Molecular and Cytological Identification of Wheat-Thinopyrum intermedium Partial Amphiploid Line 92048 with Resistance to Stripe Rust and Fusarium Head Blight.

Thinopyrum intermedium (2n = 6x = 42, EeEeEbEbStSt or JJJsJsStSt) contains a large number of genes that are highly adaptable to the environment and immune to a variety of wheat diseases, such as powdery mildew, rust, and yellow dwarf, making it an important gene source for the genetic improvement of common wheat. Currently, an important issue plaguing wheat production and breeding is the spread of pests and illnesses. Breeding disease-resistant wheat varieties using disease-resistant genes is currently the most effective measure to solve this problem. Moreover, alien resistance genes often have a stronger disease-resistant effect than the resistance genes found in common wheat. In this study, the wheat-Th. intermedium partial amphiploid line 92048 was developed through hybridization between Th. intermedium and common wheat. The chromosome structure and composition of 92048 were analyzed using ND-FISH and molecular marker analysis. The results showed that the chromosome composition of 92048 (Octoploid Trititrigia) was 56 = 42W + 6J + 4Js + 4St. In addition, we found that 92048 was highly resistant to a mixture of stripe rust races (CYR32, CYR33, and CYR34) during the seedling stage and fusarium head blight (FHB) in the field during the adult plant stage, suggesting that the alien or wheat chromosomes in 92048 had disease-resistant gene(s) to stripe rust and FHB. There is a high probability that the gene(s) for resistance to stripe rust and FHB are from the alien chromosomes. Therefore, 92048 shows promise as a bridge material for transferring superior genes from Th. intermedium to common wheat and improving disease resistance in common wheat.

Establishment and Verification of an Artificial Intelligence Prediction Model for Children With Sepsis.

BACKGROUND: Early identification of high-risk groups of children with sepsis is beneficial to reduce sepsis mortality. This article used artificial intelligence (AI) technology to predict the risk of death effectively and quickly in children with sepsis in the pediatric intensive care unit (PICU). STUDY DESIGN: This retrospective observational study was conducted in the PICUs of the First Affiliated Hospital of Sun Yat-sen University from December 2016 to June 2019 and Shenzhen Children's Hospital from January 2019 to July 2020. The children were divided into a death group and a survival group. Different machine language (ML) models were used to predict the risk of death in children with sepsis. RESULTS: A total of 671 children with sepsis were enrolled. The accuracy (ACC) of the artificial neural network model was better than that of support vector machine, logical regression analysis, Bayesian, K nearest neighbor method and decision tree models, with a training set ACC of 0.99 and a test set ACC of 0.96. CONCLUSIONS: The AI model can be used to predict the risk of death due to sepsis in children in the PICU, and the artificial neural network model is better than other AI models in predicting mortality risk.

Genome-wide QTL mapping for agronomic traits in the winter wheat cultivar Pindong 34 based on 90K SNP array.

Introduction: Agronomic traits are key components of wheat yield. Exploitation of the major underlying quantitative trait loci (QTLs) can improve the yield potential in wheat breeding. Methods: In this study, we constructed a recombinant inbred line (RIL) population from Mingxian 169 (MX169) and Pindong 34 (PD34) to determine the QTLs for grain length (GL), grain width (GW), grain length-to-width ratio (LWR), plant height (PH), spike length (SL), grain number per spike (GNS), and the thousand grain weight (TGW) across four environments using wheat 90K SNP array. Results: A QTL associated with TGW, i.e., QTGWpd.swust-6BS, was identified on chromosome 6B, which explained approximately 14.1%-16.2% of the phenotypic variation. In addition, eight QTLs associated with GL were detected across six chromosomes in four different test environments. These were QGLpd.swust-1BL, QGLpd.swust-2BL, QGLpd.swust-3BL.1, QGLpd.swust-3BL.2, QGLpd.swust-5DL, QGLpd.swust-6AL, QGLpd.swust-6DL.1, and QGLpd.swust-6DL.2. They accounted for 9.0%-21.3% of the phenotypic variation. Two QTLs, namely, QGWpd.swust-3BS and QGWpd.swust-6DL, were detected for GW on chromosomes 3B and 6D, respectively. These QTLs explained 12.8%-14.6% and 10.8%-15.2% of the phenotypic variation, respectively. In addition, two QTLs, i.e., QLWRpd.swust-7AS.1 and QLWRpd.swust-7AS.2, were detected on chromosome 7A for the grain LWR, which explained 10.9%-11.6% and 11.6%-11.2% of the phenotypic variation, respectively. Another QTL, named QGNSpd-swust-6DS, was discovered on chromosome 6D, which determines the GNS and which accounted for 11.4%-13.8% of the phenotypic variation. Furthermore, five QTLs associated with PH were mapped on chromosomes 2D, 3A, 5A, 6B, and 7B. These QTLs were QPHpd.swust-2DL, QPHpd.swust-3AL, QPHpd.swust-5AL, QPHpd.swust-6BL, and QPHpd.swust-7BS, which accounted for 11.3%-19.3% of the phenotypic variation. Lastly, a QTL named QSLpd.swust-3AL, conferring SL, was detected on chromosome 3A and explained 16.1%-17.6% of the phenotypic variation. All of these QTLs were defined within the physical interval of the Chinese spring reference genome. Discussion: The findings of this study have significant implications for the development of fine genetic maps, for genomic breeding, and for marker-assisted selection to enhance wheat grain yield.

