Miracle-dz/UniFAR-FTUs huggingface dataset:
Specific Gene Expression Responses to Parasite Genotypes Reveal Redundancy of Innate Immunity in Vertebrates

Vertebrate innate immunity is the first line of defense against an invading pathogen and has long been assumed to be largely unspecific with respect to parasite/pathogen species. However, recent phenotypic evidence suggests that immunogenetic variation, i.e. allelic variability in genes associated with the immune system, results in host-parasite genotype-by-genotype interactions and thus specific innate immune responses. Immunogenetic variation is common in all vertebrate taxa and this reflects an effective immunological function in complex environments. However, the underlying variability in host gene expression patterns as response of innate immunity to within-species genetic diversity of macroparasites in vertebrates is unknown. We hypothesized that intra-specific variation among parasite genotypes must be reflected in host gene expression patterns. Here we used high-throughput RNA-sequencing to examine the effect of parasite genotypes on gene expression patterns of a vertebrate host, the three-spined stickleback (Gasterosteus aculeatus). By infecting naïve fish with distinct trematode genotypes of the species Diplostomum pseudospathaceum we show that gene activity of innate immunity in three-spined sticklebacks depended on the identity of an infecting macroparasite genotype. In addition to a suite of genes indicative for a general response against the trematode we also find parasite-strain specific gene expression, in particular in the complement system genes, despite similar infection rates of single clone treatments. The observed discrepancy between infection rates and gene expression indicates the presence of alternative pathways which execute similar functions. This suggests that the innate immune system can induce redundant responses specific to parasite genotypes.
Pendrin Is a Novel In Vivo Downstream Target Gene of the TTF-1/Nkx-2.1 Homeodomain Transcription Factor in Differentiated Thyroid Cells

ABSTRACT Thyroid transcription factor gene 1 (TTF-1) is a homeobox-containing gene involved in thyroid organogenesis. During early thyroid development, the homeobox gene Nkx-2.5 is expressed in thyroid precursor cells coincident with the appearance of TTF-1. The aim of this study was to investigate the molecular mechanisms underlying thyroid-specific gene expression. We show that the Nkx-2.5 C terminus interacts with the TTF-1 homeodomain and, moreover, that the expression of a dominant-negative Nkx-2.5 isoform (N188K) in thyroid cells reduces TTF-1-driven transcription by titrating TTF-1 away from its target DNA. This process reduced the expression of several thyroid-specific genes, including pendrin and thyroglobulin. Similarly, down-regulation of TTF-1 by RNA interference reduced the expression of both genes, whose promoters are sensitive to and directly associate with TTF-1 in the chromatin context. In conclusion, we demonstrate that pendrin and thyroglobulin are downstream targets in vivo of TTF-1, whose action is a prime factor in controlling thyroid differentiation in vivo.
Outlier absorbing based on a Bayesian approach

The presence of outliers is prevalent in machine learning applications and may produce misleading results. In this paper a new method for dealing with outliers and anomal samples is proposed. To overcome the outlier issue, the proposed method combines the global and local views of the samples. By combination of these views, our algorithm performs in a robust manner. The experimental results show the capabilities of the proposed method.
Surface Regions of Interest for Viewpoint Selection

While the detection of the interesting regions in images has been extensively studied, relatively few papers have addressed surfaces. This paper proposes an algorithm for detecting the regions of interest of surfaces. It looks for regions that are distinct both locally and globally and accounts for the distance to the foci of attention. It is also shown how this algorithm can be adopted to saliency detection in point clouds. Many applications can utilize these regions. In this paper we explore one such application-viewpoint selection. The most informative views are those that collectively provide the most descriptive presentation of the surface. We show that our results compete favorably with the state-of-the-art results.
D-WARD: a source-end defense against flooding denial-of-service attacks

