Metabolomics analysis reveals enhanced salt tolerance in maize through exogenous Valine-Threonine-Isoleucine-Aspartic acid application.

Salt stress is a well-known abiotic constraint that hampers crop productivity, affecting more than 424 million hectares of topsoil worldwide. Applying plant growth regulators externally has proven effective in enhancing crop resilience to salt stress. Previous metabolomics studies revealed an accumulation of Valine-Threonine-Isoleucine-Aspartic acid (VTID) in salt-stressed maize seedlings, suggesting its potential to assist maize adaptation to salt stress. To explore the effectiveness of VTID in enhancing salt tolerance in maize, 10 nM VTID was applied to salt-stressed maize seedlings. The results showed a remarkable 152.29% increase in plant height and a 122.40% increase in fresh weight compared to salt-stressed seedlings. Moreover, the addition of VTID enhanced the activity of antioxidant enzymes, specifically superoxide dismutase (SOD) and catalase (CAT), while reducing the level of malondialdehyde (MDA), a marker of oxidative stress. Additionally, VTID supplementation resulted in a significant increase in osmoregulatory substances such as proline. Metabolomic analysis revealed substantial changes in the metabolite profile of maize seedlings when treated with VTID during salt stress. Differential metabolites (DMs) analysis revealed that the identified DMs primarily belonged to lipids and lipid-like molecules. The receiver operating characteristic curve and linear regression analysis determined a correlation between isodolichantoside and the height of maize seedlings under salt-stress conditions. In conclusion, these findings validate that VTID effectively regulates tolerance in maize seedlings and offers valuable insights into the potential of short peptides for mitigating salt stress.

A new modular framework for high-level application development at HEPS.

As a representative of the fourth-generation light sources, the High Energy Photon Source (HEPS) in Beijing, China, utilizes a multi-bend achromat lattice to obtain an approximately 100 times emittance reduction compared with third-generation light sources. New technologies bring new challenges to operate the storage ring. In order to meet the beam commissioning requirements of HEPS, a new framework for the development of high-level applications (HLAs) has been created. The key part of the new framework is a dual-layer physical module to facilitate the seamless fusion of physical simulation models with the real machine, allowing for fast switching between different simulation models to accommodate the various simulation scenarios. As a framework designed for development of physical applications, all variables are based on physical quantities. This allows physicists to analytically assess measurement parameters and optimize machine parameters in a more intuitive manner. To enhance both extensibility and adaptability, a modular design strategy is utilized, partitioning the entire framework into discrete modules in alignment with the requirements of HLA development. This strategy not only facilitates the independent development of each module but also minimizes inter-module coupling, thereby simplifying the maintenance and expansion of the entire framework. To simplify the development complexity, the design of the new framework is implemented using Python and is called Python-based Accelerator Physics Application Set (Pyapas). Taking advantage of Python's flexibility and robust library support, we are able to develop and iterate quickly, while also allowing for seamless integration with other scientific computing applications. HLAs for both the HEPS linac and booster have been successfully developed. During the beam commissioning process at the linac, Pyapas's ease of use and reliability have significantly reduced the time required for the beam commissioning operators. As a development framework for HLA designed for the new-generation light sources, Pyapas has the versatility to be employed with HEPS, as well as with other comparable light sources, due to its adaptability.

Automated multimodal imaging of Caenorhabditis elegans behavior in multi-well plates.

Large-scale assays of behavior in model organisms play an important role in genetic screens, drug testing, and the elucidation of gene-behavior relationships. We have developed an automated, high-throughput imaging and analysis method for assaying behaviors of the nematode C. elegans . We use high-resolution optical imaging to longitudinally record the behaviors of 96 animals at a time in multi-well plates, and computer vision software to quantify the animals' locomotor activity, behavioral states, and egg laying events. To demonstrate the capabilities of our system we used it to examine the role of serotonin in C. elegans behavior. We found that egg-laying events are preceded by a period of reduced locomotion, and that this decline in movement requires serotonin signaling. In addition, we identified novel roles of serotonin receptors SER-1 and SER-7 in regulating the effects of serotonin on egg laying across roaming, dwelling, and quiescent locomotor states. Our system will be useful for performing genetic or chemical screens for modulators of behavior.

