Search

Abstract Background Panax notoginseng (Burk) F. H. Chen is one of the most famous Chinese traditional medicinal plants. The taproot is the main organ producing triterpenoid saponins, and its development is directly linked to the quality and yield of the harvested P. notoginseng. However, the mechanisms underlying the dynamic metabolic changes occurring during taproot development of P. notoginseng are unknown. Results We carried out metabolomic and transcriptomic analyses to investigate metabolites and gene expression during the development of P. notoginseng taproots. The differentially accumulated metabolites included amino acids and derivatives, nucleotides and derivatives, and lipids in 1-year-old taproots, flavonoids and terpenoids in 2- and 3-year-old taproots, and phenolic acids in 3-year-old taproots. The differentially expressed genes (DEGs) are related to phenylpropanoid biosynthesis, metabolic pathway and biosynthesis of secondary metabolites at all three developmental stages. Integrative analysis revealed that the phenylpropanoid biosynthesis pathway was involved in not only the development of but also metabolic changes in P. notoginseng taproots. Moreover, significant accumulation of triterpenoid saponins in 2- and 3-year-old taproots was highly correlated with the up-regulated expression of cytochrome P450s and uridine diphosphate-dependent glycosyltransferases genes. Additionally, a gene encoding RNase-like major storage protein was identified to play a dual role in the development of P. notoginseng taproots and their triterpenoid saponins synthesis. Conclusions These results elucidate the molecular mechanism underlying the accumulation of and change relationship between primary and secondary metabolites in P. notoginseng taproots, and provide a basis for the quality control and genetic improvement of P. notoginseng.

IntroductionSpecific interactions between root exudates and soil microorganisms has been proposed as one of the reasons accounting for the continuous cropping obstacle (CCO) of Panax notoginseng. However, rotation of other crops on soils planted with P. notoginseng (SPP) did not show CCO, suggesting that root exudates of different crops differentially regulate soil microorganisms in SPP.MethodsHere, we investigated the microbial community structure and specific interaction mechanisms of the root exudates of the four plant species, P. notoginseng (Pn), Zea mays (Zm), Nicotiana tabacum (Nt) and Perilla frutescens (Pf), in SPP by static soil culture experiment.ResultsThe results showed that the chemical diversity of root exudates varied significantly among the four plant species. Pn had the highest number of unique root exudates, followed by Nt, Zm and Pf. Terpenoids, flavonoids, alkaloids and phenolic acids were the most abundant differentially accumulated metabolites (DAMs) in Pn, Nt, Zm and Pf, respectively. However, lipids were the most abundant common DAMs among Zm Nt and Pf. Pn root exudates decreased the relative abundance of bacteria, but increased that of fungi. While specific DAMs in Pn enriched Phenylobacterium_zucineum, Sphingobium_yanoikuyae, Ophiostoma_ulmi and functional pathways of Nucleotide excision repair, Streptomycin biosynthesis, Cell cycle-Caulobacter and Glycolysis/Gluconeogenesis, it inhibited Paraburkholderia _caledonica and Ralstonia_pickettii. However, common DAMs in Zm, Nt and Pf had opposite effects. Moreover, common DAMs in Zm, Nt and Pf enriched Ralstonia_pseudosolanacearum and functional pathway of Xylene degradation; unique DAMs in Zm enriched Talaromyces_purcureogeneus, while inhibiting Fusarium_tricinctum and functional pathways of Nucleotide excision repair and Alanine, aspartate and glutamate metabolism; unique DAMs in Pf enriched Synchytrium_taraxaci.DiscussionThe core strains identified that interact with different root exudates will provide key clues for regulation of soil microorganisms in P. notoginseng cultivation to alleviate CCO.

