The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis.

Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the application of big data and machine learning (ML) techniques in PTSD. We found 873 studies meet the inclusion criteria and a total of 31 of those in a sample of 210,001 were included in quantitative analysis. ML algorithms were able to discriminate PTSD with an overall accuracy of 0.89. Pooled estimates of classification accuracy from multi-dimensional data (0.96) are higher than single data types (0.86 to 0.90). ML techniques can effectively classify PTSD and models using multi-dimensional data perform better than those using single data types. While selecting optimal combinations of data types and ML algorithms to be clinically applied at the individual level still remains a big challenge, these findings provide insights into the classification, identification, diagnosis and treatment of PTSD.

Sex-based differences in brain morphometry under chronic stress: A pilot MRI study.

Background: Sex-based differences are known to be a significant feature of chronic stress; however, the morphological mechanisms of the brain underlying these differences remain unclear. The present study aimed to use magnetic resonance imaging (MRI) and voxel-based morphometry (VBM) to investigate the effects of sex on gray matter volume (GMV) changes under conditions of chronic stress. Methods: A total of 32 subjects were included for analysis in the present study: 16 participants experiencing chronic stress and 16 healthy controls. T1-weighted (T1WI) images from a 3 T MRI scanner were extracted from the OpenfMRI database. Images were segmented into gray matter using VBM analysis. A two-way analysis of variance (ANOVA) with a 2 × 2 full factorial design was used to evaluate the main and interaction effects of chronic stress and sex on GMV changes, and then post hoc testing was used to verify each simple effect. Results: Two-way ANOVA showed a chronic stress × sex interaction effect on GMV. Simple effects analysis indicated that the GMV of the bilateral pre- and post-central gyri, the right cuneus and superior occipital gyrus was decreased in males, whereas that of the bilateral pre- and post-central gyri, the right superior occipital gyrus and the left middle frontal gyrus and orbital middle frontal gyrus was increased in females, under chronic stress. Additionally, in the control group, the GMV of the bilateral pre- and post-central gyri, the right cuneus and superior occipital gyrus was greater in males than females. While in the chronic stress group, the above sex-based differences were no longer significant. Conclusions: This study preliminarily shows that there are significant differences in gray matter volume changes between males and females under chronic stress. These findings provide a basis for future studies investigating the volumetric mechanisms of sex differences under chronic stress.

Multi-hierarchy Network Configuration Can Predict Brain States and Performance.

The brain is a hierarchical modular organization that varies across functional states. Network configuration can better reveal network organization patterns. However, the multi-hierarchy network configuration remains unknown. Here, we proposed an eigenmodal decomposition approach to detect modules at multi-hierarchy, which can identify higher-layer potential submodules, and is consistent with the brain hierarchical structure. We defined three metrics: node configuration matrix, combinability, and separability. Node configuration matrix represents network configuration changes between layers. Separability reflects network configuration from global to local, whereas combinability shows network configuration from local to global. First, we created a random network to verify the feasibility of the method. Results show that separability of real networks is larger than that of random networks, whereas combinability is smaller than random networks. Then, we analyzed a large data set incorporating fMRI data from resting and seven distinct tasking conditions. Experiment results demonstrates the high similarity in node configuration matrices for different task conditions, whereas the tasking states have less separability and greater combinability between modules compared with the resting state. Furthermore, the ability of brain network configuration can predict brain states and cognition performance. Crucially, derived from tasks are highlighted with greater power than resting, showing that task-induced attributes have a greater ability to reveal individual differences. Together, our study provides novel perspectives for analyzing the organization structure of complex brain networks at multi-hierarchy, gives new insights to further unravel the working mechanisms of the brain, and adds new evidence for tasking states to better characterize and predict behavioral traits.

Attentional bias modification and attention control training in PTSD: a systematic review and meta-analysis.

