Miracle-dz/UniFAR-FTUs huggingface dataset:
Persistence of Circulating Memory B Cell Clones with Potential for Dengue Virus Disease Enhancement for Decades following Infection

ABSTRACT Symptomatic dengue virus infection ranges in disease severity from an influenza-like illness to life-threatening shock. One model of the mechanism underlying severe disease proposes that weakly neutralizing, dengue serotype cross-reactive antibodies induced during a primary infection facilitate virus entry into Fc receptor-bearing cells during a subsequent secondary infection, increasing viral replication and the release of cytokines and vasoactive mediators, culminating in shock. This process has been termed antibody-dependent enhancement of infection and has significantly hindered vaccine development. Much of our understanding of this process has come from studies using mouse monoclonal antibodies (MAbs); however, antibody responses in mice typically exhibit less complexity than those in humans. A better understanding of the humoral immune response to natural dengue virus infection in humans is sorely needed. Using a high-efficiency human hybridoma technology, we isolated 37 hybridomas secreting human MAbs to dengue viruses from 12 subjects years or even decades following primary or secondary infection. The majority of the human antibodies recovered were broadly cross-reactive, directed against either envelope or premembrane proteins, and capable of enhancement of infection in vitro; few exhibited serotype-specific binding or potent neutralizing activity. Memory B cells encoding enhancing antibodies predominated in the circulation, even two or more decades following infection. Mapping the epitopes and activity of naturally occurring dengue antibodies should prove valuable in determining whether the enhancing and neutralizing activity of antibodies can be separated. Such principles could be used in the rational design of vaccines that enhance the induction of neutralizing antibodies, while lowering the risk of dengue shock syndrome.
A Protection Motivation Theory Approach to Home Wireless Security

Research in socio-technical factors in computer security has traditionally focused on employees and their work practice within the premises of the organization. However, with universal access to computing and the diverse means of connecting such devices to each another and to the global Internet, work carried out has shifted outside one central physical location to encompass a variety of possible points-of-access that include homes, modern cafes, and public libraries. Given the ubiquity of wireless devices used for last-hop access to the Internet from home, we look at the security of home wireless networks. In this work, we identify the variables that affect the decision of home wireless network users to implement security features on their networks. Our study is based on the protection motivation theory. A survey was conducted on 189 home users to identify and characterize predictors that differentiate between users who secure their home wireless networks and those who don’t. Results of the analysis identified the following variables as significant: perceived severity, response efficacy, self efficacy, and response cost.
Hierarchical 3D fully convolutional networks for multi-organ segmentation

Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of full volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of seven abdominal structures (artery, vein, liver, spleen, stomach, gallbladder, and pancreas) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training organ-specific models. To this end, we propose a two-stage, coarse-to-fine approach that trains an FCN model to roughly delineate the organs of interest in the first stage (seeing $\sim$40% of the voxels within a simple, automatically generated binary mask of the patient's body). We then use these predictions of the first-stage FCN to define a candidate region that will be used to train a second FCN. This step reduces the number of voxels the FCN has to classify to $\sim$10% while maintaining a recall high of $>$99%. This second-stage FCN can now focus on more detailed segmentation of the organs. We respectively utilize training and validation sets consisting of 281 and 50 clinical CT images. Our hierarchical approach provides an improved Dice score of 7.5 percentage points per organ on average in our validation set. We furthermore test our models on a completely unseen data collection acquired at a different hospital that includes 150 CT scans with three anatomical labels (liver, spleen, and pancreas). In such challenging organs as the pancreas, our hierarchical approach improves the mean Dice score from 68.5 to 82.2%, achieving the highest reported average score on this dataset.
Effect of epolene E‐43 as a compatibilizer on the mechanical properties of palm fiber–poly(propylene) composites

Composites of palm fibers and poly(propylene) (PP) were compounded in an extruder at 200°C. The composites were subsequently injection molded into standard tensile specimens for mechanical characterization. The fracture morphology of the specimens was analyzed by scanning electron microscopy. It was observed that the composite modulus increased with the increase of fiber content, indicating the existence of adhesion between PP and the much stiffer palm fibers. However, the adhesion was not satisfactory and resulted in a decrease in the composite tensile strength with fiber addition. The compatibilizer Epolene E-43 was used to minimize this incompatibility between the wood fibers and the PP matrix. The maleated PP additive enhanced the fiber–matrix adhesion, resulting in an improvement in composite performance. Also, small fibers showed better mechanical properties than those of long fibers. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 2581–2592, 2004
Event-triggered output consensus of heterogeneous linear multi-agent systems with immeasurable disturbances

