Calmodulins and calmodulin-like proteins-mediated plant organellar calcium signaling networks under abiotic stress

Plant calmodulins(CaMs)and calmodulin-like proteins(CMLs)mediate Ca~(2+)signaling in response to abiotic stresses.Manipulation of this signaling in crops could increase stress tolerance.We review methods for detecting Ca~(2+)signals,regulatory roles of Ca Ms and CMLs,binding targets,and Ca~(2+)networks under abiotic stress in organelles.

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).

Social-ecological perspective on the suicidal behaviour factors of early adolescents in China:a network analysis

Background In early adolescence,youth are highly prone to suicidal behaviours.Identifying modifiable risk factors during this critical phase is a priority to inform effective suicide prevention strategies.Aims To explore the risk and protective factors of suicidal behaviours(ie,suicidal ideation,plans and attempts)in early adolescence in China using a social-ecological perspective.Methods Using data from the cross-sectional project‘Healthy and Risky Behaviours Among Middle School Students in Anhui Province,China',stratified random cluster sampling was used to select 5724 middle school students who had completed self-report questionnaires in November 2020.Network analysis was employed to examine the correlates of suicidal ideation,plans and attempts at four levels,namely individual(sex,academic performance,serious physical llness/disability,history of self-harm,depression,impulsivity,sleep problems,resilience),family(family economic status,relationship with mother,relationship with father,family violence,childhood abuse,parental mental illness),school(relationship with teachers,relationship with classmates,school-bullying victimisation and perpetration)and social(social support,satisfaction with society).Results In total,37.9%,19.0%and 5.5%of the students reported suicidal ideation,plans and attempts in the past 6 months,respectively.The estimated network revealed that suicidal ideation,plans and attempts were collectively associated with a history of self-harm,sleep problems,childhood abuse,school bullying and victimisation.Centrality analysis indicated that the most influential nodes in the network were history of self-harm and childhood abuse.Notably,the network also showed unique correlates of suicidal ideation(sex,weight=0.60;impulsivity,weight=0.24;family violence,weight=0.17;relationship with teachers,weight=-0.03;school-bullying perpetration,weight=0.22),suicidal plans(social support,weight=-0.15)and suicidal attempts(relationship with mother,weight=-0.10;parental mental llness,weight=0.61).Conclusions This study identified the correlates of suicidal ideation,plans and attempts,and provided practical implications for suicide prevention for young adolescents in China.Firstly,this study highlighted the importance of joint interventions across multiple departments.Secondly,the common risk factors of suicidal ideation,plans and attempts were elucidated.Thirdly,this study proposed target interventions to address the unique influencing factors of suicidal ideation,plans and attempts.

Inhibition of protein degradation increases the Bt protein concentration in Bt cotton

Bacillus thuringiensis(Bt)cotton production is challenged by two main problems,i.e.,the low concentration of Bt protein at the boll setting stage and the lowest insect resistance in bolls among all the cotton plant’s organs.Therefore,increasing the Bt protein concentration at the boll stage,especially in bolls,has become the main goal for increasing insect resistance in cotton.In this study,two protein degradation inhibitors(ethylene diamine tetra acetic acid(EDTA)and leupeptin)were sprayed on the bolls,subtending leaves,and whole cotton plants at the peak flowering stage of two Bt cultivars(medium maturation Sikang 1(SK1))and early maturation Zhongmian 425(ZM425)in 2019 and 2020.The Bt protein content and protein degradation metabolism were assessed.The results showed that the Bt protein concentrations were enhanced by 21.3 to 38.8%and 25.0 to 38.6%in the treated bolls of SK1 and ZM425 respectively,while they were decreased in the subtending leaves of these treated bolls.In the treated leaves,the Bt protein concentrations increased by 7.6 to 23.5%and 11.2 to 14.9%in SK1 and ZM425,respectively.The combined application of EDTA and leupeptin to the whole cotton plant increased the Bt protein concentrations in both bolls and subtending leaves.The Bt protein concentrations in bolls were higher,increasing by 22.5 to 31.0%and 19.6 to 32.5%for SK1 and ZM425,respectively.The organs treated with EDTA or/and leupeptin showed reduced free amino acid contents,protease and peptidase activities and significant enhancements in soluble protein contents.These results indicated that inhibiting protein degradation could improve the protein content,thus increasing the Bt protein concentrations in the bolls or/and leaves of cotton plants.Therefore,the increase in the Bt protein concentration without yield reduction suggested that these two protein degradation inhibitors may be applicable for improving insect resistance in cotton production.

