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Bitcoin is the highest market capitalization product among virtual currencies today, attracting investors from all over the world. The attitudes and policies of governments towards bitcoin are a key concern for investors, and policy changes may have some impact on the bitcoin market. In this paper, the main topic is the changes in bitcoin market returns and volatility for different periods (one week, two weeks, and 1 month) after the U.S. and China proposed policies that have a negative impact on bitcoin by using an ARMA-GARCH model. The study shows that in the long-term perspective, the seemingly devastating policies did not generate much volatility in the overall market to shake investors' long-term investment enthusiasm. This study provides countervailing evidence to support investors who are overly concerned about Bitcoin's reduced investment value due to tough government regulation, boosting investor confidence.

The study of social behaviors in animals is essential for understanding their survival and reproductive strategies. However, accurately tracking and analyzing the social interactions of free-moving animals has remained a challenge. Existing multi-animal pose estimation techniques suffer from drawbacks such as the need for extensive manual annotation and difficulty in discriminating between similar-looking animals in close social interactions. In this paper, we present the Social Behavior Atlas (SBeA), a novel computational framework that solves these challenges by employing a deep learning-based video instance segmentation model, 3D pose reconstruction, and unsupervised dynamic behavioral clustering. SBeA framework also involves a multi-camera setup to prevent occlusion, and a novel approach to identify individual animals in close social interactions. We demonstrate the effectiveness of SBeA in tracking and mapping the 3D close interactions of free-moving animals using the example of genetic mutant mice, birds, and dogs. Our results show that SBeA is capable of identifying subtle social interaction abnormalities, and the models and frameworks developed can be applied to a wide range of animal species. SBeA is a powerful tool for researchers in the fields of neuroscience and ecology to study animal social behaviors with a high degree of accuracy and reliability.

Various animal models of anxiety have been developed to evaluate anxiety and anxiolytic drugs. However, non-uniform measuring paradigms, variability in apparatus use and individual differences in animals confound study results. In this study, when all animals were included in the data analysis, we found no significant differences between control and stressed mice using standard behavioral paradigms for assessing anxiety (elevated plus maze and open field test). To provide a better assessment of anxiety, we therefore used a machine learning approach to analyze the behavioral patterns of each animal, and selected typical subjects in each group for use as a training set according to classical anxiety parameters. Spontaneous behaviors in these animals were captured by multi-view cameras and decomposed into sub-second modules using Behavior Atlas, and six behavioral features providing statistically significant difference between stressed and control mice were identified. Combined with low-dimensional embedding and clustering, new features were used to discriminate stressed mice from controls, in both the training set and all objects. Our results show Behavior Atlas is a powerful approach for identifying new potential biomarkers in an unbiased fashion. Our approach can complement classical measuring paradigms to objectively and comprehensively evaluate anxiety-like behaviors.

Many membraneless organelles are liquid-like domains that form inside the active, viscoelastic environment of living cells through phase separation. To investigate the potential coupling of phase separation with the cytoskeleton, we quantify the structural correlations of membraneless organelles (stress granules) and cytoskeletal filaments (microtubules) in a human-derived epithelial cell line. We find that microtubule networks are substantially denser in the vicinity of stress granules. When microtubules are depolymerized, the sub-units localize near the surface of the stress granules. We interpret these data using a thermodynamic model of partitioning of particles to the surface and bulk of the droplets. In this framework, our data are consistent with a weak (≲kBT) affinity of the microtubule sub-units for stress granule interfaces. As microtubules polymerize, their interfacial affinity increases, providing sufficient adhesion to deform droplets and/or the network. Our work suggests that proteins and other objects in the cell have a non-specific affinity for droplet interfaces that increases with the contact area and becomes most apparent when they have no preference for the interior of a droplet over the rest of the cytoplasm. We validate this basic physical phenomenon in vitro through the interaction of a simple protein–RNA condensate with microtubules., Nature Physics, 18 (5), ISSN:1745-2473, ISSN:1745-2481