In this paper, we consider the output consensus problem of linear multi-agent systems subject to different agent dynamics by utilizing event-triggered control strategies. Both the dynamics and output of the agents are affected by non-identical disturbances, which are not measurable. The proposed event-triggered control scheme has a desirable feature that each individual agent independently decides when to communicate with its neighbors while a positive minimum inter-event interval is guaranteed. By using the self-triggered strategy, we further solve the continuous monitoring problem usually existed with the event-triggered strategies. The effectiveness of the results is demonstrated by an example.
Gene expression A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data

Motivation: False discovery rate (FDR) is defined as the expected percentage of false positives among all the claimed positives. In practice, with the true FDR unknown, an estimated FDR can serve as a criterion to evaluate the performance of various statistical methods under the condition that the estimated FDR approximates the true FDR well, or at least, it does not improperly favor or disfavor any particularmethod.Permutationmethods havebecomepopular to estimate FDR in genomic studies. The purpose of this paper is 2-fold. First, we investigate theoretically and empirically whether the standard permutation-based FDR estimator is biased, and if so, whether the bias inappropriately favors or disfavors any method. Second, we propose a simplemodification of the standard permutation to yield a better FDR estimator, which can in turn serve as a more fair criterion to evaluate various statistical methods. Results:Bothsimulatedand real dataexamplesareused for illustration and comparison. Three commonly used test statistics, the sample mean, SAM statistic and Student’s t-statistic, are considered. The results show that the standard permutation method overestimates FDR.Theoverestimation is themost severe for the samplemeanstatistic while the least for the t-statistic with the SAM-statistic lying between the twoextremes,suggesting thatonehas tobecautiouswhenusing the standard permutation-based FDR estimates to evaluate various statistical methods. In addition, our proposed FDR estimation method is simple and outperforms the standard method. Contact: yangxie@biostat.umn. ed
Genetic Inactivation of Pyruvate Dehydrogenase Kinases Improves Hepatic Insulin Resistance Induced Diabetes

Pyruvate dehydrogenase kinases (PDK1-4) play a critical role in the inhibition of the mitochondrial pyruvate dehydrogenase complex especially when blood glucose levels are low and pyruvate can be conserved for gluconeogenesis. Under diabetic conditions, the Pdk genes, particularly Pdk4, are often induced, and the elevation of the Pdk4 gene expression has been implicated in the increased gluconeogenesis in the liver and the decreased glucose utilization in the peripheral tissues. However, there is no direct evidence yet to show to what extent that the dysregulation of hepatic Pdk genes attributes to hyperglycemia and insulin resistance in vivo. To address this question, we crossed Pdk2 or Pdk4 null mice with a diabetic model that is deficient in hepatic insulin receptor substrates 1 and 2 (Irs1/2). Metabolic analyses reveal that deletion of the Pdk4 gene had better improvement in hyperglycemia and glucose tolerance than knockout of the Pdk2 gene whereas the Pdk2 gene deletion showed better insulin tolerance as compared to the Pdk4 gene inactivation on the Irs1/2 knockout genetic background. To examine the specific hepatic effects of Pdks on diabetes, we also knocked down the Pdk2 or Pdk4 gene using specific shRNAs. The data also indicate that the Pdk4 gene knockdown led to better glucose tolerance than the Pdk2 gene knockdown. In conclusion, our data suggest that hepatic Pdk4 may be critically involved in the pathogenesis of diabetes.
Awareness Levels about Breast Cancer Risk Factors, Early Warning Signs, and Screening and Therapeutic Approaches among Iranian Adult Women: A large Population Based Study Using Latent Class Analysis

Background and Objective. Breast cancer (BC) continues to be a major cause of morbidity and mortality among women throughout the world and in Iran. Lack of awareness and early detection program in developing country is a main reason for escalating the mortality. The present research was conducted to assess the Iranian women's level of knowledge about breast cancer risk factors, early warning signs, and therapeutic and screening approaches, and their correlated determinants. Methods. In a cross-sectional study, 2250 women before participating at a community based screening and public educational program in an institute of cancer research in Isfahan, Iran, in 2012 were investigated using a self-administered questionnaire about risk factors, early warning signs, and therapeutic and screening approaches of BC. Latent class regression as a comprehensive statistical method was used for evaluating the level of knowledge and its correlated determinants. Results. Only 33.2%, 31.9%, 26.7%, and 35.8% of study participants had high awareness levels about screening approaches, risk factors, early warning signs and therapeutic modalities of breast cancer, respectively, and majority had poor to moderate knowledge levels. Most effective predictors of high level of awareness were higher educational qualifications, attending in screening and public educational programs, personal problem, and family history of BC, respectively. Conclusion. Results of current study indicated that the levels of awareness among study population about key elements of BC are low. These findings reenforce the continuing need for more BC education through conducting public and professional programs that are intended to raise awareness among younger, single women and those with low educational attainments and without family history.
Experimental investigation on emission parameters of transesterified Adelfa oil (Methyl Ester of Nerium Oil).