Mediating effect of the NLR on the relationship between HbA1c and left atrial stiffness in overweight patients with hypertension.

BACKGROUND: We aimed to investigate the association between hemoglobin A1c (HbA1c) and left atrial (LA) stiffness in patients with hypertension and to explore the mediating effect of the neutrophil/lymphocyte ratio (NLR) on this association. METHODS: Essential hypertensive patients (n=292) aged 18 to 83 years were enrolled and divided into two groups based on the LA stiffness index (LASI): Group I (LASI≤0.32, n=146) and Group II (LASI>0.32, n=146). The LASI was defined as the ratio of early diastolic transmitral flow velocity/lateral mitral annulus myocardial velocity (E/e') to LA reservoir strain. Multivariate linear regression analysis was performed to determine the independent predictors of the LASI. RESULTS: Age, BMI, SBP, HbA1c, CRP and the NLR were significantly greater in Group II than in Group I (P<0.05). Additionally, Group II had a greater LA volume index (LAVI), left ventricular mass index (LVMI), and E/e' and lower LA reservoir, conduit and booster pump strains than Group I (P<0.001). Univariate and multivariate linear regression models revealed that age, SBP, HbA1c, and the NLR were independently associated with the LASI. Further mediation analysis was performed to determine the mediating effect of the NLR on the association between HbA1c and the LASI and revealed that the NLR had a mediating role only in overweight hypertensive patients, and the proportion of the mediating effect was 21.9%. CONCLUSIONS: The NLR was independently correlated with the LASI and played a mediating role in the relationship between HbA1c and the LASI in overweight hypertensive patients.

Simultaneous Measurement of Local Pulse Wave Velocities in Radial Arteries Using a Soft Sensor Based on the Fiber Bragg Grating Technique.

Arterial stiffness has been proved to be an important parameter in the evaluation of cardiovascular diseases, and Pulse Wave Velocity (PWV) is a strong indicator of arterial stiffness. Compared to regional PWV (PWV among different arteries), local PWV (PWV within a single artery) outstands in providing higher precision in indicating arterial properties, as regional PWVs are highly affected by multiple parameters, e.g., variations in blood vessel lengths due to individual differences, and multiple reflection effects on the pulse waveform. However, local PWV is less-developed due to its high dependency on the temporal resolution in synchronized signals with usually low signal-to-noise ratios. This paper presents a method for the noninvasive simultaneous measurement of two local PWVs in both left and right radial arteries based on the Fiber Bragg Grating (FBG) technique via correlation analysis of the pulse pairs at the fossa cubitalis and at the wrist. Based on the measurements of five male volunteers at the ages of 19 to 21 years old, the average left radial PWV ranged from 9.44 m/s to 12.35 m/s and the average right radial PWV ranged from 11.50 m/s to 14.83 m/s. What is worth mentioning is that a stable difference between the left and right radial PWVs was observed for each volunteer, ranging from 2.27 m/s to 3.04 m/s. This method enables the dynamic analysis of local PWVs and analysis of their features among different arteries, which will benefit the diagnosis of early-stage arterial stiffening and may bring more insights into the diagnosis of cardiovascular diseases.

Supramolecular Assembly in Live Cells Mapped by Real-Time Phasor-Fluorescence Lifetime Imaging.

