Influence of microorganisms on flavor substances and functional components of sojae semen praeparatum during fermentation: A study integrating comparative metabolomics and high-throughput sequencing.

Sojae semen praeparatum (SSP), a fermented product known for its distinctive flavor and medicinal properties, undergoes a complex fermentation process due to the action of various microorganisms. Despite its widespread use, the effect of these microorganisms on the flavor compounds and functional components of SSP remains poorly understood. This study aimed to shed light on this aspect by identifying 20 metabolites as potential key flavor substances in SSP. Moreover, glycine and lysine were identified as crucial flavor substances. Additionally, 24 metabolites were identified as key functional components. The dominant microorganisms involved in the fermentation process were examined, revealing six genera of fungi and 12 genera of bacteria. At the species level, 16 microorganisms were identified as dominant through metagenome sequencing. Spearman correlation analysis demonstrated a strong association between dominant microorganisms and both flavor substances and functional components. Furthermore, the study validated the significance of four core functional microorganisms in improving the flavor and quality of SSP. This comprehensive exploration of functional microorganisms of SSP on key flavor substances/functional components during SSP fermentation. The study findings serve as a valuable reference for enhancing the overall flavor and quality of SSP.

[Rapid determination of 15 N-nitrosamines in air-dried yak meat using one-step QuEChERS-gas chromatography-triple quadrupole mass spectrometry].

A method based on gas chromatography-triple quadrupole mass spectrometry (GC-MS/MS) coupled with one-step QuEChERS technique was developed for the simultaneous determination of 15 N-nitrosamines in air-dried yak meat. The hydration volume, extraction solvent, extracting salt, and cleaning material were optimized according to the characteristics of the N-nitrosamines and sample matrix. The optimized conditions were as follows: 10 mL of purified water for sample hydration, acetonitrile as the extraction solvent for the sample after hydration, 4.0 g of anhydrous MgSO4 and 1.0 g of NaCl as extracting salts, 500 mg of MgSO4+25 mg of C18+50 mg of PSA as cleaning materials. Favorable recoveries of the 15 N-nitrosamines were obtained when the extraction solution was incompletely dried. Thus, the final extract was dried to below 0.5 mL under a mild nitrogen stream and then redissolved to 0.5 mL with acetonitrile. After filtration, 200 µL of the sample was transferred to an autosampler vial for GC-MS/MS analysis. The 15 N-nitrosamines were determined using GC-MS/MS on a DB-HeavyWAX column (30 m×0.25 mm×0.25 µm) with an electron impact ion source in multiple-reaction monitoring (MRM) mode, and quantified using an external standard method. Under the optimized experimental conditions, the results showed that the calibration curves exhibited good linearities for the 15 N-nitrosamines, with correlation coefficients (r2) greater than 0.9990. The limits of detection (LODs) and the limits of quantification (LOQs) ranged from 0.05 to 0.20 µg/kg and from 0.10 to 0.50 µg/kg, respectively. At spiked levels of 1LOQ, 2LOQ, and 10LOQ, the average recoveries were 79.4%-102.1%, 80.6%-109.5%, and 83.0%-110.6%, respectively, and the relative standard deviations were in the range of 0.8%-16.0%. The low matrix effects of the 15 N-nitrosamines indicated the high sensitivity of the proposed method. The method was applied to detect representative commercial air-dried yak meat samples obtained using different processing techniques. Seven N-nitrosamines, including N-nitrosodimethylamine, N-nitrosodiisobutylamine, N-nitrosodibutylamine, N-methyl-N-phenylnitrous amide, N-ethyl-N-nitrosoaniline, N-nitrosopyrrolidine, and N-nitrosodiphenylamine were detected in all samples. The average contents of the seven N-nitrosamines was 0.08-20.18 µg/kg. The detection rates and average contents of the N-nitrosamines in cooked air-dried yak meat samples were higher than those in traditional raw air-dried yak meat samples. Compared with the manual QuEChERS method, the one-step QuEChERS method developed integrated the extraction and clean-up procedures into one single run, and the detection efficiency was considerably improved. The developed method is simple, rapid, highly sensitive, and insusceptible to human errors. Thus, it is useful for the determination of N-nitrosamines in air-dried yak meat and can be extended to the qualitative and quantitative analysis of N-nitrosamines in other meat products. It also provides method support and a data reference for the general determination of N-nitrosamines, which is of great significance for food safety.

