Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Basal Ganglia in Sleep–Wake Regulation: Neural Circuitry and Clinical Significance

Researchers over the last decade have made substantial progress toward understanding the roles of dopamine and the basal ganglia (BG) in the control of sleep–wake behavior. In this review, we outline recent advancements regarding dopaminergic modulation of sleep through the BG and extra-BG sites. Our main hypothesis is that dopamine promotes sleep by its action on the D2 receptors in the BG and promotes wakefulness by its action on D1 and D2 receptors in the extra-BG sites. This hypothesis implicates dopamine depletion in the BG (such as in Parkinson's disease) in causing frequent nighttime arousal and overall insomnia. Furthermore, the arousal effects of psychostimulants (methamphetamine, cocaine, and modafinil) may be linked to the ventral periaquductal gray (vPAG) dopaminergic circuitry targeting the extra-BG sleep–wake network.
Using Energy Efficient Relative Neighborhood Graph for AODV Routing Protocol in MANETs

Mobile Ad-hoc Networks (MANET) are self-organizing networks composed of mobile nodes without any fixed infrastructure. The design and the implementation of efficient and scalable routing protocols constitute one main issue. However, routing protocols for MANETs are mainly based on computing the shortest paths and could not be energy efficient. Nodes failure based on power deficiency could affect the overall network lifetime. In our previous work, since in AODV, the processing time of packets stored in nodes queues in- creases as the number of packets increases, we have introduced an enhanced version, named QAODV(26), of the standard AODV (SAODV) protocol. In this enhanced version, we have included a technique to control the packet waiting time inside queues. Simulations have been conducted to compare QAODV protocol with SAODV in terms of network lifetime as well as packet delivery ratio. Simulation results showed that QAODV outperforms SAODV. In this paper, we further study the impact of using an efficient energy relative neigh- borhood graph (e-RNG) on the performance of QAODV in MANETs. Simulation results show that e-RNG provides better performance while decreasing the energy consumption.
The Hippo Pathway Controls a Switch between Retinal Progenitor Cell Proliferation and Photoreceptor Cell Differentiation in Zebrafish

The precise regulation of numbers and types of neurons through control of cell cycle exit and terminal differentiation is an essential aspect of neurogenesis. The Hippo signaling pathway has recently been identified as playing a crucial role in promoting cell cycle exit and terminal differentiation in multiple types of stem cells, including in retinal progenitor cells. When Hippo signaling is activated, the core Mst1/2 kinases activate the Lats1/2 kinases, which in turn phosphorylate and inhibit the transcriptional cofactor Yap. During mouse retinogenesis, overexpression of Yap prolongs progenitor cell proliferation, whereas inhibition of Yap decreases this proliferation and promotes retinal cell differentiation. However, to date, it remains unknown how the Hippo pathway affects the differentiation of distinct neuronal cell types such as photoreceptor cells. In this study, we investigated whether Hippo signaling regulates retinogenesis during early zebrafish development. Knockdown of zebrafish mst2 induced early embryonic defects, including altered retinal pigmentation and morphogenesis. Similar abnormal retinal phenotypes were observed in zebrafish embryos injected with a constitutively active form of yap [(yap (5SA)]. Loss of Yap’s TEAD-binding domain, two WW domains, or transcription activation domain attenuated the retinal abnormalities induced by yap (5SA), indicating that all of these domains contribute to normal retinal development. Remarkably, yap (5SA)-expressing zebrafish embryos displayed decreased expression of transcription factors such as otx5 and crx, which orchestrate photoreceptor cell differentiation by activating the expression of rhodopsin and other photoreceptor cell genes. Co-immunoprecipitation experiments revealed that Rx1 is a novel interacting partner of Yap that regulates photoreceptor cell differentiation. Our results suggest that Yap suppresses the differentiation of photoreceptor cells from retinal progenitor cells by repressing Rx1-mediated transactivation of photoreceptor cell genes during zebrafish retinogenesis.
BRoPH: A compact and efficient binary 3D feature descriptor

Growing availability and use of 3D data acquisition devices have spurred interest in 3D vision field. Feature descriptor is essential to feature matching. Current 3D feature descriptors are mostly represented in float vector. In this paper, we propose a binary 3D feature descriptor, called BRoPH, by turning the binary description of the 3D point cloud into a series of binarization of projected 2D image patches. We demonstrate through experiments that BRoPH achieves a comparable descriptiveness and robustness with respect to the state-of-the-art 3D feature descriptors while outperforms the others in terms of compactness and efficiency.
Multiple feature fusion for face recognition