Measuring eye movement is a fundamental approach in cognitive science as it provides a variety of insightful parameters that reflect brain states such as visual attention and emotions. Combining eye-tracking with multimodal neural recordings or manipulation techniques is beneficial for understanding the neural substrates of cognitive function. Many commercially-available and custom-built systems have been widely applied to awake, head-fixed small animals. However, the existing eye-tracking systems used in freely-moving animals are still limited in terms of their compatibility with other devices and of the algorithm used to detect eye movements. Here, we report a novel system that integrates a general-purpose, easily compatible eye-tracking hardware with a robust eye feature-detection algorithm. With ultra-light hardware and a detachable design, the system allows for more implants to be added to the animal’s exposed head and has a precise synchronization module to coordinate with other neural implants. Moreover, we systematically compared the performance of existing commonly-used pupil-detection approaches, and demonstrated that the proposed adaptive pupil feature-detection algorithm allows the analysis of more complex and dynamic eye-tracking data in free-moving animals. Synchronized eye-tracking and electroencephalogram recordings, as well as algorithm validation under five noise conditions, suggested that our system is flexibly adaptable and can be combined with a wide range of neural manipulation and recording technologies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12264-022-00834-9.

The prevailing view is that parvalbumin (PV) interneurons play modulatory roles in emotional response through local medium spiny projection neurons (MSNs). Here, we show that PV activity within the nucleus accumbens shell (sNAc) is required for producing anxiety-like avoidance when mice are under anxiogenic situations. Firing rates of sNAcPV neurons were negatively correlated to exploration time in open arms (threatening environment). In addition, sNAcPV neurons exhibited high excitability in a chronic stress mouse model, which generated excessive maladaptive avoidance behavior in an anxiogenic context. We also discovered a novel GABAergic pathway from the anterior dorsal bed nuclei of stria terminalis (adBNST) to sNAcPV neurons. Optogenetic activation of these afferent terminals in sNAc produced an anxiolytic effect via GABA transmission. Next, we further demonstrated that chronic stressors attenuated the inhibitory synaptic transmission at adBNSTGABA → sNAcPV synapses, which in turn explains the hyperexcitability of sNAc PV neurons on stressed models. Therefore, activation of these GABAergic afferents in sNAc rescued the excessive avoidance behavior related to an anxious state. Finally, we identified that the majority GABAergic input neurons, which innervate sNAcPV cells, were expressing somatostatin (SOM), and also revealed that coordination between SOM- and PV- cells functioning in the BNST → NAc circuit has an inhibitory influence on anxiety-like responses. Our findings provide a potentially neurobiological basis for therapeutic interventions in pathological anxiety.

The spinal cord is critical to the perception of peripheral information under sensory-guided motor behaviors in health and disease. However, the cellular activity underlie spinal cord function in freely behaving animals is not clear. Here, we developed a new method for imaging the spinal cord at cellular and subcellular resolution over weeks under naturalistic conditions. The method involves an improved surgery to reduce spinal movement, and the installation of a miniaturized two-photon microscope to obtain high-resolution imaging in moving mice. In vivo calcium imaging demonstrated that dorsal horn neurons show a sensorimotor program-dependent synchronization and heterogeneity under distinct cutaneous stimuli in behaving mice. The long-term imaging of sensory neurons revealed that in the spinal cord, healthy mice demonstrated stereotyped responses. However, in a neuropathic pain model, plasticity changes and neuronal sensitization were observed. We provide a practical method to study the function of spinal cord on sensory perception and disorders in freely behaving mice.

