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Background Accurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpatient visits are also affected by the doctors’ scheduling and the effects are not pure random. Thinking about the impure specialty, this paper presents a new forecasting model that takes cyclicity and the day of the week effect into consideration. Methods We formulate a seasonal ARIMA (SARIMA) model on a daily time series and then a single exponential smoothing (SES) model on the day of the week time series, and finally establish a combinatorial model by modifying them. The models are applied to 1 year of daily visits data of urban outpatients in two internal medicine departments of a large hospital in Chengdu, for forecasting the daily outpatient visits about 1 week ahead. Results The proposed model is applied to forecast the cross-sectional data for 7 consecutive days of daily outpatient visits over an 8-weeks period based on 43 weeks of observation data during 1 year. The results show that the two single traditional models and the combinatorial model are simplicity of implementation and low computational intensiveness, whilst being appropriate for short-term forecast horizons. Furthermore, the combinatorial model can capture the comprehensive features of the time series data better. Conclusions Combinatorial model can achieve better prediction performance than the single model, with lower residuals variance and small mean of residual errors which needs to be optimized deeply on the next research step.

Tumor differentiation is a therapeutic strategy aimed at reactivating the endogenous differentiation program of cancer cells and inducing cancer cells to mature and differentiate into other types of cells. It has been found that a variety of natural small-molecule drugs can induce tumor cell differentiation both in vitro and in vivo. Relevant molecules involved in the differentiation process may be potential therapeutic targets for tumor cells. Compared with synthetic drugs, natural small-molecule antitumor compounds have the characteristics of wide sources, structural diversity and low toxicity. In addition, natural drugs with structural modification and transformation have relatively concentrated targets and enhanced efficacy. Therefore, using natural small-molecule compounds to induce malignant cell differentiation represents a more targeted and potential low-toxicity means of tumor treatment. In this review, we focus on natural small-molecule compounds that induce differentiation of myeloid leukemia cells, osteoblasts and other malignant cells into functional cells by regulating signaling pathways and the expression of specific genes. We provide a reference for the subsequent development of natural small molecules for antitumor applications and promote the development of differentiation therapy.

Stress granules (SGs) are essential cytoplasmic, membraneless organelles that form in response to cellular stress, functioning to prevent mRNA translation and protect mRNA from damage. However, the mechanism of SG formation remains largely unknown. Here, utilizing a systems-level technique for quantification of RNA cap epitranscriptome, we found that mRNA Cap1 and non-canonical caps are predominantly enriched in SGs, with the composition of mRNA caps in SGs of mammalian cells differing between different stress conditions. Knockdown of RNA guanine-7-methyltransferase (RNMT) and phosphorylated CTD interacting factor 1(PCIF1) both resulted in substantial changes in the content and composition of mRNA caps and RNMT knockdown caused failure of SG formation under different stress conditions. Furthermore, proteomic, Co-IP and confocal immunofluorescence analysis of these knockdown cells and SGs revealed that mRNAs partition into SGs through cap-protein interactions. These findings collectively revealed the significant role of mRNA cap in SG formation and stress response in mammalian cells.

There are a lot of redundant data in wireless sensor networks (WSNs). If these redundant data are processed and transmitted, the node energy consumption will be too fast and will affect the overall lifetime of the network. Data fusion technology compresses the sampled data to eliminate redundancy, which can effectively reduce the amount of data sent by the node and prolong the lifetime of the network. Due to the dynamic nature of WSNs, traditional data fusion techniques still have many problems. Compressed sensing (CS) theory has introduced new ideas to solve these problems for WSNs. Therefore, in this study we analyze the data fusion scheme and propose an algorithm that combines improved clustered (ICL) algorithm low energy adaptive clustering hierarchy (LEACH) and CS (ICL-LEACH-CS). First, we consider the factors of residual energy, distance, and compression ratio and use the improved clustered LEACH algorithm (ICL-LEACH) to elect the cluster head (CH) nodes. Second, the CH uses a Gaussian random observation matrix to perform linear compressed projection (LCP) on the cluster common (CM) node signal and compresses the N-dimensional signal into M-dimensional information. Then, the CH node compresses the data by using a CS algorithm to obtain a measured value and sends the measured value to the sink node. Finally, the sink node reconstructs the signal using a convex optimization method and uses a least squares algorithm to fuse the signal. The signal reconstruction optimization problem is modeled as an equivalent ℓ 1 -norm problem. The simulation results show that, compared with other data fusion algorithms, the ICL-LEACH-CS algorithm effectively reduces the node’s transmission while balancing the load between the nodes.

Precision spraying technology has attracted increasing attention in orchard production management. Traditional chemical pesticide application relies on subjective judgment, leading to fluctuations in pesticide usage, low application efficiency, and environmental pollution. This study proposes a machine vision-based precision spraying control system for orchards. First, a canopy leaf wall area calculation method was developed based on a multi-iteration GrabCut image segmentation algorithm, and a spray volume calculation model was established. Next, a fuzzy adaptive control algorithm based on an extended state observer (ESO) was proposed, along with the design of flow and pressure controllers. Finally, the precision spraying system’s performance tests were conducted in laboratory and field environments. The indoor experiments consisted of three test sets, each involving six citrus trees, totaling eighteen trees arranged in two staggered rows, with an interrow spacing of 3.4 m and an intra-row spacing of 2.5 m; the nozzle was positioned approximately 1.3 m from the canopy surface. Similarly, the field experiments included three test sets, each selecting eight citrus trees, totaling twenty-four trees, with an average height of approximately 1.5 m and a row spacing of 3 m, representing a typical orchard environment for performance validation. Experimental results demonstrated that the system reduced spray volume by 59.73% compared to continuous spraying, by 30.24% compared to PID control, and by 19.19% compared to traditional fuzzy control; meanwhile, the pesticide utilization efficiency increased by 61.42%, 26.8%, and 19.54%, respectively. The findings of this study provide a novel technical approach to improving agricultural production efficiency, enhancing fruit quality, reducing pesticide use, and promoting environmental protection, demonstrating significant application value.