The complex dynamics and transience of assembly pathways in living systems complicate the understanding of these molecular to nanoscale processes. Current technologies are unable to track the molecular events leading to the onset of assembly, where real-time information is imperative to correlate their rich biology. Using a chemically designed pro-assembling molecule, we map its transformation into nanofibers and their fusion with endosomes to form hollow fiber clusters. Tracked by phasor-fluorescence lifetime imaging (phasor-FLIM) in epithelial cells (L929, A549, MDA-MB 231) and correlative light-electron microscopy and tomography (CLEM), spatiotemporal splicing of the assembly events shows time-correlated metabolic dysfunction. The biological impact begins with assembly-induced endosomal disruption that reduces glucose transport into the cells, which, in turn, stymies mitochondrial respiration.

Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is underdiagnosed with the current gold standard measure pulmonary function test (PFT). A more sensitive and simple option for early detection and severity evaluation of COPD could benefit practitioners and patients. METHODS: In this multicenter retrospective study, frontal chest X-ray (CXR) images and related clinical information of 1055 participants were collected and processed. Different deep learning algorithms and transfer learning models were trained to classify COPD based on clinical data and CXR images from 666 subjects, and validated in internal test set based on 284 participants. External test including 105 participants was also performed to verify the generalization ability of the learning algorithms in diagnosing COPD. Meanwhile, the model was further used to evaluate disease severity of COPD by predicting different grads. RESULTS: The Ensemble model showed an AUC of 0.969 in distinguishing COPD by simultaneously extracting fusion features of clinical parameters and CXR images in internal test, better than models that used clinical parameters (AUC = 0.963) or images (AUC = 0.946) only. For the external test set, the AUC slightly declined to 0.934 in predicting COPD based on clinical parameters and CXR images. When applying the Ensemble model to determine disease severity of COPD, the AUC reached 0.894 for three-classification and 0.852 for five-classification respectively. CONCLUSION: The present study used DL algorithms to screen COPD and predict disease severity based on CXR imaging and clinical parameters. The models showed good performance and the approach might be an effective case-finding tool with low radiation dose for COPD diagnosis and staging.

Lipid metabolism-related long noncoding RNA RP11-817I4.1 promotes fatty acid synthesis and tumor progression in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common types of tumors. The influence of lipid metabolism disruption on the development of HCC has been demonstrated in published studies. AIM: To establish an HCC prognostic model for lipid metabolism-related long non-coding RNAs (LMR-lncRNAs) and conduct in-depth research on the specific role of novel LMR-lncRNAs in HCC. METHODS: Correlation and differential expression analyses of The Cancer Genome Atlas data were used to identify differentially expressed LMR-lncRNAs. Quantitative real-time polymerase chain reaction analysis was used to evaluate the expression of LMR-lncRNAs. Nile red staining was employed to observe intracellular lipid levels. The interaction between RP11-817I4.1, miR-3120-3p, and ATP citrate lyase (ACLY) was validated through the performance of dual-luciferase reporter gene and RIP assays. RESULTS: Three LMR-lncRNAs (negative regulator of antiviral response, RNA transmembrane and coiled-coil domain family 1 antisense RNA 1, and RP11-817I4.1) were identified as predictive markers for HCC patients and were utilized in the construction of risk models. Additionally, proliferation, migration, and invasion were reduced by RP11-817I4.1 knockdown. An increase in lipid levels in HCC cells was significantly induced by RP11-817I4.1 through the miR-3120-3p/ACLY axis. CONCLUSION: LMR-lncRNAs have the capacity to predict the clinical characteristics and prognoses of HCC patients, and the discovery of a novel LMR-lncRNAs, RP11-817I4.1, revealed its role in promoting lipid accumulation, thereby accelerating the onset and progression of HCC.

Ultrasonic seed treatment improved seed germination, growth, and yield of rice by modulating associated physio-biochemical mechanisms.

