Miracle-dz/UniFAR-FTUs huggingface dataset:
DNA Sequencing Confirms the Involvement of Leishmania (L.) Amazonensis in American Tegumentary Leishmaniasis in the State of São Paulo, Brazil

INTRODUCTION American tegumentary leishmaniasis (ATL) represents one of the most important public health issues in the world. An increased number of autochthonous cases of ATL in the Northeastern region of São Paulo State has been documented in the last few years, leading to a desire to determine the Leishmania species implicated. METHODS PCR followed by DNA sequencing was carried out to identify a 120bp fragment from the universal kDNA minicircle of the genus Leishmania in 61 skin or mucosal biopsies from patients with ATL. RESULTS DNA sequencing permitted the identification of a particular 15bp fragment (5’ …GTC TTT GGG GCA AGT. 3’) in all samples. Analysis by the neighbor-joining method showed the occurrence of two distinct groups related to the genus Viannia (V) and Leishmania (L), each with two subgroups. Autochthonous cases with identity to a special Leishmania sequence not referenced in Genbank predominated in subgroup V.1, suggesting the possible existence of a subtype or mutation of Leishmania Viannia in this region. In the subgroup L.2, which showed identity with a known sequence of L. (L.) amazonensis, there was a balanced distribution of autochthonous and non-autochthonous cases, including the mucosal and mucocutaneus forms in four patients. The last observation may direct us to new concepts, since the mucosal compromising has commonly been attributed to L. (V.) braziliensis, even though L. (L.) amazonensis is more frequent in the Amazonian region. CONCLUSIONS These results confirm the pattern of distribution and possible mutations of these species, as well as the change in the clinical form presentation of ATL in the São Paulo State.
Multi-criteria search algorithm: An efficient approximate k-NN algorithm for image retrieval

We propose a new method for approximate k-NN search in large scale image databases, based on top-k multi-criteria search techniques. The method defines a simple index structure based on sorted lists, which provides a good compromise between fast retrieval, storage requirements and update cost. The search algorithm delivers approximate results with guarantees about false negatives, with fast emergence of good approximations, monotonically improved and leading if necessary to an exact result. Experiments with the on-disk implementation show that our method produces very good approximate results several times faster than the Baseline method.
Cancer exosomes trigger mesenchymal stem cell differentiation into pro-angiogenic and pro-invasive myofibroblasts

Stromal fibroblasts become altered in response to solid cancers, to exhibit myofibroblastic characteristics, with disease promoting influence. Infiltrating mesenchymal stem cells (MSC) may contribute towards these changes, but the factors secreted by cancer cells that impact MSC differentiation are poorly understood. We investigated the role of nano-metre sized vesicles (exosomes), secreted by prostate cancer cells, on the differentiation of bone-marrow MSC (BM-MSC), and the subsequent functional consequences of such changes. Purified exosomes impaired classical adipogenic differentiation, skewing differentiation towards alpha-smooth muscle actin (αSMA) positive myofibroblastic cells. A single exosomes treatment generated myofibroblasts secreting high levels of VEGF-A, HGF and matrix regulating factors (MMP-1, −3 and −13). Differentiated MSC had pro-angiogenic functions and enhanced tumour proliferation and invasivity assessed in a 3D co-culture model. Differentiation was dependent on exosomal-TGFβ, but soluble TGFβ at matched dose could not generate the same phenotype. Exosomes present in the cancer cell secretome were the principal factors driving this phenotype. Prostate cancer exosomes dominantly dictate a programme of MSC differentiation generating myofibroblasts with functional properties consistent with disease promotion.
Building Large Scale Text Corpus for Tibetan Natural Language Processing by Extracting Text from Web Pages