Segmentation-free measurement of locomotor frequency in Caenorhabditis elegans using image invariants.

An animal's locomotor rate is an important indicator of its motility. In studies of the nematode C. elegans , assays of the frequency of body bending waves have often been used to discern the effects of mutations, drugs, or aging. Traditional manual methods for measuring locomotor frequency are low in throughput and subject to human error. Most current automated methods depend on image segmentation, which requires high image quality and is prone to errors. Here, we describe an algorithm for automated estimation of C. elegans locomotor frequency using image invariants, i.e., shape-based parameters that are independent of object translation, rotation, and scaling. For each video frame, the method calculates a combination of 8 Hu's moment invariants and a set of Maximally Stable Extremal Regions (MSER) invariants. The algorithm then calculates the locomotor frequency by computing the autocorrelation of the time sequence of the invariant ensemble. Results of our method show excellent agreement with manual or segmentation-based results over a wide range of frequencies. We show that compared to the segmentation method that analyzes a worm's shape, our method is more robust to low image quality. We demonstrate the system's capabilities by testing the effects of serotonin and serotonin pathway mutants on locomotor frequency.

Upregulation of UHRF1 Promotes PINK1-mediated Mitophagy to Alleviates Ferroptosis in Diabetic Nephropathy.

Diabetic nephropathy (DN) is a common diabetic complication. Studies show that mitophagy inhibition induced-ferroptosis plays a crucial role in DN progression. UHRF1 is associated with mitophagy and is highly expression in DN patients, however, the effect of UHRF1 on DN is still unclear. Thus, in this study, we aimed to investigate whether UHRF1 involves DN development by the mitophagy/ferroptosis pathway. We overexpressed UHRF1 using an adeno-associated virus 9 (AAV9) system in high-fat diet/streptozotocin-induced diabetic mice. Renal function index, pathological changes, mitophagy factors, and ferroptosis factors were detected in vivo. High-glucose cultured human renal proximal tubular (HK-2) cells were used as in vitro models to investigate the mechanism of UHRF1 in DN. We found that diabetic mice exhibited kidney damage, which was alleviated by UHRF1 overexpression. UHRF1 overexpression promoted PINK1-mediated mitophagy and inhibited the expression of thioredoxin interacting protein (TXNIP), a factor associated with mitochondrial dysfunction. Additionally, UHRF1 overexpression alleviated lipid peroxidation and free iron accumulation, and upregulated the expression of GPX4 and Slc7a11, indicating the inhibition effect of UHRF1 overexpression on ferroptosis. We further investigated the mechanism of UHRF1 in the mitophagy/ferroptosis pathway in DN. We found that UHRF1 overexpression promoted PINK1-mediated mitophagy via inhibiting TXNIP expression, thus suppressing ferroptosis. These findings confirmed that upregulation of UHRF1 expression alleviates DN, indicating that UHRF1 has a reno-protective effect against DN.

A robotic system for automated genetic manipulation and analysis of Caenorhabditis elegans.

The nematode Caenorhabditis elegans is one of the most widely studied organisms in biology due to its small size, rapid life cycle, and manipulable genetics. Research with C. elegans depends on labor-intensive and time-consuming manual procedures, imposing a major bottleneck for many studies, especially for those involving large numbers of animals. Here, we describe a general-purpose tool, WormPicker, a robotic system capable of performing complex genetic manipulations and other tasks by imaging, phenotyping, and transferring C. elegans on standard agar media. Our system uses a motorized stage to move an imaging system and a robotic arm over an array of agar plates. Machine vision tools identify animals and assay developmental stage, morphology, sex, expression of fluorescent reporters, and other phenotypes. Based on the results of these assays, the robotic arm selectively transfers individual animals using an electrically self-sterilized wire loop, with the aid of machine vision and electrical capacitance sensing. Automated C. elegans manipulation shows reliability and throughput comparable with standard manual methods. We developed software to enable the system to autonomously carry out complex protocols. To validate the effectiveness and versatility of our methods, we used the system to perform a collection of common C. elegans procedures, including genetic crossing, genetic mapping, and genomic integration of a transgene. Our robotic system will accelerate C. elegans research and open possibilities for performing genetic and pharmacological screens that would be impractical using manual methods.