Ultrasonic seed (US) treatment could alter seed germination mechanism, however, US induced alterations in morph-physiological attributes and yield of fragrant rice were rarely reported. In the present study, the seeds of three fragrant rice cultivars viz., Xiangyaxiangzhan, Meixiangzhan 2, Ruanhuayou 6100 and one non-fragrant rice viz., Wufengyou 615 were exposed to ultrasonic waves at 20-40 kHz for 1.5 min (T) whereas the seeds without exposure were taken as control (CK). Results showed that US treatment caused minor cracks on seed surface while improved seed germination rate (1.79 %-11.09 %) and 3-indoleacetic acid (IAA) (3.36 %-46.91 %). Furthermore, peroxidase (POD) activity and methionine sulfoxide reductase activity was increased by 29.15 %-74.13 % and 11.26 %-20.87 %, respectively; however, methionine sulfoxide reductase related protein repairing gene MSRA4 was down-regulated by 17.93 % -41.04 % under T, compared to CK. Besides, US treatment also improved soluble protein in flag leaf (0.92 %-40.79 %), photosynthesis (3.37 %-16.46 %), biomass (5.17 %-31.87 %), as well as 2-acetyl-1-pyrroline content (4.77 %-15.48 %) in rice grains. In addition, multivariate analysis showed that the dry weight at the maturity stage were significantly related to the POD, glutathione reductase (GR) activity, IAA, and abscisic acid (ABA) content while germination rate was positively related to the GR activity, ABA content, and yield, but which were negatively related to the IAA and gibberellic acid content.

Improving the second-tier classification of methylmalonic acidemia patients using a machine learning ensemble method.

INTRODUCTION: Methylmalonic acidemia (MMA) is a disorder of autosomal recessive inheritance, with an estimated prevalence of 1:50,000. First-tier clinical diagnostic tests often return many false positives [five false positive (FP): one true positive (TP)]. In this work, our goal was to refine a classification model that can minimize the number of false positives, currently an unmet need in the upstream diagnostics of MMA. METHODS: We developed machine learning multivariable screening models for MMA with utility as a secondary-tier tool for false positives reduction. We utilized mass spectrometry-based features consisting of 11 amino acids and 31 carnitines derived from dried blood samples of neonatal patients, followed by additional ratio feature construction. Feature selection strategies (selection by filter, recursive feature elimination, and learned vector quantization) were used to determine the input set for evaluating the performance of 14 classification models to identify a candidate model set for an ensemble model development. RESULTS: Our work identified computational models that explore metabolic analytes to reduce the number of false positives without compromising sensitivity. The best results [area under the receiver operating characteristic curve (AUROC) of 97%, sensitivity of 92%, and specificity of 95%] were obtained utilizing an ensemble of the algorithms random forest, C5.0, sparse linear discriminant analysis, and autoencoder deep neural network stacked with the algorithm stochastic gradient boosting as the supervisor. The model achieved a good performance trade-off for a screening application with 6% false-positive rate (FPR) at 95% sensitivity, 35% FPR at 99% sensitivity, and 39% FPR at 100% sensitivity. CONCLUSIONS: The classification results and approach of this research can be utilized by clinicians globally, to improve the overall discovery of MMA in pediatric patients. The improved method, when adjusted to 100% precision, can be used to further inform the diagnostic process journey of MMA and help reduce the burden for patients and their families.

Biochar derived from alkali-treated sludge residue regulates anaerobic digestion: Enhancement performance and potential mechanisms.

Biochar produced from bio-wastes has been widely used to promote the performance of anaerobic digestion. Waste activated sludge (WAS) is considered as a kind of popular precursor for biochar preparation, but the abundant resources in WAS were neglected previously. In this study, the roles of biochar prepared from raw, pretreated, and fermented sludge on anaerobic digestion were investigated. That is, parts of carbon sources and nutrients like polysaccharides, proteins, and phosphorus were firstly recovered after sludge pretreatment or fermentation, and then the sludge residuals were used as raw material to prepare biochar. The methane yield improved by 22.1% with adding the biochar (AK-BC) prepared by sludge residual obtained from alkaline pretreatment. Mechanism study suggested that the characteristics of AK-BC like specific surface area and defect levels were updated. Then, the conversion performance of intermediate metabolites and electro-activities of extracellular polymeric substances were up-regulated. As a result, the activity of electron transfer was increased with the presence of AK-BC, with increase ratio of 21.4%. In addition, the electroactive microorganisms like Anaerolineaceae and Methanosaeta were enriched with the presence of AK-BC, and the potential direct interspecies electron transfer was possibly established. Moreover, both aceticlastic and CO2-reducing methanogenesis pathways were improved by up-regulating related enzymes. Therefore, the proposed strategy can not only obtain preferred biochar but also recover abundant resources like carbon source, nutrients, and bioenergy.

EZH2/G9a interact to mediate drug resistance in non-small-cell lung cancer by regulating the SMAD4/ERK/c-Myc signaling axis.

