TRIM44 Promotes Rabies Virus Replication by Autophagy-Dependent Mechanism.

Tripartite motif (TRIM) proteins are a multifunctional E3 ubiquitin ligase family that participates in various cellular processes. Recent studies have shown that TRIM proteins play important roles in regulating host-virus interactions through specific pathways, but their involvement in response to rabies virus (RABV) infection remains poorly understood. Here, we identified that several TRIM proteins are upregulated in mouse neuroblastoma cells (NA) after infection with the rabies virus using RNA-seq sequencing. Among them, TRIM44 was found to regulate RABV replication. This is supported by the observations that downregulation of TRIM44 inhibits RABV replication, while overexpression of TRIM44 promotes RABV replication. Mechanistically, TRIM44-induced RABV replication is brought about by activating autophagy, as inhibition of autophagy with 3-MA attenuates TRIM44-induced RABV replication. Additionally, we found that inhibition of autophagy with rapamycin reverses the TRIM44-knockdown-induced decrease in LC3B expression and autophagosome formation as well as RABV replication. The results suggest that TRIM44 promotes RABV replication by an autophagy-dependent mechanism. Our work identifies TRIM44 as a key host factor for RABV replication, and targeting TRIM44 expression may represent an effective therapeutic strategy.

Melatonin ameliorates 10-hydroxycamptothecin-induced oxidative stress and apoptosis via autophagy-regulated p62/Keap1/Nrf2 pathway in mouse testicular cells.

10-Hydroxycamptothecin (HCPT) is a widely used clinical anticancer drug but has a significant side effect profile. Melatonin has a beneficial impact on the chemotherapy of different cancer cells and reproductive processes, but the effect and underlying molecular mechanism of melatonin's involvement in the HCPT-induced side effects in cells, especially in the testicular cells, are poorly understood. In this study, we found that melatonin therapy significantly restored HCPT-induced testicular cell damage and did not affect the antitumor effect of HCPT. Further analysis found that melatonin therapy suppressed HCPT-induced DNA damage associated with ataxia-telangiectasia mutated- and Rad3-related and CHK1 phosphorylation levels in the testis. Changes in apoptosis-associated protein levels (Bax, Bcl-2, p53, and Cleaved caspase-3) and in reactive oxygen species-associated proteins (Nrf2 and Keap1) and index (malondialdehyde and glutathione) suggested that melatonin treatment relieved HCPT-induced cell apoptosis and oxidative damage, respectively. Mechanistically, melatonin-activated autophagy proteins (ATG7, Beclin1, and LC3bII/I) may induce p62-dependent autophagy to degrade Keap1, eliciting Nrf2 from Keap1-Nrf2 interaction to promote antioxidant enzyme expression such as HO-1, which would salvage HCPT-induced ROS production and mitochondrial dysfunction. Collectively, this study reveals that melatonin therapy may protect testicular cells from HCPT-induced damage via the activation of autophagy, which alleviates oxidative stress, mitochondrial dysfunction, and cell apoptosis.

Enhancing Concrete Mechanical Properties through Basalt Fibers and Calcium Sulfate Whiskers: Optimizing Compressive Strength, Elasticity, and Pore Structure.

To study the effects of basalt fibers (BFs), calcium sulfate whiskers (CSWs), and modified calcium sulfate whiskers (MCSWs) on the compressive strength and dynamic modulus of elasticity of concrete, this paper utilizes Mercury Intrusion Porosimetry (MIP) to measure the microstructure of concrete and calculate the fractal dimension of pore surface area. The results indicate that both CSWs and BFs can increase the compressive strength of concrete. CSWs can enhance the dynamic modulus of elasticity of concrete, while the effect of BFs on the dynamic modulus of elasticity is not significant. The improvement in compressive strength and dynamic modulus of elasticity provided by MCSWs is significantly greater than that provided by CSWs. Both CSWs and BFs can effectively improve the pore structure of concrete and have a significant impact on the surface fractal dimension. CSWs inhibit the formation of ink-bottle pores, while BFs increase the number of ink-bottle pores. Due to the ink-bottle pore effect, the fractal dimension of the capillary pore surface is generally greater than three, lacking fractal characteristics. The compressive strength and dynamic modulus of elasticity of concrete have a good correlation with the fractal dimensions of large pores and transition pores.

Novel Umami Peptides from Mushroom (Agaricus bisporus) and Their Umami Enhancing Effect via Virtual Screening and Molecular Simulation.

