Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Human Norovirus Nonstructural Protein NS1.2 Involved in the Induction of the Filamentous Endoplasmic Reticulum, Enlarged Lipid Droplets, LC3 Recruitment, and Interaction with NTPase and NS4

Human noroviruses (HuNVs) are the leading cause of gastroenteritis worldwide. NS1.2 is critical for HuNV pathogenesis, but the function is still unclear. The GII NS1.2 of HuNVs, unlike GI NS1.2, was localized to the endoplasmic reticulum (ER) and lipid droplets (LDs) and is accompanied by a distorted-filamentous ER morphology and aggregated-enlarged LDs. LC3 was recruited to the NS1.2-localized membrane through an autophagy-independent pathway. NS1.2, expressed from a cDNA clone of GII.4 norovirus, formed complexes with NTPase and NS4, which exhibited aggregated vesicle-like structures that were also colocalized with LC3 and LDs. NS1.2 is structurally divided into three domains from the N terminus: an inherently disordered region (IDR), a region that contains a putative hydrolase with the H-box/NC catalytic center (H-box/NC), and a C-terminal 251–330 a.a. region containing membrane-targeting domain. All three functional domains of NS1.2 were required for the induction of the filamentous ER. The IDR was essential for LC3 recruitment by NS1.2. Both the H-Box/NC and membrane-targeting domains are required for the induction of aggregated-enlarged LDs, NS1.2 self-assembly, and interaction with NTPase. The membrane-targeting domain was sufficient to interact with NS4. The study characterized the NS1.2 domain required for membrane targeting and protein–protein interactions, which are crucial for forming a viral replication complex.
Scalar Turbulence in Convective Boundary Layers by Changing the Entrainment Flux

Alarge-eddysimulationmodelisadoptedtoinvestigatetheevolutionofscalarstransportedbyatmospheric cloud-free convective boundary layer flows. Temperature fluctuations due to the ground release of sensible heat and concentration fluctuations of a trace gas emitted at the homogeneous surface are mixed by turbulence within the unstable boundary layer. On the top, the entrainment zone is varied to obtain two distinct situations: (i) the temperature inversion is strong and the trace gas increment across the entrainment region is small, yieldingto a small topflux with respect to the surface emission; (ii) the temperature inversionat the top of the convective boundary layer is weak, and the scalar increment large enough to achieve a concentration flux toward the free atmosphere that overwhelms the surface flux. In both cases, an estimation of the entrainment flux is obtained within a simple model, and it is tested against numerical data. The evolution of the scalar profiles is discussed in terms of the different entrainment‐surface flux ratios. Results show that, when entrainment at the top of the boundary layer is weak, temperature and trace gas scalar fields are strongly correlated, particularly in the lower part of the boundary layer. This means that they exhibit similar behavior from the largest down to the smallest spatial scales. However, when entrainment is strong, as moving from the surface, differences in the transport of the two scalars arise. Finally, it is shown that, independently of the scalar regime, the temperature field exhibits more intermittent fluctuations than the trace gas.
Fit for purpose? Assessing the accessibility, theory of action, and accountability of digital technology interventions for sexual and gender-based violence prevention and response

Abstract This paper presents the results of a scoping review of Information and Communications Technologies for Development (ICT4D) interventions designed to address sexual and gender-based violence (SGBV). Our analysis considers the extent to which these ICT4D interventions align with established strategies for preventing and responding to SGBV from gender equality and global health practitioner communities. Using a feminist lens, we propose three parameters against which design features of digitally-based SGBV interventions should be assessed: (1) accessibility, (2) theory of action, and (3) accountability. Reading the intervention landscape through these parameters, our results indicate that ICT interventions to address SGBV deploy creative use of various technologies, from mapping software to social networks and document storage. That said, we also find significant scope for improving the accessibility of existing interventions through use of features that remove literacy barriers. Our findings around the accountability of interventions highlight the need for far greater engagement with emerging conceptualizations of data rights. Finally, we show that existing theories of action are only partially aligned with “offline” best practices.
Robust PSS design considering multi-operating conditions

