A Multipath Routing Protocol Based on Clustering and Ant Colony Optimization for Wireless Sensor Networks

For monitoring burst events in a kind of reactive wireless sensor networks (WSNs), a multipath routing protocol (MRP) based on dynamic clustering and ant colony optimization (ACO) is proposed. Such an approach can maximize the network lifetime and reduce the energy consumption. An important attribute of WSNs is their limited power supply, and therefore some metrics (such as energy consumption of communication among nodes, residual energy, path length) were considered as very important criteria while designing routing in the MRP. Firstly, a cluster head (CH) is selected among nodes located in the event area according to some parameters, such as residual energy. Secondly, an improved ACO algorithm is applied in the search for multiple paths between the CH and sink node. Finally, the CH dynamically chooses a route to transmit data with a probability that depends on many path metrics, such as energy consumption. The simulation results show that MRP can prolong the network lifetime, as well as balance of energy consumption among nodes and reduce the average energy consumption effectively

Specific Volumetric Weight-Driven Shift in Microbiota Compositions With Saccharifying Activity Change in Starter for Chinese Baijiu Fermentation

Chinese starter Jiuqu, traditionally produced by spontaneous fermentation and always squeezed into bricks, serves as a vital saccharifying agent for simultaneous saccharification and fermentation of Chinese Baijiu. It is important to reveal the key saccharifying microbiota and the driving force to improve the quality of Jiuqu. Here we studied the compositions of the microbiota by high-throughput amplicons sequencing analysis in Jiuqu, and revealed eight bacterial and seven fungal genera as the dominant community members. Among them, Lactobacillus, Aspergillus, Pichia, Saccharomyces, Rhizopus were the main contributors of proteins by metaproteomics analysis. Whereas, only Lactobacillus, Pichia, Rhizopus appeared as key actors for saccharification by secreting three glycosidases and two glycosyltransferases, and it indicated they were the key saccharifying microbiota in Jiuqu. Especially, Rhizopus secreted the most abundant glucoamylase. Interestingly, these three active genera significantly decreased and the key saccharifying enzymes were down-expressed, when Jiuqu was produced in diffused shape with a low volumetric weight. Rhizopus microsporus, the main producer of glucoamylase, was positively correlated with volumetric weight of Jiuqu. It indicated volumetric weight was the major driving force of the key saccharifying microbiota in Jiuqu. This work provides deep insights of key saccharifying microbiota, and indicates the main driving force for the key microbe. Furthermore, this finding can contribute to the improvement of saccharifying agent for food fermentation

Investigation of lipid metabolism dysregulation and the effects on immune microenvironments in pan-cancer using multiple omics data.

BACKGROUND: Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression. RESULTS: Through systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets. CONCLUSIONS: Our study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors

Neuropathologic damage induced by radiofrequency ablation at different temperatures

Objective: To explore the molecular mechanism of neuropathologic damage induced by radiofrequency ablation at different temperatures. Methods: This is basic research, and 36 SD rats were used to construct the neuropathological injury model. The rats were subjected to radiofrequency stimulation at different temperatures and were divided into 6 groups according to the temperature injury: 42°, 47°, 52°, 57°, 62°, and 67°C groups. Conduction time, conduction distance, and nerve conduction velocity were recorded after temperature injury. HE-staining was used to observe the histopathological morphology of the sciatic nerve. The expression of SCN9A, SCN3B, and NFASC protein in sciatic nerve tissue were detected by western blot. Results: With the increase in temperature, nerve conduction velocity gradually decreased, and neurons were damaged when the temperature was 67°C. HE-staining showed that the degrees of degeneration of neurons in rats at 47°, 52°, 57°, 62°, and 67°C were gradually increased. The expression of SCN9A, SCN3B protein in 57°, 62°, 67°C groups were much higher than that of NC, 42°, 47°, 52°C groups. However, the expression of NFASC protein in 57°, 62°, 67°C groups was much lower than that of the NC, 42°, 47°, 52°C groups. Conclusion: There was a positive correlation between temperature caused by the radiofrequency stimulation to neuropathological damage. The mechanism is closely related to the expression of SCN9A, SCN3B, and NFASC protein in nerve tissue caused by heat transfer injury

The Advantage of Low-Delta Electroencephalogram Phase Feature for Reconstructing the Center-Out Reaching Hand Movements

