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Precocial Eriocheir sinensis, showing advanced sexual maturity, are a serious impediment to crab industry. The aim of this study was to identify miRNAs and its related pathways involved in regulation of precocity in E. sinensis. High-throughput sequencing was used to evaluate hepatopancreas of precocious and normal E. sinensis at the same period. A total of 101 differentially expressed miRNAs were identified as candidate target genes. KEGG analysis revealed retinol metabolism may contribute to the regulation of gonadogenesis. Aldehyde dehydrogenase 1 family member A1 (aldh1a1) was identified as the core gene in the retinol metabolism by gene expression, and subsequently, gene cloning was performed to further explore its function. The aldh1a1 gene was 1740 bp, including a 189 bp 5′ untranslated region (UTR), 75 bp 3′ UTR and a 1476 bp open-reading frame, encoding 491 amino acids. The corresponding protein is 87.98 % homologous in Portunus trituberculatus. The expression of aldh1a1 was highest in ovary, and then in the hepatopancreas. In addition, dual-luciferase reporter assay indicated that there was negative correlation between NW_020868524.1_2213 and aldh1a1. NW_020868524.1_2213 agomir treatment in the normal group increased testosterone and estradiol levels significantly, down-regulated aldh1a1 expression, and activated the retinol metabolism. By contrast, the administration of NW_020868524.1_2213 antagomir after a short-term induction of retinol resulted in increase in aldh1a1 expression, decrease in sex hormone levels, and inhibition in retinol metabolism. In short, NW_020868524.1_2213 in the hepatopancreas could activate the retinol metabolic pathway via aldh1a1, thus regulating precocious and gonadal development in E. sinensis. These findings would provide a potential pathway for decreasing crab precocity in practice.

Background: Akebia trifoliata is a widely distributed medicine and food homology plant. Its fruit is used to treat tumors and the seed’s weight proportion exceeds 50%. However, the anti-tumor effect of A. trifoliata seed remains poorly studied. Methods: UHPLC-Q-Orbitrap-MS was used to identify the chemical components of A. trifoliata seed water extract (WE) and ethanolic extract (EE), and their cytotoxicity was evaluated using MTT assay. Further, colony formation and cell cycle assays examined the anti-proliferative effect of A. trifoliata seed EE. Next, morphology observation, AO/EB staining, Hoechst 33,258 staining, Annexin V-PE/7-AAD staining, and JC-1 assays were employed to detect apoptosis. The influence of A. trifoliata seed EE on A549 cell metastasis was assessed by wound healing and transwell invasion tests. In addition, western blotting and network pharmacology were applied to further analyze its anticancer mechanism. Results: Eighty-two components were characterized from the A. trifoliata seed WE and EE. For anticancer activity, A. trifoliata seed EE exhibited high cytotoxicity against cancer cells A549 (IC50: 52.38 ± 1.04 μg/mL) and NCI-H1299 (IC50: 70.29 ± 0.58 μg/mL) and revealed low cytotoxicity against non-cancer cells MRC-5 (IC50: 101.28 ± 2.84 μg/mL) and L929 (IC50: 113.44 ± 0.55 μg/mL). A. trifoliata seed EE suppressed A549 cell proliferation by inducing S-phase arrest via down-regulation of CDK2 and p21 as well as up-regulation of cyclin E1 and E2. It increased the Bax/Bcl-2 ratio, reduced mitochondrial membrane potential (ΔΨm), activated caspase-9 and caspase-3, cleaved PARP, and triggered apoptosis through the mitochondrial pathway. Besides, it repressed migratory and invasive ability by reducing MMP-2, MMP-9, and N-cadherin levels. Conclusion: A. trifoliata seed EE exhibits outstanding anticancer properties and has the exploitation potential as an anticancer drug in the pharmaceutical industry.

This systematic review aimed to statistically profile the medication burden and associated influencing factors, and outcomes in patients with dialysis-dependent chronic kidney disease (DD-CKD). Studies of medication burden in patients with DD-CKD in the last 10 years from 1 January 2013 to 31 March 2024 were searched from PubMed, Embase, and Cochrane databases. Newcastle-Ottawa Scale (NOS) or Agency for Healthcare Research and Quality (AHRQ) methodology checklist was used to evaluate quality and bias. Data extraction and combining from multiple groups of number (n), mean, and standard deviation (SD) were performed using R programming language (version4.3.1; R Core Team, Vienna, Austria). A total of 10 studies were included, and the results showed a higher drug burden in patients with DD-CKD. The combined pill burden was 14.57 ± 7.56 per day in hemodialysis (HD) patients and 14.63 ± 6.32 in peritoneal dialysis (PD) patients. The combined number of medications was 9.74 ± 3.37 in HD and 8 ± 3 in PD. Four studies described the various drug classes and their proportions, in general, antihypertensives and phosphate binders were the most commonly used drugs. Five studies mentioned factors associated with medication burden. A total of five studies mentioned medication burden-related outcomes, with one study finding that medication-related burden was associated with increased treatment burden, three studies finding that poor medication adherence was associated with medication burden, and another study finding that medication complexity was not associated with self-reported medication adherence. Limitations: meta-analysis was not possible due to the heterogeneity of studies.

Background: Convolutional neural network (CNN) can capture the structural features changes of brain aging based on MRI, thus predict brain age in healthy individuals accurately. However, most studies use single feature to predict brain age in healthy individuals, ignoring adding information from multiple sources and the changes in brain aging patterns after mild traumatic brain injury (mTBI) were still unclear. Methods: Here, we leveraged the structural data from a large, heterogeneous dataset (N = 1464) to implement an interpretable 3D combined CNN model for brain-age prediction. In addition, we also built an atlas-based occlusion analysis scheme with a fine-grained human Brainnetome Atlas to reveal the age-sstratified contributed brain regions for brain-age prediction in healthy controls (HCs) and mTBI patients. The correlations between brain predicted age gaps (brain-PAG) following mTBI and individual's cognitive impairment, as well as the level of plasma neurofilament light were also examined. Results: Our model utilized multiple 3D features derived from T1w data as inputs, and reduced the mean absolute error (MAE) of age prediction to 3.08 years and improved Pearson's r to 0.97 on 154 HCs. The strong generalizability of our model was also validated across different centers. Regions contributing the most significantly to brain age prediction were the caudate and thalamus for HCs and patients with mTBI, and the contributive regions were mostly located in the subcortical areas throughout the adult lifespan. The left hemisphere was confirmed to contribute more in brain age prediction throughout the adult lifespan. Our research showed that brain-PAG in mTBI patients was significantly higher than that in HCs in both acute and chronic phases. The increased brain-PAG in mTBI patients was also highly correlated with cognitive impairment and a higher level of plasma neurofilament light, a marker of neurodegeneration. The higher brain-PAG and its correlation with severe cognitive impairment showed a longitudinal and persistent nature in patients with follow-up examinations. Conclusion: We proposed an interpretable deep learning framework on a relatively large dataset to accurately predict brain age in both healthy individuals and mTBI patients. The interpretable analysis revealed that the caudate and thalamus became the most contributive role across the adult lifespan in both HCs and patients with mTBI. The left hemisphere contributed significantly to brain age prediction may enlighten us to be concerned about the lateralization of brain abnormality in neurological diseases in the future. The proposed interpretable deep learning framework might also provide hope for testing the performance of related drugs and treatments in the future.