Background: Cognitive models of post-traumatic stress disorder (PTSD) highlighted the effect of maladaptive cognitive processing in the development and maintenance of PTSD. PTSD is related to attentional bias (AB) toward threatening stimuli and greater attentional bias variability (ABV). Attentional bias modification (ABM) and attention control training (ACT) have demonstrated the effect of improving PTSD, but the results of randomized controlled trials (RCTs) are controversial. Objectives: The current study aimed to evaluate the extent of evidence supporting the efficacy of ABM in the treatment of PTSD. Design: Systematic review and meta-analysis. Methods: We searched PUBMED, PsycINFO, EMBASE, ClinicalTrials.gov, and the Cochrane Central Register of Controlled Trials for articles published between 1980 and 2022. RCTs of ABM for adult participants with PTSD symptoms were identified. The primary outcome was changes in PTSD severity, and the second outcome was changes in AB and ABV. Trial quality was assessed using the Cochrane Risk of Bias Tool. Publication bias was assessed using the Doi plot and Luis Furuya-Kanamori (LFK) index. Results: Eight RCTs comparing the effect of ABM to ACT were included in the review, and six studies were meta-analyzed. Meta-analysis favored ACT in improving PTSD symptoms and ABV, and the effect size was large. ABM and ACT demonstrated similar effects in improving AB. Conclusion: ACT should not only be seen as a control training condition but also has therapeutic values. However, since the current meta-analysis only included a limited number of studies, further research was still needed to examine the clinical value of ACT in PTSD treatment.

Attentional bias modification and attention control training in PTSD We summarized and analyzed studies on attentional bias modification (ABM) and attention control training (ACT) in PTSD. Our findings indicated that ACT was a more effective treatment condition. This study highlights the therapeutic value of ACT.

Research Progress on New Functions of Animal and Plant Proteins.

Protein is composed of peptides, essential nutrients for human survival and health, and the easy absorption of peptides further promotes human health. According to the source of the protein, it can be divided into plants, animals, and micro-organisms, which have important physiological effects on the health of the body, especially in enhancing immunity. The most widely used raw materials are animal protein and plant protein, and the protein composition formed by the two in a certain proportion is called "double protein". In recent years, China's State Administration for Market Regulation has issued an announcement on the "Implementation Rules for the Technical Evaluation of New Functions and Products of Health Foods (Trial)", which provides application conditions and listing protection for the research and development of new functions of health foods. At present, some researchers and enterprises have begun to pay attention to the potential of animal and plant proteins to be used in new functions. In this article, the research progress of animal and plant proteins in the new functions of Chinese health food is reviewed in detail, and suggestions for future research on animal and plant proteins are put forward.

Network analysis of the comorbidity between post-traumatic stress, depression and anxiety symptoms among frontline healthcare workers during the COVID-19 pandemic.

Background: Coronavirus disease 2019 pandemic pointed out significant mental symptoms of frontline healthcare workers (HCWs). Objective: We aimed to estimate the prevalence and comorbidity of post-traumatic stress symptoms (PTSS), depression and anxiety symptoms in HCWs from Fangcang shelter hospitals during the pandemic. Design: Demographic information, post-traumatic stress disorder checklist for DSM-5 (PCL-5), Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder Questionnaire (GAD-7) were obtained online based on stratified random sampling design during April 2022, with 284 eligible responses. Method: Hierarchical regression analyses were applied to investigate independent variables associated with psychological status outcomes (PHQ-9, GAD-7 and PCL-5), and the network analyses were applied to explore the comorbidity using all items of PCL-5, PHQ-9 and GAD-7. Results: (1) 10.56%, 13.03% and 8.10% of HCWs reported PTSS, depression and anxiety symptoms. Fifty-three (18.66%) HCWs experienced at least one mental health disorder, among which 26.42-37.74% HCWs had comorbidity of two or three mental disorders; (2) several influence factors of mental health were identified, including medical professions, working hours, contacted patients (p < 0.05); (3) prominent bridge symptoms between PTSS and depression were sleep problems, suicide ideation, concentration difficulties and recklessness. Comorbidity between PTSS and anxiety was thought to mainly stem from negative affect, such as afraid, anxious, annoyed and worrying. Depressed mood and worry might be good targets during treatment of comorbidity of depression and anxiety. Conclusion: Our data suggest mild level of PTSS, depression and anxiety symptoms among HCWs during the pandemic and might give novel insights into assessment and intervention of comorbidity.

