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With the development of wearable electronic technology, the flexible photodetectors have attracted widespread attention, and it is of great significance to develop flexible, eco‐friendly and low‐cost photodetectors. Cellulose paper, as a flexible, eco‐friendly, low‐cost, lightweight, customizable, biodegradable and renewable material, has received enthusiastic attention and rapid development in the field of photodetectors in recent years. In this review, it is focused on the research progress of paper‐based (PB) photodetectors. First, the fabrication methods of PB photodetectors are discussed, including electrode materials and optoelectronic functional materials. Then, this review systematically summarizes and analyzes the achievements of PB photodetectors on photoelectric performances (spectral response range, responsivity, detectivity, response/recovery times and on/off ratio) and flexibility characteristics (bending angle and bending cycle). In terms of key performance indicators, the PB photodetectors reported so far can detect multiple wavelengths of light from UV to near‐infrared with the maximum detectivity of 1013 Jones. In addition, the various applications of PB photodetectors is reviewed and discussed. Finally, it is look forward to the future development of PB photodetectors in terms of fabrication methods, photoelectric and flexible performances, and applications. With this review, it is hope that it will promoted the future development of PB photodetectors. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this study is to investigate the associated risk factors of pulmonary infection in individuals diagnosed with chronic kidney disease (CKD). The primary goal is to develop a predictive model that can anticipate the likelihood of pulmonary infection during hospitalization among CKD patients. Methods: This retrospective cohort study was conducted at two prominent tertiary teaching hospitals. Three distinct models were formulated employing three different approaches: (1) the statistics-driven model, (2) the clinical knowledge-driven model, and (3) the decision tree model. The simplest and most efficient model was obtained by comparing their predictive power, stability, and practicability. Results: This study involved a total of 971 patients, with 388 individuals comprising the modeling group and 583 individuals comprising the validation group. Three different models, namely Models A, B, and C, were utilized, resulting in the identification of seven, four, and eleven predictors, respectively. Ultimately, a statistical knowledge-driven model was selected, which exhibited a C-statistic of 0.891 (0.855–0.927) and a Brier score of 0.012. Furthermore, the Hosmer–Lemeshow test indicated that the model demonstrated good calibration. Additionally, Model A displayed a satisfactory C-statistic of 0.883 (0.856–0.911) during external validation. The statistical-driven model, known as the A-C2GH2S risk score (which incorporates factors such as albumin, C2 [previous COPD history, blood calcium], random venous blood glucose, H2 [hemoglobin, high-density lipoprotein], and smoking), was utilized to determine the risk score for the incidence rate of lung infection in patients with CKD. The findings revealed a gradual increase in the occurrence of pulmonary infections, ranging from 1.84% for individuals with an A-C2GH2S Risk Score ≤ 6, to 93.96% for those with an A-C2GH2S Risk Score ≥ 18.5. Conclusion: A predictive model comprising seven predictors was developed to forecast pulmonary infection in patients with CKD. This model is characterized by its simplicity, practicality, and it also has good specificity and sensitivity after verification. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to determine the effects of iRoot BP Plus on receptor activator of nuclear factor‐κB ligand (RANKL)‐induced osteoclastogenesis in vitro and inflammation‐mediated bone resorption in vivo and investigated the underlying molecular mechanisms. Methodology: CCK‐8 was performed to test cell viability in RANKL‐induced RAW 264.7 cells and BMDMs in response to iRoot BP Plus. The effect of iRoot BP Plus on osteoclastogenesis was determined using TRAP staining and phalloidin staining, respectively. Pit formation assay was conducted to measure osteoclast resorptive capacity. Western blot and qPCR were performed to examine osteoclast‐related proteins and gene expression, respectively. Western blot was also used to investigate the signalling pathways involved. For in vivo experiments, an LPS‐induced mouse calvarial bone resorption model was established to analyse the effect of iRoot BP Plus on bone resorption (n = 6 per group). At 7 days, mouse calvaria were collected and prepared for histological analysis. Results: We identified that iRoot BP Plus extracts significantly attenuated RANKL‐induced osteoclastogenesis, reduced sealing zone formation, restrained osteolytic capacity and decreased osteoclast‐specific gene expression (p <.01). Mechanistically, iRoot BP Plus extracts reduced TRAF6 via proteasomal degradation, then suppressed the phosphorylation of mitogen‐activated protein kinases (MAPKs), blocked the nuclear translocation of c‐Fos and diminished nuclear factor‐κB (NF‐κB) p65 and NFATc1 accumulation. Consistent with the in vitro results, iRoot BP Plus extracts attenuated osteoclast activity thus protecting against inflammatory bone resorption in vivo (p <.05), which was accompanied by a suppression of TRAF6, c‐Fos, NFATc1 and cathepsin K expression. Conclusion: These findings provide valuable insights into the signalling mechanisms underlying nanoparticulate bioceramic putty‐mediated bone homeostasis. [ABSTRACT FROM AUTHOR]