In order to reduce CO₂ emissions from industrial processes, countries have commenced the vigorous development of CCUS (carbon capture, utilization and storage) technology. The high geographical overlap between China’s extensive coal mining regions and CO2-emitting industrial parks provides an opportunity for the more efficient reduction in CO2 emissions through the development of Enhanced Coal Bed Methane (ECBM) Recovery for use with CCUS technology. Furthermore, the high geographical overlap and proximity of these regions allows for a shift in the transportation mode from pipelines to tanker trucks, which are more cost-effective and logistically advantageous. The issue of transportation must also be considered in order to more accurately assess the constructed cost function and CCUS source–sink matching model for the implementation of ECBM. The constructed model, when considered in conjunction with the actual situation in Shanxi Province, enables the matching of emission sources and sequestration sinks in the province to be realized through the use of ArcGIS 10.8 software, and the actual transport routes are derived as a result. After analyzing the matching results, it is found that the transportation cost accounts for a relatively small proportion of the total cost. In fact, the CH4 price has a larger impact on the total cost, and a high replacement ratio is not conducive to profitability. When the proportion of CO2 replacing CH4 increases from 1 to 3, the price of CH4 needs to increase from $214.41/t to $643.23/t for sales to be profitable. In addition, electric vehicle transportation costs are lower compared to those of fuel and LNG vehicles, especially for high-mileage and frequent-use scenarios. In order to reduce the total cost, it is recommended to set aside the limitation of transportation distance when matching sources and sinks.

The accurate measurement of pulverized coal concentration (PCC) is crucial for optimizing the production efficiency and safety of coal-fired power plants. Traditional microwave attenuation methods typically rely on a single frequency for analysis while neglecting valuable information in the frequency domain, making them susceptible to the varying sensitivity of the signal at different frequencies. To address this issue, we proposed an innovative frequency selection method based on principal component analysis (PCA) and orthogonal matching pursuit (OMP) algorithms and implemented a multi-frequency microwave sensing system for PCC measurement. This method transcended the constraints of single-frequency analysis by employing a developed hardware system to control multiple working frequencies and signal paths. It measured insertion loss data across the sensor cross-section at various frequencies and utilized PCA to reduce the dimensionality of high-dimensional full-path insertion loss data. Subsequently, the OMP algorithm was applied to select the optimal frequency signal combination based on the contribution rates of the eigenvectors, enhancing the measurement accuracy through multi-dimensional fusion. The experimental results demonstrated that the multi-frequency microwave sensing system effectively extracted features from the high-dimensional PCC samples and selected the optimal frequency combination. Filed experiments conducted on five coal mills showed that, within a common PCC range of 0–0.5 kg/kg, the system achieved a minimum mean absolute error (MAE) of 1.41% and a correlation coefficient of 0.85. These results indicate that the system could quantitatively predict PCC and promptly detect PCC fluctuations, highlighting its immediacy and reliability.

In a high-speed railway (HSR) corridor, railway companies provide diverse train services with different features such as departure time, running speed, and fare. Furthermore, passengers have varying preferences, which influence their travel choices. Focusing on this problem, this paper proposed a novel train timetable optimization model based on departure time and pricing strategy to analyze their impact on passengers’ travel choices with a consistent operating speed. Additionally, an extended time-space-fare network is constructed to visualize this process. The generalized cost of each arc in the network is used as a criterion for evaluating passenger selection. Then, we adapted a passenger flow distribution method based on the user equilibrium (UE) principle to allocate the passenger flow to each train. We also developed a bi-level planning model with passenger allocation as the lower-level model and timetable decision as the upper-level one to optimize collaboratively. Finally, a genetic algorithm integrated with the Frank-Wolfe method is designed to seek the optimal timetable and pricing strategy and a concrete numerical example in Lanzhou-Xi’an HSR is taken to investigate the effectiveness of the proposed model and algorithm.

Normally, proper fermentation can be an efficient and widely used method to improve feed quality in animal rearing; however, the studies on crustaceans, especially Eriocheir sinensis, remain limited. This study aimed to investigate whether feed fermentation could meliorate dietary nutritional value and benefit E. sinensis rearing. First, non-fermented feed (NFD) and fermented feed (FD) were produced and assessed, respectively. Then, the “Y” maze feed choice behavior test (180 times; 30 times, 6 rounds) was conducted to assess the attractiveness of these 2 feeds for crabs. Finally, a total of 80 crabs (44.10 ± 0.80 g) were randomly assigned into 2 groups with 4 replicates, and fed the experimental diets for 8 weeks to evaluate the effects of each feed on growth, antioxidant capacity, meat flavor, and intestinal microbiota. In this study, FD showed higher levels of crude protein (P

Camellia oleifera is a medicine food homology plant widely cultivated in the Yangtze River Basin and southern China due to its camellia oil. Camellia oleifera bud and fruit exist simultaneously, and its bud is largely discarded as waste. However, C. oleifera bud has been used in traditional Chinese medicine to treat a variety of ailments. Thus, the purpose of this study was to identify the chemical components of C. oleifera bud ethanol extract (EE) and first evaluate its anticancer effects in non-small cell lung cancer A549 cells. Based on UHPLC-Q-Orbitrap-MS analysis, seventy components were identified. For anticancer activity, C. oleifera bud EE had remarkable cytotoxic effect on non-small cell lung cancer A549 (IC50: 57.53 ± 1.54 μg/mL) and NCI-H1299 (IC50: 131.67 ± 4.32 μg/mL) cells, while showed lower cytotoxicity on non-cancerous MRC-5 (IC50 > 320 μg/mL) and L929 (IC50: 179.84 ± 1.08 μg/mL) cells. It dramatically inhibited the proliferation of A549 cells by inducing cell cycle arrest at the G1 phase. Additionally, it induced apoptosis in A549 cells through a mitochondria-mediated pathway, which decreased mitochondrial membrane potential, upregulated Bax, activated caspase 9 and caspase 3, and resulted in PARP cleavage. Wound healing and transwell invasion assays demonstrated that C. oleifera bud EE inhibited the migration and invasion of A549 cells in a dose-dependent manner. The above findings indicated that C. oleifera bud EE revealed notable anticancer effects by inhibiting proliferation, inducing apoptosis, and suppressing migration and invasion of A549 cells. Hence, C. oleifera bud ethanol extract could serve as a new source of natural anticancer drugs.

