Miracle-dz/UniFAR-FTUs huggingface dataset:
Deficient glucose and glutamine metabolism in Aralar/AGC1/Slc25a12 knockout mice contributes to altered visual function

Purpose To characterize the vision phenotype of mice lacking Aralar/AGC1/Slc25a12, the mitochondrial aspartate-glutamate carrier mutated in global cerebral hypomyelination (OMIM 612949). Methods We tested overnight dark-adapted control and aralar-deficient mice for the standard full electroretinogram (ERG) response. The metabolic stress of dark-adaptation was reduced by 5 min illumination after which the ERG response was monitored in darkness. We used the electrical response to two identical saturating light flashes (paired-flash stimulation) to isolate the inner retina and photoreceptor responses. Retinal morphology was examined with hematoxylin and eosin staining, immunohistochemistry of antibodies against retinal cells, and 4',6-diamidino-2-phenylindole (DAPI) labeling. Results Aralar plays a pivotal role in retina metabolism as aralar provides de novo synthesis pathway for glutamine, protects glutamate from oxidation, and is required for efficient glucose oxidative metabolism. Aralar-deficient mice are not blind as their retinas have light-evoked activity. However, we report an approximate 50% decrease in the ERG amplitude response in the light-evoked activity of dark-adapted retinas from aralar-deficient mice, in spite of normal retina histology. The defective response is partly reversed by exposure to a brief illumination period, which lowers the metabolic stress of dark-adaptation. The metabolic stress and ERG alteration takes place primarily in photoreceptors, but the response to two flashes applied in fast succession also revealed an alteration in synaptic transmission consistent with an imbalance of glutamate and an energy deficit in the inner retina neurons. Conclusions We propose that compromised glucose oxidation and altered glutamine and glutamate metabolism in the absence of aralar are responsible for the phenotype reported.
The populist breeding ground and religion: the case of the Greek mainstream and Radical Right

ABSTRACT This article examines whether the decade preceding the financial crisis fuelled a culture of political resentment in Greece, and the role of religion in this development. To this end, it explores empirically how religiosity affected mainstream and Radical Right attitudes and voting in the period 2000–2010, and to what extent political resentment might have worked as a reinforcing or moderating factor to this relationship. The article primarily demonstrates that religiosity fosters both mainstream and Radical Right attitudes and voting; religiosity is negatively correlated with political resentment; and political resentment reinforces the positive effect of religiosity on mainstream Right attitudes, but mitigates the positive effect of religiosity on Radical Right attitudes.
Deep-Learning-Based Classification and Diagnosis of Alzheimer's Disease

Alzheimer's is the most common form of dementia in India and it is one of the leading causes of death in the world. Currently it is diagnosed by calculating the MSME score and by manual study of MRI scan. In this chapter, the authors develop and compare different methods to diagnose and predict Alzheimer's disease by processing structural magnetic resonance image scans (MRI scans) with deep learning neural networks. The authors implement one model of deep-learning networks which are convolution neural network (CNN). They use four different architectures of CNN, namely Lenet-5, AlexNet, ZFNet, and R-CNN architecture. The best accuracies for 75-25 cross validation and 90-10 cross validation are 97.68% and 98.75%, respectively, and achieved by ZFNet architecture of convolution neural network. This research will help in further studies on improving the accuracy of Alzheimer's diagnosis and prediction using neural networks.
3-D interaction with a large wall display using transparent markers

In this paper we proposed a new interface for interacting with large displays via small video devices such as a cell phone. We estimate the location of the camera relative to the display using a matrix of transparent markers embedded on display. As a result, our interface allows the user to interact with digital contents without being distracted by opaque visual markers. Our interface enables intuitive interactions such as pointing, rotating, dragging and dropping. Moreover, our use of a small hand-held camera device allows for interaction with large scale displays without the need for direct contact with the display surface. Thus our system is well suited for interactions when there is some distance between the user and the display. Our proposed system has applications to large scale advertisement displays and can enable interactions between individuals and large scale digital content.
Robot designed for walking and climbing based on abstracted cockroach locomotion mechanisms