Abstract Influences of photosynthesis of leaf and silique on seeds yield and quality of oilseed rape (Brassica napus L.) were explored in this study. A field comparing experiment with several rapeseed varieties was conducted and the results showed, that the leaf area index (LAI), silique surface area index (SAI), siliques number per plant, and biological yield were statistically classified as the first principal factors which greatly influenced seeds yield, the leaf net photosynthetic rate (P n) and silique P n were the second principal factors; the stomatal conductance (G s) and chlorophyll a (Chl a) content were the first principal factors which influenced leaf P n and silique P n. A shading experiment was conducted and the results showed that, under treatments of the ZH1, ZH2, and ZH3 (shading rapeseed plants during flowering stage, during time from initial flowering until seeds ripening, and during time from flowering ending until seeds ripening, respectively), respectively the seeds yield per plant was reduced by 34.6%, 84.3%, and 86.1%, the seed protein content was significantly increased. The treatment ZH1 Not, but the ZH2 and ZH3 caused significant decrease in both seed oil content and oleic acid (C18:1) content in seed oil, and the contents of linoleic acid (C18:2), linolenic acid (C18:3) in oil were significantly increased, gene expression of the ACCase (Acetyl-CoA carboxylase), FAD2 (fatty acid desaturase), and FAD3 (ω-3 fatty acid dehydrogenase) in green seeds was restrained/changed. Thus the LAI, SAI, siliques number per plant, biological yield per plant, leaf P n, silique P n, and the G s, Chl a content of leaf and silique formed an indexes system to be used in screening rapeseed variety with higher light efficiency and seeds yield; the silique photosynthesis inhibition and the photosynthates deficiency in rapeseed plant after flowering stage predominately influenced seeds yield and quality.

Abstract Background and objective Sudden cardiac death (SCD) is one of the leading causes of death in cardiovascular diseases. Monitoring the state of the heart in real time and giving early warning of possible dangers by using ambulate electrocardiogram signals are the keys to prevent cardiovascular death. However, due to the diversity inducing factors of SCD and great individual differences, accurate prediction of SCD using electrocardiogram is a hard task, especially applied in portable electrocardiograph. Methods This paper proposed a multi-domain features fusion algorithm to predict SCD. Heart rate variability (HRV) signals was used to investigate the characters of SCD. A multiscale variation feature extracted from multiscale poincare plots was proposed to demonstrate the dynamic changes of HRV along different scales. A time-domain feature, Shannon entropy and this multiscale variation feature were combined by using SVM classifier to classify SCD. HRV signals from different time periods prior to SCD onset were used to test the effectiveness of the SCD prediction algorithm. And the dynamic variation characteristics of SCD prediction accuracy for each minute were also studied. Results In the prediction of SCD using the 70-min HRV signals before the onset of SCD, the average prediction accuracy only using the multiscale variation feature reached to 85.83%, which verified the effectiveness and high specificity of this multiscale variation feature. By combining time domain, Shannon entropy and the multiscale variation feature, the average prediction accuracy was improved to 91.22%. Through fusing multi-domain feature extracted in this paper, the advance prediction time was increased to 70 min before the onset of SCD. Conclusions A feature with high sensitivity and specificity is proposed to predict SCD. By fusing multi-domain features of HRV signals, a high prediction accuracy is achieved and the advance prediction ability is improved. The algorithm is low computational complexity and easy to integrate into cardiovascular intelligent monitoring equipment, making the intelligent monitoring and real-time early warning of SCD becomes possible.