A Single Component, Single Layer Flexile Foam Evaporator with the Higher Efficiency for Water Generation.

One of the greenest and promising ways to solve the problem of freshwater crisis is surface solar steam generation from seawater. A great number of photothermal materials with multi-component and multi-layered delicate yet complex structures often suffer from either low evaporation rate or high energy loss. Here, this work presents a single component foam evaporator with steam generation rate of up to 4.32 kg m-2 h-1 under 1 sun irradiation. The evaporator is constructed from an aniline oligomer as a single light-absorbing component, covalent linked with polyethylene glycol to form a monolithic polymer foam. Floating on the seawater, the foam has absorbance of 99.5% over the entire solar spectral range and low thermal conductivity (0.0077 W K-1m-1) that effectively retains heat in the material and at the interface. After 3 months of continuous outdoor natural sunlight irradiation, the evaporator maintains a stable and durable evaporation rate. Moreover, the materials have good mechanical properties (7.48 MPa young's modulus and 57.38% elongation at break) and excellent chemical resistance in 10 common organic solvents and aqueous solutions of pH = 1 to 14. This study provides a new system and strategy for desalination, steam power generation, treatment of polluted water and sewage, etc.

First-Principles Study of Discharge Products and Their Stability for Lithium-Nitrogen Batteries.

Li-N2 batteries present a relatively novel approach to N2 immobilization, and an advanced N2/Li3N cycling method is introduced in this study. The low operating overpotential of metal-air batteries is quite favorable to their stable cycling performance, providing a prospect for the development of a new type of battery with extreme voltage. The battery system of Li-N2 uses N2 as the positive electrode, lithium metal as the negative electrode, and a conductive medium containing soluble lithium salts as the electrolyte. In accordance with its voltage-distribution trend, a variety of lithium-nitrogen molecule intermediates are produced during the discharge process. There is a lack of theoretical description of material changes at the microscopic level during the discharge process. In this paper, the first-principles approach is used to simulate and analyze possible material changes during the discharge process of Li-N2 batteries. The discharge process is simulated on a 4N-graphene anode substrate model, and simulations of its electrostatic potential, Density of States (DOS), HOMO (Highest Occupied Molecular Orbital) and LUMO (Lowest Unoccupied Molecular Orbital) aspects confirm that the experimentally found Li3N becomes the final stabilized product of the Li-N2 battery. It can also be seen in the density of states that graphene with adsorption of 4N transforms from semiconducting to metallic properties. In addition, the differential charge also indicates that the Li-N2 material has a strong adsorption effect on the substrate, which can play the dual role of electricity storage and nitrogen fixation.

Turtle peptide and its derivative peptide ameliorated DSS-induced ulcerative colitis by inhibiting inflammation and modulating the composition of the gut microbiota.