Understanding the behavior of mice relies on the precise tracking of its limbs' movement. In recent years, deep learning based animal pose estimation methods can track the user-defined body points of mice from single-view camera. However, mice perform the behavior in three dimensional (3D) space, and there is still no mature device to acquire them. Hence, this paper proposed an integrated 3D behavioral tracking system of free-moving mice. This system uses four multi-view cameras to capture the behavior of mice synchronously. The automatic calibration module simplifies the most time-consuming calibration step of multi-view cameras by merging Zhang's calibration method with a movable mechanical checkboard. With the calibration parameters, the 3D frame poses of mice can be reconstructed using the deep learning pose estimation trajectories of each camera. This 3D behavioral tracking system can avoid the occlusion problem caused by single-view camera, and calibrate multi-view camera efficiently, which can provide high-precision 3D poses data of free-moving mice with less human supervision.

Understanding the connection between brain and behavior in animals requires precise monitoring of their behaviors in three-dimensional (3-D) space. However, there is no available three-dimensional behavior capture system that focuses on rodents. Here, we present MouseVenue3D, an automated and low-cost system for the efficient capture of 3-D skeleton trajectories in markerless rodents. We improved the most time-consuming step in 3-D behavior capturing by developing an automatic calibration module. Then, we validated this process in behavior recognition tasks, and showed that 3-D behavioral data achieved higher accuracy than 2-D data. Subsequently, MouseVenue3D was combined with fast high-resolution miniature two-photon microscopy for synchronous neural recording and behavioral tracking in the freely-moving mouse. Finally, we successfully decoded spontaneous neuronal activity from the 3-D behavior of mice. Our findings reveal that subtle, spontaneous behavior modules are strongly correlated with spontaneous neuronal activity patterns.

In the fields of technology such as aerospace, anomaly detection is critical to the overall system. With the large increase in data volume and dimensions, the traditional detection methods have great limitations, and thus anomaly detection algorithms based on deep learning have received widespread attention. In this paper, based on autoencoder: standard autoencoder, denoising autoencoder, and sparse autoencoder, an ensemble detection model that can extract more feature information is proposed. To make more use of these feature information, inspired by the idea of pooling layer of the CNN, two feature fusion methods are proposed. Finally, the experiment verifies that the result of this model is better than the single autoencoder model.

Animal behavior usually has a hierarchical structure and dynamics. Therefore, to understand how the neural system coordinates with behaviors, neuroscientists need a quantitative description of the hierarchical dynamics of different behaviors. However, the recent end-to-end machine-learning-based methods for behavior analysis mostly focus on recognizing behavioral identities on a static timescale or based on limited observations. These approaches usually lose rich dynamic information on cross-scale behaviors. Here, inspired by the natural structure of animal behaviors, we address this challenge by proposing a parallel and multi-layered framework to learn the hierarchical dynamics and generate an objective metric to map the behavior into the feature space. In addition, we characterize the animal 3D kinematics with our low-cost and efficient multi-view 3D animal motion-capture system. Finally, we demonstrate that this framework can monitor spontaneous behavior and automatically identify the behavioral phenotypes of the transgenic animal disease model. The extensive experiment results suggest that our framework has a wide range of applications, including animal disease model phenotyping and the relationships modeling between the neural circuits and behavior., Animal behavior usually has a hierarchical structure and dynamics. Here, the authors propose a parallel and multi-layered framework to learn the hierarchical dynamics and generate an objective metric to map the behaviour into the feature space.

Previous studies have reported that nanoparticles (NPs) and heavy metals are toxic to the environment. However, the jointed toxicity is not yet well understood. This study was aimed to investigate the combined toxicity of TiO2 NPs and the heavy metal cadmium (Cd) to plants. Rice (Oryzasativa L.) was selected as the target plant. The rice seedlings were randomly separated into 12 groups and treated with CdCl2 (0, 10 and 20 mg/L) and TiO2 NPs (0, 10, 100 and 1000 mg/L). The plant height, biomass and root length indicated significant toxicity of Cd to the growth, but TiO2 NPs exhibited the potential ability to alleviate the Cd toxicity. Transmission electron microscopy (TEM) and energy dispersive spectrometer (EDS) confirmed the existence of TiO2 NPs in plants. Elemental analysis of Ti and Cd suggested that the presences of Cd significantly decreased the Ti accumulation in the rice roots in the co-exposure treatments. Interestingly, TiO2 NPs could lower the Cd uptake and distribution in rice roots and leaves. The results of antioxidant enzyme activity, lipid peroxide as well as phytohormones varied in the different treatments. Comparing with the Cd alone treatment, the net photosynthetic rate and chlorophyll content were significantly increased in the co-exposure treatments, suggesting that TiO2 NPs could tremendously reduce the Cd toxicity.

