Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of endotoxin on the hypothalamic-pituitary-adrenal axis in sheep.

Endotoxin has been shown to stimulate ACTH and cortisol secretion through an action at the hypothalamic level. However, the nature of hypothalamic neurohormones, corticotropin-releasing hormone (CRH) and especially arginine vasopressin (AVP), involved in that regulation is still controversial. The purpose of this study was to determine the effects of an acute i.v. endotoxin administration on CRH and AVP secretion into hypophysial portal blood (HPB). The experiment has been performed in sheep since it is possible to collect HPB and quantify CRH and AVP secretion in this animal under physiological conditions. The release of both peptides into HPB was stimulated by endotoxin injection, the increase in portal AVP being more pronounced than that of CRH. An initial, transient, increase in jugular AVP concentrations was observed, probably due to the activation of magnocellular AVP neurons. In conclusion, our data indicate that the activation of the pituitary-adrenal axis after endotoxin injection is associated with an increased release of both CRH and AVP into HPB. Magnocellular AVP neurons are initially stimulated while parvocellular CRH and AVP neurons are stimulated throughout the experiment.
A general model for digitalizing human being's thought: MiMaS

A General Model For Digitalizing Human Being's Thought MIMAS proposes a general model including both hardware and software to determine and recognize some states of thinking of human. The hardware is a device that receives, pre-processes, and digitalizes brain wave signals which are then sent to the software to be processed. The software using some algorithms Independent Component Analysis, Expectation Maximization, and Gaussian mixture model classifier to process data, extract features, total up data and storage digitalized thinking of human. Our model has been experimented with over 50 persons and our accuracy is 87%.
An environment for synchronous software development

Collaboration is essential for the development of complex software systems. When a team of developers is geographically distributed, collaboration aspects -such as awareness, communication and synchronization- are compromised by physical distance. We present an approach, named synchronous development, to reduce the negative distance effects on collaboration. We use a fine-grained change tracking mechanism and instantaneously notify any developer working on the system about changes being performed by other developers. We believe that our approach can augment each developer's knowledge of the project and, consequently, promote interaction and increase productivity.
Incorporating Prior Knowledge in Support Vector Machines: Retrospect and Prospect

In the last few years, several works in the literature have addressed the problem of incorporating knowledge into support vector machines. The importance of this problem derives from the fact that, once incorporated, the knowledge can act as numerous training instances by which the machine performance would be considerably enhanced. In this paper, we propose a taxonomy for characterizing knowledge incorporation, briefly survey major knowledge incorporation methods described in the literatures, and provide a prospect for knowledge incorporation. Hopefully, this work will stimulate other studies aimed at a more comprehensive analysis of knowledge incorporation into support vector machines.
Microlensing Halo Models with Abundant Brown Dwarfs

All previous attempts to understand the microlensing results toward the Large Magellanic Cloud (LMC) have assumed homogeneous present-day mass functions (PDMFs) for the lensing populations. Here, we present an investigation into the microlensing characteristics of halos with spatially varying PDMFs and anisotropic velocity dispersion tensors. One attractive possibility—suggested by baryonic dark cluster formation in pregalactic and protogalactic cooling flows—is that the inner halo is dominated by stellar mass objects, whereas low-mass brown dwarfs become more prevalent on moving outward. The contribution to the microlensing rate must be dominated by dark remnants (~0.5 M☉) to recover the observed timescales of the microlensing experiments. But, even though stellar remnants control the rate, they do not dominate the mass of the baryonic halo, and so the well-known enrichment and mass budget problems are much less severe. Using a simple Ansatz for the spatial variation of the PDMF, models are constructed in which the contribution of brown dwarfs to the mass of the baryonic halo is ~55% and to the total halo is ~30%. An unusual property of the models is that they predict that the average timescale of events toward M31 is shorter than the average timescale toward the LMC. This is because the longer line of sight toward M31 probes more of the far halo in which brown dwarfs are the most common constituent.
Exploiting Discriminative Point Process Models for Spoken Term Detection

State-of-the-art spoken term detection (STD) systems are built on top of large vocabulary speech recognition engines, which generate lattices that encode candidate occurrences of each invocabulary query. These lattices specifiy start and stop times of hypothesized term occurrences, providing a clear opportunity to return to the acoustics to incorporate novel confidence measures for verification. In this paper, we introduce a novel exemplar distance metric to the recently proposed discriminative point process modeling (DPPM) framework and use the resulting whole word models to generate STD confidence scores. In doing so, we introduce STD to a completely distinct acoustic modeling pipeline, trading Gaussian mixture models (GMM) for multi-layer perceptrons and replacing dictionary-derived hidden Markov models (HMM) with exemplar-based point process models. We find that whole word DPPM scores both perform comparably and are complementary to lattice posterior scores produced by a state-of-the-art speech recognition engine.
Organic vs. Sponsored Content: From Ads to Native Ads

