Miracle-dz/UniFAR-FTUs huggingface dataset:
The Systemic Lupus Erythematosus Infection Predictive Index (LIPI): A Clinical-Immunological Tool to Predict Infections in Lupus Patients

Among autoimmune diseases, systemic lupus erythematosus (SLE) patients have a unique predisposition to develop infections, which represents one of their main causes of morbidity and mortality. Many infections occur at disease diagnosis in the absence of immunosuppressive therapy, suggesting that the immunological abnormalities in SLE patients might be fundamental for the development of this complication. The aim of this study was to address the main clinical and immunological features associated with the development of infection and to create and validate a compound clinical-immunological infection predictive index in a cohort of SLE patients. We included 55 SLE patients with < 5 years since diagnosis. The clinical and immunological features were evaluated periodically and patients were followed-up during 1 year, searching for the development of infection. Immunophenotyping was performed by multiparametric flow cytometry and neutrophil extracellular traps (NETs) were assessed by confocal microscopy. Eighteen patients (32.7%) presented 19 infectious events, 5 (26.3%) were severe. For the construction of the index, we performed a logistic regression analysis and the cutoff points were determined with ROC curves. Increased numbers of peripheral Th17 cells, B cell lymphopenia, and lower TLR2 expression in monocytes, as well as the use of cyclophosphamide were the major risk factors for the development of infection and thus were included in the index. Besides, patients that developed infection were characterized by increased numbers of low-density granulocytes (LDGs) and higher expression of LL-37 in NETs upon infection. Finally, we validated the index retrospectively in a nested case-control study. A score >1.5 points was able to predict infection in the following year (AUC = 0.97; LR– = 0.001, specificity 100%, P = 0.0003). Our index encompasses novel immunological features able to prospectively predict the risk of infection in SLE patients.
Aspirin Inhibits Natural Killer/T-Cell Lymphoma by Modulation of VEGF Expression and Mitochondrial Function

Extranodal nasal-type natural killer/T-cell lymphoma (NKTCL) is an Epstein-Barr virus (EBV)-associated lymphoma with a strong tendency relapse or be refractory in response to chemotherapy. Development of a new strategy for NKTCL treatment is still quite necessary. In this study, we found that aspirin treatment suppresses VEGF expression in NKTCL SNK-6 cells. Further investigation showed that aspirin treatment increases histone methylation in the range of −100~0 that is proximal to the transcription start site on the VEGF promoter, subsequently decreasing the binding ability of Sp1 to the VEGF promoter with VEGF suppression. Furthermore, aspirin treatment modulates mitochondrial function with increased ROS formation and apoptosis in NKTCL cells. Aspirin treatment alone slightly inhibits NKTCL SNK-6 tumor growth and EBV replication; while in the presence of histone deacetylase inhibitor (HDACi) chidamide (CDM), aspirin significantly suppresses the VEGF signaling pathway with increased ROS overgeneration and EBV inhibition. We also showed that with the addition of chidamide, aspirin significantly suppresses NKTCL tumor growth in both in vitro cell culture and in vivo mouse model with prolonged mouse survival. This is the first time that the potential mechanism for aspirin-mediated VEGF suppression and anti-tumor effect has been discovered, and this study provides a new strategy for anti-tumor drug development for NKTCL treatment based on aspirin-mediated targeting of the VEGF signaling pathway and ROS formation.
Advantages of Hydrogel-Based 3D-Printed Enzyme Reactors and Their Limitations for Biocatalysis

