Adaptation and Validation of Learned Helplessness Scale in Chinese Law School Students.

Purpose: This study aims to translate and validate the Learned Helplessness Scale (LHS) for use in the educational context and specifically among Chinese law school students. Understanding learned helplessness in the context of Chinese law students can provide unique insights into the interaction of legal education, psychological health, and cultural influences, thereby contributing to a more nuanced understanding of learned helplessness. Methods: A total of 711 Chinese college students from two law schools participated in this study. The Learned Helplessness Scale (LHS) was translated into Chinese using forward and backward translation. Exploratory and confirmatory factor analysis, and construct validity were conducted to assess the dimensionality of the Chinese version of the LHS (Chinese LHS). Results: The exploratory factor analysis indicated that the Chinese LHS has a four-factor structure consisting of 14 items, which accounted for 50% of the total variance. The subsequent confirmatory factor analysis further supported this four-factor structure. The internal consistency of the Chinese LHS was found to be medium to high, with Cronbach's α values ranging from 0.63 to 0.87 for the subfactors, and 0.79 for the total scale. In addition, concurrent validity is also confirmed. Conclusion: The 14-item version of the Chinese LHS is a psychometric sound instrument for assessing learned helplessness among Chinese law school students.

Identification of a ceRNA Network Driven by Copy Number Variations in Esophageal Cancer.

BACKGROUND: Copy number variation (CNV) is associated with progression of esophageal cancer (EC), a common gastrointestinal neoplasm. METHODS: Using sequencing data, CNV data, and clinical data of EC transcriptome samples obtained from public databases, we performed differential expression analysis on sequencing data. Differentially expressed CNV-driven lncRNAs were screened using the chi-square test, and CNV-driven lncRNA-associated miRNAs and mRNAs were predicted. Cytoscape software was then used to construct ceRNA networks. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to investigate biological functions of mRNAs in the ceRNA network. Survival curves were plotted to explore correlations between lncRNAs in the ceRNA network and overall survival of CNV patients. Multiple databases were used to predict lncRNAs-related drugs. RESULTS: A dysregulated lncRNA-associated ceRNA network driven by CNV in EC, including 11 lncRNAs, 11 miRNAs and 159 mRNAs, was constructed. Downstream enrichment of mRNAs was related to biological processes such as extracellular matrix organization, indicating that these mRNAs mainly participate in intercellular exchange between tumor cells. Additionally, expression of all lncRNAs in the ceRNA network, except LINC00950, LINC01270 and MIR181A1HG, was correlated with patients' CNV. In addition, none of the 11 lncRNAs was significantly correlated with overall survival of CNV patients. CH5424802 and PD-033299CNV mainly affected the RTK signaling pathway and the cell cycle of tumor cells via RP11-180N14.1 and RP11-273 G15.2 in the ceRNA network. CONCLUSIONS: This study identified 11 CNV-driven lncRNAs that might affect EC development, 2 of which have promising effects if applied to drug treatment. These findings might assist in identifying novel treatments for EC.

PC-Phos Enabled Catalytic Palladium-heteroallyl Asymmetric Cycloaddition.

Asymmetric cycloaddition reactions are the most powerful tool to the expeditious construction of enantioenriched cyclic motifs in organic chemistry. In sharp contrast to well-developed cycloaddition reactions via the palladium-trimethylenemethane (Pd-TMM) intermediate, hetero (3 + 2) cycloadditions of the heteroallyl cations remain rare, largely due to their thermally forbidden nature. To the best of our knowledge, there is no example of asymmetric version leading to enantioenriched heterocycles reported so far. Herein we enabled the first example of catalytic asymmetric (3 + 2) cycloaddition of electrophilic palladium-heteroallyl zwitterion intermediate (Pd-OTMM or Pd-NTMM) with cyclic or acyclic 1,3-dienes via a pathway terminated with C-N or C-O bond formation, delivering the highly substituted or fused pyrrolidine and tetrahydrofuran rings in high yields with excellent regio-, diastereo-, and enantioselectivity. Engineering the PC-Phos, one of the chiral sulfinamide phosphine (Sadphos) type ligands, by introducing the di-tert-butyl or/and 3,5-difluorophenyl group is a vital component in achieving excellent catalytic reactivity and enantioselectivity.

A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods.

