Miracle-dz/UniFAR-FTUs huggingface dataset:
Ablation of Gabra5 Influences Corticosterone Levels and Anxiety-like Behavior in Mice

Stress responses are activated by the hypothalamic-pituitary-adrenal axis (HPA axis), culminating in the release of glucocorticoids. During prolonged periods of secretion of glucocorticoids or inappropriate behavioral responses to a stressor, pathologic conditions may occur. Increased glucocorticoid concentration is linked to generalized anxiety, and there are knowledge gaps regarding its regulation. It is known that the HPA axis is under GABAergic control, but the contribution of the individual subunits of the GABA receptor is largely unknown. In this study, we investigated the relationship between the α5 subunit and corticosterone levels in a new mouse model deficient for Gabra5, which is known to be linked to anxiety disorders in humans and phenologs observed in mice. We observed decreased rearing behavior, suggesting lower anxiety in the Gabra5−/− animals; however, such a phenotype was absent in the open field and elevated plus maze tests. In addition to decreased rearing behavior, we also found decreased levels of fecal corticosterone metabolites in Gabra5−/− mice indicating a lowered stress response. Moreover, based on the electrophysiological recordings where we observed a hyperpolarized state of hippocampal neurons, we hypothesize that the constitutive ablation of the Gabra5 gene leads to functional compensation with other channels or GABA receptor subunits in this model.
Solitary-wave decay in a cold plasma

A numerical investigation of the evolution of localized disturbances in the form of solitary waves is performed for the full system of equations describing planar waves in a cold collisionless quasineutral plasma. It is shown that, for a wide range of angles of inclination of the magnetic field vector to the direction of wave propagation, such a kind of initial disturbance never results in a wave propagating steadily, but leads to decay due to radiation of periodic waves. These results confirm the recent theoretical conclusion of Il'ichev [J. Plasma Phys.55, 181–194 (1996)] about the non-existence of solitary waves in a cold plasma for the range of angles of inclination in question.
Magneto-optic disk storage

Recent researches on materials and systems for magneto-optic disk storage are reviewed. An amorphous double-layered GdCo film is suitable to use for this purpose. An experimentaI result on recording color TV signals in real time on a GdCo disk is also presented. Fairly good motion pictures were reproduced. From this work, it can be said that magneto-optic disk storage system will be put into practice in the near future.
The Surprising Effectiveness of Deep Orthogonal Procrustes Alignment in Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledge from a labeled source domain to an unlabeled target domain. Traditionally, geometry-based alignment methods, e.g., Orthogonal Procrustes Alignment (OPA), formed an important class of solutions to this problem. Despite their mathematical tractability, they rarely produce effective adaptation performance with the recent benchmarks. Instead, state-of-the-art approaches rely on sophisticated distribution alignment strategies such as adversarial training. In this paper, we show that, conventional OPA, when coupled with powerful deep feature extractors and a novel bi-level optimization formulation, is indeed an effective choice for handling challenging distribution shifts. When compared to existing UDA methods, our approach offers the following benefits: computational efficiency: Through the isolation of alignment and classifier training steps during adaptation, and the use of deep OPA, our approach is computationally very effective (typically requiring only 700 K parameters more than the base feature extractor as compared to millions of extra parameters required by state-of-the-art UDA baselines); (ii) data efficiency: Our approach does not require updating our feature extractor during adaptation and hence can be effective even with limited target data; (iii) improved generalization: The resulting models are intrinsically well-regularized and demonstrate effective generalization even in the challenging partial DA setting, i.e., target domain contains only a subset of the classes observed in the source domain. ; and (iv) incremental training: Our approach allows progressive adaptation of models to novel domains (unseen during training) without requiring retraining of the model from scratch.
Three‐dimensional modelling and inversion of dc resistivity data incorporating topography – II. Inversion

SUMMARY We present a novel technique for the determination of resistivity structures associated with arbitrary surface topography. The approach represents a triple-grid inversion technique that is based on unstructured tetrahedral meshes and finite-element forward calculation. The three grids are characterized as follows: A relatively coarse parameter grid defines the elements whose resistivities are to be determined. On the secondary field grid the forward calculations in each inversion step are carried out using a secondary potential (SP) approach. The primary fields are provided by a one-time simulation on the highly refined primary field grid at the beginning of the inversion process. We use a Gauss‐Newton method with inexact line search to fit the data within error bounds. A global regularization scheme using special smoothness constraints is applied. The regularization parameter compromising data misfit and model roughness is determined by an L-curve method and finally evaluated by the discrepancy principle. To solve the inverse subproblem efficiently, a least-squares solver is presented. We apply our technique to synthetic data from a burial mound to demonstrate its effectiveness. A resolution-dependent parametrization helps to keep the inverse problem small to cope with memory limitations of today’s standard PCs. Furthermore, the SP calculation reduces the computation time significantly. This is a crucial issue since the forward calculation is generally very time consuming. Thus, the approach can be applied to large-scale 3-D problems as encountered in practice, which is finally proved on field data. As a by-product of the primary potential calculation we obtain a quantification of the topography effect and the corresponding geometric factors. The latter are used for calculation of apparent resistivities to prevent the reconstruction process from topography induced artefacts.
Geometry dependence of nonlinear effects in high temperature superconducting transmission lines at microwave frequencies

