Miracle-dz/UniFAR-FTUs huggingface dataset:
MS²-GNN: Exploring GNN-Based Multimodal Fusion Network for Depression Detection

Major depressive disorder (MDD) is one of the most common and severe mental illnesses, posing a huge burden on society and families. Recently, some multimodal methods have been proposed to learn a multimodal embedding for MDD detection and achieved promising performance. However, these methods ignore the heterogeneity/homogeneity among various modalities. Besides, earlier attempts ignore interclass separability and intraclass compactness. Inspired by the above observations, we propose a graph neural network (GNN)-based multimodal fusion strategy named modal-shared modal-specific GNN, which investigates the heterogeneity/homogeneity among various psychophysiological modalities as well as explores the potential relationship between subjects. Specifically, we develop a modal-shared and modal-specific GNN architecture to extract the inter/intramodal characteristics. Furthermore, a reconstruction network is employed to ensure fidelity within the individual modality. Moreover, we impose an attention mechanism on various embeddings to obtain a multimodal compact representation for the subsequent MDD detection task. We conduct extensive experiments on two public depression datasets and the favorable results demonstrate the effectiveness of the proposed algorithm.
Light propagation properties of two-dimensional photonic crystal channel filters with elliptical micro-cavities

Light propagation through a channel filter based on two-dimensional photonic crystals with elliptical-rod defects is studied by the finite-difference time-domain method. Shape alteration of the defects from the usual circle to an ellipse offers a powerful approach to engineer the resonant frequency of channel filters. It is found that the resonant frequency can be flexibly adjusted by just changing the orientation angle of the elliptical defects. The sensitivity of the resonant wavelength to the alteration of the oval rods' shape is also studied. This kind of multi-channel filter is very suitable for systems requiring a large number of output channel filters.
Antiferromagnetism in a bosonic mixture of rubidium (Rb87) and potassium (K41)

We simulate the experimental possibility of observing antiferromagnetic (AF) order in bosonic mixtures of rubidium ({sup 87}Rb) and potassium ({sup 41}K) in a two-dimensional optical lattice in the presence of harmonic confinement. By tuning the interspecies interactions and the lattice heights, we have found the ground states, within the mean-field approximation, that interpolate from phase separation to AF order. For a moderate lattice height, the coexistence of the Mott and AF phases is possible for the Rb atoms whereas the K atoms remain in the AF-superfluid phase. This observation may provide an experimentally feasible route to hitherto unobserved AF order for {sup 87}Rb-{sup 41}K mixtures.
Direct-reading instruments for aerosols: A review for occupational health and safety professionals part 2: Applications

Abstract Direct reading instruments (DRIs) for aerosols have been used in industrial hygiene practice for many years, but their potential has not been fully realized by many occupational health and safety professionals. Although some DRIs quantify other metrics, this article will primarily focus on DRIs that measure aerosol number, size, or mass. This review addresses three applications of aerosol DRIs that occupational health and safety professionals can use to discern, characterize, and document exposure conditions and resolve aerosol-related problems in the workplace. The most common application of aerosol DRIs is the evaluation of engineering controls. Examples are provided for many types of workplaces and situations including construction, agriculture, mining, conventional manufacturing, advanced manufacturing (nanoparticle technology and additive manufacturing), and non-industrial sites. Aerosol DRIs can help identify the effectiveness of existing controls and, as needed, develop new strategies to reduce potential aerosol exposures. Aerosol concentration mapping (ACM) using DRI data can focus attention on emission sources in the workplace spatially illustrate the effectiveness of controls and constructively convey concerns to management and workers. Examples and good practices of ACM are included. Video Exposure Monitoring (VEM) is another useful technique in which video photography is synced with the concentration output of an aerosol DRI. This combination allows the occupational health and safety professional to see what tasks, environmental situations, and/or worker actions contribute to aerosol concentration and potential exposure. VEM can help identify factors responsible for temporal variations in concentration. VEM can assist with training, engage workers, convince managers about necessary remedial actions, and provide for continuous improvement of the workplace environment. Although using DRIs for control evaluation, ACM and VEM can be time-consuming, the resulting information can provide useful data to prompt needed action by employers and employees. Other barriers to adoption include privacy and security issues in some worksites. This review seeks to provide information so occupational health and safety professionals can better understand and effectively use these powerful applications of aerosol DRIs.
Epilepsy and its neurobehavioral comorbidities: Insights gained from animal models