Faster depletion of fossil fuels, day to day increase of automotives and increasing cost of petroleum fuel triggered the interest of researchers to arrive at an alternative fuel which is most suitable to compression ignition engines. It is already proven that vegetable oil could be used in the compression ignition engines, by Mr. Rudolf Diesel, inventor of diesel engine in an exhibition. Further researchers reported few problems such as, clogging of fuel filter, nozzle choking, starting trouble during winter season etc., with prolonged usage of vegetable oil. Transesterification process is used for producing the NOME from raw Adelfa oil, to reduce its viscosity and to make it more suitable for compression ignitions without major modification. In this experimental work esterified Adelfa oil is being tested and compared with petroleum diesel. Adelfa (Nerium Oil Methyl EsterNOME) is blended with diesel by 20% by volume and various emission tests have been carried out. Promising results show a good sign of using A20 (Adelfa 20% by volume with diesel) as a fuel for compression engines.
GeoWatch : Online detection of Geo-Correlated Information Trends In Social Networks

Detecting information trends in online social networks is an important problem that has attracted the attention of both the industry and the research community in recent years. Global trends, information items that are trendy in the entire social network, can be detected using existing data streams techniques. However, detecting global trends is only the first step in understanding online social networks. As The First Law of Geography states “Everything is related to everything else, but near things are more related than distant things”. This spatial significance has implications in various applications, trend detection being one of them. To this end, in this paper we propose a new algorithmic tool, GeoWatch, to detect geo-trends. GeoWatch is a data streams solution that detects correlations between topics and locations in a sliding window in addition to providing tools for analyzing topics and locations independently. The degree of correlation as well as the sliding window size can be set to arbitrary values thus enabling a flexible framework. GeoWatch has theoretical guarantees for detecting all trending correlated pairs while requiring only sublinear space and running time. Experiments on Twitter show that in addition to providing perfect recall, GeoWatch has near-perfect precision. As the Twitter analysis demonstrates, GeoWatch successfully filters out topics without geo-intent and detects various local interests such as emergency events, local political demonstrations or cultural events.
Macrophage Migration Inhibitory Factor Inhibition Is Deleterious for High-Fat Diet-Induced Cardiac Dysfunction

Aims Development of metabolic syndrome is associated with impaired cardiac performance, mitochondrial dysfunction and pro-inflammatory cytokine increase, such as the macrophage migration inhibitory factor MIF. Depending on conditions, MIF may exert both beneficial and deleterious effects on the myocardium. Therefore, we tested whether pharmacological inhibition of MIF prevented or worsened metabolic syndrome-induced myocardial dysfunction. Methods and Results C57BL/6J mice were fed for ten weeks with 60% fat-enriched diet (HFD) or normal diet (ND). MIF inhibition was obtained by injecting mice twice a week with ISO-1, for three consecutive weeks. Then, triglycerides, cholesterol, fat mass, glucose intolerance, insulin resistance, ex vivo cardiac contractility, animal energetic substrate utilization assessed by indirect calorimetry and mitochondrial respiration and biogenesis were evaluated. HFD led to fat mass increase, dyslipidemia, glucose intolerance and insulin resistance. ISO-1 did not alter these parameters. However, MIF inhibition was responsible for HFD-induced cardiac dysfunction worsening. Mouse capacity to increase oxygen consumption in response to exercise was reduced in HFD compared to ND, and further diminished in ISO-1-treated HFD group. Mitochondrial respiration was reduced in HFD mice, treated or not with ISO-1. Compared to ND, mitochondrial biogenesis signaling was upregulated in the HFD as demonstrated by mitochondrial DNA amount and PGC-1α expression. However, this increase in biogenesis was blocked by ISO-1 treatment. Conclusion MIF inhibition achieved by ISO-1 was responsible for a reduction in HFD-induced mitochondrial biogenesis signaling that could explain majored cardiac dysfunction observed in HFD mice treated with MIF inhibitor.
PRINCIPAL COMPONENTS ANALYSIS BASED ON A FUZZY SETS APPROACH

As with any other multivariate statistical method, Principal Components Analysis is sensitive to outliers, missing data, and poor linear correlation between variables due to poorly distributed variables. As a result data transformations have a large impact upon PCA. This paper introduces a powerful approach to improve PCA: robust fuzzy PCA algorithm (FPCA). The matrix data is fuzzified, thus diminishing the influence of the outliers.
Feature Fusion Reveals Slow and Fast Visual Memories