Online advertising is dominated by traditional techniques such as pop ups, banners, emails etc. Users are more likely to engage with content they find relevant and interesting, and ads generally disrupt their browsing experience. Hence an effective advertisement is one that simultaneously satisfies the marketing goals of the advertiser and also seamlessly embeds into the user experience. Such advertisements should be engaging, and presumably share features with organic (i.e. non-advertisement content). In this preliminary work, we aim at discovering features which best discriminate organic content from advertisements. For the advertiser it provides the utility of user targeting and revenue, but it also provides utility to the user, by making ads less of a nuisance and be interesting within a specific context.
Over-representation of correlation analysis (ORCA): a method for identifying associations between variable sets

MOTIVATION Often during the analysis of biological data, it is of importance to interpret the correlation structure that exists between variables. Such correlations may reveal patterns of co-regulation that are indicative of biochemical pathways or common mechanisms of response to a related set of treatments. However, analyses of correlations are usually conducted by either subjective interpretation of the univariate covariance matrix or by applying multivariate modeling techniques, which do not take prior biological knowledge into account. Over-representation analysis (ORA) is a simple method for objectively deciding whether a set of variables of known or suspected biological relevance, such as a gene set or pathway, is more prevalent in a set of variables of interest than we expect by chance. However, ORA is usually applied to a set of variables differentiating a single experimental variable and does not take into account correlations. RESULTS Over-representation of correlation analysis (ORCA) is a novel combination of ORA and correlation analysis that provides a means to test whether more associations exist between two specific groups of variables than expected by chance. The method is exemplified by application to drug sensitivity and microRNA expression data from a panel of cancer cell lines (NCI60). ORCA highlighted a previously reported correlation between sensitivity to alkylating anticancer agents and topoisomerase inhibitors. We also used this approach to validate microRNA clusters predicted by mRNA correlations. These observations suggest that ORCA has the potential to reveal novel insights from these data, which are not readily apparent using classical ORA. AVAILABILITY AND IMPLEMENTATION The R code of the method is available at https://github.com/ORCABioinfo/ORCAcode.
Redecentralizing the Web with Distributed Ledgers

The web was originally conceived as decentralized and universal, but during its popularization, its big value was built on centralized servers and nonuniversal access. A key element to redecentralize the web is to be able to generate trustable, secure, and accountable updates among autonomous participants without a central server. The authors believe that the marriage between distributed ledgers and linked data can provide this functionality and unlock the web's true potential. As a first step toward it, the authors propose a minimal vocabulary to describe and link distributed ledgers.
Functional results after tape removal for chronic pelvic pain following tension-free vaginal tape or transobturator tape

and 49% were pN0 (p<0.001). The strongest predictor of progression and mortality was bGS. PSA >20 ng/ml associated with bGS </=7 resulted in 10-yr PCSM of 5%; when associated with bGS >/=8, PCSM was 35%. The main limitations of the study were retrospective design and varying treatment modalities. Conclusions: PCa patients with PSA >20 ng/ml have varying risk levels of disease progression and PCSM. Considering additional risk factors further stratifies this group into four subgroups that can guide the clinician in preoperative patient counselling.
Neurobehavioral Evaluation of C 57 BL / 6 J Mice Submitted to Tannery Effluents Intake

The emission of tannery effluents into the environment may cause serious harm to human and environmental health. Nevertheless, few studies have investigated the possible effects of intake of these effluents in mammalian. Thus, this study aimed to evaluate the neurobehavioral effects of chronic intake of different tannery effluents concentrations (0.1%, 1% and 5%) (120 days) in male C57BL/6J mice. The animals were subjected to behavioral tests, predictive of anxiety, depression and memory deficits. By the elevated plus maze test was observed that the mice in the 5% of tannery effluents group showed higher anxiety scores. At neophobia test, all the animals exposed to chronic intake of tannery effluents showed higher latency time to start eating, which corresponds to an anxiogenic behavior. Regarding the forced swim test, it was observed that the animals of groups 1% and 5% of tannery effluents had longer in immobility behavior. Finally, the object recognition test showed that the treatments did not cause damage to the animals’ memory. The recognition rate of the new object did not differ among the experimental groups. Thus, it is concluded that male C57BL/6J mice exposed to tannery effluents have predictive neurobehavioral changes of anxiety and depression, with no memory deficit.
Alpine lee cyclogenesis influence on air‐sea heat exchanges and marine atmospheric boundary layer thermodynamics over the western Mediterranean during a Tramontane/Mistral event