Abstract Background The exact optimal timing of dialysis for ESKD patients remains unknown. This study systematically reviewed the available evidence with regard to the optimal initiation of maintenance dialysis in ESKD patients. Methods An electronic search was performed in Embase, PubMed and the Cochrane Library in order to find studies investigating associations between variables reference to “start of dialysis” and outcomes. Quality assessment and bias assessment were performed by the Newcastle–Ottawa scale and the ROBINSI tool. Due to the heterogeneity of studies, a meta-analysis could not be performed. Results Thirteen studies were included; four studies included only haemodialysis patients, three peritoneal dialysis, six both; study outcomes included mortality, cardiovascular events, technique failure, quality of life and others. Nine studies mainly focused on the optimal GFR of maintenance dialysis initiation; five studies showed none association between GFR and mortality or other adverse outcomes, two studies showed dialysis initiation at higher GFR levels were with poor prognosis, and 2 studies showed higher GFR levels with better prognosis. Three studies paid attention to comprehensive assessment of uremic signs and/or symptoms for optimal dialysis initiation; uremic burden based on 7 uremic indicators (hemoglobin, serum albumin, blood urea nitrogen, serum creatinine, potassium, phosphorus, and bicarbonate) were not associated with mortality; another equation (combination of sex, age, serum creatinine, blood urea nitrogen, serum albumin, haemoglobin, serum phosphorus, diabetes mellitus, and heart failure) based on fuzzy mathematics to assess the timing of haemodialysis initiation was accuracy to prognose 3-year survival; the third study found that volume overload or hypertension was associated with the highest risk for subsequent mortality. Two studies compared urgent or optimal start in dialysis, a study reported increased survival in optimal start patients, another reported no differences between Urgent-Start-PD and Early-Start-PD regarding 6-month outcomes. Limitations: Heterogeneity among the studies was quite high, with differences in sample size, variable and group characteristics; no RCT studies were included, which weakened the strength of evidences. Conclusions The criteria for dialysis initiation were varied. Most studies proved that GFR at dialysis initiation was not associated with mortality, timing of dialysis initiation should not be based on GFR, assessments of volume load and patient’s tolerance to volume overload are prospective approaches.

The grain moisture content of maize inbred lines at maturity is one of the most important indicators for mechanical harvesting of kernels. In this study, 116 maize inbred lines from a wide range of sources were used as research materials and 30 traits of grain moisture content were analyzed using multivariate statistical analysis. The results showed that all 30 traits had some correlations. Principal component analysis downscaled the 30 traits into 10 principal component factors, reflecting 77.674% of the information in the original traits. Cluster analysis categorized the 116 inbred lines into 5 major groups containing 26, 29, 31, 16 and 14 inbred lines. Based on the D value of the overall evaluation, discriminant analysis reclassified the maize inbred lines by principal component scores and 98 maize inbred lines were correctly discriminated with a probability of 84.48%, which can be regarded as a relatively reliable clustering result. The stepwise regression method was further used to screen seven traits: GMC2, GDR1, HMC3, NH, GDR2, CD and EL and to establish a comprehensive evaluation model for the grain moisture content of maize inbred lines. Among 116 maize inbred lines, 14, represented by H21 and MS71, had the lowest grain moisture content at maturity.

Maize Ustilago maydis is a disease that severely affects maize yield and quality. In this paper, we employed transcriptome sequencing and GWAS analysis to identify candidate genes and reveal disease-resistant germplasm resources, thereby laying the foundation for further analysis of the molecular mechanism of maize Ustilago maydis resistance and genetic improvement. The results of transcriptome sequencing revealed that a considerable number of receptor kinase genes, signal-transduction-related protein genes, redox-response-related genes, WRKYs, and P450s genes were significantly upregulated. There was a wide range of mutations of Ustilago maydis in maize inbred lines. Thirty-two high-resistance maize inbred lines were selected, and 16 SNPs were significantly associated with the disease index. By integrating the results of GWAS and RNA-seq, five genes related to disease resistance were identified, encoding the chitinase 1 protein, fatty acid elongase (FAE), IAA9, GATA TF8, and EREB94, respectively. It provides a certain reference for the cloning of maize anti-tumor smut genes and the breeding of new varieties.

Atmospheric forcings are significant physical factors that influence the variation of sea surface temperature (SST) and are often used as essential input variables for ocean numerical models. However, their contribution to the prediction of SST based on machine-learning methods still needs to be tested. This study presents a prediction model for SST in the East China Sea (ECS) using two machine-learning methods: Random Forest and SA-ConvLSTM algorithms. According to the Random Forest feature importance scores and correlation coefficients R, 2 m air temperature and longwave radiation were selected as the two most important key atmospheric factors that can affect the SST prediction performance of machine-learning methods. Four datasets were constructed as input to SA-ConvLSTM: SST-only, SST-T2m, SST-LWR, and SST-T2m-LWR. Using the SST-T2m and SST-LWR, the prediction skill of the model can be improved by about 9.9% and 9.43% for the RMSE and by about 8.97% and 8.21% for the MAE, respectively. Using the SST-T2m-LWR dataset, the model’s prediction skill can be improved by 10.75% for RMSE and 9.06% for MAE. The SA-ConvLSTM can represent the SST in ECS well, but with the highest RMSE and AE in summer. The findings of the presented study requires much more exploration in future studies.

The gut microbiota plays a crucial role in the host’s metabolic processes. Many studies have shown significant changes in the gut microbiota of mammals during hibernation to adapt to the changes in the external environment, but there is limited research on the colonic epithelial tissue and gut microbiota of the wild chipmunks during hibernation. This study analyzed the diversity, composition, and function of the gut microbiota of the wild chipmunk during hibernation using 16S rRNA gene high-throughput sequencing technology, and further conducted histological analysis of the colon. Histological analysis of the colon showed an increase in goblet cells in the hibernation group, which was an adaptive change to long-term fasting during hibernation. The dominant gut microbial phyla were Bacteroidetes, Firmicutes, and Proteobacteria, and the relative abundance of them changed significantly. The analysis of gut microbiota structural differences indicated that the relative abundance of Helicobacter typhlonius and Mucispirillum schaedleri increased significantly, while unclassified Prevotella-9, unclassified Prevotellaceae-UCG-001, unclassified Prevotellaceae-UCG-003 and other species of Prevotella decreased significantly at the species level. Alpha diversity analysis showed that hibernation increased the diversity and richness of the gut microbiota. Beta diversity analysis revealed significant differences in gut microbiota diversity between the hibernation group and the control group. PICRUSt2 functional prediction analysis of the gut microbiota showed that 15 pathways, such as lipid metabolism, xenobiotics biodegradation and metabolism, amino acid metabolism, environmental adaptation, and neurodegenerative diseases, were significantly enriched in the hibernation group, while 12 pathways, including carbohydrate metabolism, replication and repair, translation, and transcription, were significantly enriched in the control group. It can be seen that during hibernation, the gut microbiota of the wild chipmunk changes towards taxa that are beneficial for reducing carbohydrate consumption, increasing fat consumption, and adapting more strongly to environmental changes in order to better provide energy for the body and ensure normal life activities during hibernation.

