Miracle-dz/UniFAR-FTUs huggingface dataset:
Divergence between the Highly Virulent Zoonotic Pathogen Helicobacter heilmannii and Its Closest Relative, the Low-Virulence “Helicobacter ailurogastricus” sp. nov

ABSTRACT Helicobacter heilmannii naturally colonizes the stomachs of dogs and cats and has been associated with gastric disorders in humans. Nine feline Helicobacter strains, classified as H. heilmannii based on ureAB and 16S rRNA gene sequences, were divided into a highly virulent and a low-virulence group. The genomes of these strains were sequenced to investigate their phylogenetic relationships, to define their gene content and diversity, and to determine if the differences in pathogenicity were associated with the presence or absence of potential virulence genes. The capacities of these helicobacters to bind to the gastric mucosa were investigated as well. Our analyses revealed that the low-virulence strains do not belong to the species H. heilmannii but to a novel, closely related species for which we propose the name Helicobacter ailurogastricus. Several homologs of H. pylori virulence factors, such as IceA1, HrgA, and jhp0562-like glycosyltransferase, are present in H. heilmannii but absent in H. ailurogastricus. Both species contain a VacA-like autotransporter, for which the passenger domain is remarkably larger in H. ailurogastricus than in H. heilmannii. In addition, H. ailurogastricus shows clear differences in binding to the gastric mucosa compared to H. heilmannii. These findings highlight the low-virulence character of this novel Helicobacter species.
Does the Combined Intervention Program Matter for College-Attending Hispanic and Other Minority Young Adults?

This evaluation study reports the effects of a combined alcohol-use and sex education intervention program on the knowledge, attitudes, and risk-taking behaviors among college-attending Hispanic and other minority young adults. A random sample of Hispanic, Black, and other racial minority college students aged 18–24 was selected to participate in an intervention study from 2014 to 2016 at a minority-serving institution (MSI) in South Texas. Results show that the combined intervention program has significantly increased minority young adults’ awareness of risks associated with unprotected sex, safe-sex negotiation skills, and HIV knowledge. Moreover, the combined intervention program has also decreased minority young adults’ past-30-day use of alcohol. These results support the assertion that a combined intervention program can serve as an important strategy to help prevent the risk of HIV/STD transmission among college attending Hispanic and racial minority young adults in South Texas.
High accuracy extend Kalman filter for posture measurement based on attitude and heading reference system

Due to the drift of gyroscope, there is lots of accumulated error in the posture measurement. The gyroscope can't complete a long time and high accurate attitude measurement independently. Therefore, the common method is the multi-sensor information fusion technology. The measurement system based on gyroscope, accelerometer and magnetometer is presented by this paper. The original data is compensated and filtered by extend Kalman filter. And the sensornoise is reduced. The Kalman state equation is established by the gyroscope data calculated by quaternion. The Kalman measurement equation is established by the output of accelerometer and magnetometer. The experiments' results show that the error of the attitude angle is effectively suppressed by this extend Kalman filter.
Recovery of Software Defined Network from Multiple Failures: Openstate Vs Openflow

Software Defined Network (SDN) is an emerging network with clearer separation between control and data planes. Like non-SDN, SDN undergoes a recovery process upon occurrence of failures. The recovery process should take less than 50 msec to comply with service level agreement of Internet Service Providers (ISPs). Currently, it is possible for SDN to meet the requirement with Openstate standard or protocol. However, the results were based on single failures only. Multiple failures may also occur and unavoidable. In this research, formulae to estimate recovery times of Openflow and Openstate are derived and proposed. It is obvious from the formulae that Openstate recovery time is better or equivalent to Openflow. Thus, we also conducted experiments to analyze the performance of Openstate with multiple failures using Mininet simulation software, in terms of three metrics: communication overhead, computation overhead of controller and packet loss. The performance of Openstate is compared to Openflow to observe how much the former is better than the later. From the simulation results, we conclude that Openstate has faster recovery time than Openflow. On the average, recovery time is 70 msec and 85 msec for Openstate and Openflow, respectively. This indicates that Openstate should be improved for faster recovery time.
Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors

OBJECTIVES To identify the most important demographic risk factors for a diagnosis of type 2 diabetes mellitus (T2DM) using a neural network model. METHODS This study was conducted on a sample of 234 individuals, in whom T2DM was diagnosed using hemoglobin A1c levels. A multilayer perceptron artificial neural network was used to identify demographic risk factors for T2DM and their importance. The DeLong method was used to compare the models by fitting in sequential steps. RESULTS Variables found to be significant at a level of p<0.2 in a univariate logistic regression analysis (age, hypertension, waist circumference, body mass index [BMI], sedentary lifestyle, smoking, vegetable consumption, family history of T2DM, stress, walking, fruit consumption, and sex) were entered into the model. After 7 stages of neural network modeling, only waist circumference (100.0%), age (78.5%), BMI (78.2%), hypertension (69.4%), stress (54.2%), smoking (49.3%), and a family history of T2DM (37.2%) were identified as predictors of the diagnosis of T2DM. CONCLUSIONS In this study, waist circumference and age were the most important predictors of T2DM. Due to the sensitivity, specificity, and accuracy of the final model, it is suggested that these variables should be used for T2DM risk assessment in screening tests.
Reviewing KLEE's Sonar-Search Strategy in Context of Greybox Fuzzing

