Miracle-dz/UniFAR-FTUs huggingface dataset:
Lexicons for Arabic Social Media

Existing Arabic sentiment lexicons have low coverage—only a few thousand entries. In this paper, we present several large sentiment lexicons that were automatically generated using two different methods: (1) by using distant supervision techniques on Arabic tweets, and (2) by translating English sentiment lexicons into Arabic using a freely available statistical machine translation system. We compare the usefulness of new and old sentiment lexicons in the downstream application of sentence-level sentiment analysis. Our baseline sentiment analysis system uses numerous surface form features. Nonetheless, the system benefits from using additional features drawn from sentiment lexicons. The best result is obtained using the automatically generated Dialectal Hashtag Lexicon and the Arabic translation of the NRC Emotion Lexicon (accuracy of 66.6%). Finally, we describe a qualitative study of the automatic translations of English sentiment lexicons into Arabic, which shows that about 88% of the automatically translated entries are valid for Arabic as well. Close to 10% of the invalid entries are the result of gross mistranslation, close to 40% are due to translation into a related word, and about 50% are due to differences in how the word is used in Arabic.
Impact of the initialisation of a blind unmixing method dealing with intra-class variability

In hyperspectral imagery, unmixing methods are often used to analyse the composition of the pixels. Such methods usually suppose that a single spectral signature, called an endmember, can be associated with each pure material present in the scene. Such an assumption is no more valid for materials that exhibit spectral variability due to illumination conditions, weathering, slight variations of the composition, etc. In this paper, we investigate a new method based on the assumption of a linear mixing model, that deals with intra-class spectral variability. A new formulation of the linear mixing is provided. In our model a pure material cannot be described by a single spectrum in the image but it can in a pixel. An approach method is presented to handle this new model. It is based on pixel-by-pixel Nonnegative Matrix Factorization (NMF) methods. The methods are tested on a semi-synthetic data set built with spectra extracted from a real hyperspectral image and mixtures of these spectra. We particularly focused our tests to study the impact of the initialisation of our methods.
PRINS: Resistive CAM Processing in Storage

Near-data in-storage processing research has been gaining momentum in recent years. Typical processing-in-storage architecture places a single or several processing cores inside the storage and allows data processing without transferring it to the host CPU. Since this approach replicates von Neumann architecture inside storage, it is exposed to the problems faced by von Neumann architecture, especially the bandwidth wall. We present PRINS, a novel in-data processing-in-storage architecture based on Resistive Content Addressable Memory (RCAM). PRINS functions simultaneously as a storage and a massively parallel associative processor. PRINS alleviates the bandwidth wall faced by conventional processing-in-storage architectures by keeping the computing inside the storage arrays, thus implementing in-data, rather than near-data, processing. We show that PRINS may outperform a reference computer architecture with a bandwidth-limited external storage. The performance of PRINS Euclidean distance, dot product and histogram implementation exceeds the attainable performance of a reference architecture by up to four orders of magnitude, depending on the dataset size. The performance of PRINS SpMV may exceed the attainable performance of such reference architecture by more than two orders of magnitude.
Stevens - Johnson Syndrome and Intestinal Perforation following regorafenib Administration for Metastatic Rectal Cancer: A Case Report

C l i n M e d International Library Citation: Muranaka T, Komatsu Y, Sawada K, Nakatsumi H, Kawamoto Y, et al. (2016) Stevens Johnson Syndrome and Intestinal Perforation following regorafenib Administration for Metastatic Rectal Cancer: A Case Report. Clin Med Rev Case Rep 3:088 Received: January 06, 2016: Accepted: February 06, 2016: Published: February 09, 2016 Copyright: © 2016 Muranaka T, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Volume 3 | Issue 2
Coarse-to-fine Seam Estimation for Image Stitching

Seam-cutting and seam-driven techniques have been proven effective for handling imperfect image series in image stitching. Generally, seam-driven is to utilize seam-cutting to find a best seam from one or finite alignment hypotheses based on a predefined seam quality metric. However, the quality metrics in most methods are defined to measure the average performance of the pixels on the seam without considering the relevance and variance among them. This may cause that the seam with the minimal measure is not optimal (perception-inconsistent) in human perception. In this paper, we propose a novel coarse-to-fine seam estimation method which applies the evaluation in a different way. For pixels on the seam, we develop a patch-point evaluation algorithm concentrating more on the correlation and variation of them. The evaluations are then used to recalculate the difference map of the overlapping region and reestimate a stitching seam. This evaluation-reestimation procedure iterates until the current seam changes negligibly comparing with the previous seams. Experiments show that our proposed method can finally find a nearly perception-consistent seam after several iterations, which outperforms the conventional seam-cutting and other seam-driven methods.
Supply and Demand in mHealth Apps for Persons With Multiple Sclerosis: Systematic Search in App Stores and Scoping Literature Review

