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Abstract Background and Purpose Neoadjuvant chemotherapy (NACT) has become an essential component of the comprehensive treatment of cervical squamous cell carcinoma (CSCC). However, not all patients respond to chemotherapy due to individual differences in sensitivity and tolerance to chemotherapy drugs. Therefore, accurately predicting the sensitivity of CSCC patients to NACT was vital for individual chemotherapy. This study aims to construct a machine learning radiomics model based on magnetic resonance imaging (MRI) to assess its efficacy in predicting NACT susceptibility among CSCC patients. Methods This study included 234 patients with CSCC from two hospitals, who were divided into a training set (n = 180), a testing set (n = 20), and an external validation set (n = 34). Manual radiomic features were extracted from transverse section MRI images, and feature selection was performed using the recursive feature elimination (RFE) method. A prediction model was then generated using three machine learning algorithms, namely logistic regression, random forest, and support vector machines (SVM), for predicting NACT susceptibility. The model's performance was assessed based on the area under the receiver operating characteristic curve (AUC), accuracy, and sensitivity. Results The SVM approach achieves the highest scores on both the testing set and the external validation set. In the testing set and external validation set, the AUC of the model was 0.88 and 0.764, and the accuracy was 0.90 and 0.853, the sensitivity was 0.93 and 0.962, respectively. Conclusions Machine learning radiomics models based on MRI images have achieved satisfactory performance in predicting the sensitivity of NACT in CSCC patients with high accuracy and robustness, which has great significance for the treatment and personalized medicine of CSCC patients.

The Agaricus bisporus (Button mushroom) stands out as one of the most prolific edible fungi which offers robust flavor and nutrition. Nonetheless, this mushroom contains high moisture levels and intense respiration. Without appropriate postharvest preservation techniques, the button mushroom readily experiences browning and senescence. To ensure optimum quality, prompt cooling and appropriate storage conditions are essential. This present research investigated the postharvest quality of button mushrooms stored in a controlled atmosphere (CA) with different initial gas compositions. The findings revealed that button mushrooms in the CA group demonstrated considerable enhancements in appearance and overall quality, effectively delaying browning and senescence compared to those in the control group. The optimal gas composition is 1–3% O2 and 15–17% CO2 (CAII), which effectively inhibited the expression of polyphenol oxidase (PPO)- and lactase (LAC)-related genes in the button mushroom, maintaining a high L* value. Furthermore, the application of 1–3% O2 and 15–17% CO2 (CAII) not only preserved visual quality but also extended the postharvest shelf life of the button mushroom by minimizing metabolic activities that contribute to senescence. Moreover, 1–3% O2 and 15–17% CO2 (CAII) storage also reduced the expression levels of genes associated with ethylene synthesis, which is reflected in the gradual decrease in cell membrane permeability. Consequently, this research underscores the critical importance of controlled atmosphere storage in improving the marketability and sustainability of this widely consumed mushroom. [ABSTRACT FROM AUTHOR]

During early pregnancy in mice, the establishment of uterine receptivity and endometrial decidualization require the extensive proliferation and differentiation of endometrial epithelial cells or stromal cells. Pin1 has been suggested to act as a molecular 'timer' of the cell cycle and is involved in the regulation of cellular proliferation and differentiation by binding many cell‐cycle regulatory proteins. However, its physiological role during early pregnancy is still not fully understood. Here, we employed immunohistochemistry to determine the spatiotemporal pattern of Pin1 expression during early pregnancy. We found that Pin1 was mainly localized in subluminal stromal cells on day 4, in the decidual zone on days 5 to 8 of pregnancy and in artificial decidualization. Using a uterine stromal cell culture system, we found that progesterone, but not estrogen, induced the expression of Pin1 in a progesterone receptor‐dependent manner. Inhibition of Pin1 in the uterus leads to impaired embryo implantation and decidualization in mice. Notably, a decrease in Pin1 activation affected the functional execution of several implantation‐ or decidualization‐related factors. These findings provide new evidence for a previously unknown function of Pin1 in mediating embryo implantation and decidualization during successful pregnancy establishment and maintenance. [ABSTRACT FROM AUTHOR]

Partition control of the fan array within a direct air‐cooling condenser (DACC) is recognized as a highly effective energy‐saving measure and a means to mitigate the adverse effects of ambient winds. This study focuses on the development of a dynamic partition model, which establishes the relationship between mass flow rate and fan array speed under windy conditions. The model is grounded in multivariable Hammerstein controlled autoregressive moving average (H‐CARMA) systems. Additionally, an innovative extended stochastic gradient algorithm, founded on the hierarchical identification principle, is introduced to estimate the model's unknown parameters. The recursive least square is employed as a benchmark to verify the accuracy and efficiency of the approach. Subsequently, leveraging the identified model, the model predictive controller based on the differential evolution method is designed to derive the optimal control law. Finally, simulation test and analysis are carried out to testify the effectiveness of our control strategy. The experiment result shows that the dynamic characteristics of the system have been significantly improved and the fan power consumption is reduced by 3.5% compared to the PID controller. [ABSTRACT FROM AUTHOR]

PCB defect detection aims to identify the presence of gaps, open circuits, short circuits, and other defects in the PCB boards produced in the industry. Designing effective deep learning algorithms is crucial to finding a solution. Previously proposed PCB defect detection algorithms are limited in detecting tiny objects in high-density. Directly applying previous models to tackle PCB defect detection tasks will cause serious issues, such as missed detection and false detection. In this paper, we present a detection algorithm for tiny PCB defect targets in high-density regions to solve the above-mentioned problems. We firstly propose a detection head to detect tiny objects. Then, we design a four-channel feature fusion mechanism to fuse four different scale features and add an attention mechanism to find the attention region in scenarios with dense objects. Finally, we achieved accurate detection of tiny targets in high-density areas. Experiments were performed on the publicly available PCB defect dataset from Peking University. Our mAP@.5:.95 achieves 48.6%, while mAP@0.5 exceeds 90%. Compared with YOLOX and YOLOv5, our improved model can better localize tiny objects in high-density scenes. The experimental results certify that our model can obtain higher performance in comparison with the baseline and the state of the art. [ABSTRACT FROM AUTHOR]

Bioluminogenic probes emerged as powerful tools for imaging and analysis of various bioanalyses, but traditional approaches would be limited to the low sensitivity during determine the low activity of protease in clinical specimens. Herein, we proposed a caged luciferase inhibitor‐based bioluminescence‐switching strategy (CLIBS) by using a cleavable luciferase inhibitor to modulate the activity of luciferase reporter to amplify the detective signals, which led to the enhancement of detection sensitivity, and enabled the determination of circulating Aminopeptidase N (APN) activity in thousands of times diluted serum. By applying the CLIBS to serum samples in non–small cell lung cancer (NSCLC) patients from two clinical cohorts, we revealed that, for the first time, higher circulating APN activities but not its concentration, were associated with more NSCLC metastasis or higher metastasis stages by subsequent clinical analysis, and can serve as an independent factor for forecasting NSCLC patients' risk of metastasis. [ABSTRACT FROM AUTHOR]