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This essay focuses on the topic of learning, examining it from two perspectives: the learning process of the main character Kya in the novel Where the Crawdads Sing, and the learning experiences of the young generation growing up in the information age. The essay has explored the elements of grit (passion and persistence), boredom, self-complexity, learning outcomes, and the learning environment through Kya's learning journey, drawing insights from psychological research and educational theories in the first section of the essay. Additionally, the essay analyzes the significant changes in the learning environment brought about by the shift from a static information age to the dynamic era of technology. It delves into the learning challenges and obstacles faced by the young generation, primarily drawing from Dr. Jean Marie Twenge's research on this demographic. In the conclusion, a potential solution is proposed by applying Kya's learning spirit to address the contemporary learning issues faced by these young digital natives.

Recently, latent models have continued to find advantages in statistical process monitoring, especially with multifarious extensions coping with the nonlinearity and dynamics confronted by the pattern extraction. However, when later performing the famous T2 statistic on the patterns, the issue of non-Gaussianity has rarely been focused alongside, hence vitiating the control of Type-I and -II error in practice. In this paper, a model, in the name of recurrent Kalman variational auto-encoder (RKVAE), is theoretically derived for handling these properties simultaneously, and proposed for its use on monitoring general complex processes. The main idea is to build a monitoring panel on the basis of further reasoning the dynamics underlying the nonlinearly extracted Gaussian features. The model hierarchically consists of three parts: the vanilla VAE for encoding latent Gaussian features, a linear Gaussian state-space model (LGSSM) for temporally reasoning these features, and an RNN for recurrently updating the online LGSSM by fusing several candidate parameters. For the latter K in the name, it is the Kalman filter (KF) that facilitates the feature noise filtering and LGSSM identification. The model has, structurally, a good capacity to be extensible from both the depth and width when modelling complex processes. Finally, based on the explicit formula of LGSSM, a detection panel is designed, and two case studies are given to demonstrate its effectiveness in monitoring and relevant analysis.

Lingyi Zhang,1,* Zhuoling Zheng,2,* Wudi Ma,1 Shuyu Zhang,3,4 Faling Xue,1 Haini Wang,5 Yongqi He,6 Fang Ye,1 Shouning Zhou,6 Yongzi Wen,3,4 Xiaoyan Li,2 Wenqi Huang,1 Min Huang,3,4 Jiali Li,3,4,* Zhongxing Wang1,* 1Department of Anesthesiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China; 2Department of Pharmacy, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People’s Republic of China; 3Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China; 4Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China; 5Department of Pharmacy, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, People’s Republic of China; 6Department of Pharmacy, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhongxing WangDepartment of Anesthesiology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People’s Republic of ChinaTel +86-20-87755766Email wzhxing@mail.sysu.edu.cnJiali LiInstitute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, 132 Waihuan Dong Road, University City, Guangzhou, 510006, People’s Republic of ChinaTel +86-20-39943034Email lijiali5@mail.sysu.edu.cnPurpose: Propofol is one of the most commonly used intravenous sedatives in general anesthesia, while the individual variations of propofol are apparent. The objective of this study was to investigate the influence of genetic variations in GABAergic neurons and glutamatergic neurons on time to loss of consciousness (LOC) and the incidence of hypotension during anest

Jiangqing Huang,1 Shengcen Zhang,1 Shuyu Zhang,2 Zhichang Zhao,3 Yingping Cao,1 Min Chen,2 Bin Li1 1Department of Clinical Laboratory, Fujian Medical University Union Hospital, Fuzhou, Fujian 350001, People’s Republic of China; 2Department of Laboratory Medicine, Fujian Medical University, Fuzhou, Fujian 350001, People’s Republic of China; 3Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, Fujian 350001, People’s Republic of ChinaCorrespondence: Bin LiDepartment of Clinical Laboratory, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, Fujian 350001, People’s Republic of ChinaEmail leonlee307@hotmail.comBackground: Sequence type 1193 is a new such lineage among fluoroquinolone-resistant Escherichia coli, which has risen dramatically within the last several years. However, reasons for rapid emergence and successful spread of E. coli ST1193 remain unclear. The aim of this study was to compare the pathogenicity and survivability features of E. coli ST1193 with global epidemic lineage, ST131.Methods: A total of 30 E. coli were used in this study. Isolates were divided into two groups, ST1193 (n=15) and ST131 (n=15). Adhesion and invasion to T24 cells and resistance to serum were quantified and compared among two groups. Biofilm formation capacity was assessed by crystal violet assay. Macrocolony formation was assessed on macrocolony formation plates. Resistance to hydrogen peroxide was performed by broth microdilution. RAW264.7 cells were used to assess the anti-phagocytic function of different isolates.Results: Adhesion and invasion assays revealed that E. coli ST1193 could adhere and invade T24 cells (p < 0.05). 93.3% of E. coli ST1193 could form biofilms. The majority of E. coli ST1193 (66.7%) possessed no curli/no cellulose on macrocolony formation plates. E. coli ST1193 showed significant growth in serum and hydrogen peroxide and illustrated higher anti-phagocytic function to RAW