Object: This study aims to investigate the changes in gut microbiota and metabolism of patients with chronic kidney disease (CKD) stage 1–2, as well as the potential impact of hyperuricemia (HUA) on these factors in CKD stage 1–2 patients. Methods: In this study, fecal samples were collected from CKD stage 1–2 without HUA patients (CKD-N group), CKD stage 1–2 with HUA patients (CKD-H group), and healthy people controls (HCs group). The samples were then subjected to the microbiome (16S rRNA gene sequencing) and metabolome (liquid chromatography-tandem mass spectrometry) analyses. The multi-omics datasets were analyzed individually and integrated for combined analysis using various bioinformatics approaches. Results: Gut microbial dysbiosis was found in CKD-N and CKD-H patients. At the phylum level, compared to HCs group, Bacteroidetes decreased but Proteobacteria increased in CKD-H group significantly. Fusobacteria in CKD-N group was significantly lower than HCs group. At genus level, [Eubacterium]_ventriosum_group, Fusobacterium, Agathobacter, Parabacteroides, and Roseburia significantly changed in CKD groups. [Ruminococcus]_gnavus_group was significantly lower in CKD-H group than CKD-N group. Moreover, the fecal metabolome of CKD-N and CKD-H altered significantly. d-glutamine and d-glutamate metabolism, arginine and proline metabolism, histidine metabolism, and lysine biosynthesis were down-regulated in the CKD-N group. Phenylalanine metabolism, arginine and proline metabolism, purine metabolism, and beta-alanine metabolism were up-regulated in the CKD-H group. There was a significant difference between the two CKD groups in phenylalanine metabolism. The abundance change of [Ruminococcus]_gnavus_group, [Eubacterium]_ventriosum_group, UCG-002, Alistipes, and Bifidobacterium had a close correlation with differential metabolites. Conclusion: The gut microbiota and metabolic status undergo significant changes in CKD patients compared to healthy people. Additionally, HUA has been found to impact the gut microbiota of CKD patients, as well as their metabolism. The close association between gut microbiota and metabolites suggests that the former plays a crucial role in metabolism.

It is very important to promote plant growth and decrease the nitrogen leaching in soil, to improve nitrogen (N) utilization efficiency. In this experiment, we designed a new fertilization strategy, fruit tree hole storage brick (FTHSB) application under subsurface drip irrigation, to characterise the effects of FTHSB addition on N absorption and utilization in grapes. Three treatments were set in this study, including subsurface drip irrigation (CK) control, fruit tree hole storage brick A (T1) treatment, and fruit tree hole storage brick B (T2) treatment. Results showed that the pore number and size of FTHSB A were significantly higher than FTHSB B. Compared with CK, T1 and T2 treatments significantly increased the biomass of different organs of grape, N utilization and 15N content in the roots, stems and leaves, along with more prominent promotion at T1 treatment. When the soil depth was 15–30 cm, the FTHSB application significantly increased the soil 15N content. But when the soil depth was 30–45 cm, it reduced the soil 15N content greatly. T1 and T2 treatments obviously increased the activities of nitrite reductase (NR) and glutamine synthetase (GS) in grape leaves, also the urease activity(UR) in 30 cm of soil. Our findings suggest that FTHSB promoted plant N utilization by reducing N loss in soil and increasing the enzyme activity related to nitrogen metabolism. In addition, this study showed that FTHSB A application was more effective than FTHSB B in improving nitrogen utilization in grapes.

This study proposes a high-frequency pneumatic system and demodulation method based on polydimethylsiloxane (PDMS) film-embedded fiber-optic Fabry-Perot sensor to meet the conflicting requirements of small sizes and wide frequency-response range not achieved by conventional pneumatic probes. The high modulus of elasticity of PDMS films provides the potential for an upper limit of a pneumatic frequency response up to 20 kHz, and the new demodulation algorithm, based on the ability of the F-P cavity to return to its initial length from the maximum cavity length change, is utilized to analyze its variation. The process of recovering F-P cavity corresponds with the sparse part of the signal, which takes advantage of the unique characteristics of interference fringes in this part. Moreover, the relationship between the relative change of the F-P cavity length and pneumatic pressure, as well as the relationship between the duration of the relative change of the F-P cavity length and the frequency are analyzed comprehensively. The rationality of the proposed demodulation scheme is verified from the angle-error analyses. These analyses could be helpful for integrated and high-frequency response pneumatic detection.