MechaRoach II is a hexapod robot under development that can walk on horizontal surfaces, climb on inclined or vertical mesh surfaces, and test strategies for transitioning between the two. The locomotion principles that allow cockroaches to make these transitions have been studied and mechanisms using abstractions of those principles have been developed for the robot. These principles include usage of features of leg and foot morphology, leg compliance, gait adaptation, and body flexion. MechaRoach II has a single drive motor, a motor for steering, and a motor to actuate a body flexion joint. The single drive motor powers all six legs, and each leg uses 4-bar mechanisms to recreate cockroach-like foot trajectories. Cockroaches have been shown to flex their bodies downward between the first and second thoracic segments or rear their bodies upward using their middle legs during transitions between climbing on vertical surfaces and walking on horizontal ones. Similarly, MechaRoach II's body joint rears the front of the robot upward or downward during transitioning. The robot normally walks in a tripod gait with contralateral legs 180 degrees out of phase, but uses passive torsionally compliant devices to bring contralateral legs into phase for climbing
Performance evaluation of the MDOAM and SSM methods

The Modified Direction-of-Arrival (DOA) matrix (MDOAM) method and the Signal Space Matrix (SSM) method are two Subspace Rotation-Based (SRB) techniques that estimate 2-D DOA angles of narrowband signals impinging on a nonlinear sensor array with a built-in displacement invariant structure, First, the paper evaluates the performance of the MDOAM and SSM methods for locating far-field sources by relating the estimation error to the Cramer-Rao lower bound. Second, the paper identifies the conditions under which the methods can be used to detect near-field sources. We evaluate the MDOAM and SSM methods because they have many advantages over the previous SRB techniques of ESPRIT, the DOAM method, and the SR method. <<ETX>>
Heuristic Evaluation on Mobile Interfaces: A New Checklist

The rapid evolution and adoption of mobile devices raise new usability challenges, given their limitations (in screen size, battery life, etc.) as well as the specific requirements of this new interaction. Traditional evaluation techniques need to be adapted in order for these requirements to be met. Heuristic evaluation (HE), an Inspection Method based on evaluation conducted by experts over a real system or prototype, is based on checklists which are desktop-centred and do not adequately detect mobile-specific usability issues. In this paper, we propose a compilation of heuristic evaluation checklists taken from the existing bibliography but readapted to new mobile interfaces. Selecting and rearranging these heuristic guidelines offer a tool which works well not just for evaluation but also as a best-practices checklist. The result is a comprehensive checklist which is experimentally evaluated as a design tool. This experimental evaluation involved two software engineers without any specific knowledge about usability, a group of ten users who compared the usability of a first prototype designed without our heuristics, and a second one after applying the proposed checklist. The results of this experiment show the usefulness of the proposed checklist for avoiding usability gaps even with nontrained developers.
On the Capacity Region of Multi-Radio Multi-Channel Wireless Mesh Networks

Next generation fixed wireless broadband networks are being increasingly deployed as mesh networks in order to provide and extend access to the internet. These networks are characterized by the use of multiple orthogonal channels and nodes with the ability to simultaneously communicate with many neighbors using multiple radios (interfaces) over orthogonal channels. Networks based on the IEEE 802.11a/b/g and 802.16 standards are examples of these systems. However, due to the limited number of available orthogonal channels, interference is still a factor in such networks. Recent results have attempted to characterize the achievable capacity of such networks, both in the deterministic[1] and asymptotic[2] sense. In this paper, we build on these previous works and provide an experimental verification of asymptotic capacity result in [2] by using the deterministic model first proposed in [1]. Our simulation results are based on a novel joint channel assignment and scheduling algorithm which is shown to achieve near-optimal capacity limits in random network topologies. The results in this paper are the first to provide an experimental verification of theoretical results in [2] and give an insight into the behavior of the capacity region of a multi-hop multi-radio mesh network.
Variable-size burst based on information-theoretic criterion for interference suppression in wireless communications