Toll-gates are crucial points of management and key congestion bottleneck for the freeway. In order to avoid traffic deterioration and alleviate traffic congestion in advance, it is necessary to predict and evaluate the congestion in toll-gates scattering in large-scale region of freeway network. In this paper, traffic volume and operational delay time are selected from various traffic indicators to evaluate congestion considering the particular characteristics of the traffic flow within the toll-gate area. The congestion prediction method is designed including two modules: a deep learning (DL) prediction and a fuzzy evaluation. We propose a modified deep learning method based on graph convolutional network (GCN) structure in the fusion of dilated causal mechanism and optimize the method for spatial feature extraction by constructing a new adjacency matrix. This new AI network could process spatiotemporal information of traffic volume and operational delay time, that extracted from large-scaled toll-gates spontaneously, and predict key indicators in 15/30/60min future time. The evaluation module is proposed based on these predicted results. Then, fuzzy C-means algorithm (FCM) is further modified by determining coupling weight for these two key indicators to detect congestion state. Original traffic data are obtained from the current 186 toll-gates served on the freeway network in Shaanxi Province, China. Experimental tests are carried out based on historical data of four months after preprogressing. The comparative tests show the proposed CPT-DF (congestion prediction on toll-gates using deep learning and fuzzy evaluation) outperforms the current-used other models by 6-15%. The successful prediction could extend to the real-time prediction and early warning of traffic congestion in the toll system to improve the intelligent level of traffic emergency management and guidance on the key road of disasters to some extent., Author(s): Tongtong Shi [1,2]; Ping Wang (corresponding author) [1,2]; Xudong Qi [2]; Jiacheng Yang [3]; Rui He [2]; Jingwen Yang [2]; Yu Han (corresponding author) [1,4] 1. Introduction Freeway is [...]

Despite the considerable amount of research devoted to understanding fraud, few studies have examined how the physical environment can influence the likelihood of committing fraud. One recent study found a link between room brightness and occurrence of human fraud behaviors. Therefore, the present study aims to investigate how temperature may affect fraud. Based on a power analysis using the effect size observed in a pilot study, we recruited 105 participants and randomly divided them into three temperature groups (warm, medium, and cool). We then counted fraud behaviors in each group and tested for potential significant differences with a Kruskal-Wallis test. Additionally, we used a correlation analysis to determine whether the perceived temperature affected fraud. As a result, regardless of participants' subjective sensory experience or their physical environment, we did not find that temperature-related factors influence the incidence of fraud. We discussed the potential reason for the results and suggested directions for future research.

Craniosynostosis is defined as congenital premature fusion of one or more cranial sutures. While the genetic basis for about 30% of cases is known, the causative genes for the diverse presentations of the remainder of cases are unknown. The recently discovered cranial suture stem cell population affords an opportunity to identify early signaling pathways that contribute to craniosynostosis. We previously demonstrated that enhanced BMP signaling in neural crest cells (caA3 mutants) leads to premature cranial suture fusion resulting in midline craniosynostosis. Since enhanced mTOR signaling in neural crest cells leads to craniofacial bone lesions, we investigated the extent to which mTOR signaling is involved in the pathogenesis of BMP-mediated craniosynostosis by affecting the suture stem cell population. Our results demonstrate a loss of suture stem cells in the caA3 mutant mice by the newborn stage. We have found increased activation of mTOR signaling in caA3 mutant mice during embryonic stages, but not at the newborn stage. Our study demonstrated that inhibition of mTOR signaling via rapamycin in a time specific manner partially rescued the loss of the suture stem cell population. This study provides insight into how enhanced BMP signaling regulates suture stem cells via mTOR activation.

Non-destructive stress characterization is essential for gate-all-around (GAA) nanosheet (NS) transistors technology, while it is a big challenge to be realized on nanometer-sized GAA devices by using traditional Micro-Raman spectroscopy due to its light spot far exceeding the device. In this work, a non-destructive stress characterization methodology of confocal Raman spectroscopy was proposed and performed for GAANS device fabrication. Channel stress evolution along the fabrication process was successfully characterized by designing high-density NS array and analyzing the linear scanned spectra in different structures. The related mechanism of stress evolution was systematically studied by Sentaurus process simulation. Additionally, applying this methodology on detecting the bending of suspended NS after channel release process was demonstrated. Therefore, this work might provide a promising solution to realize in-line characterization of channel stress in GAA NS transistors and process monitor of NS channel integrity.