Major royal-jelly proteins intake modulates immune functions and gut microbiota in mice

In this study,we investigated the effects of major royal jelly proteins(MRJPs)on the estrogen,gut microbiota,and immunological responses in mice.Mice given 250 or 500 mg/kg,not 125 mg/kg of MRJPs,enhanced the proliferation of splenocytes in response to mitogens.The splenocytes and mesenteric lymphocytes activated by T-cell mitogens(Con A and anti-CD3/CD28 antibodies)released high levels of IL-2 but low levels of IFN-γand IL-17A.The release of IL-4 was unaffected by MRJPs.Additionally,splenocytes and mesenteric lymphocytes activated by LPS were prevented by MRJPs at the same dose as that required for producing IL-1βand IL-6,two pro-inflammatory cytokines.The production of IL-1β,IL-6,and IFN-γwas negatively associated with estrogen levels,which were higher in the MRJP-treated animals than in the control group.Analysis of the gut microbiota revealed that feeding mice 250 mg/kg of MRJPs maintained the stability of the natural intestinal microflora of mice.Additionally,the LEf Se analysis identified biomarkers in the MRJP-treated mice,including Prevotella,Bacillales,Enterobacteriales,Gammaproteobacteria,Candidatus_Arthromitus,and Shigella.Our results showed that MRJPs are important components of royal jelly that modulate host immunity and hormone levels and help maintain gut microbiota stability.

The pathogenic mechanism of TAR DNA-binding protein 43(TDP-43)in amyotrophic lateral sclerosis

The onset of amyotrophic lateral sclerosis is usually characterized by focal death of both upper and/or lower motor neurons occurring in the motor cortex,basal ganglia,brainstem,and spinal cord,and commonly involves the muscles of the upper and/or lower extremities,and the muscles of the bulbar and/or respiratory regions.However,as the disease progresses,it affects the adjacent body regions,leading to generalized muscle weakness,occasionally along with memory,cognitive,behavioral,and language impairments;respiratory dysfunction occurs at the final stage of the disease.The disease has a complicated pathophysiology and currently,only riluzole,edaravone,and phenylbutyrate/taurursodiol are licensed to treat amyotrophic lateral sclerosis in many industrialized countries.The TAR DNA-binding protein 43 inclusions are observed in 97%of those diagnosed with amyotrophic lateral sclerosis.This review provides a preliminary overview of the potential effects of TAR DNAbinding protein 43 in the pathogenesis of amyotrophic lateral sclerosis,including the abnormalities in nucleoplasmic transport,RNA function,post-translational modification,liquid-liquid phase separation,stress granules,mitochondrial dysfunction,oxidative stress,axonal transport,protein quality control system,and non-cellular autonomous functions(e.g.,glial cell functions and prion-like propagation).

Image super‐resolution via dynamic network

Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction block.The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution.To enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes.To prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained features.Also,a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem.Finally,a construction block is responsible for reconstructing high‐quality images.Designed heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital devices.Experimental results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and complexity.The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.

Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction

Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering design.However,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow field.In this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity data.Specifi-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored before.In our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder.This method significantly reduces the workload of data measure-ment in practical engineering applications.Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects.Furthermore,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field points.Compared with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields.This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios.The code is available at https://github.com/zhangzm0128/MSU.

Applying an Improved Dung Beetle Optimizer Algorithm to Network Traffic Identification

Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.

