Miracle-dz/UniFAR-FTUs huggingface dataset:
Clostridium difficile ribotypes in humans and animals in Brazil

Clostridium difficile is an emerging enteropathogen responsible for pseudomembranous colitis in humans and diarrhoea in several domestic and wild animal species. Despite its known importance, there are few studies aboutC. difficile polymerase chain reaction (PCR) ribotypes in Brazil and the actual knowledge is restricted to studies on human isolates. The aim of the study was therefore to compare C. difficileribotypes isolated from humans and animals in Brazil. Seventy-six C. difficile strains isolated from humans (n = 25), dogs (n = 23), piglets (n = 12), foals (n = 7), calves (n = 7), one cat, and one manned wolf were distributed into 24 different PCR ribotypes. Among toxigenic strains, PCR ribotypes 014/020 and 106 were the most common, accounting for 14 (18.4%) and eight (10.5%) samples, respectively. Fourteen different PCR ribotypes were detected among human isolates, nine of them have also been identified in at least one animal species. PCR ribotype 027 was not detected, whereas 078 were found only in foals. This data suggests a high diversity of PCR ribotypes in humans and animals in Brazil and support the discussion of C. difficile as a zoonotic pathogen.
Exchange Matters: Perspectives from Social Capital, Neighborhoods, and Modern Culture

Everyday exchanges of ideas, goods, and time made by people are fundamental for life. This article explores exchange relationships of different kinds and maintains that they are an essential and common component of economic activity and of Judeo-Christian morality. Although risks and costs must be faced, individuals and regions can become better off through self-interested trade and investment exchanges. Jesus tied exchange behaviors to what life is like in the kingdom of heaven and to the spiritual well-being of individuals.
A Case Study of Higher Order Tikhonov Regularization Terms Influence on the Convergence of Active Contours

An explicit snake is a smooth closed curve which deforms towards the desired features in an image. There are two types of force controlling the motion of the snake: internal and external forces. The former usually constrains the snake’s curvature and tension, through first and second Tikhonov smoothness force terms, while the latter generates attraction forces. To investigate the possible role of higher Tikhonov constraint parameters, third and fourth force regularizing terms are added in this study. The related theoretical equations are derived and the respective influence of the four internal force terms are examined and followed on test images. While still at the preliminary stage, the present study shows that the added internal force terms may improve the smoothness and convergence of the snake.
Tracer Trails Urban Transport System of the City of Villahermosa: Case of Transbus

The city of Villahermosa, considered the gateway to southern Mexico, has experienced growth due to the economic activities of the region. This growth has not been based on an urban development plan where a suitable system of streets and avenues is contemplated, which impacts the service of the public transportation system, which consists of the mass transit system urban (vans), the rental car service (taxis), Transbus system (with routes within the city) and Transmetropolitano system (covering the metropolitan area of the city). According to the above, it is necessary to have an application support to determine the transfer of a part of the city to another in an efficient and intuitive way. The proposed tool (in the first phase only Transbus system) makes use of the PRM (Probabilistic Road Map) and Dijkstra algorithm to find a path Shuttle routes Transbus using interfaces in his version of HTML 5, jQuery, CSS and GoogleMaps, having the ability to adapt to different screen sizes of devices, thus enhancing the ways in which the user can have the tool.
A Scalable Dependability Scheme for Routing Fabric of SRAM-Based Reconfigurable Devices

With the continual scaling of feature size, system failure due to soft errors is getting more frequent in CMOS technology. Soft errors have particularly severe effects in static random-access memory (SRAM)-based reconfigurable devices (SRDs) since an error in SRD configuration bits can permanently change the functionality of the system. Since interconnect resources are the dominant contributor to the overall configuration memory upsets in SRD-based designs, the system failure rate can be significantly reduced by mitigating soft errors in routing fabric. This paper first presents a comprehensive analysis of SRD switch box susceptibility to short and open faults. Based on this analysis, we present a dependable routing fabric by efficiently employing asymmetric SRAM cells in configuration memory of SRDs. The proposed scheme is highly scalable and capable of achieving any desired level of dependability. In the proposed scheme, we also present a fault masking mechanism to mitigate the effect of soft errors in the routing circuitry. A routing algorithm is also proposed to take the advantage of the proposed routing fabric. Experimental results over the Microelectronics Center of North Carolina benchmarks show that the proposed scheme can mitigate both single and multiple event upsets in the routing fabric and can reduce system failure rate orders of magnitude as compared with the conventional protection techniques.
Metallicity calibration of theoretical stellar SEDs using UBVRIJHKL photometry of globular clusters