Multifingered robotic hands (usually referred to as dexterous hands) are designed to achieve human-level or human-like manipulations for robots or as prostheses for the disabled. The research dates back 30 years ago, yet, there remain great challenges to effectively design and control them due to their high dimensionality of configuration, frequently switched interaction modes, and various task generalization requirements. This article aims to give a brief overview of multifingered robotic manipulation from three aspects: a) the biological results, b) the structural evolvements, and c) the learning methods, and discuss potential future directions. First, we investigate the structure and principle of hand-centered visual sensing, tactile sensing, and motor control and related behavioral results. Then, we review several typical multifingered dexterous hands from task scenarios, actuation mechanisms, and in-hand sensors points. Third, we report the recent progress of various learning-based multifingered manipulation methods, including but not limited to reinforcement learning, imitation learning, and other sub-class methods. The article concludes with open issues and our thoughts on future directions.

Palladium-Catalyzed Asymmetric Tandem Denitrogenative Heck/Tsuji-Trost of Benzotriazoles with 1,3-Dienes.

The asymmetric denitrogenative cycloaddition has emerged as a powerful tool to build chiral aza-heterocyles. However, only one example of asymmetric denitrogenative cycloaddition of benzotriazole with unsaturated hydrocarbons has been explored so far, because the ring-opening of benzotriazole to generate α-imino metal carbenoid species is a thermodynamically unfavorable process. We herein report an efficient asymmetric denitrogenative cycloaddition of benzotriazoles with cyclic and acyclic 1,3-dienes enabled by Pd and chiral sulfonamide phosphine ligand. A variety of substituted hexahydrocarbazoles and indolines were delivered in good yields with high ee values. Interestingly, a pair of enantiomers could be obtained with the use of Xu1 and PC2 with the same absolute configuration. The synthetic utilities of the optically active hexahydrocarbazoles were also showcased.

Simultaneous construction of axial and planar chirality by gold/TY-Phos-catalyzed asymmetric hydroarylation.

The simultaneous construction of two different chiralities via a simple operation poses considerable challenge. Herein a cationic gold-catalyzed asymmetric hydroarylation of ortho-alkynylaryl ferrocenes derivatives is developed, which enable the simultaneous construction of axial and planar chirality. The here identified TY-Phos derived gold complex is responsible for the high yield, good diastereoselectivity (>20:1 dr), high enantioselectivities (up to 99% ee) and mild conditions. The catalyst system also shows potential application in the synthesis of chiral biaryl compounds. The cause of high enantioselectivity of this hydroarylation is investigated with density functional theory caculation.

Deep Learning Method on Target Echo Signal Recognition for Obscurant Penetrating Lidar Detection in Degraded Visual Environments.

With the rapid development of autonomous vehicles and mobile robotics, the desire to advance robust light detection and ranging (Lidar) detection methods for real world applications is increasing. However, this task still suffers in degraded visual environments (DVE), including smoke, dust, fog, and rain, as the aerosols lead to false alarm and dysfunction. Therefore, a novel Lidar target echo signal recognition method, based on a multi-distance measurement and deep learning algorithm is presented in this paper; neither the backscatter suppression nor the denoise functions are required. The 2-D spectrogram images are constructed by using the frequency-distance relation derived from the 1-D echo signals of the Lidar sensor individual cell in the course of approaching target. The characteristics of the target echo signal and noise in the spectrogram images are analyzed and determined; thus, the target recognition criterion is established accordingly. A customized deep learning algorithm is subsequently developed to perform the recognition. The simulation and experimental results demonstrate that the proposed method can significantly improve the Lidar detection performance in DVE.

Modeling and Characterization of Capacitive Coupling Intrabody Communication in an In-Vehicle Scenario.

Intrabody communication (IBC) has drawn extensive attention in the field of ubiquitous healthcare, entertainment, and more. Until now, most studies on the modeling and characterization of capacitive coupling IBC have been conducted in open space, while influences when using metallic-enclosed environments such as a car, airplane, or elevator have not yet been considered. In this paper, we aimed to systematically investigate the grounding effect of an enclosed metal wall of a vehicle on the transmission path loss, utilizing the finite element method (FEM) to model capacitive coupling IBC in an in-vehicle scenario. The results of a simulation and experimental validation indicated that the system gain in an in-vehicle scenario increased up to 7 dB compared to in open space. The modeling and characterization achieved in this paper of capacitive coupling IBC could facilitate an intrabody sensor design and an evaluation with great flexibility to meet the performance needs of an in-vehicle use scenario.

An improved optimization algorithm of the three-compartment model with spillover and partial volume corrections for dynamic FDG PET images of small animal hearts in vivo.

