Miracle-dz/UniFAR-FTUs huggingface dataset:
The Search for Multiple Populations in Magellanic Cloud Clusters III: No evidence for Multiple Populations in the SMC cluster NGC 419

We present the third paper about our ongoing HST survey for the search for multiple stellar populations (MPs) within Magellanic Cloud clusters. We report here the analysis of NGC 419, a $\sim 1.5$ Gyr old, massive ($\gtrsim 2 \times 10^5 \, {\rm M_{\odot}}$) star cluster in the Small Magellanic Cloud (SMC). By comparing our photometric data with stellar isochrones, we set a limit on [N/Fe] enhancement of $\lesssim$+0.5 dex and hence we find that no MPs are detected in this cluster. This is surprising because, in the first two papers of this series, we found evidence for MPs in 4 other SMC clusters (NGC 121; Lindsay 1, NGC 339, NGC 416), aged from 6 Gyr up to $\sim 10-11$ Gyr. This finding raises the question whether age could play a major role in the MPs phenomenon. Additionally, our results appear to exclude mass or environment as the only key factors regulating the existence of a chemical enrichment, since all clusters studied so far in this survey are equally massive ($\sim 1-2 \times 10^5 \, {\rm M_{\odot}}$) and no particular patterns are found when looking at their spatial distribution in the SMC.
Analysis of multilayered SAW based gas sensor

In this paper, we present the comparison of single-layered and multi-layered Surface Acoustic Waves based sensor for detection of Di-Chloromethane (DCM) gas. Finite Element Analysis has been carried to detect the behaviour of sensor when exposed to DCM. Two structures PIB/LiTaO3 and PIB/Al2O3/LiTaO3 are simulated in COMSOL Multiphysics. Multi-layered SAW based sensor shows high sensitivity comparatively with resonant frequency of 1.31 GHz approximately towards DCM. Also, the effect of thickness of spacer layer and atmospheric conditions on frequency shift is studied.
CodY-Mediated c-di-GMP-Dependent Inhibition of Mammalian Cell Invasion in Listeria monocytogenes

ABSTRACT Elevated levels of the second messenger c-di-GMP suppress virulence in diverse pathogenic bacteria, yet mechanisms are poorly characterized. In the foodborne pathogen Listeria monocytogenes, high c-di-GMP levels inhibit mammalian cell invasion. Here, we show that invasion is impaired because of the decreased expression levels of internalin genes whose products are involved in invasion. We further show that at high c-di-GMP levels, the expression of the entire virulence regulon is suppressed, and so is the expression of the prfA gene encoding the master activator of the virulence regulon. Analysis of mechanisms controlling prfA expression pointed to the transcription factor CodY as a c-di-GMP-sensitive component. In high-c-di-GMP strains, codY gene expression is decreased, apparently due to the lower activity of CodY, which functions as an activator of codY transcription. We found that listerial CodY does not bind c-di-GMP in vitro and therefore investigated whether c-di-GMP levels affect two known cofactors of listerial CodY, branched-chain amino acids and GTP. Our manipulation of branched-chain amino acid levels did not perturb the c-di-GMP effect; however, our replacement of listerial CodY with the streptococcal CodY homolog, whose activity is GTP independent, abolished the c-di-GMP effect. The results of this study suggest that elevated c-di-GMP levels decrease the activity of the coordinator of metabolism and virulence, CodY, possibly via lower GTP levels, and that decreased CodY activity suppresses L. monocytogenes virulence by the decreased expression of the PrfA virulence regulon. IMPORTANCE Listeria monocytogenes is a pathogen causing listeriosis, a disease responsible for the highest mortality rate among foodborne diseases. Understanding how the virulence of this pathogen is regulated is important for developing treatments to decrease the frequency of listerial infections in susceptible populations. In this study, we describe the mechanism through which elevated levels of the second messenger c-di-GMP inhibit listerial invasion in mammalian cells. Inhibition is caused by the decreased activity of the transcription factor CodY that coordinates metabolism and virulence.
Modifiable Risk Factors Versus Age on Developing High Predicted Cardiovascular Disease Risk in Blacks

