Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender Differences in Associations of Glutamate Decarboxylase 1 Gene (GAD1) Variants with Panic Disorder

Background Panic disorder is common (5% prevalence) and females are twice as likely to be affected as males. The heritable component of panic disorder is estimated at 48%. Glutamic acid dehydrogenase GAD1, the key enzyme for the synthesis of the inhibitory and anxiolytic neurotransmitter GABA, is supposed to influence various mental disorders, including mood and anxiety disorders. In a recent association study in depression, which is highly comorbid with panic disorder, GAD1 risk allele associations were restricted to females. Methodology/Principal Findings Nineteen single nucleotide polymorphisms (SNPs) tagging the common variation in GAD1 were genotyped in two independent gender and age matched case-control samples (discovery sample n = 478; replication sample n = 584). Thirteen SNPs passed quality control and were examined for gender-specific enrichment of risk alleles associated with panic disorder by using logistic regression including a genotype×gender interaction term. The latter was found to be nominally significant for four SNPs (rs1978340, rs3762555, rs3749034, rs2241165) in the discovery sample; of note, the respective minor/risk alleles were associated with panic disorder only in females. These findings were not confirmed in the replication sample; however, the genotype×gender interaction of rs3749034 remained significant in the combined sample. Furthermore, this polymorphism showed a nominally significant association with the Agoraphobic Cognitions Questionnaire sum score. Conclusions/Significance The present study represents the first systematic evaluation of gender-specific enrichment of risk alleles of the common SNP variation in the panic disorder candidate gene GAD1. Our tentative results provide a possible explanation for the higher susceptibility of females to panic disorder.
Analytical Cartography : Past , Present , and Future

Past: The field of Analytical Cartography was defined in the late 1960s by Prof. Waldo Tobler. His approach has been to use mathematical and quantitative analysis to solve conceptual cartographic problems. The basic thrust has been to extend the conceptual and theoretical basis of the field. Present: For the last three decades Tobler and his colleagues such as Moellering, Clarke, Nyerges, Cromley, Franklin, Kimerling, Chrisman, Saalfeld, and Armstrong have striven to expand the base of analytical and conceptual theory in the spatial data sciences. One can cite many conceptual advances developed as part of this effort ranging from cartographic transformations, real/virtual maps through various mathematical operators to considerations of spatial computability. This theoretical and conceptual work meshes very nicely with current work in GIScience that has been developing for the last few years. What Analytical Cartography has been working towards for the last three decades has been joined by GIScience in the last five to ten years. Future: Scientific work to extend these and other newer topics will continue as a concerted effort is made to extend and expand the base of spatial scientific theory for the spatial data sciences. Research workers in the field have been challenged to address more of the grand problems in the field such as the possibility of a general theory of spatial data. The discussion is from the North American point of view.
Logical/Linear Operators for Image Curves

We propose a language for designing image measurement operators suitable for early vision. We refer to them as logical/linear (L/L) operators, since they unify aspects of linear operator theory and Boolean logic. A family of these operators appropriate for measuring the low-order differential structure of image curves is developed. These L/L operators are derived by decomposing a linear model into logical components to ensure that certain structural preconditions for the existence of an image curve are upheld. Tangential conditions guarantee continuity, while normal conditions select and categorize contrast profiles. The resulting operators allow for coarse measurement of curvilinear differential structure (orientation and curvature) while successfully segregating edge-and line-like features. By thus reducing the incidence of false-positive responses, these operators are a substantial improvement over (thresholded) linear operators which attempt to resolve the same class of features. >
Abandoned object position prediction based on mobile robot

Visual surveillance with dynamic background is one of the most active research topics in computer vision. Especially, researches for abandoned detection in the public place have been caused by preventing terror and accident. Previous researches for abandoned detection have been based on a static image and used an image difference. In this paper we present an algorithm for a mobile robot and we use optical flow for detecting a static object and distances from abandoned objects. For this purpose, we find abandoned objects and predict a position of objects using a mobile robot visual system with a single camera.
Semistability, Finite-Time Stability, Differential Inclusions, and Discontinuous Dynamical Systems Having a Continuum of Equilibria

This paper focuses on semistability and finite-time semistability for discontinuous dynamical systems having a continuum of equilibria. Semistability is the property whereby the solutions of a dynamical system converge to Lyapunov stable equilibrium points determined by the system initial conditions. In this paper, we extend the theory of semistability to discontinuous autonomous dynamical systems. In particular, Lyapunov-based tests for semistability and finite-time semistability for autonomous differential inclusions are established.
The Role of Data Mining in Design and Implementation of Intrusion Detection Systems