Ulcerative colitis (UC) is a recurrent intestinal disease with an increasing incidence worldwide that seriously affects the life of patients. Turtle peptide (TP) is a bioactive peptide extracted from turtles that has anti-inflammatory, antioxidant and anti-aging properties. However, studies investigating the effect of TP on the progression of UC are lacking. The aim of this study was to investigate effects and underlying mechanisms of TP and its derivative peptide GPAGPIGPV (GP-9) in alleviating UC in mice. The results showed that 500 mg/kg TP treatment significantly ameliorated colitis symptoms and oxidative stress in UC mice. TP alleviated intestinal barrier damage in UC mice by promoting mucosal repair and increasing the expression of tight junction proteins (ZO1, occludin and claudin-1). TP also modulated the composition of the gut microbiota by increasing the abundance of the beneficial bacteria Anaerotignum, Prevotellaceae_UCG-001, Alistipes, and Lachno-spiraceae_NK4A136_group and decreasing the abundance of the harmful bacteria Prevotella_9 and Parasutterella. Furthermore, we characterized the peptide composition of TP and found that GP-9 ameliorated the symptoms of dextran sodium sulfate (DSS)-induced colitis in mice by inhibiting the TLR4/NF-κB signaling pathway. In conclusion, TP and its derivative peptides ameliorated DSS-induced ulcerative colitis by inhibiting the expression of inflammatory factors and modulating the composition of the intestinal microbiota; this study provides a theoretical basis for the application of TP and its derivative peptides for their anti-inflammatory activity.

Parallel wavelength-division-multiplexed signal transmission and dispersion compensation enabled by soliton microcombs and microrings.

The proliferation of computation-intensive technologies has led to a significant rise in the number of datacenters, posing challenges for high-speed and power-efficient datacenter interconnects (DCIs). Although inter-DCIs based on intensity modulation and direct detection (IM-DD) along with wavelength-division multiplexing technologies exhibit power-efficient and large-capacity properties, the requirement of multiple laser sources leads to high costs and limited scalability, and the chromatic dispersion (CD) restricts the transmission length of optical signals. Here we propose a scalable on-chip parallel IM-DD data transmission system enabled by a single-soliton Kerr microcomb and a reconfigurable microring resonator-based CD compensator. We experimentally demonstrate an aggregate line rate of 1.68 Tbit/s over a 20-km-long SMF. The extrapolated energy consumption for CD compensation of 40-km-SMFs is ~0.3 pJ/bit, which is calculated as being around 6 times less than that of the commercial 400G-ZR coherent transceivers. Our approach holds significant promise for achieving data rates exceeding 10 terabits.

Low-abundance proteins-based label-free SERS approach for high precision detection of liver cancer with different stages.

BACKGROUND: Surface-enhanced Raman scattering (SERS) technology have unique advantages of rapid, simple, and highly sensitive in the detection of serum, it can be used for the detection of liver cancer. However, some protein biomarkers in body fluids are often present at ultra-low concentrations and severely interfered with by the high-abundance proteins (HAPs), which will affect the detection of specificity and accuracy in cancer screening based on the SERS immunoassay. Clearly, there is a need for an unlabeled SERS method based on low abundance proteins, which is rapid, noninvasive, and capable of high precision detection and screening of liver cancer. RESULTS: Serum samples were collected from 60 patients with liver cancer (27 patients with stage T1 and T2 liver cancer, 33 patients with stage T3 and T4 liver cancer) and 40 healthy volunteers. Herein, immunoglobulin and albumin were separated by immune sorption and Cohn ethanol fractionation. Then, the low abundance protein (LAPs) was enriched, and high-quality SERS spectral signals were detected and obtained. Finally, combined with the principal component analysis-linear discriminant analysis (PCA-LDA) algorithm, the SERS spectrum of early liver cancer (T1-T2) and advanced liver cancer (T3-T4) could be well distinguished from normal people, and the accuracy rate was 98.5% and 100%, respectively. Moreover, SERS technology based on serum LAPs extraction combined with the partial least square-support vector machine (PLS-SVM) successfully realized the classification and prediction of normal volunteers and liver cancer patients with different tumor (T) stages, and the diagnostic accuracy of PLS-SVM reached 87.5% in the unknown testing set. SIGNIFICANCE: The experimental results show that the serum LAPs SERS detection combined with multivariate statistical algorithms can be used for effectively distinguishing liver cancer patients from healthy volunteers, and even achieved the screening of early liver cancer with high accuracy (T1 and T2 stage). These results showed that serum LAPs SERS detection combined with a multivariate statistical diagnostic algorithm has certain application potential in early cancer screening.