The development of process steps catalyzed by immobilized enzymes usually encompasses the screening of enzyme variants, as well as the optimization of immobilization protocols and process parameters. Direct immobilization of biocatalysts by physical entrapment into hydrogels can be applied to reduce the effort required for immobilization, as the enzyme-specific optimization of the immobilization procedure is omitted. Physical entrapment is applicable for purified enzymes as well as crude cell extracts. Therefore, it can be used to quickly assess and compare activities of immobilized enzymes. For the application in flow reactors, we developed 3D-printed hydrogel lattices for enzyme entrapment as well as matching housings, also manufactured by 3D-printing. Testing the resulting enzyme reactors for three different enzymes, namely alcohol dehydrogenase from Lactobacillus brevis, benzoylformate decarboxylase from Pseudomonas putida and β-galactosidase from Aspergillus oryzae, and four different enzymatic reactions showed the broad applicability of the approach but also its limitations. The activity of the immobilized biocatalysts was measured in batch experiments and compared to the kinetics of the respective free enzymes in solution. This comparison yields an effectiveness factor, which is a key figure to describe the extent the immobilized catalyst is effectively utilized. For the examined systems the effectiveness factor ranged between 6 and 14% and decreased with increasing absolute activity of the entrapped enzymes due to mass transfer limitations. To test the suitability of the hydrogel lattices for continuous operation, they were inserted into 3D-printed reactor housings and operated at constant flow. Stable product formation could be monitored over a period of 72 h for all four enzymatic systems, including two reactions with redox cofactor regeneration. Comparing calculated and experimental conversion in the continuous setup, higher values of the effectiveness factor in batch experiments also hint at good performance in continuous flow. This can be used to optimize complex biocatalytic reactions on a small scale.
The Impact of the Content of the Label on the Buying Intention of a Wine Consumer

This paper aims to analyze the influence of the content of the label of wine bottles on the purchase intention of wine. The sample was 114 individuals (51, 32% women and 48, 67% men) inside simulated Supermarket at Javeriana University of the city of Cali (Colombia). They were viewed for 2 min in front of a grocery shelving with 100 wines. The study uses eye tracking to estimate the behavior related to wine attributes included on the label of wine bottles, specifically the denomination of origin, nutritional information, and health warnings. The authors use the hierarchical model methodology, which generates a pattern of relationships among variables. Among the results, it is possible to determine a consistent model for purchase intention, where the mentioned components of the label that are related to wine attributes and their interactions constitute important factors in the possibility of influencing a purchase intention. However, when executing a group division of the audience attending to the experience in wine consumption, it is evident that based on their experience, consumers read the labels’ information differently, causing certain effects in their buying intention.
METFORMIN FOR PREVENTION AND TREATMENT OF PREECLAMPSIA: POSSIBLE MECHANISMS AND FUTURE RESEARCHES

Correspondence: Dr. Hamidreza Mahboobi, Infectious and Tropical Diseases Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas, Iran, Tel: +98. 9172121995, Email: hamidrezamahboobi@yahoo.com Received: June 29, 2016, Accepted: August 3, 2016, Published: September 2016 iThenticate screening: July 1, 2016, English editing: August 16, 2016, Quality control: August 23, 2016 © 2016 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercialNoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. Page 78 METFORMIN FOR PREVENTION AND TREATMENT OF PREECLAMPSIA: POSSIBLE MECHANISMS AND FUTURE RESEARCHES
Sentinel: Breaking the Bottleneck of Energy Utilization Efficiency in RF-Powered Devices

As a result of the limited available energy, radio frequency (RF)-powered devices must be capable of efficiently utilizing scarce energy by planning task execution according to the current harvested energy. However, the energy utilization efficiency is challenging to be improved in RF-powered devices, since sensing the harvested energy consumes a significant amount of energy that should be used for task execution. In this paper, we propose Sentinel, a novel low power method to sense the harvested energy. Sentinel is fully delegated to detect the energy for the device, while the device does not participate in the energy sensing. By this means, the computing overhead of the device is reduced. Sentinel works with low energy consumption, and functions as a trigger to activate the device when, and only when, the energy reaches an expected energy threshold. We also present a lightweight scheme to set the desired thresholds so that Sentinel achieves detecting any expected thresholds. We implement Sentinel by off-the-shelf components and conduct experiments to show that Sentinel consumes only 5.2% of energy overhead of the general energy sensing technique. With Sentinel, we show that the energy utilization efficiency can be improved up to 94.9%, outperforming the best existing works at 64.7% in the WISP platform.
Reading mathematical texts : cognitive processes and mental representations