Background Clinical guidelines recommend using predicted atherosclerotic cardiovascular disease (ASCVD) risk to inform treatment decisions. The objective was to compare the contribution of changes in modifiable risk factors versus aging to the development of high 10‐year predicted ASCVD risk. Methods and Results A prospective follow‐up was done of the Jackson Heart Study, an exclusively black cohort at visit 1 (2000–2004) and visit 3 (2009–2012). Analyses included 1115 black participants without high 10‐year predicted ASCVD risk (<7.5%), hypertension, diabetes mellitus, or ASCVD at visit 1. We used the Pooled Cohort equations to calculate the incidence of high (≥7.5%) 10‐year predicted ASCVD risk at visit 3. We recalculated the percentage with high 10‐year predicted ASCVD risk at visit 3 assuming each risk factor (age, systolic blood pressure, antihypertensive medication use, diabetes mellitus, smoking, total and high‐density lipoprotein cholesterol), one at a time, did not change from visit 1. The mean age at visit 1 was 45.2±9.5 years. Overall, 30.9% (95% CI 28.3–33.4%) of participants developed high 10‐year predicted ASCVD risk. Aging accounted for 59.7% (95% CI 54.2–65.1%) of the development of high 10‐year predicted ASCVD risk compared with 32.8% (95% CI 27.0–38.2%) for increases in systolic blood pressure or antihypertensive medication initiation and 12.8% (95% CI 9.6–16.5%) for incident diabetes mellitus. Among participants <50 years, the contribution of increases in systolic blood pressure or antihypertensive medication initiation was similar to aging. Conclusions Increases in systolic blood pressure and antihypertensive medication initiation are major contributors to the development of high 10‐year predicted ASCVD risk in blacks, particularly among younger adults.
The Sectoral Adaptive Capacity Index of Hungarian Road Transport

The understanding of different adaptive capacities is a prerequisite for targeting interventions aiming to reduce the adverse effects of climate change. Indicators and indices are common tools in this process, but their construction is fraught with many uncertainties. This paper aims to investigate the application of the Adaptive Capacity Index applied to the road transport sector. Authors sought to describe the effects of climate change on road transport and have modeled its adaptive capacity as a probability function of a combination of variables. The authors have designed and created the Sectoral Adaptive Capacity Index for the Hungarian transport sector, and investigated the trends in the historical data series of this indicator.
Approximate computation of multidimensional aggregates of sparse data using wavelets

Computing multidimensional aggregates in high dimensions is a performance bottleneck for many OLAP applications. Obtaining the exact answer to an aggregation query can be prohibitively expensive in terms of time and/or storage space in a data warehouse environment. It is advantageous to have fast, approximate answers to OLAP aggregation queries. In this paper, we present a novel method that provides approximate answers to high-dimensional OLAP aggregation queries in massive sparse data sets in a time-efficient and space-efficient manner. We construct a compact data cube, which is an approximate and space-efficient representation of the underlying multidimensional array, based upon a multiresolution wavelet decomposition. In the on-line phase, each aggregation query can generally be answered using the compact data cube in one I/O or a smalll number of I/Os, depending upon the desired accuracy. We present two I/O-efficient algorithms to construct the compact data cube for the important case of sparse high-dimensional arrays, which often arise in practice. The traditional histogram methods are infeasible for the massive high-dimensional data sets in OLAP applications. Previously developed wavelet techniques are efficient only for dense data. Our on-line query processing algorithm is very fast and capable of refining answers as the user demands more accuracy. Experiments on real data show that our method provides significantly more accurate results for typical OLAP aggregation queries than other efficient approximation techniques such as random sampling.
Multi-Engine Packet Classification Hardware Accelerator