Recent studies show face recognition (FR) with additional features achieves better performance than that with single one. Different features can represent different characteristics of human faces, and utilizing different features effectively will have positive effect on FR. Meanwhile, the advances of sparse coding enable researchers to develop various recognition methods to cooperate with multiple features. However, even if these methods achieve very encouraging performances, there still exist some intrinsic problems. Firstly, these methods directly encode the multiple features over the original training set, by which way some redundant, noisy and trivial information are incorporated and the recognition performance can be compromised. Moreover, when the training data increase in number, the jointly-encoding process can be very time-consuming. Thirdly, these methods ignore some semantic relationships among the features, which can boost the FR performance. Thus, coarsely utilizing all the features not only adds extra computation burden, but also prevent further improvement. To address these issues, we propose to fuse the multiple features into a more preferable presentation, which is more compact and more discriminative for better FR performance. As well, we take advantage of the dictionary learning framework to derive an effective recognition scheme. We evaluate our model by comparing it with other state-of-the-art approaches, and the experimental results demonstrate the effectiveness of our approach.
Sex difference in appropriate shocks but not mortality during long-term follow-up in patients with implantable cardioverter-defibrillators

Abstract Aims Implantable cardioverter-defibrillators (ICDs) have been shown to improve survival, although a considerable number of patients never receive therapy. Implantable cardioverter-defibrillators are routinely implanted regardless of sex. There is continuing controversy whether major outcomes differ between men and women. Methods and results In this retrospective single-centre study, 1151 consecutive patients (19% women) undergoing ICD implantation between 1998 and 2010 were followed for mortality and first appropriate ICD shock over 4.9 ± 2.7 years. Sex-related differences were investigated using multivariable Cox models adjusting for potential confounders. During follow-up, 318 patients died, a rate of 5.9% per year among men and 4.6% among women (uncorrected P = 0.08); 266 patients received a first appropriate ICD shock (6.3% per year among men vs. 3.6% among women, P = 0.002). After multivariate correction, independent predictors of all-cause mortality were age (hazard ratio, HR = 1.04 per year of age, 95% confidence interval (CI) [1.03–1.06], P < 0.001), left ventricular ejection fraction (HR = 0.98 per %, 95% CI [0.97–1.00], P = 0.025), renal function (HR = 0.99 per mL/min/1.73 m2, 95% CI [0.99–1.00], P = 0.009), use of diuretics (HR = 1.81, 95% CI [1.29–2.54], P = 0.0023), peripheral arterial disease (HR = 2.21, 95% CI [1.62–3.00], P < 0.001), and chronic obstructive pulmonary disease (HR = 1.48, 95% CI [1.13–1.94], P = 0.029), but not sex. Female sex (HR = 0.51, 95% CI [0.33–0.81], P = 0.013), older age (HR = 0.98, 95% CI [0.97–0.99], P < 0.001), and primary prophylactic ICD indication (HR = 0.69, 95% CI [0.52–0.93], P = 0.043) were independent predictors for less appropriate shocks. Conclusion Women receive 50% less appropriate shocks than men having similar mortality in this large single-centre population. These data may pertain to individually improved selection of defibrillator candidates using risk factors, e.g. sex as demonstrated in this study.
An Efficient Sampling Scheme For Comparison of Large Graphs

As new graph structured data is being generated, graph comparison has become an important and challenging problem in application areas such as molecular biology, telecommunications, chemoinformatics, and social networks. Graph kernels have recently been proposed as a theoretically sound approach to this problem, and have been shown to achieve high accuracies on benchmark datasets. Different graph kernels compare different types of subgraphs in the input graphs. So far, the choice of subgraphs to compare is rather ad-hoc and is often motivated by runtime considerations. There is no clear indication that certain types of subgraphs are better than the others. On the other hand, comparing all possible subgraphs has been shown to be NP-hard, thus making it practically infeasible. These difficulties seriously limit the practical applicability of graph kernels. In this article, we attempt to rectify the situation, and make graph kernels applicable for data mining on large graphs and large datasets. Our starting point is the matrix reconstruction theorem, which states that any matrix of size 5 or above can be reconstructed given all its principal minors. By applying this to the adjacency matrix of a graph, we recursively define a graph kernel and show that it can be efficiently computed by using the distribution of all size 4 subgraphs of a graph. This distribution, we argue, is similar to a sufficient statistic of the graph, especially when the graph is large. Exhaustive enumeration of these subgraphs is prohibitively expensive, scaling as O(n4). But, by bounding the deviation of the empirical estimates of the distribution from the true distribution, it suffices to sample a fixed number of subgraphs. Incidentally, our bounds are stronger than those found in the bio-informatics literature for similar techniques. In our experimental evaluation, our graph kernel outperforms state-of-the-art graph kernels both in times of time and classification accuracy.
Commonsense Morality and the Ethics of Killing in War: An Experimental Survey of the Israeli Population