This study aimed to identify novel umami peptides in Agaricus bisporus and investigate their umami enhancing effect. We virtually screened 155 potential umami peptides from the ultrasound-assisted A. bisporus hydrolysate according to Q values, iUmami-SCM, Umami_YYDS, and Tastepeptides_DM models, and molecular docking. Five peptides (AGKNTNGSQF, DEAVARGATF, REESDFQSSF, SEETTTGVHH, and WNNDAFQSSTN) were synthesized for sensory evaluation and kinetic analysis. The result showed that the umami thresholds of the five peptides were in the range of 0.21-0.40 mmol/L. Notably, REESDFQSSF, SEETTTGVHH, and WNNDAFQSSTN had low dissociation constant (KD) values and high affinity for the T1R1-VFT receptor. The enhancing effect of the three peptides with MSG or IMP was investigated by sensory evaluation, kinetic analysis, and molecular dynamics simulations. In stable complexes, ARG_277 in T1R1 played a major role in umami peptide binding to T1R1-VFT. These results provide a theoretical basis for future screening of umami peptides and improving the umami taste of food containing mushrooms.

On-Demand Optimization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform.

Synthesizing the best material globally is challenging; it needs to know what and how much the best ingredient composition should be for satisfying multiple figures of merit simultaneously. Traditional one-variable-at-a-time methods are inefficient; the design-build-test-learn (DBTL) method could achieve the optimal composition from only a handful of ingredients. A vast design space needs to be explored to discover the possible global optimal composition for on-demand materials synthesis. This research developed a hypothesis-guided DBTL (H-DBTL) method combined with robots to expand the dimensions of the search space, thereby achieving a better global optimal performance. First, this study engineered the search space with knowledge-aware chemical descriptors and customized multiobjective functions to fulfill on-demand research objectives. To verify this concept, this novel method was used to optimize colorimetric ammonia sensors across a vast design space of as high as 19 variables, achieving two remarkable optimization goals within 1 week: first, a sensing array was developed for ammonia quantification of a wide dynamic range, from 0.5 to 500 ppm; second, a new state-of-the-art detection limit of 50 ppb was reached. This work demonstrates that the H-DBTL approach, combined with a robot, develops a novel paradigm for the on-demand optimization of functional materials.

Evaluating combustion kinetics and quantifying fuel-N conversion tendency of shoe manufacturing waste.

Combustion is an effective and cost-efficient thermochemical conversion method for solid waste, showing promise for the resource utilization of shoe manufacturing waste (SMW). However, SMW is generally composed of different components, which can lead to unstable combustion and excessive pollutant emissions, especially NOx. To date, combustion characteristics, reaction mechanism and fuel nitrogen (fuel-N) conversion of different SMW components remain unclear. In this work, the combustion behavior of typical SMW components combustion was investigated using Thermogravimetric coupled with Fourier transform infrared spectrum (TG-FTIR). A simplified single-step reaction mechanism was proposed according to the temperature interval to estimate reaction mechanism of SMW. Additionally, the relationship between fuel-N conversion tendency and fuel properties was established. The results indicate that the values for the comprehensive combustion performance index (S) and flammability index (C) range from 1.65 to 0.44 and 3.98 to 1.37, respectively. This demonstrates the significant variability in combustion behavior among different SMW components. Cardboard, leather and sponge have higher values of S and C, suggesting a better ignition characteristic and a stable combustion process. During the combustion of SMW, nitrogen oxides (NO and N2O) are the main nitrogen-containing compounds in the flue gases, with NO being the major contributor, accounting for over 82.97 % of the nitrogen oxides. NO has a negative correlation with nitrogen content, but it is opposite for N2O, HCN and NH3. Furthermore, the conversion of NO, N2O and NH3 is proportional to logarithmic values of O/N, while its conversion to HCN is proportional to logarithmic values of VM/N. These findings facilitate the prediction of the fuel-N conversion of solid waste combustion. This work might shed light on combustion optimization and in-situ pollutant emission control in solid waste combustion.

Cross-Subject Motor Imagery Decoding by Transfer Learning of Tactile ERD.

