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DEK is associated with DNA replication and break repair, mRNA splicing, and transcriptional regulation, which had been studied in humans and mammals. The function of DEK in plants was poorly understood. In this study, GhDEK2D was identified in Gossypium hirsutum by genome-wide and post-translational modifications. GhDEK2D had been phosphorylated, acetylated and ubiquitylated under Verticillium dahliae (Vd) challenge. The GhDEK2D-silenced cotton decreased resistance against Vd. In GhDEK2D-silenced cotton plants, the reactive oxygen species was activated, the callose, xylogen, hypersensitive reaction (HR) and expression levels of defense-related genes were reduced. Homozygous overexpressing-GhDEK2D transgenic Arabidopsis lines were more resistant to Verticillium wilt (Vw). We propose that GhDEK2D was a potential molecular target for improving resistance to Vw in cotton.

BackgroundNonalcoholic fatty liver disease (NAFLD) poses a substantial socioeconomic burden and is becoming the fastest growing driver of chronic liver disease, potentially accompanied by a poor prognosis. ObjectiveWe aim to elucidate the global and regional epidemiologic changes in NAFLD during the past 30 years and explore the interconnected diseases. MethodsData on NAFLD incidence, prevalence, death, and disability-adjusted life-years (DALYs) were extracted from the Global Burden of Disease Study 2019. The age-standardized incident rate (ASIR), age-standardized prevalent rate (ASPR), age-standardized death rate (ASDR), and age-standardized DALYs were calculated to eliminate the confounding effects of age when comparing the epidemiologic changes between different geographical regions. In addition, we also investigated the correlation between the NAFLD burden and the sociodemographic index (SDI). Finally, the associations of the 3 common comorbidities with NAFLD were determined. ResultsGlobally, the incidence and prevalence of NAFLD both increased drastically during the past 3 decades (incidence: from 88,180 in 1990 to 172,330 in 2019, prevalence: from 561,370,000 in 1990 to 1,235,700,000 in 2019), mainly affecting young adults who were aged from 15 to 49 years. The ASIR increased slightly from 1.94 per 100,000 population in 1990 to 2.08 per 100,000 population in 2019, while ASPR increased from 12,070 per 100,000 population in 1990 to 15,020 per 100,000 population in 2019. In addition, the number of deaths and DALYs attributable to NAFLD increased significantly as well from 93,760 in 1990 to 168,970 in 2019 and from 2,711,270 in 1990 to 4,417,280 in 2019, respectively. However, the ASDR and age-standardized DALYs presented decreasing trends with values of estimated annual percentage change equaling to –0.67 and –0.82, respectively (ASDR: from 2.39 per 100,000 population in 1990 to 2.09 per 100,000 population in 2019; age-standardized DALYs: from 63.28 per 100,000 population in 1990 to 53.33 per 100,000 population in 2019). Thereinto, the burden of death and DALYs dominated the patients with NAFLD who are older than 50 years. Moreover, SDI appeared to have obvious negative associations with ASPR, ASDR, and age-standardized DALYs among 21 regions and 204 countries, although there is no marked association with ASIR. Finally, we found that the incidence and prevalence of NAFLD were positively related to those of diabetes mellitus type 2, stroke, and ischemic heart disease. ConclusionsNAFLD is leading to increasingly serious health challenges worldwide. The morbidity presented a clear shift toward the young populations, while the heavier burden of death and DALYs in NAFLD was observed in the aged populations and in regions with relatively low SDI. Comprehensive acquisition of the epidemiologic pattern for NAFLD and the identification of high-risk comorbidities may help policy makers and clinical physicians develop cost-effective prevention and control strategies, especially in countries with a high NAFLD burden.