Existing traffic flow forecasting approaches by deep learning models achieve excellent success based on a large volume of datasets gathered by governments and organizations. However, these datasets may contain lots of user's private data, which is challenging the current prediction approaches as user privacy is calling for the public concern in recent years. Therefore, how to develop accurate traffic prediction while preserving privacy is a significant problem to be solved, and there is a trade-off between these two objectives. To address this challenge, we introduce a privacy-preserving machine learning technique named federated learning and propose a Federated Learning-based Gated Recurrent Unit neural network algorithm (FedGRU) for traffic flow prediction. FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism rather than directly sharing raw data among organizations. In the secure parameter aggregation mechanism, we adopt a Federated Averaging algorithm to reduce the communication overhead during the model parameter transmission process. Furthermore, we design a Joint Announcement Protocol to improve the scalability of FedGRU. We also propose an ensemble clustering-based scheme for traffic flow prediction by grouping the organizations into clusters before applying FedGRU algorithm. Through extensive case studies on a real-world dataset, it is shown that FedGRU's prediction accuracy is 90.96% higher than the advanced deep learning models, which confirm that FedGRU can achieve accurate and timely traffic prediction without compromising the privacy and security of raw data., Comment: This paper is in the second round of under review of the IEEE Internet of Things Journal

Winterwheat is one of themajor cereal crops inChina. The spatial distribution ofwinterwheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer's and user's accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 datawere combined. As our results also revealed, itwas always good to classifymulti-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase's and reviving phase's data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical s

Zhenyu Wang1 *, Wenhao Shen1 *, Xugang Li,2 Yang Feng,1 Kun Qian,1 Gaoren Wang,3 Yiying Gao,1 Xiaohui Xu,4 Shuyu Zhang,1 Ling Yue,1 Jianping Cao1 1School of Radiation Medicine and Protection (SRMP) of Soochow University, State Key Laboratory of Radiation Medicine and Protection, Suzhou 215123, People’s Republic of China; 2Department of Radiotherapy, Anshan Cancer Hospital, Anshan 114036, People’s Republic of China; 3Nantong Tumor Hospital, Affiliated Tumor Hospital of Nantong University, Nantong 226361, People’s Republic of China; 4Department of General Surgery, The First People’s Hospital of Taicang, Taicang Affiliated Hospital of Soochow University, Suzhou 215400, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jianping Cao; Ling Yue Tel +86-512-65880037Email jpcao@suda.edu.cn; yueling@suda.edu.cnPurpose: As radiation therapy is widely used for the management of pancreatic cancer, identifying novel targets to improve the radiosensitivity of cancer cells is beneficial. Rosiglitazone, a specific peroxisome proliferator-activated receptor γ (PPARγ) agonist, has an inhibitory effect on various types of cancer cells. The purpose of this paper is to investigate the effect of rosiglitazone on the radiosensitivity of pancreatic cancer cells and the potential mechanism.Materials and Methods: PPARγ expression in pancreatic cancer and adjacent tissues was evaluated using immunohistochemistry analysis. The viability, migration and invasion ability of PANC1 and PaTu8988 cells were detected using MTT assay, scratch-wound assay and transwell invasion assay. The effect of rosiglitazone on radiosensitivity of the cells was determined using the clonogenic assay. PANC1 cells were inoculated into BALB/c mice to establish tumors. Microarray was used to investigate changes of genes involved.Results: Higher PPARγ expression was demonstrated in pancreatic can