Subject-independent EEG classification based on a hybrid neural network.

A brain-computer interface (BCI) based on the electroencephalograph (EEG) signal is a novel technology that provides a direct pathway between human brain and outside world. For a traditional subject-dependent BCI system, a calibration procedure is required to collect sufficient data to build a subject-specific adaptation model, which can be a huge challenge for stroke patients. In contrast, subject-independent BCI which can shorten or even eliminate the pre-calibration is more time-saving and meets the requirements of new users for quick access to the BCI. In this paper, we design a novel fusion neural network EEG classification framework that uses a specially designed generative adversarial network (GAN), called a filter bank GAN (FBGAN), to acquire high-quality EEG data for augmentation and a proposed discriminative feature network for motor imagery (MI) task recognition. Specifically, multiple sub-bands of MI EEG are first filtered using a filter bank approach, then sparse common spatial pattern (CSP) features are extracted from multiple bands of filtered EEG data, which constrains the GAN to maintain more spatial features of the EEG signal, and finally we design a convolutional recurrent network classification method with discriminative features (CRNN-DF) to recognize MI tasks based on the idea of feature enhancement. The hybrid neural network proposed in this study achieves an average classification accuracy of 72.74 ± 10.44% (mean ± std) in four-class tasks of BCI IV-2a, which is 4.77% higher than the state-of-the-art subject-independent classification method. A promising approach is provided to facilitate the practical application of BCI.

A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI.

Introduction: Brain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insufficient movement execution features in EEG signals. Methods: This paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement. Results: A classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%. Discussion: This approach can be used to develop a hybrid EEG-EMG brain-computer interface to provide patients with more accurate neural feedback to aid their recovery.

A proprioceptive feedback circuit drives Caenorhabditis elegans locomotor adaptation through dopamine signaling.

An animal adapts its motor behavior to navigate the external environment. This adaptation depends on proprioception, which provides feedback on an animal's body postures. How proprioception mechanisms interact with motor circuits and contribute to locomotor adaptation remains unclear. Here, we describe and characterize proprioception-mediated homeostatic control of undulatory movement in the roundworm Caenorhabditis elegans. We found that the worm responds to optogenetically or mechanically induced decreases in midbody bending amplitude by increasing its anterior amplitude. Conversely, it responds to increased midbody amplitude by decreasing the anterior amplitude. Using genetics, microfluidic and optogenetic perturbation response analyses, and optical neurophysiology, we elucidated the neural circuit underlying this compensatory postural response. The dopaminergic PDE neurons proprioceptively sense midbody bending and signal to AVK interneurons via the D2-like dopamine receptor DOP-3. The FMRFamide-like neuropeptide FLP-1, released by AVK, regulates SMB head motor neurons to modulate anterior bending. We propose that this homeostatic behavioral control optimizes locomotor efficiency. Our findings demonstrate a mechanism in which proprioception works with dopamine and neuropeptide signaling to mediate motor control, a motif that may be conserved in other animals.

APOBEC mutational signature predicts prognosis and immunotherapy response in nonsmoking patients with lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is the most common type of non-small cell lung cancer (NSCLC) with poor survival in advanced stage. Nowadays the rate of nonsmoking patients has dramatically increased and may be associated with the presence of driver mutations. Better understanding of the mutation profile data of nonsmoking LUAD patients are critical to predict survival and provide greater benefits to more patients. The apolipoprotein B mRNA editing enzyme catalytic polypeptide-like (APOBEC) has been shown to play an important role in molecular tumorigenesis of NSCLC. However, the clinical relevance of APOBEC in nonsmoking LUAD remains to be understood. Methods: LUAD patients with somatic mutation and RNA sequencing data obtained from The Cancer Genome Atlas (TCGA) were assessed and screened in the Gene Expression Omnibus. Transcriptome data and mutational signatures were analyzed using R package. Then, we used the least absolute shrinkage and selection operator (LASSO) regression model to construct the APOBEC3 score (APOBEC3 score) model. The prognostic value was evaluated using Kaplan-Meier analysis. Finally, the functional enrichment analysis of differential expressed genes (DEGs) and the immune-related features were also estimated using R package. Results: By analyzing the mutational profile data of NSCLC in the TCGA database, we found that different mutation patterns existed between smoking and nonsmoking patients, and the APOBEC3 family played an important role in the mutation pattern of nonsmoking patients with LUAD. We established an APOBEC3 score and found that TCW (W = A or T) mutation counts were significantly greater in the high APOBEC3 score group than in the low APOBEC3 score group. Furthermore, there were different immune feathers and prognostic values between the high and low APOBEC3 score patients, suggesting an independent prognostic factor of APOBEC3 in nonsmoking LUAD patients. Conclusions: We established a comprehensive view of APOBEC3 mutations in nonsmoking LUAD patients. Our review provides new insights into using the APOBEC3 mutation to predict prognosis and improve the immunotherapy response for future applications.