Background Multiple sclerosis (MS) is a non-curable chronic inflammatory disease of the central nervous system that affects more than 2 million people worldwide. MS-related symptoms impact negatively on the quality of life of persons with MS, who need to be active in the management of their health. mHealth apps could support these patient groups by offering useful tools, providing reliable information, and monitoring symptoms. A previous study from this group identified needs, barriers, and facilitators for the use of mHealth solutions among persons with MS. It is unknown how commercially available health apps meet these needs. Objective The main objective of this review was to assess how the features present in MS apps meet the reported needs of persons with MS. Methods We followed a combination of scoping review methodology and systematic assessment of features and content of mHealth apps. A search strategy was defined for the two most popular app stores (Google Play and Apple App Store) to identify relevant apps. Reviewers independently conducted a screening process to filter apps according to the selection criteria. Interrater reliability was assessed through the Fleiss-Cohen coefficient (k=.885). Data from the included MS apps were extracted and explored according to classification criteria. Results An initial total of 581 potentially relevant apps was found. After removing duplicates and applying inclusion and exclusion criteria, 30 unique apps were included in the study. A similar number of apps was found in both stores. The majority of the apps dealt with disease management and disease and treatment information. Most apps were developed by small and medium-sized enterprises, followed by pharmaceutical companies. Patient education and personal data management were among the most frequently included features in these apps. Energy management and remote monitoring were often not present in MS apps. Very few contained gamification elements. Conclusions Currently available MS apps fail to meet the needs and demands of persons with MS. There is a need for health professionals, researchers, and industry partners to collaborate in the design of mHealth solutions for persons with MS to increase adoption and engagement.
MiR-137 silencing of BRD 4 suppresses oral squamous cell carcinoma cells proliferation , migration and invasion

Objective: In order to study the role of miR-137 in oral squamous cell carcinoma (OSCC). Materials and methods: The gene expression level of miR-137 was detected in OSCC tissues and cell lines by using qRT-PCR. OSCC cells were transfected with miR-137 mimics and mimic controls. The proliferation, invasion and migration abilities were measured by MTT, colony formation assay, transwell and wound healing analysis. The targeting gene of miR137 was measured by western blot, qRT-PCR and luciferase activity assays. Results: We demonstrated that the level of miR-137 was decreased in OSCC tissues when compared with the adjacent normal tissues, it also downregulated in OSCC cell lines. Overexpression of miR-137 suppressed OSCC cells proliferation, invasion and migration. In addition, we demonstrated that bromodomain 4 (BRD4) was a target gene of miR-137. Upregulation of BRD4 can ameliorate the inhibiting effect of miR-137 on tumor cells proliferation and migration. Conclusions: MiR-137 acted as an anticarcinogenic miRNA, partly through targeting BRD4 in oralsquamous cell carcinoma, it would become a therapeutic target for OSCC.
Être sourd et apprendre à lire : Une étude de cas des stratégies de lecture d’un élève de 14 ans

Montréal. Il a pour mission la promotion et la valorisation de la recherche. Érudit offre des services d'édition numérique de documents scientifiques depuis 1998. Pour communiquer avec les responsables d'Érudit : info@erudit.org Article « Être sourd et apprendre à lire : une étude de cas des stratégies de lecture d'un élève de 14 ans » Note : les règles d'écriture des références bibliographiques peuvent varier selon les différents domaines du savoir. Ce document est protégé par la loi sur le droit d'auteur. L'utilisation des services d'Érudit (y compris la reproduction) est assujettie à sa politique d'utilisation que vous pouvez consulter à l'URI
A new method to identify individuals using signals from the brain

A new method to identify individuals using signals from the brain is proposed. In the method, brain signals specifically known as visual evoked potential (VEP) signals are recorded from 61 electrodes located on the scalp while the individuals see a picture. Next, spectral features are computed and classified by a simplified fuzzy ARTMAP (SFA) neural network (NN). The spectral features consist of power in gamma band (30-50 Hz). The experimental results using 800 VEP signals from 20 subjects show give average classification of 94.18%. This pilot investigation shows that the proposed method of identifying individuals using their brain signals is worth further study.
Optimization for Testing the Sensitivity and Specificity of Antibodies to Brg1

