Role of a Signalling System in Gene Interaction in Inheritance of Root System Characteristics of Arabidopsis thaliana (L.) Heynh.

The central problem of genetics is gene interaction since genes in the course of individual organism development interact with other genes, that’s why their effects may change. Studies for the last 100 years managed to discover that the entire diversity of inter-gene interactions is presented in four major forms: complementarity, epistasis, polymery, and modifying effect of genes. However, gene interaction mechanism which is reflected on the segregation nature of variously crossed hybrids has not been sufficiently studied. Exclusive of molecular genetics, biochemistry and physiology, a genetic analysis of inheritance of characteristics in gene interaction taken by itself cannot reveal nature of this interaction. Lately, molecular-genetic and physiological studies on A. thaliana mutants have enabled to isolate and sequence a wide range of genes controlling certain links of the signalling chain. At the same time, effect of the plant development regulation signalling system on interaction of these genes in inheritance of characteristics of Arabidopsis root system haven’t been studied so far which was a cause for our studies. Gene interaction problem is closely related to the plant development regulation signalling system. Mechanism involved in gene interaction may be explained based on current idea of molecular principles of biological response. Affected by mutations occurring in various genes that control certain links of the signalling chain, signalling path to the cell nucleus and response are blocked partially or in full which leads to distortion in expression of the characteristic on the plant level in general or its organ level. Such phenomenon is observed in realization of many characteristics in animals and plants, including in A. thaliana. In inheritance of such characteristics, as a rule, both allelic, and non-allelic gene interactions are observed. Results of a study of the plant signalling system interconnection and gene interaction in inheritance of characteristics of Arabidopsis root system are presented. It is established that complementary interaction of genes RHD3 and SAR1 is observed in the second crossing generation for plants of rhd3-1 × sar-1 mutant lines. When gpa1-3 × slr-1 mutant-line plants are crossed, recessive epistasis (slr-1 slr-1 > GPA1_) occurs in F2 generation. Polymeric interaction of genes SHY2 and MSG1 is observed in F2 in crossing of shy2-2 × msg1-2 mutant-line plants.

Traditional Chinese medicine Pien-Tze-Huang ameliorates LPS-induced sepsis through bile acid-mediated activation of TGR5-STAT3-A20 signalling

Pien Tze Huang(PZH),a class-1 nationally protected traditional Chinese medicine(TCM),has been used to treat liver diseases such as hepatitis;however,the effect of PZH on the progression of sepsis is unknown.Here,we reported that PZH attenuated lipopolysaccharide(LPS)-induced sepsis in mice and reduced LPS-induced production of proinflammatory cytokines in macrophages by inhibiting the activation of mitogen-activated protein kinase(MAPK)and nuclear factor-kappa B(NF-κB)signalling.Mechanistically,PZH stimulated signal transducer and activator of transcription 3(STAT3)phosphorylation to induce the expression of A20,which could inhibit the activation of NF-κB and MAPK signalling.Knockdown of the bile acid(BA)receptor G protein-coupled bile acid receptor 1(TGR5)in macrophages abolished the effects of PZH on STAT3 phosphorylation and A20 induction,as well as the LPS-induced inflammatory response,suggesting that BAs in PZH may mediate its anti-inflammatory effects by activating TGR5.Consistently,deprivation of BAs in PZH by cholestyramine resin reduced the effects of PZH on the expression of phosphorylated-STAT3 and A20,the activation of NF-κB and MAPK signalling,and the production of proinflammatory cytokines,whereas the addition of BAs to cholestyramine resin-treated PZH partially restored the inhibitory effects on the production of proinflammatory cytokines.Overall,our study identifies BAs as the effective components in PZH that activate TGR5-STAT3-A20 signalling to ameliorate LPS-induced sepsis.

Reduction of the oxidative damage to H_(2)O_(2)-induced HepG2 cells via the Nrf2 signalling pathway by plant flavonoids Quercetin and Hyperoside