In this paper, we propose an approach to build a large scale text corpus for Tibetan natural language processing. We find the distribution of Tibetan web pages on the internet with a crawler which can identify whether or not a web page contains Tibetan text. Three biggest web sites are selected, and topic pages are selected with a rule based method by checking the url. The layout structures of selected pages are analysed, and topic related information are extracted based on web site specific rules. Consequently, we get a corpus including more than 65 thousands documents, nearly 1.59 million sentences or 35 million syllables in total. Title and Abstract in Chinese 抽取网页文本为藏文自然语言处理构建大规模文本语料库 在本文中,我们提出了一种为藏文自然语言处理构建大规模文本语料库的方法。 我们首先 使用网络爬虫技术,并结合藏文网页的编码识别技术判断一个网页中是否包含藏文文本, 并据此考察互联网上藏文网页的分布情况。 然后,我们选择了三个最大的藏文网站,根据 网页的 URL,利用预先定义的规则,判断网页是 Hub 页面还是 Topic 页面。 之后,我 们分析了每个网站的 Topic 页面的布局结构特点,并利用正则表达式编制了相应的 Topic 相关文本的抽取规则。 采用上述方法,我们构建了一个包含 6.5 万文档,共计 159 万句、 3500 万藏文音节字的文本语料库。
Kinematics of a 6-DOF Feeding and Unloading Manipulator

In order to improve the efficiency of kinematics modeling in analyzing robots, this document used Lie groups and screw theory to describe rigid motion of the robot system, established a kinematic model of operating arm based on Product of Exponentials (POE) formula, and analyzed forward and inverse kinematics of chain topology structure robot, as well as several sub-inverse problem of inverse kinematics. A 6-DOF series-wound configuration loading and unloading manipulator has been used as an example, its kinematics equations are established by POE formula combined with screw theory, the special configuration of end effector under a specific task planning are calculated using Mathematica software. By contrast with D-H parameters method, we can draw a conclusion that POE formula and D-H parameters method are essentially equivalent. However, POE formula and motion screw have more definite geometric and physical meaning, neat expression form, convenient algebraic operation, are able to overcome limitations, such as calculation complexity and singularity in solving process, can meet kinematic analysis and real-time control of robot.
A Least Biased Fuzzy Clustering Method

A new operational definition of cluster is proposed, and a fuzzy clustering algorithm with minimal biases is formulated by making use of the maximum entropy principle to maximize the entropy of the centroids with respect to the data points (clustering entropy). The authors make no assumptions on the number of clusters or their initial positions. For each value of an adimensional scale parameter /spl beta/', the clustering algorithm makes each data point iterate towards one of the cluster's centroids, so that both hard and fuzzy partitions are obtained. Since the clustering algorithm can make a multiscale analysis of the given data set one can obtain both hierarchy and partitioning type clustering. The relative stability with respect to /spl beta/' of each cluster structure is defined as the measurement of cluster validity. The authors determine the specific value of /spl beta/' which corresponds to the optimal positions of cluster centroids by minimizing the entropy of the data points with respect to the centroids (clustered entropy). Examples are given to show how this least biased method succeeds in getting perceptually correct clustering results. >
Young Adults’ Information Seeking Following Celebrity Suicide: Considering Involvement With the Celebrity and Emotional Distress in Health Communication Strategies

ABSTRACT Young adults (N = 357) were surveyed following the suicide of celebrity Robin Williams to better understand how involvement with the actor and emotional responses to his death influenced searches for information concerning depression, suicide, and mental health. Emotional distress following the actor’s death mediated the relationship between involvement and certain types of information searches. Most respondents sought information about the celebrity’s career, suicide, and depression using portable devices such as smartphones and laptop computers to access news websites for information. Those respondents who sought information about the suicide reported changes in their thoughts about suicide, most often dealing with the difficulty in spotting warning signs and the idea that “it can happen to anyone. ” Findings suggest placement of health messages within existing material about celebrity announcements on online websites and social media to drive more traffic toward general informational outlets. Messages that acknowledge emotional distress might be best placed within content specific to the celebrity’s tragedy, rather than specific to the celebrity’s career or performances.
A survey on secure data offloading using auction based mechanism

