Miracle-dz/UniFAR-FTUs huggingface dataset:
Diverse Genetic Markers Concordantly Identify Bovine Origin Escherichia coli O157 Genotypes Underrepresented in Human Disease

ABSTRACT Genetic markers previously reported to occur at significantly different frequencies in isolates of Escherichia coli O157:H7 obtained from cattle and from clinically affected humans concordantly delineate at least five genetic groups. Isolates in three of these groups consistently carry one or more markers rarely found among clinical isolates.
Microbial Dynamics during Industrial Rearing, Processing, and Storage of Tropical House Crickets (Gryllodes sigillatus) for Human Consumption

ABSTRACT In this study, the microbiota during industrial rearing, processing, and storage of the edible tropical house cricket, Gryllodes sigillatus, was investigated. To this end, we analyzed samples from the cricket feed, obtained before feeding as well as from the cages, and from the crickets during rearing, after harvest, and after processing into frozen, oven-dried, and smoked and oven-dried (smoked/dried) end products. Although the feed contained lower microbial numbers than the crickets, both were dominated by the same species-level operational taxonomic units, as determined by Illumina MiSeq sequencing. They corresponded, among others, to members of Porphyromonadaceae, Fusobacterium, Parabacteroides, and Erwinia. The harvested crickets contained high microbial numbers, but none of the investigated food pathogens Salmonella spp. , Listeria monocytogenes, Bacillus cereus, or coagulase-positive staphylococci. However, some possible mycotoxin-producing fungi were isolated from the crickets. A postharvest heat treatment, shortly boiling the crickets, reduced microbial numbers, but an endospore load of 2.4 log CFU/g remained. After processing, an increase in microbial counts was observed for the dried and smoked/dried crickets. Additionally, in the smoked/dried crickets, a high abundance of a Bacillus sp. was observed. Considering the possible occurrence of food-pathogenic species from this genus, it is advised to apply a heat treatment which is sufficient to eliminate spores. Nevertheless, the microbial numbers remained constant over a 6-month storage period, whether frozen (frozen end product) or at ambient temperature (oven-dried and smoked/dried end products). IMPORTANCE The need for sustainable protein sources has led to the emergence of a new food sector, producing and processing edible insects into foods. However, insight into the microbial quality of this new food and into the microbial dynamics during rearing, processing, and storage of edible insects is still limited. Samples monitored for their microbiota were obtained in this study from an industrial rearing and processing cycle. The results lead first to the identification of process steps which are critical for microbial food safety. Second, they can be used in the construction of a Hazard Analysis and Critical Control Points (HACCP) plan and of a Novel Food dossier, which is required in Europe for edible insects. Finally, they confirm the shelf-life period which was determined by the rearer.
Valuing the Effects of Air and Noise Pollution on Health Status in Turkey

This study explores the determinants of health status in Turkey. Moreover, this study explores the willingness to pay for reducing the air and noise pollution. The estimates are based on data from the annual Income and Living Conditions Survey (ILCS) in Turkey which took place in period 2006-2012. The effects of air and noise pollution on individuals’ health status and whether an individual suffers from chronic illness are estimated and their monetary value is calculated. This is the first study which examines the effects of noise and air pollution in Turkey using a great variety of econometric models as ordered Logit and binary Logit models for cross sectional data. Moreover using a pseudo panel data created based on age and region cohorts various panel data econometric approaches are followed. Regarding the health status the first model is the adapted Probit fixed effects, the “Blow-Up and Cluster” (BUC) and Ferrer-i-Carbonell and Frijters (FCF) estimators to account for intercept heterogeneity. The second approach is the Random Effects Generalized Logit Model to account for slope heterogeneity. Finally, two and three stage least squares instrumental variables approaches are followed using wind direction and regional complaint rates on air and noise pollution as instruments. Income and education are the most important determinants of health status. Based on the favoured estimates individuals who reported problems with air and noise pollution are willing to pay for air and noise quality improvement more by 20.00-21.00 Turkish Liras (TL) and 22.80-25.00 TL respectively than the individuals than did not report any complaint. Finally, the MWTP values of air and noise pollution effects on wages, working hours lost, house rents and expenses and moving dwelling are calculated.
Watershed-based segmentation of cell nuclei boundaries in Pap smear images

In this work we present a fully automated method for the accurate detection of cell nuclei boundaries in conventional Pap smear images, based on the watershed transform. For the extraction of nuclei and cytoplasm markers, which are used as starting points for the flooding process, a morphological reconstruction step is initially performed in each image. The watershed transform is then applied in the color morphological gradient image, which shows the boundaries of the more pronounced nuclei. For the elimination of false positive findings, salient features of shape and intensity of the detected regions were calculated and a clustering step is performed. The method was evaluated with a data set of 19 images containing 3616 recognized cells nuclei. The performance of the method was evaluated in terms of the correct detection of the positions of the nuclei. Comparisons with the segmentation results of the gradient vector flow (GVF) deformable model showed that the segmentation of the watershed transform captures more accurately the boundaries of nuclei, leading to a better performance of the clustering algorithm.
High scoring segment selection for pairwise whole genome sequence alignment with the maximum scoring subsequence and GPUs