The three-compartment model with spillover (SP) and partial volume (PV) corrections has been widely used for noninvasive kinetic parameter studies of dynamic 2-[18F] fluoro-2deoxy-D-glucose (FDG) positron emission tomography images of small animal hearts in vivo. However, the approach still suffers from estimation uncertainty or slow convergence caused by the commonly used optimization algorithms. The aim of this study was to develop an improved optimization algorithm with better estimation performance. Femoral artery blood samples, image-derived input functions from heart ventricles and myocardial time-activity curves (TACs) were derived from data on 16 C57BL/6 mice obtained from the UCLA Mouse Quantitation Program. Parametric equations of the average myocardium and the blood pool TACs with SP and PV corrections in a three-compartment tracer kinetic model were formulated. A hybrid method integrating artificial immune-system and interior-reflective Newton methods were developed to solve the equations. Two penalty functions and one late time-point tail vein blood sample were used to constrain the objective function. The estimation accuracy of the method was validated by comparing results with experimental values using the errors in the areas under curves (AUCs) of the model corrected input function (MCIF) and the 18F-FDG influx constant K i . Moreover, the elapsed time was used to measure the convergence speed. The overall AUC error of MCIF for the 16 mice averaged -1.4 ± 8.2%, with correlation coefficients of 0.9706. Similar results can be seen in the overall K i error percentage, which was 0.4 ± 5.8% with a correlation coefficient of 0.9912. The t-test P value for both showed no significant difference. The mean and standard deviation of the MCIF AUC and K i percentage errors have lower values compared to the previously published methods. The computation time of the hybrid method is also several times lower than using just a stochastic algorithm. The proposed method significantly improved the model estimation performance in terms of the accuracy of the MCIF and K i , as well as the convergence speed.

An Objective Balance Error Scoring System for Sideline Concussion Evaluation Using Duplex Kinect Sensors.

Sports-related concussion is a common sports injury that might induce potential long-term consequences without early diagnosis and intervention in the field. However, there are few options of such sensor systems available. The aim of the study is to propose and validate an automated concussion administration and scoring approach, which is objective, affordable and capable of detecting all balance errors required by the balance error scoring system (BESS) protocol in the field condition. Our approach is first to capture human body skeleton positions using two Microsoft Kinect sensors in the proposed configuration and merge the data by a custom-made algorithm to remove the self-occlusion of limbs. The standing balance errors according to BESS protocol were further measured and accessed automatically by the proposed algorithm. Simultaneously, the BESS test was filmed for scoring by an experienced rater. Two results were compared using Pearson coefficient r, obtaining an excellent consistency (r = 0.93, p < 0.05). In addition, BESS test-retest was performed after seven days and compared using intraclass correlation coefficients (ICC), showing a good test-retest reliability (ICC = 0.81, p < 0.01). The proposed approach could be an alternative of objective tools to assess postural stability for sideline sports concussion diagnosis.

An Energy Efficient Technique Using Electric Active Shielding for Capacitive Coupling Intra-Body Communication.

Capacitive coupling intra-body communication (CC-IBC) has become one of the candidates for healthcare sensor networks due to its positive prevailing features of energy efficiency, transmission rate and security. Under the CC-IBC scheme, some of the electric field emitted from signal (SIG) electrode of the transmitter will couple directly to the ground (GND) electrode, acting equivalently as an internal impedance of the signal source and inducing considerable energy losses. However, none of the previous works have fully studied the problem. In this paper, the underlying theory of such energy loss is investigated and quantitatively evaluated using conventional parameters. Accordingly, a method of electric active shielding is proposed to reduce the displacement current across the SIG-GND electrodes, leading to less power loss. In addition, the variation of such loss in regard to frequency range and positions on human body was also considered. The theory was validated by finite element method simulation and experimental measurement. The prototype result shows that the receiving power has been improved by approximate 5.5 dBm while the total power consumption is maximally 9 mW less using the proposed technique, providing an energy efficient option in physical layer for wearable and implantable healthcare sensor networks.

KSRP suppresses cell invasion and metastasis through miR-23a-mediated EGR3 mRNA degradation in non-small cell lung cancer.