An approach for robust PSS design is presented in this paper, in which multi-operating conditions of a power system is considered by the probabilistic technique. With the assumption of normal distribution, the distribution properties of eigenvalues are obtained by the probabilistic eigenvalue analysis, and described by expectations and variances. To enhance system damping by using PSSs, effects of PSSs on both eigenvalue expectation and variance have been determined, and the conventional eigenvalue sensitivity analysis has been extended to a probabilistic environment. Two types of probabilistic sensitivity indices (PSIs) are formed and employed for robust PSS site selection and PSS parameters adjustment.
Optimization design of filter banks for wavelet denoising

We present a new optimization based method for designing orthonormal filter banks in wavelet denoising. We formulate the design problem as a nonlinear optimization problem whose objective is to minimize the mean squared error (MSE) between the original and the denoised signal. In contrast to previous methods that design filter banks separately from the other operations in noise suppression, our formulation allows us to search for the filters in the context of a denoising algorithm to minimize the MSE. Due to the nonlinear nature of the performance metric, the optimization problem is solved by using the simulated annealing global-search method. We apply the optimization method to find good filter banks for different training signals corrupted by impulsive noise and select the one that performs best across all training signals to be the final solution. In experimental results, we show that the filter bank designed by our method reduces the MSE of the best existing filter bank on sixteen benchmark signals contaminated by either impulsive or Gaussian noise.
Regimes of low-frequency variability in a three-layer quasi-geostrophic ocean model

The temporal variability of the midlatitude double-gyre wind-driven ocean circulation is studied in a three-layer quasi-geostrophic model over a broad range in parameter space. Four different types of flow regimes are found, each characterized by a specific time-mean state and spatio-temporal variability. As the lateral friction is decreased, these regimes are encountered in the following order: the viscous antisymmetric regime, the asymmetric regime, the quasi-homoclinic regime and the inertial antisymmetric regime. The variability in the viscous and the inertial antisymmetric regimes (at high and low lateral friction, respectively) is mainly caused by Rossby basin modes. Lowfrequency variability, i.e. on interannual to decadal time-scales, is present in the asymmetric and quasi-homoclinic regime and can be related to relaxation oscillations originating from low-frequency gyre modes. The focus of this paper is on the mechanisms of the transitions between the different regimes. The transition from the viscous antisymmetric regime to the asymmetric regime occurs through a symmetry-breaking pitchfork bifurcation. There are strong indications that the quasihomoclinic regime is introduced through the existence of a homoclinic orbit. The transition to the inertial antisymmetric regime is due to the symmetrization of the time-mean state zonal velocity field through rectification effects.
Dynamic Regulation Genes at Microtubule Plus Ends: A Novel Class of Glioma Biomarkers

