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This study developed a scientific process parameter setup based on nozzle pressure and screw position, with the process parameter search sequence being injection speed, V/P switchover position, packing pressure, and packing time. Unlike previous studies, this study focuses on the scientific process parameter setup of experiments and simulations, as well as on the implementation of calibration. Experiments and simulations had the same trend of results in the scientific process parameter setup. Although the experiments and simulations had the same trend, the machine response caused parameter errors. After setting the time constant of the simulations, injection speed profiles from the experiments and simulations became closely aligned. The simulation results for the injection speed and V/P switchover position became closer to the experiment results than the results of the uncalibrated simulation. The error between the simulated and experimental injection speed was reduced from 20% to 6% after applying time constant calibration. The V/P switchover point error was also reduced from 11% to 5%, highlighting the effectiveness of the time constant to calibrate the simulation.

Injection molding is a highly nonlinear procedure that is easily influenced by various external factors, thereby affecting the stability of the product’s quality. High-speed injection molding is required for production due to the rapid cooling characteristics of thin-walled parts, leading to increased manufacturing complexity. Consequently, establishing appropriate process parameters for maintaining quality stability in long-term production is challenging. This study selected a hot runner mold with a thin wall fitted with two external sensors, a nozzle pressure sensor and a tie-bar strain gauge, to collect data regarding the nozzle peak pressure, the timing of peak pressure, the viscosity index, and the clamping force difference value. The product weight was defined as the quality indicator, and a standardized parameter optimization process was constructed, including injection speed, V/P switchover point, packing, and clamping force. Finally, the optimized process parameters were applied to the adaptive process control experiments using the developed control system operated within the micro-controller unit (MCU). The results revealed that the control system effectively stabilized the product weight variation and standard deviation of 0.677% and 0.0178 g, respectively.

The injection-molding process is a non-linear process, and the product quality and long-term production stability are affected by several factors. To stabilize the product quality effected by these factors, this research establishes a standard process parameter setup procedure and an adaptive process control system based on the data collected by a nozzle pressure sensor and a tie-bar strain gauge to achieve this goal. In this research, process parameters such as the V/P switchover point, injection speed, packing pressure, and clamping force are sequentially optimized based on the characteristics of the pressure profile. After the optimization process, this research defines the standard quality characteristics through the optimized process parameters and combines it with the adaptive process control system in order to achieve the purpose of automatic adjustment of the machine and maintain high-quality production. Finally, three different viscosity materials are used to verify the effectiveness of the optimization procedure and the adaptive process control system. With the system, the variation of product weight was reduced to 0.106%, 0.092%, and 0.079%, respectively.

This study established a real-time measurement system to monitor the melt quality in an injection molding process using a pressure sensor installed on the nozzle and a strain gauge installed on the tie bar. Based on the sensing curves from these two external sensors, the characteristic values of nozzle pressure and clamping force were used to optimize parameters. This study defined product weight as a quality indicator and developed a scientific molding parameter setup process. The optimization sequence of parameters is injection speed, V/P switchover point, packing pressure, packing time, and clamping force. Finally, an adaptive process control system was established based on the online quality characteristic values to maintain product quality consistency. Continuous production experiments were conducted at two sites to verify the system’s effectiveness. The results revealed that the optimized process parameters can ensure product weight stability during long-term production. Furthermore, using the adaptive process control system further enhanced product weight stability at both sites, reducing the standard deviation of product weight to 0.0289 g and 0.0148 g, and the coefficient of variation to 0.065% and 0.035%, respectively.

Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer ( http://www.cellchat.org/ ) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues. Single-cell methods record molecule expressions of cells in a given tissue, but understanding interactions between cells remains challenging. Here the authors show by applying systems biology and machine learning approaches that they can infer and analyze cell-cell communication networks in an easily interpretable way.

Mitochondria are critical for providing energy to maintain cell viability. Oxidative phosphorylation involves the transfer of electrons from energy substrates to oxygen to produce adenosine triphosphate. Mitochondria also regulate cell proliferation, metastasis, and deterioration. The flow of electrons in the mitochondrial respiratory chain generates reactive oxygen species (ROS), which are harmful to cells at high levels. Oxidative stress caused by ROS accumulation has been associated with an increased risk of cancer, and cardiovascular and liver diseases. Glutathione (GSH) is an abundant cellular antioxidant that is primarily synthesized in the cytoplasm and delivered to the mitochondria. Mitochondrial glutathione (mGSH) metabolizes hydrogen peroxide within the mitochondria. A long-term imbalance in the ratio of mitochondrial ROS to mGSH can cause cell dysfunction, apoptosis, necroptosis, and ferroptosis, which may lead to disease. This study aimed to review the physiological functions, anabolism, variations in organ tissue accumulation, and delivery of GSH to the mitochondria and the relationships between mGSH levels, the GSH/GSH disulfide (GSSG) ratio, programmed cell death, and ferroptosis. We also discuss diseases caused by mGSH deficiency and related therapeutics.