RtNAC055 promotes drought tolerance via a stomatal closure pathway linked to methyl jasmonate/hydrogen peroxide signaling in Reaumuria trigyna.

The stomata regulate CO2 uptake and efficient water usage, thereby promoting drought stress tolerance. NAC proteins (NAM, ATAF1/2, and CUC2) participate in plant reactions following drought stress, but the molecular mechanisms underlying NAC-mediated regulation of stomatal movement are unclear. In this study, a novel NAC gene from Reaumuria trigyna, RtNAC055, was found to enhance drought tolerance via a stomatal closure pathway. It was regulated by RtMYC2 and integrated with jasmonic acid signaling and was predominantly expressed in stomata and root. The suppression of RtNAC055 could improve jasmonic acid and H2O2 production and increase the drought tolerance of transgenic R. trigyna callus. Ectopic expression of RtNAC055 in the Arabidopsis atnac055 mutant rescued its drought-sensitive phenotype by decreasing stomatal aperture. Under drought stress, overexpression of RtNAC055 in poplar promoted ROS (H2O2) accumulation in stomata, which accelerated stomatal closure and maintained a high photosynthetic rate. Drought upregulated the expression of PtRbohD/F, PtP5CS2, and PtDREB1.1, as well as antioxidant enzyme activities in heterologous expression poplars. RtNAC055 promoted H2O2 production in guard cells by directly binding to the promoter of RtRbohE, thus regulating stomatal closure. The stress-related genes RtDREB1.1/P5CS1 were directly regulated by RtNAC055. These results indicate that RtNAC055 regulates stomatal closure by maintaining the balance between the antioxidant system and H2O2 level, reducing the transpiration rate and water loss, and improving photosynthetic efficiency and drought resistance.

Secreted Clusterin Inhibits Tumorigenesis by Modulating Tumor Cell and Macrophage in Human Meningioma.

BACKGROUND: Meningioma is the most common primary intracranial tumor with high frequency of postoperative recurrence, yet the biology of meningioma malignancy process is still obscure. METHODS: To identify potential therapeutic targets and tumor suppressors, we performed single-cell transcriptome analysis through meningioma malignancy, which included 18 samples spanning normal meninges, benign and high grade in situ tumors, and lung metastases, for extensive transcriptome characterization. Tumor suppressor candidate gene and molecular mechanism were functionally validated at animal model and cellular level. RESULTS: Comprehensive analysis and validation in mice and clinical cohorts indicated Clusterin (CLU) had suppressive function for meningioma tumorigenesis and malignancy by inducing mitochondria damage and triggering type I interferon pathway dependent on its secreted isoform, and the inhibition effect was enhanced by TNFα as TNFα also induced type I interferon pathway. The expression of CLU was upregulated by histone deacetylase inhibition. Meanwhile, both intra- and extra-cellular CLU overexpression enhanced macrophage polarization towards M1 phenotype and TNFα production, thus promoted tumor killing and phagocytosis. CONCLUSIONS: CLU might be a key brake of meningioma malignance by synchronous modulating tumor cells and their microenvironment. Our work provides comprehensive insights into meningioma malignancy and a potential therapeutic strategy.

Enhanced multi-label cardiology diagnosis with channel-wise recurrent fusion.