— Since data mining is one of the most emerging field, when we talk about intrusion detection systems. This paper, proposes a system, named " The role of data mining in design and implementation of intrusion detection system " , which will capture live data packets and will compare with our training data sets to find out normal and abnormal behavior of the packet,the data mining algorithm which we are using is more efficient.Basically, this paper uses the data mining approach like association rule mining and clustering technique for improvement of IDS and further provides lessons learned in this task based upon our experiences in getting started on this type of research work. Results are showing better approach to intrusion detection system.
The scientific study of literary experience: Sampling the state of the art

In this state-of-the-art review, I start with an illustrative example of behavioral data collected during the reading of a love poem reflecting one of many aspects that form the object of the Scientific Study of Literature, i.e., literary experience. A further section discusses key ingredients of literary experience, i.e., immersive and aesthetic processes. The paper’s core part analyses four recent representative empirical studies covering perspectives ranging from phenomenology to cognitive neuroscience. In the final sections, a number of critical theoretical and methodological issues are considered. Systematic research on the ontogeny of literary experiences is identified as a main desideratum of the field and further recommendations for the future scientific study of literature are proposed.
Enhanced Bit-Loading Techniques for Adaptive MIMO Bit-Interleaved Coded OFDM Systems

When channel state information (CSI) is available at the transmitter, the system throughput can be enhanced by adaptive transmissions and opportunistic multiuser scheduling. In this paper, we consider multi-input multi-output (MIMO) systems employing bit-interleaved coded orthogonal frequency division multiplexing (BIC-OFDM). We first propose a bit- loading algorithm based on the Levin-Campello algorithm for the BIC-OFDM. Then we will apply this algorithm to the MIMO system with a finite set of constellations, by reassigning residual power on each stream. Simulation results show that proposed bit-loading scheme which takes the residual power into account improves the system performance especially at high signal-to- noise ratio (SNR) range.
STUDENTS' PERSPECTIVE OF PRACTICAL WORK IN LEARNING SCIENCES VIA DISTANCE EDUCATION

This article focuses on students' perspectives on various aspects of learning science at a distance, such as ‘expectations,’ ‘learning outcomes,’ and ‘satisfaction’. Two foundation science courses offered by the School of Science and Technology at the Open University of Hong Kong (OUHK) were selected for the study. A series of indepth interviews was carried out with students enrolled in these courses, followed by questionnaire surveys and field observations in order to validate and triangulate the interview data. The findings revealed that students placed a high value on practical work in science courses. Moreover, the more familiar with the course subject, the less difficulty they anticipate in learning science via distance education. Two significant correlations were also found, between the perception of practical work and satisfaction, and between the attendance rate and the perception of practical work with distance learning at OUHK. This research adds to knowledge of disciplinebased distance education research in terms of empirical data in both qualitative and quantitative forms. Further investigation on this issue may help instructional designers and developed of science courses in the decision on various aspects of course design and development.
Youth, Insecurity and Education: Guest Editorial

The University Repository is a digital collection of the research output of the University, available on Open Access. Copyright and Moral Rights for the items on this site are retained by the individual author and/or other copyright owners. Users may access full items free of charge; copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational or not-for-profit purposes without prior permission or charge, provided:
Preparation of Biodiesel from Soybean Catalyzed by Basic Ionic Liquids [Hnmm]OH

A morpholine alkaline basic ionic liquid (IL) 1-butyl-3-methyl morpholine hydroxide ([Hnmm]OH) was synthesized and characterized by 1H NMR and FT-IR. [Hnmm]OH is highly active in catalyzing the synthesis of biodiesel from the reaction of methanol with soybean oil. The influence of the reaction conditions, including the [Hnmm]OH catalyst amount, the molar ratio of methanol to soybean oil, reaction temperature and time, was investigated. Moreover, the pH and thermal stability of the catalyst was studied. The catalytic activity was affected by its alkalinity. The optimum reaction conditions were found as [Hnmm]OH amount of 4% (mass fraction), the methanol to soybean oil molar ratio of 8, temperature 70 °C and reaction time 1.5 h, the yield of Biodiesel reached 97.0%, and exhibits high stability upon recycling, the yield of Biodiesel is still more than 90% even after being reused for five times. A great advantage of using ILs is that it is very easy to separate the final products. After the reaction, a biphasic system was obtained. The top phase contains biodiesel and a little bit of methanol. Pure biodiesel can be isolated by vacuum evacuating the methanol. The bottom phase contains methanol, glycerol and ILs. Pure glycerol can be obtained simply by distillation. After distillation, pure ILs was obtained, which can be used directly for another reaction. The as prepared biodiesel shows very appealing properties.
The Proapoptotic BCL-2 Homology Domain 3-Only Protein Bim Is Not Critical for Acute Excitotoxic Cell Death