KH-type splicing regulatory protein (KSRP) is a single-strand RNA binding protein which regulates mRNA stability either by binding to AU-rich elements (AREs) of mRNA 3'UTR or by facilitating miRNA biogenesis to target mRNA. Unlike its well-characterized function at the molecular level in maintaining RNA homeostasis, the role of KSRP in cancer progression remains largely unknown. Here we investigate the role of KSRP in non-small cell lung cancer (NSCLC). We first examined KSRP expression by immunohistochemistry in a cohort containing 196 NSCLC patients and observed a strong positive correlation between KSRP expression and survival of NSCLC patients. Multivariate analysis further identified KSRP as an independent prognostic factor. Manipulating KSRP expression significantly affected in vitro cell mobility and in vivo metastatic ability of NSCLC cells. Microarray analysis identified an ARE-containing gene, EGR3, as a downstream effector of KSRP in NSCLC. Interestingly, we found that KSRP decreased EGR3 mRNA stability in an ARE-independent manner. By screening KSRP-regulated miRNAs in NSCLC cells, we further found that miR-23a directly binds to EGR3 3'UTR, reducing EGR3 expression and thereby inhibiting NSCLC cell mobility. Our findings implicate a targetable KSRP/miR-23a/EGR3 signaling axis in advanced tumor phenotypes.

Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning.

Recently, many biologically inspired visual computational models have been proposed. The design of these models follows the related biological mechanisms and structures, and these models provide new solutions for visual recognition tasks. In this paper, based on the recent biological evidence, we propose a framework to mimic the active and dynamic learning and recognition process of the primate visual cortex. From principle point of view, the main contributions are that the framework can achieve unsupervised learning of episodic features (including key components and their spatial relations) and semantic features (semantic descriptions of the key components), which support higher level cognition of an object. From performance point of view, the advantages of the framework are as follows: 1) learning episodic features without supervision-for a class of objects without a prior knowledge, the key components, their spatial relations and cover regions can be learned automatically through a deep neural network (DNN); 2) learning semantic features based on episodic features-within the cover regions of the key components, the semantic geometrical values of these components can be computed based on contour detection; 3) forming the general knowledge of a class of objects-the general knowledge of a class of objects can be formed, mainly including the key components, their spatial relations and average semantic values, which is a concise description of the class; and 4) achieving higher level cognition and dynamic updating-for a test image, the model can achieve classification and subclass semantic descriptions. And the test samples with high confidence are selected to dynamically update the whole model. Experiments are conducted on face images, and a good performance is achieved in each layer of the DNN and the semantic description learning process. Furthermore, the model can be generalized to recognition tasks of other objects with learning ability.

Noninvasive Detection of Early Metabolic Left Ventricular Remodeling in Systemic Hypertension.

OBJECTIVES: Hypertension (HTN) is a common cause of left ventricular hypertrophy (LVH). Sustained pressure overload induces a permanent myocardial switch from fatty-acid to glucose metabolism. In this study, we tested the hypothesis that metabolic remodeling, characterized by increased myocardial glucose uptake, precedes structural and functional remodeling in HTN-induced LVH. METHODS: We recruited 31 patients: 11 with HTN only, 9 with HTN and LVH and 11 normotensive controls without LVH. Transthoracic echocardiography was performed to assess the function, mass, wall thickness and diastolic function of the left ventricle. Positron emission tomography imaging was performed, and the rate of myocardial 2-deoxy-2-[18F]fluoro-D-glucose uptake, Ki, was determined using a 3-compartment kinetic model. RESULTS: The mean Ki values were significantly higher in HTN patients than in those with HTN and LVH (p < 0.001) and in controls (p = 0.003). The unexpected decrease in Ki with LVH may be secondary to a decreased Ki with diastolic dysfunction (DD), 0.039 ± 0.032 versus 0.072 ± 0.013 (p = 0.004). There was also a significant stepwise decrease in Ki with increasing DD grade (p = 0.04). CONCLUSION: Glucose metabolic remodeling is detectable in hypertensive patients before the development of LVH. Furthermore, lower glucose uptake rates are observed in patients with DD. The mechanism for this last finding requires further investigation.

Enhanced HMAX model with feedforward feature learning for multiclass categorization.