The analysis of critical states during fracture of wood materials is crucial for wood building safety monitoring, wood processing, etc. In this paper, beech and camphor pine are selected as the research objects, and the acoustic emission signals during the fracture process of the specimens are analyzed by three-point bending load experiments. On the one hand, the critical state interval of a complex acoustic emission signal system is determined by selecting characteristic parameters in the natural time domain. On the other hand, an improved method of b_value analysis in the natural time domain is proposed based on the characteristics of the acoustic emission signal. The K-value, which represents the beginning of the critical state of a complex acoustic emission signal system, is further defined by the improved method of b_value in the natural time domain. For beech, the analysis of critical state time based on characteristic parameters can predict the "collapse" time 8.01 s in advance, while for camphor pines, 3.74 s in advance. K-value can be analyzed at least 3 s in advance of the system "crash" time for beech and 4 s in advance of the system "crash" time for camphor pine. The results show that compared with traditional time-domain acoustic emission signal analysis, natural time-domain acoustic emission signal analysis can discover more available feature information to characterize the state of the signal. Both the characteristic parameters and Natural_Time_b_value analysis in the natural time domain can effectively characterize the time when the complex acoustic emission signal system enters the critical state. Critical state analysis can provide new ideas for wood health monitoring and complex signal processing, etc.

Abstract INTRODUCTION We evaluated how the apolipoprotein E (APOE) ε4 allele modulated the spatial patterns of longitudinal atrophy in the Alzheimer's disease–vulnerable brain areas of patients with mild traumatic brain injury (mTBI) from the acute to chronic phase post injury. METHODS Fifty‐nine adult patients with acute mTBI and 48 healthy controls with APOE ε4 allele testing underwent T1‐weighted magnetic resonance imaging and neuropsychological assessments with 6 to 12 months of follow‐up. Progressive brain volume loss was compared voxel‐wise in the temporal lobes. RESULTS Patients with the APOE ε4 allele presented significant longitudinal atrophy in the left superior and middle temporal gyri, where the progressive gray matter volume loss predicted longitudinal impairment in language fluency, whereas mTBI APOE ε4 allele noncarriers showed mainly significant longitudinal atrophy in the medial temporal lobes, without significant neuropsychological relevance. DISCUSSION The atrophy progression observed in mTBI patients with the APOE ε4 allele may increase the possibility of developing a specific phenotype of Alzheimer's disease with language dysfunction. Highlights The apolipoprotein E (APOE) ε4 allele and mild traumatic brain injury (mTBI) are risk factors for Alzheimer's disease (AD) progression. It is unclear how the interaction of mTBI with the APOE ε4 allele impacts the progressive atrophy topography in AD‐vulnerable brain regions. In this study, patients with the APOE ε4 allele showed progressive atrophy patterns similar to the early stage of logopenic variant of primary progressive aphasia (lvPPA) phenotype of AD. APOE ε4 allele carriers with mTBI history may be at the risk of developing a given AD phenotype with language dysfunction.

Premna puberula, a medicine food homologous plant, is utilized in the preparation of cold tofu and as traditional Chinese medicine. This study aimed to identify the chemical components of P. puberula root petroleum ether extract (PEE) and clarify its anticancer properties and associated mechanisms. The MTT results of the four extracts (petroleum ether extract, ethyl acetate extract, n-butanol extract, and water extract) from P. puberula root showed that the PEE had good cytotoxic activity against A549 cells (IC50 = 38.01 ± 3.36 μg/mL) and low toxicity to normal cells (IC50 = 76.85 ± 3.18 μg/mL). The GC-FID/MS analysis revealed fifty compounds, which made up 98.6 % of the total PEE. Further antitumor assay demonstrated that PEE induced cell cycle arrest in the S phase by up-regulating p21 and cyclin E2 expression, thereby inhibiting the proliferation of A549 cells. Simultaneously, it induces apoptosis through the mitochondrial-mediated apoptosis pathway, which significantly up-regulated the ratio of Bax/Bcl-2, down-regulated mitochondrial membrane potential (ΔΨm), promoted the release of Cyt c, activated caspase-9 and caspase-3, thereby resulting in PARP cleavage. It prevented the migration and invasion of A549 cells by reducing the expression of MMP-2 and N-cadherin. In conclusion, P. puberula root PEE can be a new source of antitumor agents and has significant anticancer activity in vitro.

There is still an unmet need for effective and safe drugs to treat deep vein thrombosis during pregnancy, a life-threatening condition for the mother and fetus. Here, the authors show that engineered multifunctional nanoparticles can site-specifically dissolve thrombi and reverse deep vein thrombosis-mediated intrauterine growth restriction and delayed development of fetuses in pregnant rats.

Abstract Background Stalk lodging is one of the main factors affecting maize (Zea mays L.) yield and limiting mechanized harvesting. Developing maize varieties with high stalk lodging resistance requires exploring the genetic basis of lodging resistance-associated agronomic traits. Stalk strength is an important indicator to evaluate maize lodging and can be evaluated by measuring stalk rind penetrometer resistance (RPR) and stalk buckling strength (SBS). Along with morphological traits of the stalk for the third internodes length (TIL), fourth internode length (FIL), third internode diameter (TID), and the fourth internode diameter (FID) traits are associated with stalk lodging resistance. Results In this study, a natural population containing 248 diverse maize inbred lines genotyped with 83,057 single nucleotide polymorphism (SNP) markers was used for genome-wide association study (GWAS) for six stalk lodging resistance-related traits. The heritability of all traits ranged from 0.59 to 0.72 in the association mapping panel. A total of 85 significant SNPs were identified for the association mapping panel using best linear unbiased prediction (BLUP) values of all traits. Additionally, five candidate genes were associated with stalk strength traits, which were either directly or indirectly associated with cell wall components. Conclusions These findings contribute to our understanding of the genetic basis of maize stalk lodging and provide valuable theoretical guidance for lodging resistance in maize breeding in the future.

BackgroundModifications in the gut microbiota may be a crucial factor in the efficacy of canagliflozin (Cana) in managing patients with type 2 diabetes mellitus (T2DM). However, the interplay between oral and ocular surface microbiota and this treatment remains poorly explored.AimThis study aimed to assess alterations in the gut, oral, and ocular surface microbiota pre- and post-Cana treatment in patients with T2DM.MethodsIn this 30-day, controlled before-and-after study, 21 treatment-naïve patients with T2DM received sole treatment with Cana (100 mg/day), and were matched with 10 healthy controls based on gender and age. Using 16S rRNA sequencing, changes in the gut, oral, and ocular surface microbiota pre- and post-Cana treatment were assessed and compared with those of healthy controls. Concurrently, diabetes-related clinical parameters were recorded over the study period. The trial was registered in the Chinese Clinical Trial Registry (ChiCTR200034878).ResultsA noticeable shift was observed in the gut, oral, and ocular surface microbiota pre- and post-Cana treatment. The post-Cana treatment gut microbiota was more similar to that of the healthy controls. Network correlation analysis revealed that modifications in the gut, oral, and ocular surface microbiota were related to changes in clinical parameters, especially for the ocular surface microbiota.Clinical parametersA significant decrease in fasting plasma glucose (8.22 ± 2.19 vs 6.87 ± 1.09 mmol/L), glycated serum protein [291.00 (264.00, 353.00) vs 275.00 (251.00, 342.50) μmol/L], hemoglobin A1c (7.39 ± 1.18 vs 7.12 ± 1.33%), body mass index (25.32 ± 2.99 vs 24.83 ± 2.95 kg/m2), systolic blood pressure (129.05 ± 17.51 vs 123.43 ± 14.82 mmHg), and urinary creatinine [158.40 (74.75, 219.15) vs 79.70 (56.25, 138.10) μmmol/kg] levels was noted after 30-day Cana monotherapy (P < 0.05).Gut microbiomeTreatment with Cana resulted in an increase in the relative abundance of short-chain fatty acid (SCFA)-producing bacteria, particularly Lachnospiraceae UCG 004, Bacteroides, and Lachnospiraceae NK4A136 group.Oral microbiotaAfter Cana treatment, a significant increase of Prevotella and Veillonella, both of which are known to be closely associated with SCFAs, was observed.Ocular surface microbiotaPost-Cana administration, the ocular surface microbiota exhibited the most distinct changes in structure and composition. Remarkably, the majority of the increased ocular surface microbiota could produce SCFAs within the gut microbiota.ConclusionCana effectively improved the dysregulated glucose metabolism in patients with T2DM. This improvement can potentially be attributed to the restoration of balance among the gut, oral, and ocular surface microbial communities.Clinical trial registrationhttps://www.chictr.org.cn/showproj.html?proj=56487, identifier ChiCTR2000034878.