Hyperoside and quercetin are similar in molecular structures.In this study,the antioxidant regulatory targets of hyperoside and quercetin are mainly in the nuclear factor(erythroid-2-derived)-related factor 2(Nrf2)pathway predicted by network pharmacology.And the antioxidant effect and mechanism of hyperoside and quercetin were measured and compared in H_(2)O_(2)-induced Hep G2 cells and Caenorhabditis elegans.The findings indicated that quercetin was more effective than hyperoside in reducing oxidative damage,which was proved by improved cell viability,decreased reactive oxygen species(ROS)production,decreased cellular apoptosis,and alleviated mitochondrial damage.In addition,quercetin was more efficient than hyperoside in enhancing the expression of Nrf2-associated m RNAs,increasing the activities of superoxide dismutase(SOD),glutathione peroxidase(GSH-Px),and catalase(CAT),and reducing the cellular malondialdehyde(MDA)content.Quercetin was superior to hyperoside in prolonging the lifespan of worms,decreasing the accumulation of lipofuscin,inhibiting ROS production,and increasing the proportion of skn-1 in the nucleus.With the Nrf2 inhibitor ML385,we verified that quercetin and hyperoside primarily protected the cells against oxidative damage via the Nrf2 signalling pathway.Furthermore,molecular docking and dynamics simulations demonstrated that the quercetin-Kelch-like ECH-associated protein 1(Keap1)complex was more stable than the hyperoside-Keap1 complex.The stable structure of the complex might hinder the binding of Nrf2 and Keap1 to release Nrf2 and facilitate its entry into the nucleus to play an antioxidant role.Overall,quercetin had a better antioxidant than hyperoside.

Digital signal acquisition system for complex nuclear reaction experiments

A digital data-acquisition system based on XIA LLC products was used in a complex nuclear reaction experiment using radioactive ion beams.A flexible trigger system based on a field-programmable gate array(FPGA)parametrization was developed to adapt to different experimental sizes.A user-friendly interface was implemented,which allows converting script language expressions into FPGA internal control parameters.The proposed digital system can be combined with a conventional analog data acquisition system to provide more flexibility.The performance of the combined system was veri-fied using experimental data.

An artificial systems,computational experiments and parallel execution-based surface electromyogram-driven anti-disturbance zeroing neurodynamic strategy for upper limb human-robot interaction control

In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be ameliorated.Specially,the efficacy and robustness of the HRI control algorithm in the presence of unknown external disturbances deserve to be addressed.To deal with these urgent issues,in this study,artificial systems,computational experiments and a parallel execution intelligent control framework are constructed for the HRI control.The upper limb-robotic exoskeleton system is re-modelled as an artificial system.Depending on surface electromyogram-based subject's active motion intention in the practical system,a non-convex function activated anti-disturbance zeroing neurodynamic(NC-ADZND)controller is devised in the artificial system for parallel interaction and HRI control with the practical system.Furthermore,the linear activation function-based zeroing neurodynamic(LAF-ZND)controller and proportionalderivative(posterior deltoid(PD))controller are presented and compared.Theoretical results substantiate the global convergence and robustness of the proposed controller in the presence of different external disturbances.In addition,the simulation results verify that the NC-ADZND controller is better than the LAF-ZND and the PD controllers in respect of convergence order and anti-disturbance characteristics.

Early monitoring values of oncogenic signalling molecules for hepatocellular carcinoma

The prevention and early diagnosis of liver cancer remains a global medical challenge.During the malignant transformation of hepatocytes,a variety of oncogenic cellular signalling molecules,such as novel high mobility group-Box 3,angiopoietin-2,Golgi protein 73,glypican-3,Wnt3a(a signalling molecule in the Wnt/β-catenin pathway),and secretory clusterin,can be expressed and secreted into the blood.These signalling molecules are derived from different signalling pathways and may not only participate in the malignant transformation of hepatocytes but also become early diagnostic indicators of hepatocarcinogenesis or specific targeted molecules for hepatocellular carcinoma therapy.This article reviews recent progress in the study of several signalling molecules as sensitive biomarkers for monitoring hepatocarcinogenesis.

Low-complexity signal detection for massive MIMO systems via trace iterative method

Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.

Electroacupuncture targeting the immune system to alleviate sepsis

Sepsis is a life-threatening inflammatory syndrome with high morbidity and mortality rates.However,options for sepsis are still limited to general treatment in intensive care units(ICUs),and effective therapies that improve sepsis survival are required.Immune disturbances play a vital role in the pathology of sepsis and are associated with protracted inflammation,susceptibility to infections,and death.Therefore,many investigators have focused on the potential benefits of immunomodulation therapy for sepsis.Electroacupuncture(EA)has been practiced in clinics for many years and has shown advantages in treating infectious diseases.Over the last few decades,our understanding of the efficacy and mechanisms of EA in sepsis has undergone considerable developments.We searched the literature regarding“CNKI,Wan Fang Data,VIP Database,PubMed,and Ingenta Connect”from 2010 to 2023,using the keywords“sepsis”“septic”and“electroacupuncture”and 336 sources were searched.Finally,we included 82 studies that targeted the immune system to determine EA’s anti-inflammatory and immunomodulatory effects on sepsis.In this review,we found that EA has clinical benefits in relieving septic inflammation,improving immune function,and attenuating related multi-organ injury through several mechanisms,such as activation of the cholinergic anti-inflammatory pathway(CAP),vagaladrenal axis,inhibition of the nuclear factor Kappa-B(NF-κB)signaling pathway,signal transducers and activators of transcription(STAT)signaling pathway,and improvement of immune cell function.Therefore,EA may be a promising complementary therapy for sepsis treatment.We also expect these data will contribute to further studies on EA in sepsis.