Whole genome alignment programs use string matching with hash tables to identify high scoring fragments between a query and target sequence around which a full alignment is then built. A recent study comparing alignment programs showed that while evolutionary similar genomes were easy to align, divergent genomes still posed a challenge to existing methods. To fill this gap we explore the use of the maximum scoring subsequence to identify high scoring fragments. We split the query genome into several fragments and align them to the target with a previously published parallel algorithm for short read alignment. We then pass such high scoring fragments on to the LASTZ program to obtain a more complete alignment. On simulated data we obtain an average of at least 20% higher accuracy than the alignment given by LASTZ at the expense of few hours of additional runtime. Our source code is freely available at http://web.njit. edu/usman/MSGA
Game Theoretic Analysis of Distributed Certification Authority in Mobile Ad Hoc Networks

Certification authority (CA) in mobile ad hoc networks is implemented in a distributed fashion using threshold cryptography. There are two kinds of distributed certification authority according to the variableness of CA nodes. In this paper, we analyze whether the distributed certification schemes operate solidly when the nodes in ad hoc networks are selfish. We model the schemes using various games and find Nash equilibria. Our findings are that the CA nodes should be changed continually and a proper reputation system is needed for the stable certification service.
Are Linked Datasets fit for Open-domain Question Answering? A Quality Assessment

The current decade is a witness to an enormous explosion of data being published on the Web as Linked Data to maximise its reusability. Answering questions that users speak or write in natural language is an increasingly popular application scenario for Web Data, especially when the domain of the questions is not limited to a domain where dedicated curated datasets exist, like in medicine. The increasing use of Web Data in this and other settings has highlighted the importance of assessing its quality. While quite some work has been done with regard to assessing the quality of Linked Data, only few efforts have been dedicated to quality assessment of linked data from the question answering domain's perspective. From the linked data quality metrics that have so far been well documented in the literature, we have identified those that are most relevant for QA. We apply these quality metrics, implemented in the Luzzu framework, to subsets of two datasets of crucial importance to open domain QA -- DBpedia and Wikidata -- and thus present the first assessment of the quality of these datasets for QA. From these datasets, we assess slices covering the specific domains of restaurants, politicians, films and soccer players. The results of our experiments suggest that for most of these domains, the quality of Wikidata with regard to the majority of relevant metrics is higher than that of DBpedia.
Correlating Events for Monitoring Business Processes

With the increasing demand for real-time information on critical performance indicators of business processes, the capturing, transformation and correlation of real-world events with minimal latency are a prerequisite for improving the speed and effectiveness of an organization's business operations. Events often include key business information about their relationship to other events that can be utilized to collect relevant event data for the calculation of business performance indicators. In this paper we introduce an approach for correlating events of business processes that uses correlation sessions to represent correlation knowledge. Correlation sessions facilitate the processing of data across multiple events and thereby enable a calculating of business metrics in near real-time. The benefit over existing approaches is that it is tailored to instrument business processes and business applications that may operate in a heterogeneous software environment. We propose a Java-based, container-managed environment which provides a distributed, scalable, near-real time processing of events and which includes a correlation service that effectively manages correlation sessions. We also show a complete example that illustrates how correlation sessions can be utilized for computing the cycle time of business processes.
An FPGA implementation of Kak's instantaneously-trained, Fast-Classification neural networks

Motivated by a biologically plausible short-memory sketchpad, Kak's Fast Classification (FC) neural networks are instantaneously trained by using a prescriptive training scheme. Both weights and the topology for an FC network are specified with only two presentations of the training samples. Compared with iterative learning algorithms such as Backpropagation (which may require many thousands of presentations of the training data), the training of FC networks is extremely fast and learning convergence is always guaranteed. Thus FC networks are suitable for applications where real-time classification and adaptive filtering are needed. In this paper we show that FC networks are "hardware friendly" for implementation on FPGAs. Their unique prescriptive learning scheme can be integrated with the hardware design of the FC network through parameterization and compile-time constant folding.
Principal depth estimation using cost-volume filtering for fast stereo matching

This paper presents an effective method to reduce computational cost of Cost-volume filtering for stereo matching. We first obtain initial depth values by using pixel-wise cost volume and estimate principal depth values for reducing computational cost. The proposed method can be applied to advanced driver assistance system, object recognition, and other algorithms that require 3-D depth information.
Analyzing Web Chat Messages for Recommending Items from a Digital Library