There is a need to distinguish between understanding a text and learning from a text, something that can be done by looking at different levels of mental representations created when reading. This study is focusing on the mental representations created in two types of situations, where different kinds of cognitive processes are dominant, either a more perception-like process or a more problem-solving process. The purpose is to test an analytical tool of different levels of mental representations when used for mathematical texts, and to see examples of students’ mental representations when reading different kinds of mathematical texts.
Quantitative Methods for Stratified Medicine

Stratified medicine has tremendous potential to deliver more effective therapeutic intervention to improve public health. For practical implementation, reliable prediction models and clinically meaningful categorization of some comprehensible summary measures of individual treatment effect are vital elements to aid the decision-making process and bring stratified medicine to fruitful realization. We tackle the quantitative issues involved from three fronts : 1) prediction model building and selection; 2) reproducibility assessment; and 3) stratification. First, we propose a systematic model development strategy that integrates cross-validation and predictive accuracy measures in the prediction model building and selection process. Valid inference is made possible via internal holdout sample or external data evaluation to enhance generalizability of the selected prediction model. Second, we employ parametric or semi-parametric modeling to derive individual treatment effect scoring systems. We introduce a stratification algorithm with constrained optimization by utilizing dynamic programming and supervised-learning techniques to group patients into different actionable categories. We integrate the stratification and newly proposed prediction performance metric into the model development process. The methodologies are first presented in single treatment case, and then extended to two treatment cases. Finally, adapting the concept of uplift modeling, we provide a framework to identify the subgroup(s) with the most beneficial prospect; wasteful, harmful, and futile subgroups to save resources and reduce unnecessary exposure to treatment adverse effects. The contribution of this dissertation is to provide an operational framework to bridge predictive modeling and decision making for more practical applications in stratified medicine.
Pengenalan Action Unit (au) Pada Alis Mata Manusia Menggunakan Local Phase Quantitation From Three Orthogonal Planes (lpq-top) Dan Adaboost-svm

Alis mata pada wajah manusia memiliki peran penting dalam menciptakan sebuah ekspresi / emosi pada wajah manusia, hal ini membuat penelitian pada tugas akhir ini fokus kepada alis mata wajah manusia. Dalam sebuah eskpresi wajah manusia, gerakan alis mata mata merupakan salah satu aksi unit terkecil pada wajah manusia dimana nantinya akan diproses dengan metode Facial Action Coding System (FACS). Aksi unit pada gerakan alis mata terdiri dari tiga, yaitu Action Unit 1 (AU1), Action Unit 2 (AU2), dan Action Unit 4 (AU4). AU menjadi sangat penting dalam menentukan hasil ekspresi wajah manusia oleh FACS, sehingga dalam tugas akhir ini akan diteliti mengenai AU pada alis mata manusia dengan menggunakan LPQ-TOP dan Adaboost-SVM. Hasil akurasi penelitian terbaik diperoleh sebesar 83.81% dimana yang dideteksi oleh sistem adalah AU pada alis mata dan AU dalam kondisi normal. Parameter terbaik dalam mendeteksi AU diperoleh dari hasil mencari LPQ-TOP dengan parameter yang optimal, kemudian seleksi ciri antara 400 sampai 768 ciri dimana sebelumnya hasil ekstraksi ciri pada LPQTOP adalah 768 ciri, kemudian mencari iterasi adaboost yang optimal, dan klasifikasi SVM dengan parameter yang optimal. Kata kunci : FACS, AU, LPQ-TOP, Adaboost-SVM
Activity of red wine polyphenols on endothelial no-synthase (eNOS)