Although stellar SED models are constantly improving and seem to show similar differential tendencies as measured spectra, their absolute properties (eg. absolute flux in different wavelength regions) can still differ significantly from the measured ones. Therefore, it is necessary to "color-calibrate" the theoretical spectra to obtain realistic model-based stellar spectra, which can then be used for many purposes, such as population synthesis models. In order to calibrate a hybrid library (consisting of the Kurucz -, the Allard and Hauschild -, and the Scholz-models) of theoretical SEDs, UBVRIJHKLM photometric data of solar neighborhood stars were collected from the literature, and the solar metallicity models were calibrated using an algorithm developed by Cuisinier et. al. The corrections were then propagated through the whole grid of models, resulting in the "semi-empirical" models (available on ftp from the university of Basel). In order to extend the calibration to non-solar abundances, we have collected UBVRIJHKL photometric data from well-known Galactic globular clusters, covering a wide range of metallicities. We shall present first results, using the metallicity-calibrated library for calculating isochrones of metal-poor clusters. In particular, the observed slope and/or curvature of the RGB appears to be reproducible in a systematically correct manner for the first time.
On-line learning of sequence data based on Self-Organizing Incremental Neural Network

This paper presents an on-line, continuously learning mechanism for sequence data. The proposed approach is based on SOINN-DTW method (Okada and Hasegawa, 2007), which is designed for learning of sequence data. It is based on self-organizing incremental neural network (SOINN) and dynamic time warping (DTW). Using SOINNpsilas function represents the topological structure of online input data, the output distribution of each states is represented and adapted in a self-organizing manner corresponding to online input data. Consequently, this method can train a network and estimate parameters of the output distribution using new (on-line) data continuously, based on scarce batch-training data. Through online learning, the recognition accuracy is improved continuously. To confirm the effectiveness of the on-line learning mechanism of SOINN-DTW, we present an extensive set of experiments that demonstrate how our method outperforms the online learning method of HMM in classifying phoneme data.
Corticotropin-releasing factor (CRF) over-expression down-regulates hippocampal dopamine receptor protein expression and CREB activation in mice.

BACKGROUND Stress results in hyperactivity of the hypothalamic-pituitary-adrenal (HPA) axis, characterized by increased central CRF activity, elevated circulating glucocorticoid levels, impaired glucocorticoid-mediated negative feedback and abnormal hippocampal functions, possibly contributing to the development of behavioral pathologies, such as depression. The hippocampus is critically involved in the control of the HPA axis as well as in explicit memory, contextual aspects of fear, organization of the behavioral response to environmental novelty and in habituation. We have previously shown that mice that over-express CRF in the brain exhibit impaired novelty detection and altered psychophysiological and behavioral habituation, functions linked to dopamine receptor-dependent hippocampal plasticity. OBJECTIVE AND METHODS Therefore, the aim of the present study was to measure D1 and D2 dopamine receptor expression and related signaling, such as CREB and ERK protein levels and phosphorylation, in the hippocampus and other brain regions of mice with post-natal CRF over-expression (CRF-OE mice). RESULTS We found a region-specific down-regulation of both D1 and D2 protein expression, without altered CRF receptor protein expression, in the hippocampus in CRF-OE mice. This was accompanied by an impaired phosphorylation of hippocampal CREB, but not ERK1 and ERK2, in the same animals. CONCLUSIONS These results suggest that post-natal onset CRF over-expression results in an impairment of dopamine signaling in the hippocampus, which may underlie cognitive and motivational aspects of stress-related, CRF-driven mood disorders.
Network type and mortality risk in later life.

PURPOSE The purpose of this study was to examine the association of baseline network type and 7-year mortality risk in later life. DESIGN AND METHODS We executed secondary analysis of all-cause mortality in Israel using data from a 1997 national survey of adults aged 60 and older (N=5,055) that was linked to records from the National Death Registry up to 2004. We considered six network types--diverse, friend focused, neighbor focused, family focused, community-clan, and restricted--in the analysis, controlling for population group, sociodemographic background, and health factors. We carried out Cox proportional hazards regressions for the entire sample and separately by age group at baseline: 60-69, 70-79, and 80 and older. RESULTS Network types were associated with mortality in the 70-79 and 80 and older age groups. Respondents located in diverse and friend-focused network types, and to a lesser degree those located in community-clan network types, had a lower risk of mortality compared to individuals belonging to restricted networks. IMPLICATIONS Gerontological practitioners should address older adults' social networks in their assessments of clients. The parameters used to derive network types in this study can serve toward the development of practical network type inventories. Moreover, practitioners should tailor the interventions they implement to the different network types in which their elderly clients are embedded.
Recent Advances in the Application of Metabolomics to Studies of Biogenic Volatile Organic Compounds (BVOC) Produced by Plant

