Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Hippocampal Interneuronal Subtypes Identified Using Transgenic Mice That Express Green Fluorescent Protein in GABAergic Interneurons

The chief inhibitory neurons of the mammalian brain, GABAergic neurons, are comprised of a myriad of diverse neuronal subtypes. To facilitate the study of these neurons, transgenic mice were generated that express enhanced green fluorescent protein (EGFP) in subpopulations of GABAergic neurons. In one of the resulting transgenic lines, called GIN ( GFP-expressing Inhibitory Neurons), EGFP was found to be expressed in a subpopulation of somatostatin-containing GABAergic interneurons in the hippocampus and neocortex. In both live and fixed brain preparations from these mice, detailed microanatomical features of EGFP-expressing interneurons were readily observed. In stratum oriens of the hippocampus, EGFP-expressing interneurons were comprised almost exclusively of oriens/alveus interneurons with lacunosum-moleculare axon arborization (O-LM cells). In the neocortex, the somata of EGFP-expressing interneurons were largely restricted to layers II-IV and upper layer V. In hippocampal area CA1, two previously uncharacterized subtypes of interneurons were identified using the GIN mice: stratum pyramidale interneurons with lacunosum-moleculare axon arborization (P-LM cells) and stratum radiatum interneurons with lacunosum-moleculare axon arborization (R-LM cells). These newly identified interneuronal subtypes appeared to be closely related to O-LM cell, as they selectively innervate stratum lacunosum-moleculare. Whole-cell patch-clamp recordings revealed that these cells were fast-spiking and showed virtually no spike frequency accommodation. The microanatomical features of these cells suggest that they function primarily as “input-biasing” neurons, in that synaptic volleys in stratum radiatum would lead to their activation, which in turn would result in selective suppression of excitatory input from the entorhinal cortex onto CA1 pyramidal cells.
A reduced cerebral metabolic ratio in exercise reflects metabolism and not accumulation of lactate within the human brain

During maximal exercise lactate taken up by the human brain contributes to reduce the cerebral metabolic ratio, O2/(glucose + 1/2 lactate), but it is not known whether the lactate is metabolized or if it accumulates in a distribution volume. In one experiment the cerebral arterio‐venous differences (AV) for O2, glucose (glc) and lactate (lac) were evaluated in nine healthy subjects at rest and during and after exercise to exhaustion. The cerebrospinal fluid (CSF) was drained through a lumbar puncture immediately after exercise, while control values were obtained from six other healthy young subjects. In a second experiment magnetic resonance spectroscopy (1H‐MRS) was performed after exhaustive exercise to assess lactate levels in the brain (n = 5). Exercise increased the AVO2 from 3.2 ± 0.1 at rest to 3.5 ± 0.2 mm (mean ±s.e.m.; P < 0.05) and the AVglc from 0.6 ± 0.0 to 0.9 ± 0.1 mm (P < 0.01). Notably, the AVlac increased from 0.0 ± 0.0 to 1.3 ± 0.2 mm at the point of exhaustion (P < 0.01). Thus, maximal exercise reduced the cerebral metabolic ratio from 6.0 ± 0.3 to 2.8 ± 0.2 (P < 0.05) and it remained low during the early recovery. Despite this, the CSF concentration of lactate postexercise (1.2 ± 0.1 mm; n= 7) was not different from baseline (1.4 ± 0.1 mm; n= 6). Also, the 1H‐MRS signal from lactate obtained after exercise was smaller than the estimated detection limit of ∼1.5 mm. The finding that an increase in lactate could not be detected in the CSF or within the brain rules out accumulation in a distribution volume and indicates that the lactate taken up by the brain is metabolized.
Assessment of Microbial Communities by Graph Partitioning in a Study of Soil Fungi in Two Alpine Meadows

ABSTRACT Understanding how microbial community structure and diversity respond to environmental conditions is one of the main challenges in environmental microbiology. However, there is often confusion between determining the phylogenetic structure of microbial communities and assessing the distribution and diversity of molecular operational taxonomic units (MOTUs) in these communities. This has led to the use of sequence analysis tools such as multiple alignments and hierarchical clustering that are not adapted to the analysis of large and diverse data sets and not always justified for characterization of MOTUs. Here, we developed an approach combining a pairwise alignment algorithm and graph partitioning by using MCL (Markov clustering) in order to generate discrete groups for nuclear large-subunit rRNA gene and internal transcript spacer 1 sequence data sets obtained from a yearly monitoring study of two spatially close but ecologically contrasting alpine soils (namely, early and late snowmelt locations). We compared MCL with a classical single-linkage method (Ccomps) and showed that MCL reduced bias such as the chaining effect. Using MCL, we characterized fungal communities in early and late snowmelt locations. We found contrasting distributions of MOTUs in the two soils, suggesting that there is a high level of habitat filtering in the assembly of alpine soil fungal communities. However, few MOTUs were specific to one location.
Learning Behaviors in Agents Systems with Interactive Dynamic Influence Diagrams

