Appendix_I – Supplemental material for CEO long-term orientation and elite university education

Supplemental material, Appendix_I for CEO long-term orientation and elite university education by Danny Miller and Xiaowei Xu in Strategic Organization</p

Chemical Labeling of 5-Iodo-2′-deoxyuridine with 4-Ethynyl-N-ethyl-1,8-naphthalimide Using Copper-Free Sonogashira Cross-Coupling in Aqueous Medium

<div><p></p><p>The utility of Sonogashira cross-coupling reactions in organic synthesis is indisputable. However, the palladium catalytic Sonogashira reaction in mild condition is still in its infancy. Here, we reported the synthesis of a fluorescent nucleoside analog by using a copper-free Sonogashira cross-coupling reaction in a satisfactory yield. This reaction occurred at 37 °C in the open air and aqueous medium, and avoided the toxicity of Cu(I). The mild Sonogashira reaction provided the possibility of fluorescent labeling of nucleoside mimics in living cells.</p> <p>[Supplementary materials are available for this article. Go to the publisher's online edition of <i>Synthetic Communications®</i> for the following free supplemental resource(s): Full experimental and spectral details.]</p> </div

Automatic delineation of malignancy in histopathological head and neck slides-0

<p><b>Copyright information:</b></p><p>Taken from "Automatic delineation of malignancy in histopathological head and neck slides"</p><p>http://www.biomedcentral.com/1471-2105/8/S7/S17</p><p>BMC Bioinformatics 2007;8(Suppl 7):S17-S17.</p><p>Published online 1 Nov 2007</p><p>PMCID:PMC2099485.</p><p></p>r 128 × 128 subimages. The four green points are the centers of the 4 subimages. Each subimage has a 50% overlap with another subimage

Table_1_DeepCausality: A general AI-powered causal inference framework for free text: A case study of LiverTox.XLSX

Causality plays an essential role in multiple scientific disciplines, including the social, behavioral, and biological sciences and portions of statistics and artificial intelligence. Manual-based causality assessment from a large number of free text-based documents is very time-consuming, labor-intensive, and sometimes even impractical. Herein, we proposed a general causal inference framework named DeepCausality to empirically estimate the causal factors for suspected endpoints embedded in the free text. The proposed DeepCausality seamlessly incorporates AI-powered language models, named entity recognition and Judea Pearl's Do-calculus, into a general framework for causal inference to fulfill different domain-specific applications. We exemplified the utility of the proposed DeepCausality framework by employing the LiverTox database to estimate idiosyncratic drug-induced liver injury (DILI)-related causal terms and generate a knowledge-based causal tree for idiosyncratic DILI patient stratification. Consequently, the DeepCausality yielded a prediction performance with an accuracy of 0.92 and an F-score of 0.84 for the DILI prediction. Notably, 90% of causal terms enriched by the DeepCausality were consistent with the clinical causal terms defined by the American College of Gastroenterology (ACG) clinical guideline for evaluating suspected idiosyncratic DILI (iDILI). Furthermore, we observed a high concordance of 0.91 between the iDILI severity scores generated by DeepCausality and domain experts. Altogether, the proposed DeepCausality framework could be a promising solution for causality assessment from free text and is publicly available through https://github.com/XingqiaoWang/https-github.com-XingqiaoWang-DeepCausality-LiverTox.</p

Table_3_DeepCausality: A general AI-powered causal inference framework for free text: A case study of LiverTox.XLSX

Causality plays an essential role in multiple scientific disciplines, including the social, behavioral, and biological sciences and portions of statistics and artificial intelligence. Manual-based causality assessment from a large number of free text-based documents is very time-consuming, labor-intensive, and sometimes even impractical. Herein, we proposed a general causal inference framework named DeepCausality to empirically estimate the causal factors for suspected endpoints embedded in the free text. The proposed DeepCausality seamlessly incorporates AI-powered language models, named entity recognition and Judea Pearl's Do-calculus, into a general framework for causal inference to fulfill different domain-specific applications. We exemplified the utility of the proposed DeepCausality framework by employing the LiverTox database to estimate idiosyncratic drug-induced liver injury (DILI)-related causal terms and generate a knowledge-based causal tree for idiosyncratic DILI patient stratification. Consequently, the DeepCausality yielded a prediction performance with an accuracy of 0.92 and an F-score of 0.84 for the DILI prediction. Notably, 90% of causal terms enriched by the DeepCausality were consistent with the clinical causal terms defined by the American College of Gastroenterology (ACG) clinical guideline for evaluating suspected idiosyncratic DILI (iDILI). Furthermore, we observed a high concordance of 0.91 between the iDILI severity scores generated by DeepCausality and domain experts. Altogether, the proposed DeepCausality framework could be a promising solution for causality assessment from free text and is publicly available through https://github.com/XingqiaoWang/https-github.com-XingqiaoWang-DeepCausality-LiverTox.</p