Antibodies are vital experimental tools that are used extensively in basic science and in clinical settings. With the evolution of personalized medicine and targeted therapies, the development of reliable diagnostic antibodies will be in greater demand, as they are used for a variety of assays such as immunocytochemistry (ICC), immunohistochemistry (IHC), immunofluorescence (IF), enzyme-linked immunosorbent assay (ELISA), western blot (WB) and immunoprecipitation (IP), among others. While antibodies are largely produced commercially, the performance standards are most often defined by individual investigators who use an antibody for specific assays. Central to the reliability and validity is to what degree the antibody has been produced and tested to assure its specificity and selectivity. This, in turn, often requires specific scientific expertise and knowledge which often lie with individual investigators and not necessarily with commercial companies. Specifically, detailed knowledge is required to remove possible cross-reactive proteins which results from multiple epitopes with a similar sequence or with an overlapping sequence(s); these antigens can be easily eliminated with a well-designed double immunopurification step. Often, multiple antibodies are commercially available such that the decision becomes more complicated as one has to determine which of the antibodies are sensitive and not cross-reactive. Hence, it is sometimes difficult for investigators to find the "right" antibodies. The generation of antibodies against the SWI/SNF subunit BRG1 (Brahma- related gene 1, SMARCA4), which has a high sequence homology to its counterpart BRM (Brahma, SMARCA2) represents such a paradigm. Within this article, we discuss a series of steps that scientists, pathologists and technicians can use to identify the "right" antibody for their purposes. Using a set of commercially-available antibodies to BRG, we detail a cadre of steps to assure that the chosen antibody possesses the prerequisite sensitivity and specificity. This is important as many companies do not rigorously test and perform thorough work-ups of their antibodies.
Temporal variability of spawning site selection in the frog Rana dalmatina: consequences for habitat management

We evaluated whether R. dalmatina females laid their eggs randomly within a pond or preferred particular microhabitats. The same measures were performed in the same area in two consecutive years to determine whether the pattern remained constant over time. In 2003, we observed a significant selection for areas with more submerged deadwood and vegetation, presence of emergent ground and low water depth. However, these results were not confirmed in the subsequent year when none of the microhabitat features measured had a significant effect. Although microhabitat features can strongly influence tadpoles, the temporal variability of habitat at this spatial scale suggests that habitat management could be more effective if focused on a a wider spatial scale.
“Three-Dimensional Printing” – A New Vista for Periodontal Regeneration: A Review

Periodontitis is a multi-factorial disease with a broad spectrum of inflammatory and destructive responses leading to loss of periodontium and tooth-supporting bone. The aim of periodontal therapy is to regenerate the periodontium lost due to periodontal disease. Tissue regeneration in the oral cavity is regulated by signalling molecules, cells, and by matrix formation. Maintenance of the integrity of healthy periodontium and regeneration of the periodontium are achieved by a balance between bone formation and bone resorption termed as bone coupling. Traditional regeneration techniques using grafts and membranes were unpredictable and could not achieve complete regeneration. Periodontal tissue engineering focuses on regenerating the form and function of hard and soft tissues using signalling molecules, scaffolds, and cells. Bony defects may vary in size from small intrabony defects to large horizontal and vertical bone defects in periodontal diseases that prove critical for implant rehabilitation. For decades, efforts have been made to achieve predictable and reliable bone regeneration using various methods. This review focuses on the various materials and methods that are currently being used and which are in the research stage for 3D printing of patient-specific custom-made scaffolds for periodontal regeneration.
Analysis of Pore Structure Effects on Diffusive Reactive Transport in Bentonite via Pore Network Models - 14068

Bentonite is widely considered as one of the most suitable backfill materials for deep geological disposal of radioactive nuclear waste. In order to assess the long-term containment performance of radioactive waste repositories, good knowledge of transport behaviour of radionuclides in bentonite is required. While it is possible to study the complex diffusion phenomena using pore network models, accurate description of the morphology of real porous media is required to support model parameterisation (i.e. pore size distribution, throat size 1 distribution and connectivity parameters). However, for bentonites with tight pore spaces that are of interest to potential geological repository applications, current experimental techniques do not allow resolution of sufficient number of throat sizes (if at all). Consequently, often only pore size distribution is available, while connectivity data, e.g. pore coordination statistics/spectra are lacking. In order to circumvent this problem, a meso-scale model for diffusion of foreign species through porous media is proposed. The model considers diffusion as a continuum process operating on a discrete geometrical structure dictated by the pore size distribution. The effect of connectivity can be derived indirectly from macroscopically measured mass transport. Local diffusivities and hence connectivity are seen to be dependent on the size of the diffusing species and the sorption of those species onto the pore walls. As such, the bulk diffusivity of the medium can be analysed by considering the effects of pore structure alone, and in combination with sorption. This paper presents initial results for the effects of solute sizes on the topological and physical properties of the porous network. Besides emulating the effect of sorption, the approach could in principle be extended to other pore space changing mechanisms (e.g. precipitation, dissolution, micro-cracking, etc.) for deriving mechanism-based evolution laws for the transport parameters of porous media. 1 A standard parameter in Pore Network Model that represents the cross-sectional area of the passage links between the pore voids.
Energy-efficient clustering routing algorithm for heterogeneous wireless sensor networks