We investigate the nature of low-power nonlinear effects in high-temperature superconducting microwave devices by measuring third harmonic generation at 76 K in coplanar waveguide transmission lines of different geometries fabricated from YBa2Cu3O7−δ thin films. The measured power in the third-harmonic signal changes systematically with film thickness, center conductor linewidth, and line length. We analyze these results using a simple model for a transmission line with a nonlinear inductance arising from a current-dependent superconducting penetration depth. This analysis describes quantitatively the observed differences in harmonic generation for transmission lines of different dimensions, and yields a single geometry-independent parameter (the nonlinear scaling current density J0) to quantify the observed nonlinear behavior. For the thin film samples studied here J0=3.0×107 A/cm2 at 76 K for all geometries investigated. These results provide the means to establish a lower limit for the expected nonline.
Effect of Core Stability Training on Postpartum Mother's Sexual Desire

Background: Women may experience different problems in every aspect of their sexual life throughout their lives; therefore, it is essential to check women's sexual health and try to improve it. The present research aims to assess core stability trainings on postpartum mothers' sexual desire. Materials and Methods: This quasi-experimental research was conducted by random sampling on 72 mothers who referred to comprehensive health centers in Isfahan in 2019 in postpartum period. The samples were divided into experimental and control groups by random placement method (blocking). Core stability exercises were performed in the experimental group for 24 sessions. The demographic questionnaire and Female Sexual Function Index (FSFI) were filled out in two stages (before and one month after the intervention) by the samples, and the data were analyzed by Mann-Whitney, independent t-test, paired t-test, Chi-square. Results: The result of this study showed that the average score of the sexual desire after intervention in the experimental group was significantly higher than that in the control group (p = 0.03). The average score of the sexual desire after the intervention in the experimental group was significantly higher than that before the intervention (p < 0.001). The average scores of the sexual desire before and after the intervention in the control group were not significantly different (p = 0.40). Conclusions: Eight weeks of core stabilization exercises can improve endurance of the pelvic floor muscles and the central part of the body, subsequently promoting females' sexual desire. The findings of this study can be considered in the fields of education, health, clinical, and policy.
Concerning the Number of Independent Variables of the Classical Electromagnetic Field

The classical electromagnetic fields are usually described by six independent functions of space and time, namely the three components of the electric field and the three components of the magnetic field, satisfying Maxwell's equations. On the other hand it is well known that all observable effects of these fields can be derived from five components of the electromagnetic stress-energy tensor. Consequently the usual description contains one superabundance, that can be described by one parameter φ which cannot be measured experimentally but must be fixed by an arbitrary convention. Implicit conventions concerning φ, underlying the usual approach to electromagnetic fields tend to obscure the understanding and should be replaced by an explicitly stated convention. It is recommended that this convention be that φ be defined zero for electrons.
Investigating Deep Stock Market Forecasting with Sentiment Analysis

When forecasting financial time series, incorporating relevant sentiment analysis data into the feature space is a common assumption to increase the capacities of the model. In addition, deep learning architectures and state-of-the-art schemes are increasingly used due to their efficiency. This work compares state-of-the-art methods in financial time series forecasting incorporating sentiment analysis. Through an extensive experimental process, 67 different feature setups consisting of stock closing prices and sentiment scores were tested on a variety of different datasets and metrics. In total, 30 state-of-the-art algorithmic schemes were used over two case studies: one comparing methods and one comparing input feature setups. The aggregated results indicate, on the one hand, the prevalence of a proposed method and, on the other, a conditional improvement in model efficiency after the incorporation of sentiment setups in certain forecast time frames.
Exploring N -way tables with sums-of-products models

Abstract Sums-of-products models can be characterized as a variant of the analysis of variance model, where interaction terms are assumed to be products of single-factor scales and where the assumption that main effects and interaction terms have zero sums is dropped. Because of the ordinal structure inherent in sums of products, these models are appropriate when data satisfy single-factor independence (the direction of the effects of a factor is independent of other factors) but have amplificatory violations of joint independence (the direction of the joint effects of a set of factors may depend on other factors, because a given factor amplifies the effects of some factors more than the effects of others). This paper describes data analysis methods for sums-of-products models that determine which main effect and interaction terms exist. The methods are exploratory in that no error theory is provided. A key concept is that of multi-factor residual with respect to a set of factors, computed by successively "taking out the means" of these factors. The methods involve analysis of multi-factor residuals in terms of their sums of squares and their N -way matrix ranks.
Continuous‐Time Stochastic Processes with Cyclical Long‐Range Dependence