1 Unité de Pharmacologie, Département des Sciences fondamentales et biologiques, Faculté des Sciences de la Santé 01 BP 188 ; Université d’Abomey Calavi (UAC) Cotonou (Rép. Du Bénin). 2 Laboratoire de Biomembranes et Signalisation Cellulaire, Département de Physiologie Animale, Faculté des Sciences et Techniques 01 BP 4521 Université d’Abomey Calavi Cotonou (Rép. du Bénin). 3 Unité de pharmacognosie, Département des Sciences Pharmaceutiques, UFR PharmacieFaculté des Sciences de la Santé 01 BP 188; Université d’Abomey Calavi (UAC) Cotonou (Rép. Bénin). 4 School of Health Sciences and Healthcare administrations, University of Atlanta-Georgia, USA.
Multi-Channel Time Division Scheduling for Beacon Frame Collision Avoidance in Cluster-tree Wireless Sensor Networks

In beacon-enabled mode, beacon collision is a significant problem for the scalability of cluster-tree wireless sensor networks. In this paper, multi-channel time division scheduling (MCTS) is proposed to prevent beacon collisions and provide scalability. A coordinator broadcasts a beacon frame, including information on allocated channels and time-slots, and a new node determines its own channel and time-slot. The performance of the proposed method is evaluated by comparing the proposed approach with a typical ZigBee. MCTS prevents beacon collisions in cluster-tree wireless sensor networks. It enables large-scale wireless sensor networks based on a cluster tree to be scalable and effectively constructed.
Cardiac Pacing, Defibrillation and Resynchronization: A Clinical Approach

We may not be able to make you love reading, but cardiac pacing defibrillation and resynchronization a clinical approach will lead you to love reading starting from now. Book is the window to open the new world. The world that you want is in the better stage and level. World will always guide you to even the prestige stage of the life. You know, this is some of how reading will give you the kindness. In this case, more books you read more knowledge you know, but it can mean also the bore is full.
Determination of entrance skin dose from diagnostic X-ray of human chest at Federal Medical Centre Keffi, Nigeria

patient during x-ray examination in Federal Medical Centre, Keffi in Nasarawa state, Nigeria. Entrance skin doses (ESDs) for a common type of x-ray procedures, namely chest AP/PA (anterior/posterior) were measured. A total of 200 data were collected from patients who were exposed to diagnostic X-ray during their routine chest X-ray examinations. The age of the patients ranged from 15 to 68 years old while the weight and height of these patients ranged from 37.5kg to 98.5kg and 130.0cm to 175cm, respectively. The patent’s skin dose were determined using Edmond’s formula, which is based on the X-ray tube and the radiographic exposure parameters of kVp, mAS, SSD and the total filtration of the beams. The calculated mean skin dose ranges from 0.013± 0.01mGy to 0.851±0.023mGy. In general, the ESDs measured for this type of x-ray procedures were found to be lower than or in agreement with the guidance level set by the Nigerian Basic Ionizing Radiation Regulation (NBIRR, 2003) standard and other international bodies and does not pose any significant health risk to the patience or the workers. Keywords : Entrance skin Dose, X-ray, Anterior-Posterior, Exposure, and Radiation.
Multi-Layer Hidden Markov Model Based Intrusion Detection System

The all IP nature of the next generation (5G) networks is going to open a lot of doors for new vulnerabilities which are going to be challenging in preventing the risk associated with them. Majority of these vulnerabilities might be impossible to detect with simple networking traffic monitoring tools. Intrusion Detection Systems (IDS) which rely on machine learning and artificial intelligence can significantly improve network defense against intruders. This technology can be trained to learn and identify uncommon patterns in massive volume of traffic and notify, using such as alert flags, system administrators for additional investigation. This paper proposes an IDS design which makes use of machine learning algorithms such as Hidden Markov Model (HMM) using a multi-layer approach. This approach has been developed and verified to resolve the common flaws in the application of HMM to IDS commonly referred as the curse of dimensionality. It factors a huge problem of immense dimensionality to a discrete set of manageable and reliable elements. The multi-layer approach can be expanded beyond 2 layers to capture multi-phase attacks over longer spans of time. A pyramid of HMMs can resolve disparate digital events and signatures across protocols and platforms to actionable information where lower layers identify discrete events (such as network scan) and higher layers new states which are the result of multi-phase events of the lower layers. The concepts of this novel approach have been developed but the full potential has not been demonstrated.
Hole-Filling for DIBR Based on Depth and Gradient Information