Beta-informativeness-diffusion multilayer graph embedding for brain network analysis.

Brain network analysis provides essential insights into the diagnosis of brain disease. Integrating multiple neuroimaging modalities has been demonstrated to be more effective than using a single modality for brain network analysis. However, a majority of existing brain network analysis methods based on multiple modalities often overlook both complementary information and unique characteristics from various modalities. To tackle this issue, we propose the Beta-Informativeness-Diffusion Multilayer Graph Embedding (BID-MGE) method. The proposed method seamlessly integrates structural connectivity (SC) and functional connectivity (FC) to learn more comprehensive information for diagnosing neuropsychiatric disorders. Specifically, a novel beta distribution mapping function (beta mapping) is utilized to increase vital information and weaken insignificant connections. The refined information helps the diffusion process concentrate on crucial brain regions to capture more discriminative features. To maximize the preservation of the unique characteristics of each modality, we design an optimal scale multilayer brain network, the inter-layer connections of which depend on node informativeness. Then, a multilayer informativeness diffusion is proposed to capture complementary information and unique characteristics from various modalities and generate node representations by incorporating the features of each node with those of their connected nodes. Finally, the node representations are reconfigured using principal component analysis (PCA), and cosine distances are calculated with reference to multiple templates for statistical analysis and classification. We implement the proposed method for brain network analysis of neuropsychiatric disorders. The results indicate that our method effectively identifies crucial brain regions associated with diseases, providing valuable insights into the pathology of the disease, and surpasses other advanced methods in classification performance.

Machine learning methods to discriminate posttraumatic stress disorder: A protocol of systematic review and meta-analysis.

Introduction: Recent years have witnessed a persistent threat to public mental health, especially during and after the COVID-19 pandemic. Posttraumatic stress disorder (PTSD) has emerged as a pivotal concern amidst this backdrop. Concurrently, machine learning (ML) techniques have progressively applied in the realm of mental health. Therefore, our present undertaking seeks to provide a comprehensive assessment of studies employing ML methods that use diverse data modalities on the classification of people with PTSD. Methods and analysis: In pursuit of pertinent studies, we will search both English and Chinese databases from January 2000 to May 2022. Two researchers will independently conduct screening, extract data and assess study quality. We intend to employ the assessment framework introduced by Luis Francisco Ramos-Lima in 2020 for quality evaluation. Rate, standard error and 95% CIs will be utilized for effect size measurement. A Cochran's Q test will be applied to assess heterogeneity. Subgroup and sensitivity analysis will further elucidate the source of heterogeneity and funnel plots and Egger's test will detect publication bias. Ethics and dissemination: This systematic review and meta-analysis does not encompass patient interactions or engagements with healthcare providers. The outcomes of this research will be disseminated through scholarly channels, including presentations at scientific conferences and publications in peer-reviewed journals.PROSPERO registration number CRD42023342042.

The benefits of hypoglycemic therapy for patients with obstructive sleep apnea.

PURPOSE: Obstructive sleep apnea (OSA) is often associated with glycemic abnormalities. This study is conducted to investigate the effects of hypoglycemic therapy on OSA-related indicators. METHOD: We systematically searched Web of Science, PubMed, Embase, and the Cochrane Library for articles on OSA patients receiving any hypoglycemic drugs, published until December 25, 2022. Seven original studies were finally included. The proposal was registered with PROSPERO (CRD42022351206). RESULTS: In summary, in addition to reduced glycosylated hemoglobin A1c (HbA1c), we found that hypoglycemic treatment can lower the apnea-hypopnea index (AHI) by 7.07/h (p = 0.0001). Although long-term treatment (> 12 weeks) achieved a more significant reduction in HbA1c (- 1.57% vs. - 0.30%) compared to short-term treatment (≤ 12 weeks), there was no significant difference between the two in terms of AHI (intergroup p-value = 0.27). We also found that patients using sodium glucose cotransporter 2 inhibitors (SGLT2i) experienced a greater reduction in AHI (- 11.00/h, p < 0.00001). Additionally, hypoglycemic treatment also showed certain improvements in related indicators like Epworth Sleepiness Scale, body mass index, and blood pressure. CONCLUSIONS: Our results affirm the benefits of hypoglycemic treatment for OSA patients and highlight the notable effect of SGLT2i. Further researches are needed to help doctors gain a comprehensive understanding of the interaction between OSA and glycemic abnormalities.