In recent years, the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields. Many biologically inspired visual models are proposed, and among them, the Hierarchical Max-pooling model (HMAX) is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal (PIT) layer of the primate visual cortex, which could generate a series of position- and scale- invariant features. However, it could be improved with attention modulation and memory processing, which are two important properties of the primate visual cortex. Thus, in this paper, based on recent biological research on the primate visual cortex, we still mimic the first 100-150 ms of visual cognition to enhance the HMAX model, which mainly focuses on the unsupervised feedforward feature learning process. The main modifications are as follows: (1) To mimic the attention modulation mechanism of V1 layer, a bottom-up saliency map is computed in the S1 layer of the HMAX model, which can support the initial feature extraction for memory processing; (2) To mimic the learning, clustering and short-term memory to long-term memory conversion abilities of V2 and IT, an unsupervised iterative clustering method is used to learn clusters with multiscale middle level patches, which are taken as long-term memory; (3) Inspired by the multiple feature encoding mode of the primate visual cortex, information including color, orientation, and spatial position are encoded in different layers of the HMAX model progressively. By adding a softmax layer at the top of the model, multiclass categorization experiments can be conducted, and the results on Caltech101 show that the enhanced model with a smaller memory size exhibits higher accuracy than the original HMAX model, and could also achieve better accuracy than other unsupervised feature learning methods in multiclass categorization task.

Determination of Fatty Acid Metabolism with Dynamic [11C]Palmitate Positron Emission Tomography of Mouse Heart In Vivo.

The goal of this study was to establish a quantitative method for measuring fatty acid (FA) metabolism with partial volume (PV) and spill-over (SP) corrections using dynamic [(11)C]palmitate positron emission tomographic (PET) images of mouse heart in vivo. Twenty-minute dynamic [(11)C]palmitate PET scans of four 18- to 20-week-old male C57BL/6 mice under isoflurane anesthesia were performed using a Focus F-120 PET scanner. A model-corrected blood input function, by which the input function with SP and PV corrections and the metabolic rate constants (k1-k5) are simultaneously estimated from the dynamic [(11)C]palmitate PET images of mouse hearts in a four-compartment tracer kinetic model, was used to determine rates of myocardial fatty acid oxidation (MFAO), myocardial FA esterification, myocardial FA use, and myocardial FA uptake. The MFAO thus measured in C57BL/6 mice was 375.03 ± 43.83 nmol/min/g. This compares well to the MFAO measured in perfused working C57BL/6 mouse hearts ex vivo of about 350 nmol/g/min and 400 nmol/min/g. FA metabolism was measured for the first time in mouse heart in vivo using dynamic [(11)C]palmitate PET in a four-compartment tracer kinetic model. MFAO obtained with this model was validated by results previously obtained with mouse hearts ex vivo.

Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment.

Recently, many computational models have been proposed to simulate visual cognition process. For example, the hierarchical Max-Pooling (HMAX) model was proposed according to the hierarchical and bottom-up structure of V1 to V4 in the ventral pathway of primate visual cortex, which could achieve position- and scale-tolerant recognition. In our previous work, we have introduced memory and association into the HMAX model to simulate visual cognition process. In this paper, we improve our theoretical framework by mimicking a more elaborate structure and function of the primate visual cortex. We will mainly focus on the new formation of memory and association in visual processing under different circumstances as well as preliminary cognition and active adjustment in the inferior temporal cortex, which are absent in the HMAX model. The main contributions of this paper are: 1) in the memory and association part, we apply deep convolutional neural networks to extract various episodic features of the objects since people use different features for object recognition. Moreover, to achieve a fast and robust recognition in the retrieval and association process, different types of features are stored in separated clusters and the feature binding of the same object is stimulated in a loop discharge manner and 2) in the preliminary cognition and active adjustment part, we introduce preliminary cognition to classify different types of objects since distinct neural circuits in a human brain are used for identification of various types of objects. Furthermore, active cognition adjustment of occlusion and orientation is implemented to the model to mimic the top-down effect in human cognition process. Finally, our model is evaluated on two face databases CAS-PEAL-R1 and AR. The results demonstrate that our model exhibits its efficiency on visual recognition process with much lower memory storage requirement and a better performance compared with the traditional purely computational methods.

Introducing memory and association mechanism into a biologically inspired visual model.

A famous biologically inspired hierarchical model (HMAX model), which was proposed recently and corresponds to V1 to V4 of the ventral pathway in primate visual cortex, has been successfully applied to multiple visual recognition tasks. The model is able to achieve a set of position- and scale-tolerant recognition, which is a central problem in pattern recognition. In this paper, based on some other biological experimental evidence, we introduce the memory and association mechanism into the HMAX model. The main contributions of the work are: 1) mimicking the active memory and association mechanism and adding the top down adjustment to the HMAX model, which is the first try to add the active adjustment to this famous model and 2) from the perspective of information, algorithms based on the new model can reduce the computation storage and have a good recognition performance. The new model is also applied to object recognition processes. The primary experimental results show that our method is efficient with a much lower memory requirement.