For Brain-Computer Interface (BCI) based on motor imagery (MI), the MI task is abstract and spontaneous, presenting challenges in measurement and control and resulting in a lower signal-to-noise ratio. The quality of the collected MI data significantly impacts the cross-subject calibration results. To address this challenge, we introduce a novel cross-subject calibration method based on passive tactile afferent stimulation, in which data induced by tactile stimulation is utilized to calibrate transfer learning models for cross-subject decoding. During the experiments, tactile stimulation was applied to either the left or right hand, with subjects only required to sense tactile stimulation. Data from these tactile tasks were used to train or fine-tune models and subsequently applied to decode pure MI data. We evaluated BCI performance using both the classical Common Spatial Pattern (CSP) combined with the Linear Discriminant Analysis (LDA) algorithm and a state-of-the-art deep transfer learning model. The results demonstrate that the proposed calibration method achieved decoding performance at an equivalent level to traditional MI calibration, with the added benefit of outperforming traditional MI calibration with fewer trials. The simplicity and effectiveness of the proposed cross-subject tactile calibration method make it valuable for practical applications of BCI, especially in clinical settings.

Computed tomography-based radiomics improves non-invasive diagnosis of Pneumocystis jirovecii pneumonia in non-HIV patients: a retrospective study.

BACKGROUND: Pneumocystis jirovecii pneumonia (PCP) could be fatal to patients without human immunodeficiency virus (HIV) infection. Current diagnostic methods are either invasive or inaccurate. We aimed to establish an accurate and non-invasive radiomics-based way to identify the risk of PCP infection in non-HIV patients with computed tomography (CT) manifestation of pneumonia. METHODS: This is a retrospective study including non-HIV patients hospitalized for suspected PCP from January 2010 to December 2022 in one hospital. The patients were randomized in a 7:3 ratio into training and validation cohorts. Computed tomography (CT)-based radiomics features were extracted automatically and used to construct a radiomics model. A diagnostic model with traditional clinical and CT features was also built. The area under the curve (AUC) were calculated and used to evaluate the diagnostic performance of the models. The combination of the radiomics features and serum ß-D-glucan levels was also evaluated for PCP diagnosis. RESULTS: A total of 140 patients (PCP: N = 61, non-PCP: N = 79) were randomized into training (N = 97) and validation (N = 43) cohorts. The radiomics model consisting of nine radiomic features performed significantly better (AUC = 0.954; 95% CI: 0.898-1.000) than the traditional model consisting of serum ß-D-glucan levels (AUC = 0.752; 95% CI: 0.597-0.908) in identifying PCP (P = 0.002). The combination of radiomics features and serum ß-D-glucan levels showed an accuracy of 95.8% for identifying PCP infection (positive predictive value: 95.7%, negative predictive value: 95.8%). CONCLUSIONS: Radiomics showed good diagnostic performance in differentiating PCP from other types of pneumonia in non-HIV patients. A combined diagnostic method including radiomics and serum ß-D-glucan has the potential to provide an accurate and non-invasive way to identify the risk of PCP infection in non-HIV patients with CT manifestation of pneumonia. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05701631).

Efficient Estimation of Directed Connectivity in Nonlinear and Nonstationary Spiking Neuron Networks.

OBJECTIVE: Studying directed connectivity within spiking neuron networks can help understand neural mechanisms. Existing methods assume linear time-invariant neural dynamics with a fixed time lag in information transmission, while spiking networks usually involve complex dynamics that are nonlinear and nonstationary, and have varying time lags. METHODS: We develop a Gated Recurrent Unit (GRU)-Point Process (PP) method to estimate directed connectivity within spiking networks. We use a GRU to describe the dependency of the target neuron's current firing rate on the source neurons' past spiking events and a PP to relate the target neuron's firing rate to its current 0-1 spiking event. The GRU model uses recurrent states and gate/activation functions to deal with varying time lags, nonlinearity, and nonstationarity in a parameter-efficient manner. We estimate the model using maximum likelihood and compute directed information as our measure of directed connectivity. RESULTS: We conduct simulations using artificial spiking networks and a biophysical model of Parkinson's disease to show that GRU-PP systematically addresses varying time lags, nonlinearity, and nonstationarity, and estimates directed connectivity with high accuracy and data efficiency. We also use a non-human-primate dataset to show that GRU-PP correctly identifies the biophysically-plausible stronger PMd-to-M1 connectivity than M1-to-PMd connectivity during reaching. In all experiments, the GRU-PP consistently outperforms state-of-the-art methods. CONCLUSION: The GRU-PP method efficiently estimates directed connectivity in varying time lag, nonlinear, and nonstationary spiking neuron networks. SIGNIFICANCE: The proposed method can serve as a directed connectivity analysis tool for investigating complex spiking neuron network dynamics.