From static to dynamic:live observation of the support system after ischemic stroke by two photon-excited fluorescence laser-scanning microscopy

Ischemic stroke is one of the most common causes of mortality and disability worldwide.However,treatment efficacy and the progress of research remain unsatisfactory.As the critical support system and essential components in neurovascular units,glial cells and blood vessels(including the bloodbrain barrier)together maintain an optimal microenvironment for neuronal function.They provide nutrients,regulate neuronal excitability,and prevent harmful substances from entering brain tissue.The highly dynamic networks of this support system play an essential role in ischemic stroke through processes including brain homeostasis,supporting neuronal function,and reacting to injuries.However,most studies have focused on postmortem animals,which inevitably lack critical information about the dynamic changes that occur after ischemic stroke.Therefore,a high-precision technique for research in living animals is urgently needed.Two-photon fluorescence laser-scanning microscopy is a powerful imaging technique that can facilitate live imaging at high spatiotemporal resolutions.Twophoton fluorescence laser-scanning microscopy can provide images of the whole-cortex vascular 3D structure,information on multicellular component interactions,and provide images of structure and function in the cranial window.This technique shifts the existing research paradigm from static to dynamic,from flat to stereoscopic,and from single-cell function to multicellular intercommunication,thus providing direct and reliable evidence to identify the pathophysiological mechanisms following ischemic stroke in an intact brain.In this review,we discuss exciting findings from research on the support system after ischemic stroke using two-photon fluorescence laser-scanning microscopy,highlighting the importance of dynamic observations of cellular behavior and interactions in the networks of the brain’s support systems.We show the excellent application prospects and advantages of two-photon fluorescence laser-scanning microscopy and predict future research developments and directions in the study of ischemic stroke.

Deep Learning Based Signal Detector for OFDM Systems

In this paper,we propose a novel deep learning(DL)-based receiver design for orthogonal frequency division multiplexing(OFDM)systems.The entire process of channel estimation,equalization,and signal detection is replaced by a neural network(NN),and hence,the detector is called a NN detector(N^(2)D).First,an OFDM signal model is established.We analyze both temporal and spectral characteristics of OFDM signals,which are the motivation for DL.Then,the generated data based on the simulation of channel statistics is used for offline training of bi-directional long short-term memory(Bi-LSTM)NN.Especially,a discriminator(F)is added to the input of Bi-LSTM NN to look for subcarrier transmission data with optimal channel gain(OCG),which can greatly improve the performance of the detector.Finally,the trained N^(2)D is used for online recovery of OFDM symbols.The performance of the proposed N^(2)D is analyzed theoretically in terms of bit error rate(BER)by Monte Carlo simulation under different parameter scenarios.The simulation results demonstrate that the BER of N^(2)D is obviously lower than other algorithms,especially at high signal-to-noise ratios(SNRs).Meanwhile,the proposed N^(2)D is robust to the fluctuation of parameter values.

A Two-Layer Fuzzy Control Strategy for the Participation of Energy Storage Battery Systems in Grid Frequency Regulation

To address the frequency fluctuation problem caused by the power dynamic imbalance between the power system and the loadwhen a large number of newenergy sources are connected to the grid,a two-layer fuzzy control strategy is proposed for the participation of the energy storage battery system in FM.Firstly,considering the coordination of FM units responding to automatic power generation control commands,a comprehensive allocation strategy of two signals under automatic power generation control commands is proposed to give full play to the advantages of two FM signals while enabling better coordination of two FM units responding to FM commands;secondly,based on the grid FM demand and battery FM capability,a double-layer fuzzy control strategy is proposed for FM units responding to automatic power generation control commands in a coordinated manner under dual-signal allocation mode to precisely allocate the power output depth of FM units,which can control the fluctuation of frequency deviation within a smaller range at a faster speed while maintaining the battery charge state;finally,the proposed Finally,the proposed control strategy is simulated and verified inMatlab/Simulink.The results show that the proposed control strategy can control the frequency deviation within a smaller range in a shorter time,better stabilize the fluctuation of the battery charge level,and improve the utilization of the FM unit.