SUMO specific peptidase 6 regulates the crosstalk between podocytes and glomerular endothelial cells in diabetic kidney disease.

There is increasing evidence that the crosstalk between podocytes and glomerular endothelial cells (GECs) exacerbates the progression of diabetic kidney disease (DKD). Here, we investigated the underlying role of SUMO specific peptidase 6 (SENP6) in this crosstalk. In the diabetic mice, SENP6 was decreased in glomerular tissues and its knockdown further exacerbated glomerular filtration barrier injury. In the mouse podocyte cell line MPC5 cells, SENP6 overexpression reversed HG-induced podocyte loss by suppressing the activation of Notch1 signaling. Notch1 intracellular domain (N1ICD) is the active form of Notch1. SENP6 upregulated the ubiquitination of N1ICD by deSUMOylating Notch1, thereby reducing N1ICD and suppressing Notch1 signaling activation in MPC5 cells. Endothelin-1 (EDN1) is a protein produced by podocytes and has been reported to promote GEC dysfunction. The supernatant from HG-treated MPC5 cells induced mitochondrial dysfunction and surface layer injury in GECs, and the supernatant from SENP6-deficient podocytes further exacerbated the above GEC dysfunction, while this trend was reversed by an EDN1 antagonist. The following mechanism study showed that SENP6 deSUMOylated KDM6A (a histone lysine demethylase) and then decreased the binding potency of KDM6A to EDN1. The latter led to the upregulation of H3K27me2 or H3K27me3 of EDN1 and suppressed its expression in podocytes. Taken together, SENP6 suppressed the HG-induced podocyte loss and ameliorated GEC dysfunction caused by crosstalk between podocytes and GECs, and the protective effect of SENP6 on DKD is attributed to its deSUMOylation activity.

Identification and validation of hub genes in CD5-positive diffuse large B-cell lymphoma.

CD5+ diffuse large B-cell lymphoma (DLBCL), as a significant heterogeneity category of DLBCL, is reflected in both the molecular biological and genetic levels, which in turn induces ever-changing clinical manifestations, and what mediates tumor survival mechanisms are still unclear. This study aimed to predict the potential hub genes in CD5+ DLBCL. A total of 622 patients with DLBCL diagnosed between 2005 and 2019 were included. High expression of CD5 was correlated with IPI, LDH, and Ann Arbor stage, patients with CD5-DLBCL have longer overall survival. We identified 976 DEGs between CD5-negative and positive DLBCL patients in the GEO database and performed GO and KEGG enrichment analysis. After intersecting the genes obtained through the Cytohubba and MCODE, further external verification was performed in the TCGA database. Three hub genes were screened: VSTM2B, GRIA3, and CCND2, of which CCND2 were mainly involved in cell cycle regulation and JAK-STAT signaling pathways. Analysis of clinical samples showed that the expression of CCND2 was found to be correlated with CD5 (p = 0.001), and patients with overexpression of CCND2 in CD5+ DLBCL had poor prognosis (p = 0.0455). Cox risk regression analysis showed that, for DLBCL, CD5, and CCND2 double positive was an independent poor prognostic factor (HR: 2.545; 95% CI: 1.072-6.043; p = 0.034). These findings demonstrate that CD5 and CCND2 double-positive tumors should be stratified into specific subgroups of DLBCL with poor prognosis. CD5 may regulate CCND2 through JAK-STAT signaling pathways, mediating tumor survival. This study provides independent adverse prognostic factors for risk assessment and treatment strategies for newly diagnosed DLBCL.

