Proteomic analysis of rat serum revealed the effects of chronic sleep deprivation on metabolic, cardiovascular and nervous system

Sleep is an essential and fundamental physiological process that plays crucial roles in the balance of psychological and physical health. Sleep disorder may lead to adverse health outcomes. The effects of sleep deprivation were extensively studied, but its mechanism is still not fully understood. The present study aimed to identify the alterations of serum proteins associated with chronic sleep deprivation, and to seek for potential biomarkers of sleep disorder mediated diseases. A label-free quantitative proteomics technology was used to survey the global changes of serum proteins between normal rats and chronic sleep deprivation rats. A total of 309 proteins were detected in the serum samples and among them, 117 proteins showed more than 1.8-folds abundance alterations between the two groups. Functional enrichment and network analyses of the differential proteins revealed a close relationship between chronic sleep deprivation and several biological processes including energy metabolism, cardiovascular function and nervous function. And four proteins including pyruvate kinase M1, clusterin, kininogen1 and profilin-1were identified as potential biomarkers for chronic sleep deprivation. The four candidates were validated via parallel reaction monitoring (PRM) based targeted proteomics. In addition, protein expression alteration of the four proteins was confirmed in myocardium and brain of rat model. In summary, the comprehensive proteomic study revealed the biological impacts of chronic sleep deprivation and discovered several potential biomarkers. This study provides further insight into the pathological and molecular mechanisms underlying sleep disorders at protein level

Renaissance for magnetotactic bacteria in astrobiology

Capable of forming magnetofossils similar to some magnetite nanocrystals observed in the Martian meteorite ALH84001, magnetotactic bacteria (MTB) once occupied a special position in the field of astrobiology during the 1990s and 2000s. This flourish of interest in putative Martian magnetofossils faded from all but the experts studying magnetosome formation, based on claims that abiotic processes could produce magnetosome-like magnetite crystals. Recently, the rapid growth in our knowledge of the extreme environments in which MTB thrive and their phylogenic heritage, leads us to advocate for a renaissance of MTB in astrobiology. In recent decades, magnetotactic members have been discovered alive in natural extreme environments with wide ranges of salinity (up to 90 g L-1), pH (1-10), and temperature (0-70 °C). Additionally, some MTB populations are found to be able to survive irradiated, desiccated, metal-rich, hypomagnetic, or microgravity conditions, and are capable of utilizing simple inorganic compounds such as sulfate and nitrate. Moreover, MTB likely emerged quite early in Earth's history, coinciding with a period when the Martian surface was covered with liquid water as well as a strong magnetic field. MTB are commonly discovered in suboxic or oxic-anoxic interfaces in aquatic environments or sediments similar to ancient crater lakes on Mars, such as Gale crater and Jezero crater. Taken together, MTB can be exemplary model microorganisms in astrobiology research, and putative ancient Martian life, if it ever occurred, could plausibly have included magnetotactic microorganisms. Furthermore, we summarize multiple typical biosignatures that can be applied for the detection of ancient MTB on Earth and extraterrestrial MTB-like life. We suggest transporting MTB to space stations and simulation chambers to further investigate their tolerance potential and distinctive biosignatures to aid in understanding the evolutionary history of MTB and the potential of magnetofossils as an extraterrestrial biomarker

Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves

Leaf nitrogen concentration (LNC) is a critical indicator of crop nutrient status. In this study, the feasibility of using visible and near-infrared spectroscopy combined with deep learning to estimate LNC in cotton leaves was explored. The samples were collected from cotton’s whole growth cycle, and the spectra were from different measurement environments. The random frog (RF), weighted partial least squares regression (WPLS), and saliency map were used for characteristic wavelength selection. Qualitative models (partial least squares discriminant analysis (PLS-DA), support vector machine for classification (SVC), convolutional neural network classification (CNNC) and quantitative models (partial least squares regression (PLSR), support vector machine for regression (SVR), convolutional neural network regression (CNNR)) were established based on the full spectra and characteristic wavelengths. Satisfactory results were obtained by models based on CNN. The classification accuracy of leaves in three different LNC ranges was up to 83.34%, and the root mean square error of prediction (RMSEP) of quantitative prediction models of cotton leaves was as low as 3.36. In addition, the identification of cotton leaves based on the predicted LNC also achieved good results. These results indicated that the nitrogen content of cotton leaves could be effectively detected by deep learning and visible and near-infrared spectroscopy, which has great potential for real-world application

CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM

Recent advances in convolutional neural networks (CNNs) for image feature extraction have achieved extraordinary performance, but back-propagation algorithms tend to fall into local minima. To alleviate this problem, this paper proposes a coordinate attention-support vector machine-convolutional neural network (CASVM). This proposed to enhance the model’s ability by introducing coordinate attention while obtaining enhanced image features. Training is carried out by back-propagating the loss function of support vector machines (SVMs) to improve the generalization capability, which can effectively avoid falling into local optima. The image datasets used in this study for benchmark experiments are Fashion-MNIST, Cifar10, Cifar100, and Animal10. Experimental results show that compared with softmax, CASVM can improve the image classification accuracy of the original model under different image resolution datasets. Under the same structure, CASVM shows better performance and robustness and has higher accuracy. Under the same network parameters, the loss function of CASVM enables the model to realize a lower loss value. Among the standard CNN models, the highest accuracy rate can reach 99%, and the optimal number of accuracy indicators is 5.5 times that of softmax, whose accuracy rate can be improved by up to 56%

Forward kinematics solutions of a special six-degree-of-freedom parallel manipulator with three limbs

This article presents a special 6-degree-of freedom parallel manipulator, and the mechanical structure of this robot has been introduced; with this structure, the kinematic constrain equations are decoupled. Based on this character, the polynomial solutions of the forward kinematics problem are also presented. In this method, the closed-loop kinematic chain of the manipulator is divided into two parts, the solution forward position kinematics is obtained by a first-degree polynomial equation first, and then an eighth-degree polynomial equation in a single variable for the forward orientation kinematics is obtained. Based on those solutions, the configurations of the robot, including position and orientation of the end-effector, are graphically displayed. A numerical simulation is given to verify the algorithm, and the result implies that for a given set of input values, the manipulator can be assembled in eight different configurations at most. And a set of experiments illustrate the motion ability for forward kinematics of the prototype of this manipulator

CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM

Recent advances in convolutional neural networks (CNNs) for image feature extraction have achieved extraordinary performance, but back-propagation algorithms tend to fall into local minima. To alleviate this problem, this paper proposes a coordinate attention-support vector machine-convolutional neural network (CASVM). This proposed to enhance the model’s ability by introducing coordinate attention while obtaining enhanced image features. Training is carried out by back-propagating the loss function of support vector machines (SVMs) to improve the generalization capability, which can effectively avoid falling into local optima. The image datasets used in this study for benchmark experiments are Fashion-MNIST, Cifar10, Cifar100, and Animal10. Experimental results show that compared with softmax, CASVM can improve the image classification accuracy of the original model under different image resolution datasets. Under the same structure, CASVM shows better performance and robustness and has higher accuracy. Under the same network parameters, the loss function of CASVM enables the model to realize a lower loss value. Among the standard CNN models, the highest accuracy rate can reach 99%, and the optimal number of accuracy indicators is 5.5 times that of softmax, whose accuracy rate can be improved by up to 56%

Linking employee boundary spanning behavior to task performance: The influence of informal leader emergence and group power distance

Driven by fierce global competition, flatter organizational structures and the growing complexity of tasks, boundary spanning behavior (BSB) in externally dependent work teams has increasingly been emphasized in both theory and practice. The current study aims to answer the questions of whether, when and how an individual’s BSB impacts his or her task performance within a team. Based on a sample of 272 employees from 57 new product development teams in China, we found that informal leader emergence mediated the relationship between an individual’s BSB and his or her performance within a team. Moreover, group-level power distance positively moderated the association between BSB and informal leader emergence. An overall mediated moderation model of the effect of the interaction between BSB and group power distance (PD) on task performance via informal leadership emergence was also confirmed. In particular, the relationship between BSB and task performance via informal leadership emergence was stronger for teams with less PD than for those with more PD. The implications of the research are discussed