Drug resistance is the leading problem in non-small-cell lung cancer (NSCLC) therapy. The contribution of histone methylation in mediating malignant phenotypes of NSCLC is well known. However, the role of histone methylation in NSCLC drug-resistance mechanisms remains unclear. Here, our data show that EZH2 and G9a, two histone methyltransferases, are involved in the drug resistance of NSCLC. Gene manipulation results indicate that the combination of EZH2 and G9a promotes tumor growth and mediates drug resistance in a complementary manner. Importantly, clinical study demonstrates that co-expression of both enzymes predicts a poor outcome in patients with NSCLC. Mechanistically, G9a and EZH2 interact and promote the silencing of the tumor-suppressor gene SMAD4, activating the ERK/c-Myc signaling pathway. Finally, SU08, a compound targeting both EZH2 and G9a, is demonstrated to sensitize resistant cells to therapeutic drugs by regulating the SMAD4/ERK/c-Myc signaling axis. These findings uncover the resistance mechanism and a strategy for reversing NSCLC drug resistance.

Global-scale magnetosphere convection driven by dayside magnetic reconnection.

Plasma convection on a global scale is a fundamental feature of planetary magnetosphere. The Dungey cycle explains that steady-state convection within the closed part of the magnetosphere relies on magnetic reconnection in the nightside magnetospheric tail. Nevertheless, time-dependent models of the Dungey cycle suggest an alternative scenario where magnetospheric convection can be solely driven by dayside magnetic reconnection. In this study, we provide direct evidence supporting the scenario of dayside-driven magnetosphere convection. The driving process is closely connected to the evolution of Region 1 and Region 2 field-aligned currents. Our global simulations demonstrate that intensified magnetospheric convection and field-aligned currents progress from the dayside to the nightside within 10-20 minutes, following a southward turning of the interplanetary magnetic field. Observational data within this short timescale also reveal enhancements in both magnetosphere convection and the ionosphere's two-cell convection. These findings provide insights into the mechanisms driving planetary magnetosphere convection, with implications for the upcoming Solar-Wind-Magnetosphere-Ionosphere Link Explorer (SMILE) mission.

A co-located QTL for seven spike architecture-related traits shows promising breeding use potential in common wheat (Triticum aestivum L.).

KEY MESSAGE: A co-located novel QTL for TFS, FPs, FMs, FFS, FFPs, KWS, and KWPs with potential of improving wheat yield was identified and validated. Spike-related traits, including fertile florets per spike (FFS), kernel weight per spike (KWS), total florets per spike (TFS), florets per spikelet (FPs), florets in the middle spikelet (FMs), fertile florets per spikelet (FFPs), and kernel weight per spikelet (KWPs), are key traits in improving wheat yield. In the present study, quantitative trait loci (QTL) for these traits evaluated under various environments were detected in a recombinant inbred line population (msf/Chuannong 16) mainly genotyped using the 16 K SNP array. Ultimately, we identified 60 QTL, but only QFFS.sau-MC-1A for FFS was a major and stably expressed QTL. It was located on chromosome arm 1AS, where loci for TFS, FPs, FMs, FFS, FFPs, KWS, and KWPs were also simultaneously co-mapped. The effect of QFFS.sau-MC-1A was further validated in three independent segregating populations using a Kompetitive Allele-Specific PCR marker. For the co-located QTL, QFFS.sau-MC-1A, the presence of a positive allele from msf was associate with increases for all traits: + 12.29% TFS, + 10.15% FPs, + 13.97% FMs, + 17.12% FFS, + 14.75% FFPs, + 22.17% KWS, and + 19.42% KWPs. Furthermore, pleiotropy analysis showed that the positive allele at QFFS.sau-MC-1A simultaneously increased the spike length, spikelet number per spike, and thousand-kernel weight. QFFS.sau-MC-1A represents a novel QTL for marker-assisted selection with the potential for improving wheat yield. Four genes, TraesCS1A03G0012700, TraesCS1A03G0015700, TraesCS1A03G0016000, and TraesCS1A03G0016300, which may affect spike development, were predicted in the physical interval harboring QFFS.sau-MC-1A. Our results will help in further fine mapping QFFS.sau-MC-1A and be useful for improving wheat yield.

Effects of the Timing of Intense Physical Activity on Hypertension Risk in a General Population: A UK-Biobank Study.