The prevailing view is that parvalbumin (PV) interneurons play modulatory roles in emotional response through local medium spiny projection neurons (MSNs). Here, we show that PV activity within the nucleus accumbens shell (sNAc) is required for producing anxiety-related avoidance when mice are under anxiogenic situations; sNAcPV neurons exhibited high excitability in chronically stressed mice model, which generated excessive maladaptive avoidance behavior in an anxiogenic context. We also discovered a novel GABAergic projections from the anterior dorsal bed nuclei of stria terminalis (adBNST) to sNAcPV neurons; optogenetic activation of these afferent terminals in sNAc produced an anxiolytic effect via GABA transmission. Next, we further demonstrated that chronic stressors attenuated the inhibitory synaptic transmission at adBNSTGABA sNAcPV synapses, which in turn explains the hyperexcitability of sNAcPV neurons on stressed models; therefore, activation of these GABAergic afferents in sNAc rescued the excessive avoidance behavior related to anxious state.Our findings reveal the coordination between BNST and NAc through an inhibitory architecture in controlling of anxiety-related response and provide a neurobiological basis for therapeutic interventions in pathological anxiety.

Nanoparticles (NPs) are an increasingly common contaminant in agro-environments, and their potential effect on genetically modified (GM) crops has been largely unexplored. GM crop exposure to NPs is likely to increase as both technologies develop. To better understand the implications of nanoparticles on GM plants in agriculture, we performed a glasshouse study to quantify the uptake of Fe2O3 NPs on transgenic and non-transgenic rice plants. We measured nutrient concentrations, biomass, enzyme activity, and the concentration of two phytohormones, abscisic acid (ABA) and indole-3-acetic acid (IAA), and malondialdehyde (MDA). Root phytohormone inhibition was positively correlated with Fe2O3 NP concentrations, indicating that Fe2O3 had a significant influence on the production of these hormones. The activities of antioxidant enzymes were significantly higher as a factor of low Fe2O3 NP treatment concentration and significantly lower at high NP concentrations, but only among transgenic plants. There was also a positive correlation between the treatment concentration of Fe2O3 and iron accumulation, and the magnitude of this effect was greatest among non-transgenic plants. The differences in root phytohormone production and antioxidant enzyme activity between transgenic and non-transgenic rice plants in vivo suggests that GM crops may react to NP exposure differently than conventional crops. It is the first study of NPs that may have an impact on GM crops, and a realistic significance for food security and food safety.

In the cloud computing system environment, combined with the first-level scheduling model of task-virtual machine resource nodes, the individual coding, fitness function, selection replication and cross-variation process are redesigned, and the cloud computing resource scheduling model based on genetic algorithm is established. Corresponding to fireflies and virtual machine resource nodes, this paper redesigned the firefly decision domain update method, selected attraction probability formula and location movement strategy, and combined with genetic algorithm to establish cloud computing resource scheduling model based on firefly-genetic algorithm. Experiment with the CloudSim cloud computing simulation platform. The results show that the task completion time of the resource scheduling model is smaller than that of the single genetic algorithm. The virtual machine load is more balanced, the task completion time is short, and the overall optimization effect of the resource scheduling scheme is obvious.