This paper introduces continuous‐time random processes whose spectral density is unbounded at some non‐zero frequencies. The discretized versions of these processes have asymptotic properties similar to those of discrete‐time Gegenbauer processes. The paper presents some properties of the covariance function and spectral density as well as a theory of statistical estimation of the mean and covariance function of such processes. Some directions for further generalizations of the results are indicated.
OCT and ERG Techniques in High-Throughput Phenotyping of Mouse Vision

The purpose of the study is to demonstrate coherent optical tomography and electroretinography techniques adopted from the human clinical practice to assess the morphology and function of the mouse retina in a high-throughput phenotyping environment. We present the normal range of wild-type C57Bl/6NCrl retinal parameters in six age groups between 10 and 100 weeks as well as examples of mild and severe pathologies resulting from knocking out a single protein-coding gene. We also show example data obtained by more detailed analysis or additional methods useful in eye research, for example, the angiography of a superficial and deep vascular complex. We discuss the feasibility of these techniques in conditions demanding a high-throughput approach such as the systemic phenotyping carried out by the International Mouse Phenotyping Consortium.
Mixed-phase deconvolution and wavelet estimation

The seismic convolutional model describes seismic wave propagation as a linear system. A seismic trace then consists of a wavelet or pulse convolved with the reflection response or reflectivity series plus additive noise. An important problem in seismic data processing is the removal of the effect of the wavelet (spiking deconvolution or inverse filtering) in order to estimate the reflectivity series. When the wavelet is known, designing an optimal spiking filter by a least-squares approach is straightforward. For a mixed-delay wavelet, we must delay the position of the desired output spike. This delay can later be removed so that the effective filter is no longer causal but operates on both past and future input data values with respect to the output position. When the wavelet is unknown, we must determine both the wavelet and the reflectivity series from the input data. Solving this ambiguous problem requires additional assumptions about the wavelet, the reflectivity series, and the noise. Common assumptions are that the reflectivity series is a stationary random process and that it is uncorrelated to the stationary random noise. Furthermore, if it is assumed that the signal-to-noise ratio is large, the autocorrelation function (ACF) of the seismic trace can be used as an estimate of the ACF of the wavelet. If, in addition, it is assumed (as it commonly is) that the wavelet is minimum delay, meaning that all roots of its associated polynomial have amplitude greater than 1, an optimal least-squares inverse filter can be computed by solving the normal equations using a Levinson algorithm. The normal equations are also called the Yule-Walker (YW) equations. However, problems can arise with minimum-phase inverse filtering when the wavelet is not minimum delay. In a tutorial on spiking deconvolution ( TLE , 1995), Leinbach showed that applying a minimum-phase filter to an air-gun …
A Siamese Network-Based Method for Improving the Performance of Sleep Staging with Single-Channel EEG

Sleep staging is of critical significance to the diagnosis of sleep disorders, and the electroencephalogram (EEG), which is used for monitoring brain activity, is commonly employed in sleep staging. In this paper, we propose a novel method for improving the performance of sleep staging models based on Siamese networks, based on single-channel EEG. Our proposed method consists of a Siamese network architecture and a redesigned loss with distance metrics. Two encoders are used in the Siamese network to generate latent features of the EEG epochs, and the contrastive loss, which is also a distance metric, is used to compare the similarity or differences between EEG epochs from the same or different sleep stages. We evaluated our method on single-channel EEGs from different channels (Fpz-Cz and F4-EOG (left)) from two public datasets SleepEDF and MASS-SS3 and achieved the overall accuracies MF1 and Cohen’s kappa coefficient of 85.2%, 78.3% and 0.79 on SleepEDF and 87.2%, 82.1% and 0.81 on MASS-SS3. The results show that our method can significantly improve the performance of sleep staging models and outperform the state-of-the-art sleep staging methods. The performance of our method also confirms that the features captured by Siamese networks and distance metrics are useful for sleep staging.
IMPACT OF EXPORT DIVERSIFICATION ON THE ECONOMIC GROWTH IN MALAYSIA

This study aims to analyse the impact of export diversification on the economic growth in Malaysia in both the short and long run. Computed data of degree of specialisation and diversification (DSD) in Malaysia from 1987 to 2017 were utilised. Furthermore, this study takes into consideration other determinants such as foreign direct investment and human capital to examine their impacts on the economic growth in Malaysia. The findings indicate that Malaysia is experiencing diversification in its export basket. Furthermore, all the independent variables were found to be positive and significant in relation to economic growth in the long run. However, there was no short-run relationship found throughout the studies amongst the dependent and independent variables. The research findings suggested that Malaysia should diversify further in terms of export commodities and cultivate a larger partnership socially and economically with the rest of the world.
Safety and Tolerability of the Acute Ketamine Treatment in Treatment-Resistant Depression: Focus on Comorbidities Interplay with Dissociation and Psychomimetic Symptoms