This work presents a recommender system that analyzes textual messages sent during a communication session in a private Web chat, identifies the context of each message and recommends items from a Digital Library. Recommendations are directly made to users in the chat screen and are decided by a software system through a proactive paradigm, without any request of the users. A domain ontology, containing concepts and a controlled vocabulary, is used to identify subjects in textual messages and to automatically classify items of the Digital Library.
Disease activity in axial spondyloarthritis after discontinuation of TNF inhibitors therapy

Objective Use of tumour necrosis factor inhibitors (TNFi) has proved to be an important step forward in the treatment of axial spondyloarthritis (axSpA), but the duration of the therapy as well as the management in case of low disease activity (LDA) or remission are not clearly established. Currently, the identification of potential predictors associated with the treatment discontinuation is the basic purpose of many clinical studies. The aim of this study was to analyze the influence of the discontinuation of TNFi therapy on the disease activity in patients with low disease activity. Material and methods The study included 65 patients; 47 of patients (72%) were treated with etanercept, 16 (2%) with adalimumab and 2 (3%) with infliximab. Results The mean age of the patients was 45 years, the mean BASDAI score was 6.8 and VAS for low back pain was 76 mm at baseline. 54 patients with axSpA (83%) achieved LDA after 9 months of anti-TNF therapy. During follow-up 40 patients (74% of patients with LDA) had an increase of the disease activity after mean 14 weeks and needed to restart the treatment with TNFi. After restart of the therapy LDA was regained in all patients after mean 7 weeks. 11 patients (17%) have never achieved LDA and 14 patients (22%) had LDA longer than 6 months without relapse. At baseline higher levels of CRP and ESR were observed in patients with relapse of the disease at the end of treatment and with LDA shorter than 6 months. Conclusions Changes in the values of disease activity indicators (CRP, ESR) correlated with more stable response to TNFi therapy. Over 50% of patients who were treated with TNFi needed to restart the therapy. Treatment resumption allowed to regain a good clinical effect among affected patients.
The Role of Association in Early Word-Learning

Word-learning likely involves a multiplicity of components, some domain-general, others domain-specific. Against the background of recent studies that suggest that word-learning is domain-specific, we investigated the associative component of word-learning. Seven- and 14-month-old infants viewed a pair of events in which a monkey or a truck moved back and forth, accompanied by a sung syllable or a tone, matched for pitch. Following habituation, infants were presented with displays in which the visual-auditory pairings were preserved or switched, and looked longer at the “switch” events when exposure time was sufficient to learn the intermodal association. At 7 months, performance on speech and tones conditions was statistically identical; at 14 months, infants had begun to favor speech. Thus, the associative component of word-learning does not appear (in contrast to rule-learning, Marcus et al., 2007) to initially privilege speech.
An Effective Automatic Image Annotation Model Via Attention Model and Data Equilibrium

Nowadays, a huge number of images are available. However, retrieving a required image for an ordinary user is a challenging task in computer vision systems. During the past two decades, many types of research have been introduced to improve the performance of the automatic annotation of images, which are traditionally focused on content-based image retrieval. Although, recent research demonstrates that there is a semantic gap between content-based image retrieval and image semantics understandable by humans. As a result, existing research in this area has caused to bridge the semantic gap between low-level image features and high-level semantics. The conventional method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, we propose a novel AIA model based on the deep learning feature extraction method. The proposed model has three phases, including a feature extractor, a tag generator, and an image annotator. First, the proposed model extracts automatically the high and low-level features based on dual tree continues wavelet transform (DT-CWT), singular value decomposition, distribution of color ton, and the deep neural network. Moreover, the tag generator balances the dictionary of the annotated keywords by a new log-entropy auto-encoder (LEAE) and then describes these keywords by word embedding. Finally, the annotator works based on the long-short-term memory (LSTM) network in order to obtain the importance degree of specific features of the image. The experiments conducted on two benchmark datasets confirm that the superiority of proposed model compared to the previous models in terms of performance criteria.
Heart Disease Prediction Using Hybrid Genetic Fuzzy Model