Energy heterogeneity is ubiquitous in wireless sensor networks. Because of nodes' initial energy of multi-level energy heterogeneity in wireless sensor networks distributing within a certain scope randomly, this paper proposed an Energy-efficient clustering routing algorithm which is suit to the heterogeneous environments in wireless sensor networks to economize the nodes' energy exploiting the energy heterogeneity and to extend the stability period of the network. The nodes of the cluster select the cluster head based on the current residual energy and the distance to the cluster centroid, and then accomplish the collection of data and the two-hop data transmission. The simulations indicate that the proposed algorithm balances the energy consumption among sensor nodes much better and prolongs the WSNs life time efficiently. Index Terms - Wireless sensor network, energy heterogeneity, energy efficiency, clustering routing algorithm. I. Introduction
BRCA 1 gene mutation rate in breast cancer patients and its clinical significance

Breast cancer commonly occurs in females with increasing incidence. BRCA1 is one tumor suppressor gene and participates in DNA injury and repair. The dysfunction of BRCA1 impairs DNA and elevates the risk of breast or ovarian cancer. This study thus investigated the mutation rate of BRCA gene and its implication for prognosis. A total of 116 breast cancer patients were collected for peripheral blood. Leukocyte DNA was extracted to test germ-line mutation of BRCA1. Cancer tissues were also extracted for DNA, from which somatic mutation of BRCA1 was tested, in addition to MLPA approach. From clinical and pathological parameters, their correlation with BRCA1 gene mutation along with patient prognosis wasanalyzed. In addition, Kaplan-Meier method was used to analyze the survival rate. The overall mutation rate of BRCA1 gene in patients is 20.7%. Among those the mutation rate was 57.1% in all patients with inherited breast/ovarian cancer, significantly higher than those rates in sporadic breast cancer. Further analysis of clinical pathology parameters revealed no significant correlation between BRCA1 gene mutation and patient’s age, menstrual status and metastasis. Kaplan-Meier method analyzed patient’s diagnosis showed better RFS and OS within five years in patients with BRCA1 mutation compared to wild type ones. By sequencing and MLPA test, we found significantly higher mutation rate of BRCA1 gene in those patients with family history of breast/ovarian cancer compared to those in sporadic cancer patients. Meanwhile those breast cancer patients with BRCA1 mutation had more favorable prognosis.
Microfinance for Women: Are There Economical Reasons? Evidence from Latin America

Our main research question is whether, using more robust statistical methods, the effect of the percentage of female borrowers of a Microfinance Institution (MFI) on the delinquency of the portfolio still holds. By using two samples of Latin American MFIs, we show that there is no relation between the percentage of female borrowers in an MFI portfolio and MFI economic outcomes. The relation between portfolio-at-risk and gender is not found when using a dynamic panel to account for serial autocorrelation, thus not accepting the hypothesis that women repay better than men. Hence, this paper suggests that MFIs lend more to women for reasons beyond just "economic" ones, such as empowering women and helping poor people, otherwise unable to access credit lines, get loans in Latin America.
A k-NN-Based Localization Approach for Crowdsourced Air Traffic Communication Networks

In this paper, we argue that current state-of-the-art methods of aircraft localization such as multilateration are insufficient, in particular for modern crowdsourced air traffic networks with random, unplanned deployment geometry. We propose an alternative, a grid-based localization approach using the k-nearest neighbor (k -NN) algorithm, to deal with the identified shortcomings. Our proposal does not require any changes to the existing air traffic protocols and transmitters, and is easily implemented using only low-cost, commercial-off-the-shelf hardware. Using an algebraic multilateration algorithm for comparison, we evaluate our approach using real-world flight data collected with our collaborative sensor network OpenSky. We quantify its effectiveness in terms of aircraft location accuracy, surveillance coverage, and the verification of false position data. Our results show that the grid-based k-NN approach can increase the effective air traffic surveillance coverage compared to multilateration by a factor of up to 2.5. As it does not suffer from dilution of precision to the same extent, it is more robust in noisy environments and performs better in pre-existing, unplanned receiver deployments. We further find that the mean aircraft location accuracy can be increased by up to 41% in comparison with multilateration while also being able to pinpoint the origin of potential spoofing attacks conducted from the ground.