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Optical imaging through the intact mouse skull is challenging because of skull-induced aberrations and scattering. We found that three-photon excitation provided improved optical sectioning compared with that obtained with two-photon excitation, even when we used the same excitation wavelength and imaging system. Here we demonstrate three-photon imaging of vasculature through the adult mouse skull at >500-μm depth, as well as GCaMP6s calcium imaging over weeks in cortical layers 2/3 and 4 in awake mice, with 8.5 frames per second and a field of view spanning hundreds of micrometers.

Deep learning methods have been introduced for fault diagnosis of rotating machinery. Most methods have good performance when processing bearing data at a certain rotating speed. However, most rotating machinery in industrial practice has variable working speed. When processing the bearing data with variable rotating speed, the existing methods have low accuracies, or need complex parameter adjustments. To solve this problem, a fault diagnosis method based on continuous wavelet transform scalogram (CWTS) and Pythagorean spatial pyramid pooling convolutional neural network (PSPP-CNN) is proposed in this paper. In this method, continuous wavelet transform is used to decompose vibration signals into CWTSs with different scale ranges according to the rotating speed. By adding a PSPP layer, CNN can process CWTSs in different sizes. Then the fault diagnosis of variable rotating speed bearing can be carried out by a single CNN model without complex parameter adjustment. Compared with a spatial pyramid pooling (SPP) layer that has been used in CNN, a PSPP layer locates as front layer of CNN. Thus, the features obtained by PSPP layer can be delivered to convolutional layers for further feature extraction. According to experiment results, this method has higher diagnosis accuracy for variable rotating speed bearing than other methods. In addition, the PSPP-CNN model trained by data at some rotating speeds can be used to diagnose bearing fault at full working speed.

Systemic acquired resistance (SAR) is a long-lasting broad-spectrum plant defense mechanism induced in distal systemic tissues by mobile signals generated at the primary infection site. Despite the discoveries of multiple potential mobile signals, how these signals cooperate to trigger downstream SAR signaling is unknown. Here, we show that endogenous extracellular nicotinamide adenine dinucleotide (phosphate) [eNAD(P)] accumulates systemically upon pathogen infection and that both eNAD(P) and the lectin receptor kinase (LecRK), LecRK-VI.2, are required in systemic tissues for the establishment of SAR. Moreover, putative mobile signals, e.g., N-hydroxypipecolic acid (NHP), trigger de novo systemic eNAD(P) accumulation largely through the respiratory burst oxidase homolog RBOHF-produced reactive oxygen species (ROS). Importantly, NHP-induced systemic immunity mainly depends on ROS, eNAD(P), LecRK-VI.2, and BAK1, indicating that NHP induces SAR primarily through the ROS-eNAD(P)-LecRK-VI.2/BAK1 signaling pathway. Our results suggest that mobile signals converge on eNAD(P) in systemic tissues to trigger SAR through LecRK-VI.2. Systemic acquired resistance (SAR) is a plant immune response triggered by mobile signals generated at the primary infection site. Here the authors show that one such mobile signal, N-hydroxypipecolic acid, can trigger production of eNAD(P) that activates SAR through the LecRK-VI.2 receptor.

While antidepressants are effective in alleviating symptoms, their association with mortality remains unclear. This research investigated the link between antidepressant usage and all-cause mortality among depressed patients. We performed a real-world study on 5,947 adults with depression using a dataset from the National Health and Nutrition Examination Survey (2005-2018). Depression was identified by a Patient Health Questionnaire-9 score ≥10, or the use of antidepressants, with all-cause mortality assessed through the National Death Index. Covariates included demographics, socioeconomic status, lifestyle factors, and chronic conditions. The study performed weighted Cox proportional-hazards models, propensity score methods, and inverse probability of treatment weighting (IPTW) to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for comparing mortality risk between patients treated with antidepressants and those who were not. We conducted sensitivity analyses to evaluate the robustness of our findings. During the median 82-month follow-up period, 15.0% of participants (n = 894) died. Antidepressant users (n = 3,925) had a crude mortality rate of 16.5%, compared to 12.2% in non-users (n = 2,022). The crude Cox proportional-hazards analysis indicated that antidepressant use was linked to a non-significant elevation in mortality (HR = 1.18, 95% CI 0.95-1.47, P = 0.126). This association attenuated completely after covariate adjustment (adjusted HR = 0.92, 95% CI 0.75-1.13). Propensity score analyses indicated no significant link between antidepressant use and mortality (IPTW, HR = 0.96, 95% CI 0.80-1.16, P = 0.707). Across all methods, no statistically significant association was observed. All-cause mortality is not significantly affected by the overall use of antidepressants in individuals with depression; however, future studies should investigate safety differences between specific drug classes.

Existing research has demonstrated links between airborne particulate matter and ulcerative colitis (UC) onset. Through Mendelian randomization, this study aims to further delineate the causal association between specific types of airborne particulates and UC. A two-sample Mendelian randomization analysis was undertaken to investigate the causality between airborne particulate matter and UC. Genetic datasets for both airborne particulates and UC were derived from accessible genome-wide association studies (GWAS). We employed a range of MR techniques, such as inverse variance weighted (IVW), weighted median, MR-Egger, and Wald Ratio, to validate the causality. In addition, sensitivity assessments were executed to ensure result reliability. The data indicate a probable positive correlation between PM2.5 exposure and UC risk (OR: 3.6; 95% CI: [1.2-11.3]; P = 0.026). The statistical strength for causal determination via the IVW approach stood at 0.87, with a Type I error rate set at 0.025. Assessments using Cochran's Q test, MR-Egger intercept, MR-PRESSO, and leave-one-out sensitivity analyses did not identify notable heterogeneity, pleiotropy, or biases in the overall relationship between PM2.5 and UC. Furthermore, the MR-Steiger assessment indicated that PM2.5 exposure level determinants predominantly affect UC vulnerability. The findings underscore the potential involvement of PM2.5 in UC pathogenesis.