Automatic delineation of malignancy in histopathological head and neck slides-1

<p><b>Copyright information:</b></p><p>Taken from "Automatic delineation of malignancy in histopathological head and neck slides"</p><p>http://www.biomedcentral.com/1471-2105/8/S7/S17</p><p>BMC Bioinformatics 2007;8(Suppl 7):S17-S17.</p><p>Published online 1 Nov 2007</p><p>PMCID:PMC2099485.</p><p></p>or areas, which is green area in the figure. The positive color areas are cell nuclei, which build the features for classification

table1_Application Potential of Plant-Derived Medicines in Prevention and Treatment of Platinum-Induced Peripheral Neurotoxicity.docx

As observed with other chemotherapeutic agents, the clinical application of platinum agents is a double-edged sword. Platinum-induced peripheral neuropathy (PIPN) is a common adverse event that negatively affects clinical outcomes and patients’ quality of life. Considering the unavailability of effective established agents for preventing or treating PIPN and the increasing population of cancer survivors, the identification and development of novel, effective interventions are the need of the hour. Plant-derived medicines, recognized as ideal agents, can not only help improve PIPN without affecting chemotherapy efficacy, but may also produce synergy. In this review, we present a brief summary of the mechanisms of platinum agents and PIPN and then focus on exploring the preventive or curative effects and underlying mechanisms of plant-derived medicines, which have been evaluated under platinum-induced neurotoxicity conditions. We identified 11 plant extracts as well as 17 plant secondary metabolites, and four polyherbal preparations. Their effects against PIPN are focused on oxidative stress and mitochondrial dysfunction, glial activation and inflammation response, and ion channel dysfunction. Also, ten clinical trials have assessed the effect of herbal products in patients with PIPN. The understanding of the molecular mechanism is still limited, the quality of clinical trials need to be further improved, and in terms of their efficacy, safety, and cost effectiveness studies have not provided sufficient evidence to establish a standard practice. But plant-derived medicines have been found to be invaluable sources for the development of natural agents with beneficial effects in the prevention and treatment of PIPN.</p

Table_2_DeepCausality: A general AI-powered causal inference framework for free text: A case study of LiverTox.XLSX

Causality plays an essential role in multiple scientific disciplines, including the social, behavioral, and biological sciences and portions of statistics and artificial intelligence. Manual-based causality assessment from a large number of free text-based documents is very time-consuming, labor-intensive, and sometimes even impractical. Herein, we proposed a general causal inference framework named DeepCausality to empirically estimate the causal factors for suspected endpoints embedded in the free text. The proposed DeepCausality seamlessly incorporates AI-powered language models, named entity recognition and Judea Pearl's Do-calculus, into a general framework for causal inference to fulfill different domain-specific applications. We exemplified the utility of the proposed DeepCausality framework by employing the LiverTox database to estimate idiosyncratic drug-induced liver injury (DILI)-related causal terms and generate a knowledge-based causal tree for idiosyncratic DILI patient stratification. Consequently, the DeepCausality yielded a prediction performance with an accuracy of 0.92 and an F-score of 0.84 for the DILI prediction. Notably, 90% of causal terms enriched by the DeepCausality were consistent with the clinical causal terms defined by the American College of Gastroenterology (ACG) clinical guideline for evaluating suspected idiosyncratic DILI (iDILI). Furthermore, we observed a high concordance of 0.91 between the iDILI severity scores generated by DeepCausality and domain experts. Altogether, the proposed DeepCausality framework could be a promising solution for causality assessment from free text and is publicly available through https://github.com/XingqiaoWang/https-github.com-XingqiaoWang-DeepCausality-LiverTox.</p

Group, meta sports resources utilization efficiency and TGR.

Group, meta sports resources utilization efficiency and TGR.</p

Table A1.

The efficient allocation of sports resources for optimal outcomes remains a pressing national endeavour in China. Over the past two decades, substantial investments by provincial and national governments have been directed toward sports infrastructure development. This initiative aims to foster sports talent, facilitate excellence, host major sporting events, and enhance national pride and soft power. This study employs a comprehensive methodology encompassing Data Envelopment Analysis-Slack Based Measure (DEA-SBM), Meta Frontier Analysis, and Malmquist Productivity Index to assess Sports Resource Utilization Efficiency (SRUE), Technological Gap Ratio (TGR), and Productivity Growth (MI) across China’s 31 provinces and 3 distinct regions for the period 2010–2021. The findings indicate that China’s average SRUE stands at 0.6307, revealing an inefficiency of 36.93% in sports resource utilization. Noteworthy efficiency was observed in Beijing, Chongqing, Henan, Shaanxi, Shanghai, and Tianjin during the study duration. Furthermore, a consistent upward trajectory in SRUE from 2010 to 2021 highlights gradual and sustained progress. Comparatively, the eastern region showcases higher technological advancement (average TGR of 0.7598) than the central and western regions. The Malmquist Productivity Index (MI), with an average value of 1.05391, highlights a substantial 5.39% productivity growth. Notably, technological advancement emerges as the primary driver of this productivity increase, evident through the higher Total Factor Productivity Change (TC) of 1.0312 compared to the Efficiency Change (EC) of 1.0209. The Central region’s outperforming productivity growth is noteworthy relative to the Eastern and Western regions. Conclusively, the Kruskal-Wallis test confirms significant disparities in the average MI, EC, TC, and TGR among all three regions of China.</div