Miracle-dz/UniFAR-FTUs huggingface dataset:
An Enteric Helminth Infection Protects Against an Allergic Response to Dietary Antigen1

Although helminths induce a polarized Th2 response they have been shown, in clinical studies, to confer protection against allergies. To elucidate the basis for this paradox, we have examined the influence of an enteric helminth infection on a model of food allergy. Upon Ag challenge, mice fed peanut (PN) extract plus the mucosal adjuvant cholera toxin (CT) produced PN-specific IgE that correlated with systemic anaphylactic symptoms and elevated plasma histamine. PN-specific IgE was not induced in helminth-infected mice fed PN without CT. Moreover, when PN plus CT was fed to helminth-infected mice, both PN-specific IgE and anaphylactic symptoms were greatly diminished. The down-regulation of PN-specific IgE was associated with a marked reduction in the secretion of IL-13 by PN-specific T cells. When helminth-infected PN plus CT-sensitized mice were treated with neutralizing Abs to IL-10, the PN-specific IgE response and anaphylactic symptoms were similar to, or greater than, those seen in mice that receive PN and CT alone. Taken together, these results suggest that helminth-dependent protection against allergic disease involves immunoregulatory mechanisms that block production of allergen-specific IgE.
The Common State Filter for SLAM

This paper presents the common state filter (CSF), a novel and efficient suboptimal multiple hypothesis slam (MHSLAM) method for Kalman Filter-based SLAM algorithms. Conventional MHSLAM algorithms require the entire vehicle and map state to be copied for each hypothesis. The CSF, by contrast, maintains a single, common instance of the vast majority of the map and only copies the map portion that varies substantially across different hypotheses. We demonstrate the performance of the algorithm on the Victoria Park data set.
SVD-based Feature Extraction from Time-series Motion Data and Its Application to Gesture Recognition

Singular value decomposition is used to extract features from time-series motion data. A matrix consisting of the time-series data is decomposed into left singular vectors which represent the patterns of the motion and singular values as a scalar, by which each corresponding left singular vector affects the matrix. Gesture recognition using the extracted features suggest the effectiveness of the method.
Automatic Derivation of Surface Text Patterns for a Maximum Entropy Based Question Answering System

In this paper we investigate the use of surface text patterns for a Maximum Entropy based Question Answering (QA) system. These text patterns are collected automatically in an unsupervised fashion using a collection of trivia question and answer pairs as seeds. These patterns are used to generate features for a statistical question answering system. We report our results on the TREC-10 question set.
Semi-adaptive channel estimation technique for LTE systems

Channel estimation in orthogonal frequency division multiplexing (OFDM) systems is required for coherent demodulation. This paper deals with channel estimation in the OFDM based LTE system. In particular, two pilot-aided techniques are presented: the low-pass filter based estimator (LPFE), and the robust minimum mean square error estimator (MMSE). These techniques are characterized by their low complexity relative to other complex estimation techniques. A semi-adaptive channel estimator is proposed in this paper. In the proposed estimator, channel delay spread and Doppler frequency are used to adapt the interpolation filters in order to enhance the estimator performance while maintaining low complexity relative to the fully adaptive estimator. Simulation results show that there is performance improvement using the proposed adaptive scheme when applied to LTE systems.
Forest stem volume and storm damage mapping in Finland and Russia

Storms and heavy winds can induce degradations on wide areas of forest, which have a severe impact on the ecological habitat and on economical value. During ten days in July and August 2010, the thunderstorms named Asta, Veera, Lahja and Sylvi raged in southern and middle Finland. About 8.1 million m3 of forest was damaged, and the storms caused severe damage to the country infrastructure (e.g. buildings, power lines and railroads). The estimated financial losses of Asta storm alone were over 20 million euros.
Real-Time Timeline Summarisation for High-Impact Events in Twitter