Results previously obtained show that a variable-size burst has the potential to improve spectral efficiency in burst-by-burst wireless communication systems. However the methods proposed were applicable to only a certain class of channels. In this work, an alternative strategy for constructing a variable-size burst based on the minimum description length criterion is proposed. Use of this information-theoretic criterion enhances the applicability and efficacy of a variable-size burst, in a wider gamut of scenarios. The cost for this improved optimality is an increase in computational complexity compared to the previously proposed methods.
Impact of Global Transcriptional Regulation by ArcA, ArcB, Cra, Crp, Cya, Fnr, and Mlc on Glucose Catabolism in Escherichia coli

ABSTRACT Even though transcriptional regulation plays a key role in establishing the metabolic network, the extent to which it actually controls the in vivo distribution of metabolic fluxes through different pathways is essentially unknown. Based on metabolism-wide quantification of intracellular fluxes, we systematically elucidated the relevance of global transcriptional regulation by ArcA, ArcB, Cra, Crp, Cya, Fnr, and Mlc for aerobic glucose catabolism in batch cultures of Escherichia coli. Knockouts of ArcB, Cra, Fnr, and Mlc were phenotypically silent, while deletion of the catabolite repression regulators Crp and Cya resulted in a pronounced slow-growth phenotype but had only a nonspecific effect on the actual flux distribution. Knockout of ArcA-dependent redox regulation, however, increased the aerobic tricarboxylic acid (TCA) cycle activity by over 60%. Like aerobic conditions, anaerobic derepression of TCA cycle enzymes in an ArcA mutant significantly increased the in vivo TCA flux when nitrate was present as an electron acceptor. The in vivo and in vitro data demonstrate that ArcA-dependent transcriptional regulation directly or indirectly controls TCA cycle flux in both aerobic and anaerobic glucose batch cultures of E. coli. This control goes well beyond the previously known ArcA-dependent regulation of the TCA cycle during microaerobiosis.
Enhancing Teacher Effectiveness in Spain: A Pilot Study of The RULER Approach to Social and Emotional Learning

This study examined the effects of an evidence-based social and emotional learning (SEL) program, The RULER Approach to Social and Emotional Learning (RULER), on teacher self reports of engagement, teacher-student interactions, and burnout. Participants were 47 teachers from 19 public schools in Spain who either volunteered for training on RULER (n = 24) or eLearning (n = 23). Multivariate Analysis of Covariance (MANCOVA) was conducted separately for each outcome. Teachers in the RULER as compared to eLearning group had significantly higher scores on many outcomes even after controlling for gender, age, trait affect, and personality, as well as pre-test scores on all outcomes. These findings extend the literature on the effectiveness of SEL programs for the improvement of teacher practices; they also advance our understanding of possible mechanisms for promoting high-quality professional development.
Emergence of statistically validated financial intraday lead-lag relationships

According to the leading models in modern finance, the presence of intraday lead-lag relationships between financial assets is negligible in efficient markets. With the advance of technology, however, markets have become more sophisticated. To determine whether this has resulted in an improved market efficiency, we investigate whether statistically significant lagged correlation relationships exist in financial markets. We introduce a numerical method to statistically validate links in correlation-based networks, and employ our method to study lagged correlation networks of equity returns in financial markets. Crucially, our statistical validation of lead-lag relationships accounts for multiple hypothesis testing over all stock pairs. In an analysis of intraday transaction data from the periods 2002–2003 and 2011–2012, we find a striking growth in the networks as we increase the frequency with which we sample returns. We compute how the number of validated links and the magnitude of correlations change with increasing sampling frequency, and compare the results between the two data-sets. Finally, we compare topological properties of the directed correlation-based networks from the two periods using the in-degree and out-degree distributions and an analysis of three-node motifs. Our analysis suggests a growth in both the efficiency and instability of financial markets over the past decade.
Process Induced Random Variation Models of Nanoscale MOS Performance : Efficient tool for the nanoscale regime analog / mixed signal CMOS statistical / variability aware design