It is well established that epilepsy is associated with numerous neurobehavioral comorbidities, with a bidirectional relationship; people with epilepsy have an increased incidence of depression, anxiety, learning and memory difficulties, and numerous other psychosocial challenges, and the occurrence of epilepsy is higher in individuals with those comorbidities. Although the cause‐and‐effect relationship is uncertain, a fuller understanding of the mechanisms of comorbidities within the epilepsies could lead to improved therapeutics. Here, we review recent data on epilepsy and its neurobehavioral comorbidities, discussing mainly rodent models, which have been studied most extensively, and emphasize that clinically relevant information can be gained from preclinical models. Furthermore, we explore the numerous potential factors that may confound the interpretation of emerging data from animal models, such as the specific seizure induction method (e.g., chemical, electrical, traumatic, genetic), the role of species and strain, environmental factors (e.g., laboratory environment, handling, epigenetics), and the behavioral assays that are chosen to evaluate the various aspects of neural behavior and cognition. Overall, the interplay between epilepsy and its neurobehavioral comorbidities is undoubtedly multifactorial, involving brain structural changes, network‐level differences, molecular signaling abnormalities, and other factors. Animal models are well poised to help dissect the shared pathophysiological mechanisms, neurological sequelae, and biomarkers of epilepsy and its comorbidities.
DUX4 expression activates JNK and p38 MAP kinases in myoblasts

ABSTRACT Facioscapulohumeral muscular dystrophy (FSHD) is caused by misexpression of the DUX4 transcription factor in skeletal muscle that results in transcriptional alterations, abnormal phenotypes and cell death. To gain insight into the kinetics of DUX4-induced stresses, we activated DUX4 expression in myoblasts and performed longitudinal RNA sequencing paired with proteomics and phosphoproteomics. This analysis revealed changes in cellular physiology upon DUX4 activation, including DNA damage and altered mRNA splicing. Phosphoproteomic analysis uncovered rapid widespread changes in protein phosphorylation following DUX4 induction, indicating that alterations in kinase signaling might play a role in DUX4-mediated stress and cell death. Indeed, we demonstrate that two stress-responsive MAP kinase pathways, JNK and p38, are activated in response to DUX4 expression. Inhibition of each of these pathways ameliorated DUX4-mediated cell death in myoblasts. These findings uncover that the JNK pathway is involved in DUX4-mediated cell death and provide additional insights into the role of the p38 pathway, a clinical target for the treatment of FSHD.
The deep OB star population in Carina from the VST Photometric Hα Survey (VPHAS

© The Authors 2016. Published by Oxford University Press on behalf of The Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction

Objective This study aimed to investigate whether a deep learning reconstruction (DLR) method improves the image quality, stent evaluation, and visibility of the valve apparatus in coronary computed tomography angiography (CCTA) when compared with filtered back projection (FBP) and hybrid iterative reconstruction (IR) methods. Materials and Methods CCTA images of 51 patients (mean age ± standard deviation [SD], 63.9 ± 9.8 years, 36 male) who underwent examination at a single institution were reconstructed using DLR, FBP, and hybrid IR methods and reviewed. CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and stent evaluation, including 10%–90% edge rise slope (ERS) and 10%–90% edge rise distance (ERD), were measured. Quantitative data are summarized as the mean ± SD. The subjective visual scores (1 for worst -5 for best) of the images were obtained for the following: overall image quality, image noise, and appearance of stent, vessel, and aortic and tricuspid valve apparatus (annulus, leaflets, papillary muscles, and chordae tendineae). These parameters were compared between the DLR, FBP, and hybrid IR methods. Results DLR provided higher Hounsfield unit (HU) values in the aorta and similar attenuation in the fat and muscle compared with FBP and hybrid IR. The image noise in HU was significantly lower in DLR (12.6 ± 2.2) than in hybrid IR (24.2 ± 3.0) and FBP (54.2 ± 9.5) (p < 0.001). The SNR and CNR were significantly higher in the DLR group than in the FBP and hybrid IR groups (p < 0.001). In the coronary stent, the mean value of ERS was significantly higher in DLR (1260.4 ± 242.5 HU/mm) than that of FBP (801.9 ± 170.7 HU/mm) and hybrid IR (641.9 ± 112.0 HU/mm). The mean value of ERD was measured as 0.8 ± 0.1 mm for DLR while it was 1.1 ± 0.2 mm for FBP and 1.1 ± 0.2 mm for hybrid IR. The subjective visual scores were higher in the DLR than in the images reconstructed with FBP and hybrid IR. Conclusion DLR reconstruction provided better images than FBP and hybrid IR reconstruction.
A systems medicine strategy to predict the efficacy of drugs for monogenic epilepsies

Monogenic epilepsies are rare but often severe. Because of their rarity, they are neglected by traditional drug developers. Hence, many lack effective treatments. Treatments for a disease can be discovered more quickly and economically by computationally predicting drugs that can be repurposed for it. We aimed to create a computational method to predict the efficacy of drugs for monogenic epilepsies, and to use the method to predict drugs for Dravet syndrome, as (1) it is the archetypal monogenic catastrophic epilepsy; (2) few antiseizure medications are efficacious in Dravet syndrome; and (3) predicting the effect of drugs on Dravet syndrome is challenging, because Dravet syndrome is typically caused by an SCN1A mutation, but some antiseizure medications that are efficacious in Dravet syndrome do not affect SCN1A, and some antiseizure medications that affect SCN1A aggravate seizures in Dravet syndrome.
Leveraging Geometric Structure for Label-Efficient Semi-Supervised Scene Segmentation

Label-efficient scene segmentation aims to achieve effective per-pixel classification with reduced labeling effort. Recent approaches for this task focus on leveraging unlabelled images by formulating consistency regularization or pseudo labels for individual pixels. Yet most of these methods ignore the 3D geometric structures naturally conveyed by image scenes, which is free for enhancing training segmentation models with better discrimination of image details. In this work, we present a novel Geometric Structure Refinement (GSR) framework to explicitly exploit the geometric structures of image scenes to enhance the semi-supervised training of segmentation models. In the training phase, we generate initial dense pseudo labels based on fast and coarse annotations, and then utilize the free unsupervised 3D reconstruction of the image scene to calibrate the dense pseudo labels with more reliable details. With the calibrated pseudo groundtruth, we are able to conveniently train any existing image segmentation models without increasing the costs of annotations or modifying the models’ architectures. Moreover, we explore different strategies for allocating labeling effort in semi-supervised scene segmentation, and find that a combination of finely-labeled samples and coarsely-labeled samples performs better than the traditional dense-fine only annotations. Extensive experiments on datasets including Cityscapes and KITTI are conducted to evaluate our proposed methods. The results demonstrate that GSR can be easily applied to boost the performance of existing models like PSPNet, DeepLabv3+, etc with reduced annotations. With half of the annotation effort, GSR achieves 99% of the accuracy of its fully supervised state-of-the-art counterparts.
Vacuum fluctuations in a supersymmetric model in FRW spacetime

We study a noninteracting supersymmetric model in an expanding FRW spacetime. A soft supersymmetry breaking induces a nonzero contribution to the vacuum energy density. A short distance cutoff of the order of Planck length provides a scale for the vacuum energy density comparable with the observed cosmological constant. Assuming the presence of a dark energy substance in addition to the vacuum fluctuations of the field an effective equation of state is derived in a selfconsistent approach. The effective equation of state is sensitive to the choice of the cut-off but no fine tuning is needed.
Quantum-information approach to the quantum phase transition in the Kitaev honeycomb model