Precursors and formation pathways of furfural in sugarcane juice during thermal treatment.

As a potent aromatic compound, furfural may have adverse effects on sugarcane juice quality. In this study, simplified sugarcane juice models containing glucose, fructose and amino acids were used to explore the potential precursors and formation pathways of furfural. The changes of precursors and intermediates involved in furfural formation were quantified. The results indicated that fructose contributed more to furfural formation than glucose. Serine was the main amino acid precursor for furfural formation. Furfural could be generated through 3 pathways in sugarcane juice: 1) Streaker reaction of serine, 2) caramelization of glucose and fructose via 3-deoxyglucosone, 3) formed from reducing sugars (glucose or fructose) and serine via N-(1-Deoxy-d-fructos-1-yl)-l-serine intermediate, which further converted to 3-deoxyglucosone. At the first 10 min, furfural was mainly produced through the caramelization of fructose. Subsequently, furfural was produced in the above three ways. Furfural was more effectively formed by caramelization than Maillard reaction in sugarcane juice.

Novel lncRNA-prader willi/angelman region RNA, SNRPN neighbour (PWARSN) aggravates tubular epithelial cell pyroptosis by regulating TXNIP via dual way in diabetic kidney disease.

OBJECTIVES: Elevated thioredoxin-interacting protein (TXNIP)-induced pyroptosis contributes to the pathology of diabetic kidney disease (DKD). However, the molecular mechanisms in dysregulated TXNIP in DKD remain largely unclear. MATERIALS AND METHODS: Transcriptomic analysis identified a novel long noncoding RNA-Prader Willi/Angelman region RNA, SNRPN neighbour (PWARSN)-which was highly expressed in a proximal tubular epithelial cell (PTEC) under high glucose conditions. We focused on revealing the functions of PWARSN in regulating TXNIP-mediated pyroptosis in PTECs by targeting PWARSN expression via lentivirus-mediated overexpression and CRISPR-Cas9-based knockout in vitro and overexpressing PWARSN in the renal cortex by AAV-9 targeted injection in vivo. A number of molecular techniques disclosed the mechanisms of PWARSN in regulating TXNIP induced-pyroptosis in DKD. RESULTS: TXNIP-NOD-like receptor thermal protein domain associated protein 3 (NLRP3) inflammasome and PTEC pyroptosis were activated in the renal tubules of patients with DKD and in diabetic mice. Then we explored that PWARSN enhanced TXNIP-driven PTECs pyroptosis in vitro and in vivo. Mechanistically, cytoplasmic PWARSN sponged miR-372-3p to promote TXNIP expression. Moreover, nuclear PWARSN interacted and facilitated RNA binding motif protein X-linked (RBMX) degradation through ubiquitination, resulting in the initiation of TXNIP transcription by reducing H3K9me3-enrichment at the TXNIP promoter. Further analysis indicated that PWARSN might be a potential biomarker for DKD. CONCLUSIONS: These findings illustrate distinct dual molecular mechanisms for PWARSN-modulated TXNIP and PTECs pyroptosis in DKD, presenting PWARSN as a promising therapeutic target for DKD.

Regulation of salt tolerance in the roots of Zea mays by L-histidine through transcriptome analysis.

