Miracle-dz/UniFAR-FTUs huggingface dataset:
Few-Electrode EEG from the Wearable Devices Using Domain Adaptation for Depression Detection

Nowadays, major depressive disorder (MDD) has become a crucial mental disease that endangers human health. Good results have been achieved by electroencephalogram (EEG) signals in the detection of depression. However, EEG signals are time-varying, and the distributions of the different subjects’ data are non-uniform, which poses a bad influence on depression detection. In this paper, the deep learning method with domain adaptation is applied to detect depression based on EEG signals. Firstly, the EEG signals are preprocessed and then transformed into pictures by two methods: the first one is to present the three channels of EEG separately in the same image, and the second one is the RGB synthesis of the three channels of EEG. Finally, the training and prediction are performed in the domain adaptation model. The results indicate that the domain adaptation model can effectively extract EEG features and obtain an average accuracy of 77.0 ± 9.7%. This paper proves that the domain adaptation method can effectively weaken the inherent differences of EEG signals, making the diagnosis of different users more accurate.
Computation of the Energy Spectrum in Homogeneous Two‐Dimensional Turbulence

Two‐dimensional and three‐dimensional turbulence have different properties, but both contain the two basic ingredients of randomness and convective nonlinearity, and some of the statistical hypotheses which have been proposed for three‐dimensional turbulence should be applicable to two‐dimensional motion. This justifies a numerical integration of the unaveraged equations of motion in two dimensions with random initial conditions as a means of testing the soundness of ideas such as those leading to the Kolmogoroff equilibrium theory. In spatially homogeneous two‐dimensional turbulence, the mean‐square vorticity is unaffected by convection and can only decrease under the action of viscosity. Consequently the rate of dissipation of energy tends to zero with the viscosity (v). On the other hand, the mean‐square vorticity gradient is increased by convective mixing, and it seems likely that the rate of decrease of mean‐square vorticity tends to a nonzero limit κ as ν → 0. This suggests the existence of a “casca.
Fast deterministic switching in orthogonal spin torque devices via the control of the relative spin polarizations

We model 100 ps pulse switching dynamics of orthogonal spin transfer (OST) devices that employ an out-of-plane polarizer and an in-plane polarizer. Simulation results indicate that increasing the spin polarization ratio, CP = PIPP/POPP, results in deterministic switching of the free layer without over-rotation (360° rotation). By using spin torque asymmetry to realize an enhanced effective PIPP, we experimentally demonstrate this behavior in OST devices in parallel to anti-parallel switching. Modeling predicts that decreasing the effective demagnetization field can substantially reduce the minimum CP required to attain deterministic switching, while retaining low critical switching current, Ip ∼ 500 μA.
Sequence-Based Prediction of Protein Phase Separation: The Role of Beta-Pairing Propensity

The formation of droplets of bio-molecular condensates through liquid-liquid phase separation (LLPS) of their component proteins is a key factor in the maintenance of cellular homeostasis. Different protein properties were shown to be important in LLPS onset, making it possible to develop predictors, which try to discriminate a positive set of proteins involved in LLPS against a negative set of proteins not involved in LLPS. On the other hand, the redundancy and multivalency of the interactions driving LLPS led to the suggestion that the large conformational entropy associated with non specific side-chain interactions is also a key factor in LLPS. In this work we build a LLPS predictor which combines the ability to form pi-pi interactions, with an unrelated feature, the propensity to stabilize the β-pairing interaction mode. The cross-β structure is formed in the amyloid aggregates, which are involved in degenerative diseases and may be the final thermodynamically stable state of protein condensates. Our results show that the combination of pi-pi and β-pairing propensity yields an improved performance. They also suggest that protein sequences are more likely to be involved in phase separation if the main chain conformational entropy of the β-pairing maintained droplet state is increased. This would stabilize the droplet state against the more ordered amyloid state. Interestingly, the entropic stabilization of the droplet state appears to proceed according to different mechanisms, depending on the fraction of “droplet-driving“ proteins present in the positive set.
Importance of annexin V N-terminus for 2D crystal formation and quick purification protocol of recombinant annexin V