Defenses against flooding distributed denial-of-service (DDoS) commonly respond to the attack by dropping the excess traffic, thus reducing the overload at the victim. The major challenge is the differentiation of the legitimate from the attack traffic, so that the dropping policies can be selectively applied. We propose D-WARD, a source-end DDoS defense system that achieves autonomous attack detection and surgically accurate response, thanks to its novel traffic profiling techniques, the adaptive response and the source-end deployment. Moderate traffic volumes seen near the sources, even during the attacks, enable extensive statistics gathering and profiling, facilitating high response selectiveness. D-WARD inflicts an extremely low collateral damage to the legitimate traffic, while quickly detecting and severely rate-limiting outgoing attacks. D-WARD has been extensively evaluated in a controlled testbed environment and in real network operation. Results of selected tests are presented in the paper.
Emotion Detection from Facial Expression using Support Vector Machine

The objective of this paper is to apply Support Vector Machine to the problem of classifying emotion on images of human faces. This well defined problem is complicated by the natural variation in people’s faces, requiring the classification algorithm to distinguish the small number of relevant features from the large pool of input features. Three different kernels i.e., linear kernel, polynomial kernel and RBF kernel are used to recognise eight facial expressions, anger, contempt, disgust, fear, happiness, neutral, sadness and surprise of human beings in still images. Accuracy of the three kernels is compared to judge the best kernel for facial expression recognition.
BIOGEME: a free package for the estimation of discrete choice models

Reference TRANSP-OR-CONF-2006-048 URL: http://transp-or.epfl. ch/documents/proceedings/Bier03.pdf URL: http://www.strc. ch/Paper/bierlaire.pdf Record created on 2008-02-15, modified on 2017-12-15
Blood Parasites of Two Costa Rican Amphibians with Comments on Detection and Microfilaria Density Associated with Adult Filarial Worm Intensity

The 2 objectives of this study were: (1) to compare parasite detectability in blood smears obtained from toe-clips versus the heart from amphibian hosts; and (2) to test whether microfilariae density is correlated with adult filarial worm intensity. We examined blood parasites of 2 species of amphibians, Rana vaillanti (n = 45) and Eleutherodactylus fitzingeri (n = 36), from Costa Rica collected during the summer of 2003. Separate blood smears were obtained from toe-clips and the heart during necrospy. Eight species of blood parasites were identified from R. vaillanti and 1 from E. fitzingeri. Each parasite species was counted in a 2 × 2.2-cm2 area on each blood smear, and the density of host red blood cells (RBCs) was estimated using a sub-sampling approach, allowing parasite infections to be expressed as individuals per RBC. The detection failure rate for toe-cut smears ranged from 71–100% (x̄ = 92.3%) and from 0–9% (x̄ = 2.4%) for heart smears, depending on parasite species. The density of RBCs was significantly higher in smears produced from heart samples and may explain the differences in detectability. Foleyellides striatus microfilariae densities (per RBC) were significantly correlated with adult female worm intensity (R2 = 0.32, P = 0.011).
HDminer: Efficient Mining of High Dimensional Frequent Closed Patterns from Dense Data

Frequent closed pattern mining has been developed for decades, mostly on a two dimensional matrix. This paper addresses the problem of mining high dimensional frequent closed patterns (nFCPs) from dense binary dataset, where the dataset is represented by a high dimensional cube. As existing FP-tree or enumeration tree based algorithms do not suit for n-dimensional dense data, we are motivated to propose a novel algorithm called HDminer for nFCPs mining. HDminer employs effective search space partition and pruning strategies to enhance the mining efficiency. We have implemented HDminer, and the performance studies on synthetic data and real microarray data show its superiority over existing algorithms.
Assessment the Potential of Using Carbon Nanotubes Reinforcements for Improving the Tensile/Flexural Strength and Fracture Toughness of Portland Cement Paste for Damage Resistant Concrete Transportation Infrastructures