Soil salinization is an important worldwide environmental problem and the main reason to reduce agricultural productivity. Recent findings suggested that histidine is a crucial residue that influences the ROS reduction and improves the plants' tolerance to salt stress. Herein, we conducted experiments to understand the underlying regulatory effects of histidine on maize root system under salt stress (100 mM NaCl solution system). Several antioxidant enzymes were determined. The related expressed genes (DEGs) with its pathways were observed by Transcriptome technologies. The results of the present study confirmed that histidine can ameliorate the adverse effects of salt stress on maize root growth. When the maize roots exposed to 100 mM NaCl were treated with histidine, the accumulation of superoxide anion radicals, hydrogen peroxide, and malondialdehyde, and the content of nitrate nitrogen and ammonium nitrogen were significantly reduced; while the activities of superoxide dismutase, peroxidase, catalase, nitrate reductase, glutamine synthetase, and glutamate synthase were significantly increased. Transcriptome analysis revealed that a total of 454 (65 up-regulated and 389 down-regulated) and 348 (293 up-regulated and 55 down-regulated) DEGs were observed when the roots under salt stress were treated with histidine for 12 h and 24 h, respectively. The pathways analysis of those DEGs showed that a small number of down-regulated genes were enriched in phytohormone signaling and phenylpropanoid biosynthesis at 12 h after histidine treatment, and the DEGs involved in the phytohormone signaling, glycolysis, and nitrogen metabolism were significantly enriched at 24 h after treatment. These results of gene expression and enzyme activities suggested that histidine can improve the salt tolerance of maize roots by enriching some DEGs involved in plant hormone signal transduction, glycolysis, and nitrogen metabolism pathways.

A Semi-Supervised Progressive Learning Algorithm for Brain-Computer Interface.

Brain-computer interface (BCI) usually suffers from the problem of low recognition accuracy and large calibration time, especially when identifying motor imagery tasks for subjects with indistinct features and classifying fine grained motion control tasks by electroencephalogram (EEG)-electromyogram (EMG) fusion analysis. To fill the research gap, this paper presents an end-to-end semi-supervised learning framework for EEG classification and EEG-EMG fusion analysis. Benefiting from the proposed metric learning based label estimation strategy, sampling criterion and progressive learning scheme, the proposed framework efficiently extracts distinctive feature embedding from the unlabeled EEG samples and achieves a 5.40% improvement on BCI Competition IV Dataset IIa with 80% unlabeled samples and an average 3.35% improvement on two public BCI datasets. By employing synchronous EMG features as pseudo labels for the unlabeled EEG samples, the proposed framework further extracts deep level features of the synergistic complementarity between the EEG signals and EMG features based on the deep encoders, which improves the performance of hybrid BCI (with a 5.53% improvement for the Upper Limb Motion Dataset and an average 4.34% improvement on two hybrid datasets). Moreover, the ablation experiments show that the proposed framework can substantially improve the performance of the deep encoders (with an average 5.53% improvement). The proposed framework not only largely improves the performance of deep networks in the BCI system, but also significantly reduces the calibration time for EEG-EMG fusion analysis, which shows great potential for building an efficient and high-performance hybrid BCI for the motor rehabilitation process.

Comparisons of Glutamate in the Brains of Alzheimer's Disease Mice Under Chemical Exchange Saturation Transfer Imaging Based on Machine Learning Analysis.

Chemical exchange saturation transfer (CEST) is one of the molecular magnetic resonance imaging (MRI) techniques that indirectly measures low-concentration metabolite or free protein signals that are difficult to detect by conventional MRI techniques. We applied CEST to Alzheimer's disease (AD) and analyzed both region of interest (ROI) and pixel dimensions. Through the analysis of the ROI dimension, we found that the content of glutamate in the brains of AD mice was higher than that of normal mice of the same age. In the pixel-dimensional analysis, we obtained a map of the distribution of glutamate in the mouse brain. According to the experimental data of this study, we designed an algorithm framework based on data migration and used Resnet neural network to classify the glutamate distribution images of AD mice, with an accuracy rate of 75.6%. We evaluate the possibility of glutamate imaging as a biomarker for AD detection for the first time, with important implications for the detection and treatment of AD.

Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System.

The brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) has received more and more attention due to its vast application potential in emotion recognition. However, the relatively insufficient investigation of the feature extraction algorithms limits its use in practice. In this article, to improve the performance of fNIRS-based BCI, we proposed a method named R-CSP-E, which introduces EEG signals when computing fNIRS signals' features based on transfer learning and ensemble learning theory. In detail, we used the Independent Component Analysis (ICA) algorithm for the correspondence between the sources of the two signals. We then introduced the EEG signals when computing the spatial filter based on a modified Common Spatial Pattern (CSP) algorithm. Experimental results on public datasets show that the proposed method in this paper outperforms traditional methods without transfer. In general, the mean classification accuracy can be increased by up to 5%. To our knowledge, it is an innovation that we tried to apply transfer learning between EEG and fNIRS. Our study's findings not only prove the potential of the transfer learning algorithm in cross-model brain-computer interface, but also offer a new and innovative perspective to research the hybrid brain-computer interface.