Interactive dynamic influence diagrams (I-DIDs) are a well recognized decision model that explicitly considers how multiagent interaction affects individual decision making. To predict behavior of other agents, I-DIDs require models of the other agents to be known ahead of time and manually encoded. This becomes a barrier to I-DID applications in a human-agent interaction setting, such as development of intelligent non-player characters (NPCs) in real-time strategy (RTS) games, where models of other agents or human players are often inaccessible to domain experts. In this paper, we use automatic techniques for learning behavior of other agents from replay data in RTS games. We propose a learning algorithm with improvement over existing work by building a full profile of agent behavior. This is the first time that data-driven learning techniques are embedded into the I-DID decision making framework. We evaluate the performance of our approach on two test cases.
Scene-Domain Active Part Models for Object Representation

In this paper, we are interested in enhancing the expressivity and robustness of part-based models for object representation, in the common scenario where the training data are based on 2D images. To this end, we propose scene-domain active part models (SDAPM), which reconstruct and characterize the 3D geometric statistics between object's parts in 3D scene-domain by using 2D training data in the image-domain alone. And on top of this, we explicitly model and handle occlusions in SDAPM. Together with the developed learning and inference algorithms, such a model provides rich object descriptions, including 2D object and parts localization, 3D landmark shape and camera viewpoint, which offers an effective representation to various image understanding tasks, such as object and parts detection, 3D landmark shape and viewpoint estimation from images. Experiments on the above tasks show that SDAPM outperforms previous part-based models, and thus demonstrates the potential of the proposed technique.
Painting style transfer for head portraits using convolutional neural networks

Head portraits are popular in traditional painting. Automating portrait painting is challenging as the human visual system is sensitive to the slightest irregularities in human faces. Applying generic painting techniques often deforms facial structures. On the other hand portrait painting techniques are mainly designed for the graphite style and/or are based on image analogies; an example painting as well as its original unpainted version are required. This limits their domain of applicability. We present a new technique for transferring the painting from a head portrait onto another. Unlike previous work our technique only requires the example painting and is not restricted to a specific style. We impose novel spatial constraints by locally transferring the color distributions of the example painting. This better captures the painting texture and maintains the integrity of facial structures. We generate a solution through Convolutional Neural Networks and we present an extension to video. Here motion is exploited in a way to reduce temporal inconsistencies and the shower-door effect. Our approach transfers the painting style while maintaining the input photograph identity. In addition it significantly reduces facial deformations over state of the art.
Authority and Legitimacy: A Quantitative Study of Youth's Perceptions on the Brazilian Police

Today, the majority of research has focused on legitimacy, while much less attention has been given to the sources of trust in the police (Nix et al., 2014). Limited attention has been also paid to the examination of legitimacy of the police services as viewed by those they serve (Bottoms & Tankebe, 2013), especially by the young people. Hence, the current study aims to add to this body of research in three ways. First, it examines the factors that influence peoples’ beliefs about the police and their intentions to cooperate or exhibit confidence. Second, this study constitutes one of the first empirical analyses that highlight the importance of examining the relationship between police authority and legitimacy in Brazil. Aiming to understand the dynamics among those notions in relation to trust and obedience, the empirical part of the present study is conducted in the district of Jardim Ângela (Sao Paolo); once considered as the most violent urban region in the world. The final contribution lies in its focus on early adolescence as the particular age forms a crucial period in peoples’ legal socialization (Dirikx & Van den Bulck, 2014). The statistical analysis shows significant relationships between the frequency of obedience in laws and trust in the police, and dimensions of perceived legitimacy. Nikolaos Stamatakis University of Sao Paolo, Brazil Authority and Legitimacy
Multicentre patch test study of air‐oxidized ethoxylated surfactants

Frequent exposure to water and surfactants is considered to be the main cause of hand eczema from wet work. Ethoxylated surfactants are susceptible to oxidation and some of the oxidation products formed have proved to be contact sensitizers in guinea pigs. The question of human sensitization to oxidized surfactants was addressed in a multicentre study in the Stockholm region. 528 consecutive dermatitis patients were patch tested with widely used ethoxylated surfactants in oxidized and non‐oxidized form as well as certain identified oxidation compounds. 61 patients presented with mild, clearly irritant reactions to some of the surfactants tested. 18 patients showed not only erythema but also oedema and/or papules and vesicles, using a morphologic descriptive system for reading the patch test reactions. These reactions occurred mostly to oxidized surfactants and oxidation products. When retesting 9 of these 18 patients only an allergic reaction to acetaldehyde was confirmed. We conclude that oxidized ethoxylated surfactants have increased irritant potential compared to non‐oxidized material. Our working hypothesis is that oxidized surfactants of technical quality exert a lower risk of sensitization than do oxidized homologous pure surfactants. Among the potential allergens formed during autoxidation, formaldehyde and acetaldehyde must be considered as a source of unexpected exposure.
Medical image retrieval based on low level feature and high level semantic feature