LncRNA RP11-10E18.7 cooperates with lncRNA RP11-481C4.2 to affect the overall survival of breast cancer patients: a TCGA-based retrospective study.

Background: As either oncogenes or tumor suppressor genes, long non-coding RNAs (lncRNAs) have a major role in both tumorigenesis and progression of human cancers, including breast cancer (BC). However, the statistical correlation between the lncRNA-lncRNA interaction and prognosis of BC remains unclear. Methods: We analyzed the fragments per kilobase per million (FPKM) lncRNA expression data in tumor tissue samples from 890 female patients with BC in The Cancer Genome Atlas (TCGA) between May 2021 and October 2022. The Cox proportional hazards model adjusted for age, race, clinical stage, neoadjuvant therapy, estrogen receptor (ER), and progesterone receptor (PR) was adopted to evaluate the lncRNA-lncRNA interaction regarding overall survival (OS) of BC. The multiple comparison was corrected by Bonferroni method. Results: RP11-10E18.7×RP11-481C4.2 was significantly associated with OS of BC patients [hazard ratio (HR)interaction =1.04, 95% confidence interval (CI): 1.03-1.06, P=3.35×10-9]. Then, gene-gene interaction analysis was performed for genes co-expressed with lncRNAs. FOXA1×U2SURP (HRinteraction =1.49, 95% CI: 1.28-1.73, P=2.16×10-7) was found to have a similar interactive pattern to RP11-10E18.7×RP11-481C4.2. after classifying the patients by intersection (3.47), we observed that the effect of FOXA1 opposite in patients with different U2SURP expression level (HRhigh vs. low =0.58, 95% CI: 0.34-0.99, P=0.046 in low expression of U2SURP; HRhigh vs. low =1.56, 95% CI: 1.18-2.87, P=0.029 in high expression of U2SURP). Conclusions: Our comprehensive study identified RP11-10E18.7×RP11-481C4.2 as a potential biomarker of BC prognosis. The results play an essential role in the impact of lncRNA-lncRNA interaction on BC survival. Our findings elucidated potential molecular mechanisms of BC progression under complex association patterns and provided potential dynamic and reversible therapeutic targets for BC patients.

Relationship between machining accuracy and optical properties of convex blazed grating in ultra-precision cutting.

For ultra-precision machining of convex blazed grating elements there are inevitable machining errors, surface defects, and surface roughness, all of which can have an impact on their diffraction efficiency. In this paper, we use PCGrate software based on the integration method to establish the machining error model, surface defect model, and surface roughness model of convex spherical blazed grating with a curvature radius of 41.104 mm, a substrate diameter of 14 mm, a grating density of 53.97 line/mm and a blazed angle of 3.86° as the basic specification. To investigate the effect of base curvature radius error, grating period error, blazed angle error, grating ridge and valley passivation radius, Poisson burr height, and blaze surface roughness on their -1 order diffraction efficiency in the 0.95-2.5 µm spectral range. The results show that when the curvature radius error of the spherical base is less than ±80µm, the influence on diffraction efficiency can be ignored. Among the three groups of grating microstructure parameters, the influence of blazed angle on grating diffraction efficiency is the largest, followed by a grating period, and the influence of grating apex angle is the smallest, among which when the error of blazed angle is less than ±0.1° and the error of grating period is less than ±0.1µm, the influence on diffraction efficiency can be ignored. The effect of the passivation radius of the grating valley on the diffraction efficiency is smaller than that of the passivation radius of the grating ridge, and the maximum reduction of diffraction efficiency is 0.096 and 0.144 when the grating ridge and valley passivation radius are 50nm∼650 nm, respectively. The diffraction efficiency decreases significantly in the wavelength range of 1.9-2.5 µm with the increase of Poisson burr height and blaze surface roughness, and its effect on the diffraction efficiency can be neglected when the Poisson burr height is less than 0.5 µm and the blaze surface roughness value is less than RMS 1 nm. The machining error, surface defect, and surface roughness models of the convex blazed grating are optimized to provide a comprehensive machining accuracy basis for ultra-precision cutting of convex grating components.

LCMR1 Promotes Large-Cell Lung Cancer Proliferation and Metastasis by Downregulating HLA-Encoding Genes.