Previous studies have reported that nanoparticles (NPs) and heavy metals are toxic to the environment. However, the jointed toxicity is not yet well understood. This study was aimed to investigate the combined toxicity of TiO

Nanoparticles (NPs) are an increasingly common contaminant in agro-environments, and their potential effect on genetically modified (GM) crops has been largely unexplored. GM crop exposure to NPs is likely to increase as both technologies develop. To better understand the implications of nanoparticles on GM plants in agriculture, we performed a glasshouse study to quantify the uptake of Fe2O3 NPs on transgenic and non-transgenic rice plants. We measured nutrient concentrations, biomass, enzyme activity, and the concentration of two phytohormones, abscisic acid (ABA) and indole-3-acetic acid (IAA), and malondialdehyde (MDA). Root phytohormone inhibition was positively correlated with Fe2O3 NP concentrations, indicating that Fe2O3 had a significant influence on the production of these hormones. The activities of antioxidant enzymes were significantly higher as a factor of low Fe2O3 NP treatment concentration and significantly lower at high NP concentrations, but only among transgenic plants. There was also a positive correlation between the treatment concentration of Fe2O3 and iron accumulation, and the magnitude of this effect was greatest among non-transgenic plants. The differences in root phytohormone production and antioxidant enzyme activity between transgenic and non-transgenic rice plants in vivo suggests that GM crops may react to NP exposure differently than conventional crops. It is the first study of NPs that may have an impact on GM crops, and a realistic significance for food security and food safety.

The crystallization of 16 proteins was carried out using 60 wells on board Shenzhou 3 in 2002. Although the mission was only 7 days, careful and concerted planning at all stages made it possible to obtain crystals of improved quality compared to their ground controls for some of the proteins. Significantly improved resolutions were obtained from diffracted crystals of 4 proteins. A complete data set from a space crystal of the PEP carboxykinase yielded significantly higher resolution (1.46A vs. 1.87A), I/sigma (22.4 vs. 15.5), and a lower average temperature factor (29.2A(2) vs. 42.9A(2)) than the best ground-based control crystal. The 3-D structure of the enzyme is well improved with significant ligand density. It has been postulated that the reduced convection and absence of macromolecule sedimentation under microgravity have advantages/benefits for protein crystal growth. Improvements in experimental design for protein crystal growth in microgravity are ongoing.

Background SiO2 nanoparticle is one of the most popular nanomaterial which has been used in various fields, such as wastewater treatment, environmental remediation, food processing, industrial and household applications, biomedicine, disease labeling, and biosensor, etc. In agriculture, the use of SiO2 nanoparticles as insecticide, carriers in drug delivery, or in uptake and translocation of nutrient elements, etc., has been given attention. However, the effects of nanoparticles on plants have been seldom studied. In this work, the toxicity of SiO2 nanoparticles and their uptake, transport, distribution and bio-effects have been investigated in Bt-transgenic cotton. Methods The phytotoxic effects of SiO2 nanoparticles were exhibited in Bt-transgenic cotton with different SiO2 concentrations of 0, 10, 100, 500 and 2000 mg.L−1 for 3 weeks through dry biomasses, nutrient elements, xylem sap, enzymes activities, and hormone concentrations. The uptake and distribution of nanoparticles by the plants were confirmed using transmission electron microscopy (TEM). Results The SiO2 nanoparticles decreased significantly the plant height, shoot and root biomasses; the SiO2 nanoparticles also affected the contents of Cu, Mg in shoots and Na in roots of transgenic cotton; and SOD activity and IAA concentration were significantly influenced by SiO2 nanoparticles. In addition, SiO2 nanoparticles were present in the xylem sap and roots as examined by TEM showing that the SiO2 nanoparticles were transported from roots to shoots via xylem sap. Conclusions This is the first report of the transportation of SiO2 nanoparticles via xylem sap within Bt-transgenic cotton. This study provides direct evidence for the bioaccumulation of SiO2 nanoparticles in plants, which shows the potential risks of SiO2 nanoparticles impact on food crops and human health.