[1] Data from a recent field campaign are used to analyze the nonstationary aspects of air-sea heat exchanges and marine atmospheric boundary layer (MABL) thermodynamics over the Gulf of Lion (GoL) in connection with synoptic forcing. The data set includes measurements made from a wide range of platforms (sea-borne, airborne, and space-borne) as well as three-dimensional atmospheric modeling. The analysis focuses on the 24 March 1998 Tramontane/Mistral event. It is shown that the nonstationary nature of the wind regime over the GoL was controlled by the multistage evolution of an Alpine lee cyclone over the Tyrrhenian Sea (between Sardinia and continental Italy). In the early stage (low at 1014 hPa) the Tramontane flow prevailed over the GoL. As the low deepened (1010 hPa), the prevailing wind regime shifted to a well-established Mistral that peaked around 1200 UTC. In the afternoon the Mistral was progressively disrupted by a strengthening outflow coming from the Ligurian Sea in response to the deepening low over the Tyrrhenian Sea (1008 hPa) and the channelling induced by the presence of the Apennine range (Italy) and the Alps. In the evening the Mistral was again well established over the GoL as the depression continued to deepen (1002 hPa) but moved to the southeast, reducing the influence of outflow from the Ligurian Sea on the flow over the GoL. The air-sea heat exchanges and the structure of the MABL over the GoL were observed to differ significantly between the established Mistral period and the disrupted Mistral period. In the latter period, surface latent and sensible heat fluxes were reduced by a factor of 2, on average. During that latter period, air-sea moisture exchanges were mainly driven by dynamics, whereas during the former period, both winds and vertical moisture gradients controlled moisture exchanges. The MABL was shallower during the latter period (0.7 km instead of 1.2 km) because of reduced surface turbulent heat fluxes and increased wind shear at the top of the MABL in connection with the outflow from the Ligurian Sea. In the period of established Mistral, gravity waves above the MABL were observed to have an influence on the MABL structure. In the perturbed Mistral case this influence was not observed. Over the GoL the ubiquitous presence of sheltered regions (i.e., regions of reduced wind speed in the MABL) in the lee of the three major mountain ranges surrounding the GoL (namely, the Pyrenees, the Massif Central, and the Alps) was shown to have an impact on surface turbulent heat fluxes. The position of these sheltered regions, which evolved with the synoptic conditions, was the key to a correct interpretation of multiplatform surface turbulent flux measurements made over the GoL on 24 March 1998.
Agent Based Modeling of Human Gut Microbiome Interactions and Perturbations

Background Intestinal microbiota plays an important role in the human health. It is involved in the digestion and protects the host against external pathogens. Examination of the intestinal microbiome interactions is required for understanding of the community influence on host health. Studies of the microbiome can provide insight on methods of improving health, including specific clinical procedures for individual microbial community composition modification and microbiota correction by colonizing with new bacterial species or dietary changes. Methodology/Principal Findings In this work we report an agent-based model of interactions between two bacterial species and between species and the gut. The model is based on reactions describing bacterial fermentation of polysaccharides to acetate and propionate and fermentation of acetate to butyrate. Antibiotic treatment was chosen as disturbance factor and used to investigate stability of the system. System recovery after antibiotic treatment was analyzed as dependence on quantity of feedback interactions inside the community, therapy duration and amount of antibiotics. Bacterial species are known to mutate and acquire resistance to the antibiotics. The ability to mutate was considered to be a stochastic process, under this suggestion ratio of sensitive to resistant bacteria was calculated during antibiotic therapy and recovery. Conclusion/Significance The model confirms a hypothesis of feedbacks mechanisms necessity for providing functionality and stability of the system after disturbance. High fraction of bacterial community was shown to mutate during antibiotic treatment, though sensitive strains could become dominating after recovery. The recovery of sensitive strains is explained by fitness cost of the resistance. The model demonstrates not only quantitative dynamics of bacterial species, but also gives an ability to observe the emergent spatial structure and its alteration, depending on various feedback mechanisms. Visual version of the model shows that spatial structure is a key factor, which helps bacteria to survive and to adapt to changed environmental conditions.
A coevolutionary algorithm to automatic test case selection and mutant in Mutation Testing