The objective of the work is to diagnose heart disease using computing techniques like genetic algorithm and fuzzy logic. The system would help the doctors to automate heart disease diagnosis and to enhance the medical care. In this paper a hybrid genetic-fuzzy heart disease diagnosis system is designed. The genetic algorithm is used for a stochastic search that provides the optimal solution to the feature selection problem. The relevant features selected from the dataset help the diagnosing system to develop a classification model using fuzzy inference system. The rules for the fuzzy system are generated from the sample data. Among the entire rule set the important and relevant subset of rules are selected using genetic algorithm. The proposed work uses the benefits of genetic algorithms and fuzzy inference system for effective prediction of heart disease in patients. The selected features are sex, serum cholesterol (chol), maximum heart rate achieved (thalach), Exercise induced angina (exang), ST depression induced by exercise relative to rest (oldpeak), number of major vessels coloured (ca) and thal value. Fuzzification using Fuzzy Gaussian membership function and defuzzification using centroid method improves the performance of the system. The work has been evaluated using the performance metrics like accuracy, specificity, sensitivity, confusion matrix that help in proving the efficiency of the work. The obtained classification accuracy is 86% using the stratified k fold technique with the values for specificity and sensitivity as . 90 and . 80 respectively. The number of attributes has been reduced from 13 to 7 from heart disease dataset available in the UCI Machine learning repository. When compared with the existing system the accuracy of the proposed work has been increased by 1.54%. The proposed model is named as GAFL model called Genetic Algorithm Fuzzy Logic model for effective heart disease prediction. It is easy to build the model thereby providing an easy option to be used in hospitals and medical centers for the aid of the physicians.
MINING SINGLE NUCLEOTIDE POLYMORPHISM FROM PUBLICLY AVAILABLE ESTS OF BREAD WHEAT (TRITICUM AESTIVUM L.)

Objective: The present study was undertaken to discover Single Nucleotide Polymorphisms (SNPs) in bread wheat with reference to leaf rust disease. Methods: Next Generation Sequencing platform sequencing by Oligonucleotide Ligation and Detection (SOLiD) was performed on four Serial Analysis of Gene Expression (SAGE) libraries of mock and leaf rust pathogen infected near-isogenic lines HD2329±Lr28. CLC Genomics Workbench was used for computational prediction of the SNPs. The predicted SNPs were filtered by Blast using wheat Expressed Sequence Tags (ESTs). The SNP-containing ESTs were annotated, and their expression was checked in response to inoculation of Puccinia triticina. Results: We have identified 191 SNPs from data obtained through the These EST-SNPs participated in various physiological and biochemical processes that influence important traits, such as cell rescue, defense and disease resistance. Conclusion: Very little knowledge exists on SNPs in hexaploid bread wheat ( Triticum aestivum L.) because of the difficulty to discern the true polymorphic loci. This study has revealed fast and costs effective approach for SNP discovery which will be helpful in molecular breeding with important agronomic traits.
Query Bootstrapping: A Visual Mining Based Query Expansion

Bag of Visual Words (BoVW) is an effective framework for image retrieval. Query expansion (QE) further boosts retrieval performance by refining a query with relevant visual words found from the geometric consistency check between the query image and highly ranked retrieved images obtained from the first round of retrieval. Since QE checks the pairwise consistency between query and highly ranked images, its performance may deteriorate when there are slight degradations in the query image. We propose Query Bootstrapping as a variant of QE to circumvent this problem by using the consistency of highly ranked images instead of pairwise consistency. In so doing, we regard frequently co-occurring visual words in highly ranked images as relevant visual words. Frequent itemset mining (FIM) is used to find such visual words efficiently. However, the FIM-based approach requires sensitive parameters to be fine-tuned, namely, support (min/max-support) and the number of top ranked images (top-k). Here, we propose an adaptive support algorithm that adaptively determines both the minimum support and maximum support by referring to the first round’s retrieval list. Selecting relevant images by using a geometric consistency check further boosts retrieval performance by reducing outlier images from a mining process. An important parameter for the LO-RANSAC algorithm that is used for the geometric consistency check, namely, inlier threshold, is automatically determined by our algorithm. We further introduce tf-fi-idf on top of tf-idf in order to take into account the frequency of inliers (fi) in the retrieved images. We evaluated the performance of QB in terms of mean average precision (mAP) on three benchmark datasets and found that it gave significant performance boosts of 5.37%, 9.65%, and 8.52% over that of state-of-the-art QE on Oxford 5k, Oxford 105k, and Paris 6k, respectively. key words: image retrieval, instance search, query expansion, frequent itemset mining, visual word mining, query bootstrapping, adaptive support, adaptive inlier threshold
Building workflows definitions based on business cases

The maturity of infrastructures that support e-services allows organizations to incorporate Web services as part of their business processes. One prominent solution to manage, coordinate, and orchestrate Web services is the use of workflow technology. While workflow management systems architectures, language specifications, and workflow analysis techniques have been extensively studied there is a lack of tools and methods to assist process development. The purpose of our study is to present a framework to assist process analysts and designers in their task, allowing the creation of processes (Web processes and workflows) with a higher quality. The framework structures a comprehensive set of steps that drives the analysis and design of processes based on requirements gathered from communication with managers and experts.