Simple Summary Microtubule plus-end-related genes (MPERGs) encode a group of proteins that specifically aggregate at the microtubule plus ends to play critical biological roles in the cell cycle, cell movement, ciliogenesis, and neuronal development by coordinating microtubule assembly and dynamics; however, the MPERG correlations and their clinical significance in glioma are not fully understood. This study is the first to systematically analyze and define a seven-gene signature (CTTNBP2, KIF18A, NAV1, SLAIN2, SRCIN1, TRIO, and TTBK2) and nomogram model closely associated with clinical factors and the tumor microenvironment as a reliable and independent prognostic biomarker to guide personalized choices of immunotherapy and chemotherapy for glioma patients. Abstract Glioma is the most prevalent and aggressive primary nervous system tumor with an unfavorable prognosis. Microtubule plus-end-related genes (MPERGs) play critical biological roles in the cell cycle, cell movement, ciliogenesis, and neuronal development by coordinating microtubule assembly and dynamics. This research seeks to systematically explore the oncological characteristics of these genes in microtubule-enriched glioma, focusing on developing a novel MPERG-based prognostic signature to improve the prognosis and provide more treatment options for glioma patients. First, we thoroughly analyzed and identified 45 differentially expressed MPERGs in glioma. Based on these genes, glioma patients were well distinguished into two subgroups with survival and tumor microenvironment infiltration differences. Next, we further screened the independent prognostic genes (CTTNBP2, KIF18A, NAV1, SLAIN2, SRCIN1, TRIO, and TTBK2) using 36 prognostic-related differentially expressed MPERGs to construct a signature with risk stratification and prognostic prediction ability. An increased risk score was related to the malignant progression of glioma. Therefore, we also designed a nomogram model containing clinical factors to facilitate the clinical use of the risk signature. The prediction accuracy of the signature and nomogram model was verified using The Cancer Genome Atlas and Chinese Glioma Genome Atlas datasets. Finally, we examined the connection between the signature and tumor microenvironment. The signature positively correlated with tumor microenvironment infiltration, especially immunoinhibitors and the tumor mutation load, and negatively correlated with microsatellite instability and cancer stemness. More importantly, immune checkpoint blockade treatment and drug sensitivity analyses confirmed that this prognostic signature was helpful in anticipating the effect of immunotherapy and chemotherapy. In conclusion, this research is the first study to define and validate an MPERG-based signature closely associated with the tumor microenvironment as a reliable and independent prognostic biomarker to guide personalized choices of immunotherapy and chemotherapy for glioma patients.
DEVELOPMENT AND VALIDATION OF DERIVATIVE SPECTOSCOPIC METHOD FOR SIMULTANEOUS DETERMINATION OF ATORVASTATIN AND RAMIPRIL IN COMBINED DOSAGE FORM

A novel, simple, accurate, sensitive, reproducible, the economical spectroscopic method has been developed and validated for the determination of Atorvastatin and Ramipril in combined dosage form. First order derivative spectroscopy method is adopted to eliminate spectral interference. The method obeys Beer's Law in concentration range of 15-35 µg/ml for Atorvastatin and 7.5-17.5 µg/ml for Ramipril. The method was validated for linearity, range, accuracy, precision and specificity as per ICH guidelines. Zero crossing point for Atorvastatin and Ramipril was 223 nm and 296 nm respectively in methanol. The developed method as successfully used for the quantitative analysis of commercially available dosage form.
Kernel Fisher Discriminant Analysis for Indoor Localization

In this paper we introduce Kernel Fisher Discriminant Analysis (KFDA) to transform our database of received signal strength (RSS) measurements into a smaller dimension space to maximize the difference between reference points (RP) as possible. By KFDA, we can efficiently utilize RSS data than other method so that we can achieve a better performance.
The Role of Gasotransmitter-Dependent Signaling Mechanisms in Apoptotic Cell Death in Cardiovascular, Rheumatic, Kidney, and Neurodegenerative Diseases and Mental Disorders

Cardiovascular, rheumatic, kidney, and neurodegenerative diseases and mental disorders are a common cause of deterioration in the quality of life up to severe disability and death worldwide. Many pathological conditions, including this group of diseases, are based on increased cell death through apoptosis. It is known that this process is associated with signaling pathways controlled by a group of gaseous signaling molecules called gasotransmitters. They are unique messengers that can control the process of apoptosis at different stages of its implementation. However, their role in the regulation of apoptotic signaling in these pathological conditions is often controversial and not completely clear. This review analyzes the role of nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H2S), and sulfur dioxide (SO2) in apoptotic cell death in cardiovascular, rheumatic, kidney, and neurodegenerative diseases. The signaling processes involved in apoptosis in schizophrenia, bipolar, depressive, and anxiety disorders are also considered. The role of gasotransmitters in apoptosis in these diseases is largely determined by cell specificity and concentration. NO has the greatest dualism; scales are more prone to apoptosis. At the same time, CO, H2S, and SO2 are more involved in cytoprotective processes.
One-Way Light Speed Determination Using the Range Measurement Equation of the GPS

The one-way speed of light is determined using the range measurement equation of the Global Positioning System. This equation has been rigorously and extensively tested and verified in the Earth-Centred Inertial frame, a frame that moves with the Earth as it revolves around the Sun but does not share its rotation. The result is a simple demonstration of one-way light speed anisotropy depending on the direction of propagation relative to the rotating Earth.
On the principle of stripline circulation