Single session of interpretation bias modification helped to improve fear of COVID-19 and COVID-19-related post-traumatic stress symptoms.

BACKGROUND: Post-traumatic stress symptoms (PTSS) are frequently observed in those who have experienced trauma events like the COVID-19 outbreak. The cognitive model of PTSS highlights the relationship between PTSS and negative interpretation bias. OBJECTIVE: The present study aimed to modify interpretation bias and to improve PTSS as well as PTSS-related fear. METHODS: 59 participants with high PTSS levels were recruited and randomly allocated to either the interpretation modification programme (IMP) intervention group or the interpretation control condition (ICC) control group. PTSS, negative interpretation bias, fear of COVID-19, and depression and anxiety symptoms were assessed before and after training. FINDINGS: Intention-to-treat analyses showed that compared with ICC, participants receiving IMP generated fewer negative interpretations for ambiguous scenarios, and the group-by-time interaction effect was significant. IMP also illustrated a more significant change in fear after training compared with ICC. Although no effects of training conditions were found on PTSS, the interaction of training conditions with fear reduction could predict PTSS improvement. CONCLUSIONS: IMP could improve negative interpretations and fear related to COVID-19 and might help to ameliorate PTSS. CLINICAL IMPLICATIONS: The role of PTSS-related emotion should be considered when exploring the effectiveness of IMP. IMP is a flexible approach that can be tailored to the specific characteristics of the traumatic event, which makes it suitable for a broader range of traumatised individuals.

More fear reduction, better posttraumatic stress symptoms resilience: 1 year longitudinal study based on the psychological trauma recover project-5-6.

OBJECTIVE: The outbreak of COVID-19 not only raised public health concerns but also caused tremendous psychological distress. Deficits in fear played a role in the development and maintenance of posttraumatic stress symptoms (PTSS). We aimed to investigate the relationship between fear and PTSS during COVID-19 at two points. METHOD: The questionnaire at the first time of investigation (T1) was conducted through WeChat or phone from January 30, 2020 to February 25, 2020 as part of the psychological trauma recover project-5-6 in China. And the questionnaire at the second time of investigation (T2) was collected from March 17, 2021 to June 17, 2021. After 12 months, data from 150 participants were included in the final analysis. Fear was measured by a self-reported question. Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5) was designed to assess PTSS. Pearson correlation, multivariate regression analysis, and multiple mediator model were used as statistical analyses. RESULTS: Fear reduced significantly over time. Participants with higher fear presented worse PTSS in both T1 and T2. Positive correlations between fear and PTSS of participants were found over time. Occupation and change of fear are significant predictors in the severity of PTSS over time. Change of fear partially mediated the relationship between the PCL-5 total score in T1 and the development of PTSS. CONCLUSION: Fear reduction was beneficial to trauma resilience. Future interventions could be developed to reduce excessive fear in facing natural disasters or following epidemics. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The impact of isolation on comorbidity of PTSD symptoms and depression: evidence from PTRP-5-6 in China.