Abstract The morality of killing in war, which has been the subject of debate among philosophers and legal scholars, focuses on two main approaches: the “traditional” and the “revisionist. ” The traditional approach argues for symmetry: soldiers of both sides are liable to be killed, whether or not the war they fight is just; civilians are immune from being harmed whether or not they are responsible for the evil against which the just side fights. Recently this approach has been challenged by a revisionist viewpoint, which derives the morality of killing in war from facts concerning the responsibility of individuals for wrongful threats. Killing soldiers, who fight a just war, is morally impermissible, whereas in certain cases killing civilians who are involved in an unjust aggression is permissible. The present study uses an experimental survey to examine whether moral attitudes toward targeting individuals in war are shaped by the factors underlying this debate: the cause of the war, the status of the targeted individuals as soldiers or civilians, and their involvement in the war effort. Respondents comprised a representative sample of the Jewish population in Israel. Our findings reveal a complex interplay between the factors, demonstrating the relevance of each factor to moral judgments. Notably, participants did not consider decisions on the battlefield to be independent of the justification of the war, a result which is consistent with the revisionist approach. We discuss the potential consequences of our findings on the understanding of commonsense morality and on the law. We also suggest that concepts derived from the social-psychological literature concerning moral self-regulation may help explain some of our findings.
EMDR in the Treatment of Adults Abused as Children

This book shows therapists how to integrate EMDR (eye movement desensitization and reprocessing) into the treatment so that adults who have been abused as children clear their trauma more rapidly, escape falling into the victim mentality, and proceed to lead full, productive lives. For therapists already familiar with EMDR, it covers the primary treatment issues and symptomatology of these clients and specific alterations of the standard EMDR protocol. For therapists experienced with treating abuse survivors, it introduces a safe and effective way to process trauma. Emphasizing the practical, Laurel Parnell not only teaches many techniques to help the therapist when an impasse is reached, but also provides a selection of treatment choices. She demonstrates how EMDR can be used in the beginning phase of therapy for ego strengthening and the development and installation of resources. This prepares clients for trauma processing in the middle phase. Finally, in the end phase, clients integrate their experiences and often feel an awakening of their creativity and spirituality. Cases are used throughout to provide therapists with a deeper, more grounded understanding of different kinds of abuse cases and their treatment. Readers will find that Laurel Parnell is an empathic, sensitive, and knowledgeable guide to the difficult terrain of working with adults abused as children using EMDR.
Microtexturing of the Conductive PEDOT:PSS Polymer for Superhydrophobic Organic Electrochemical Transistors

Superhydrophobic surfaces are bioinspired, nanotechnology artifacts, which feature a reduced friction coefficient, whereby they can be used for a number of very practical applications including, on the medical side, the manipulation of biological solutions. In this work, we integrated superhydrophobic patterns with the conducting polymer PEDOT:PSS, one of the most used polymers in organic electronics because highly sensitive to ionized species in solution. In doing so, we combined geometry and materials science to obtain an advanced device where, on account of the superhydrophobicity of the system, the solutions of interest can be manipulated and, on account of the conductive PEDOT:PSS polymer, the charged molecules dispersed inside can be quantitatively measured. This original substrate preparation allowed to perform electrochemical measurements on ionized species in solution with decreasing concentration down to 10−7 molar. Moreover, it was demonstrated the ability of the device of realizing specific, combined time and space resolved analysis of the sample. Collectively, these results demonstrate how a tight, interweaving integration of different disciplines can provide realistic tools for the detection of pathologies. The scheme here introduced offers breakthrough capabilities that are expected to radically improve both the pace and the productivity of biomedical research, creating an access revolution.
Examining task engagement in sensor-based statistical models of human interruptibility

The computer and communication systems that office workers currently use tend to interrupt at inappropriate times or unduly demand attention because they have no way to determine when an interruption is appropriate. Sensor? based statistical models of human interruptibility offer a potential solution to this problem. Prior work to examine such models has primarily reported results related to social engagement, but it seems that task engagement is also important. Using an approach developed in our prior work on sensor? based statistical models of human interruptibility, we examine task engagement by studying programmers working on a realistic programming task. After examining many potential sensors, we implement a system to log low? level input events in a development environment. We then automatically extract features from these low? level event logs and build a statistical model of interruptibility. By correctly identifying situations in which programmers are non? interruptible and minimizing cases where the model incorrectly estimates that a programmer is non? interruptible, we can support a reduction in costly interruptions while still allowing systems to convey notifications in a timely manner.
Two-step detection of water sound events for the diagnostic and monitoring of dementia