Lung cancer is notorious for its high global morbidity and mortality. Here, we examined whether the LCMR1 gene, which we previously cloned from a human large-cell lung carcinoma cell line, contributes to the proliferation and metastasis of large-cell lung carcinoma. To this end, we performed pan-cancer and non-small cell lung cancer (NSCLC) cell line-based LCMR1 expression profiling. Results revealed that LCMR1 was expressed at high levels in most solid tumors, including NSCLC. LCMR1 expression was the highest in the 95D large cell lung cancer cell line. Functional studies using lentivirus-based knockdown revealed that LCMR1 was critical for the proliferation, migration, and invasion of cultured large cell lung cancer cells. Moreover, blocking this gene significantly reduced tumor growth in a 95D cell xenograft mouse model. A multiple sequence-based assay revealed a mechanism by which LCMR1 diminished the RNA Pol II occupancy at the promoter of human leukocyte antigen (HLA)-encoding genes to prevent their transcription. The HLA genes play vital roles in cancer-specific antigen presentation and anticancer immunity. A correlation assay using TCGA database identified a negative relationship between the expression levels of LCMR1 and HLA coding genes. Taken together, our findings demonstrate that LCMR1 is required for large cell lung cancer cell growth and invasion and suggest its potential as a valid target in clinical treatment.

Enhanced Motor Imagery Decoding by Calibration Model-Assisted With Tactile ERD.

OBJECTIVE: In this study, we propose a tactile-assisted calibration method for a motor imagery (MI) based Brain-Computer Interface (BCI) system. METHOD: In the proposed calibration, tactile stimulation was applied to the hand wrist to assist the subjects in the MI task, which is named SA-MI task. Then, classifier training in the SA-MI Calibration was performed using the SA-MI data, while the Conventional Calibration employed the MI data. After the classifiers were trained, the performance was evaluated on a common MI dataset. RESULTS: Our study demonstrated that the SA-MI Calibration significantly improved the performance as compared with the Conventional Calibration, with a decoding accuracy of (78.3% vs. 71.3%). Moreover, the average calibration time could be reduced by 40%. This benefit of the SA-MI Calibration effect was further validated by an independent control group, which showed no improvement when tactile stimulation was not applied during the calibration phase. Further analysis showed that when compared with MI, greater motor-related cortical activation and higher R 2 value in the alpha-beta frequency band were induced in SA-MI. CONCLUSION: Indeed, the SA-MI Calibration could significantly improve the performance and reduce the calibration time as compared with the Conventional Calibration. SIGNIFICANCE: The proposed tactile stimulation-assisted MI Calibration method holds great potential for a faster and more accurate system setup at the beginning of BCI usage.

CircPTEN suppresses human clear cell renal carcinoma progression and resistance to mTOR inhibitors by targeting epigenetic modification.

Renal cell carcinoma (RCC) is known to be the most commonly diagnosed kidney cancer. Clear cell RCC (ccRCC) represents approximately 85 % of diagnosed RCC cases. Targeted therapeutics, such as multi-targeted tyrosine kinase inhibitors (TKI) and mTOR inhibitors, are widely used in ccRCC therapy. However, patients treated with mTOR and TKI inhibitors easily acquire drug resistance, making the therapy less effective. Here, we demonstrated that circPTEN inhibits the expression of its parental gene PTEN by reducing methylation of the PTEN promotor and inhibits GLUT1 expression by reducing m6A methylation of GLUT1, which suppresses ccRCC progression and resistance to mTOR inhibitors.

The effects of body dissatisfaction, sleep duration, and exercise habits on the mental health of university students in southern China during COVID-19.