The timely detection of abnormal electrocardiogram (ECG) signals is vital for preventing heart disease. However, traditional automated cardiology diagnostic methods have the limitation of being unable to simultaneously identify multiple diseases in a segment of ECG signals, and do not consider the potential correlations between the 12-lead ECG signals. To address these issues, this paper presents a novel network architecture, denoted as Branched Convolution and Channel Fusion Network (BCCF-Net), designed for the multi-label diagnosis of ECG cardiology to achieve simultaneous identification of multiple diseases. Among them, the BCCF-Net incorporates the Channel-wise Recurrent Fusion (CRF) network, which is designed to enhance the ability to explore potential correlation information between 12 leads. Furthermore, the utilization of the squeeze and excitation (SE) attention mechanism maximizes the potential of the convolutional neural network (CNN). In order to efficiently capture complex patterns in space and time across various scales, the multi branch convolution (MBC) module has been developed. Through extensive experiments on two public datasets with seven subtasks, the efficacy and robustness of the proposed ECG multi-label classification framework have been comprehensively evaluated. The results demonstrate the superior performance of the BCCF-Net compared to other state-of-the-art algorithms. The developed framework holds practical application in clinical settings, allowing for the refined diagnosis of cardiac arrhythmias through ECG signal analysis.

Prognostic Factors and Construction of Nomogram Prediction Model of Lung Cancer Patients Using Clinical and Blood Laboratory Parameters.

Objective: This work aimed to explore the prognostic risk factors of lung cancer (LC) patients and establish a line chart prediction model. Methods: A total of 322 LC patients were taken as the study subjects. They were randomly divided into a training set (n = 202) and a validation set (n = 120). Basic information and laboratory indicators were collected, and the progression-free survival (PFS) and overall survival (OS) were followed up. Single-factor and cyclooxygenase (COX) multivariate analyses were performed on the training set to construct a Nomogram prediction model, which was validated with 120 patients in the validation set, and Harrell's consistency was analyzed. Results: Single-factor analysis revealed significant differences in PFS (P<0.05) between genders, body mass index (BMI), carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), squamous cell carcinoma antigen (SCCA), treatment methods, treatment response evaluation, smoking status, presence of pericardial effusion, and programmed death ligand 1 (PD-L1) at 0 and 1-50%. Significant differences in OS (P<0.05) were observed for age, tumor location, treatment methods, White blood cells (WBC), uric acid (UA), CA125, pro-gastrin-releasing peptide (ProGRP), SCCA, cytokeratin fragment 21 (CYFRA21), and smoking status. COX analysis identified male gender, progressive disease (PD) as treatment response, and SCCA > 1.6 as risk factors for LC PFS. The consistency indices of the line chart models for predicting PFS and OS were 0.782 and 0.772, respectively. Conclusion: Male gender, treatment response of PD, and SCCA > 1.6 are independent risk factors affecting the survival of LC patients. The PFS line chart model demonstrates good concordance.

Danggui-Shaoyao-San (DSS) ameliorates the progression of osteoarthritis via suppressing the NF-κB signaling pathway: an in vitro and in vivo study combined with bioinformatics analysis.

BACKGROUND: Osteoarthritis (OA) is a common chronic age-related joint disease characterized primarily by inflammation of synovial membrane and degeneration of articular cartilage. Accumulating evidence has demonstrated that Danggui-Shaoyao-San (DSS) exerts significant anti-inflammatory effects, suggesting that it may play an important role in the treatment of knee osteoarthritis (KOA). METHODS: In the present study, DSS was prepared and analyzed by high-performance liquid chromatography (HPLC). Bioinformatics analyses were carried out to uncover the functions and possible molecular mechanisms by which DSS against KOA. Furthermore, the protective effects of DSS on lipopolysaccharide (LPS)-induced rat chondrocytes and cartilage degeneration in a rat OA model were investigated in vivo and in vitro. RESULTS: In total, 114 targets of DSS were identified, of which 60 candidate targets were related to KOA. The target enrichment analysis suggested that the NF-κB signaling pathway may be an effective mechanism of DSS. In vitro, we found that DSS significantly inhibited LPS-induced upregulation of inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), interleukin-6 (IL-6), matrix metalloproteinase-3 (MMP3), and matrix metalloproteinase-13 (MMP13). Meanwhile, the degradation of collagen II was also reversed by DSS. Mechanistically, DSS dramatically suppressed LPS-induced activation of the nuclear factor kappa B (NF-κB) signaling pathway. In vivo, DSS treatment prevented cartilage degeneration in a rat OA model. CONCLUSIONS: DSS could ameliorate the progression of OA through suppressing the NF-κB signaling pathway. Our findings indicate that DSS may be a promising therapeutic approach for the treatment of KOA.