Various shipping emission restrictions have recently been implemented locally and nationally, which might mitigate their impacts on regional air quality, climate change, and human health. In this study, the daily trace metal elements in PM1 were measured in a coastal megacity in Northern China, from autumn to winter from 2018 to 2022, spanning DECA 1.0 (domestic emission control area), DECA 2.0, IMO 2020, and Pre-OWG Beijing 2022 stages. The trace element changes of V, Ni, Pb, and Zn in PM1 were analyzed. The concentrations of V declined with shipping emission regulations implemented in 2018–2022 at 3.61 ± 3.01, 1.07 ± 1.04, 0.84 ± 0.62, and 0.68 ± 0.61 ng/m3, respectively, with the V/Ni ratio decreasing at 1.14 ± 0.79, 0.93 ± 1.24, 0.35 ± 0.24, and 0.22 ± 0.18. The V/Ni ratio was dominated by the shipping emissions in the DECA 1.0 stage but has been more affected by the inland sources since DECA 2.0. The V/Ni ratio of local transport air mass was higher than that of long-distance transportation, indicating that some ships were still using high-sulfur fuel oil, especially for the ships 12 nautical miles from the coastline. The multiple linear regression model showed a better fit using V as a tracer for ship emission sources of ambient SO2 in the DECA 1.0 stage, while the indication effect reduced since DECA 2.0. The V and V/Ni ratios should be carefully used as indicators of ship sources as more vessels will use clean fuels for energy, and the contribution of inland sources to V and Ni will gradually increase.

ObjectiveMajor depressive disorder (MDD) has a relapse rate that cannot be ignored and places a tremendous burden on the patient in the prevention and treatment process. Yoga, a combination of physical and mental exercises, is effective and acceptable for the adjunctive treatment of MDD. This study aimed to explore further the evidence of yoga’s efficacy for patients with MDD.MethodsPubMed, Embase, Cochrane library, PsycINFO, SinoMed, CNKI, Wanfang, and VIP databases from their inception to 13 October 2022 were searched by a pre-defined search strategy. RCTs of patients with MDD who met diagnostic criteria for yoga treatment were included. RoB2.0 was used to evaluate the quality of the literature. Improvement in depressive symptoms was assessed by the Beck Depression Inventory (BDI), Hamilton Depression Rating Scale (HAMD), or other scales were used as primary outcome indicators, and improvement in anxiety was assessed by the Hamilton Anxiety Scale (HAMA) and State–Trait Anxiety Inventory (STAI) scale as secondary outcome indicators. RR and Cohen’s d at 95% CI were used as effect size estimates, and Q and I2 were used to evaluate the size of heterogeneity, with a p-value less than 0.05 indicating statistical significance.ResultsThirty-four RCT studies, including 1,269 patients in the treatment group and 1,072 patients in the control group, 48.4% of whom were women, were included in the study. Compared to the control group, the BDI-II results yielded a moderate effect of yoga on the improvement of depressive symptoms (Cohen’s d = −0.60; 95% CI: −1.00 to −0.21; p

Background: As an objective method to detect the neural electrical activity of the brain, electroencephalography (EEG) has been successfully applied to detect major depressive disorder (MDD). However, the performance of the detection algorithm is directly affected by the selection of EEG channels and brain regions. Methods: To solve the aforementioned problems, nonlinear feature Lempel–Ziv complexity (LZC) and frequency domain feature power spectral density (PSD) were extracted to analyze the EEG signals. Additionally, effects of different brain regions and region combinations on detecting MDD were studied with eyes closed and opened in a resting state. Results: The mean LZC of patients with MDD was higher than that of the control group, and the mean PSD of patients with MDD was generally lower than that of the control group. The temporal region is the best brain region for MDD detection with a detection accuracy of 87.4%. The best multi brain regions combination had a detection accuracy of 92.4% and was made up of the frontal, temporal, and central brain regions. Conclusions: This paper validates the effectiveness of multiple brain regions in detecting MDD. It provides new ideas for exploring the pathology of MDD and innovative methods of diagnosis and treatment.