There is evidence for ketamine use in treatment-resistant depression (TRD). Several safety concerns arise regarding adverse drug reactions in specific subpopulations. The aim of this study was to investigate the safety of intravenous ketamine treatment in relation to dissociative and psychotic measures in TRD inpatients with Major Depressive Disorder (MDD) and Bipolar depression (BP) with comorbidities. In total, 49 inpatients with MDD or BP were treated with ketamine following the registered naturalistic observational protocol in a tertiary reference unit for mood disorders (NCT04226963). This dataset represents an intermittent analysis of an observational study performed for interim modeling of observational learning. The observations were applied to the inhomogeneous TRD population in a single site with no blinding and were limited to acute administration. The presence of epilepsy was significantly associated with an elevation in the BPRS over time (p = 0.008). Psychotic symptomatology with BPRS scores for comorbid conditions excluding epilepsy turned out to be insignificant (p = 0.198) regardless of the diagnosis. However, for a subgroup of patients with epilepsy (n = 6), a substantial fluctuation was seen across all administrations in the time course of the study. The study results contribute to the literature on the safety and tolerability profile of CNS adverse drug reactions in short-term treatment with intravenous ketamine as an add-on intervention to current standard-of-care psychotropic medication in TRD-MDD and TRD-BP inpatients with comorbidities. The careful consideration of comorbidities and concomitant medication is needed with ketamine administration along with close-clinical supervision at every visit.
Characterization of magnetic multilayers by grazing incidence x-ray reflectivity

The application of grazing incidence X-ray reflectivity measurements to the characterization of metallic multilayers is described. Use of simulation permits interface roughness to be determined to a precision of about 0.1 nm r.m.s. (root mean square) from the specular reflectivity profile. Measurement of the diffuse scatter permits interdiffusion to be distinguished in principle from interface roughness. Localization of strong diffuse scatter around the Bragg peaks of superlattices provides evidence for coherency in the roughness through the superlattice thickness. >
Phylogeny of the Poritiinae (Lepidoptera: Lycaenidae), butterflies with ant associations and unusual lichenivorous diets

The Poritiinae are a diverse subfamily of lycaenid butterflies with about 700 species divided into two major groups: the Asian endemic tribe Poritiini, and the African endemic tribe Liptenini. Among these, the Liptenini are notable for their lichenivorous diet and the strong but apparently non‐mutualistic ant associations of many species. We present the first molecular phylogeny for this subfamily, based on data from 14 gene regions, and including 218 representatives from 177 taxa (approximately 25% of species) in 50 of the 58 (86%) recognized genera. From this analysis, we confirm the division of the subfamily into two tribes, and we rearrange the Liptenini tribe into six subtribes, Durbaniina, Pentilina, Liptenina, Iridanina and Epitolina, plus a new tribe, Cooksoniina subtrib. n. , to fill a gap in the nomenclature revealed by the phylogenetic analysis. We also point to several genera in need of further taxonomic revision. Ancestral range reconstruction could not infer the range of the common ancestor of the Poritiinae; however, the common ancestor of the Poritiini was likely Asian, while that of the Liptenini was likely African, with subsequent narrowing of ranges in several lineages.
Basic Principles of Structural Equation Modeling: An Introduction to LISREL and EQS

Contents: Preface Frequently Used Symbols and Notation List of Figures and Tables CHAPTER 1: Linear Regression and Classical Path Analysis Overview and Key Points. Linear Ordinary Least Squares Regression. Classical Path Analysis. Summary. Exercises. Recommended Readings. CHAPTER 2: Confirmatory Factor Analysis. Overview and Key Points. Specification and Identification of a CFA Model. Data-Model Fit. Model Modification. Validity and Reliability from a CFA Perspective. Summary. Exercises. Recommended Readings. CHAPTER 3: General Structural Equation Modeling. Overview and Key Points. Specification and Identification of a General Structural Equation Model. The Direct, Indirect, and Total Structural Effect Components. Parameter Estimation. The Structural Equation Modeling Process: An Illustrated Review and Summary. Conclusion. Exercises. Recommended Readings. APPENDIX A: The Simplis Command Language. APPENDIX B: Location, Dispersion and Association. APPENDIX C: Matrix Algebra APPENDIX D: Descriptive Statistics for the SES Analysis. APPENDIX E: Descriptive Statistics for the HBI Analysis. References. Index.