Annexin V forms trimeric structures which further assemble into two-dimensional crystal (2D crystal) lattices on negatively charged phospholipid bilayer in a Ca2+-dependent manner. It is also known that annexin V 2D crystals show two types of symmetric patterns with six-fold symmetry (p6) and three-fold symmetry (p3). The p6 lattice also contains additional trimers in the gaps between the p6 axes, which are also referred to as non-p6 trimers because they do not participate in the formation of the p6 lattice. We here show that the annexin V N-terminal has significant influence on 2D crystal formation using high-speed atomic force microscopy (HS-AFM) observations. We also present a quick purification method to purify recombinant annexin V without any residual affinity tag after protein purification in ~3h.
A Dual-Stage Attention Model for Tool Wear Prediction in Dry Milling Operation

The intelligent monitoring of tool wear status and wear prediction are important factors affecting the intelligent development of the modern machinery industry. Many scholars have used deep learning methods to achieve certain results in tool wear prediction. However, due to the instability and variability of the signal data, some neural network models may have gradient decay between layers. Most methods mainly focus on feature selection of the input data but ignore the influence degree of different features to tool wear. In order to solve these problems, this paper proposes a dual-stage attention model for tool wear prediction. A CNN-BiGRU-attention network model is designed, which introduces the self-attention to extract deep features and embody more important features. The IndyLSTM is used to construct a stable network to solve the gradient decay problem between layers. Moreover, the attention mechanism is added to the network to obtain the important information of output sequence, which can improve the accuracy of the prediction. Experimental study is carried out for tool wear prediction in a dry milling operation to demonstrate the viability of this method. Through the experimental comparison and analysis with regression prediction evaluation indexes, it proves the proposed method can effectively characterize the degree of tool wear, reduce the prediction errors, and achieve good prediction results.
High-frequency waves in the solar coronal plasma

We derived numerical solutions of a dispersion equation in order to analyze the effect of finite plasma temperature on the highfrequency wave dispersion characteristics in conditions of hot magnetized plasma in the solar corona. Spectra of the high-frequency eigen modes of these plasma were determined in conditions when the electron gyrofrequency is lower than the plasma one and when the eigen modes frequencies are higher than the electron gyrofrequency. The longitudinal wave mode is shown to turn to the Z-mode at refractive index n < 1. At refractive index n � 1, the longitudinal wave frequency increases when n grows, and these waves go to strongly damped ones with an anomalous dispersion. We interpret some spectral features of type II and IV radio bursts in the solar corona.
Next-generation RSV vaccines avoid flipping out

In clinical trials, RSV prefusion F protein induced higher neutralizing antibodies and more activated memory B cells than postfusion F protein (Chang et al.). Description In clinical trials, RSV prefusion F protein induced higher neutralizing antibodies and more activated memory B cells than postfusion F protein (Chang et al.).
Spondylolysis and Isthmic Spondylolisthesis: A Guide to Diagnosis and Management

Introduction: Spondylolysis and isthmic spondylolisthesis are commonly implicated as organic causes of low back pain in this population. Many patients involved in sports that require repetitive hyperextension of the lumbar spine like diving, weightlifting, gymnastics and wrestling develop spondylolysis and isthmic spondylolisthesis. While patients are typically asymptomatic in mild forms, the hallmark of symptoms in more advanced disease include low back pain, radiculopathy, postural changes and rarely, neurologic deficits. Methods: We conducted a narrative review of the literature on the clinical presentation, diagnosis, prognosis and management of spondylolysis and isthmic spondylolisthesis. Results: A comprehensive physical exam and subsequent imaging including radiographs, CT and MRI play a role in the diagnosis of this disease process. While the majority of patients improve with conservative management, others require operative management due to persistent symptoms. Conclusion: Due to the risk of disease progression, referral to a spine surgeon is recommended for any patient suspected of having these conditions. This review provides information and guidelines for practitioners to promote an actionable awareness of spondylolysis and isthmic spondylolisthesis.
A spectroscopic study of the DR 21 outflow source. II. The vibrational H2 line emission

Large-scale mapping and high-spectral resolution profile measurements of the vibrational H 2 v = 1-0 S(1) line are used to investigate the morphology and kinematic structure of the hot, shocked gas associated with the ultraluminous DR 21 young-stellar outflow. It is found that the H 2 line profiles at certain locations within the outflow possess high-velocity wings which extend to beyond 100 km s -1 from the DR 21 rest velocity. It is argued that the jets, which shock the surrounding molecular cloud medium and produce the bright H 2 line emission, are composed primarily of hot atomic gas which is confined by the thermal pressure of the ambient cloud medium.
The impact of price transparency of bundled vacation packages on travel decision making: An experimental study

