing workload intensities, numerical results show the extent to which increasing the redundancy level reduces the mean response time of storage reads and significantly flattens the tail of their distribution; this is demonstrated at medium-high quantiles, up to the 99th. The quantitative reduction in response time achieved by two policies for canceling redundant tasks is also shown: for cancel-at-finish and cancel-at-start, which limits the additional load introduced whilst losing the benefit of selectivity amongst fragment service times.
Alleviation of UV-B radiation damages by sodium hydrosulfide (H2S donor) pre-treatment in Borage seedlings

ABSTRACT Hydrogen sulfide (H2S) is a highly diffusible, gaseous molecule that has been proved to control a range of physiological responses in many plant stresses. Exposure to UV-B radiation leads to the generation of reactive oxygen species that cause oxidative damages. In this study, sodium hydrosulfide (NaHS) was used as a H2S donor for the alleviation of damage caused by UV-B in Borage plant. Lipid peroxidation, protein oxidation, and H2O2 content were increased in plant exposed to UV-B. Results showed that ascorbate peroxidase, guaiacol peroxidase, and catalase activities and reduced glutathione (GSH) did not change significantly in stressed plants when compared with control, but flavonoids concentration was increased considerably. Pre-treatment of plants with NaHS (120 μM), significantly decreased the Malondialdehyde content, carbonyl groups, and H2O2. Activities of ascorbate peroxidase and guaiacol peroxidase enzymes increased in plants which were pre-treated while catalase activity decreased. The reduced glutathione content increased in borage plant leaves. HPLC analysis of flavonoids demonstrated that pre-treatment with NaHS caused a decrease in flavonoid content. These findings suggest that H2S alleviates UV-B damages may occur through enhancement of antioxidative enzyme activities and GSH content in borage seedlings.
Design and development of bit arrow: a web-based programming learning environment

In Japan, all high school students will study programming in next teaching guidelines. It is important to use suitable environment or tool for programming education. Some current textbooks show programs written in JavaScript and instruct students to use text editor and browser as development environment. But such environment has some problems; it requires students to manage multiple applications, to find errors with little information and to type long statements. We developed "Bit Arrow", an online programming environment. The environment helps students to find errors. Also the environment provides API to write statements shortly. In this report, we describe design and evaluation of Bit Arrow from students' log data.
Anomaly based Detection Analysis for Intrusion Detection System using Big Data Technique with Learning Vector Quantization (LVQ) and Principal Component Analysis (PCA)

Data security has become a very serious parf of any organizational information system. More and more threats across the Internet has evolved and capable to deceive firewall as well as antivirus software. In addition, the number of attacks become larger and become more dificult to be processed by the firewall or antivirus software. To improve the security of the system is usually done by adding Intrusion Detection System(IDS), which divided into anomaly-based detection and signature-based detection. In this research to process a huge amount of data, Big Data technique is used. Anomaly-based detection is proposed using Learning Vector Quantization Algorithm to detect the attacks. Learning Vector Quantization is a neural network technique that learn the input itself and then give the appropriate output according to the input. Modifications were made to improve test accuracy by varying the test parameters that present in LVQ. Varying the learning rate, epoch and k-fold cross validation resulted in a more efficient output. The output is obtained by calculating the value of information retrieval from the confusion matrix table from each attack classes. Principal Component Analysis technique is used along with Learning Vector Quantization to improve system performance by reducing the data dimensionality. By using 18-Principal Component, dataset successfully reduced by 47.3%, with the best Recognition Rate of 96.52% and time efficiency improvement up to 43.16%.
Preferences of functional food without or with genetically modified technology in the perspective of perceived health risk related to metabolic syndrome

The objective is to investigate the importance of perceived health risk for consumers’ preferences to purchase functional food without and with genetically modified technology. An interview-assisted questionnaire on attitudes towards the metabolic syndrome, potential agro-food technologies and genetics in nutrition was applied within a Polish representative sample of 1005 adults. There was a tendency to consider central obesity as medium-risk condition (34.5%), high blood pressure as high and very high-risk condition (42.4%), high stress level as both
Healthcare Provider Discrimination toward Pregnant Women with Rifampin-Resistant Tuberculosis

Little is known about the treatment experiences of pregnant women with rifampin-resistant tuberculosis. We conducted qualitative interviews with 10 women who had this condition; 9 reported facing discrimination from healthcare providers. Our findings underscore an urgent need to ensure a human-rights–based, patient-centered approach for women with rifampin-resistant tuberculosis who are pregnant.
Convolutional Neural Network Protection Method of Lenet-5-Like Structure

In this paper, we build and describe a convolutional neural network protection method of Lenet-5-like structure. The protected convolutional neural network is obtained by adding the protection lock module in forward propagation algorithm in different layers so that unauthorized user can not use protected convolutional neural network of Lenet-5-like structure. The experimental results show that protection lock module be added in first and middle layers would gain the best protection effect. Authorized users can use protected convolutional neural network of Lenet-5-like structure after adding unlocking module. The accuracy of unlocked convolutional neural network is not affected by adding protection lock module and unlocking module. To add protection lock module realize protection on the convolutional neural network of Lenet-5-like structure.
Evaluating the role of HPV as a primary screening method for cancer cervix: A pilot study

Cervical cancer is the leading genital malignancy in India, there is a dire need to schedule screening programs on a regular basis. Objectives: 1) To compare the efficacy of visual, cytological and HPV DNA testing as primary cancer screening methods for the detection cervical intraepithelial neoplasia and cancer. 2) Colposcopic evaluation and directed biopsy for positive cases. Methodology: Prospective observational study including 100 married women in the age group 35-50 years for opportunistic screening were enrolled and they underwent a visual inspection of the cervix followed by cytology (PAP smear) and HPV DNA testing at the same visit. Results: Visual inspection and Pap smear as screening methods had good specificity but low sensitivity values, as compared to HPV. Conclusion: HPV testing serves as a more reliable and cost effective screening method, can be considered as a gold standard one step screening method for preinvasive lesions of the cervix.
Deep Learning Based Non-rigid Device Tracking in Ultrasound Image

Cardiac interventional procedure is an important type of minimally invasive surgery that deals with catheter-based treatment of cardiovascular disease, it is usually accompanied by X-ray fluoroscopy imaging as a typical setup, this is not desirable because the operator will be exposed to a significant radiation dose for a long time. Motivated by reducing the radiation hazard for cardiologists and sonographers during intervention, this research is aiming to develop a fully automatic and robust non-rigid device tracking and segmentation platform on pure ultrasound images. Based on the dataset collected on a tissue mimicking cardiac phantom, this research focuses on building up the automatic non-rigid device tracking model via deep learning method. Before training, the target tip is localized with an external EM tracking device, then the tracking framework is realized by automatic tip segmentation using U-net on both 2D and 3D TOE images.
EEG-Based Attention Feedback to Improve Focus in E-Learning

Neurons are cells that process and transmit information via electrical and chemical signalling. The paper aims to provide an E-learning utility tool that can enable users to monitor and evaluate the effect of real-time attention feedback over a span of time. The tool uses Neurosky Mindwave headset, a non-invasive, EEG (Electroencephalogram) based Brain-Computer Interface to measure the attention level of users. We present an experiment to study the impact of real-time attention-based neurofeedback in improving the performance and average attention of users while performing a reading comprehension task. The experiment results indicate an improvement in the average attention as well as improvement in the test scores of the users. Our proposed E-learning attention based tool can be used to develop low cost, user-friendly E-learning and IoT based applications.