As line rates increase, the task of designing high performance architectures with reduced power consumption for the processing of router traffic remains important. In this paper, we present a multi-engine packet classification hardware accelerator, which gives increased performance and reduced power consumption. It follows the basic idea of decision-tree based packet classification algorithms, such as HiCuts and HyperCuts, in which the hyperspace represented by the ruleset is recursively divided into smaller subspaces according to some heuristics. Each classification engine consists of a Trie Traverser which is responsible for finding the leaf node corresponding to the incoming packet, and a Leaf Node Searcher that reports the matching rule in the leaf node. The packet classification engine utilizes the possibility of ultra-wide memory word provided by FPGA block RAM to store the decision tree data structure, in an attempt to reduce the number of memory accesses needed for the classification. Since the clock rate of an individual engine cannot catch up to that of the internal memory, multiple classification engines are used to increase the throughput. The implementations in two different FPGAs show that this architecture can reach a searching speed of 169 million packets per second (mpps) with synthesized ACL, FW and IPC rulesets. Further analysis reveals that compared to state of the art TCAM solutions, a power savings of up to 72% and an increase in throughput of up to 27% can be achieved.
Composition control of alloy coatings and composition design of cathode targets in multi-arc ion plating

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Sequence Learning with Passive RFID Sensors for Real-Time Bed-Egress Recognition in Older People

Getting out of bed and ambulating without supervision is identified as one of the major causes of patient falls in hospitals and nursing homes. Therefore, increased supervision is proposed as a key strategy toward falls prevention. An emerging generation of batteryless, lightweight, and wearable sensors are creating new possibilities for ambulatory monitoring, where the unobtrusive nature of such sensors makes them particularly adapted for monitoring older people. In this study, we investigate the use of a batteryless radio-frequency identification (RFID) tag response to analyze bed-egress movements. We propose a bed-egress movement detection framework that includes a novel sequence learning classifier with a set of features derived from bed-egress motion analysis. We analyzed data from 14 healthy older people (66–86 years old) who wore a wearable embodiment of a batteryless accelerometer integrated RFID sensor platform loosely attached over their clothes at sternum level, and undertook a series of activities including bed-egress in two clinical room settings. The promising results indicate the efficacy of our batteryless bed-egress monitoring framework.
Radiation vulcanization of thermoplastic low-density polyethylene/bromobutyl rubber blends

Blends of thermoplastic low-density polyethylene (LDPE) with bromobutyl rubber (BIIR) with varying ratios have been prepared. Vulcanization of prepared blends has been induced by gamma ionizing radiation of varying doses of up to 250 kGy. Physical properties, namely gel fraction percentage, and mechanical properties, namely tensile strength, tensile modulus at 50% elongation, percentage elongation at break, hardness and permanent set have been followed up as a function of irradiation dose and blend compositions. Moreover, measurements of thermogravimetric analysis and heat shrinking properties have been carried out. The results indicated that blending with LDPE has improved the properties of BIIR rubber. Moreover, the presence of BIIR enhances the heat shrinking properties of the obtained blends.
Outpatient prescriptions for atypical antipsychotics for African Americans, Hispanics, and whites in the United States.