Although most indoor positioning systems use radio waves, such as Wi-Fi, Bluetooth, or RFID, for application in department stores, exhibition halls, stations, and airports, the accuracy of such technology is easily affected by human shadowing and multipath propagation delay. This study combines the earth’s magnetic field strength and Wi-Fi signals to obtain the indoor positioning information with high availability. Wi-Fi signals are first used to identify the user’s area under several kinds of environment partitioning methods. Then, the signal pattern comparison is used for positioning calculations using the strength change in the earth’s magnetic field among the east–west, north–south, and vertical directions at indoor area. Finally, the k-nearest neighbors (KNN) method and fingerprinting algorithm are used to calculate the fine-grained indoor positioning information. The experiment results show that the average positioning error is 0.57 m in 12-area partitioning, which is almost a 90% improvement in relation to that of one area partitioning. This study also considers the positioning error if the device is held at different angles by hand. A rotation matrix is used to convert the magnetic sensor coordinates from a mobile phone related coordinates into the geographic coordinates. The average positioning error is decreased by 68%, compared to the original coordinates in 12-area partitioning with a 30-degree pitch. In the offline procedure, only the northern direction data are used, which is reduced by 75%, to give an average positioning error of 1.38 m. If the number of reference points is collected every 2 m for reducing 50% of the database requirement, the average positioning error is 1.77 m.

In recent years, there have been efforts to utilize surface water as a power source, material, and food. However, these efforts are impeded due to the vast amounts of contaminants and emerging contaminants introduced by anthropogenic activities. Herbicides such as Glyphosate and Glufosinate are commonly known to contaminate surface water through agricultural industries. In contrast, some emerging contaminants, such as rare earth elements, have started to enter the surface water from the production and waste of electronic products. Duckweeds are angiosperms from the Lemnaceae family and have been used for toxicity tests in aquatic environments, mainly those from the genus Lemna, and have been approved by OECD. In this study, we used duckweed from the genus Wolffia, which is smaller and considered a good indicator of metal pollutants in the aquatic environment. The growth rate of duckweed is the most common endpoint in observing pollutant toxicity. In order to observe and mark the fronds automatically, we used StarDist, a machine learning-based tool. StarDist is available as a plugin in ImageJ, simplifying and assisting the counting process. Python also helps arrange, manage, and calculate the inhibition percentage after duckweeds are exposed to contaminants. The toxicity test results showed Dysprosium to be the most toxic, with an IC50 value of 14.6 ppm, and Samarium as the least toxic, with an IC50 value of 279.4 ppm. In summary, we can provide a workflow for automatic frond counting using StarDist integrated with ImageJ and Python to simplify the detection, counting, data management, and calculation process.

The Laney p′ control chart is a new type of attribute control chart that can be applied in situations where the process exhibits either overdispersion or underdispersion. While it has gained acceptance in the industry, there is still limited knowledge about its effectiveness in detecting process variation. It is well known that before applying a control chart, understanding its performance is crucial, especially when the parameters of the control chart need to be estimated from historical data. In this study, we used simulations to investigate the ability of the Laney p′ control chart to detect process variations when the parameters are estimated. We designed appropriate experiments to assess the impact of overdispersion on the average run length (ARL) performance. In this study, we assumed that the overdispersion comes from the variation in the mean fraction nonconforming of each sample. The mean value varies according to a uniform distribution. This study evaluated the performance of the Laney p′ control chart using the average of the ARL (AARL) and the standard deviation of the ARL (SDARL). Additionally, real-world data were utilized to illustrate the practical applications of the Laney p′ control chart in the PCB and IC substrate industries. The research findings can serve as valuable guidance for practical implementation.

Statistical process control (SPC) charts are commonly used to monitor quality characteristics in manufacturing processes. When monitoring two or more related quality characteristics simultaneously, multivariate T2 control charts are often employed. Like univariate control charts, control chart pattern recognition (CCPR) plays a crucial role in multivariate SPC. The presence of non-random patterns in T2 control charts indicates that a process is influenced by one or more assignable causes and that corrective actions should be taken. In this study, we developed a deep learning-based classification model for recognizing control chart patterns in multivariate processes. To address the problem of the insufficient representation of one-dimensional (1D) data, we explore the advantages of using two-dimensional (2D) image data obtained from a threshold-free recurrence plot. A multi-channel deep convolutional neural network (MCDCNN) model was developed to incorporate both 1D and 2D representations of control chart data. This model was tested on multivariate processes with different covariance matrices and compared with other traditional algorithms. Moreover, the effects of imbalanced datasets and dataset size on classification performance were analyzed. Simulation studies revealed that the developed MCDCNN model outperforms other techniques in identifying multivariate non-random patterns. For the most significant one, our proposed MCDCNN method achieved a 10% improvement over traditional methods. The overall results suggest that the developed MCDCNN model can be beneficial for intelligent SPC.