Computing Power Network:A Survey

With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect.To overcome these problems and improve network efficiency,a new network computing paradigm is proposed,i.e.,Computing Power Network(CPN).Computing power network can connect ubiquitous and heterogenous computing power resources through networking to realize computing power scheduling flexibly.In this survey,we make an exhaustive review on the state-of-the-art research efforts on computing power network.We first give an overview of computing power network,including definition,architecture,and advantages.Next,a comprehensive elaboration of issues on computing power modeling,information awareness and announcement,resource allocation,network forwarding,computing power transaction platform and resource orchestration platform is presented.The computing power network testbed is built and evaluated.The applications and use cases in computing power network are discussed.Then,the key enabling technologies for computing power network are introduced.Finally,open challenges and future research directions are presented as well.

Impact of apolipoprotein E isoforms on sporadic Alzheimer's disease:beyond the role of amyloid beta

The impact of apolipoprotein E(ApoE)isoforms on sporadic Alzheimer's disease has long been studied;however,the influences of apolipoprotein E gene(APOE)on healthy and pathological human brains are not fully understood.ApoE exists as three common isoforms(ApoE2,ApoE3,and ApoE4),which differ in two amino acid residues.Traditionally,ApoE binds cholesterol and phospholipids and ApoE isoforms display diffe rent affinities for their receptors,lipids transport and distribution in the brain and periphery.The role of ApoE in the human depends on ApoE isoforms,brain regions,aging,and neural injury.APOE E4 is the strongest genetic risk factor for sporadic Alzheimer's disease,considering its role in influencing amyloid-beta metabolism.The exact mechanisms by which APOE gene variants may increase or decrease Alzheimer's disease risk are not fully understood,but APOE was also known to affect directly and indirectly tau-mediated neurodegeneration,lipids metabolism,neurovascular unit,and microglial function.Consistent with the biological function of ApoE,ApoE4 isoform significantly alte red signaling pathways associated with cholesterol homeostasis,transport,and myelination.Also,the rare protective APOE variants confirm that ApoE plays an important role in Alzheimer's disease pathogenesis.The objectives of the present mini-review were to describe classical and new roles of various ApoE isoforms in Alzheimer's disease pathophysiology beyond the deposition of amyloid-beta and to establish a functional link between APOE,brain function,and memory,from a molecular to a clinical level.APOE genotype also exerted a heterogeneous effect on clinical Alzheimer's disease phenotype and its outcomes.Not only in learning and memory but also in neuro psychiatric symptoms that occur in a premorbid condition.Cla rifying the relationships between Alzheimer's disease-related pathology with neuropsychiatric symptoms,particularly suicidal ideation in Alzheimer's disease patients,may be useful for elucidating also the underlying pathophysiological process and its prognosis.Also,the effects of anti-amyloid-beta drugs,recently approved for the treatment of Alzheimer's disease,could be influenced by the APOE genotype.

GmSTF accumulation mediated by DELLA protein GmRGAs contributes to coordinating light and gibberellin signaling to reduce plant height in soybean

Plant height influences plant architecture,lodging resistance,and yield performance.It is modulated by gibberellic acid(GA)metabolism and signaling.DELLA proteins,acting as central repressors of GA signaling,integrate various environmental and hormonal signals to regulate plant growth and development in Arabidopsis.We examined the role of two DELLA proteins,GmRGAa and GmRGAb,in soybean plant height control.Knockout of these proteins led to longer internodes and increased plant height,primarily by increasing cell elongation.GmRGAs functioned under different light conditions,including red,blue,and far-red light,to repress plant height.Interaction studies revealed that GmRGAs interacted with the blue light receptor GmCRY1b.Consistent with this,GmCRY1b partially regulated plant height via GmRGAs.Additionally,DELLA proteins were found to stabilize the protein GmSTF1/2,a key positive regulator of photomorphogenesis.This stabilization led to increased transcription of GmGA2ox-7b and subsequent reduction in plant height.This study enhances our understanding of DELLA-mediated plant height control,offering Gmrgaab mutants for soybean structure and yield optimization.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

Responses of growth performance,antioxidant function,small intestinal morphology and mRNA expression of jejunal tight junction protein to dietary iron in yellow-feathered broilers