It is an emerging frontier of research on the use of neural signals for prosthesis control, in order to restore lost function to amputees and patients after spinal cord injury. Compared to the invasive neural signal based brain-machine interface (BMI), a non-invasive alternative, i.e., the electroencephalogram (EEG)-based BMI would be more widely accepted by the patients above. Ideally, a real-time continuous neuroprosthestic control is required for practical applications. However, conventional EEG-based BMIs mainly deal with the discrete brain activity classification. Until recently, the literature has reported several attempts for achieving the real-time continuous control by reconstructing the continuous movement parameters (e.g., speed, position, etc.) from the EEG recordings, and the low-frequency band EEG is consistently reported to encode the continuous motor control information. Previous studies with executed movement tasks have extensively relied on the amplitude representation of such slow oscillations of EEG signals for building models to decode kinematic parameters. Inspired by the recent successes of instantaneous phase of low-frequency invasive brain signals in the motor control and sensory processing domains, this study examines the extension of such a slow-oscillation phase representation to the reconstructing two-dimensional hand movements, with the non-invasive EEG signals for the first time. The data for analysis are collected on five healthy subjects performing 2D hand center-out reaching along four directions in two sessions. On representative channels over the cortices encoding the execution information of reaching movements, we show that the low-delta EEG phase representation is characterized by higher signal-to-noise ratio and stronger modulation by the movement tasks, compared to the low-delta EEG amplitude representation. Furthermore, we have tested the low-delta EEG phase representation with two commonly used linear decoding models. The results demonstrate that the low-delta EEG phase based decoders lead to superior performance for 2D executed movement reconstruction to its amplitude based counterparts, as well as the other-frequency band amplitude and power based features. Thus, our study contributes to improve the movement reconstruction from EEG by introducing a new feature set based on the low-delta EEG phase patterns, and demonstrates its potential for continuous fine motion control of neuroprostheses

Increased Variation in Body Weight and Food Intake Is Related to Increased Dietary Fat but Not Increased Carbohydrate or Protein in Mice

Funding This study was funded by the National Key R&D Program of China (2019YFA0801900) to JS and the Postdoctoral Innovation Fund (2021) to YW. The original diet exposure experiment was funded by the Chinese Academy of Sciences Strategic Program (XDB13030100). JS was also supported during this work by a PIFI professorial fellowship from CAS and a Wolfson merit award from the UK Royal Society. CORRECTION article Front. Nutr., 21 October 2022 Sec. Nutrition and Metabolism https://doi.org/10.3389/fnut.2022.1049766 Corrigendum: Increased variation in body weight and food intake is related to increased dietary fat but not increased carbohydrate or protein in micePeer reviewedPublisher PD

A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization

Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO) has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO) is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems

Recognizing emotions induced by wearable haptic vibration using noninvasive electroencephalogram

The integration of haptic technology into affective computing has led to a new field known as affective haptics. Nonetheless, the mechanism underlying the interaction between haptics and emotions remains unclear. In this paper, we proposed a novel haptic pattern with adaptive vibration intensity and rhythm according to the volume, and applied it into the emotional experiment paradigm. To verify its superiority, the proposed haptic pattern was compared with an existing haptic pattern by combining them with conventional visual–auditory stimuli to induce emotions (joy, sadness, fear, and neutral), and the subjects’ EEG signals were collected simultaneously. The features of power spectral density (PSD), differential entropy (DE), differential asymmetry (DASM), and differential caudality (DCAU) were extracted, and the support vector machine (SVM) was utilized to recognize four target emotions. The results demonstrated that haptic stimuli enhanced the activity of the lateral temporal and prefrontal areas of the emotion-related brain regions. Moreover, the classification accuracy of the existing constant haptic pattern and the proposed adaptive haptic pattern increased by 7.71 and 8.60%, respectively. These findings indicate that flexible and varied haptic patterns can enhance immersion and fully stimulate target emotions, which are of great importance for wearable haptic interfaces and emotion communication through haptics

Effects of dietary macronutrients and body composition on glucose homeostasis in mice

Funding This work was supported by the Chinese Academy of Sciences Strategic Programs (XDA12030209 and XDB13030100), the 1000 Talents Program and a Wolfson Merit Award to J.R.SPeer reviewedPublisher PD