The Kitaev honeycomb model with a topological phase transition at zero temperature is studied using the quantum-information method. Based on the exact solution of the ground state, the mutual information between two nearest sites and between two bonds with the greatest distance are obtained. It is found that the mutual information shows some singularities at the critical point where the system transits from the gapless phase to the gapped phase. Finite-size effects and scaling behavior are also studied. Our results indicate that the mutual information can serve as a good indicator of the topological phase transition. This is because the mutual information is believed to be able to catch some global correlation properties of the system. Meanwhile, this method has the advantages that the phase transition can be determined easily and the order parameters, which are hard to obtain for some topological phase transitions, are not necessarily known.
The blue sky of GJ3470b: the atmosphere of a low-mass planet unveiled by ground-based photometry

GJ3470b is a rare example of a “hot Uranus” transiting exoplanet orbiting a nearby M1.5 dwarf. It is crucial for atmospheric studies because it is one of the most inflated low-mass planets known, bridging the boundary between “super-Earths” and Neptunian planets. We present two new ground-based light curves of GJ3470b gathered by the LBC camera at the Large Binocular Telescope. Simultaneous photometry in the ultraviolet (λc = 357.5 nm) and optical infrared (λc = 963.5 nm) allowed us to detect a significant change in the effective radius of GJ3470b as a function of wavelength. This can be interpreted as a signature of scattering processes occurring in the planetary atmosphere, which should be cloud-free and with a low mean molecular weight. The unprecedented accuracy of our measurements demonstrates that the photometric detection of Earth-sized planets around M dwarfs is achievable using 8−10 m size ground-based telescopes. We provide updated planetary parameters and a greatly improved orbital ephemeris for any forthcoming study of this planet.
Outcomes of isolated tricuspid valve replacement: a systematic review and meta-analysis of 5,316 patients from 35 studies.

BACKGROUND Transcatheter tricuspid valve replacement (TTVR) is rapidly emerging as a therapeutic option amongst patients with secondary tricuspid regurgitation. Historical data from surgical tricuspid valve replacement (TVR) studies may serve as a benchmark for the development of TTVR trials. AIMS The aim of the study was to investigate the early and late outcomes following isolated surgical TVR. METHODS Multiple electronic databases were searched to identify studies on isolated surgical TVR. The prespecified primary endpoint was operative mortality; secondary endpoints were early and late outcomes. Overall estimates of proportions and incidence rates with 95% confidence intervals (CI) were calculated using random-effects models. Multiple sensitivity analyses accounting for baseline characteristics, country and the operative period were applied. Results: A total of 35 studies (5,316 patients) were included in this meta-analysis. The operative period ranged from 1974 to 2019. The overall rate of operative mortality was 12% (95% CI: 9-15), with higher mortality for patients who were operated on before 1995, who had prior cardiac surgeries, or who had liver disease. The most frequent clinical events were pacemaker implantation (10% [95% CI: 6-16]), bleeding (12% [95% CI: 8-17]), acute kidney injury (15% [95% CI: 9-24]) and respiratory complications (15% [95% CI: 12-20]). At follow-up analysis of the bioprosthetic TVR, there was an incidence rate per 100 person-years of 6 (95% CI: 2-13) for death and 8 (95% CI: 5-13) for recurrence of significant tricuspid regurgitation. CONCLUSIONS This meta-analysis provides an overview of the historical clinical outcomes following isolated surgical TVR. These findings can support the development of future clinical trials in the tricuspid space by providing thresholds for clinical outcomes.
Generalized Patterson-Sullivan measures for products of Hadamard spaces