In many plants, biogenic volatile organic compounds (BVOCs) are produced as specialized metabolites that contribute to the characteristics of each plant. The varieties and composition of BVOCs are chemically diverse by plant species and the circumstances in which the plants grow, and also influenced by herbivory damage and pathogen infection. Plant-produced BVOCs are receptive to many organisms, from microorganisms to human, as both airborne attractants and repellants. In addition, it is known that some BVOCs act as signals to prime a plant for the defense response in plant-to-plant communications. The compositional profiles of BVOCs can, thus, have profound influences in the physiological and ecological aspects of living organisms. Apart from that, some of them are commercially valuable as aroma/flavor compounds for human. Metabolomic technologies have recently revealed new insights in biological systems through metabolic dynamics. Here, the recent advances in metabolomics technologies focusing on plant-produced BVOC analyses are overviewed. Their application markedly improves our knowledge of the role of BVOCs in chemosystematics, ecological influences, and aroma research, as well as being useful to prove the biosynthetic mechanisms of BVOCs.
Modeling Piezoelectric Actuator Hysteresis with Singularity Free Prandtl-Ishlinskii Model

Actuators like piezoelectric, magnetostrictive and shape memory alloy are gaining importance in applications involving high frequency, high precision and yet compactness is needed. The PI operator, while being able to model the hysteresis behaviour of a piezoelectric actuator well, has one major inadequacy: the inverse of the operator does not exist when the slope of the hysteretic curve is not positive definite, i.e. singularity occurs when the PI weights les 0. There is an inevitable trade-off between modeling accuracy and inversion stability. The modeling of the hysteretic loop gets better with the number of backlash operators used in the modeling. However, as the piecewise continuous interval represented by each backlash operator shrinks, there is a greater chance for the reciprocal of the PI weights to be ill conditioned, especially at the hysteretic curve turning points. Similar ill conditioned situations also arise when the actuators are used to actuate heavy loads or when operating at high frequency. This paper proposes extension to the PI operator to map the hysteresis data through a linear transformation onto another domain, where the inversion would be better behaved. The inverse weights are evaluated in this domain and are subsequently used to compute the inverse hysteresis model, which is to be used in the feed-forward controller, before the inverse model is transformed back to the original domain.
A new sliding mode-based learning control scheme

A new sliding mode-based learning control scheme for a class of SISO dynamic systems is developed in this paper. It is seen that, based on the most recent information on the closed-loop stability, a recursive learning chattering-free sliding mode controller can be designed to drive the closed-loop dynamics to reach the sliding mode surface in a finite time, on which the desired closed-loop dynamics with the zero-error convergence can be achieved.
Watershed Segmentation Using a Multiscale Ramp Edge Merging Strategy

For the watershed segmentation algorithm to be successful it must be implemented on a realistic gradient image. In most watershed implementations, gradients are extracted using an operator optimal for ideal step edges. However, image edges are never ideal steps and more closely resemble ramp edges at multiple scales. Therefore this strategy results in an inaccurate measure of image gradients and in turn lessens segmentation performance. In this paper we propose a new multiscale gradient operator for ramp edges. This strategy merges the properties of accurate gradient estimation of a large scale kernel with accurate localization of a small scale kernel by tracking gradients from larger to smaller scales. Quantitative performance evaluation of segmentation results shows this approach to outperform a traditional single small scale gradient operator optimal for step edges.
Hybrid Community Participation in Crowdsourced Early Warning Systems

In this paper we present Aurorasaurus: a website, a mobile application, and a citizen science initiative that allows a community of users to report and verify sightings of the Aurora Borealis. Through ad-hoc data indirectly offered through social media, a community of citizen scientists verify sightings of the Aurora Borealis. These verified data are tested against currently existing aurora-forecasting models. The insights these data provide are transformed into map and text-based forms. In addition, notifications are sent to interested participants in a timely manner. This is a design test-bed for an early warning system (EWS) that is capable of detecting and communicating the earliest signs of disaster to community members in near real time. Most importantly, this system incorporates community participation in improving the quality of data mined from Twitter and direct community contributions.
Facial reactions to emotional stimuli: Automatically controlled emotional responses