The focus of this study was on exploring the use of nanotechnology-based nano-filaments, such as carbon nanotubes (CNTs) and nanofibers (CNFs), as reinforcement in improving the mechanical properties of Portland cement paste as a construction material. Due to their ultra-high strength and very high aspect ratios, CNTs and CNFs have been used as excellent reinforcements in enhancing the physical and mechanical properties of polymer, metallic, and ceramic composites. Very little attention has been devoted to explore the use of nano-filaments in the transportation industry. Therefore, this study aimed to bridge the gap between nano-filaments and transportation materials. This was achieved by testing the integration of CNTs and CNFs in ordinary Portland cement paste through state-of-the-art techniques. Different mixes in fixed proportions (e.g. water-to-cement ratio, air content, admixtures) along with varying concentrations of CNTs or CNFs were prepared. Different techniques commonly used for other materials (like polymers) were used in achieving uniform dispersion of nano-filaments in the cement paste matrix and strong nano-filaments/cement bonding. Small-scale specimens were prepared for mechanical testing in order to measure the modified mechanical properties as a function of nano-filaments concentration, type, and distribution. With 0.1% CNFs, the ultimate strain capacity increased by 142%, the flexural strength increased by 79%, and the fracture toughness increased by 242%. A scanning electron microscope (SEM) was used to discern the difference between crack bridging and fiber pullout. Test results showed that the strength, ductility, and fracture toughness can be improved with the addition of low concentrations of either CNTs or CNFs.
Wnt signaling directs a metabolic program of glycolysis and angiogenesis in colon cancer

Much of the mechanism by which Wnt signaling drives proliferation during oncogenesis is attributed to its regulation of the cell cycle. Here, we show how Wnt/β‐catenin signaling directs another hallmark of tumorigenesis, namely Warburg metabolism. Using biochemical assays and fluorescence lifetime imaging microscopy (FLIM) to probe metabolism in vitro and in living tumors, we observe that interference with Wnt signaling in colon cancer cells reduces glycolytic metabolism and results in small, poorly perfused tumors. We identify pyruvate dehydrogenase kinase 1 (PDK1) as an important direct target within a larger gene program for metabolism. PDK1 inhibits pyruvate flux to mitochondrial respiration and a rescue of its expression in Wnt‐inhibited cancer cells rescues glycolysis as well as vessel growth in the tumor microenvironment. Thus, we identify an important mechanism by which Wnt‐driven Warburg metabolism directs the use of glucose for cancer cell proliferation and links it to vessel delivery of oxygen and nutrients.
The Neurobiology of Decision-Making and Responsibility: Reconciling Mechanism and Mindedness

This essay reviews recent developments in neurobiology which are beginning to expose the mechanisms that underlie some elements of decision-making that bear on attributions of responsibility. These “elements” have been mainly studied in simple perceptual decision tasks, which are performed similarly by humans and non-human primates. Here we consider the role of neural noise, and suggest that thinking about the role of noise can shift the focus of discussions of randomness in decision-making away from its role in enabling alternate possibilities and toward a potential grounding role for responsibility.
LET's GO: improving spoken dialog systems for the elderly and non-natives

With the recent improvements in speech technology, it is now possible to build spoken dialog systems that basically work. However, such systems are designed and tailored for the general population. When users come from less general sections of the population, such as the elderly and non-native speakers of English, the accuracy of dialog systems degrades. This paper describes Let’s Go, a dialog system specifically designed to allow dialog experiments to be carried out on the elderly and non-native speakers in order to better tune such systems for these important populations. Let’s Go is designed to provide Pittsburgh area bus information. The basic system is described and our initial experiments are outlined.
Subgrain Size-Distributions, Dislocation Structures, Stacking- and Twin Faults and Vacancy Concentrations in SPD Materials Determined by X-Ray Line Profile Analysis