PURPOSE OF REVIEW: It has recently been suggested that the timing of exercise is important in the subsequent development of hypertension. We used the UK Biobank database which prospectively collates data in over 500,000 people aged between 40 and 69 years to determine the relationship between the chronoactivity pattern of exercise and the risk of incident hypertension. RECENT FINDINGS: We analyzed data from 70,617 participants with 7-day Axivity AX3 triaxial accelerometry information available. Comparisons were made by a K-means clustering analysis separating groups according to the daily timing of physical activity and intensity. Subgroup, sensitivity analyses, and Cox proportional hazard model were performed. The mean age of the cohort was 61.17 (± 7.89) years with 40.05% men, and there was a mean follow-up of 7.54 (± 1.65) years. Participants were separated into 4 clusters with 6341 developing hypertension. Cluster 1 (early morning physical activity) and Cluster 2 (early morning and later physical activity) had a significantly reduced risk of incident hypertension (adjusted HR 0.870 [95%CI 0.812-0.932) vs. 0.895 [95%CI 0.825-0.972], respectively) when compared with Cluster 3 (physical activity intensity spread evenly throughout the day). Cluster 1 and Cluster 2 cases with High Intensity physical activity had a lower risk of hypertension; however, Low Intensity physical activity in Cluster 1 still reduced the risk of incident hypertension. There was a lower risk of hypertension in Cluster 1 and Cluster 2 in both morning and evening sleep chronotypes. The development of incident hypertension is significantly reduced in those who engage in some level of physical activity earlier in the day. Hypertension (high blood pressure) is a global problem with a high economic health burden that has been shown to be a major risk factor for diabetes, cardiovascular, and kidney disease. Our study has used a large maintained UK biological database to determine the impact of physical exercise on reducing the subsequent development of hypertension during follow-up from data provided by more than 70,000 participants. When we segregated patients into clusters of exercise timing, we found that the risk of developing hypertension over time was reduced for patients who performed exercise earlier in the morning than at other times of the day. This benefit was still evident even when the intensity of regular physical activity was low.

A-D-A Type Nonfullerene Acceptors Synthesized by Core Segmentation and Isomerization for Realizing Organic Solar Cells with Low Nonradiative Energy Loss.

Reducing non-radiative recombination energy loss (ΔEnonrad ) in organic solar cells (OSCs) has been considered an effective method to improve device efficiency. In this study, the backbone of PTBTT-4F/4Cl is divided into D1-D2-D3 segments and reconstructed. The isomerized TPBTT-4F/4Cl obtains stronger intramolecular charge transfer (ICT), thus leading to elevated highest occupied molecular orbital (HOMO) energy level and reduced bandgap (Eg ). According to ELoss  = Eg- qVOC , the reduced Eg and enhanced open circuit voltage (VOC ) result in lower ELoss , indicating that ELoss has been effectively suppressed in the TPBTT-4F/4Cl based devices. Furthermore, compared to PTBTT derivatives, the isomeric TPBTT derivatives exhibit more planar molecular structure and closer intermolecular stacking, thus affording higher crystallinity of the neat films. Therefore, the reduced energy disorder and corresponding lower Urbach energy (Eu ) of the TPBTT-4F/4Cl blend films lead to low ELoss and high charge-carrier mobility of the devices. As a result, benefitting from synergetic control of molecular stacking and energetic offsets, a maximum power conversion efficiency (PCE) of 15.72% is realized from TPBTT-4F based devices, along with a reduced ΔEnonrad of 0.276 eV. This work demonstrates a rational method of suppressing VOC loss and improving the device performance through molecular design engineering by core segmentation and isomerization.

Biomarker Screening by LCMS and Liquid Chip Technology in Acute Aortic Dissection.

BACKGROUND: Aortic dissection (AD) is a serious disease. Previous study, the use of peripheral blood biomarkers to diagnose AD showed strong clinical feasibility, but the possible molecular mechanism is unclear. METHODS: Sera from 79 healthy subjects, 73 patients with well-established AD, and 74 patients with well-established acute myocardial infarction (AMI) were investigated by Liquid Chromatograph-Mass Spectrometer to detect metabolites (AFMK, Glycerophosphocholine, Inosine, SPH). The cell factor expression in the 3 group were detected by Liquid Chip Technology. RESULTS: The serum content trends of 4 metabolic indexes in patients with AMI and AD group were used as the diagnostic models, and the effective diagnosis rate was 97.8%. The diagnosis rate is 89.8% in distinguishing patients with AMI from patients with AD. The expression in serum of the 3 groups showed that there were significant differences in the expression of 23 cytokines. By correlation analysis, it was found that miP-1, IL-7, MIP-1ß, EGF and other cytokines were significantly correlated with the 4 metabolic molecules. CONCLUSIONS: AFMK, Glycerophosphocholine, Inosine, Sphingfungin B (SPH) metabolites are potential biomarkers for AD, and the influence of related metabolic process may be related to the expression of miP-1, IL-7, MIP-1ß, EGF, and other cytokines.