Microbial biophotovoltaics (BPV) offers a biological solution for renewable energy production by using photosynthetic microorganisms as light absorbers. Although abiotic engineering approaches, e.g., electrode modification and device optimization, can enhance the electrochemical communication between living cells and electrodes, the power densities of BPV are still low due to the weak exoelectrogenic activity of photosynthetic microorganisms. Here, we develop a BPV based on a d -lactate mediated microbial consortium consisting of photosynthetic cyanobacteria and exoelectrogenic Shewanella . By directing solar energy from photons to d -lactate, then to electricity, this BPV generates a power density of over 150 mW·m −2 in a temporal separation setup. Furthermore, a spatial-temporal separation setup with medium replenishment enables stable operation for over 40 days with an average power density of 135 mW·m −2 . These results demonstrate the electron flow constrained microbial consortium can facilitate electron export from photosynthetic cells and achieve an efficient and durable power output. Power densities of existing microbial biophotovoltaics (BPV) are low and unendurable. Here, the authors develop a BPV based on d -lactate mediated microbial consortium, which can generate an average power density of 135 mW·m −2 for over 40 days in a spatial-temporal separation setup with medium replenishment.

Increasing evidence indicates that the systemic immune-inflammation index (SII) and lactate dehydrogenase (LDH) are correlated with the prognosis of various malignancies. This study aimed to evaluate the prognostic value of pre-treatment SII and LDH in patients with non-metastatic nasopharyngeal carcinoma (NPC). We conducted a retrospective analysis of 756 patients with non-metastatic NPC. The optimal cut-off values for SII and LDH, determined using X-tile software, were 150 and 447, respectively. Independent prognostic factors for survival outcomes were identified using Kaplan-Meier analysis and Cox regression analysis. Patients in the high SII group had significantly worse prognosis in 5-year OS (76.5 vs. 86.7%, p  < 0.001), 5-year DMFS (77.3 vs. 85.4%, p  < 0.001), and 5-year PFS (67.9 vs. 80.5%, p  < 0.001) compared to the low SII group. Patients in the high LDH group had significantly worse prognosis in 5-year OS (72.1 vs. 85.0%, p  < 0.001), 5-year DMFS (72.1 vs. 84.8%, p  < 0.001), and 5-year PFS (63.7 vs. 77.7%, p  < 0.001) compared to the low LDH group.Multivariate analysis showed that high SII and high LDH were significantly associated with poorer OS ( p  = 0.005 vs. p  < 0.001), DMFS( p  = 0.001 vs. p  < 0.001), and PFS ( p  = 0.001 vs. p  < 0.001). These results confirmed that both SII and LDH are independent prognostic factors for OS, DMFS, and PFS. In subgroup analysis, this predictive effect was more pronounced in locally advanced stages. Among patients with locally advanced NPC, the combination of SII and LDH showed the highest AUC values for predicting OS, DMFS, and PFS. Pre-treatment SII and LDH are important prognostic factors in patients with non-metastatic NPC. Furthermore, the combination of both provides a more accurate prognosis for patients with locally advanced NPC than either marker alone.

To reveal the dynamic characteristics and distribution patterns of sand particles in multiphase pumps used in closed oilfield gathering and transportation systems, this study focuses on a helical-blade multiphase pump. Through a systematic investigation of the effects of sand particle parameters (such as diameter, concentration, and shape) and pump operating parameters (including flow rate, rotational speed, and oil content) on the solid-liquid two-phase flow within the pump, the study aims to elucidate how these factors influence the internal motion behavior and spatial distribution of sand particles. The results indicate that sand particle diameter significantly influences two-phase flow trajectories, vortex structures, and particle motion, whereas the effects of concentration and shape are relatively weak. Higher flow rates and rotational speeds, combined with lower oil content, increase the kinetic energy of the fluid, thereby intensifying radial momentum fluctuations in the impeller. Axial momentum increases initially and then decreases with increasing particle size, rises with higher particle concentration, and remains unaffected by particle shape. Larger particles reduce transport efficiency, while higher concentrations increase the mass loading in key regions of the pump. High flow rates reduce the internal sand concentration, thereby facilitating transport; low rotational speeds increase sand concentration in critical locations. Increased oil content enhances fluid viscosity and drag forces, facilitating sand discharge. These findings provide theoretical insights into the coupled mechanisms of particle motion and energy dissipation in sand-laden multiphase flows, and offer practical guidance for anti-wear design and operational optimization of helical-blade multiphase pumps.

The authors provide an epidemiologic analysis of the first 425 confirmed cases of infection with the novel coronavirus in Wuhan, China. This analysis provides estimates of the epidemic doubling time and the basic reproductive number and shows clear evidence of sustained human-to-human transmission.

To further evaluate the causal relationships between inflammatory cytokines and migraine, we conducted a bidirectional, two-sample Mendelian randomization (MR) analysis using genetic data from publicly available genome-wide association studies (GWAS). We used several MR methods, including random-effect inverse-variance weighting (IVW), weighted median, MR-Egger, to test the causal relationships. Sensitivity analyses were also conducted to evaluate the robustness of the results. The results showed that hepatocyte growth factor (HGF) was positively associated with the risk of migraine (odds ratio [OR], 1.004; 95% confidence interval [CI], 1.001–1.008; P = 0.022). In addition, Interleukin-2 (IL-2) was considered a downstream consequence of migraine (OR, 0.012; 95% CI, 0.000–0.0929; P = 0.046). These findings suggest that HGF may be a factor associated with the etiology of migraine, while IL-2 is more likely to be involved in the downstream development of migraine.