Abstract Lodging is one of the main problems affecting the maize production. In this study, 220 maize inbred lines were used for determining the lodging resistance. Analysis methods such as the correlation analysis, the principal component analysis, the cluster analysis, the stepwise discriminate analysis, and the ridge regression analysis were deployed for data interpretation. The results of the correlation analysis showed that 17 characters were correlated with varying degrees. Strong and positive correlations between TID and FID (r = 0.910), TIL and FIL (r = 0.898), NSVB and NLVB (r = 0.775), ASVB and ALVB (r = 0.746), and LC and HC (r = 0.656) were observed. The first six principal components explained 79.13% of the phenotypic variation of the 17 characters, with the contribution rates being 20.77%, 18.12%, 14.09%, 11.17%, 8.66%, and 6.32%, respectively. Five categories were clustered in the 220 inbred lines. The result of stepwise discriminate analysis showed that 211 inbred lines were correctly discriminated and the identification rate was 95.91%, and 9 inbred lines were incorrectly discriminated and the identification rate was 4.09%, which meant that the result of cluster analysis was accurate and reliable. The stalk bending strength, ear height, cellulose content, number of small vascular bundles, and cross‐sectional area were selected, and the regression model of lodging resistance of inbred lines was established by using the ridge regression method. Thirty of the 220 inbred lines, including R1656, 4003, and LD61, showed the highest lodging resistance. The results provide a reference for the selection of lodging‐resistant germplasm resources in breeding for the lodging resistance hybrids.

Rhynchanthus beesianus is a medicine food homology plant, and its rhizome is a food spice and traditional Chinese medicine used to treat inflammation-related disorders. Still, little research has been done on its phytochemicals and biological activities. Therefore, this study firstly reported the phytochemical analysis by UHPLC-Q-Orbitrap-MS, antioxidant, anti-enzymatic, and anti-inflammatory effects of R. beesianus rhizome water extract (WE) and 70% ethanol extract (EE). For phytochemical analysis, WE and EE had high total phenolic (25.57–28.19 mg GAE/g extract) and flavonoid (10.57–28.08 mg RE/g extract) contents, and further UHPLC-Q-Orbitrap-MS test identified sixty-one compounds, including twelve phenolic compounds and six flavonoids. In the antioxidant assay, WE and EE showed significant DPPH (IC50: 83.93 ± 5.90 and 69.48 ± 1.57 μg/mL, respectively) and ABTS (IC50: 73.46 ± 0.69 and 40.72 ± 0.67 μg/mL, respectively) radical scavenging ability, especially the efficacy of EE was superior or equivalent to that of positive control BHT. Moreover, the WE and EE exhibited poor inhibition on acetylcholinesterase (IC50: 3.31 ± 0.16 and 3.53 ± 0.14 mg/mL, respectively), butyrylcholinesterase (IC50: 1.29 ± 0.02 and 0.75 ± 0.08 mg/mL, respectively), and tyrosinase (IC50: 5.91 ± 0.63 and 21.30 ± 1.52 mg/mL, respectively). Interestingly, EE showed remarkable inhibition on NO, PGE2, IL-6, and TNF-α produced by LPS stimulation without cytotoxicity. It reduced ROS generation and down-regulated mRNA and protein levels of iNOS and COX-2 in LPS-stimulated RAW264.7 macrophages. Besides, EE decreased LPS-stimulated MAPKs (ERK, p38, and JNK) phosphorylation and inhibited NF-κB p65 nuclear translocation by suppressing the phosphorylation and degradation of IκBα. Therefore, R. beesianus can be used as a promising natural antioxidant, enzyme inhibitor, and anti-inflammatory agent with great exploitation potential in functional food and pharmaceuticals.

Abstract Background Neutrophil gelatinase-associated lipocalin (NGAL) is not only a bone-derived factor involved in metabolism, but also a biomarker of kidney disease and cardiovascular pathophysiology. We conducted this cross-sectional observational study to explore relationships between plasma NGAL and thoracic aorta calcification (TAC) in maintenance hemodialysis (MHD) patients with and without diabetes. Methods Plasma NGAL was measured by ELISA, TAC was evaluated via computed tomography scan using a 3D quantification method or chest radiography aortic arch calcification score. Spearman correlation, Logistic regression and Partial correlation analysis were used to describe the correlations between NGAL and TAC. Results Plasma NGAL levels were lower in MHD patients with diabetes compared to those without diabetes (49.33(42.37, 55.48) vs 56.78(44.37, 674.13) ng/mL, P = 0.026). In MHD patients without diabetes, lg (NGAL) was positively correlated with ARC value(R = 0.612, P = 0.003) analyzed by Spearman correlation; for partial correlation analysis, lg (NGAL) was positively correlated with ARC value, after adjusting for age and sex (R = 0.550, P = 0.015), adjusting for age, sex and CHD (R = 0.565, P = 0.015), adjusting for age, sex, CHD and Alb (R = 0.536, P = 0.027), or adjusting for age, sex, CHD, Alb, and dialyzer membrane (polysulfone) (R = 0.590, P = 0.016); however, when adjusting for age, sex, CHD, Alb and Ca, the correlation between lg (NGAL) and ARC value disappeared. Positive correlation were found between NGAL and Ca (R = 0.644, P

“Cognitive dysmetria” theory of schizophrenia (SZ) has highlighted that the cerebellum plays a critical role in understanding the pathogenesis and cognitive impairment in SZ. Despite some studies have reported the structural disruption of the cerebellum in SZ using whole brain approach, specific focus on the voxel-wise changes of cerebellar WM microstructure and its associations with cognition impairments in SZ were less investigated. To further explore the voxel-wise structural disruption of the cerebellum in SZ, the present study comprehensively examined volume and diffusion features of cerebellar white matter in SZ at the voxel level (42 SZ vs. 52 controls) and correlated the observed alterations with the cognitive impairments measured by MATRICS Consensus Cognitive Battery. Combing voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) methods, we found, compared to healthy controls (HCs), SZ patients did not show significant alteration in voxel-level cerebellar white matter (WM) volume and tract-wise and skeletonized DTI features. In voxel-wise DTI features of cerebellar peduncles, compared to HCs, SZ patients showed decreased fractional anisotropy and increased radial diffusivity mainly located in left middle cerebellar peduncles (MCP) and inferior cerebellar peduncles (ICP). Interestingly, these alterations were correlated with overall composite and different cognitive domain (including processing speed, working memory, and attention vigilance) in HCs but not in SZ patients. The present findings suggested that the voxel-wise WM integrity analysis might be a more sensitive way to investigate the cerebellar structural abnormalities in SZ patients. Correlation results suggested that inferior and MCP may be a crucial neurobiological substrate of cognition impairments in SZ, thus adding the evidence for taking the cerebellum as a novel therapeutic target for cognitive impairments in SZ patients.