A novel computational tool for tracking cancer energy hijacking from immune cells.

Recent studies revealed a new biological process that malignant cancer cells hijack mitochondria from nearby T cells, providing another potential mechanism for immune evasion. We further confirmed this process at the single-cell genomic level through MERCI, a novel algorithm for tracking mitochondrial (MT) transfer. Applied to human cancer samples, MERCI identified a new cancer phenotype linked to MT hijacking, correlating with rapid tumour proliferation and poor patient survival. This discovery offers insights into the limitations of current cancer immunotherapies and suggests new therapeutic avenues targeting MT transfer to enhance cancer treatment efficacy.

The fatty acid-related gene signature stratifies poor prognosis patients and characterizes TIME in cutaneous melanoma.

BACKGROUND: The aim of this study is to build a prognostic model for cutaneous melanoma (CM) using fatty acid-related genes and evaluate its capacity for predicting prognosis, identifying the tumor immune microenvironment (TIME) composition, and assessing drug sensitivity. METHODS: Through the analysis of transcriptional data from TCGA-SKCM and GTEx datasets, we screened for differentially expressed fatty acids-related genes (DEFAGs). Additionally, we employed clinical data from TCGA-SKCM and GSE65904 to identify genes associated with prognosis. Subsequently, utilizing all the identified prognosis-related fatty acid genes, we performed unsupervised clustering analysis using the ConsensusClusterPlus R package. We further validated the significant differences between subtypes through survival analysis and pathway analysis. To predict prognosis, we developed a LASSO-Cox prognostic signature. This signature's predictive ability was rigorously examined through multivariant Cox regression, survival analysis, and ROC curve analysis. Following this, we constructed a nomogram based on the aforementioned signature and evaluated its accuracy and clinical utility using calibration curves, cumulative hazard rates, and decision curve analysis. Using this signature, we stratified all cases into high- and low-risk groups and compared the differences in immune characteristics and drug treatment responsiveness between these two subgroups. Additionally, in this study, we provided preliminary confirmation of the pivotal role of CD1D in the TIME of CM. We analyzed its expression across various immune cell types and its correlation with intercellular communication using single-cell data from the GSE139249 dataset. RESULTS: In this study, a total of 84 DEFAGs were identified, among which 18 were associated with prognosis. Utilizing these 18 prognosis-related genes, all cases were categorized into three subtypes. Significant differences were observed between subtypes in terms of survival outcomes, the expression of the 18 DEFAGs, immune cell proportions, and enriched pathways. A LASSO-Cox regression analysis was performed on these 18 genes, leading to the development of a signature comprising 6 DEFAGs. Risk scores were calculated for all cases, dividing them into high-risk and low-risk groups. High-risk patients exhibited significantly poorer prognosis than low-risk patients, both in the training group (p < 0.001) and the test group (p = 0.002). Multivariate Cox regression analysis indicated that this signature could independently predict outcomes [HR = 2.03 (1.69-2.45), p < 0.001]. The area under the ROC curve for the training and test groups was 0.715 and 0.661, respectively. Combining risk scores with clinical factors including metastatic status and patient age, a nomogram was constructed, which demonstrated significant predictive power for 3  and 5 years patient outcomes. Furthermore, the high and low-risk subgroups displayed differences in the composition of various immune cells, including M1 macrophages, M0 macrophages, and CD8+ T cells. The low-risk subgroup exhibited higher StromalScore, ImmuneScore, and ESTIMATEScore (p < 0.001) and demonstrated better responsiveness to immune therapy for patients with PD1-positive and CTLA4-negative or positive expressions (p < 0.001). The signature gene CD1D was found to be mainly expressed in monocytes/macrophages and dendritic cells within the TIME. Through intercellular communication analysis, it was observed that cases with high CD1D expression exhibited significantly enhanced signal transductions from other immune cells to monocytes/macrophages, particularly the (HLA-A/B/C/E/F)-CD8A signaling from natural killer (NK) cells to monocytes/macrophages (p < 0.01). CONCLUSIONS: The prognostic signature constructed in this study, based on six fatty acid-related genes, exhibits strong capabilities in predicting patient outcomes, identifying the TIME, and assessing drug sensitivity. This signature can aid in patient risk stratification and provide guidance for clinical treatment strategies. Additionally, our research highlights the crucial role of CD1D in the CM's TIME, laying a theoretical foundation for future related studies.