BACKGROUND: The Omicron pandemic struck Shanghai, China, resulting in impairments of both physical and psychological health on those patients who were confirmed and transferred to the Fangcang shelters. The way of isolation led to high risk of posttraumatic stress symptoms (PTSS) and depressive symptoms among the patients in Fangcang shelters. We aim to estimate the prevalence and comorbidity of PTSS and depressive symptoms in patients from China's Fangcang shelters during the epidemic. METHODS: Demographic information questionnaire, the posttraumatic stress disorder checklist for DSM-5 (PCL-5), and Patient Health Questionnaire (PHQ-9) were used in the study. The data were collected online via mobile phones during 10th April to 20th April, 2022, as part of our Psychological Trauma Recover Project-5-6 (PTRP-5-6), a longitudinal study focusing on individuals who have experienced trauma. RESULTS: A total of 336 subjects were included in the analysis. The results revealed (1) the prevalence of depressive symptoms, and PTSS were 30.1% (cut-off = 10) and 6% (cut-off = 33); (2) Multiple logistic regression showed that female (OR = 3.04, p < 0.05), suffering from dyspnea (OR = 5.83, p < 0.05) or gastrointestinal symptoms (OR = 6.38, p < 0.05) were risk factors for PTSS; higher education level (OR = 3.27, p < 0.05) and suffering from dizziness or headache (OR = 2.46, p < 0.05) were risk factors for depressive symptoms; (3)Respectively, 85% of the patients who reported PTSS also experienced depressive symptoms, 16.8% of the patients who reported depressive symptoms presented PTSS. CONCLUSION: In the context of COVID-19, the comorbidity rate of PTSS and depressive symptoms among patients in Fangcang shelters increased with the severity of depressive symptoms.

High-energy-density zinc ion capacitors based on 3D porous free-standing defect-reduced graphene oxide hydrogel cathodes.

Zinc ion capacitors (ZICs) have shown potential for breaking the energy density ceiling of traditional supercapacitors (SCs) via appropriate device design. Nevertheless, a significant challenge remains in advancing ZIC positive electrode materials with excellent conductivity, high specific capacitance, and reliable cycle stability. A highly attractive option for carbon-based electrode materials is reduced graphene oxide (RGO) due to its vast specific surface area, prominent porosity, and 3D cross-linked frame. However, the tight stacking of RGO sheets driven by van der Waals forces can restrict active sites, decrease specific capacitance, and elevate electrochemical impedance. To overcome these challenges, 3D defective RGO (DRGO) hydrogels were prepared by a metal Co cocatalytic gasification reaction. This method produced mesoporous defects on the surface of RGO hydrogels via a low-temperature hydrothermal self-assembly strategy. The surface of the layer has a wide and uniform distribution, which can offer abundant redox active sites, rich ion transfer channels, and fast reaction kinetics. In this work, 3D DRGO//Zn exhibited a wide operating window (0-1.8 V), high specific capacitance (189.39 F g-1 at 1 A g-1), outstanding energy density (85.23 W h kg-1 at 960.31 W kg-1; 52.36 W h kg-1 at 17454.87 W kg-1), and persistent cycling life (98.86% initial capacitance retention after 10 000 cycles at 10 A g-1). This study emphasizes the device design of ZIC and promising prospects of using 3D DRGO hydrogel as a feasible positive electrode for ZIC.

Effect of different anti-cardiovascular disease treatments on the severity of obstructive sleep apnea.

Obstructive sleep apnea (OSA) and cardiovascular co-morbidities have a mutually reinforcing effect, but existing studies have focussed only on the improvement of the associated co-morbidities by treatment for OSA. To provide fresh guidelines for the treatment of OSA from a co-morbidity standpoint, we conducted a systematic search of Web of Science, PubMed, EMBASE, and the Cochrane Library for articles published from inception up to 2 May 2023. Fourteen original studies of patients with OSA with cardiovascular co-morbidities and who received related treatment were included in the analysis. We found that diuretic treatment can reduce the apnea-hypopnea index in patients with OSA and hypertension (-19.41/h, p = 1.0 × 10-5 ), aldosterone-angiotensin inhibitors also have a 9.19/h reduction (p = 0.003), while the effect of renal sympathetic denervation is insignificant (-2.32/h, p = 0.19). The short-term treatment (<4 weeks) did not show an improvement (-2.72/h, p = 0.16), while long-term treatment (>4 weeks) produced surprising outcomes (-12.78/h, p = 0.002). Patients with milder disease (baseline AHI < 35/h) had insignificant improvements (-1.05/h, p = 0.46), whereas those with more severe disease (baseline AHI > 35/h) could achieve satisfactory outcomes (-14.74/h, p < 0.00001). In addition, it also showed some improvement in the oxygen desaturation index and blood oxygen. Our results support the additional benefit of antihypertensive treatment for OSA symptoms, and the efficacy can be affected by different therapy, treatment duration, and severity levels. It could be useful in developing clinical therapy, educating patients, and exploring interaction mechanisms. The proposal was registered with PROSPERO (CRD42022351206).