Prolonged and repetitive epileptic activity is causally linked to neuronal cell death in the brain and is most marked in vulnerable subfields of the hippocampus. The Bcl-2 family protein Bim, a proapoptotic member of the BCL-2 homology domain 3-only subfamily, has been implicated as an important mediator of neuronal cell damage in various pathological conditions, although its role in epilepsy-associated cell death is not understood. We performed intrahippocampal stereotaxic injections of the glutamate analog kainic acid as an in vivo model of acute excitotoxicity to assess neuronal injury in Bim-deficient and control wild-type mice. A variety of cell death parameters including chromatin condensation, TdT-mediated dUTP nick end labeling, and caspase-3 activity was assessed. We found no differences in the extent of hippocampal neuronal death parameters between the 2 groups. Moreover, electroencephalographic recordings after kainic acid injection revealed indistinguishable patterns of seizure activity in Bim-deficient and wild-type animals. These in vivo and histological data suggest that Bim is not critically involved in excitotoxicity-induced acute neuronal cell injury.
Identifying functional motifs on protein surfaces by structural alignment with sequence information

Protein local structures, especially the regional patterns of surface cavities, are critical to determine specific protein functions and crucial in drug design and other protein engineering [1, 2]. It is in pressing need to develop novel structural alignment methods to identify these functional structural motifs. Although many structure alignment methods have been developed [3], most of them require the implicitly incorporation of sequence order constraints, which may suit for revealing global similarity between whole structures but fails to detect local structure similarity since it not only sacrifices the accuracy of the result, but also increases the difficulty and loses the biochemical similarity implication [4]. In this paper, we propose a new method to identify functional motifs on protein surface cavities by combining sequence and structure information together in protein local structure alignment. We believe that involvement of sequence information in structure alignment may generate more robust and biologically meaningful results.
Discovering Rubik's Cube Subgroups using Coevolutionary GP: A Five Twist Experiment

This work reports on an approach to direct policy discovery (a form of reinforcement learning) using genetic programming (GP) for the 3 by 3 by 3 Rubik's Cube. Specifically, a synthesis of two approaches is proposed: 1) a previous group theoretic formulation is used to suggest a sequence of objectives for developing solutions to different stages of the overall task; and 2) a hierarchical formulation of GP policy search is utilized in which policies adapted for an earlier objective are explicitly transferred to aid the construction of policies for the next objective. The resulting hierarchical organization of policies explicitly demonstrates task decomposition and policy reuse. Algorithmically, the process makes use of a recursive call to a common approach for maintaining a diverse population of GP individuals and then learns how to reuse subsets of programs (policies) developed against the earlier objective. Other than the two objectives, we do not explicitly identify how to decompose the task or mark specific policies for reuse. Moreover, at the end of evolution we return a population solving 100% of 17,675,698 different initial Cubes for the two objectives currently in use.
Random finite set for data association in multiple camera tracking

Most methods for multiple camera tracking rely on accurate calibration to associate data from multiple cameras. However, it often is not easy to have an accurate calibration in some real applications due to practical reasons. The inaccurate calibration can lead to wrong data association of objects between cameras. In this paper, we propose a method to handle the data association of objects in multiple cameras under inaccurate ground plane homography by using the RFS Bayes filter. Our method is based on modeling measurements from cameras to a random finite set. This random finite set includes the primary measurement from the object, extraneous measurements of the object, and clutter. Experimental results show the efficiency and robustness of our method through challenging cases such as occlusions, merged and split persons.
Lbl-33634 Neural Network for Recognizing Cerenkov Radiation Patterns 3

A three layer feed forward neural network has been developed to distinguish between true and false Cerenkov radiation signals that are present in certain nuclear reactions. The network consists of an input layer of phototubes on a spherical surface, two hidden layers of neurons, and an output neuron. The rst layer connections are developed by principal component analysis based on our measurements of the spatiotemporal correlation of Cerenkov radiation patterns. The connections of the second and third layers are learned by back error propagation on the rst layer output. It is shown that temporal information in addition to spatial information is very important for identifying di erent patterns. The network, modelled after the Sudbury Neutrino Observatory detector geometry, is capable of recognizing certain classes of event patterns with a high degree of accuracy. This work was supported by the Director, O ce of Energy Research, Division of Nuclear Physics of the O ce of High Energy and Nuclear Physics of the U.S. Department of Energy under Contract No. DE-AC03-76SF00098. y Future Address: Beckman Institute 139-74, California Institute of Technology, Pasadena, CA 91125. Email: dawei@hope.caltech. edu.
Towards A Sociology For Childhood Thinking From Children S Lives