Abstract. In this research, the novel models of random variation in Ids which is a key parameter of any MOS transistor, have been proposed in this research as the probability density functions. Both triode and saturation regions have been explored. Unlike the previous researches, this research has been performed based upon the up to dated nanoscale regime MOS equations. The proposed models for both regions have been verified at 65 nm technology by using the Monte Carlo simulation and the Kolmogorov-Smirnof goodness of fit test (KS-test). These models are very accurate since they can fit the Monte Carlo based distribution with 99% confidence. Hence, the proposed models have been found to be efficient for the statistical/variability aware design of various CMOS analog/mixed signal circuits and systems in the nanoscale regime.
Unsupervised clustering technique to harness ideas from an Ideas Portal

Supervised learning techniques have long been used to analyze unstructured natural language text documents. However, supervised learning techniques are not only computationally intensive but also often require large training corpora. Supervised techniques often fail when such training corpora is either (a) not available or (b) when available, is not statistically significant to enable learning. In many practical scenarios, unsupervised learning techniques become de-facto since the training corpus is not available. In this paper we first describe an unsupervised text analysis technique and demonstrate its usefulness in addressing a real life application to harness ideas from aggregating ideas posted on our company Ideas Portal website.
THE RAILMAPPER – A DEDICATED MOBILE LIDAR MAPPING SYSTEM FOR RAILWAY NETWORKS

Abstract. The Mobile LiDAR Mapping System StreetMapper from IGI and 3D Laser Mapping (Bingham Nottingham, UK) is mounted on a large variety of road vehicles to cover different mission specifications. In addition to the operation on the road, the system finds its applications on other kinds of vehicles, like boats or trains. The modular and flexible system concept even allows utilizing the same LiDAR Mapping system for Mobile Mapping on the ground and for airborne missions on helicopters, respectively. Besides this general flexibility, each application has its own special requirements. Special hardware and software components are needed to complete the core components, like the laser scanner and the GNSS/IMU systems, to build a dedicated system for the chosen task. Compared to the typical dynamics of a road vehicle mounted Mobile Mapping system, a dedicated rail mapping system operates under conditions that are much more challenging for a high accuracy GNSS/IMU trajectory determination. Furthermore, the typical rail mapping tasks, like the exact measurement of the rail track geometry, require the operation of the most accurate laser scanners and of specialized post-processing software. In this paper, the RailMapper , a specialized Mobile Mapping system for railway surveys is presented. The system is described with focus on the railway specific requirements and results of practical surveys are given.
Transmit diversity over quasi-static fading channels using multiple antennas and random signal mapping

We introduce a new scheme that achieves a diversity gain for coded systems under static fading conditions by using multiple antennas and random signal mapping. It is shown that as the number of transmit antennas N goes to infinity, the effective channel behaves as if it were perfectly interleaved (i.e. as if the fading was independent). When N is small, a further performance gain can be achieved by expanding the original signal constellation. Simulation results show that N/spl ap/8 is enough to achieve close to the performance of a perfectly interleaved system.
Similarity of salivary microbiome in parents and adult children