A circular disc of gyromagnetic material, when placed between two infinite plane conductors and magnetized axially, can support cylindrical modes having no field variation in the axial direction. In terms of a superposition of some of these (radiating) modes the physical principle of the stripline circulator can be explained, at least tentatively. Moreover, from this theory some information can be obtained about the dependence of physical dimensions (such as disc radius and stripline width) on frequency, magnetic bias field and magnetization. Reference is made to the eigen resonances of the ferrite disc. Some experimental results are given and compared with the theory.
Early Alzheimer’s Disease Diagnosis Using Wearable Sensors and Multilevel Gait Assessment: A Machine Learning Ensemble Approach

Alzheimer’s disease (AD) is a progressive neurological disorder, and mild cognitive impairment (MCI) is a stage between cognitive normal (CN) and AD. Although timely diagnosis is the key to treatment, the conventional diagnostic methods make periodic diagnosis impossible due to various issues, such as pain and cost. Therefore, we propose a method for early diagnosing by focusing on gait, which is safe and efficient. Seven wearable devices with a built-in inertial measurement unit were used to collect gait data from 145 subjects, and seven gait experiment paradigms, including multilevel subtasks, were developed to clarify the characteristics of gait of each severity. Based on the acquired gait datasets, we proposed a machine learning (ML)-based classification model—an elimination method-based ensemble and oversampling model—which is applied to our proposed method. Experimental results show that our proposed model is effective in detecting the early stages of AD and demonstrate the potential of using an auxiliary diagnostic tool.
Video super-resolution via nonlocal deformable alignment and frame recursive progressive fusion network

Abstract. Video super-resolution (VSR) is a process of high-resolution reconstruction of low-resolution video. To address the problems of the previous VSR methods with poor temporal consistency and unsatisfactory SR results, we proposed a nonlocal deformable alignment and frame recursive progressive fusion (RPF) network combining sliding window and recursive methods, which uses nonlocal operations to align sequential frame features and later applies recursion to temporally model the hidden information and alignment features of the previous moment, thus improving temporal consistency. The RPF unit is used to fully fuse the hidden information with the currently aligned features, acquiring more supporting information to be obtained from adjacent frames, resulting in better SR results. The results were evaluated on the three public VSR datasets of Vid4, udm10, and Vimeo-90K, and the experimental results show that the proposed method can achieve state-of-the-art performance on VSR task.
New model and algorithm for solving the KKT system of optimal power flow

The market revolution of the power industry presents higher requirement for the computing precision and speed of optimal power flowOPF.Considering that there exist plentiful boundary constraints of reactive power in OPF problems,the general inequality constraints and the boundary constraints are treated separately.Then,by introducing a diagonal matrix and the nonlinear complementarity function,the Karush-Kuhn-Tucker(KKT) system of OPF is transformed equivalently to non-smooth constrained equations.Moreover,based on the new model,a projected semi-smooth Newton algorithm is presented,which has convergent guarantee in theory.Compared with the traditional nonlinear system of KKT system of OPF and the method of nonlinear complementarity function,the new method not only keeps the advantage that it need not identify active set,but also reduces the dimension of the problem.Furthermore, the proposed approach retains the feasible property of boundary constraints for reactive power.Numerical examples of some standard tested IEEE systems show that the new model and algorithm have better effect of computation.
HIGHLY CONCENTRATED NEBULAR NOBLE GASES IN POROUS NANOCARBON SEPARATES FROM THE SARATOV (L4) METEORITE