Depth image-based rendering (DIBR) is a method for generating new virtual images from known viewpoints. However, holes often appear in the rendered virtual images due to occlusion and inaccurate depth information. In this paper, we present a novel hole-filling algorithm to improve the image quality of DIBR. In the proposed method, depth information is added to the priority calculation function when determining the order of hole-filling. Then, the gradient information is used as auxiliary information when searching for the optimal matching block. Experimental results show that the proposed algorithm achieves better objective quality and also improves the subjective quality of the rendered images.
La contraception masculine.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. La contraception masculine Françoise Petite Roulet
A Review on Classification Techniques for Human Activity Recognition

Recognizing human actions from video sequences has many important applications like video surveillance, patient monitoring, human computer interaction, dance choreography analysis, analysis of sports events and entertainment environments. It involves processing the video into frames firstly and finding out the interest points, then extracting the features and lastly specifying and labeling the videos following an appropriate classifying approach like Support Vector Machine, bag of words or nearest neighbor. This paper provides a detailed overview of various state-of-the-art research papers on human activity recognition using different types of classifiers. We surveyed various challenges exhibited by computer vision researchers like the problem of occlusion, 2D/3D pose estimation, variations in viewpoints, human body modeling especially of a person who is paralyzed or injured. From this survey, we can make conclusion of various advantageous and disadvantageous facts about different classifiers used in the detection and classification task.
Implementing Spatial Data Analysis Software Tools in R

This article reports on work in progress on the implementation of functions for spatial statistical analysis, in particular of lattice/area data in the R language environment. The underlying spatial weights matrix classes, as well as methods for deriving them from data from commonly used geographical information systems are presented, handled using other contributed R packages. Since the initial release of some functions in 2001, and the release of the spdep package in 2002, experience has been gained in the use of various functions. The topics covered are the ingestion of positional data, exploratory data analysis of positional, attribute, and neighborhood data, and hypothesis testing of autocorrelation for univariate data. It also provides information about community building in using R for analyzing spatial data.
THE MATHEMATICAL MODELING AND NUMERICAL SIMULATIONS FOR THE MOTION OF SOAP BUBBLES

In this paper, we develop the appropriate equations for bubble motions depending on curvature using the level set methodology. Our method consists of an appropriate finite difference scheme for solving our model equations and a level set approach for capturing the complicated motion between the bubbles. Results indicate that our models and the level set methods can handle complicated interfacial motions and topology changes, and that they can numerically simulate many of the physical features of bubble motions.
Characterizing and Improving Web Page Load Times

Characterizing and Improving Web Page Load Times Xiao Wang Co-Chairs of the Supervisory Committee: Prof. David J. Wetherall Computer Science and Engineering Prof. Arvind Krishnamurthy Computer Science and Engineering Web page load time (PLT) is a key performance metric that many techniques aim to improve. PLT is much slower than lower-level latencies, but the reason was not well understood. This dissertation first characterizes the Web page load time by abstracting a dependency model between network and computation activities. We have built a tool WProf based on this model, that identifies the bottlenecks of PLTs of hundreds of Web pages, and that provides basis for evaluating PLT-reducing techniques. Next, we evaluate SPDY’s contributions to PLTs and find that SPDY’s impact on PLTs is largely limited by the dependencies and browser computation. This suggests that the page load process should be restructured to remove the dependencies so as to improve PLTs. Thus, we propose SplitBrowser that preprocesses Web pages on a proxy server and migrate carefully crafted state to the client so as to simplify the client-side page load process. We have shown that SplitBrowser reduces PLTs by more than half under a variety of mobile settings that span less compute power and slower networks.