Background Human saliva contains approximately 700 bacterial species. It has been reported that the salivary microbiome of a large family of closely related individuals consisting of multiple households is similar but the relatedness of salivary bacteria between generations of parents and their children has not yet been investigated. The objectives were to investigate the entirety of salivary bacterial DNA profiles and whether and how families share these profiles and also compare these communities between grandparents and their first daughter generations (F1) using 16S rRNA gene amplicon sequencing. Results The most abundant phyla in two separate families were Firmicutes, Bacteroidetes, Proteobacteria, Fusobacteria and Actinobacteria. Family ties explained 13% of the variance between individuals’ bacterial communities (R2 = 0.13; P = 0.001). Mothers shared more OTUs with adult children compared to fathers, but this linkage seemed to be weaker in the nuclear family with older adult children. We identified 29 differentially abundant genus level OTUs (FDR < 0.05) between families, which accounted for 31% of the total identified genus level OTUs. Conclusions Our results indicate that adult family members share bacterial communities and adult children were more similar to mothers than fathers. The observed similarity in oral microbiome between parent–child pairs seemed to weaken over time. We suggest that our analysis approach is suitable for relatedness study of multigenerational salivary bacteria microbiome.
Sizing and economic assessment of energy Storage with real-time pricing and ancillary services

Energy storage units (ESU) can generate profit through providing multiple services. In order to maximize profit while meeting battery lifetime constraints, it is necessary to select the capacity and charge/discharge schedule of the battery. This paper proposes a methodology to calculate the necessary ESU battery capacity for a given power rating and the lifetime profitability of the ESU. Because the price of electricity is not known more than 24 hours in advance, forecasting is necessary when scheduling the ESU. The reduction in profitability of the ESU because of forecasting errors must be taken into account. The methodology applies ridge regression for price forecasting to overcome the problem of having many highly correlated inputs, which lead to poor performance of the forecaster. The methodology demonstrates the encouraging result that the profitability of an ESU is not highly sensitive to forecaster error, as the scheduler is insensitive to the types of errors introduced by the forecaster. This is because of the nature of the variance in electricity market price.
Shapes as property restrictions and property-based similarity

In this work we look into the details of modeling shapes in an ontology as property restrictions on classes. In this way shapes do not have to be categorized in an exhaustive hierarchy and there is no need to take immediate decisions on how to group the objects, it is rather possible to individualize some important characteristics of shapes and use them as the basis for their categorization and comparison. This approach also makes it possible to use the adequate similarity measure based on properties which helps find similar shapes in different contexts, depending on the relevance of the properties in the particular situation.
Gender Differences in Suicide Rates

Objective: There is limited research on the trends in suicide rates by gender in Turkey. The purpose of the study is to examine suicide rates by gender over time and whether suicide rates by gender differ significantly. Method: The data on suicide from 2007 to 2016 were obtained from the Turkish Statistical Institute. The number of suicide cases was 28,760 (72.5% male, 27.5% female). Direct standardization method was used to calculate gender specific suicide rates. Numerical (correlation) and graphical (line charts) methods were used to reveal the trends in suicide rates by gender. Then, paired-sample t-test was conducted to determine whether suicide rates significantly differed by gender. Results: The increase in suicide rates among males was not statistically significant while there was a statistically significant decrease in suicide rates among females over time. The ratio of male to female suicide rate increased significantly over time. In addition, the difference in suicide rates between males and females was statistically significant. Male suicide rates were 2.5 times higher than that of female. Conclusion: Suicide rates differ by gender.
A Web Recommender System Based on Dynamic Sampling of User Information Access Behaviors

Abstract — In this study, we propose a Gradual Adaption Model for a Web recommender system. This model is used to track users’ focus of interests and its transition by analyzing their information access behaviors, and recommend appropriate information. A set of concept classes are extracted from Wikipedia. The pages accessed by users are classified by the concept classes, and grouped into three terms of short, medium and long periods, and two categories of remarkable and exceptional for each concept class, which are used to describe users’ focus of interests, and to establish reuse probability of each concept class in each term for each user by Full Bayesian Estimation as well. According to the reuse probability and period, the information that a user is likely to be interested in is recommended. In this paper, we propose a new approach by which short and medium periods are determined based on dynamic sampling of user information access behaviors. We further present experimental simulation results, and show the validity and effectiveness of the proposed system.