Price transparency is a vital factor in consumers’ judgements and decisions. When selecting a bundled vacation package, travelers are often influenced by transparency in the prices of individual elements of the package. However, because of the diversity of elements bundled in a vacation package, it is a challenge to research the impact of price transparency. To try to overcome this challenge, our study used five experiments to examine the primary impact of element price transparency on travelers’ purchases, along with the moderating effects of consumer involvement and the vertical position of element prices in product descriptions. For the primary effect, we found that tourists preferred vacation packages with low transparency in element prices. We also found that the primary effect of price transparency remained consistent and robust across both revised and actual vacation packages. For moderating effects, we found that tourists with low involvement attached greater importance to price transparency than those with high involvement when the element price was presented higher in the product description of the travel package. The findings of the five experiments have theoretical implications for price transparency and Heuristic-systematic Model and practical implications for tourism professionals designing and marketing vacation packages.
Biophysical, Calorimetric, Zeta Potential, Voltammetric and Computational Insights into the Interaction of An Antimuta-genic Agent with Human Serum Albumin

Abstract Several biological processes in the body involve the ligand-protein association and thereby exert a wide spectrum of pharmaceutical activities. Consequently, the research community worked hard for a long time to explore the phenomenon of drug-protein binding. Insights into the molecular and physiological processes of a flavonoid, galangin (GAL) upon binding to human serum albumin (HSA) have been explored in vitro via both experimental and theoretical approaches. GAL is a natural, multifunctional and a potential anticancer drug and HSA is a plentiful protein in the circulatory system of humans. UV absorption, calorimetric, temperature-dependent ellipticity, fluorescence lifetime decay, FRET, electrochemical, zeta potential and in silico studies were used to further investigate GAL-HSA interaction. Results of these investigations suggested the interaction between GAL and HSA. The location of GAL on HSA was identified by site probe experiments and further confirmed by molecular docking and simulation studies. GRAPHICAL ABSTRACT
Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance

Alzheimer’s disease (AD) takes a more aggressive course in women than men, with higher prevalence and faster progression. Amnestic AD specifically targets the default mode network (DMN), which subserves short-term memory; past research shows relative hyperconnectivity in the posterior DMN in aging women. Higher reliance on this network during memory tasks may contribute to women’s elevated AD risk. Here, we applied connectome-based predictive modeling (CPM), a robust linear machine-learning approach, to the Lifespan Human Connectome Project-Aging (HCP-A) dataset (n=579). We sought to characterize sex-based predictors of memory performance in aging, with particular attention to the DMN. Models were evaluated using cross-validation both across the whole group and for each sex separately. Whole-group models predicted short-term memory performance with accuracies ranging from ρ=0.21-0.45. The best-performing models were derived from an associative memory task-based scan. Sex-specific models revealed significant differences in connectome-based predictors for men and women. DMN activity contributed more to predicted memory scores in women, while within- and between-visual network activity contributed more to predicted memory scores in men. While men showed more segregation of visual networks, women showed more segregation of the DMN. We demonstrate that women and men recruit different circuitry when performing memory tasks, with women relying more on intra-DMN activity and men relying more on visual circuitry. These findings are consistent with the hypothesis that women draw more heavily upon the DMN for recollective memory, potentially contributing to women’s elevated risk of AD.
Multi-center integrated analysis of non-coding CRISPR screens

The ENCODE Consortium’s efforts to annotate non-coding, cis-regulatory elements (CREs) have advanced our understanding of gene regulatory landscapes which play a major role in health and disease. Pooled, non-coding CRISPR screens are a promising approach for systematically investigating gene regulatory mechanisms. Here, the ENCODE Functional Characterization Centers report 109 screens comprising 346,970 individual perturbations across 13.3Mb of the genome, using a variety of methods, readouts, and statistical analyses. Across 332 functionally confirmed CRE-gene links, we identify principles for screening endogenous, non-coding elements for causal regulatory mechanisms. Nearly all CREs show strong evidence of open chromatin, and targeting accessibility peak summits is a critical component of our proposed sgRNA design rules. We provide experimental guidelines to accurately detect CREs with variable, often low, transcriptional effects. We discover a previously undescribed DNA strand-bias for CRISPRi in transcribed regions with implications for screen design and analysis. Benchmarking five screen analysis tools, we find CASA produces the most conservative CRE calls and is robust to artifacts of low-specificity sgRNAs. Together, we provide an accessible data resource, predesigned sgRNAs targeting 3,275,697 ENCODE SCREEN candidate CREs, and screening guidelines to accelerate functional characterization of the non-coding genome.
7-Tesla evidence for columnar and rostral–caudal organization of the human periaqueductal gray response in the absence of threat: a working memory study