Judong Luo,1,* Xin Jiang,1,* LiLi Cao,2,* Kejun Dai,1 Shuyu Zhang,3,4 Xin Ge,3,4 Xifa Zhou,1 Xujing Lu1 1Department of Radiotherapy, Changzhou Tumor Hospital, Soochow University, Changzhou, People's Republic of China; 2Department of Molecular Radiobiology, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan; 3School of Radiation Medicine and Protection and Jiangsu Provincial Key Laboratory of Radiation Medicine and Protection, 4Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions and School for Radiological and Interdisciplinary Sciences (RAD-X), Soochow University, Suzhou, People's Republic of China  *These authors contributed equally to this work Objective: Esophageal squamous cell carcinoma (ESCC) is one of the deadliest cancers worldwide. Yin Yang 1 (YY1) is a ubiquitous and multifunctional zinc-finger transcription factor that plays important biological functions in cell homeostasis and tumorigenesis. The purpose of this study was to investigate the expression of YY1 in different ESCC tissues and the potential relationship with clinicopathological features. Methods: One hundred and four ESCC tissues were collected in this study. The protein levels of YY1 were measured by immunohistochemistry. TE-1 cell invasion in vitro was assessed using the Transwell assay. Results: There were no obvious differences between expression levels in patients over age 64 and those younger than 64, and no noticeable distinction was observed between males and females. However, the YY1 protein level was significantly higher in ESCC tissues with lymph node metastasis than those without lymph node metastasis (P=0.042). Furthermore, the expression of the YY1 protein was stronger in stage III–IV patients than in stage I–II patients (P=0.002), but the protein levels between different histological grades (well, moderate, or poor) showed no statistical significance

Thermal flying-height control (TFC) is now a key technology used in hard-disk drives (HDD) as an effective way to push the magnetic spacing to sub-5 nm. Precise control of the TFC sliders’ actuated flying-height (FH) is a major consideration in improving the read/write capability as well as reducing the reliability problem. In this paper, we investigate the response of TFC sliders to altitude changes with a focus on the variation of actuated FH. Numerical and experimental results both indicate an increase in the actuated FH at higher altitudes. Simulations show that the increased TFC protrusion and less air bearing pushback on sliders at higher altitudes contribute to this increase. This study is of practical importance for improving heater and air bearing surface design to reduce the TFC sliders’ sensitivities to altitude changes.

Hong Liu-Seifert, Shuyu Zhang, Deborah D’Souza, Vladimir SkljarevskiLilly Research Laboratories, Indianapolis, IN, USAPurpose: The baseline-observation-carried-forward (BOCF) approach is one method to handle missing data from early treatment discontinuation. We examined modifications of this approach, taking into consideration treatment-related and nontreatment-related reasons for discontinuation.Methods: Two duloxetine chronic pain trials (placebo-controlled) were used to examine the impact of different analytical methods on study outcome. Reasons for discontinuation were categorized as treatment-related and nontreatment-related. Missing data in the primary efficacy outcome were handled using five statistical methods: mixed-model repeated measures (MMRM), last-observation-carried-forward (LOCF), BOCF, modified BOCF (mBOCF, discontinuation due to treatment-related reasons, ie, adverse events [AEs] or lack of efficacy), and aeBOCF (discontinuation due to AEs only).Results: Duloxetine was superior to placebo on mean change from baseline in Brief Pain Inventory average pain rating, using MMRM (study 1, P = 0.004; study 2, P < 0.001), LOCF (study 1, P = 0.019; study 2, P < 0.001), BOCF (study 1, P = 0.019; study 2, P = 0.013), and mBOCF (study 1, P = 0.041; study 2, P = 0.005). Using aeBOCF, duloxetine was superior to placebo in study 2 (P = 0.005) and numerically better in study 1 (P = 0.075).Conclusion: Due to the different assumptions made by various methods regarding accounting for missing data, the analytical methods chosen may influence the interpretation of study results. Consideration should be given to the effect of actual treatment outcomes from patients. Employing different statistical approaches such as sensitivity analyses may help to assess the robustness of the study results and provide a more accurate reflection of the treatment outcome.Keywords: discontinuation, treatment-related, pain, statistical