Abstract High quality is the main goal of today’s maize breeding and the investigation of grain quality traits would help to breed high-quality varieties in maize. In this study, genome-wide association studies in a set of 248 diverse inbred lines were performed with 83,057 single nucleotide polymorphisms (SNPs), and five grain quality traits were investigated in diverse environments for two years. The results showed that maize inbred lines showed substantial natural variations of grain quality and these traits showed high broad-sense heritability. A total of 49 SNPs were found to be significantly associated with grain quality traits. Among these SNPs, four co-localized sites were commonly detected by multiple traits. The candidate genes which were searched for can be classified into 11 biological processes, 13 cellular components, and 6 molecular functions. Finally, we found 29 grain quality-related genes. These genes and the SNPs identified in the study would offer essential information for high-quality varieties breeding programs in maize.

Deep learning methods have excellent prospects for application in wave forecasting research. This study employed the convolutional LSTM (ConvLSTM) algorithm to predict the South China Sea (SCS) significant wave height (SWH). Three prediction models were established to investigate the influences of setting different parameters and using multiple training data on the forecasting effects. Compared with the SWH data from the China–France Ocean Satellite (CFOSAT), the SWH of WAVEWATCH III (WWIII) from the pacific islands ocean observing system are accurate enough to be used as training data for the ConvLSTM-based SWH prediction model. Model A was preliminarily established by only using the SWH from WWIII as the training data, and 20 sensitivity experiments were carried out to investigate the influences of different parameter settings on the forecasting effect of Model A. The experimental results showed that Model A has the best forecasting effect when using three years of training data and three hourly input data. With the same parameter settings as the best prediction performance Model A, Model B and C were also established by using more different training data. Model B used the wind shear velocity and SWH as training and input data. When making a 24-h SWH forecast, compared with Model A, the root mean square error (RMSE) of Model B is decreased by 17.6%, the correlation coefficient (CC) is increased by 2.90%, and the mean absolute percentage error (MAPE) is reduced by 12.2%. Model C used the SWH, wind shear velocity, wind and wave direction as training and input data. When making a 24-h SWH forecast, compared with Model A, the RMSE of Model C decreased by 19.0%, the CC increased by 2.65%, and the MAPE decreased by 14.8%. As the performance of the ConvLSTM-based prediction model mainly rely on the SWH training data. All the ConvLSTM-based prediction models show a greater RMSE in the nearshore area than that in the deep area of SCS and also show a greater RMSE during the period of typhoon transit than that without typhoon. Considering the wind shear velocity, wind, and wave direction also used as training data will improve the performance of SWH prediction.

In China, the transportation capacity of high-speed railways is gradually sufficient to provide services for high-value express freight besides meeting passenger demands. However, similar as the passengers, express freight demands fluctuate and show clear peak and trough periods daily. Therefore, optimizing running numbers of express freight trains on high-speed railway by periods is quite necessary to guarantee the revenues of railway industry and to meet the various requirements of all consignees simultaneously. First, a space-period-pattern three-dimensional network with virtual arcs is built to describe the departure period selection and the stopping or skip-stopping operations. Second, by constructing the arcs' impedance function, the user equilibrium principle is introduced to optimize the express freight flow distribution for each pattern in each period. To elaborate the comprehensive goal of balancing the relationship between profits and the flow distribution, a bi-level programming model is established. The upper model addresses the railway industry's maximum profits, and the lower model addresses the minimum and similar impedance values of the final express freight flow distribution. Finally, through the use of a hybrid algorithm that combines the heuristic genetic algorithm with the Frank-Wolfe algorithm, an experimental case of 10 stations on the Beijing-Xi'an high-speed railway corridor is used to validate the study. The results show that the express freight volume is reasonably distributed to each kind of virtual arc, the impedance value of each pattern is minimized and almost equalized, and the profits of railway industry are maximized by optimizing the number and departure time of trains by each pattern in each period on the basis of meeting all the express freight demands.

Abstract Background Hepatitis B virus (HBV) infection is acknowledged as the main cause of hepatocellular carcinoma (HCC). Moreover, previous studies have revealed that microRNAs (miRNAs) widely participate in regulation of various cellular processes, such as viral replication. Hence, the purpose of this study was to investigate the roles of aquaporin 5 (AQP5) and miR-325-3p in the proliferation and apoptosis of HBV-related HCC cells. Methods AQP5 and miR-325-3p expression in both normal and HBV-HCC tissues or cells (both Huh7–1.3 and HepG2.2.15) was detected using qRT-PCR. AQP5 expression was knocked down in HBV-related Huh7–1.3 and HepG2.2.15 cells using small interfering RNA (siRNA) technology. Down-regulation was confirmed using real-time PCR and Western blot analysis. Effects of AQP5 down-regulation on the proliferation and apoptosis were assessed. Dual luciferase reporter gene assay, Western blot and qRT-PCR were employed to evaluate the effect of miR-325-3p on the luciferase activity and expression of AQP5. Moreover, miR-325-3p mimic-induced changes in cellular proliferation and apoptosis were detected through CCK-8 assay, BrdU assay, flow cytometry analysis and ELISA. Results In this study, the expression of AQP5 was up-regulated in human HBV-HCC tissue, Huh7–1.3 and HepG2.2.15 cells. Knockdown of AQP5 significantly inhibited the proliferation and promoted apoptosis of HBV-HCC cells. Next, miR-325-3p was obviously down-regulated in HBV-HCC. In concordance with this, MiR-325-3p directly targeted AQP5, and reduced both mRNA and protein levels of AQP5, which promoted cell proliferation and suppressed cell apoptosis in HCC cells. Overexpression of miR-325-3p dramatically inhibited cell proliferation and induced cell apoptosis. Conclusions Our findings clearly demonstrated that introduction of miR-325-3p inhibited proliferation and induced apoptosis of Huh7–1.3 and HepG2.2.15 cells by directly decreasing AQP5 expression, and that silencing AQP5 expression was essential for the pro-apoptotic effect of miR-325-3p overexpression on Huh7–1.3 and HepG2.2.15 cells. It is beneficial to gain insight into the mechanism of HBV infection and pathophysiology of HBV-related HCC.