Theranostics, an integrated therapeutic and diagnostic system, can simultaneously monitor the real-time response of therapy. Different imaging modalities can combine with a variety of therapeutic moieties in theranostic nanoagents. In this study, a multi-functionalized, integrated theranostic nanoagent based on folate-conjugated reducible polyethylenimine passivated carbon dots (fc-rPEI-Cdots) is developed and characterized. These nanoagents emit visible blue photoluminescence under 360 nm excitation and can encapsulate multiple siRNAs (EGFR and cyclin B1) followed by releasing them in intracellular reductive environment. In vitro cell culture study demonstrates that fc-rPEI-Cdots is a highly biocompatible material and a good siRNA gene delivery carrier for targeted lung cancer treatment. Moreover, fc-rPEI-Cdots/pooled siRNAs can be selectively accumulated in lung cancer cells through receptor mediated endocytosis, resulting in better gene silencing and anti-cancer effect. Combining bioimaging of carbon dots, stimulus responsive property, gene silencing strategy and active targeting motif, this multi-functionalized, integrated theranostic nanoagent may provide a useful tool and platform to benefit clinicians adjusting therapeutic strategy and administered drug dosage in real time response by monitoring the effect and tracking the development of carcinomatous tissues in diagnostic and therapeutic aspects.

Influenza is one of the most common human respiratory diseases, and represents a serious public health concern. However, the high mutability of influenza viruses has hampered vaccine development, and resistant strains to existing anti-viral drugs have also emerged. Novel anti-influenza therapies are urgently needed, and in this study, we describe the anti-viral properties of a Spirulina ( Arthrospira platensis ) cold water extract. Anti-viral effects have previously been reported for extracts and specific substances derived from Spirulina, and here we show that this Spirulina cold water extract has low cellular toxicity, and is well-tolerated in animal models at one dose as high as 5,000 mg/kg, or 3,000 mg/kg/day for 14 successive days. Anti-flu efficacy studies revealed that the Spirulina extract inhibited viral plaque formation in a broad range of influenza viruses, including oseltamivir-resistant strains. Spirulina extract was found to act at an early stage of infection to reduce virus yields in cells and improve survival in influenza-infected mice, with inhibition of influenza hemagglutination identified as one of the mechanisms involved. Together, these results suggest that the cold water extract of Spirulina might serve as a safe and effective therapeutic agent to manage influenza outbreaks, and further clinical investigation may be warranted.

Many studies have recently used several deep learning methods for detecting skin cancer. However, hyperspectral imaging (HSI) is a noninvasive optics system that can obtain wavelength information on the location of skin cancer lesions and requires further investigation. Hyperspectral technology can capture hundreds of narrow bands of the electromagnetic spectrum both within and outside the visible wavelength range as well as bands that enhance the distinction of image features. The dataset from the ISIC library was used in this study to detect and classify skin cancer on the basis of basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and seborrheic keratosis (SK). The dataset was divided into training and test sets, and you only look once (YOLO) version 5 was applied to train the model. The model performance was judged according to the generated confusion matrix and five indicating parameters, including precision, recall, specificity, accuracy, and the F1-score of the trained model. Two models, namely, hyperspectral narrowband image (HSI-NBI) and RGB classification, were built and then compared in this study to understand the performance of HSI with the RGB model. Experimental results showed that the HSI model can learn the SCC feature better than the original RGB image because the feature is more prominent or the model is not captured in other categories. The recall rate of the RGB and HSI models were 0.722 to 0.794, respectively, thereby indicating an overall increase of 7.5% when using the HSI model.

Background Powdery mildew (PM) is one of the important soybean diseases, and host resistance could practically contribute to soybean PM management. To date, only the Rmd locus on chromosome (Chr) 16 was identified through traditional QTL mapping and GWAS, and it remains unclear if the bulk segregant RNA-Seq (BSR-Seq) methodology is feasible to explore additional PM resistance that might exist in other varieties. Results BSR-Seq was applied to contrast genotypes and gene expressions between the resistant bulk (R bulk) and the susceptible bulk (S bulk), as well as the parents. The ∆(SNP-index) and G’ value identified several QTL and significant SNPs/Indels on Chr06, Chr15, and Chr16. Differentially expressed genes (DEGs) located within these QTL were identified using HISAT2 and Kallisto, and allele-specific primers (AS-primers) were designed to validate the accuracy of phenotypic prediction. While the AS-primers on Chr06 or Chr15 cannot distinguish the resistant and susceptible phenotypes, AS-primers on Chr16 exhibited 82% accuracy prediction with an additive effect, similar to the SSR marker Satt431. Conclusions Evaluation of additional AS-primers in the linkage disequilibrium (LD) block on Chr16 further confirmed the resistant locus, derived from the resistant parental variety ‘Kaohsiung 11’ (‘KS11’), not only overlaps with the Rmd locus with unique up-regulated LRR genes (Glyma.16G213700 and Glyma.16G215100), but also harbors a down-regulated MLO gene (Glyma.16G145600). Accordingly, this study exemplified the feasibility of BSR-Seq in studying biotrophic disease resistance in soybean, and showed the genetic makeup of soybean variety ‘KS11’ comprising the Rmd locus and one MLO gene.