BACKGROUND New antipsychotic medications introduced during the past decade-clozapine (1990), risperidone (1994), olanzapine (1996), and quetiapine fumarate (1997)-offer a decrease in serious adverse effects compared with traditional antipsychotic medications, but at up to 10 times the cost. We examined whether ethnic minorities achieve access to these new advanced treatments. METHODS Using national data on physician office and hospital outpatient department visits from the National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey from 1992 through 2000, we selected all patient visits at which an antipsychotic medication (atypical or traditional) was prescribed or continued and the patient was aged between 18 and 69 years. We performed a series of cross-sectional logistic regression analyses to determine the association of ethnic group and receipt of an atypical antipsychotic prescription over time, adjusted for potential confounders such as age, diagnosis, and health insurance type. RESULTS Antipsychotic medication was prescribed or continued in 5032 visits; 33% of overall visits involved an atypical antipsychotic prescription. During 1992 to 1994, the adjusted relative odds of receipt of an atypical antipsychotic prescription for African Americans was 0.50 (95% confidence interval [CI], 0.26-0.96) and for Hispanics was 0.43 (95% CI, 0.16-1.18) compared with whites. During 1995 to 1997, the odds of receipt of a prescription for atypical antipsychotics increased for African Americans (odds ratio [OR], 0.69; 95% CI, 0.54-0.85) and for Hispanics (OR, 0.84; 95% CI, 0.65-1.07) compared with whites; and during 1998 to 2000, the relative odds continued to increase for African Americans (OR, 0.88; 95% CI, 0.78-0.97) and for Hispanics (OR, 1.05; 95% CI, 0.92-1.16) compared with whites. For visits specified for psychotic disorders, receipt of atypical antipsychotics was still lower for African Americans by 1998 to 2000 (adjusted OR, 0.74; 95% CI, 0.61-0.89) compared with whites, while for Hispanics the relative odds was equivalent (adjusted OR, 1.05; 95% CI, 0.87-1.19). CONCLUSION Early gaps between ethnic groups in receipt of atypical antipsychotic prescriptions decreased throughout the 1990s but persisted for African Americans with psychotic disorders.
Detecting Review Spammer Groups

With an increasing number of paid writers posting fake reviews to promote or demote some target entities through Internet, review spammer detection has become a crucial and challenging task. In this paper, we propose a three-phase method to address the problem of identifying review spammer groups and individual spammers, who get paid for posting fake comments. We evaluate the effectiveness and performance of the approach on a real-life online shopping review dataset from amazon.com. The experimental result shows that our model achieved comparable or better performance than previous work on spammer detection.
Hybrid Attention Networks for Chinese Short Text Classification

To improve the classiﬁcation performance for Chinese short text with automatic semantic feature selection, in this paper we propose the Hybrid Attention Networks (HANs) which combines the word- and character-level selective attentions. The model ﬁrstly applies RNN and CNN to extract the semantic features of texts. Then it captures class-related attentive representation from word- and character-level features. Finally, all of the features are concatenated and fed into the output layer for classiﬁcation. Experimental results on 32-class and 5-class datasets show that, our model outperforms multiple baselines by combining not only the word- and character-level features of the texts, but also class-related semantic features by attentive mechanism.
Fuzzy vector implementation on manifold embedding for head pose estimation with degraded images using fuzzy nearest distance

Advancement of image acquisition and processing technology have triggered the development of 3D face recognition and, along with it, the head poses estimation. The problem arises when image degradation occurred thus reducing the capability of the system to analyze the image. We seek to minimize the problem by constructing a system that handles imprecision data with no significant problem. This paper introduces an alternative approach on manifold embedding head pose estimation on 3D space with 2D intensity image. We employ fuzzy vector used to make the system works with imprecision data thus minimize the negative effect coming from noise and image degradation. On the training set, crisp vector representation of images on specific pose will be transformed to its fuzzy vector representation using a specific triangle fuzzification method. Then, a linear interpolation will be used to construct a manifold, adding data points to improve the precision of pose estimation. In the testing phase, we transform every unknown data image to its fuzzy-vector representation using the parameter we obtained from training phase. We then project the unknown fuzzy vector to the manifolds using a technique called fuzzy nearest distance. The output will be the fuzzy points that mostly represent the unknown fuzzy vector given. This system is applied to recognize pose on images from our database which some of them are influenced by noises. Experimental poses range widely from -90° to 90° horizontally and 0° to 70° vertically. The experimental result shows that the system can correctly recognize horizontal poses with 44.4% success rate and vertical poses with 49.4% success rate.
Person Re-Identification by Discriminative Selection in Video Ranking