Adaptive responses of eelgrass (Zostera marina L.) to ocean warming and acidification.

Ocean warming (OW) and ocean acidification (OA), driven by rapid global warming accelerating at unprecedented rates, are profoundly impacting the stability of seagrass ecosystems. Yet, our current understanding of the effects of OW and OA on seagrass remains constrained. Herein, we investigated the response of eelgrass (Zostera marina L.), a representative seagrass species, to OW and OA through comprehensive transcriptomic and metabolomic analyses. The results showed notable variations in plant performance under varying conditions: OW, OA, and OWA (a combination of both conditions). Specifically, under average oceanic temperature conditions for eelgrass growth over the past 20 years -from May to November-OA promoted the production of differentially expressed genes and metabolites associated with alanine, aspartate, and glutamate metabolism, as well as starch and sucrose metabolism. Under warming condition, eelgrass was resistant to OA by accelerating galactose metabolism, along with glycine, serine, and threonine metabolism, as well as the tricarboxylic acid (TCA) cycle. Under the combined OW and OA condition, eelgrass stimulated fructose and mannose metabolism, glycolysis, and carbon fixation, in addition to galactose metabolism and the TCA cycle to face the interplay. Our findings suggest that eelgrass exhibits adaptive capacity by inducing different metabolites and associated genes, primarily connected with carbon and nitrogen metabolism, in response to varying degrees of OW and OA. The data generated here support the exploration of mechanisms underlying seagrass responses to environmental fluctuations, which hold critical significance for the future conservation and management of these ecosystems.

Visual fatigue a comprehensive review of mechanisms of occurrence, animal model design and nutritional intervention strategies.

When the eyes work intensively, it is easy to have eye discomfort such as blurred vision, soreness, dryness, and tearing, that is, visual fatigue. Visual fatigue not only affects work and study efficiency, but long-term visual fatigue can also easily affect physical and mental health. In recent years, with the popularization of electronic products, although it has brought convenience to the office and study, it has also caused more frequent visual fatigue among people who use electronic devices. Moreover, studies have reported that the number of people with visual fatigue is showing a trend of increasing year by year. The range of people involved is also extensive, especially students, people who have been engaged in computer work and fine instruments (such as microscopes) for a long time, and older adults with aging eye function. More and more studies have proposed that supplementation with the proper nutrients can effectively relieve visual fatigue and promote eye health. This review discusses the physiological mechanisms of visual fatigue and the design ideas of animal experiments from the perspective of modern nutritional science. Functional food ingredients with the ability to alleviate visual fatigue are discussed in detail.

Estimation of heart rate and respiratory rate by monitoring cardiopulmonary signals with flexible sensor.

Monitoring of cardiopulmonary signals plays an important role in many clinical applications. A portable magnetic induction cardiopulmonary signal monitoring system with the flexible sensor of double micro-coils is presented in this paper. The detection of cardiopulmonary signals is realized with double micro-coils. The proposed system is safe, non-invasive, simple, and portable compared with traditional direct contact methods. The Hilbert-Huang transform (HHT) based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to cardiopulmonary signal processing, decomposing cardiopulmonary signal effectively. The sensor to monitor respiration rate and heart rate is validated and demonstrated with healthy volunteers. The root mean squared errors (RMSE) of heart rate, respiration rate under deep breathing and normal breathing are 3.8 beats/min, 0.61 times/min, and 0.98 times/min respectively. The flexible sensor of double micro-coils has little influence on the measurement results at the bending curvature of 33.9 m-1. Therefore, a suggested solution for monitoring and decomposition of cardiopulmonary signals is easy-to-use, and quick, which can be applied as a respected analytical device on mobile occasions in this study.