The ocean chlorophyll-a (Chl-a) concentration is an important variable in the marine environment, the abnormal distribution of which is closely related to the hazards of red tides. Thus, the accurate prediction of its concentration in the East China Sea (ECS) is greatly important for preventing water eutrophication and protecting the coastal ecological environment. Processed by two different pre-processing methods, 10-year (2011–2020) satellite-observed chlorophyll-a data and logarithmic data were used as the long short-term memory (LSTM) neural network training datasets in this study. The 2021 data were used for comparison to prediction results. The past 15 days’ data were used to predict the concentration of chlorophyll-a for the five following days. Results showed that the predictions obtained by both pre-processing methods could simulate the seasonal distribution of the Chl-a concentration in the ECS effectively. Moreover, the prediction performance of the model driven by the original values was better in the medium- and low-concentration regions. However, in the high-concentration region, the prediction of extreme concentrations by the two data-driven LSTM models showed underestimation, considering that the prediction performance of the model driven by the original values was better. Results of sensitivity experiments showed that the prediction accuracy of the model decreased considerably when the backward prediction time step increased. In this study, the neural network was driven only by chlorophyll-a, whose concentration in the ECS was forecasted, and the effect of other relevant marine elements on Chl-a was not considered, which is the current weakness of this study.

Alpinia galanga is widely cultivated for its essential oil (EO), which has been used in cosmetics and perfumes. Previous studies of A. galanga focussed mostly on the rhizome but seldom on the flower. Therefore, this study was designed to identify the chemical composition of A. galanga flower EO and firstly estimate its antioxidant, antibacterial, enzyme inhibitory, and anticancer activities. According to the results of the gas chromatography with flame ionization or mass selective detection (GC-FID/MS) analysis, the most abundant component of the EO was farnesene (64.3%), followed by farnesyl acetate (3.6%), aceteugenol (3.2%), eugenol (3.1%), E-nerolidol (2.9%), decyl acetate (2.4%), octyl acetate (2.0%), sesquirosefuran (1.9%), (E)-β-farnesene (1.7%), and germacrene D (1.5%). For the bioactivities, the EO exhibited moderate DPPH and ABTS radical scavenging effects with IC50 values of 138.62 ± 3.07 μg/mL and 40.48 ± 0.49 μg/mL, respectively. Moreover, the EO showed strong-to-moderate antibacterial activities with various diameter of inhibition zone (DIZ) (8.79–14.32 mm), minimal inhibitory concentration (MIC) (3.13–6.25 mg/mL), and minimal bactericidal concentration (MBC) (6.25–12.50 mg/mL) values against Staphylococcus aureus, Bacillus subtilis, Enterococcus faecalis, Pseudomonas aeruginosa, Escherichia coli, and Proteus vulgaris. Interestingly, the EO possessed remarkable α-glucosidase inhibition (IC50 = 0.16 ± 0.03 mg/mL), which was equivalent to that of the positive control acarbose (IC50 = 0.15 ± 0.01 mg/mL) (p > 0.05). It showed moderate tyrosinase inhibition (IC50 = 0.62 ± 0.09 mg/mL) and weak inhibitory activity on acetylcholinesterase (AChE) (IC50 = 2.49 ± 0.24 mg/mL) and butyrylcholinesterase (BChE) (IC50 = 10.14 ± 0.59 mg/mL). Furthermore, the EO exhibited considerable selective cytotoxicity to K562 cells (IC50 = 41.55 ± 2.28 μg/mL) and lower cytotoxicity to non-cancerous L929 cells (IC50 = 120.54 ± 8.37 μg/mL), and it induced K562 cell apoptosis in a dose-dependent manner. Hence, A. galanga flower EO could be regarded as a bioactive natural product with great application potential in the pharmaceutical field.

Sea surface temperature (SST) is an important physical factor in the interaction between the ocean and the atmosphere. Accurate monitoring and prediction of the temporal and spatial distribution of SST are of great significance in dealing with climate change, disaster prevention, disaster reduction, and marine ecological protection. This study establishes a prediction model of sea surface temperature for the next five days in the East China Sea using long-term and short-term memory neural networks (LSTM). It investigates the influence of different parameters on prediction accuracy. The sensitivity experiment results show that, based on the same training data, the length of the input data of the LSTM model can improve the model’s prediction performance to a certain extent. However, no obvious positive correlation is observed between the increase in the input data length and the improvement of the model’s prediction accuracy. On the contrary, the LSTM model’s performance decreases with the prediction length increase. Furthermore, the single-point prediction results of the LSTM model for the estuary of the Yangtze River, Kuroshio, and the Pacific Ocean are accurate. In particular, the prediction results of the point in the Pacific Ocean are the most accurate at the selected four points, with an RMSE of 0.0698 °C and an R2 of 99.95%. At the same time, the model in the Pacific region is migrated to the East China Sea. The model was found to have good mobility and can well represent the long-term and seasonal trends of SST in the East China Sea.

General combining ability (GCA) is an important index for inbred lines breeding of maize. To identify the genetic loci of GCA and associated agronomic traits, an association analysis with 195 SSRs was made in phenotypic traits of 240 F1 derived from 120 elite inbred lines containing current breeding resources of maize crossed with 2 testers (Zheng58 and Chang7-2) in two places in 2018. All of the 20 association loci detected for grain yield (GY), plant height (PH), ear height (EH) and GCA for the three traits in two places could explain a phenotypic variation range of 7.31%-9.29%. Among the 20 association loci, 9 (7.31%-9.04%) were associated with GY, 4 (7.22%-8.91%) were related to GCA of GY, 1 (7.56%) was associated with PH, and 3 (7.53%-8.96%) were related to EH. In addition, 3 loci (9.14%-9.29%) were associated with GCA of PH whereas no locus was identified for GCA of EH. In the comparison of the association loci detected in Baoding and Handan, interestingly, one locus (7.69% and 8.11%) was identified in both environments and one locus (7.52% and 7.82%) was identified for yield and GCA of yield. Therefore, the identification of GY-, PH-, EH- and GCA-related association loci could not only provide references for high yield breeding of maize, but also help us comprehend the relationships among GY, agricultural traits and GCA.

Parasitic weeds represent a major threat to agricultural production across the world. Little is known about which host genetic pathways determine compatibility for any host–parasitic plant interaction. We developed a quantitative assay to characterize the growth of the parasitic weed Phelipanche aegyptiaca on 46 mutant lines of the host plant Arabidopsis thaliana to identify host genes that are essential for susceptibility to the parasite. A. thaliana host plants with mutations in genes involved in jasmonic acid biosynthesis/signaling or the negative regulation of plant immunity were less susceptible to P. aegyptiaca parasitization. In contrast, A. thaliana plants with a mutant allele of the putative immunity hub gene Pfd6 were more susceptible to parasitization. Additionally, quantitative PCR revealed that P. aegyptiaca parasitization leads to transcriptional reprograming of several hormone signaling pathways. While most tested A. thaliana lines were fully susceptible to P. aegyptiaca parasitization, this work revealed several host genes essential for full susceptibility or resistance to parasitism. Altering these pathways may be a viable approach for limiting host plant susceptibility to parasitism.