One of the main problems to perform the Software Testing is to find a set of tests (subset from input domain of the problem) which is effective to detect the remaining bugs in the software. The Search-Based Software Testing (SBST) approach uses metaheuristics to find low cost set of tests with a high effectiveness to detect bugs. From several existing test criteria, Mutation Testing is considered quite promising to reveal bugs, despite its high computational cost, due to the great quantity of mutant programs generated. Therefore, this paper addresses the problem of selecting mutant programs and test cases in Mutation Testing context. To this end, it is proposed a Coevolutionary Genetic Algorithm (CGA) and the concept of Genetic Effectiveness, describing a new representation and implementing new genetic operators. The CGA is applied in five benchmarks and the results are compared to other five methods, showing a better performance of the proposed algorithm in subsets automatic selection with better mutation score and greater reduction of computational cost, specifically the amount of testing, when compared with exhaustive test.
Multiple kernel representation and classification of multivariate satellite-image time-series for crop mapping

ABSTRACT Multivariate satellite-image time-series (MSITS) are a valuable source of information for a wide range of agricultural applications. Image classification, one of the main applications of this type of data, is a challenging task. It is mainly because MSITS are generated by a complex interaction among several sources of information, which are known as the factors of variation. These factors contain different information with different levels of relevance to a classification task. Thus, a proper representation of MSITS data is required in order to extract and model the most useful information from these factors for classification purpose. To this end, this article proposes three multiple kernel representations of MSITS data. These representations extract the most classification-related information from these data through combining the basis kernels constructed from different factors of variation of the MSITS data. In the proposed representations, the combination of the basis kernels was achieved by using the multiple kernel learning algorithms. The efficiency of the proposed multiple kernel representations was evaluated based both on analysing the relevance of their kernels to the classification task and their classification performances. Two different MSITS data sets composed of 10 RapidEye imageries of an agricultural area were used to evaluate the performances of the proposed methods. In addition, the classification results of both MSITS using a single kernel were considered as the baseline for comparison. The results showed an increase of up to 14% in overall accuracy of the classification maps by using the multiple kernel representations. Moreover, these particular representations for classification of time-series observations were able to handle the undesirable effects in image data such as the presence of clouds and their shadows.
Negative priming effect of three kinds of biochar on the mineralization of native soil organic carbon

This study was conducted to improve our understanding of the effect of biochar on the degradation of original soil organic carbon (SOC) and the resulting net C sequestration. A 120‐day incubation experiment was conducted to detect the CO2 evolution from soil amended with three kinds of biochar, namely, rice husk biochar (RH), pecan shell biochar (PS), and bamboo biochar (BB), at biochar to soil ratios of 0% (as control), 1%, 2%, and 5%, as well as 100% pure biochar, and to evaluate their priming effect on SOC mineralization. Results showed that the cumulative mineralized amounts of SOC decreased by 7.95–10.7% with 5% RH, PS, and BB treatments, whereas there were no significant differences in 1% or 2% biochar treatments after 120 days of incubation compared with the control. Biochar had a suppressing influence on native SOC decomposition, and the effect increased with the amount of applied biochar increasing. The negative priming effect of biochar on the mineralization of SOC was mainly observed after 15 days of incubation and increased with incubation time. The negative effects of BB and RH were significantly stronger than that of PS at the 5% application ratio. This was mainly due to the less pores and smaller specific surface area of PS, which resulted in the weaker encapsulation and adsorption protection effect of PS than RH and BB, and inhibited the degradation of the adsorbed native SOC. Therefore, BB and RH have great potential for C sequestration and mitigation, especially in soils with low organic C.
Dissociating Self-Generated from Passively Applied Head Motion: Neural Mechanisms in the Vestibular Nuclei

The ability to distinguish sensory inputs that are a consequence of our own actions from those that result from changes in the external world is essential for perceptual stability and accurate motor control. To accomplish this, it has been proposed that an internal prediction of the consequences of our actions is compared with the actual sensory input to cancel the resultant self-generated activation. Here, we provide evidence for this hypothesis at an early stage of processing in the vestibular system. Previous studies have shown that neurons in the vestibular nucleus, which receive direct inputs from vestibular afferent fibers, are responsive to passively applied head movements. However, these same neurons do not reliably encode head velocity resulting from self-generated movements of the head on the body. In this study, we examined the mechanism that underlies the selective elimination of vestibular sensitivity to active head-on-body rotations. Individual neurons were recorded in monkeys making active head movements. The correspondence between intended and actual head movement was experimentally controlled. We found that a cancellation signal was gated into the vestibular nuclei only in conditions in which the activation of neck proprioceptors matched that expected on the basis of the neck motor command. This finding suggests that vestibular signals that arise from self-generated head movements are inhibited by a mechanism that compares the internal prediction of the sensory consequences by the brain to the actual resultant sensory feedback. Because self-generated vestibular inputs are selectively cancelled early in processing, we propose that this gating is important for the computation of spatial orientation and control of posture by higher-order structures.