3D Hierarchical Ti3C2TX@PANI-Reduced Graphene Oxide Heterostructure Hydrogel Anode and Defective Reduced Graphene Oxide Hydrogel Cathode for High-Performance Zinc Ion Capacitors.

The practical application of supercapacitors (SCs) has been known to be restricted by low energy density, and zinc ion capacitors (ZICs) with a capacitive cathode and a battery-type anode have emerged as a unique technology that can effectively mitigate the issue. To this end, the design of electrodes with low electrochemical impedance, high specific capacitance, and outstanding reaction stability represents a critical first step. Herein, we report the synthesis of hierarchical Ti3C2TX@PANI heterostructures by uniform deposition of conductive polyaniline (PANI) polymer nanofibers on the exposed surface of the Ti3C2TX nanosheets, which are then assembled into a three-dimensional (3D) cross-linking framework by a graphene oxide (GO)-assisted self-convergence hydrothermal strategy. This resulting 3D Ti3C2TX@PANI-reduced graphene oxide (Ti3C2TX@PANI-RGO) heterostructure hydrogel shows a large surface area (488.75 F g-1 at 0.5 A g-1), outstanding electrical conductivity, and fast reaction kinetics, making it a promising electrode material. Separately, defective RGO (DRGO) hydrogels are prepared by a patterning process, and they exhibit a broad and uniform distribution of mesopores, which is conducive to ion transport with an excellent specific capacitance (223.52 F g-1 at 0.5 A g-1). A ZIC is subsequently constructed by utilizing Ti3C2TX@PANI-RGO as the anode and DRGO as the cathode, which displays an extensive operating voltage (0-3.0 V), prominent energy density (1060.96 Wh kg-1 at 761.32 W kg-1, 439.87 Wh kg-1 at 9786.86 W kg-1), and durable cycle stability (retaining 67.9% of the original capacitance after 4000 cycles at 6 A g-1). This study underscores the immense prospect of the Ti3C2TX-based heterostructure hydrogel and DRGO as a feasible anode and cathode for ZICs, respectively.

Population evolution of seagrasses returning to the ocean.

Seagrasses are higher flowering plants that live entirely in marine environments, with the greatest habitat variation occurring from land to sea. Genetic structure or population differentiation history is a hot topic in evolutionary biology, which is of great significance for understanding speciation. Genetic information is obtained from geographically distributed subpopulations, different subspecies, or strains of the same species using next-generation sequencing techniques. Genetic variation is identified by comparison with reference genomes. Genetic diversity is explored using population structure, principal component analysis (PCA), and phylogenetic relationships. Patterns of population genetic differentiation are elucidated by combining the isolation by distance (IBD) model, linkage disequilibrium levels, and genetic statistical analysis. Demographic history is simulated using effective population size, divergence time, and site frequency spectrum (SFS). Through various population genetic analyses, the genetic structure and historical population dynamics of seagrass can be clarified, and their evolutionary processes can be further explored at the molecular level to understand how evolutionary processes contributed to the formation of early ecological species and provide data support for seagrass conservation.

Understanding CaCl2 induces surface gelatinization to promote cold plasma modified maize starch: Structure-effect relations.

To investigate the influence of surface gelatinization on cold plasma (CP) modification of starch, this study used CaCl2 to modify maize starch by surface gelatinization, further combined with CP treatment and characterized its multi-scale structure and physicochemical properties. The results revealed that starch surface gelatinization causes roughness and fragmentation on the granule surface, and CP undergoes etching effects. The synergistic modification promotes starch degradation, as evidenced by molecular weight decrease and short-chain ratio increase. Although the growth rings, FT-IR patterns, and crystal types of starch remained unchanged, the synergistic modification induced a reduction in the short-range orderliness and crystallinity of starch, thus causing a decrease in the pasting properties and contributing to its solubility. Notably, the CP treatment improved the RDS and SDS contents of the gelatinized starch due to more active sites on the granule surface after gelatinization, and this finding may provide insight into the deep processing of starch.