This study aimed to investigate the dose-effect of iron on growth performance,antioxidant function.intestinal morphology,and mRNA expression of jejunal tight junction protein in 1-to21-d-old yellow-feathered broilers.A total of 7201-d-old yellow-feathered maleb roilers were allocated to 9 treatments with 8 replicate cages of 10 birds per cage.The dietary treatments were consisted of a basal diet(contained 79.6 mg Fe kg^(-1))supplemented with 0,20,40,60,80,160,320,640,and 1,280 mg Fe kg^(-1)in the form of FeSO_(4)·7H_(2)O.Compared with the birds in the control group,birds supplemented with 20mg Fe kg^(-1)had higher average daily gain(ADG)(P<0.0001).Adding 640 and 1,280 mg Fe kg^(-1)significantly decreased ADG(P<0.0001)and average daily feed intake(ADFI)(P<0.0001)compared with supplementation of 20mg Fe kg^(-1).Malondialdehyde(MDA)concentration in plasma and duodenum increased linearly(P<0.0001),but MDA concentration in liver and jejunum increased linearly(P<0.05)or quadratically(P<0.05)with increased dietary Fe concentration.The villus height(VH)in duodenum and jejunum,and the ratio of villus height to crypt depth(V/C)in duodenum decreased linearly(P?0.05)as dietary Feincreased.As dietary Fe increased,the jejunal relative mRNA abundance of claudin-1 decreased linearly(P=0.001),but the jejunal relative mRNA abundance of zona occludens-1(ZO-1)and occludin decreased linearly(P?0.05)or quadratically(P?0.05).Compared with the supplementation of 20 mg Fe kg^(-1),the supplementation of640 mg Fe kg^(-1)or higher increased(P?0.05)MDA concentrations in plasma,duodenum,and jejunum,decreased VH in the duodenum and jejunum,and the addition of 1,280 mg Fe kg^(-1)reduced(P?0.05)the jejunal tight junction protein(claudin-1,ZO-1,occludin)mRNA abundance.In summary,640 mg of supplemental Fe kg^(-1)or greater was associated with decreased growth performance,increased oxidative stress,disrupted intestinal morphology,and reduced mRNA expression of jejunal tight junction protein.

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.

Insights into microbiota community dynamics and flavor development mechanism during golden pomfret(Trachinotus ovatus)fermentation based on single-molecule real-time sequencing and molecular networking analysis

Popular fermented golden pomfret(Trachinotus ovatus)is prepared via spontaneous fermentation;however,the mechanisms underlying the regulation of its flavor development remain unclear.This study shows the roles of the complex microbiota and the dynamic changes in microbial community and flavor compounds during fish fermentation.Single-molecule real-time sequencing and molecular networking analysis revealed the correlations among different microbial genera and the relationships between microbial taxa and volatile compounds.Mechanisms underlying flavor development were also elucidated via KEGG based functional annotations.Clostridium,Shewanella,and Staphylococcus were the dominant microbial genera.Forty-nine volatile compounds were detected in the fermented fish samples,with thirteen identified as characteristic volatile compounds(ROAV>1).Volatile profiles resulted from the interactions among the microorganisms and derived enzymes,with the main metabolic pathways being amino acid biosynthesis/metabolism,carbon metabolism,and glycolysis/gluconeogenesis.This study demonstrated the approaches for distinguishing key microbiota associated with volatile compounds and monitoring the industrial production of high-quality fermented fish products.

Emulsifying property,antioxidant activity,and bitterness of soybean protein isolate hydrolysate obtained by Corolase PP under high hydrostatic pressure

Enzymatic hydrolysis of proteins can enhance their emulsifying properties and antioxidant activities.However,the problem related to the hydrolysis of proteins was the generation of the bitter taste.Recently,high hydrostatic pressure(HHP)treatment has attracted much interest and has been used in several studies on protein modification.Hence,the study aimed to investigate the effects of enzymatic hydrolysis by Corolase PP under different pressure treatments(0.1,100,200,and 300 MPa for 1-5 h at 50℃)on the emulsifying property,antioxidant activity,and bitterness of soybean protein isolate hydrolysate(SPIH).As observed,the hydrolysate obtained at 200 MPa for 4 h had the highest emulsifying activity index(47.49 m^(2)/g)and emulsifying stability index(92.98%),and it had higher antioxidant activities(44.77%DPPH free radical scavenging activity,31.12%superoxide anion radical scavenging activity,and 61.50%copper ion chelating activity).At the same time,the enhancement of emulsion stability was related to the increase of zeta potential and the decrease of mean particle size.In addition,the hydrolysate obtained at 200 MPa for 4 h had a lower bitterness value and showed better palatability.This study has a broad application prospect in developing food ingredients and healthy foods.