Twitter has become a valuable source of event-related information, namely, breaking news and local event reports. Due to its capability of transmitting information in real-time, Twitter is further exploited for timeline summarisation of high-impact events, such as protests, accidents, natural disasters or disease outbreaks. Such summaries can serve as important event digests where users urgently need information, especially if they are directly affected by the events. In this paper, we study the problem of timeline summarisation of high-impact events that need to be generated in real-time. Our proposed approach includes four stages: classification of real-world events reporting tweets, online incremental clustering, post-processing and sub-events summarisation. We conduct a comprehensive evaluation of different stages on the “Ebola outbreak” tweet stream, and compare our approach with several baselines, to demonstrate its effectiveness. Our approach can be applied as a replacement of a manually generated timeline and provides early alarms for disaster surveillance.
Integrating data‐deficient species in analyses of evolutionary history loss

Abstract There is an increasing interest in measuring loss of phylogenetic diversity and evolutionary distinctiveness which together depict the evolutionary history of conservation interest. Those losses are assessed through the evolutionary relationships between species and species threat status or extinction probabilities. Yet, available information is not always sufficient to quantify the threat status of species that are then classified as data deficient. Data‐deficient species are a crucial issue as they cause incomplete assessments of the loss of phylogenetic diversity and evolutionary distinctiveness. We aimed to explore the potential bias caused by data‐deficient species in estimating four widely used indices: HEDGE, EDGE, PDloss, and Expected PDloss. Second, we tested four different widely applicable and multitaxa imputation methods and their potential to minimize the bias for those four indices. Two methods are based on a best‐ vs. worst‐case extinction scenarios, one is based on the frequency distribution of threat status within a taxonomic group and one is based on correlates of extinction risks. We showed that data‐deficient species led to important bias in predictions of evolutionary history loss (especially high underestimation when they were removed). This issue was particularly important when data‐deficient species tended to be clustered in the tree of life. The imputation method based on correlates of extinction risks, especially geographic range size, had the best performance and enabled us to improve risk assessments. Solving threat status of DD species can fundamentally change our understanding of loss of phylogenetic diversity. We found that this loss could be substantially higher than previously found in amphibians, squamate reptiles, and carnivores. We also identified species that are of high priority for the conservation of evolutionary distinctiveness.
Fabrication of the metal wire-grid polarizer in visible light

The polarization characteristics of the metal wire-grid polarizer are investigated by Finite Difference Time Domain method. To achieve a high extinction ratio and transmissivity in visible light, the metal wire-grid profile was optimized. The simulation results show that the period should be less than 200 nm. When the period, the glass substrate etching depth and the Al groove depth of metal wire-grid were at 180nm, 20nm and 120nm respectively, the extinction ratio could reach the maximum 40 dB; the transmissivity could reach the maximum 85%. Herein, considering the high reflection of metal aluminum in the manufacturing process, we propose using the copper as anti-reflection coating. Finally, a prototype of the metal wire-grid polarizer has been fabricated by holographic-immersion lithography and ion beam etching.
Human Identification Method : SCLERA RECOGNITION

- The structure of blood vessels in the sclera-the white part of the human eye, is unique for every individual, hence it is best suited for human identification. However, this is a challenging research because it has a high insult rate (the number of occasions the valid user is rejected). In this survey firstly a brief introduction is presented about the sclera based biometric authentication. In addition, a literature survey is presented. We have proposed simplified method for sclera segmentation, a new method for sclera pattern enhancement based on histogram equalization and line descriptor based feature extraction and pattern matching with the help of matching score between the two segment descriptors. We attempt to increase the awareness about this topic, as much of the research is not done in this area.
A multicriteria logistics-outsourcing decision making using the analytic hierarchy process