The periaqueductal gray (PAG) is a small midbrain structure that surrounds the cerebral aqueduct, regulates brain–body communication, and is often studied for its role in “fight-or-flight” and “freezing” responses to threat. We used ultra-high field 7-Tesla fMRI to resolve the PAG in humans and distinguish it from the cerebral aqueduct, examining its in vivo function in humans during a working memory task (N = 87). Relative to baseline fixation, both mild and moderate task-elicited cognitive demands elicited bilateral BOLD increases in ventrolateral PAG (vlPAG), a region previously observed to show increased activity during anticipated painful threat in both non-human and human animals. The present task posed only the most minimal (if any) “threat”. The mild-demand condition involved a task easier than remembering a phone number, elicited a heart rate decrease relative to baseline, yet nonetheless elicited a bilateral vlPAG response. Across PAG voxels, BOLD signal intensity correlated with changes in physiological reactivity (relative to baseline) and showed some evidence of spatial organization along the rostral–caudal axis. These findings suggest that the PAG may have a broader role in coordinating brain—body communication during a minimally to moderately demanding task, even in the absence of threat.
Centers of Down–Up Algebras☆

Abstract Down–up algebras which originated in the study of differential posets were recently defined and studied by Benkart and Roby. Benkart posed an open problem in her paper: to determine the centers of all down–up algebras. Here in this paper we completely solve this problem. As an application we also get the centers of homogenizations of down–up algebras.
Measuring of Similarity between Pair of Words Using Word Net

In the current era, the digital data size increased enormously and available abundantly. Retrieving the relevant and accurate information from the available data still a big challenge. In this paper we are finding the similarity of noun-noun pairs and verb-verb pairs using Word-Net as corpus. Computation of similarity between noun-noun pairs in a sentence using different semantic algorithm computed and analysed.it has been observed that computation of similarity between verb-pair is found to be not as easy as computation of similarity between noun-pair. There are two challenges observed during the experimentation of this work. The first one is no standard data set available for verb pair and second is no exact hierarchy of verb of available in word-net.
A Novel Trio-Hybrid for Detecting Fraudulent Credit Card Transactions

In this era of digitization, credit card frauds shake down the spirits of not only the customers but also the merchants, which incurs a loss of billions of dollars globally. To combat such frauds, a robust and responsive system is needed that can flag the fraudulent transaction instantly before it happens. The existing systems are great at detecting and battling with fraud after it has happened but slouch in case of prevention of such crimes. They aren’t good¬¬ at optimization and also struggle in terms of response time. The inefficiency of existing systems is attributed to either working on a single machine learning technique, or just combining two of them. We present a Trio-Hybrid of K-means, Genetic algorithm, and artificial neural network approaches to deal with the aforementioned problems. The K-means algorithm helps in reducing the training time of neural networks and the genetic algorithm helps in feature selection to prevent the neural network from being over-trained, thereby reducing the cost of the system. We leverage the benefits provided by these three techniques and put them together into a trio for the first time and achieve an accuracy of 99.94% in detecting the fraudulent credit card transactions.
Characterization of the stability radius via bifurcation techniques

Robustness of stability of linear time-invariant systems using the relationship between the structured complex stability radius and a parametrized algebraic Riccati equation is analysed. Our approach is based on the observation that the algebraic Riccati equation can be viewed as a bifurcation problem. It is proved that the stability radius is, under certain assumptions, associated with a turning point of the bifurcation problem given by the parametrized algebraic Riccati equation. As a byproduct, the stability radius can be computed via path following. Some numerical examples are presented.
Does Creativity Help Us Survive? A Possible Approach with Quantum-Driven Robots

How can we relate quantum computing, robotics, and music? This is a position paper where we try to connect these fields. We discuss one main question regarding computational creativity and we imagine an experimental setup to test our hypothesis. Artificial intelligence and autonomous machines can accomplish simple creative tasks such as reorganizing given material. Because creativity is a human (and not only) resource to survive, we wonder if also artificial agents, such as robots, might develop creativity at a higher level to ensure self-survival. Then, we design an experimental setup with three robots, playing and dancing. If music and movement flow is regular (with the beat difference below a certain threshold), the voltage given to robots is constant. Otherwise, if there are inconsistencies, it drops, activating robots’ alert signal sensors, and triggering new musical activity. We use the paradigm of quantum computing to formalize our claims. This test might be performed in situ in a robotic lab.