Epilepsy is marked by hypersynchronous bursts of neuronal activity, and seizures can propagate variably to any and all areas, leading to brain network dynamic organization. However, the relationship between the network characteristics of scalp EEG and blood oxygenation level-dependent (BOLD) responses in epilepsy patients is still not well known. In this study, simultaneous EEG and fMRI data were acquired in 18 juvenile myoclonic epilepsy (JME) patients. Then, the adapted directed transfer function (ADTF) values between EEG electrodes were calculated to define the time-varying network. The variation of network information flow within sliding windows was used as a temporal regressor in fMRI analysis to predict the BOLD response. To investigate the EEG-dependent functional coupling among the responding regions, modulatory interactions were analyzed for network variation of scalp EEG and BOLD time courses. The results showed that BOLD activations associated with high network variation were mainly located in the thalamus, cerebellum, precuneus, inferior temporal lobe and sensorimotor-related areas, including the middle cingulate cortex (MCC), supplemental motor area (SMA), and paracentral lobule. BOLD deactivations associated with medium network variation were found in the frontal, parietal, and occipital areas. In addition, modulatory interaction analysis demonstrated predominantly directional negative modulation effects among the thalamus, cerebellum, frontal and sensorimotor-related areas. This study described a novel method to link BOLD response with simultaneous functional network organization of scalp EEG. These findings suggested the validity of predicting epileptic activity using functional connectivity variation between electrodes. The functional coupling among the thalamus, frontal regions, cerebellum and sensorimotor-related regions may be characteristically involved in epilepsy generation and propagation, which provides new insight into the pathophysiological mechanisms and intervene targets for JME. Keywords: Juvenile myoclonic epilepsy, Simultaneous EEG and fMRI, Network variation, Functional coupling, Modulatory interaction

Purpose: The purpose of this study was to comprehensively evaluate alterations of resting-state spontaneous brain activity in patients with idiopathic generalized epilepsy (IGE) and its subgroups [juvenile myoclonic epilepsy (JME) and generalized tonic-clonic seizures (GTCS)].Methods: Resting state functional magnetic resonance imaging (fMRI) data were acquired from 60 patients with IGE and 60 healthy controls (HCs). Amplitude of low frequency fluctuation (ALFF), global functional connectivity density (gFCD), local FCD (lFCD), and long range FCD (lrFCD) were used to evaluate spontaneous brain activity in the whole brain. Moreover, the coupling between ALFF and FCDs (gFCD, lFCD, and lrFCD) was analyzed on both voxel-wise and subject-wise levels. Two-sample t-tests were used to analyze the difference in ALFF, FCDs and coupling on a subject-wise level between the two groups. Nonparametric permutation tests were used to evaluate differences in coupling on a voxel-wise level.Key findings: Patients with IGE and its subgroups showed reduced ALFF, gFCD and lrFCD in posterior regions of the default mode network (DMN). In addition, decreased ALFF and increased coupling with FCD were found in the cerebellum, while decreased coupling was observed in the bilateral pre- and postcentral gyrus in IGE compared with the coupling in HCs. Similar findings were found in the analysis between each of the two subgroups of IGE (JME and GTCS) and HCs, and JME patients had increased coupling in the cerebellum and bilateral middle occipital gyrus compared with coupling in the GTCS patients.Significance: This study demonstrated a multifactor abnormality of the DMN in IGE and emphasized that the abnormality in the cerebellum was associated with dysfunctional motor symptoms during seizures and might participate in the regulation of GSWDs in IGE.

To identify electrical vehicle (EV) distribution paths with high robustness, insensitivity to uncertainty factors, and detailed road-by-road schemes, optimization of the distribution path problem of EV with multiple distribution centers and considering the charging facilities is necessary. With the minimum transport time as the goal, a robust optimization model of EV distribution path with adjustable robustness is established based on Bertsimas' theory of robust discrete optimization. An enhanced three-segment genetic algorithm is also developed to solve the model, such that the optimal distribution scheme initially contains all road-by-road path data using the three-segment mixed coding and decoding method. During genetic manipulation, different interlacing and mutation operations are carried out on different chromosomes, while, during population evolution, the infeasible solution is naturally avoided. A part of the road network of Xifeng District in Qingyang City is taken as an example to test the model and the algorithm in this study, and the concrete transportation paths are utilized in the final distribution scheme. Therefore, more robust EV distribution paths with multiple distribution centers can be obtained using the robust optimization model.

The purpose of this study was to evaluate the spatiotemporal Consistency of spontaneous activities in local brain regions in patients with generalized tonic-clonic seizures (GTCS). The resting-state fMRI data were acquired from nineteen patients with GTCS and twenty-two matched healthy subjects. FOur-dimensional (spatiotemporal) Consistency of local neural Activities (FOCA) metric was used to analyze the spontaneous activity in whole brain. The FOCA difference between two groups were detected using a two sample t-test analysis. Correlations between the FOCA values and features of seizures were analyzed. The findings of this study showed that patients had significantly increased FOCA in motor-related cortex regions, including bilateral supplementary motor area, paracentral lobule, precentral gyrus and left basal ganglia, as well as a substantial reduction of FOCA in regions of default mode network (DMN) and parietal lobe. In addition, several brain regions in DMN demonstrated more reduction with longer duration of epilepsy and later onset age, and the motor-related regions showed higher FOCA value in accompany with later onset age. These findings implicated the abnormality of motor-related cortical network in GTCS which were associated with the genesis and propagation of epileptiform activity. And the decreased FOCA in DMN might reflect the intrinsic disturbance of brain activity. Moreover, our study supported that the FOCA might be potential tool to investigate local brain spontaneous activity related with the epileptic activity, and to provide important insights into understanding the underlying pathophysiological mechanisms of GTCS.

Abstract The role of circulating metabolome in cognitive impairment is inconclusive, and whether the associations are in the severity-dependent manner remains unclear. We aimed to identify plasma metabolites associated with cognitive impairment and evaluate the added predictive capacity of metabolite biomarkers on incident cognitive impairment beyond traditional risk factors. In the Rugao Longevity and Ageing Study (RuLAS), plasma metabolome was profiled by nuclear magnetic resonance spectroscopy. Participants were classified into the cognitively normal, moderately impaired, and severely impaired groups according to their performance in two objective cognitive tests. A two-step strategy of cross-sectional discovery followed by prospective validation was applied. In the discovery stage, we included 1643 participants (age: 78.9 ± 4.5 years) and conducted multinomial logistic regression. In the validation stage, we matched 68 incident cases of cognitive impairment (moderately-to-severely impaired) during the 2-year follow-up with 204 cognitively normal controls by age and sex at a 1:3 ratio, and conducted conditional logistic regression. We identified 28 out of 78 metabolites cross-sectionally related to severely impaired cognition, among which IDL particle number, ApoB in IDL, leucine, and valine were prospectively associated with 28%, 28%, 29%, and 33% lower risk of developing cognitive impairment, respectively. Incorporating 13 metabolite biomarkers selected through Lasso regression into the traditional risk factors-based prediction model substantially improved the ability to predict incident cognitive impairment (AUROC: 0.839 vs. 0.703, P