Abstract. Objective:. The impact of dairy fat on inflammatory bowel disease remained inconclusive. We aimed to compare the effects of whole-milk and skimmed-milk consumption on the risk of inflammatory bowel disease using a Mendelian randomization analysis. Methods:. We conducted a genome-wide association study of the preference for whole versus skimmed milk using data for 20,200 whole-milk consumers and 67,847 skimmed-milk consumers from the UK Biobank. The lead single nucleotide polymorphisms in the associated loci were identified at the genome-wide significance level, and were further employed as instrumental variables for whole-milk preference. We conducted a two-sample Mendelian randomization analysis with whole-milk preference as the exposure and inflammatory bowel disease as the outcome. The pleiotropic effects and heterogeneity of the instrumental variables were estimated using Mendelian randomization-Egger regression and Cochran Q test, respectively. This study was conducted using the UKB resources under the application “53536”. The UK Biobank was approved by the North West Multi-center Research Ethics Committee, the National Information Governance Board for Health and Social Care in England and Wales, and the Community Health Index Advisory Group in Scotland. Results:. The genome-wide association study identified five lead nucleotide polymorphisms associated with whole-milk preference. Mendelian randomization indicated that whole-milk preference significantly decreased the risk of inflammatory bowel disease (β=−1.735, P = 0.048). Of the two subtypes, whole-milk preference was associated with a lower risk of Crohn disease (β=−2.549, P = 0.032), but had no significant effect on the risk of ulcerative colitis (β=−1.002, P = 0.44). Conclusion:. Consumption of whole-milk fat may protect against Crohn disease, compared with skimmed milk. This conclusion was based on causal inference in a cohort study, and further validation in randomized controlled trials is warranted.

Abstract Background Nursing work is associated with great pressure, and nurses are often overwhelmed. Therefore, correct emotional regulation is essential to improve nurses’ job well-being and promote better engagement in nursing work. The purpose of this study was to establish a structural model to estimate the impact of Chinese clinical nurses’ emotional intelligence on job well-being, using multiple intermediaries to explain the internal mechanisms underlying the relationship. Methods This was a cross-sectional study of 1475 registered nurses from a Chinese hospital who provided responses to emotional intelligence, empathy, communication satisfaction, and job well-being scales. Path analysis using a multiple mediation model was performed using AMOS 23.0. Results Among all clinical nurses who participated in the survey, 1475 (98.33 %) completed the questionnaire. The nurses’ job well-being score was 83.61 ± 12.63. There was a significant positive correlation between job well-being and communication satisfaction, emotional intelligence, and empathy ability (r = 0.346–0.570, P

Infrared neural stimulation (INS), as a novel form of neuromodulation, allows modulating the activity of nerve cells through thermally induced capacitive currents and thermal sensitivity ion channels. However, fundamental questions remain about the exact mechanism of INS and how the photothermal effect influences the neural response. Computational neural modeling can provide a powerful methodology for understanding the law of action of INS. We developed a temperature-dependent model of ion channels and membrane capacitance based on the photothermal effect to quantify the effect of INS on the direct response of individual neurons and neuronal networks. The neurons were connected through excitatory and inhibitory synapses and constituted a complex neuronal network model. Our results showed that a slight increase in temperature promoted the neuronal spikes and enhanced network activity, whereas the ultra-temperature inhibited neuronal activity. This biophysically based simulation illustrated the optical dose-dependent biphasic cell response with capacitive current as the core change condition. The computational model provided a new sight to elucidate mechanisms and inform parameter selection of INS.

Acyl activating enzyme 3 (AAE3) encodes oxalyl-CoA synthetase involved in oxalate degradation. In this study, we investigated the role of AAE3 (SlAAE3) in the fruit quality of tomato (Solanum lycopersicum). The purified recombinant SlAAE3 protein from Escherichia coli exhibited a high activity toward oxalate, with a Km of 223.8 ± 20.03 μm and Vmax of 7.908 ± 0.606 μmol mg–1 protein min–1. Transient expression of SlAAE3-green fluorescent protein (GFP) fusion proteins suggests that SlAAE3 is a soluble protein without specific subcellular localization. The expression of SlAAE3 is both tissue- and development-dependent, and increased during fruit ripping. The Slaae3 knockout mutants had improved fruit quality as evidenced by the increased sugar-acid ratio and mineral nutrient content. To find the mechanism by which SlAAE3 affects fruit quality, transcriptome, and metabolome were employed on SlAAE3 over-expressed line and wide type fruits. The transcriptomic and metabolic profiles indicated that SlAAE3 in fruits mainly functions at 20 days post-anthesis (20 DPA) and mature green (MG) stages, resulting in up-regulation of amino acid derivatives, nucleotides, and derivatives, but down-regulation of lipid compounds. However, differentially expressed genes (DEGs) were mainly enriched at redox pathways. Taken together, both in vivo and in vitro results suggest that SlAAE3-encoded protein acts as an oxalyl-CoA synthetase, which also participates in redox metabolism. These data provide a further understanding of the mechanism by which SlAAE3 participates in tomato fruit quality.