Wavelet transform and deep learning-based obstructive sleep apnea detection from single-lead ECG signals.

Sleep apnea is a common sleep disorder. Traditional testing and diagnosis heavily rely on the expertise of physicians, as well as analysis and statistical interpretation of extensive sleep testing data, resulting in time-consuming and labor-intensive processes. To address the problems of complex feature extraction, data imbalance, and low model capacity, we proposed an automatic sleep apnea classification model (CA-EfficientNet) based on the wavelet transform, a lightweight neural network, and a coordinated attention mechanism. The signal is converted into a time-frequency image by wavelet transform and put into the proposed model for classification. The effects of input time window, wavelet transform type and data balancing on the classification performance are considered, and a cost-sensitive algorithm is introduced to more accurately distinguish between normal and abnormal breathing events. PhysioNet apnea ECG database was used for training and evaluation. The 3-min Frequency B-Spline wavelets transform of ECG signal was carried out, and Dice Loss was used to train the classification model of sleep breathing. The classification accuracy was 93.44%, sensitivity was 88.9%, specificity was 96.2% and most indexes were better than other related work.

Multi-scale representation of surface-enhanced Raman spectroscopy data for deep learning-based liver cancer detection.

The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early cancer detection, providing multiple advantages over conventional methods. The majority of existing cancer detection methods utilize multivariate statistical analysis to categorize SERS data. However, these methods are plagued by issues such as information loss during dimensionality reduction and inadequate ability to handle nonlinear relationships within the data. To overcome these problems, we first use wavelet transform with its multi-scale analysis capability to extract multi-scale features from SERS data while minimizing information loss compared to traditional methods. Moreover, deep learning is employed for classification, leveraging its strong nonlinear processing capability to enhance accuracy. In addition, the chosen neural network incorporates a data augmentation method, thereby enriching our training dataset and mitigating the risk of overfitting. Moreover, we acknowledge the significance of selecting the appropriate wavelet basis functions in SERS data processing, prompting us to choose six specific ones for comparison. We employ SERS data from serum samples obtained from both liver cancer patients and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding performance, surpassing the majority of multivariate statistical analysis and traditional machine learning classification methods, with an accuracy of 99.38 %, a sensitivity of 99.8 %, and a specificity of 97.0 %. These results indicate that the combination of SERS, wavelet transform, and deep learning has the potential to function as a non-invasive tool for the rapid detection of liver cancer.

MdbHLH93 confers drought tolerance by activating MdTyDC expression and promoting dopamine biosynthesis.

Dopamine and its biosynthesis-limiting enzyme tyrosine decarboxylase (TyDC) play a vital part in mediating plant growth and the response to drought stress. However, the underlying molecular mechanism remains poorly understood. Here, drought stress markedly induced the expression of Malus domestica bHLH93 (MdbHLH93), the apple basic helix-loop-helix transcription factor. Moreover, MdbHLH93 directly bound to the Malus domestica TyDC (MdTyDC) promoter and positively regulated its expression, which resulted in dopamine synthesis and enhanced drought tolerance. Furthermore, the additive effect of overexpressing MdbHLH93 and MdTyDC simultaneously promoted dopamine synthesis and drought tolerance in apples, while the interference of MdbHLH93 inhibited this effect, indicating that MdTyDC-regulated dopamine synthesis and drought tolerance were positively regulated by MdbHLH93. Taken together, these findings suggest the positive regulation of dopamine accumulation by MdbHLH93 through its transcriptional regulation of MdTyDC and show that increased dopamine content confers drought tolerance in apples.