The fundamentals of X-ray line profile analysis are summarised in terms of subgrain size and size-distribution, dislocation density and dislocation types, especially edge and screw dislocations, intrinsic and extrinsic stacking faults and twin boundaries and vacancies produced during plastic deformation. It is shown that deformation induced vacancy concentrations in the grain boundaries of compressed copper polycrystals are close to the equilibrium values at the melting temperature. The discrepancy between X-ray and TEM size values is discussed in terms subgrain- and grain size. It is shown that this apparent discrepancy might be used to determine the status of fragmentation by severe plastic deformation.
Optimal relay power allocation for Amplify-and-Forward OFDM relay networks with deliberate clipping

Deliberate clipping is a simple solution to high Peak-to-Average Power Ratio problem of OFDM signal. In Amplify-and-Forward OFDM relay networks, due to the limited resource, deliberate clipping is also suggested. However, clipping is a nonlinear process and may cause significant performance degradation. Based on Bussgang Linearization Theory, we provide a linear system model for Amplify-and-Forward OFDM relay networks. To achieve spatial diversity, we design a practical nonlinear distortion aware receiver at the destination. Considering a total relay power constraint, we propose an optimal power allocation scheme to maximize the signal-to-noise distortion ratio. Simulation results show that our optimal relay power allocation scheme can improve the system throughput and resist the non-linear distortion. It is also verified that our proposed transmission scheme outperforms other transmission schemes without considering non-linear distortion.
Mission reliability analysis of multiple-phased systems based on Bayesian network

Multiple-phased systems are defined as systems that its operational life can be divided into several disjoint periods, called “phases”. A phased mission is defined as a sequence of tasks which must be completed to achieve the system objective. In order to complete each task a different subset of the system need to function. During the mission, component or subsystem should fail at any time. However, if the fail component or subsystem is not critical in the phase, it will not affect the system performance. It is shown that if critical component of this phase is failed in the previous phase, it will lead to this phase and mission failure. So the transition from one phase to the next is a critical event. In this paper, Bayesian network reliability model is built and mission reliability of multiple-phased system is obtained. The modeling process includes two steps. In the first step, we construct phase Bayesian network for every phase which represent each phase mission by a Bayesian network. In the second step, we construct Bayesian networks of the multiple-phased system. The reliability analysis of the multiple-phased system is performed by discrete time Bayesian network. The model features are described and the approach demonstrated.
Joint Action Coordination in 2½- and 3-Year-Old Children

When acting jointly with others, adults can be as proficient as when acting individually. However, how young children coordinate their actions with another person and how their action coordination develops during early childhood is not well understood. By means of a sequential button-pressing game, which could be played jointly or individually, the action coordination of 2½- and 3-year-old children was examined. Performance accuracy and variability of response timing were taken as indicators of coordination ability. Results showed substantial improvement in joint action coordination between the age of 2½ and 3, but both age groups performed equally well when acting individually. Interestingly, 3-year-olds performed equally well in the joint and the individual condition, whereas 2½-year-olds did not yet show this adult-like pattern as indicated by less accurate performance in the joint action. The findings suggest that in contrast to 3-year-olds, 2½-year-olds still have difficulties in establishing well-coordinated joint action with an adult partner. Possible underlying cognitive abilities such as action planning and action control are discussed.
AIS Electronic Library (AISeL) AIS Electronic Library (AISeL) Switching Costs and Loyalty: Understanding How Trust Switching Costs and Loyalty: Understanding How Trust Moderates Online Consumers’ Ties to Merchants Moderates Online Consumers’ Ties to Merchants

Information technology has transformed how travelers interact with travel service providers. Due to fierce competition in the online air travel industry, e-ticketing services have focused attention on fostering customer loyalty. This is an important strategy because, in general, initial transactions with new customers are less profitable than transactions with existing customers. Drawing on research on customer loyalty, switching costs, and trust, this study develops, and proposes an empirical test, for a model incorporating trust as a moderator of the relationship between switching costs and online customer loyalty. We propose that in the presence of high customer trust, e-businesses should have less need to rely on switching costs as a driver of customer loyalty. If supported, this proposition will extend understanding of customer loyalty, switching costs, and trust in e-commerce environments and provide practical, theory-driven, guidelines to e-businesses seeking to develop customer loyalty programs.
A Taxonomic Signature of Obesity in the Microbiome? Getting to the Guts of the Matter