Background/Aims: The storage amount of liver glycogen could affect the liver fibrosis assessment made by MRI-based methods. However, it remained unclear whether glycogen amount could bias the estimation of liver fat content by proton density fat fraction. In this study, we aimed to investigate whether glycogen metabolism gene variants could contribute to the bias of PDFF by genetic association.Methods: We conducted an association study of the glycogen metabolism genes based on the PDFF data of 11,129 participants in the UK Biobank. The effect of the SNPs in these genes on non-alcoholic fatty liver disease was estimated by a meta-analysis of the available NAFLD case-control studies.Results: We identified significant associations of the SNPs near the genes encoding glycogen phosphorylase (PYGM and PYGL) and synthase (GYS2) with PDFF (FDR-corrected p value < 0.05). The genes encoding the regulatory proteins of glycogenolysis (PHKB, CALM2/3), glucose transporter (SLC2A1), and glucose kinase (GCK) were also associated with PDFF. The SNP rs5402 of SLC2A2 and rs547066 of PYGM were associated with NAFLD (p < 0.05) with others being insignificant. Except for the PYGM gene, the PDFF-associated SNPs showed no associations with NAFLD. In addition, the burden tests of rare variants in these genes were not significant after FDR correction.Conclusion: Liver glycogen metabolism genes associated with PDFF were not associated with NAFLD, which implicated a potential bias effect of glycogen storage on the quantification of liver fat content by PDFF.

Abstract Introduction This study mainly investigated the role of miR‐199a‐5p in depression. Methods qRT‐PCR and western blotting were employed to detect the expressions of miR‐199a‐5p, CREB and BDNF. Sucrose preference test, forced swimming test, and tail suspension test were performed to evaluate depression‐related symptoms. MTT assays and flow cytometry were used to examine the cell reproduction and apoptotic cells of hippocampal neuron. Results The data demonstrated that the expression levels of miR‐199a‐5p in the cerebrospinal fluids and serums of depression patient and the hippocampus of chronic unpredictable mild stress (CUMS) mouse were significantly increased. However, the expressions of WNT2, p‐CREB, and BDNF were inhibited. In addition, miR‐199a‐5p‐inhibitor enhanced sucrose preferences of CUMS mouse and decreased immobile time in sucrose preference test and forced swimming test. Knockdown of WNT2 attenuated the effects of miR‐199a‐5p‐inhibitor on cell reproduction and apoptotic cells of hippocampal neuron and the expression of WNT2, p‐CREB, and BDNF. Conclusion MiR‐199a‐5p can target WNT2 to enhance the development of depression through regulation of the CREB/BDNF signaling. Trial registration: JNU‐Hos‐49284.

Background: Examining associations between viral genomic loads of enteroviruses and clinical severity is important for promoting and improving development of antiviral drugs related to hand, foot and mouth disease (HFMD). Methods: Throat swabs were collected from HFMD cases at acute phase of illness using a standardized technique in a prospective study. The viral genomic load was categorized into low, medium, and high groups using parameters of real-time reverse transcription-polymerase chain reaction. The clinical severities were assessed with four indicators, respectively. Findings: We analysed 1109 HFMD cases, including 538 children with CV-A6, 231 with CV-A16, 156 with EV-A71, 78 with CV-A10, 59 with CV-A4, and 47 with CV-A2. EV-A71 genomic load categories were associated with risks of diagnoses of CNS complications (p = 0.016), requiring systemic corticosteroids or IVIG (p = 0.011), intensive care unit admission (p = 0.002) and length of hospital stay over 5 days (p = 0.048). In the multivariate analyses, point estimates of adjusted odds ratio (OR) tended to increase with viral genomic loads for all four severe outcomes and ORs of highest viral genomic load were all significantly larger than one for EV-A71. Interpretation: HFMD clinical severities positively associate with viral genomic loads of EV-A71 in throat swabs. Specific antiviral drugs should be developed to reduce enterovirus load and to alleviate the clinical severities for HFMD cases. Funding: National Science Fund for Distinguished Young Scholars