Following the outbreak of COVID-19 at the end of 2019, universities around the world adopted a closed management model and various restrictive measures intended to reduce human contact and control the spread of the disease. Such measures have had a profound impact on university students, with a marked increase in depression-related psychological disorders. However, little is known about the specific status and factors influencing the impact of the pandemic on student mental health. Addressing this gap, this study examines the body dissatisfaction, physical activity, and sleep of university students during the pandemic, and uses their levels of depression to provide a theoretical basis for the development of mental health interventions for university students in the post-epidemic era. To achieve this, a total of 1,258 university students were randomly recruited for this cross-sectional study. Collected data included respondents' anthropometric measurements, body dissatisfaction levels, dietary habits, sleep status, physical activity levels, and depression levels. The overall detection rate of depression was 25.4%, with higher levels of depression among women. Multiple regression analysis showed that the PSQI score (ß = 1.768, P < 0.01) and physical activity scores (ß = -0.048, P < 0.01) were significant predictors of depression in men, while the PSQI score (ß = 1.743, P < 0.01) and body dissatisfaction scores (ß = 0.917, P < 0.01) were significant predictors of depression in women. Mental health problems were prevalent among university students during the COVID-19 pandemic. Results indicate the possibility of alleviating depression among university students by improving their body dissatisfaction, physical activity, and sleep. However, as this study was limited to Ganzhou City, it is challenging to extrapolate the findings to other populations. As this was a cross-sectional study, a causal relationship between depression levels and lifestyle habits cannot be determined.

Comparative analysis of salvage partial nephrectomy versus radical nephrectomy after the failure of initial partial nephrectomy.

OBJECTIVES: To compare the oncologic outcomes and renal function discrepancy of salvage partial nephrectomy (sPN) and salvage radical nephrectomy (sRN) after an initial failed PN. MATERIALS AND METHODS: Retrospective data from multiple centers between 2008 and 2022 were analyzed in this study. Patients who received sPN or sRN after an initial failed PN were identified. Comparative analysis and propensity score matching (PSM) was performed and the RENAL score, tumor size, and pathological T stage at salvage surgery were used to match the 2 groups. Local recurrence-free survival (LRFS) and recurrence-free survival (RFS) were assessed using the Cox proportional hazards model and log-rank tests. Renal function after salvage surgery was assessed using the Wilcoxon rank sum test. RESULTS: A total of 140 patients who underwent salvage surgery were evaluated, of whom 60 were considered for PSM analysis after matching. At a median follow-up of 27.0 months, LRFS and RFS showed no significant difference between sPN and sRN, either before (LRFS, HR = 0.673 [95% CI: 0.171-2.644], P = 0.610; RFS, HR = 0.744 [95% CI: 0.271-1.344], P = 0.595) or after matching (LRFS, HR = 1.080 [95% CI: 0.067-17.30], P = 0.957; RFS, HR = 1.199 [95% CI: 0.241-5.983], P = 0.822). During long-term follow-up, sPN preserved renal function (after matching, eGFR, 71.4 vs. 54.0, P < 0.001) and prevented eGFR loss (after matching: 6.6% vs. 25.6%, P < 0.001). CONCLUSION: Salvage partial nephrectomy offers a better alternative than sRN for recurrence after initial PN, as sPN preserves renal function better while maintaining parallel tumor control and acceptable complication rates.

A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training.

Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. Endogenous BCIs based on electroencephalogram (EEG) signals, such as motor imagery (MI) BCIs, can provide some level of control. However, mastering spontaneous BCI control requires the users to generate discriminative and stable brain signal patterns by imagery, which is challenging and is usually achieved over a very long training time (weeks/months). Here, we propose a human-machine joint learning framework to boost the learning process in endogenous BCIs, by guiding the user to generate brain signals toward an optimal distribution estimated by the decoder, given the historical brain signals of the user. To this end, we first model the human-machine joint learning process in a uniform formulation. Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel "copy/new" feedback paradigm to help shape the signal generation of the subject toward the optimal distribution and 2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process. Specifically, the decoder reweighs the brain signals generated by the subject to focus more on "good" samples to cope with the learning process of the subject. Online and psuedo-online BCI experiments with 18 healthy subjects demonstrated the advantages of the proposed joint learning process over coadaptive approaches in both learning efficiency and effectiveness.

Diagnostic value of endobronchial ultrasound elastography in differentiating between benign and malignant hilar and mediastinal lymph nodes: a retrospective study.