Obesity is an important and intractable public health problem. In addition to the well-known risk factors of behavior, diet, and genetics, gut microbial communities were recently identified as another possible source of risk and a potential therapeutic target. However, human and animal-model studies have yielded conflicting results about the precise nature of associations between microbiome composition and obesity. In this paper, we use publicly available data from the Human Microbiome Project (HMP) and MetaHIT, both surveys of healthy adults that include obese individuals, plus two smaller studies that specifically examined lean versus obese adults. We find that inter-study variability in the taxonomic composition of stool microbiomes far exceeds differences between lean and obese individuals within studies. Our analyses further reveal a high degree of variability in stool microbiome composition and diversity across individuals. While we confirm the previously published small, but statistically significant, differences in phylum-level taxonomic composition between lean and obese individuals in several cohorts, we find no association between BMI and taxonomic composition of stool microbiomes in the larger HMP and MetaHIT datasets. We explore a range of different statistical techniques and show that this result is robust to the choice of methodology. Differences between studies are likely due to a combination of technical and clinical factors. We conclude that there is no simple taxonomic signature of obesity in the microbiota of the human gut.
Multiple microvenular hemangioma: A case report

The current study reports a case of multiple microvenular hemangioma (MH). A 35-year-old male presented with dark red maculopapules on the trunk and limbs that had been apparent for 5 years. The number of lesions exceeded 100 in total. A histological examination demonstrated multiple, irregular, branching venules in the dermis, without any endothelial atypia. On immunohistochemical analysis of the vascular structures, the endothelial cells stained positive for CD31, CD34 and factor VIII, and the perivascular cells stained positive for SMA and HHF-35. These observations were consistent with a diagnosis of MH, and should be differentiated from the most common differential diagnosis of patch-stage Kaposi’s sarcoma. There was no clear effect following topical application of recombinant human interferon α-2b gel.
Multiple Imputation of Missing or Faulty Values Under Linear Constraints

Many statistical agencies, survey organizations, and research centers collect data that suffer from item nonresponse and erroneous or inconsistent values. These data may be required to satisfy linear constraints, for example, bounds on individual variables and inequalities for ratios or sums of variables. Often these constraints are designed to identify faulty values, which then are blanked and imputed. The data also may exhibit complex distributional features, including nonlinear relationships and highly nonnormal distributions. We present a fully Bayesian, joint model for modeling or imputing data with missing/blanked values under linear constraints that (i) automatically incorporates the constraints in inferences and imputations, and (ii) uses a flexible Dirichlet process mixture of multivariate normal distributions to reflect complex distributional features. Our strategy for estimation is to augment the observed data with draws from a hypothetical population in which the constraints are not present, thereby taking advantage of computationally expedient methods for fitting mixture models. Missing/blanked items are sampled from their posterior distribution using the Hit-and-Run sampler, which guarantees that all imputations satisfy the constraints. We illustrate the approach using manufacturing data from Colombia, examining the potential to preserve joint distributions and a regression from the plant productivity literature. Supplementary materials for this article are available online.
Adaptive Synchronization of Complex Networks with Mixed Probabilistic Coupling Delays via Pinning Control

The problem of synchronization for a class of complex networks with probabilistic time-varying coupling delay and distributed time-varying coupling delay (mixed probabilistic time-varying coupling delays) using pinning control is investigated in this paper. The coupling configuration matrices are not assumed to be symmetric or irreducible. By adding adaptive feedback controllers to a small fraction of network nodes, a low-dimensional pinning sufficient condition is obtained, which can guarantee that the network asymptotically synchronizes to a homogenous trajectory in mean square sense. Simultaneously, two simple pinning synchronization criteria are derived from the proposed condition. Numerical simulation is provided to verify the effectiveness of the theoretical results.