At present, more patients suffer from multiple chronic diseases. However, the hospital's existing chronic disease management is carried out according to the department. This means that a patient needs to go to more than one department for a chronic disease treatment. Therefore, this study proposes 6 dimensions (organizational management, medical service support, medical service, community alliance, self-management support, management information system) and 36 questions, to help evaluate the current chronic disease management system in China's large third-class hospitals. In this study, 143 survey samples from doctors and nurses were collected. A principal component analysis was used to extract three key elements of chronic disease management service delivery system (service management organization, management information system, medical core service). Then, multiple regression was used to establish the relationship model between the overall performance of the system and the main elements. Three key service nodes of the system (medical specialist support, patient tracking management and personalized intervention) were determined according to the weight of the regression model. The regression coefficients of the above three main elements show a similar impact on the overall performance of the system, but the key service nodes under each major element have relative differences, including medical specialist support, patient tracking management and personalized intervention. Finally, to establish a chronic disease management system with multiple departmental continuous care for chronic diseases, it is necessary to improve the chronic disease management system from three aspects of medical specialty support, patient tracking management and personalized intervention. This paper proposes corresponding improvement strategies.

Optical patterning of colloidal particles is a scalable and cost-effective approach for creating multiscale functional structures. Existing methods often use high-intensity light sources and customized optical setups, making them less feasible for large-scale microfabrication processes. Here, we report an optical patterning method for semiconductor nanoparticles by light-triggered modulation of their surface charge. Rather than using light as the primary energy source, this method utilizes UV-induced cleavage of surface ligands to modify surface charges, thereby facilitating the self-assembly of nanoparticles on a charged substrate via electrostatic interactions. By using citrate-treated ZnO nanoparticles, uniform ZnO patterns with variable thicknesses can be achieved. These multilayered ZnO patterns are fabricated into a UV detector with an on/off ratio exceeding 10 4 . Our results demonstrate a simple yet effective way to pattern semiconductor nanoparticles, facilitating the large-scale integration of functional nanomaterials into emerging flexible and robotic microdevices. The integration of colloidal nanoparticles into microdevices is essential for several advanced technologies. Here, the authors have developed a scalable method of UV-triggered surface charge modulation for the rapid surface patterning with semiconductor nanoparticles.

Post-transcriptional modifications on mRNA are crucial for mRNA fate and function. The current lack of a comprehensive method for high-coverage and sensitive quantitative analysis of mRNA modifications significantly limits the discovery of new mRNA modifications and understanding mRNA modifications’ occurrence, dynamics and function. Here, a highly sensitive, high-throughput and robust LC-MS/MS-based technique, mRQuant, was developed to simultaneously detect and quantify 84 modified ribonucleosides in cellular mRNA. Using mRQuant, we quantified 32–34 modified ribonucleosides across several human cancer and non-cancer cell lines and uncovered cancer- and cancer type-specific signatures. Analyses of cisplatin- and paclitaxel-treated HeLa cells and drug-resistant variants revealed several drug resistance-associated modifications. Among them, m 1 A exhibited significant differences across multiple cell types and between cancerous and non-cancerous cells. Knocking down mRNA m 1 A writer or eraser protein resulted in altered cell viability, cell cycle and apoptosis in HeLa cells, suggesting a role of mRNA m 1 A in cancer. Transcriptomic and proteomic analyses further revealed the molecular mechanisms underlying the phenotypic variation.

Background The prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing worldwide. Accumulating evidence suggests that serum ferritin and uric acid (UA) are strongly associated with the risk of NAFLD, but no consensus has been reached. Objective We sought to demonstrate the association between serum ferritin, UA levels, and NAFLD risk in a large cohort study. Methods We separated 2,049 patients into non-NAFLD and NAFLD groups. The NAFLD group had four subgroups based on serum ferritin and four subgroups based on UA quartile levels. We used binary logistic regression to evaluate the correlation between serum ferritin, UA, and NAFLD. Additionally, an area under the curve (AUC) of receiver operating characteristic analysis (ROC) was used to predict the diagnostic value of combined serum ferritin and UA for NAFLD. Results Serum ferritin and UA levels were higher in the NAFLD group compared with the non-NAFLD group. Serum lipid and liver transaminase concentrations were elevated with the increase of serum ferritin and UA. The logistic regression results showed an independent correlation between serum ferritin, UA, and NAFLD. In the NAFLD group, the AUC value of serum ferritin and UA was 0.771. Conclusions Increased serum ferritin and UA levels are independent risk factors for NAFLD. Increased serum UA is a stronger risk factor for NAFLD than elevated serum ferritin. Serum ferritin and UA can be important predictors of NAFLD risk.