Background: Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a first-line approach for diagnosing hilar and mediastinal lymph node metastasis. Endobronchial ultrasound (EBUS) elastography is an imaging technique for describing the elasticity of intrathoracic lesions. However, the reported accuracy of EBUS elastography needs to be improved. In this study, we aimed to explore the diagnostic value of EBUS elastography for differentiating between benign and malignant hilar and mediastinal lymph nodes. Methods: We conducted a single-center, retrospective study enrolling consecutive patients who received EBUS elastography followed by EBUS-TBNA at the Chinese PLA General Hospital from October 2015 to October 2022. The pathological results of EBUS-TBNA confirmed by 6-month follow-up were used as the gold standard. The ultrasound elastography parameters of lymph nodes included strain rate, stiff area ratio, and elasticity score, along with the conventional ultrasound characteristics such as short axis diameter, shape, margin, echogenicity distribution and intensity, and blood flow. The diagnostic performance of these parameters was compared, and conjointly analyzed using multivariate logistic regression. Bootstrapping resampling was applied for internal validation of the regression model. Results: A total of 83 patients were enrolled with an average age of 57 years, and 66.3% of patients were male. In total, 131 lymph nodes were punctured, among which 79 (60.3%) were malignant. All the conventional ultrasound characteristics were significantly different between benign and malignant lymph nodes. All the ultrasound elastography parameters of malignant lymph nodes were markedly higher than those of benign lymph nodes. Multivariate logistic regression analysis showed that the margin, echogenicity intensity, blood flow, short axis diameter, and stiff area ratio were the main factors affecting the lymph node property. The diagnostic accuracy, sensitivity, and specificity were 91.8% [95% confidence interval (CI): 85.4-96.0%], 94.4% (95% CI: 86.4-98.5%), and 88.0% (95% CI: 75.7-95.5%), respectively. Bootstrap resampling validation showed a concordance index (C-index) of 0.949. The calibration plot indicated good agreement between the predicted and observed results. Conclusions: EBUS elastography is a promising approach for differentiating between benign and malignant lymph nodes. The combination of conventional EBUS and elastography can improve diagnostic efficacy, provide reliable complementary information, and guide the implementation of EBUS-TBNA more accurately.

Automatic identification and analysis of multi-object cattle rumination based on computer vision.

Rumination in cattle is closely related to their health, which makes the automatic monitoring of rumination an important part of smart pasture operations. However, manual monitoring of cattle rumination is laborious and wearable sensors are often harmful to animals. Thus, we propose a computer vision-based method to automatically identify multi-object cattle rumination, and to calculate the rumination time and number of chews for each cow. The heads of the cattle in the video were initially tracked with a multi-object tracking algorithm, which combined the You Only Look Once (YOLO) algorithm with the kernelized correlation filter (KCF). Images of the head of each cow were saved at a fixed size, and numbered. Then, a rumination recognition algorithm was constructed with parameters obtained using the frame difference method, and rumination time and number of chews were calculated. The rumination recognition algorithm was used to analyze the head image of each cow to automatically detect multi-object cattle rumination. To verify the feasibility of this method, the algorithm was tested on multi-object cattle rumination videos, and the results were compared with the results produced by human observation. The experimental results showed that the average error in rumination time was 5.902% and the average error in the number of chews was 8.126%. The rumination identification and calculation of rumination information only need to be performed by computers automatically with no manual intervention. It could provide a new contactless rumination identification method for multi-cattle, which provided technical support for smart pasture.

Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals.

Introduction: In the field of upper limb brain computer interfaces (BCIs), the research focusing on bilateral decoding mostly based on the neural signals from two cerebral hemispheres. In addition, most studies used spikes for decoding. Here we examined the representation and decoding of different laterality and regions arm motor imagery in unilateral motor cortex based on local field potentials (LFPs). Methods: The LFP signals were recorded from a 96-channel Utah microelectrode array implanted in the left primary motor cortex of a paralyzed participant. There were 7 kinds of tasks: rest, left, right and bilateral elbow and wrist flexion. We performed time-frequency analysis on the LFP signals and analyzed the representation and decoding of different tasks using the power and energy of different frequency bands. Results: The frequency range of <8 Hz and >38 Hz showed power enhancement, whereas 8-38 Hz showed power suppression in spectrograms while performing motor imagery. There were significant differences in average energy between tasks. What's more, the movement region and laterality were represented in two dimensions by demixed principal component analysis. The 135-300 Hz band signal had the highest decoding accuracy among all frequency bands and the contralateral and bilateral signals had more similar single-channel power activation patterns and larger signal correlation than contralateral and ipsilateral signals, bilateral and ipsilateral signals. Discussion: The results showed that unilateral LFP signals had different representations for bilateral motor imagery on the average energy of the full array and single-channel power levels, and different tasks could be decoded. These proved the feasibility of multilateral BCI based on the unilateral LFP signal to broaden the application of BCI technology. Clinical trial registration: https://www.chictr.org.cn/showproj.aspx?proj=130829, identifier ChiCTR2100050705.