A significant aging of world population is foreseen for the next decades. Thus, developing technologies to empower the independency and assist the elderly are becoming of great interest. In this framework, the IMMED project investigates tele-monitoring technologies to support doctors in the diagnostic and follow-up of dementia illnesses such as Alzheimer. Specifically, water sounds are very useful to track and identify abnormal behaviors form everyday activities (e.g. hygiene, household, cooking, etc.). In this work, we propose a double-stage system to detect this type of sound events. In the first stage, the audio stream is segmented with a simple but effective algorithm based on the Spectral Cover feature. The second stage improves the system precision by classifying the segmented streams into water/non-water sound events using Gammatone Cepstral Coefficients and Support Vector Machines. Experimental results reveal the potential of the combined system, yielding a F-measure higher than 80%.
Trends in the prevalence of asthma and allergic rhinitis in Italy between 1991 and 2010

The prevalence of asthma increased worldwide until the 1990s, but since then there has been no clear temporal pattern. The present study aimed to assess time trends in the prevalence of current asthma, asthma-like symptoms and allergic rhinitis in Italian adults from 1990 to 2010. The same screening questionnaire was administered by mail or phone to random samples of the general population (age 20–44 yrs) in Italy, in the frame of three multicentre studies: the European Community Respiratory Health Survey (ECRHS) (1991–1993; n=6,031); the Italian Study on Asthma in Young Adults (ISAYA) (1998–2000; n=18,873); and the Gene Environment Interactions in Respiratory Diseases (GEIRD) study (2007–2010; n=10,494). Time trends in prevalence were estimated using Poisson regression models in the centres that repeated the survey at different points in time. From 1991 to 2010, the median prevalence of current asthma, wheezing and allergic rhinitis increased from 4.1% to 6.6%, from 10.1% to 13.9% and from 16.8% to 25.8%, respectively. The prevalence of current asthma was stable during the 1990s and increased (relative risk 1.38, 95% CI 1.19–1.59) from 1998–2000 to 2007–2010, mainly in subjects who did not report allergic rhinitis. The prevalence of allergic rhinitis has increased continuously since 1991. The asthma epidemic is not over in Italy. During the past 20 yrs, asthma prevalence has increased by 38%, in parallel with a similar increase in asthma-like symptoms and allergic rhinitis.
Centroid-Based Exemplar Selection of ASL Non-Manual Expressions using Multidimensional Dynamic Time Warping and MPEG4 Features

We investigate a method for selecting recordings of human face and head movements from a sign language corpus to serve as a basis for generating animations of novel sentences of American Sign Language (ASL). Drawing from a collection of recordings that have been categorized into various types of non-manual expressions (NMEs), we define a method for selecting an exemplar recording of a given type using a centroid-based selection procedure, using multivariate dynamic time warping (DTW) as the distance function. Through intraand inter-signer methods of evaluation, we demonstrate the efficacy of this technique, and we note useful potential for the DTW visualizations generated in this study for linguistic researchers collecting and analyzing sign language corpora.
GLC based multi label image annotation

Annotation is an effective way to make image categorization and retrieval efficient. An effective method for image annotation is to decompose the problem into several independent single-label problems, but this ignores the correlations among different labels. In this paper, an image annotation system is proposed, that automatically annotates images by combining label correlation mining and visual similarity between the images. This method extracts the visually similar images from a collection of multi labelled images by using Content Based Image Retrieval (CBIR). Then the labels of resulted images are label correlated by using GLC (Grading based Label Correlation) algorithm and the correlated labels are assigned to the image. The proposed framework is applied to IAPR -TC image data sets. Experimental results of this approach indicate that the annotation is effective.
IRHDF: Iris Recognition using Hybrid Domain Features

Iris Biometric is a unique physiological noninvasive trait of human beings that remains stable over a person's life. In this paper, we propose an Iris Recognition using Hybrid Domain Features (IRHDF) as Dual Tree Complex Wavelet Transform (DTCWT) and Over Lapping Local Binary Pattern (OLBP). An eye is preprocessed to extract the complex wavelet features to obtain the Region of Interest (ROI) area from an iris. OLBP is further applied on ROI to generate features of magnitude coefficients. Resultant features are generated by fusion of DTCWT and OLBP using arithmetic addition. Euclidean Distance (ED) is used to match the test iris image with database iris features to recognize a person. We observe that the values of Equal Error Rate (EER) and Total Success Rate (TSR) are better than in [7].
Effects of extrinsic rewards on children's subsequent intrinsic interest.

GREENE, DAVID, and LEPPER, MARK R. Effects of Extrinsic Rewards on Children's Subsequent Intrinsic Interest. CHILD DEVELOPMENT, 1974, 45, 1141-1145. Preschool children were asked, in individual sessions, to engage in an activity of high initial interest, either for its own sake or in order to obtain an extrinsic reward. Subsequently, children who had undertaken the target activity as a means to some ulterior end showed less intrinsic interest in this activity, as measured unobtrusively several weeks later in the children's classrooms, than control subjects who had either received the same reward unexpectedly or had engaged in the activity without expectation or receipt of extrinsic rewards.