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شاهد أخير : قصة أول محقق في موقع الجريمة في الصين
تجمع الرواية بين عالم التحقيقات الجنائية والوقائع التاريخية، وتدور أحداثها في الصين خلال القرن الثالث عشر الميلادي، حين كانت مملكة سونغ على وشك الانهيار نتيجة هجوم البرابرة من جهة والفساد الحكومي من جهة أخرى، بطل الرواية عالم شاب أسمه سونغ تسه، تم قبوله في الخدمة الإمبراطورية، فكرس مهاراته وفطنته للوصول إلى حقيقة العديد من الجرائم المحيرة والغامضة، وحتى تلك التي مضى عليها وقت طويل، من خلال التحقيق في مسرح الجريمة ودراسة الأدلة والاعترافات.
Book
Incidentally cured psoriasis in a patient with refractory/relapsed diffuse large B-cell lymphoma receiving CD19 CAR-T cell therapy: a case report
Chimeric antigen receptor T (CAR-T) cell therapy is a new treatment for cancers, but reports on curing immune-related skin diseases are limited. We report a case of successful CAR-T-cell therapy in a patient with refractory/relapsed diffuse large B-cell lymphoma (R/R DLBCL) who was incidentally cured of chronic generalized plaque psoriasis. The patient, a 65-year-old male who had a known history of psoriasis for 45 years, did not receive immunotherapy for psoriasis during this period. Imaging, molecular biology and immunology diagnostics confirmed DLBCL. After several weeks of standard-dose R-CHOP chemotherapy, the patient achieved partial remission, but according to CT, the patient relapsed, and there was no significant improvement in her psoriasis symptoms. Subsequently, the patient was enrolled in the CD19 CAR-T-cell therapy group. Four weeks after CAR-T-cell infusion, the patient’s abdominal pain disappeared, and there was a significant improvement in overall skin lesions. One year later, follow-up results indicated complete remission of R/R DLBCL (confirmed by PET-CT), with only minimal residual psoriatic skin lesions limited to the patient’s neck. The results of using CAR-T-cell therapy to achieve an incidental cure for psoriasis highlight the potential for exploring cell-based therapies for complex autoinflammatory skin diseases.
Journal Article
The living record of scientific history : conversations with CN Yang
Professor Chen-Ning Yang is best known for his achievements in Physics. He has also made significant contributions to the development of mathematics, as mathematics is extensively used in his research. In his long and fruitful academic career, he has witnessed many important events in the fields of Physics and Mathematics, and has collaborated or interacted with many great scientists in history. This book records eight interviews with Professor Chen-Ning Yang, which were conducted by the authors from 2016 to 2019. Through Professor Yang's unique perspective, major scientific events in the 20th century were revisited vividly, elaborating the development and mutual influences of mathematics and physics, as well as unveiling the academic work, the daily lives, and the personalities of scientists, as well as their collaboration and competition, some stories unknown to the public before are also revealed in this book.
BOOK
4-Octyl itaconate inhibits aerobic glycolysis by targeting GAPDH to exert anti-inflammatory effects
Activated macrophages switch from oxidative phosphorylation to aerobic glycolysis, similar to the Warburg effect, presenting a potential therapeutic target in inflammatory disease. The endogenous metabolite itaconate has been reported to regulate macrophage function, but its precise mechanism is not clear. Here, we show that 4-octyl itaconate (4-OI, a cell-permeable itaconate derivative) directly alkylates cysteine residue 22 on the glycolytic enzyme GAPDH and decreases its enzyme activity. Glycolytic flux analysis by U
13
C glucose tracing provides evidence that 4-OI blocks glycolytic flux at GAPDH. 4-OI thereby downregulates aerobic glycolysis in activated macrophages, which is required for its anti-inflammatory effects. The anti-inflammatory effects of 4-OI are replicated by heptelidic acid, 2-DG and reversed by increasing wild-type (but not C22A mutant) GAPDH expression. 4-OI protects against lipopolysaccharide-induced lethality in vivo and inhibits cytokine release. These findings show that 4-OI has anti-inflammatory effects by targeting GAPDH to decrease aerobic glycolysis in macrophages.
Redirection of the TCA cycle intermediate aconitate to itaconate production has anti-inflammatory effects. Here the authors show that the itaconate derivative 4-octyl-itaconate is anti-inflammatory partly as a result of inhibiting GAPDH enzymatic activity and thereby glycolysis in macrophages.
Journal Article
حوكمة الصين في العلوم والتكنولوجيا والتعليم
دخلت الصين مرحلة جديدة من التطور خلال العقود الثلاثة، مع بدئها بتنفيذ سياسة الإصلاح والانفتاح، فاحتل اقتصادها في العام 2010 م، المرتبة الثانية لأكبر اقتصاد في العالم، نتيجة سنوات طويلة من العمل الشاق، لبناء دولة اشتراكية قوية، والترويج لحوكمة جديدة، إلى جانب التطور المتسارع لكل من العلوم والتكنولوجيا والتعليم، تحت قيادة الرئيس شي جين بينغ الحكيمة التي عكست وجهة نظره الثاقبة والمتمثلة في دمج النظرية بالممارسة لمواكبة الزمن. وبناء عليه، سيعالج هذا الكتاب أهم الخطوط العريضة التي قام عليها فكر شي جين بينغ في حوكمة الصين، وبناء دولة ابتكارية تعطي الأولوية لتطوير العلوم والتكنولوجيا والتعليم، وإغنائها بالمواهب الشابة، بهدف الحفاظ على استمرارية النهضة التي تشهدها الأمة الصينية حاليا، والشير أكثر فأكثر إلى الأمام.
Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches
Autonomous Vehicles (AV) technology is emerging. Field tests on public roads have been on going in several states in the US as well as in Europe and Asia. During the US public road tests, crashes with AV involved happened, which becomes a concern to the public. Most previous studies on AV safety relied heavily on assessing drivers' performance and behaviors in a simulation environment and developing automated driving system performance in a closed field environment. However, contributing factors and the mechanism of AV-related crashes have not been comprehensively and quantitatively investigated due to the lack of field AV crash data. By harnessing California's Report of Traffic Collision Involving an Autonomous Vehicle Database, which includes the AV crash data from 2014 to 2018, this paper investigates by far the most current and complete AV crash database in the US using statistical modeling approaches that involve both ordinal logistic regression and CART classification tree. The quantitative analysis based on ordinal logistic regression and CART models has successfully explored the mechanism of AV-related crash, via both perspectives of crash severity and collision types. Particularly, the CART model reveals and visualize the hierarchical structure of the AV crash mechanism with knowledge of how these traffic, roadway, and environmental contributing factors can lead to crashes of various serveries and collision types. Statistical analysis results indicate that crash severity significantly increases if the AV is responsible for the crash. The highway is identified as the location where severe injuries are likely to happen. AV collision types are affected by whether the vehicle is on automated driving mode, whether the crashes involve pedestrians/cyclists, as well as the roadway environment. The method used in this research provides a proven approach to statistically analyze and understand AV safety issues. And this benefit is potential be even enhanced with an increasing sample size of AV-related crashes records in the future. The comprehensive knowledge obtained ultimately facilitates assessing and improving safety performance of automated vehicles.
Journal Article
A Blockchain and Machine Learning-Based Drug Supply Chain Management and Recommendation System for Smart Pharmaceutical Industry
From the last decade, pharmaceutical companies are facing difficulties in tracking their products during the supply chain process, allowing the counterfeiters to add their fake medicines into the market. Counterfeit drugs are analyzed as a very big challenge for the pharmaceutical industry worldwide. As indicated by the statistics, yearly business loss of around $200 billion is reported by US pharmaceutical companies due to these counterfeit drugs. These drugs may not help the patients to recover the disease but have many other dangerous side effects. According to the World Health Organization (WHO) survey report, in under-developed countries every 10th drug use by the consumers is counterfeit and has low quality. Hence, a system that can trace and track drug delivery at every phase is needed to solve the counterfeiting problem. The blockchain has the full potential to handle and track the supply chain process very efficiently. In this paper, we have proposed and implemented a novel blockchain and machine learning-based drug supply chain management and recommendation system (DSCMR). Our proposed system consists of two main modules: blockchain-based drug supply chain management and machine learning-based drug recommendation system for consumers. In the first module, the drug supply chain management system is deployed using Hyperledger fabrics which is capable of continuously monitor and track the drug delivery process in the smart pharmaceutical industry. On the other hand, the N-gram, LightGBM models are used in the machine learning module to recommend the top-rated or best medicines to the customers of the pharmaceutical industry. These models have trained on well known publicly available drug reviews dataset provided by the UCI: an open-source machine learning repository. Moreover, the machine learning module is integrated with this blockchain system with the help of the REST API. Finally, we also perform several tests to check the efficiency and usability of our proposed system.
Journal Article
Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images
Machine-assisted pathological recognition has been focused on supervised learning (SL) that suffers from a significant annotation bottleneck. We propose a semi-supervised learning (SSL) method based on the mean teacher architecture using 13,111 whole slide images of colorectal cancer from 8803 subjects from 13 independent centers. SSL (~3150 labeled, ~40,950 unlabeled; ~6300 labeled, ~37,800 unlabeled patches) performs significantly better than the SL. No significant difference is found between SSL (~6300 labeled, ~37,800 unlabeled) and SL (~44,100 labeled) at patch-level diagnoses (area under the curve (AUC): 0.980 ± 0.014 vs. 0.987 ± 0.008,
P
value = 0.134) and patient-level diagnoses (AUC: 0.974 ± 0.013 vs. 0.980 ± 0.010,
P
value = 0.117), which is close to human pathologists (average AUC: 0.969). The evaluation on 15,000 lung and 294,912 lymph node images also confirm SSL can achieve similar performance as that of SL with massive annotations. SSL dramatically reduces the annotations, which has great potential to effectively build expert-level pathological artificial intelligence platforms in practice.
Machine-assisted recognition of colorectal cancer has been mainly focused on supervised deep learning that suffers from a significant bottleneck of requiring massive amounts of labeled data. Here, the authors propose a semi-supervised model based on the mean teacher architecture that provides pathological predictions at both patch- and patient-levels.
Journal Article
Strategies for promoting green buildings: integrating evolutionary game and SEIR models
The use of green buildings is an inevitable requirement for sustainable development. However, green buildings face the awkward situation of a slow market share increase. This study explores the changing strategies of government and developers in promoting green buildings and how they affect consumer acceptance, providing an interdisciplinary theory for promoting green buildings. Based on the stakeholder analysis, this study constructs an evolutionary game model of the government and developers. Through analyzing consumer behavioral shifts, SEIR modeling was conducted by comparing the use of green buildings to an “infectious disease”. The findings indicate that balanced government strategies of rewards and punishments are crucial for encouraging developers to promote green buildings and encouraging developers to promote green buildings. The costs and benefits for developers significantly influence their willingness to adopt green building practices. The costs and benefits for developers significantly influence their willingness to adopt green building practices. In addition, the study found that even stable promotion strategies need to be adapted to changes in consumer behavior. This interdisciplinary research approach provides a new perspective on the adaptability of green building promotion strategies. This study provides a foundation and theoretical support for green building promotion.
Journal Article