The edge sharpness of a propeller blade plays a vital role in improving energy transmission efficiency and reducing the power required to propel the vehicle. However, producing finely sharpened edges through casting is challenging due to the risk of breakage. Additionally, the blade profile of the wax model can deform during drying, making it difficult to achieve the required edge thickness. To automate the sharpening process, we propose an intelligent system consisting of a six-DoF industrial robot and a laser-vision sensor. The system improves machining accuracy through an iterative grinding compensation strategy that eliminates material residuals based on profile data from the vision sensor. An indigenously designed compliance mechanism is employed to enhance the performance of robotic grinding which is actively controlled by an electronic proportional pressure regulator to adjust the contact force and position between the workpiece and abrasive belt. The system’s reliability and functionality are validated using three different workpiece models of four-blade propellers, achieving accurate and efficient machining within the required thickness tolerances. The proposed system provides a promising solution for finely sharpened propeller blade edges, addressing challenges associated with the earlier robotic-based grinding studies.

Aging induces a series of alterations, specifically a decline in the stature and number of villi and crypts in the small intestine, thus compromising the absorbent capability of the villi. This investigation employed a senolytic combination of dasatinib and quercetin (D+Q) to examine its impact on the intestinal tract of elderly mice. Our findings demonstrate that D+Q treatment leads to a decrease in the expression of p21, p16, and Ki67, while concurrently triggering removal of apoptotic cells within the villi. Additionally, D+Q treatment exhibits the ability to promote growth in both the height and quantity of villi and crypts, along with stimulating nitric oxide (NO) production in aged mice. The study presented a model to assess strategies to alleviate age-related senescence in the intestinal tract of elderly mice. Importantly, D+Q showcases promising potential in enhancing intestinal functionality within the aging. [ABSTRACT FROM AUTHOR]

Deubiquitinating enzymes (DUBs) play a pivotal role in regulating the antiviral immune response by targeting members of the RLR signaling pathway. As a pivotal member of the RLR pathway, TRAF3 is essential for activating the MAVS/TBK-1/IRF3 signaling pathway in response to viral infection. Despite its importance, the function of DUBs in the TRAF3-mediated antiviral response is poorly understood. Ubiquitin-specific protease 26 (USP26) regulates the RLR signaling pathway to modulate the antiviral immune response. The results demonstrate that EV71 infection upregulates the expression of USP26. Knockdown of USP26 significantly enhances EV71-induced expression of IFN-β and downstream interferon-stimulated genes (ISGs). Deficiency of USP26 not only inhibits EV71 replication but also weakens the host's resistance to EV71 infection. USP26 physically interacts with TRAF3 and reduces the K63-linked polyubiquitination of TRAF3, thereby promoting pIRF3-mediated antiviral signaling. USP26 physically interacts with TRAF3 and reduces the K63-linked polyubiquitination of TRAF3, thereby promoting pIRF3-mediated antiviral signaling. Conversely, knockdown of USP26 leads to an increase in the K63-linked polyubiquitination of TRAF3. These findings unequivocally establish the essential role of USP26 in RLR signaling and significantly contribute to the understanding of deubiquitination-mediated regulation of innate antiviral responses. [ABSTRACT FROM AUTHOR]

Winds in the nighttime upper thermosphere are often observed to mimic the ionospheric plasma convection at polar latitudes, and whether the same is true for the daytime winds remains unclear. The dayside sector is subject to large temperature gradient set up by solar irradiance and it also contains the cusp, which is a hotspot of Poynting flux and a region with the strongest soft particle precipitation. We examine daytime winds using a Scanning Doppler Imager (SDI) located at the South Pole, and investigate their distribution under steadily positive and negative IMF By conditions. The results show that daytime winds exhibit significant differences from the plasma convection. Under negative IMF By conditions, winds flow in the same direction as the plasma zonally, but have a meridional component that is strongest in the auroral zone. As a result, winds are more poleward-directed than the plasma convection within the auroral zone, and more westward-directed in the polar cap. Under positive IMF By conditions, winds can flow zonally against the plasma in certain regions. For instance, they flow westward in the polar cap despite the eastward plasma convection there, forming a large angle relative to the plasma convection. The results indicate that ion drag may not be the most dominant force for daytime winds. Although the importance of various forcing terms cannot be resolved with the utilized dataset, we speculate that the pressure gradient force in the presence of cusp heating serves as one important contributor. [ABSTRACT FROM AUTHOR]