Automatic test-case generation techniques of symbolic execution and fuzzing are the most widely used methods to discover vulnerabilities in, both, academia and industry. However, both these methods suffer from fundamental drawbacks that stop them from achieving high path coverage that may, consequently, lead to discovering vulnerabilities at the numerical scale of static analysis. In this presentation, we examine systems-under-test (SUTs) at the granularity level of functions and postulate that achieving higher function coverage (execution of functions in a program at least once) than, both, symbolic execution and fuzzing may be a necessary condition for discovering more vulnerabilities than both. We will start this presentation with the design of a targeted search strategy for KLEE, sonar-search, that prioritizes paths leading to a target function, rather than maximizing overall path coverage in the program. Then, we will show that examining SUTs at the level of functions (compositional analysis) leads to discovering more vulnerabilities than symbolic execution from a single entry point. Using this finding, we will, then, demonstrate a greybox fuzzing method that can achieve higher function coverage than symbolic execution. Finally, we will present a framework to effectively manage vulnerabilities and assess their severities.
Germline Transgenic Pigs by Sleeping Beauty Transposition in Porcine Zygotes and Targeted Integration in the Pig Genome

Genetic engineering can expand the utility of pigs for modeling human diseases, and for developing advanced therapeutic approaches. However, the inefficient production of transgenic pigs represents a technological bottleneck. Here, we assessed the hyperactive Sleeping Beauty (SB100X) transposon system for enzyme-catalyzed transgene integration into the embryonic porcine genome. The components of the transposon vector system were microinjected as circular plasmids into the cytoplasm of porcine zygotes, resulting in high frequencies of transgenic fetuses and piglets. The transgenic animals showed normal development and persistent reporter gene expression for >12 months. Molecular hallmarks of transposition were confirmed by analysis of 25 genomic insertion sites. We demonstrate germ-line transmission, segregation of individual transposons, and continued, copy number-dependent transgene expression in F1-offspring. In addition, we demonstrate target-selected gene insertion into transposon-tagged genomic loci by Cre-loxP-based cassette exchange in somatic cells followed by nuclear transfer. Transposase-catalyzed transgenesis in a large mammalian species expands the arsenal of transgenic technologies for use in domestic animals and will facilitate the development of large animal models for human diseases.
Pooling body parts on feature maps for misalignment robust person re-identification

Misalignment caused by poorly detected bounding boxes or varying poses of human bodies is a critical challenge to robust person re-identification (re-ID) systems. Most previous works extract features either from the detected entire pedestrian images or from local image patches that are globally aligned or cropped according to human body landmarks. However, these methods still suffer from misalignment ofhuman bodies in different images. In this paper, we present a Deep Joint Learning (DJL) network to fulfill misalignment robust person re-ID. It locally aligns the human bodies by pooling the features around the body parts on feature maps, and jointly optimizes the global and aligned local features to further enhance the discriminative capability oflearned feature representations. Experimental results on Market-1501 and CUHK03 datasets show that our method can effectively handle the misalignment induced intra-class variations and yield competitive accuracy particularly on poorly aligned pedestrian images.
Performance analysis of least-squares khatri-rao factorization

The least-squares Khatri-Rao factorization is regarded as an important linear- and multilinear-algebraic tool and finds applications in, for instance, computation of the CP decomposition and channel estimation for two-way relaying systems. We conduct a “first-order” perturbation analysis for it, which is a crucial step towards establishing analytical performance evaluation of various schemes employing the least-squares Khatri-Rao factorization. Numerical results validating our analytical performance analysis are shown. Being new advance in perturbation analysis on matrix decompositions, the performance analysis of the least-squares Khatri-Rao factorization presented in this paper will also contribute to a promising enhancement of the SECSI-GU framework, which is able to estimate the loading matrices in a CP decomposition, both efficiently and accurately.
Automatic Defect Identification of Eddy Current Pulsed Thermography Using Single Channel Blind Source Separation