Motor imagery (MI) electroencephalography (EEG) classification is an important part of the brain-computer interface (BCI), allowing people with mobility problems to communicate with the outside world via assistive devices. However, EEG decoding is a challenging task because of its complexity, dynamic nature, and low signal-to-noise ratio. Designing an end-to-end framework that fully extracts the high-level features of EEG signals remains a challenge. In this study, we present a parallel spatial–temporal self-attention-based convolutional neural network for four-class MI EEG signal classification. This study is the first to define a new spatial-temporal representation of raw EEG signals that uses the self-attention mechanism to extract distinguishable spatial–temporal features. Specifically, we use the spatial self-attention module to capture the spatial dependencies between the channels of MI EEG signals. This module updates each channel by aggregating features over all channels with a weighted summation, thus improving the classification accuracy and eliminating the artifacts caused by manual channel selection. Furthermore, the temporal self-attention module encodes the global temporal information into features for each sampling time step, so that the high-level temporal features of the MI EEG signals can be extracted in the time domain. Quantitative analysis shows that our method outperforms state-of-the-art methods for intra-subject and inter-subject classification, demonstrating its robustness and effectiveness. In terms of qualitative analysis, we perform a visual inspection of the new spatial–temporal representation estimated from the learned architecture. Finally, the proposed method is employed to realize control of drones based on EEG signal, verifying its feasibility in real-time applications.

Background Understanding the patterns of treatment response is critical for the treatment of patients with schizophrenia; one way to achieve this is through using a longitudinal dynamic process study design. Aims This study aims to explore the response trajectory of antipsychotics and compare the treatment responses of seven different antipsychotics over 6 weeks in patients with schizoprenia (trial registration: Chinese Clinical Trials Registry Identifier: ChiCTR-TRC-10000934). Method Data were collected from a multicentre, randomised open-label clinical trial. Patients were evaluated with the Positive and Negative Syndrome Scale (PANSS) at baseline and follow-up at weeks 2, 4 and 6. Trajectory groups were classified by the method of k-means cluster modelling for longitudinal data. Trajectory analyses were also employed for the seven antipsychotic groups. Results The early treatment response trajectories were classified into a high-trajectory group of better responders and a low-trajectory group of worse responders. The results of trajectory analysis showed differences compared with the classification method characterised by a 50% reduction in PANSS scores at week 6. A total of 349 patients were inconsistently grouped by the two methods, with a significant difference in the composition ratio of treatment response groups using these two methods (χ2 = 43.37, P < 0.001). There was no differential contribution of high- and low trajectories to different drugs (χ2 = 12.52, P = 0.051); olanzapine and risperidone, which had a larger proportion in the >50% reduction at week 6, performed better than aripiprazole, quetiapine, ziprasidone and perphenazine. Conclusions The trajectory analysis of treatment response to schizophrenia revealed two distinct trajectories. Comparing the treatment responses to different antipsychotics through longitudinal analysis may offer a new perspective for evaluating antipsychotics.

Low phosphate (Pi) availability and high aluminum (Al) toxicity constitute two major plant mineral nutritional stressors that limit plant productivity on acidic soils. Advances toward the identification of genes and signaling networks that are involved in both stresses in model plants such as Arabidopsis thaliana and rice (Oryza sativa), and in other plants as well have revealed that some factors such as organic acids (OAs), cell wall properties, phytohormones, and iron (Fe) homeostasis are interconnected with each other. Moreover, OAs are involved in recruiting of many plant-growth-promoting bacteria that are able to secrete both OAs and phosphatases to increase Pi availability and decrease Al toxicity. In this review paper, we summarize these mutual mechanisms by which plants deal with both Al toxicity and P starvation, with emphasis on OA secretion regulation, plant-growth-promoting bacteria, transcription factors, transporters, hormones, and cell wall-related kinases in the context of root development and root system architecture remodeling that plays a determinant role in improving P use efficiency and Al resistance on acidic soils.