Distributed Subgradient Method With Random Quantization and Flexible Weights: Convergence Analysis.

The distributed subgradient (DSG) method is a widely used algorithm for coping with large-scale distributed optimization problems in machine-learning applications. Most existing works on DSG focus on ideal communication between cooperative agents, where the shared information between agents is exact and perfect. This assumption, however, can lead to potential privacy concerns and is not feasible when wireless transmission links are of poor quality. To meet this challenge, a common approach is to quantize the data locally before transmission, which avoids exposure of raw data and significantly reduces the size of the data. Compared with perfect data, quantization poses fundamental challenges to maintaining data accuracy, which further impacts the convergence of the algorithms. To overcome this problem, we propose a DSG method with random quantization and flexible weights and provide comprehensive results on the convergence of the algorithm for (strongly/weakly) convex objective functions. We also derive the upper bounds on the convergence rates in terms of the quantization error, the distortion, the step sizes, and the number of network agents. Our analysis extends the existing results, for which special cases of step sizes and convex objective functions are considered, to general conclusions on weakly convex cases. Numerical simulations are conducted in convex and weakly convex settings to support our theoretical results.

Riboflavin improves grain yield, 2-acetyl-1-pyrroline accumulation, and antioxidative properties of fragrant rice.

BACKGROUND: Riboflavin, a vital water-soluble vitamin with antioxidative activity, plays a critical role in maintaining overall bodily health and defense responses. However, its impact on fragrant rice yield and aroma remains unexplored. RESULTS: In a 2022 pot experiment with Meixiangzhan and Yuxiangyouzhan fragrant rice cultivars, we applied riboflavin foliar treatments at concentrations of 0 (CK), 10 (R10), 20 (R20), and 40 (R40) mg L-1 during the initial heading stage. Riboflavin increased rice yield, 2-acetyl-1-pyrroline (2-AP) content, and antioxidative properties. It boosted 2-AP level by 13.1-50.1% for Meixiangzhan and 22.3-35.3% for Yuxiangyouzhan, with the highest levels in R20 and R10 treatments. This increase is significantly correlated with elevated levels of proline, pyrroline-5-carboxylic acid, pyrroline, and methylglyoxal, as well as heightened enzyme activities, including those of proline dehydrogenase, ornithine aminotransferase, and pyrroline-5-carboxylic acid synthetase (P5CS). The R20 treatment resulted in the highest yield due to an improved seed-setting rate. Importantly, a positive correlation emerged between 2-AP content and yield, both significantly linked to superoxide dismutase, proline, hydrogen peroxide, P5CS, catalase, and pyrroline. CONCLUSION: Riboflavin maintained enzyme activities, regulated substance synthesis pathways, and increased 2-AP and yield, especially in the R20 treatment. These insights advance fragrant rice production theory by uncovering riboflavin's role in the development of fragrant rice. © 2023 Society of Chemical Industry.

Water-Soluble Dual-Channel Fluorescent Probe for Sensitive Detection of Biothiols In Vitro and In Vivo.

Benefiting from high spatiotemporal resolution, deep tissue penetration, and excellent sensitivity, fluorescence imaging technology has been widely applied in cancer diagnosis and treatment. In recent years, a large number of fluorescent probes for monitoring the levels of endogenous biothiols have been reported, which have significant implications for cancer diagnosis and treatment. However, most probes still suffer from poor biological compatibility and easy attachment by the environment. This work presents the development of a water-soluble dual-channel fluorescent probe, named MAL-NBD, for sensitively detecting biothiols. Nonfluorescent MAL-NBD is transformed into fluorescent groups MAL and NBD-SR/NR through nucleophilic substitution by biologically active thiols, producing dual-channel fluorescence signals for precise detection of biologically active thiols. Taking advantage of the excellent biocompatibility and low biotoxicity, MAL-NBD is successfully used for imaging HeLa cancer cells and zebrafish larvae, promoting its potential application for the precise detection of biological thiols involved in physiological and pathological processes.