A medical image retrieval system combined of the low-level image feature and high-level semantic is used in the paper witch includes two main parts: image preprocessing and the machine learning. In the firs part, feature tree structure is presented to reduce the semantic gap and in the latter part, a novel machine learning method based on SVM is presented to optimize the Network parameters by which improve the effect of semantic annotation and recognition rate. Preliminary test results form clinical images prove feasibility of the retrieval system and support the theory presented in the project.
Secure similarity based document retrieval system in cloud

The introduction of Cloud computing concept has been instrumental in reducing resource unavailability in cyber world. Privacy of data stored in cloud resources though is a debatable subject and a matter of concern. As the number of documents stored on cloud resources increase, a search engine will have to be employed to search for information. Due to increased concern on these search engines itself misusing data, users are apprehensive about treating cloud resources as a data storage medium. Through this project we try to improve the security in cloud computing by introducing encryption of count list, as well as documents. The search is performed with the hashed keywords. Limitations in cloud computing are addressed by maximizing the utilization of cloud computing resources.
Three dimensional imaging of electrical trees in micro and nano-filled epoxy resin

The authors have previously shown that electrical trees in unfilled polymers can be three-dimensionally (3D) imaged using X-ray computed tomography (XCT) or serial block-face scanning electron microscopy (SBFSEM). Here, we present the results of 3D imaging and analysis of electrical trees in filled epoxy systems for the first time. Electrical trees created in unfilled, micro silica-filled and nano silica-filled epoxy resin were scanned using XCT at the Diamond Light Source synchrotron and then also imaged using SBFSEM. 3D virtual replicas of the trees have been generated and their structures characterised. Imaging in micro-filled epoxy is more challenging than in nano-filled epoxy. It was found that trees in samples filled up to 20 wt% with micro silica can be imaged using either XCT or SBFSEM. In the case considered, the mean diameter of tree channels in micro-filled epoxy was found to be considerably smaller (0.6 μm) than in unfilled or nano-filled epoxies (3 μm).
Dynamic adaptability with .NET service components

In self-adaptive systems components are dynamically modified according to the execution environment requirement, where each component is a probable point of failure. Existing approaches to make such systems more vigorous and safe are both brittle and time intensive. A framework for dynamic adaptation has been designed to automate the component integration process at runtime by accessing the equivalent component from a repository of components. The . NET technology allows developers to adapt run-time components by specifying component behavior using pre- and post-assertions on the component's services. Components can be compared for behavioral equivalence by comparing the assertions. These assertions will help us to compute the utility value for each component in the repository and the component with the highest value is picked for replacement. In this paper, we describe the mechanism for component adaptation using . NET services, by considering a system in a dynamic context with proxy switcher and network switcher components.
Occupational Leadership to Facilitate Occupational Engagement in Older Adults With Visual Impairment

Research indicates that older adults with visual impairment experience self-imposed and societal restrictions in physical and social environments. The World Health Organization has identified a need to expand access to eye health services and to increase rehabilitation for individuals with visual impairment or blindness. Through the lens of occupational science, the models of Occupational Adaptation and Situational Leadership were examined to create a framework to promote occupational engagement. Occupational Leadership was developed from these models for health care practitioners to guide and facilitate the occupational engagement of older adults with visual impairment at the community level.
Optimized local blendshape mapping for facial motion retargeting

One of the popular methods for facial motion retargeting is local blendshape mapping [Pighin and Lewis 2006], where each local facial region is controlled by a tracked feature (for example, a vertex in motion capture data). To map a target motion input onto blendshapes, a pose set is chosen for each facial region with minimal retargeting error. However, since the best pose set for each region is chosen independently, the solution likely has unorganized pose sets across the face regions, as shown in Figure 1(b). Therefore, even though every pose set matches the local features, the retargeting result is not guaranteed to be spatially smooth. In addition, previous methods ignored temporal coherence which is key for jitter-free results.
Image enhancement and dynamic range compression using novel intensity-specific stochastic resonance-based parametric image enhancement model

This paper presents a noise-aided image enhancement algorithm focussed on addressing images that have a large dynamic range, i.e., images with both dark and bright regions. The application of a new mathematical model, in a shifted double-well system exhibiting stochastic resonance, is investigated for such images. The new mathematical model addresses the shortcomings of earlier SR-based enhancement model by deriving parameters purely from input values (instead of input statistics). This model is specific to spatial domain pixel representation and operates on a revised iterative equation. This iterative processing is here applied selectively to the under-illuminated regions of the image, characterized as the De Vries-Rose (DVR) region of a human psychovisual model. The idea of suitably modifying the existing universal image quality index is also proposed for its participation in iteration termination, and to gauge the property of dynamic range compression. While the iterative algorithm is terminated using the revised image quality index, entropy maximization, and contrast quality of DVR region with constraints on perceptual quality, the performance of the proposed algorithm is also characterized by observing color enhancement and subjective scores on visual quality.
C-Vine Copula Mixture Model for Clustering of Residential Electrical Load Pattern Data