As of late, there has been a fast development in portable information use fuelled by expanding universality of different cell phones (i.e., cell phones, tablets) among clients and in addition the expanding interest for versatile information utilization by every client. These gadgets are utilized for various exercises, for example, web perusing, video/sound downloading and photograph sharing. Thusly, versatile administrators have expanded the limit of their radio get to and backhaul arranges through the advancement of new advances and an inescapable organization of new sorts of base stations. In this paper, we introduce a total review of Heterogeneous Mobile information offloading strategies and concentrate the offloading useful prerequisites for now's versatile system.
Multiple imputation with sequential penalized regression

Missing data is a common issue that can cause problems in estimation and inference in biomedical, epidemiological and social research. Multiple imputation is an increasingly popular approach for handling missing data. In case of a large number of covariates with missing data, existing multiple imputation software packages may not work properly and often produce errors. We propose a multiple imputation algorithm called mispr based on sequential penalized regression models. Each variable with missing values is assumed to have a different distributional form and is imputed with its own imputation model using the ridge penalty. In the case of a large number of predictors with respect to the sample size, the use of a quadratic penalty guarantees unique estimates for the parameters and leads to better predictions than the usual Maximum Likelihood Estimation (MLE), with a good compromise between bias and variance. As a result, the proposed algorithm performs well and provides imputed values that are better even for a large number of covariates with small samples. The results are compared with the existing software packages mice, VIM and Amelia in simulation studies. The missing at random mechanism was the main assumption in the simulation study. The imputation performance of the proposed algorithm is evaluated with mean squared imputation error and mean absolute imputation error. The mean squared error ( β ^ ), parameter estimates with their standard errors and confidence intervals are also computed to compare the performance in the regression context. The proposed algorithm is observed to be a good competitor to the existing algorithms, with smaller mean squared imputation error, mean absolute imputation error and mean squared error. The algorithm’s performance becomes considerably better than that of the existing algorithms with increasing number of covariates, especially when the number of predictors is close to or even greater than the sample size. Two real-life datasets are also used to examine the performance of the proposed algorithm using simulations.
OverWatch: A Cross-Plane DDoS Attack Defense Framework with Collaborative Intelligence in SDN

Distributed Denial of Service (DDoS) attacks are one of the biggest concerns for security professionals. Traditional middle-box based DDoS attack defense is lack of network-wide monitoring flexibility. With the development of software-defined networking (SDN), it becomes prevalent to exploit centralized controllers to defend against DDoS attacks. However, current solutions suffer with serious southbound communication overhead and detection delay. In this paper, we propose a cross-plane DDoS attack defense framework in SDN, called OverWatch, which exploits collaborative intelligence between data plane and control plane with high defense efficiency. Attack detection and reaction are two key procedures of the proposed framework. We develop a collaborative DDoS attack detection mechanism, which consists of a coarse-grained flow monitoring algorithm on the data plane and a fine-grained machine learning based attack classification algorithm on the control plane. We propose a novel defense strategy offloading mechanism to dynamically deploy defense applications across the controller and switches, by which rapid attack reaction and accurate botnet location can be achieved. We conduct extensive experiments on a real-world SDN network. Experimental results validate the efficiency of our proposed OverWatch framework with high detection accuracy and real-time DDoS attack reaction, as well as reduced communication overhead on SDN southbound interface.
Multi-Parameter Based Self-Feedback Effectiveness Evaluation in a Multi-Sensor Fusion Positioning System

Based on data fusion technology, multi-sensor fusion positioning merges several positioning sources together to achieve an optimal positioning result by making full use of all the homogeneous or heterogeneous information from different fusion sensors. However, there has not been much research carried out about the effectiveness evaluation of multi-sensor fusion positioning system. In this paper, a self-feedback effectiveness evaluation algorithm is proposed which can not only evaluate multi-sensor fusion positioning systems, but also improve positioning performance by adopting feedback information. Besides traditional evaluation parameters, confidence level and plug and play capability are proposed as new evaluation parameters to estimate effectiveness of multi-sensor fusion positioning system. Simulations verify the efficiency of proposed evaluation parameters and self-feedback effectiveness evaluation algorithm.
Industrial experience with design patterns