Background: The widespread use of high-resolution computed tomography (HRCT) in lung cancer screening has allowed for an increased detection rate of ground-glass nodules (GGNs) in the lung. Hence, obtaining the correct clinical diagnosis of benign and malignant GGNs has become crucial. Objectives: Most artificial intelligence and computer-aided diagnosis (AI-CAD) systems for the classification of pulmonary GGNs fail to extract CT features. This study used HRCT and AI to analyze the CT features of GGNs to improve the prediction of benign and malignant pulmonary GGNs. Patients and Methods: This case-control study was performed on a malignant group consisting of patients and a benign group consisting of controls. A total of 204 patients with GGNs were recruited and divided into 2 groups according to their pathological results. Group A consisted of 69 cases with precursor glandular lesions (atypical adenomatous hyperplasia [AAH] and adenocarcinoma in situ [AIS]), inflammatory nodules, and benign nodules. Group B consisted of 135 cases with invasive lesions (minimally invasive adenocarcinoma [MIA], invasive adenocarcinoma [IAC], and other malignant lesions). Various CT features were compared between the 2 groups. The diagnostic efficacy of an AI-CAD system and radiologists' reports for benign and malignant nodules were analyzed. A multivariate logistic regression analysis was performed to determine independent predictors of malignant GGN. A model that combined the AI system and manual extraction of radiological features was constructed. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the model. Results: Significant differences were found between malignant and benign groups according to the following 7 CT features: The GGN size (long and short diameters), vacuole sign, air bronchogram sign, vascular convergence sign, vascular perforator sign, interlobular septal obstruction sign, and spiculation (P < 0.05). The volume and mean CT values of precursor glandular lesions of the lungs were significantly different from those of invasive lesions (P < 0.05). The logistic regression model showed that the sensitivity and specificity of the AI system in diagnosing malignant groups were 0.756 and 0.696, respectively. The sensitivity and specificity of radiologists' reports in diagnosing the malignant groups were 0.726 and 0.783, respectively. The combination of the 2 had a sensitivity of 0.768 and a specificity of 0.793. Conclusion: Prediction of the nature of GGNs based on CT features, including the vacuole sign, vascular perforator sign, and interlobular septal obstruction sign, were relatively accurate for a preliminary diagnosis. The AI system had a poorer diagnostic accuracy for GGNs than radiologists' reports of CT images. The combination of AI and radiologists' reports showed the highest diagnostic efficacy. [ABSTRACT FROM AUTHOR]

Background: Diabetic retinopathy (DR) is a common diabetes complication and was considered as the major cause of blindness among young adults. MiRNAs are a group of small non-coding RNAs regulating the expression of target genes and have been reported to be associated with the development of DR in a variety of molecular mechanisms. In this study, we aimed to identify miRNAs that are differentially expressed (DE) in the serum of DR patients.Methods: We recruited 21 type 2 diabetes mellitus (T2DM) inpatients of Chinese Han ancestry, consisting of 10 non-proliferative DR patients (DR group) and 11 non-DR T2DM patients (NDR group). MiRNA was extracted from fasting peripheral serum and quantified by RNA-seq. The expression levels of miRNA were evaluated and compared between the two groups, with adjustments made for age differences. The validated target genes of miRNAs were subjected to a pathway analysis. We also constructed a weighted polygenic risk score using the DE miRNA and evaluated its predictive power.Results: Five miRNAs were DE between DR and NDR groups (p-Value ≤ 0.01, LFC ≥ 2 or LFC ≤-2). These included miR-4448, miR-338-3p, miR-190a-5p, miR-485-5p, and miR-9-5p. In total, these miRNAs were validated to regulate 55 target genes. Four target genes were found to overlap with the NAD metabolism, sirtuin, and aging pathway, which was thought to control the vascular growth and morphogenesis. The predictive power of our polygenic risk score was apparently high (AUC = 0.909). However, it needs to be interpreted with caution.Conclusion: In this study, we discovered novel DR-specific miRNAs in human serum samples. These circulating miRNAs may represent the pathological changes in the retina in response to diabetes and may serve as non-invasive biomarkers for early DR risk prediction.