Different management pioneers and academicians have stated that an organisation's competitive advantage stems from its ability to identify, concentrate on and develop its core competencies and activities, and outsource anything which is noncore. Outsourcing of logistics systems can contribute to profits by enabling users to maximise financial benefits, focus on core competencies, reduce risk and liability, provide wider coverage and flexible capacity, provide dedicated resources, etc. In this paper, a decision-making model for outsourcing the logistics system was developed using the Analytic Hierarchy Process (AHP). With this technique, several options are considered in the decision analysis that make it possible to adequately evaluate and determine whether outsourcing the logistics distribution may be beneficial or not for the company. The case example given in this paper proves that AHP can be used effectively to analyse the outsourcing decision for logistics distribution. It is expected to provide practitioners with the systematic analysis needed to make this important decision.
Anteater: A Service-Oriented Architecture for High-Performance Data Mining

Data mining focuses on extracting useful information from large volumes of data, and thus has been the center of much attention in recent years. Building scalable, extensible, and easy-to-use data mining systems, however, has proved to be difficult. In response, the authors developed Anteater, a service-oriented architecture for data mining that relies on Web services to achieve extensibility and interoperability, offers simple abstractions for users, and supports computationally intensive processing on large amounts of data through massive parallelism
Aberrant DNA methylation profiles in the premature aging disorders Hutchinson-Gilford Progeria and Werner syndrome

DNA methylation gradiently changes with age and is likely to be involved in aging-related processes with subsequent phenotype changes and increased susceptibility to certain diseases. The Hutchinson-Gilford Progeria (HGP) and Werner Syndrome (WS) are two premature aging diseases showing features of common natural aging early in life. Mutations in the LMNA and WRN genes were associated to disease onset; however, for a subset of patients the underlying causative mechanisms remain elusive. We aimed to evaluate the role of epigenetic alteration on premature aging diseases by performing comprehensive DNA methylation profiling of HGP and WS patients. We observed profound changes in the DNA methylation landscapes of WRN and LMNA mutant patients, which were narrowed down to a set of aging related genes and processes. Although of low overall variance, non-mutant patients revealed differential DNA methylation at distinct loci. Hence, we propose DNA methylation to have an impact on premature aging diseases.
Estrogen Receptor Status in BRCA1- and BRCA2-Related Breast Cancer

Purpose: BRCA1-related breast cancers are more frequently estrogen receptor (ER) negative than are either BRCA2-related or nonhereditary breast cancers. The relationship between ER status and other clinical features of hereditary breast cancers has not been well studied. Experimental Design: ER status, grade, and histological tumor type were evaluated in 1131 women with invasive breast cancer, ascertained at 10 centers in North America. There were 208 BRCA1 mutation carriers, 88 BRCA2 carriers, and 804 women without a known mutation. We stratified the patients by mutation status, grade, age, and histological type and calculated the percentage of ER-positive tumors within each stratum. Results: BRCA1 mutation carriers were more likely to have ER-negative breast cancers than were women in other groups, after adjustment for age, grade, and histological subtype (P < 0.001). Only 3.9% of BRCA1-related breast cancers were ER-positive cancers occurring in women in their postmenopausal years. The direction and magnitude of the change in ER status with increasing age at diagnosis in BRCA1 carriers was significantly different from in BRCA2 carriers (Pintercept = 0.0002, Pslope = 0.04). Notably, changes in ER status with age at diagnosis for BRCA1 carriers and noncarriers were almost identical (Pslope = 0.98). Conclusions: The strong relationship between the presence of a BRCA1 mutation and the ER-negative status of the breast cancers is neither a consequence of the young age at onset nor the high grade but is an intrinsic property of BRCA1-related cancers. The ER-negative status of these cancers may reflect the cell of origin of BRCA1-related cancers.
Gravitational instability and clustering in a disk of planetesimals