Purinergic signaling systems across comparative models of spinal cord injury

Within the last several decades,the scientific community has made substantial progress in elucidating the complex pathophysiology underlying spinal cord injury.However,despite the many advances using conventional mammalian models,both cellular and axonal regeneration following spinal cord injury have remained out of reach.In this sense,turning to non-mammalian,regenerative species presents a unique opportunity to identify pro-regenerative cues and characterize a spinal cord microenvironment permissive to re-growth.Among the signaling pathways hypothesized to be dysregulated during spinal cord injury is the purinergic signaling system.In addition to its well-known role as energy currency in cells,ATP and its metabolites are small molecule neurotransmitters that mediate many diverse cellular processes within the central nervous system.While our understanding of the roles of the purinergic system following spinal cord injury is limited,this signaling pathway has been implicated in all injury-induced secondary processes,including cellular death,inflammation,reactive gliosis,and neural regeneration.Given that the purinergic system is also evolutionarily conserved between mammalian and non-mammalian species,comparisons of these roles may provide important insights into conditions responsible for recovery success.Here,we compare the secondary processes between key model species and the influence of purinergic signaling in each context.As our understanding of this signaling system and pro-regenerative conditions continues to evolve,so does the potential for the development of novel therapeutic interventions for spinal cord injury.

Automatic depression recognition by intelligent speech signal processing:A systematic survey

Depression has become one of the most common mental illnesses in the world.For better prediction and diagnosis,methods of automatic depression recognition based on speech signal are constantly proposed and updated,with a transition from the early traditional methods based on hand‐crafted features to the application of architectures of deep learning.This paper systematically and precisely outlines the most prominent and up‐to‐date research of automatic depression recognition by intelligent speech signal processing so far.Furthermore,methods for acoustic feature extraction,algorithms for classification and regression,as well as end to end deep models are investigated and analysed.Finally,general trends are summarised and key unresolved issues are identified to be considered in future studies of automatic speech depression recognition.

XB130 inhibits healing of diabetic skin ulcers through the PI3K/Akt signalling pathway

BACKGROUND Diabetic skin ulcers,a significant global healthcare burden,are mainly caused by the inhibition of cell proliferation and impaired angiogenesis.XB130 is an adaptor protein that regulates cell proliferation and migration.However,the role of XB130 in the development of diabetic skin ulcers remains unclear.AIM To investigate whether XB130 can regulate the inhibition of proliferation and vascular damage induced by high glucose.Additionally,we aim to determine whether XB130 is involved in the healing process of diabetic skin ulcers,along with its molecular mechanisms.METHODS We conducted RNA-sequencing analysis to identify the key genes involved in diabetic skin ulcers.We investigated the effects of XB130 on wound healing using histological analyses.In addition,we used reverse transcription-quantitative polymerase chain reaction,Western blot,terminal deoxynucleotidyl transferasemediated dUTP nick end labeling staining,immunofluorescence,wound healing,and tubule formation experiments to investigate their effects on cellular processes in human umbilical vein endothelial cells(HUVECs)stimulated with high glucose.Finally,we performed functional analysis to elucidate the molecular mechanisms underlying diabetic skin ulcers.RESULTS RNA-sequencing analysis showed that the expression of XB130 was up-regulated in the tissues of diabetic skin ulcers.Knockdown of XB130 promoted the healing of skin wounds in mice,leading to an accelerated wound healing process and shortened wound healing time.At the cellular level,knockdown of XB130 alleviated high glucose-induced inhibition of cell proliferation and angiogenic impairment in HUVECs.Inhibition of the PI3K/Akt pathway removed the proliferative effects and endothelial protection mediated by XB130.CONCLUSION The findings of this study indicated that the expression of XB130 is up-regulated in high glucose-stimulated diabetic skin ulcers and HUVECs.Knockdown of XB130 promotes cell proliferation and angiogenesis via the PI3K/Akt signalling pathway,which accelerates the healing of diabetic skin ulcers.

Nonbinary ldpc-coded probabilistic shaping scheme for MIMO systems based on signal space diversity

This paper first describes a binary Low-Density Parity-Check(LDPC)-coded Probabilistic Shaping(PS)scheme for Multiple-Input Multiple-Output(MIMO)systems based on Signal Space Diversity(SSD).Second,a Nonbinary(NB)LDPC-coded PS scheme for MIMO systems based on SSD is proposed.The first scheme can be used to obtain a shaping gain,whereas the second can also realize a coding gain.The theoretical average mutual information of the optimized rotated quadrature amplitude modulation constellations is analyzed and the simulated error per-formance with 22 and 44 MIMO schemes is investigated.The theoretical average mutual information analysis and simulation results show that the proposed NB LDPC-coded PS scheme for MIMO systems based on SSD is reliable and robust,and is therefore suitable for future wireless communication systems.