MitoTracer facilitates the identification of informative mitochondrial mutations for precise lineage reconstruction.

Mitochondrial (MT) mutations serve as natural genetic markers for inferring clonal relationships using single cell sequencing data. However, the fundamental challenge of MT mutation-based lineage tracing is automated identification of informative MT mutations. Here, we introduced an open-source computational algorithm called "MitoTracer", which accurately identified clonally informative MT mutations and inferred evolutionary lineage from scRNA-seq or scATAC-seq samples. We benchmarked MitoTracer using the ground-truth experimental lineage sequencing data and demonstrated its superior performance over the existing methods measured by high sensitivity and specificity. MitoTracer is compatible with multiple single cell sequencing platforms. Its application to a cancer evolution dataset revealed the genes related to primary BRAF-inhibitor resistance from scRNA-seq data of BRAF-mutated cancer cells. Overall, our work provided a valuable tool for capturing real informative MT mutations and tracing the lineages among cells.

Comparative physiological and transcriptome analysis of leaf nitrogen fluxes in stay-green maize during the vegetative stage.

In maize, nitrogen (N) stored in leaves is an important internal source for supporting subsequent growth and development. However, the regulation of N fluxes and photosynthesis and the molecular and genotypic regulations that modify them are less clear in source leaves during the vegetative stage. This knowledge is crucial for improving N use efficiency (NUE). By using 15N labeling and transcriptome methods, an analysis of the physiological and molecular basis of leaf N import and export processes and photosynthetic N use efficiency (PNUE) was conducted in two maize hybrids (XY335 and XY696) with different stay-green characteristics during the vegetative stage. Leaf N import and export in XY696 were 45% and 33% greater than those in XY335. However, the PNUE in XY335 was 17% greater than that in XY696 due to the higher net photosynthetic rate (A) and lower SLN. Correspondingly, the chlorophyll content and photosynthesis-related enzyme (PEPc, PEPck, PPDK) activities increased by 18∼30% in XY335. Transcriptome analysis indicated that the expression levels of several N and carbon metabolism-related genes encoding Rubisco, PEPc, Nir, GS and AS were significantly increased or decreased in XY696 in parallel with enzyme activities. Moreover, there was a large difference in the expression abundance of genes encoding nitrate/nitrite transporters and transmembrane proteins. Our results suggest that two hybrids modulate leaf N fluxes and photosynthesis differently by altering gene expression and enzyme activities. Our study contributes to understanding leaf N fluxes and PNUE regulation and serves as a crucial reference for NUE improvement in maize breeding research.

Systematic investigation of mitochondrial transfer between cancer cells and T cells at single-cell resolution.

Mitochondria (MT) participate in most metabolic activities of mammalian cells. A near-unidirectional mitochondrial transfer from T cells to cancer cells was recently observed to "metabolically empower" cancer cells while "depleting immune cells," providing new insights into tumor-T cell interaction and immune evasion. Here, we leverage single-cell RNA-seq technology and introduce MERCI, a statistical deconvolution method for tracing and quantifying mitochondrial trafficking between cancer and T cells. Through rigorous benchmarking and validation, MERCI accurately predicts the recipient cells and their relative mitochondrial compositions. Application of MERCI to human cancer samples identifies a reproducible MT transfer phenotype, with its signature genes involved in cytoskeleton remodeling, energy production, and TNF-α signaling pathways. Moreover, MT transfer is associated with increased cell cycle activity and poor clinical outcome across different cancer types. In summary, MERCI enables systematic investigation of an understudied aspect of tumor-T cell interactions that may lead to the development of therapeutic opportunities.