For a long time, gravitational instability in the disk of planetesimals has been suspected to be the main engine responsible for the beginning of dust growth, its advantage being that it provides for rapid growth. Its real importance in planetary formation is still debated, mainly because the potential presence of turbulence can prevent the settling of particles into a gravitationally unstable layer. However, several mechanisms could yield strongly inhomogeneous distributions of solids in the disk: radial drift, trapping in vortices, perturbations by other massive bodies, etc. In this paper we present a numerical study of a gravitationally unstable layer. This allows us to go beyond the classical analytical study of linear perturbations, exploring a highly non-linear regime. A hierarchical growth of structure in the presence of dissipation (gas drag) can yield large, virialized clusters of planetesimals, the first time such clusters have been observed in the context of planetesimal disks.
Visualization of Protease Activity In Vivo Using an Activatable Photo-Acoustic Imaging Probe Based on CuS Nanoparticles

Herein, we for the first time report a novel activatable photoacoustic (PA) imaging nano-probe for in vivo detection of cancer-related matrix metalloproteinases (MMPs). A black hole quencher 3 (BHQ3) which absorbs red light is conjugated to near-infrared (NIR)-absorbing copper sulfide (CuS) nanoparticles via a MMP-cleavable peptide linker. The obtained CuS-peptide-BHQ3 (CPQ) nano-probe exhibits two distinctive absorption peaks at 630 nm and 930 nm. Inside the tumor microenviorment where MMPs present, the MMP-sensitive peptide would be cleaved, releasing BHQ3 from the CuS nanoparticles, the former of which as a small molecule is then rapidly cleared out from the tumor, whereas the latter of which as large nanoparticles would retain inside the tumor for a much longer period of time. As the result, the PA signal at 680 nm which is contributed by BHQ3 would be quickly diminished while that at 930 nm would be largely retained. The PA signal ratio of 680 nm / 930 nm could thus serve as an in vivo indicator of MMPs activity inside the tumor. Our work presents a novel strategy of in vivo sensing of MMPs based on PA imaging, which should offer remarkably improved detection depth compared with traditional optical imaging techniques.
Ultraviolet‐inactivated human cytomegalovirus induces placental syncytiotrophoblast apoptosis in a Toll‐like receptor‐2 and tumour necrosis factor‐α dependent manner

Placentae obtained from newborns with congenital human cytomegalovirus (HCMV) infection often display chronic villitis and disruptions of the syncytiotrophoblast (ST). Little is known about how HCMV infection induces inflammation in the villous placenta and loss of the trophoblast. We propose that the inflammation is initiated with innate defence responses of the ST to maternal blood‐borne virus. In this paper we show with a culture model (ST derived from primary cytotrophoblasts) that UV‐inactivated HCMV (UV‐HCMV) doubled the frequency of ST apoptosis. ST cultures challenged with UV‐HCMV increased transcription and secretion of the inflammatory cytokines tumour necrosis factor α (TNFα) and interleukin‐8, and antibody to TNFα inhibited UV‐HCMV‐induced apoptosis. Treatment with cycloheximide, an inhibitor of protein translation, did not reduce UV‐HCMV‐induced TNFα gene transcription, indicating that upregulation was independent of de novo protein synthesis. Neutralizing antibody to Toll‐like receptor (TLR)2 inhibited UV‐HCMV‐induced transcription and translation of TNFα, and consequently inhibited the increase in ST apoptosis. Our results show that even transcriptionally inactive HCMV binding to TLR2 on ST can initiate inflammation, including secretion of TNFα, which leads to trophoblast death. Copyright © 2006 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
Scale-change aware locally adaptive optical flow

Optical flow is one of the key components in computer vision research area. Since the seminal work proposed by Horn and Schunck [1], numerous advanced algorithms have been proposed. Many state-of-the-art optical flow estimation algorithms optimize the data and regularization terms to solve ill-posed problems. However, despite their major advances over last decade, conventional optical flow methods utilize a single or fixed data terms without concerning scale changes in two consecutive frames of images. In this paper, we propose scale-change aware block matching data terms fused with locally adaptive models to establish dense correspondence between frames containing objects in different scales. We observed that taking scale variations into account in matching has a positive effect on optical flow accuracy.
Optical character recognition using automatically generated Fuzzy classifiers