A Smart Heart Disease Diagnostic System Using Deep Vanilla LSTM

Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics,which aid in the prevention of several diseases including heart-related abnormalities.In this context,regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram(ECG)signals has the potential to save many lives.In existing studies,several heart disease diagnostic systems are proposed by employing different state-of-the-art methods,however,improving such methods is always an intriguing area of research.Hence,in this research,a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals.The proposed framework extracts both linear and time-series information on the ECG signals and fuses them into a single framework concurrently.The linear characteristics of ECG signals are extracted by convolution layers followed by Gaussian Error Linear Units(GeLu)and time series characteristics of ECG beats are extracted by Vanilla Long Short-Term Memory Networks(LSTM).Following on,the feature reduction of linear information is done with the help of ID Generalized Gated Pooling(GGP).In addition,data misbalancing issues are also addressed with the help of the Synthetic Minority Oversampling Technique(SMOTE).The performance assessment of the proposed model is done over the two publicly available datasets named MIT-BIH arrhythmia database(MITDB)and PTB Diagnostic ECG database(PTBDB).The proposed framework achieves an average accuracy performance of 99.14%along with a 95%recall value.

Integrated system of traditional THz time-domain spectroscopy and asynchronous optical sampling

Terahertz time-domain spectroscopy(THz-TDS)system,as a new means of spectral analysis and detection,plays an increasingly pivotal role in basic scientific research.However,owing to the long scanning time of the traditional THz-TDS system and the complex control of the asynchronous optical scanning(ASOPS)system,which requires frequent calibration,we combine traditional THz-TDS and ASOPS systems to form a composite system and propose an all-fiber trigger signal generation method based on the time overlapping interference signal generated by the collinear motion of two laser pulses.Finally,the time-domain and frequency-domain spectra are obtained by using two independent systems in the integrated systems.It is found that the full width at half maximum(FWHM)of the time-domain spectra and the spectral width of the frequency-domain spectra are almost the same,but the sampling speed of the ASOPS system is significantly faster than that of the traditional THz-TDS system,which conduces to the study of the transient characteristics of substances.

Distributed Secure State Estimation of Multi-Agent Systems Under Homologous Sensor Attacks

This paper addresses the problem of distributed secure state estimation for multi-agent systems under homologous sensor attacks.Two types of secure Luenberger-like distributed observers are proposed to estimate the system state and attack signal simultaneously.Specifically,the proposed two observers are applicable to deal with the cases in the presence and absence of time delays during network communication.It is also shown that the proposed observers can ensure the attack estimations from different agents asymptotically converge to the same value.Sufficient conditions for guaranteeing the asymptotic convergence of the estimation errors are derived.Simulation examples are finally provided to demonstrate the effectiveness of the proposed results.

Recognition System for Diagnosing Pneumonia and Bronchitis Using Children’s Breathing Sounds Based on Transfer Learning

Respiratory infections in children increase the risk of fatal lung disease,making effective identification and analysis of breath sounds essential.However,most studies have focused on adults ignoring pediatric patients whose lungs are more vulnerable due to an imperfect immune system,and the scarcity of medical data has limited the development of deep learning methods toward reliability and high classification accuracy.In this work,we collected three types of breath sounds from children with normal(120 recordings),bronchitis(120 recordings),and pneumonia(120 recordings)at the posterior chest position using an off-the-shelf 3M electronic stethoscope.Three features were extracted from the wavelet denoised signal:spectrogram,mel-frequency cepstral coefficients(MFCCs),and Delta MFCCs.The recog-nition model is based on transfer learning techniques and combines fine-tuned MobileNetV2 and modified ResNet50 to classify breath sounds,along with software for displaying analysis results.Extensive experiments on a real dataset demonstrate the effectiveness and superior performance of the proposed model,with average accuracy,precision,recall,specificity and F1 scores of 97.96%,97.83%,97.89%,98.89%and 0.98,respectively,achieving superior performance with a small dataset.The proposed detection system,with a high-performance model and software,can help parents perform lung screening at home and also has the potential for a vast screening of children for lung disease.

Intelligent Sound-Based Early Fault Detection System for Vehicles

An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning.The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car.Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts.The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults.A possible fault is determined in the vehicle based on this processed sound.Binary classification is done at the first stage to differentiate between faulty and healthy cars.We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts.We used the time domain,frequency domain,and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly.We used abnormal car data to classify it into fifteen other classical vehicle problems.We experimented with various signal processing techniques and presented the comparison results.In the detection and further problem classification,random forest showed the highest results of 97%and 92%with time-frequency features.