The ongoing deployment of residential smart meters in numerous jurisdictions has led to an influx of electricity consumption data. This information presents a valuable opportunity to suppliers for better understanding their customer base and designing more effective tariff structures. In the past, various clustering methods have been proposed for meaningful customer partitioning. This paper presents a novel finite mixture modeling framework based on C-vine copulas (CVMM) for carrying out consumer categorization. The superiority of the proposed framework lies in the great flexibility of pair copulas toward identifying multidimensional dependency structures present in load profiling data. CVMM is compared to other classical methods by using real demand measurements recorded across 2613 households in a London smart-metering trial. The superior performance of the proposed approach is demonstrated by analyzing four validity indicators. In addition, a decision tree classification module for partitioning new consumers is developed and the improved predictive performance of CVMM compared to existing methods is highlighted. Further case studies are carried out based on different loading conditions and different sets of large numbers of households to demonstrate the advantages and to test the scalability of the proposed method.
Biogenesis of the glycosome in Trypanosoma brucei: the synthesis, translocation and turnover of glycosomal polypeptides.

Glycosomes, the microbodies of Trypanosoma brucei, contain a number of enzymes involved in glucose and glycerol metabolism. The biogenesis of three of these enzymes has been studied. Aldolase, D‐glyceraldehyde‐3‐phosphate dehydrogenase and NAD‐linked glycerol‐3‐phosphate dehydrogenase are all synthesized in the cytosol on free rather than on membrane‐bound polysomes. In vitro, as well as in vivo, these polypeptides are synthesized at their mature size, and no evidence was found for any processing upon entry into the glycosomes. Continuous and pulse‐chase labelling experiments with procyclic trypomastigotes revealed that the enzymes have a half‐life in the cytosol of approximately 3 min or less, and then turn over rapidly in the glycosomes, with half‐lives as short as 30 min.
A computationally efficient implementation of fictitious play in a distributed setting

The paper deals with distributed learning of Nash equilibria in games with a large number of players. The classical fictitious play (FP) algorithm is impractical in large games due to demanding communication requirements and high computational complexity. A variant of FP is presented that aims to mitigate both issues. Complexity is mitigated by use of a computationally efficient Monte-Carlo based best response rule. Demanding communication problems are mitigated by implementing the algorithm in a network-based distributed setting, in which player-to-player communication is restricted to local subsets of neighboring players as determined by a (possibly sparse, but connected) preassigned communication graph. Results are demonstrated via a simulation example.
How Much Difference is too Much Difference? Perceptions of Gay Men and Lesbians in Intercollegiate Athletics

On the surface, intercollegiate athletics provides a notable example of creating community across difference. Although students involved in sports like football, basketball, and track at Division I institutions constitute a much more diverse group than students on campuses as a whole, community seems especially strong on these teams. On these teams, students from a vast array of backgrounds integrate into a coherent whole where factors such as race and socioeconomic status assume much less meaning compared to what individuals can contribute to the team (Wolf-Wendel, Toma, & Morphew, 2001). However, another form of diversity, sexual orientation, remains a potentially divisive issue in athletics. Indeed, student-athletes, coaches, and administrators in athletics are often homophobic and heterosexist. We examined how and why those in athletics at five NCAA Division I universities accept some forms of diversity so readily, but remain closed and even hostile to issues of difference related to sexual orientation. Sexual orientation is a socially constructed phenomenon, the meaning of which is constantly changing (Nussbaum, 1997; Tierney, 1997). In other words, American society has chosen to differentiate and label people based on whether they are intimate with same sex or different sex partners, and has endowed these distinctions with stereotypes that may or may not be salient to those being labeled, either now or in the future. We present the views of "others," namely coaches, student-athletes, and athletics administrators on homosexuality in general— and gay men, lesbians, and bisexuals in athletics in particular—as constructed at a specific period of time within a specific context. The research concerning the role of gay men and lesbians in intercollegiate athletics, and in sports more generally, is limited. Hekma (1998) found that gay men and lesbians in organized nonprofessional athletics are silenced and invisible. She also concluded that in cases where lesbian athletes were more open about their sexual orientation, they encountered a higher level of discrimination. Indicative of the homophobic and heterosexual environment promulgated in athletics, Hekma was not able to find instances in which gay male athletes were open about their sexual orientation. Further, Harry (1995) found that sports ideology is clearly associated with sexist and anti-