A design pattern is a particular prose form of recording design information such that designs which have worked well in the past can be applied again in similar situations in the future. The availability of a collection of design patterns can help both the experienced and the novice designer recognize situations in which design reuse could or should occur. We have found that design patterns: 1) provide an effective "shorthand" for communicating complex concepts effectively between designers, 2) can be used to record and encourage the reuse of "best practices", 3) capture the essential parts of a design in compact form, e.g. for documentation of existing software architectures. Since the patterns community is one that shares information in an open forum and builds on the experiences of others, we chose to submit a joint paper on our industrial experiences with patterns. We focus on the lessons learned in our respective industrial settings as a first step towards answering the questions "Patterns sound very promising, but how are they actually used in the industry and what benefits, if any, do they bring in practice? " We proceed by briefly describing each of our respective experiences with patterns. This is followed by a joint "lessons learned" section and conclusion.
Olfactory processing in bipolar disorder, major depression, and anxiety

Although olfactory abnormalities are well established in schizophrenia, considerably less work has examined olfactory performance in other neuropsychiatric conditions. In the current study, we examined odor identification, odor discrimination, detection threshold, and odor hedonic processing performance in individuals with bipolar I disorder (n = 43; n = 13 with psychotic features), bipolar II disorder (n = 48), major depressive disorder (MDD) (n = 134), anxiety (n = 48), and no mental disorder (n = 72) who participated in a community‐based family study.
Masculinization-Related Genes and Cell-Mass Structures During Early Gonadal Differentiation in the African Clawed Frog Xenopus laevis

The African clawed frog Xenopus laevis has a female heterogametic ZZ/ZW-type sex-determining system. We previously discovered a W-linked female sex-determining gene dm-W that is involved in ovary formation, probably through the up-regulation of the estrogen synthesis genes cyp19a1 and foxl2. We also reported that a unique “mass-in-line structure”, which disappears from ZZ gonads during early testicular development, might serve as the basis for ovary differentiation in ZW gonads. However, the molecular mechanisms underlying early masculinization are poorly understood. To elucidate the development of bipotential gonads into testes after sex determination in this species, we focused on the orthologs of five mammalian sex-related genes: three nuclear factor genes, dax1, sf1 (also known as ad4bp), and sox9, and two genes encoding members of the tumor growth factor-β (TGF-β) family, anti-Müllerian hormone (amh) and inhibin βb (inhbb). Quantitative RT-PCR analysis revealed that the expression of dax1, sox9, amh, and inhbb or sf1 was greatly or slightly higher in ZZ than in ZW gonads during early sex development. In situ hybridization analysis revealed that amh and inhbb mRNAs were expressed in somatic cells on the inner and outer sides of cell masses in the mass-in-line structure, respectively, in the developing ZZ gonads. Interestingly, estrogen exposure prevented the disappearance of the mass-in-line structure in early developing ZZ tadpoles. These findings suggest that TGF-β signaling is involved in the destruction of the mass-in-line structure, which may be maintained by estrogen.
$k$ NN-DP: Handling Data Skewness in $kNN$ Joins Using MapReduce

In this study, we discover that the data skewness problem imposes adverse impacts on MapReduce-based parallel <italic>kNN-join</italic> operations running clusters. We propose a data partitioning approach—called <italic> kNN-DP</italic>—to alleviate load imbalance incurred by data skewness. The overarching goal of <italic>kNN-DP </italic> is to equally divide data objects into a large number of partitions, which are processed by mappers and reducers in parallel. At the heart of <italic>kNN-DP</italic> is a data partitioning module, which dynamically and judiciously partitions data to optimize <italic>kNN-join</italic> performance by suppressing data skewness on Hadoop clusters. Data partitioning decisions largely depends on data properties (e.g., distributions), the analysis of which is highly expensive for a massive amount of data. To speed up the data-property analysis, we incorporate a sampling technique to profile the data distribution of a small sample dataset representing big datasets. After building a data-partitioning cost model for parallel <italic>kNN-join</italic>s, we derive the time-complexity upper and lower bounds of parallel <italic>kNN-join</italic> algorithms. The cost model offers us a guidance to systematically investigate <italic>kNN-DP</italic>’s performance. <italic>kNN-DP</italic> obtains global nearest neighbors using local nearest neighbors. To improve the accuracy of such an approximation solution, we augment each node’s local data by a small amount of redundant data. We develop two <italic>kNN-DP</italic>-based schemes called <italic> LSH+</italic> and <italic>z-value+</italic>, which seamlessly integrate <italic>kNN-DP</italic> with the existing <italic>LSH</italic> and <italic>z-value</italic> algorithms for <italic>kNN-join</italic> computing. We implement and evaluate <italic>LSH+</italic> and <italic>z-value+</italic> on a 24-node Hadoop cluster driven by both synthetic and real-world high-dimensional datasets. The experimental results show that <italic>kNN-DP</italic> significantly improves the performance of <italic>LSH</italic> and <italic>z-value</italic> while offering high extensibility and scalability on Hadoop clusters.
Predictors of early left ventricular systolic dysfunction in duchenne muscular dystrophy patients