The majority of heavy noble gases (Ar, Kr, and Xe) in primitive meteorites are stored in a poorly understood phase called Q. Although Q is thought to be carbonaceous, the full identity of the phase has remained elusive for almost four decades. In order to better characterize phase Q and, in turn, the early solar nebula, we separated carbon-rich fractions from the Saratov (L4) meteorite. We chose this meteorite because Q is most resistant in thermal alteration among carbonaceous noble gas carriers in meteorites and we hoped that, in this highly metamorphosed meteorite, Q would be present but not diamond: these two phases are very difficult to separate from each other. One of the fractions, AJ, has the highest 132Xe concentration of 2.1 × 10−6 cm3 STP g−1, exceeding any Q-rich fractions that have yet been analyzed. Transmission electron microscopy studies of the fraction AJ and a less Q-rich fraction AI indicate that they both are primarily porous carbon that consists of domains with short-range graphene orders, with variable packing in three dimensions, but no long-range graphitic order. The relative abundance of Xe and C atoms (6:109) in the separates indicates that individual noble gas atoms are associated with only a minor component of the porous carbon, possibly one or more specific arrangements of the nanoparticulate graphene.
Generating Emotion Descriptions for Fine Art Paintings Via Multiple Painting Representations

The task of generating emotion descriptions for fine art paintings using machine learning is gaining increasing attention. However, captioning the emotions depicted in paintings is challenging due to the artistic and subtle nature of the relied-upon visual clues. Previous studies on painting emotion captioning mainly focus on content-oriented semantic features, resulting in limited performance. Recognizing that facial expressions and body language can reflect human emotions, we propose a novel painting emotion captioning model that incorporates two additional features: facial expression feature and human pose feature. Our model includes a feature fusion method to incorporate these features with commonly used object features. The experiment results on public datasets demonstrate that our proposed model outperforms the baseline. Further experiments on paintings with abstract appearances and image corruptions show the promising performance of our proposed model.
High-information data-centric weed images data identification system based on triple filter for smart agriculture

Abstract. Weeds have seriously impacted crop planting and caused serious human losses. The intelligent weed identification algorithm based on the convolutional neural network has made some achievements in intelligent agriculture. However, the current results rely on a large number of labeled weed data, which is costly to obtain. How to effectively identify without big data is a key task in the current field. We designed a high-information data-centric weed images data identification system based on a triple filter. The system consists of three modules: nearest-neighbor core metric, unobserved components model, and outlier detection. Extensive scientific experiments have shown that our system requires only a small amount of data to achieve excellent performance in weed identification tasks. Our system saves 5% to 20% of the data. When using only 80% of the data, our system achieves the model performance obtained with 100% of data training. Compared with other methods, this method improves the accuracy by 4.9% and reaches a state-of-the-art performance. Our work solves the problem of relying on image data in smart agriculture, provides a scheme for weed identification tasks, and provides valuable ideas for future intelligent agricultural research.
Automatic Registration of Panoramic Image and Point Cloud Based on the Shape of the Overall Ground Object

This paper presents a novel method for registering panoramic images and 3D point clouds using the shape of the overall ground object in the scene as registration primitives. Firstly, a semantic segmentation method is applied to the panoramic image to extract the ground object and remove the sky. Next, the cloth simulation filtering algorithm (CSF) is employed to eliminate the ground points in the 3D point cloud. The remaining 3D ground objects are then projected onto a two-dimensional plane using the imaging model of the panoramic camera to obtain the registration primitives. Finally, we adopt the whale algorithm to perform a coarse-to-fine registration, utilizing overlap degree and mutual information as the similarity measures. The proposed method is evaluated in four different scenes and compared with the other four registration methods. The results demonstrate that the proposed method is accurate and effective, with an average registration error of 11.48 pixels (image resolution is $11000\times 5500$ pixels) compared to the EOPs of the system of 101.67 pixels.
The Environmental Effect of the CMF in the Galactic Center 50 km s−1 Molecular Cloud

Abstract We present the statistical properties of molecular clumps in the Galactic center 50 km s−1 molecular cloud based on observations of the CS J = 1 − 0 emission line with the Nobeyama Millimeter Array. The CMF and size spectrum for the whole cloud can be described by power laws of dN/dM ∝ M−2.6 ± 0.1 and dN/dR ∝ R−5.9±0.3, respectively. The CMF observed in the interacting part with the Sgr A East steepens to dN/dM ∝ M−4.0±0.2. On the other hand, the interaction presumably truncates the size spectrum on the larger side of R ∼ 0.4 pc.