Current person re-identification (ReID) methods typically rely on single-frame imagery features, whilst ignoring space-time information from image sequences often available in the practical surveillance scenarios. Single-frame (single-shot) based visual appearance matching is inherently limited for person ReID in public spaces due to the challenging visual ambiguity and uncertainty arising from non-overlapping camera views where viewing condition changes can cause significant people appearance variations. In this work, we present a novel model to automatically select the most discriminative video fragments from noisy/incomplete image sequences of people from which reliable space-time and appearance features can be computed, whilst simultaneously learning a video ranking function for person ReID. Using the PRID2011, iLIDS-VID, and HDA+ image sequence datasets, we extensively conducted comparative evaluations to demonstrate the advantages of the proposed model over contemporary gait recognition, holistic image sequence matching and state-of-the-art single-/multi-shot ReID methods.
Language usage on Twitter predicts crime rates

Social networks 1 produce enormous quantity of data. Twitter, a microblogging network, consists of over 230 million active users posting over 500 million tweets every day. We propose to analyze public data from Twitter to predict crime rates. Crime rates have increased in the past recent years. Although crime stoppers are utilizing various technics to reduce crime rates, none of the previous approaches targeted utilizing the language usage (offensive vs. non-offensive) in Tweets as a source of information to predict crime rates. In this paper, we hypothesize that analyzing the language usage in tweets is a valid measure to predict crime rates in cities. Tweets were collected for a period of 3 months in the Houston and New York City by locking the collection by geographic longitude and latitude. Further, tweets regarding crime events in the two cities were collected for verification of the validity of the prediction algorithm. We utilized Support Vector Machine (SVM) classifier to create a model of prediction of crime rates based on tweets. Finally, we report the validity of prediction algorithm in predicting crime rates in cities.
The gastrointestinal microbiome and its association with the control of pathogens in broiler chicken production: A review

Abstract The microbiome of the broiler chicken gastrointestinal tract (GIT) has been extensively studied, and it has been amply demonstrated that it plays an important role in the health of the host, as it has a positive impact on the immune system, the physiology of the GIT, and productivity. Also, the microbiota is involved in reducing and preventing colonization by enteric pathogens through the process of competitive exclusion and the production of bacteriostatic and bactericidal substances. The taxonomic composition of the microbiota is affected by different factors, such as the organ, the age of the animal, diet and the use of antimicrobials. Different kinds of additives that regulate the microbial community in feed include probiotics (live microorganisms that when administered in adequate amounts confer a health benefit on the host), prebiotics (ingredients that stimulate increased beneficial microbial activity in the digestive system in order to improve the health of the host) and phytobiotics (primary or secondary components of plants that contain bioactive compounds that exert a positive effect on the growth and health of animals). Phages may potentially provide an integrated solution to modulate the intestinal microbiome of chicken intestines, as they reduce specific pathogenic microbial populations, permitting the proliferation of beneficial microbiota. Studies have shown that the use of cocktails of phages, especially in high concentrations and with short lapses of time between exposure to the bacteria and treatment with phages, optimize the reduction of Salmonella in chickens. Each of these technologies has demonstrable positive effects on the health of the host and the reduction of the pathogen load in controlled assays. This paper presents a comprehensive summary of the role of the microbiota in the broiler chicken gastrointestinal tract, and discusses the usefulness of different strategies for its modulation to control pathogens, with a particular emphasis on bacteriophages.
Learning Sparse Latent Representations with the Deep Copula Information Bottleneck

Deep latent variable models are powerful tools for representation learning. In this paper, we adopt the deep information bottleneck model, identify its shortcomings and propose a model that circumvents them. To this end, we apply a copula transformation which, by restoring the invariance properties of the information bottleneck method, leads to disentanglement of the features in the latent space. Building on that, we show how this transformation translates to sparsity of the latent space in the new model. We evaluate our method on artificial and real data.