Eddy current pulsed thermography (ECPT) is an emerging nondestructive testing and evaluation (NDT&E) technique and has been applied for a wide range of conductive materials. In this paper, a single-channel blind source separation is proposed to process the ECPT image sequences. The proposed method enables: 1) automatically extract valuable spatial and time patterns according to the whole transient response behavior without any training knowledge, 2) automatically identify defect patterns and quantify the defects, and 3) to provide guidelines of choosing the optimal contrast functions that can improve the separation results. In this paper, both mathematical and physical models are discussed and linked. The basis of the selection of separated spatial and time patterns is also presented. In addition, an artificial slot and a thermal fatigue natural crack are applied to validate the proposed method.
The smartphone as a 3D input device

This paper presents a concept for the use of a smart phone as a handheld input device for the interaction with 3D visualizations and presentations. Applications are mainly in the area of exhibitions and museums to enable visitors to interact with certain exhibits. Moreover applications for business presentations or computer games are possible. The usability of motion and position sensors in modern smart phones for the purpose of 3D navigation is examined. An algorithm to prevent from position drifting on short distances is developed. Finally a demo application presents the navigation concept and the ability for multiple users to simultaneously interact with the same visualization.
Radiological fibrosis score is strongly associated with worse survival in rheumatoid arthritis-related interstitial lung disease

Abstract Objectives: High-resolution computed tomography (HRCT) parenchymal patterns have been used to predict prognosis in patients with interstitial lung disease (ILD). In idiopathic pulmonary fibrosis, the fibrosis score (i.e. the combined extent of reticulation and honeycombing) has been associated with worse survival. This study aimed to identify HRCT patterns and patient characteristics that can predict poor prognosis in rheumatoid arthritis-related ILD (RA-ILD). Methods: We retrospectively analysed 65 patients with newly diagnosed RA-ILD from 2007 to 2016 at Kurashiki Central hospital. Using univariate and bivariate Cox regression analysis, associations with mortality, were identified. Results: During a median follow-up of 56.5 months, 16/65 (24.6%) patients died. Univariate analysis identified six significant poor prognostic factors: lower baseline % predicted forced vital capacity, total interstitial disease score, reticulation score, traction bronchiectasis score, fibrosis score, and definite UIP pattern. Fibrosis score remained to be an independently significant poor prognostic factor of survival on bivariate analysis. Patients with a fibrosis score >20% had higher mortality (HR, 9.019; 95% CI, 2.87–28.35; p < .05). Conclusion: This study showed that fibrosis score is strongly associated with worse survival in RA-ILD, and patients with fibrosis score >20% had a 9.019-fold increased risk of mortality.
The Integrated Attachment and Sexual Minority Stress Model: Understanding the Role of Adult Attachment in the Health and Well-Being of Sexual Minority Men

Gay and bisexual boys and men experience social stigma associated with their sexual minority status that can negatively influence health. In addition, experiencing sexual orientation stigma may be linked to a decreased capacity to effectively form and maintain secure attachment relationships with parents, peers, and romantic partners across the life-course. We proposed that utilizing a framework that integrates the process by which sexual minority men develop attachment relationships in the context of sexual minority stress can lead to a better understanding of health and well-being among sexual minority boys and men. In addition, we highlight where future research can expand upon the presented model in order to better understand the developmental processes through which attachment and sexual minority stress influences health and health behaviors among sexual minority boys and men.
Simulation analysis of SVPWM based on seven-phase permanent magnet synchronous motor

With regard to the pulse width modulation (PWM) of seven-phase motor system if the three-phase space vector pulse width modulation (SVPWM) is simply extended to the seven phase motor system that is the seven phase of the traditional SVPWM it will produce a great harmonic current. In order to restrain the harmonic content of phase current the voltage vector synthesized in the 3rd harmonic subspace and the 5th harmonic subspace should be zero. Based on this idea the near six vectors space vector pulse width modulation (NSV-SVPWM) algorithm is proposed and the derivation process of the algorithm is given. This paper focuses on the analysis of a novel SVPWM algorithm for seven-phase permanent magnet synchronous motor (PMSM). At the same time the mathematical model of seven-phase PMSM is deduced and the voltage space vector distribution of seven phase voltage source inverter is analyzed. The simulation model of the seven-phase PMSM vector control system is established by using the Simulink tool of MATLAB. The correctness of the algorithm is confirmed by simulation and the purpose of restraining the current harmonic is achieved.
On the effectiveness of logical device aggregation in multi-radio multi-hop networks

In this paper we investigate new features of future generation networks, where the availability of several different access technique is integrated by means of common radio resource management interfaces and, at the same time, information about the mobility context and the physical proximity of some nodes is exploited. In particular, we advocate utilizing this knowledge in order to create routing groups of adjacent nodes, which might be beneficial in order to improve connectivity, decrease signalling overhead and increase transmission efficiency. An analytical framework is proposed, which allows the performance evaluation of device aggregation algorithms, by measuring several connectivity metrics (routing hierarchy, amount of information to be exchanged, energy consumption) under the cases where either routing groups are established or not. In this way, many detailed insights for the network connectivity performance are obtained, which can be adapted to any context due to the analytical formulation. Therefore, it might be useful for the evaluation of the grouping effectiveness and its application to real cases.
Bootstrapping $Q$ -Learning for Robotics From Neuro-Evolution Results