Five commonly used traditional Chinese medicine formulas in the treatment of ulcerative colitis:A network meta-analysis

BACKGROUND Currently,traditional Chinese medicine(TCM)formulas are commonly being used as adjunctive therapy for ulcerative colitis in China.Network meta-analysis,a quantitative and comprehensive analytical method,can systematically compare the effects of different adjunctive treatment options for ulcerative colitis,providing scientific evidence for clinical decision-making.AIM To evaluate the clinical efficacy and safety of commonly used TCM for the treatment of ulcerative colitis(UC)in clinical practice through a network metaanalysis.METHODS Clinical randomized controlled trials of these TCM formulas used for the adjuvant treatment of UC were searched from the establishment of the databases to July 1,2022.Studies that met the inclusion criteria were screened and evaluated for literature quality and risk of bias according to the Cochrane 5.1 standard.The methodological quality of the studies was assessed using ReviewManager(RevMan)5.4,and a funnel plot was constructed to test for publication bias.ADDIS 1.16 statistical software was used to perform statistical analysis of the treatment measures and derive the network relationship and ranking diagrams of the various intervention measures.RESULTS A total of 64 randomized controlled trials involving 5456 patients with UC were included in this study.The adjuvant treatment of UC using five TCM formulations was able to improve the clinical outcome of the patients.Adjuvant treatment with Baitouweng decoction(BTWT)showed a significant effect[mean difference=36.22,95%confidence interval(CI):7.63 to 65.76].For the reduction of tumor necrosis factor in patients with UC,adjunctive therapy with BTWT(mean difference=−9.55,95%CI:−17.89 to−1.41),Shenlingbaizhu powder[SLBZS;odds ratio(OR)=0.19,95%CI:0.08 to 0.39],and Shaoyao decoction(OR=−23.02,95%CI:−33.64 to−13.14)was effective.Shaoyao decoction was more effective than BTWT(OR=0.12,95%CI:0.03 to 0.39),SLBZS(OR=0.19,95%CI:0.08 to 0.39),and Xi Lei powder(OR=0.34,95%CI:0.13 to 0.81)in reducing tumor necrosis factor and the recurrence rate of UC.CONCLUSION TCM combined with mesalazine is more effective than mesalazine alone in the treatment of UC.

Effect of sodium starch octenyl succinate-based Pickering emulsion on the physicochemical properties of hairtail myofibrillar protein gel subjected to multiple freeze-thaw cycles

A Pickering emulsion based on sodium starch octenyl succinate(SSOS)was prepared and its effects on the physicochemical properties of hairtail myofibrillar protein gels(MPGs)subjected to multiple freeze-thaw(F-T)cycles were investigated.The whiteness,water-holding capacity,storage modulus(G')and texture properties of the MPGs were significantly improved by adding 1%-2%Pickering emulsion(P<0.05).Meanwhile,Raman spectral analysis demonstrated that Pickering emulsion promoted the transformation of secondary structure,enhanced hydrogen bonds and hydrophobic interactions,and promoted the transition of disulfide bond conformation from g-g-g to g-g-t and t-g-t.At an emulsion concentration of 2%,theα-helix content decreased by 10.37%,while theβ-sheet content increased by 7.94%,compared to the control.After F-T cycles,the structure of the MPGs was destroyed,with an increase in hardness and a decrease in whiteness and water-holding capacity,however,the quality degradation of MPGs was reduced with 1%-2%Pickering emulsion.These findings demonstrated that SSOS-Pickering emulsions,as potential fat substitutes,can enhance the gel properties and the F-T stability of MPGs.