Character recognition using Fuzzy classifiers has been showing very promising results. However, the definition of the membership functions together with the design of the classification rules is a challenging task even considering just the 10 digits and 23 characters of the Roman alphabet. In this paper we present a solution for the semi-automatic design of a Fuzzy classifier for letters and digits to be applied on the automatic recognition of cars license plates on unstructured conditions. Based on a training set of fuzzified examples of measures, taken from digital images of single characters, the CART algorithm learns the rules that regulate the design of the different characters and generates fuzzy rules that implement the fuzzy classifiers in a completely automatic way. After, a fuzzy inference engine executes the rules to obtain the characters classification. To take advantage of syntactical correction, a hierarchical classifier with two layers of classifiers is proposed: one classifier distinguishes between letters or digits; the second layer classifies either the letters or the digits. The performance achieved by the two-layer classifier is shown and discussed.
Exploring the use of linguistic features in sentiment analysis

In this paper we describe some explorations of the potential of genre-revealing features on automatic sentiment analysis. In particular, we use a small subset of the ‘linguistic facets’ employed in recent experiments on automatic genre identification in combination with more traditional sentiment-revealing features on two different single-genre corpora: a corpus of English blogs and a corpus of French reviews(relectures). Although still preliminary, results show that linguistic facets might have a positive influence on sentiment analysis because 6 out of 14 facets used in the experiments are among the first 22 most important discriminative features.
A Secure Framework for Wireless Sensor Networks

A large disadvantage to sensor networks in factory environments is the cost of running wire, which ranges from $40 to $2000 per foot. The economical advantages of wireless sensor networks is obvious, yet current security frameworks fail to address the complete threat scenarios, and focus on specific threats such as eavesdropping and unauthorized connections. Current proposals ignore the threats of stolen devices, hardware redeployment, and unauthorized hardware reproductions. This paper describes the design and implementation of a secure and power efficient sensor network, based on off-the-shelf components such as TinyOS software, and the IEEE 802.15. 4 compatible Chipcon CC2420 radio. The described protocol conserves battery life by using the hardware-based support for 128-bit AEC-CCM encryption, and all protocol sequences initiated by the sensors. The authentication system is based on four pair-wise symmetric keys. Two keys, the manufacturer and the customer keys are installed using a serial interface. The third “bootstrap” key is created and distributed over the 802.15. 4 network, allowing the sensor to obtain the session key, used for normal operation. A variation of the Needham-Schroeder protocol is used. Session key updates, multiple session keys, device heartbeats, and software upgrades are supported by the protocol. Six Sigma methodologies were used to evaluate the completeness of the threat reduction, and maximize the completeness of the overall architecture. This protocol was developed with funding from the Department of Energy, and is being proposed as a standard for manufacturing and factory sites. Keywords—Secure, wireless, sensor, network
Modeling the Evolution of User-generated Content on a Large Video Sharing Platform

Video sharing and entertainment websites have rapidly grown in popularity and now constitute some of the most visited websites on the Internet. Despite the high usage and user engagement, most of recent research on online media platforms have restricted themselves to networking based social media sites like Facebook or Twitter. The current study is among the first to perform a large-scale empirical study using longitudinal video upload data from one of the largest online video sites. Unlike previous studies in the online media space that have focused exclusively on demand-side research questions, we model the supply-side of the crowd contributed video ecosystem on this platform. The modeling and subsequent prediction of video uploads is made complicated by the heterogeneity of video types (e.g. popular vs. niche video genres), and the inherent time trend effects. We identify distinct genre-clusters from our dataset and employ a self-exciting Hawkes point-process model on each of these clusters to fully specify and estimate the video upload process. Our findings show that using a relatively parsimonious point-process model, we are able to achieve higher model fit, and predict video uploads to the platform with a higher accuracy than competing models.