This study focused on correcting short-term wind speed forecasts extracted from GFS global forecast data sets at corresponding locations and heights. Data measurement mode and calibration method of the LiDAR data were discussed, and wind speed at three different heights (50m, 80m, and 100m) from floating LiDARs were employed. The results indicated that directly using the Kalman filter method with sliding adaptive weights to generate the average hysteresis deviation to correct the wind speed did not yield satisfactory results. Statistical analysis suggested that better standard deviation results can be obtained by lowering the ratio of the average hysteresis deviation in the forecast correction formula. When the average hysteresis deviation ratio was reduced to 0.1--0.5, the wind speed standard deviation after correction by the Kalman filtering method was generally lower than the wind speed standard deviation before correction, and the best ratio was 0.3. Using this hysteresis deviation ratio for wind speed correction at different forecast times and periods for these three LiDAR devices revealed that, except for individual data sets, more than 90% of the wind speed data sets exhibited reduced standard deviation after correction compared to the uncorrected data sets. After the introduction of this improved algorithm, the wind speed forecast improvement rate at each height ranged from 5% to 20%. [ABSTRACT FROM AUTHOR]

COVID-19 is an acute respiratory disorder that is caused by SARS-CoV-2, in which excessive systemic inflammation is associated with adverse patient clinical outcomes. Here, we observed elevated expression levels of NLRP12 (nucleotide-binding leucine-rich repeat–containing receptor 12) in human peripheral monocytes and lung tissue during infection with SARS-CoV-2. Co-immunoprecipitation analysis revealed that NLRP12 directly interacted with the M protein through its leucine-rich repeat domain. Moreover, in vitro studies demonstrated that NLRP12 interacted with TRAF3 and promoted its ubiquitination and degradation, which counteracted the inhibitory effect of TRAF3 on the NF-κB/MAPK signaling pathway and promoted the production of inflammatory cytokines. Furthermore, an in vivo study revealed that NLRP12 knockout mice displayed attenuated tissue injury and ameliorated inflammatory responses in the lungs when infected with a SARS-CoV-2 M protein–reconstituted pseudovirus and mouse coronavirus. Taken together, these findings suggest that NLRP12 mediates the inflammatory responses during coronavirus infection. [ABSTRACT FROM AUTHOR]

Background: Accumulating evidence supports the involvement of adaptive immunity in the development of radiation‐induced brain injury (RIBI). Our previous work has emphasized the cytotoxic function of CD8+ T cells in RIBI. In this study, we aimed to investigate the presence and potential roles of cytotoxic CD4+ T cells (CD4+ CTLs) in RIBI to gain a more comprehensive understanding of adaptive immunity in this context. Main Text: Utilizing single‐cell RNA sequencing (scRNA‐seq), we analyzed 3934 CD4+ T cells from the brain lesions of four RIBI patients and identified six subclusters within this population. A notable subset, the cytotoxic CD4+ T cells (CD4+ CTLs), was marked with high expression of cytotoxicity‐related genes (NKG7, GZMH, GNLY, FGFBP2, and GZMB) and several chemokine and chemokine receptors (CCL5, CX3CR1, and CCL4L2). Through in‐depth pseudotime analysis, which simulates the development of CD4+ T cells, we observed that the CD4+ CTLs exhibited signatures of terminal differentiation. Their functions were enriched in protein serine/threonine kinase activity, GTPase regulator activity, phosphoprotein phosphatase activity, and cysteine‐type endopeptidase activity involved in the apoptotic signaling pathway. Correspondingly, mice subjected to gamma knife irradiation on the brain showed a time‐dependent infiltration of CD4+ T cells, an increase of MHCII+ cells, and the existence of CD4+ CTLs in lesions, along with an elevation of apoptotic‐related proteins. Finally, and most crucially, single‐cell T‐cell receptor sequencing (scTCR‐seq) analysis at the patient level determined a large clonal expansion of CD4+ CTLs in lesion tissues of RIBI. Transcriptional factor‐encoding genes TBX21, RORB, and EOMES showed positive correlations with the cytotoxic functions of CD4+ T cells, suggesting their potential to distinguish RIBI‐related CD4+ CTLs from other subsets. Conclusion: The present study enriches the understanding of the transcriptional landscape of adaptive immune cells in RIBI patients. It provides the first description of a clonally expanded CD4+ CTL subset in RIBI lesions, which may illuminate new mechanisms in the development of RIBI and offer potential biomarkers or therapeutic targets for the disease. [ABSTRACT FROM AUTHOR]