Although the visual system can achieve a coarse classification of its inputs in a relatively short time, the synthesis of qualia-rich and detailed percepts can take substantially more time. If these prolonged computations were to take place in a retinotopic space, moving objects would generate extensive smear. However, under normal viewing conditions, moving objects appear relatively sharp and clear, suggesting that a substantial part of visual short-term memory takes place at a nonretinotopic locus. By using a retinotopic feature fusion and a nonretinotopic feature attribution paradigm, we provide evidence for a relatively fast retinotopic buffer and a substantially slower nonretinotopic memory. We present a simple model that can account for the dynamics of these complementary memory processes. Taken together, our results indicate that the visual system can accomplish temporal integration of information while avoiding smear by breaking off sensory memory into fast and slow components that are implemented in retinotopic and nonretinotopic loci, respectively.
Self-Supervised Visual Descriptor Learning for Dense Correspondence

Robust estimation of correspondences between image pixels is an important problem in robotics, with applications in tracking, mapping, and recognition of objects, environments, and other agents. Correspondence estimation has long been the domain of hand-engineered features, but more recently deep learning techniques have provided powerful tools for learning features from raw data. The drawback of the latter approach is that a vast amount of (labeled, typically) training data are required for learning. This paper advocates a new approach to learning visual descriptors for dense correspondence estimation in which we harness the power of a strong three-dimensional generative model to automatically label correspondences in RGB-D video data. A fully convolutional network is trained using a contrastive loss to produce viewpoint- and lighting-invariant descriptors. As a proof of concept, we collected two datasets: The first depicts the upper torso and head of the same person in widely varied settings, and the second depicts an office as seen on multiple days with objects rearranged within. Our datasets focus on revisitation of the same objects and environments, and we show that by training the CNN only from local tracking data, our learned visual descriptor generalizes toward identifying nonlabeled correspondences across videos. We furthermore show that our approach to descriptor learning can be used to achieve state-of-the-art single-frame localization results on the MSR 7-scenes dataset without using any labels identifying correspondences between separate videos of the same scenes at training time.
GaAs:Mn Layer Magnetization in GaAs-Based Heterostructures Containing InGaAs Quantum Well

Magnetic properties of a GaAs-based heterostructures containing InGaAs quantum well and 2 nm thick GaAs layer doped with 5 at. % Mn (GaAs:Mn) on flat and vicinal substrates were studied. Two types of ferromagnetism were found. In the heterostructures grown on the flat substrate parallel to the (001) GaAs plane the magnetization obeys the Bloch T3/2 temperature dependence while for the structures grown on the vicinal surface grown (disoriented by 3°) the magnetization follows percolation dependence.
RDR6 Has a Broad-Spectrum but Temperature-Dependent Antiviral Defense Role in Nicotiana benthamiana

ABSTRACT SDE1/SGS2/RDR6, a putative RNA-dependent RNA polymerase (RdRP) from Arabidopsis thaliana, has previously been found to be indispensable for maintaining the posttranscriptional silencing of transgenes, but it is seemingly redundant for antiviral defense. To elucidate the antiviral role of this RdRP in a different host plant and to evaluate whether plant growth conditions affect its role, we down-regulated expression of the Nicotiana benthamiana homolog, NbRDR6, and examined the plants for altered susceptibility to various viruses at different growth temperatures. The results we describe here clearly show that plants with reduced expression of NbRDR6 were more susceptible to all viruses tested and that this effect was more pronounced at higher growth temperatures. Diminished expression of NbRDR6 also permitted efficient multiplication of tobacco mosaic virus in the shoot apices, leading to serious disruption with microRNA-mediated developmental regulation. Based on these results, we propose that NbRDR6 participates in the antiviral RNA silencing pathway that is stimulated by rising temperatures but suppressed by virus-encoded silencing suppressors. The relative strengths of these two factors, along with other plant defense components, critically influence the outcome of virus infections.
Chemokine and cytokine processing by matrix metalloproteinases and its effect on leukocyte migration and inflammation

The action of matrix metalloproteinases (MMPs) was originally believed to be restricted to degradation of the extracellular matrix; however, in recent years, it has become evident that these proteases can modify many nonmatrix substrates, such as cytokines and chemokines. The use of MMP‐deficient animals has revealed that these proteases can indeed influence the progression of various inflammatory processes. This review aims to provide the reader with a concise overview of these novel MMP functions in relation to leukocyte migration.