Serum amyloid A 1 induces suppressive neutrophils through the Toll-like receptor 2-mediated signaling pathway to promote progression of breast cancer.

Immune inflammation plays a key role in breast cancer development, progression, and therapeutic efficacy. Neutrophils are crucial for the regulation of the suppressive tumor microenvironment and are associated with poor clinical survival. However, the mechanisms underlying the activation of suppressive neutrophils in breast cancer are poorly understood. Here, we report that breast cancer cells secrete abundant serum amyloid A 1 (SAA1), which is associated with the accumulation of suppressive neutrophils. High expression of SAA1 in breast cancer induces neutrophil immunosuppressive cytokine production through the activation of Toll-like receptor 2 (TLR2)-mediated signaling pathways. These include the TLR2/myeloid differentiation primary response 88 (MYD88)-mediated PI3K/nuclear factor-κB signaling pathway and p38 MAPK-associated apoptosis resistance pathway, which eventually promote the progression of breast cancer. Our study shows a mechanistic link between breast cancer cell secretion of SAA1 and suppressive neutrophils that potentiate tumor progression. These findings provide potential therapeutic targets for breast cancer.

Individual and Combined Cardiometabolic Morbidities and the Subsequent Risk of Cardiovascular Events in Chinese Adults.

CONTEXT: Diabetes, hypertension and dyslipidemia accelerates the incidence of cardiovascular disease (CVD) events. However, data regarding the association between main cardiometabolic morbidities such as diabetes, hypertension, and dyslipidemia and the subsequent risk of CVD events in Chinese adults are still limited. OBJECTIVE: To investigate the associations between individual and combined cardiometabolic morbidities and incident cardiovascular events in Chinese adults. METHODS: Baseline data were obtained from a prospective, nationwide, and population-based cohort study in China during 2011-2012. A total of 133 572 participants aged ≥40 years were included in the study. The main outcome measures were CVD events. RESULTS: Compared with participants without diabetes, hypertension and dyslipidemia, participants with only diabetes (hazard ratio [HR], 1.58; 95% CI, 1.32-1.90) or only hypertension (2.04; 1.82-2.28) exhibited significantly higher risk for CVD events, while participants with only dyslipidemia (0.97; 0.84-1.12) exhibited no significantly higher risk for CVD events. When analyzed collectively, participants with diabetes plus hypertension (HR, 2.67; 95% CI, 2.33-3.06), diabetes plus dyslipidemia (1.57; 1.32-1.87), and hypertension plus dyslipidemia (2.12; 1.88-2.39) exhibited significantly higher risk for CVD. Moreover, participants with the combination of diabetes, hypertension, and dyslipidemia exhibited the highest risk for CVD events (HR, 3.06; 95% CI, 2.71-3.46). Multivariable-adjusted HRs (95% CIs) for CVD associated with diabetes based on fasting glucose ≥7.0 mmol/L, oral glucose tolerance test 2-hour glucose ≥11.1 mmol/L, and hemoglobin A1c ≥6.5% were 1.64 (1.51-1.78), 1.57 (1.45-1.69), and 1.54 (1.42-1.66), respectively; associated with hypertension based on systolic blood pressure ≥140 mmHg and diastolic blood pressure ≥90 mmHg were 1.89 (1.76-2.03) and 1.74 (1.60-1.88), respectively; associated with dyslipidemia based on total cholesterol ≥6.22 mmol/L, low-density lipoprotein cholesterol ≥4.14 mmol/L, high-density lipoprotein cholesterol <1.04 mmol/L, and triglycerides ≥2.26 mmol/L were 1.18 (1.08-1.30), 1.30 (1.17-1.44), 1.00 (0.92-1.09), and 1.10 (1.01-1.20), respectively. CONCLUSION: Diabetes, hypertension and dyslipidemia showed additive associations with the risk of CVD events in middle-aged and elderly Chinese adults.