Reinforcement learning (RL) problems are hard to solve in a robotics context as classical algorithms rely on discrete representations of actions and states, but in robotics both are continuous. A discrete set of actions and states can be defined, but it requires an expertise that may not be available, in particular in open environments. It is proposed to define a process to make a robot build its own representation for an RL algorithm. The principle is to first use a direct policy search in the sensori-motor space, i.e., with no predefined discrete sets of states nor actions, and then extract from the corresponding learning traces discrete actions and identify the relevant dimensions of the state to estimate the value function. Once this is done, the robot can apply RL: 1) to be more robust to new domains and, if required and 2) to learn faster than a direct policy search. This approach allows to take the best of both worlds: first learning in a continuous space to avoid the need of a specific representation, but at a price of a long learning process and a poor generalization, and then learning with an adapted representation to be faster and more robust.
Molecular dynamics study of bio-manipulation in aqueous media

Nano-manipulation is one of the most important aspects of nano-robotics and nano-assembly. The positioning process is considered by many researches to be one of the most important parts of nano-assembly, but has been poorly investigated, particularly for biologic samples. This Letter is devoted to modelling the process of positioning a biomolecule with atomic force microscopy (AFM) in an aqueous media using molecular dynamics simulations. Carbon nanotube (CNT) and graphite sheet are selected as AFM tip and substrate, respectively. To consider the effects of the medium on the manipulation, several models for decreasing the calculations including implicit, coarse grained, and all-atom methods have been investigated. They examined several parameters which may affect the quality of the manipulation process such as the CNT initial position with respect to the sample and substrate, its diameter and positioning strategies (pushing or pulling). The results demonstrate that despite of the implicit solvent methods, coarse-grained model can simulate the aqueous media accurately with lower computational cost. Furthermore, pulling method with a CNT which has a larger diameter and a smaller gap with respect to the substrate is the most appropriate setting for manipulation.
Bayesian state estimation in partially observable Markov processes. (Estimation bayésienne dans les modèles de Markov partiellement observés)

Cette these porte sur l'estimation bayesienne d'etat dans les series temporelles modelisees a l'aide des variables latentes hybrides, c'est-a-dire dont la densite admet une composante discrete-finie et une composante continue. Des algorithmes generaux d'estimation des variables d'etats dans les modeles de Markov partiellement observes a etats hybrides sont proposes et compares avec les methodes de Monte-Carlo sequentielles sur un plan theorique et applique. Le resultat principal est que ces algorithmes permettent de reduire significativement le cout de calcul par rapport aux methodes de Monte-Carlo sequentielles classiques
Metabolomic Assessment of Induced and Activated Chemical Defence in the Invasive Red Alga Gracilaria vermiculophylla

In comparison with terrestrial plants the mechanistic knowledge of chemical defences is poor for marine macroalgae. This restricts our understanding in the chemically mediated interactions that take place between algae and other organisms. Technical advances such as metabolomics, however, enable new approaches towards the characterisation of the chemically mediated interactions of organisms with their environment. We address defence responses in the red alga Gracilaria vermiculophylla using mass spectrometry based metabolomics in combination with bioassays. Being invasive in the north Atlantic this alga is likely to possess chemical defences according to the prediction that well-defended exotics are most likely to become successful invaders in systems dominated by generalist grazers, such as marine macroalgal communities. We investigated the effect of intense herbivore feeding and simulated herbivory by mechanical wounding of the algae. Both processes led to similar changes in the metabolic profile. Feeding experiments with the generalist isopod grazer Idotea baltica showed that mechanical wounding caused a significant increase in grazer resistance. Structure elucidation of the metabolites of which some were up-regulated more than 100 times in the wounded tissue, revealed known and novel eicosanoids as major components. Among these were prostaglandins, hydroxylated fatty acids and arachidonic acid derived conjugated lactones. Bioassays with pure metabolites showed that these eicosanoids are part of the innate defence system of macroalgae, similarly to animal systems. In accordance with an induced defence mechanism application of extracts from wounded tissue caused a significant increase in grazer resistance and the up-regulation of other pathways than in the activated defence. Thus, this study suggests that G. vermiculophylla chemically deters herbivory by two lines of defence, a rapid wound-activated process followed by a slower inducible defence. By unravelling involved pathways using metabolomics this work contributes significantly to the understanding of activated and inducible defences for marine macroalgae.