Based on a model in which the facial muscles can be both automatically/involuntarily controlled and voluntarily controlled by conscious processes, we explore whether spontaneously evoked facial reactions can be evaluated in terms of criteria for what characterises an automatic process. In three experiments subjects were instructed to not react with their facial muscles, or to react as quickly as possible by wrinkling the eyebrows (frowning) or elevating the cheeks (smiling) when exposed to pictures of negative or positive emotional stimuli, while EMG activity was measured from the corrugator supercilii and zygomatic major muscle regions. Consistent with the proposition that facial reactions are automatically controlled, the results showed that the corrugator muscle reaction was facilitated to negative stimuli and the zygomatic muscle reaction was facilitated to positive stimuli. The results further showed that, despite the fact that subjects were required to not react with their facial muscles at all, they could not avoid producing a facial reaction that corresponded to the negative and positive stimuli.
Hybrid Thermal-Nonthermal Synchrotron Emission from Hot Accretion Flows

We investigate the effect of a hybrid electron population, consisting of both thermal and nonthermal particles, on the synchrotron spectrum, image size, and image shape of a hot accretion flow onto a supermassive black hole. We find two universal features in the emitted synchrotron spectrum: (1) a prominent shoulder at low (≲1011 Hz) frequencies that is weakly dependent on the shape of the electron energy distribution, and (2) an extended tail of emission at high (≳1013 Hz) frequencies whose spectral slope depends on the slope of the power-law energy distribution of the electrons. In the low-frequency shoulder, the luminosity can be up to 2 orders of magnitude greater than with a purely thermal plasma even if only a small fraction (<1%) of the steady state electron energy is in the nonthermal electrons. We apply the hybrid model to the Galactic center source, Sgr A*. The observed radio and IR spectra imply that at most 1% of the steady state electron energy is present in a power-law tail in this source. This corresponds to no more than 10% of the electron energy injected into the nonthermal electrons and hence 90% into the thermal electrons. We show that such a hybrid distribution can be sustained in the flow because thermalization via Coulomb collisions and synchrotron self-absorption are both inefficient. The presence of nonthermal electrons enlarges the size of the radio image at low frequencies and alters the frequency dependence of the brightness temperature. A purely thermal electron distributions produces a sharp-edged image, while a hybrid distribution causes strong limb brightening. These effects can be seen up to frequencies ~1011 Hz and are accessible to radio interferometers.
Advertising and Cigarette Consumption

The authors examine two questions that are relevant to current policy issues: (1) is there a positive response of aggregate demand to advertising? (2) what is the reaction of consumers to government health warnings and media policy? The results support the hypothesis that advertising increases aggregate demand for cigarettes. However, the advent of health warnings and media policies seems to have eradicated this aggregate advertising effect. The findings also support some previous studies that suggest that aggregate advertising effects depreciate within one year.
The Effects of Stamp Duty on the Level and Volatility of Equity Prices

This paper investigates the effects of stamp duty - the UK securities transaction tax - on the level and volatility of equity prices. The authors examine the response of the equity market to announcements of changes in stamp duty rates and compare the prices of two assets which are similar in all respects apart from their treatment for stamp duty purposes: American Depositary Receipts (ADRs) and their London Stock Exchange-traded stocks. The findings are consistent with the hypothesis that stamp duty is capitalised in prices. Using univariate GARCH models, the authors find that stamp duty has no effect on volatility, contradicting the key hypothesis put forward by proponents of transaction taxes.
A software defined OFDM modulator

Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. A software defined radio (SDR) is a radio system that allows a great degree of reprogrammability. In a SDR, all signal processing is performed by or closely controlled by software. Because of this, proponents of SDR promise dynamic and intelligent protocols for wireless networks. Orthogonal Frequency Division Multiplexing (OFDM) has emerged as a popular modulation technique for such networks. Consequently, it is desirable to make software defined OFDM available. If possible, such a radio system should be based on the PC platform. Existing solutions for OFDM on a PC based platform lack the performance or the flexibility needed for true software defined OFDM. This thesis presents the design of an OFDM transmitter for software defined radio. The transmitter is implemented on an FPGA as part of a PC based software radio platform. To demonstrate the programmability of the FPGA design, IEEE 802.11a and several other applications are demonstrated.
Using Multiobjective Evolutionary Algorithms to Understand Parkinson's Disease

The incidence of neurodegenerative diseases such as Parkinson's is increasing rapidly around the world, yet the symptoms and pathology of these diseases remain incompletely understood. As a consequence, it is challenging for clinicians to provide patients with accurate diagnoses or prognoses. In this work, we use multi-objective evolutionary algorithms to explore recordings of patients drawing neurological assessment figures, with the aim of identifying patterns of cognitive and motor signals that discriminate different disease states. As a proof of principle, we demonstrate how this approach can be used to explore the trade-off between predicting clinical measures of motor and cognitive deficit.