Thank you for downloading towards a sociology for childhood thinking from children s lives. Maybe you have knowledge that, people have look numerous times for their favorite books like this towards a sociology for childhood thinking from children s lives, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful virus inside their laptop.
Modelling Soil Water Retention Using Support Vector Machines with Genetic Algorithm Optimisation

This work presents point pedotransfer function (PTF) models of the soil water retention curve. The developed models allowed for estimation of the soil water content for the specified soil water potentials: –0.98, –3.10, –9.81, –31.02, –491.66, and –1554.78 kPa, based on the following soil characteristics: soil granulometric composition, total porosity, and bulk density. Support Vector Machines (SVM) methodology was used for model development. A new methodology for elaboration of retention function models is proposed. Alternative to previous attempts known from literature, the ν-SVM method was used for model development and the results were compared with the formerly used the C-SVM method. For the purpose of models' parameters search, genetic algorithms were used as an optimisation framework. A new form of the aim function used for models parameters search is proposed which allowed for development of models with better prediction capabilities. This new aim function avoids overestimation of models which is typically encountered when root mean squared error is used as an aim function. Elaborated models showed good agreement with measured soil water retention data. Achieved coefficients of determination values were in the range 0.67–0.92. Studies demonstrated usability of ν-SVM methodology together with genetic algorithm optimisation for retention modelling which gave better performing models than other tested approaches.
Optimizations of Power Consumption and Supply in the Smart Grid: Analysis of the Impact of Data Communication Reliability

Data communications infrastructure will play an important role to transfer various information in smart grid. In this paper, we consider the reliability of the smart grid data communications infrastructure and its impact on the power consumption and supply optimizations. For optimizing the power consumption, we consider a deferrable load scheduling method which is modeled by using a constrained Markov decision process (CMDP) model, taking into account the unavailability of the home area network (HAN) and neighborhood area network (NAN) gateways. For optimizing the power supply, we consider an economic dispatch method which is modeled by using stochastic programming (SP), taking into account the unavailability of the exact power demand and supply information. The power consumption and supply costs are analyzed. In addition, we show how these costs can be reduced through the deployment of component redundancy in the smart grid data communications infrastructure.
Thymidine Kinase 2 Deficiency-Induced mtDNA Depletion in Mouse Liver Leads to Defect β-Oxidation

Thymidine kinase 2 (TK2) deficiency in humans causes mitochondrial DNA (mtDNA) depletion syndrome. To study the molecular mechanisms underlying the disease and search for treatment options, we previously generated and described a TK2 deficient mouse strain (TK2−/−) that progressively loses its mtDNA. The TK2−/− mouse model displays symptoms similar to humans harboring TK2 deficient infantile fatal encephalomyopathy. Here, we have studied the TK2−/− mouse model to clarify the pathological role of progressive mtDNA depletion in liver for the severe outcome of TK2 deficiency. We observed that a gradual depletion of mtDNA in the liver of the TK2−/− mice was accompanied by increasingly hypertrophic mitochondria and accumulation of fat vesicles in the liver cells. The levels of cholesterol and nonesterified fatty acids were elevated and there was accumulation of long chain acylcarnitines in plasma of the TK2−/− mice. In mice with hepatic mtDNA levels below 20%, the blood sugar and the ketone levels dropped. These mice also exhibited reduced mitochondrial β-oxidation due to decreased transport of long chain acylcarnitines into the mitochondria. The gradual loss of mtDNA in the liver of the TK2−/− mice causes impaired mitochondrial function that leads to defect β-oxidation and, as a result, insufficient production of ketone bodies and glucose. This study provides insight into the mechanism of encephalomyopathy caused by TK2 deficiency-induced mtDNA depletion that may be used to explore novel therapeutic strategies.
Extremely Metal-poor Stars. IX. CS 22949-037 and the Role of Hypernovae

We have obtained high-resolution spectra of the extremely metal-poor star CS 22949-037 ([Fe/H] = -3.79) in the region of the ultraviolet NH bands at 3360-3370 Å, which confirm our earlier report that it exhibits extreme nitrogen enhancement. We find [N/Fe] = 2.3 ± 0.4. The star is also known to have large relative overabundances, of order 0.5-1 dex, of carbon and the α-elements. This abundance pattern is not explained by any standard Galactic chemical enrichment model of which we are aware. The large N enhancement is, however, predicted by the zero heavy element, M ≳ 200 M☉, hypernova models of Woosley & Weaver and Fryer, Woosley, & Heger. If the rotating models of Fryer et al. are relevant to the enrichment of CS 22949-037, one might expect it to possess a large overabundance of oxygen, with a mass fraction similar to or larger than that of nitrogen, and an [O I] λ6300. 3 line strength of ~2-8 mÅ.