Introduction: Early detection of left ventricular systolic dysfunction (LVSD) is important for therapeutic strategies for Duchenne muscular dystrophy (DMD) patients. We analyzed myocardial strain using echocardiography for early detection of LVSD and determined the predictors of early LVSD. Methods: This investigation was a cross‐sectional study of 40 DMD patients with normal left ventricular ejection fraction. Global longitudinal strain (GLS) was used to analyze subtle disturbances in longitudinal contraction of the myocardium. Patients were determined to have early LVSD (GLS > –18) or normal left ventricular systolic function (GLS ≤ –18). Results: Patients who had early LVSD were older and had a higher frequency of corticosteroid therapy and of mutations in exons 45, 46, 47, 48, 49, 50, and 52. Discussion: Myocardial strain measurements are useful for the early diagnosis of LVSD in DMD patients. Older age, use of corticosteroids, and mutations within the “hot‐spot” region of the DMD gene are associated with early LVSD. Muscle Nerve 58: 84–89, 2018
Formability of Fiber-Reinforced Thermoplastics in Hot Press Forming Process Based on Friction Properties

High performance composites are used in commercial applications in a steadily growing degree. This increase of advanced materials is accomponied with the development of fully automated fabrication processes. It aims to drive down the time and costs of the production while ensuring a high quality of the product. This can achieved by considering the process of hot press forming with continuous fiber reinforced thermoplastics. The development of the process is, however, accompanied with a few difficulties, which require more research. For example, composite materials with different architectures, lay-ups, and constituents, show large differences in formability. This research examines the effect of friction on the formability of thermoplastic composites. Both experiments and simulations were conducted. Demonstrator products have been press-formed from laminates with different materials and architectures (UD-carbon PEEK, UD-carbon-PEI, 8hs-glass PPS, 5hs-carbon PEEK and UD-glass PPS), to investigate their effects on formability. Creating a doubly curved shape from a flat laminate requires at least three deformation mechanisms, namely in-plane shear, bending and inter-ply slippage This paper focuses on the sliding mechanism and the corresponding friction. In order to quantify the amount of sliding in the press-formed product, a dot pattern has been applied to both surfaces of the laminate. The slip between the outer plies can be analyzed by means of photogrammetry. Besides, the friction coefficient of each material is measured in a special designed friction test set-up. It can be seen that the composite formability is directly linked to its friction properties. FE simulations of the press-form process will be performed based on the measured material properties, to demonstrate the influence of the materials friction coefficient.
Electronic transport in self-organizing columnar phases

This paper presents measurements of time-of-flight charge carrier mobility and thermal optical polarized microscopy on a new class of columnar liquid crystals (LC) self-organized into hexagonal columnar mesophase self-assembled form functionalized dendrons. We discuss the temperature and electric field-independent high hole mobility of 1-3.5×10-3 cm2/Vs. The anomalous temperature behavior of the mobility is consistent with polaron transport mechanisms. Studies of time-of-flight transients, which reveal evidence for dynamic defects with lifetimes in the range of 10-6 s, are presented as aspects of charge gneeration and recombination kinetics. We demonstrate the enhancement of electron transport in acene-based dendron LCs by dopign wtih TNF-based dendron molecules.