Let $\Gamma$ be a discrete group acting by isometries on a product $X=X_1\times X_2$ of Hadamard spaces. We further require that $X_1$, $X_2$ are locally compact and $\Gamma$ contains two elements projecting to a pair of independent rank one isometries in each factor. Apart from discrete groups acting by isometries on a product of CAT(-1)-spaces, the probably most interesting examples of such groups are Kac-Moody groups over finite fields acting on the Davis complex of their associated twin building. In a previous article we showed that the regular geometric limit set $\Lim$ splits as a product $F_\Gamma\times P_\Gamma$, where $F_\Gamma\subseteq\rand_1\times \rand_2$ is the projection of the geometric limit set to $\rand_1\times \rand_2$, and $P_\Gamma$ encodes the ratios of the speed of divergence of orbit points in each factor. Our aim in this paper is a description of the limit set from a measure theoretical point of view. We first study the conformal density obtained from the classical Patterson-Sullivan construction, then generalize this construction to obtain measures supported in each $\Gamma$-invariant subset of the regular limit set and investigate their properties. Finally we show that the Hausdorff dimension of the radial limit set in each $\Gamma$-invariant subset of $\Lim$ is bounded above by the exponential growth rate introduced in the previous article.
Poststroke consequences upon optimization properties of postural sway during upright stance: a cross-sectional study

ABSTRACT Background The understanding of human postural control has advanced with the introduction of optimization process modeling. These models, however, only provide control parameters, rather than analytical descriptors of optimization processes. Here, we use a newly developed direct (pattern) search algorithm to investigate changes in postural optimization process in poststroke individuals. Objective This cross-sectional study investigated the optimization properties of postural stability during upright standing in poststroke individuals. Methods Twenty-nine poststroke and 15 healthy age-matched individuals underwent posturography with a force platform while standing for 60 s for acquisition of center-of-pressure data. Poststroke individuals were grouped depending on their weight-bearing (WB) pattern and their balance capability assessed through Berg Balance Scale (BBS). The optimization properties of postural stability were computed assuming the minimization of postural sway as cost function. Results The asymmetric WB poststroke group showed larger convergence rate toward the local minimum of postural sway than the symmetric WB group. Additionally, the low-balance capability group exhibited smaller values for averaged local minima and global minimum of postural sway coordinates compared with high-balance capability group. Significant correlations were found for BBS and the local minima and global minimum (Pearson’s r ranged 0.378–0.424, P < 0.05). Conclusions In summary, the optimization properties describing postural dynamic stability, steadiness, and global reference are altered in poststroke individuals with asymmetric WB pattern and low-balance capability.
Parity-Time Symmetric Holographic Principle

Originating from the Hamiltonian of a single qubit system, the phenomenon of the avoided level crossing is ubiquitous in multiple branches of physics, including the Landau–Zener transition in atomic, molecular, and optical physics, the band structure of condensed matter physics and the dispersion relation of relativistic quantum physics. We revisit this fundamental phenomenon in the simple example of a spinless relativistic quantum particle traveling in (1+1)-dimensional space-time and establish its relation to a spin-1/2 system evolving under a PT-symmetric Hamiltonian. This relation allows us to simulate 1-dimensional eigenvalue problems with a single qubit. Generalizing this relation to the eigenenergy problem of a bulk system with N spatial dimensions reveals that its eigenvalue problem can be mapped onto the time evolution of the edge state with (N−1) spatial dimensions governed by a non-Hermitian Hamiltonian. In other words, the bulk eigenenergy state is encoded in the edge state as a hologram, which can be decoded by the propagation of the edge state in the temporal dimension. We argue that the evolution will be PT-symmetric as long as the bulk system admits parity symmetry. Our work finds the application of PT-symmetric and non-Hermitian physics in quantum simulation and provides insights into the fundamental symmetries.
Fermi Surface Evolution in an Electron-Doped High-Tc Superconductor Revealed by Magnetic Quantum Oscillations

We report on the direct probing of the Fermi surface in the bulk of the electron-doped superconductor Nd2−xCexCuO4 at different doping levels by means of magnetoresistance quantum oscillations. Our data reveal a sharp qualitative change in the Fermi surface topology, due to translational symmetry breaking in the electronic system which occurs at a critical doping level significantly exceeding the optimal doping. This result implies that the (π/a, π/a) ordering, known to exist at low doping levels, survives up to the overdoped superconducting regime.