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The optimal perioperative chemotherapeutic regimen for locally advanced gastric cancer remains undefined. We evaluated the efficacy and safety of perioperative and postoperative S-1 and oxaliplatin (SOX) compared with postoperative capecitabine and oxaliplatin (CapOx) in patients with locally advanced gastric cancer undergoing D2 gastrectomy. We did this open-label, phase 3, superiority and non-inferiority, randomised trial at 27 hospitals in China. We recruited antitumour treatment-naive patients aged 18 years or older with historically confirmed cT4a N+ M0 or cT4b Nany M0 gastric or gastro-oesophageal junction adenocarcinoma, with Karnofsky performance score of 70 or more. Patients undergoing D2 gastrectomy were randomly assigned (1:1:1) via an interactive web response system, stratified by participating centres and Lauren classification, to receive adjuvant CapOx (eight postoperative cycles of intravenous oxaliplatin 130 mg/m2 on day one of each 21 day cycle plus oral capecitabine 1000 mg/m2 twice a day), adjuvant SOX (eight postoperative cycles of intravenous oxaliplatin 130 mg/m2 on day one of each 21 day cycle plus oral S-1 40–60 mg twice a day), or perioperative SOX (intravenous oxaliplatin 130 mg/m2 on day one of each 21 day plus oral S-1 40–60 mg twice a day for three cycles preoperatively and five cycles postoperatively followed by three cycles of S-1 monotherapy). The primary endpoint, assessed in the modified intention-to-treat population, is 3-year disease-free survival to assess the superiority of perioperative-SOX compared with adjuvant-CapOx and the non-inferiority (hazard ratio non-inferiority margin of 1·33) of adjuvant-SOX compared with adjuvant-CapOx. Safety analysis were done in patients who received at least one dose of the assigned treatment. This study is registered with ClinicalTrials.gov, NCT01534546. Between Aug 15, 2012, and Feb 28, 2017, 1094 patients were screened and 1022 (93%) were included in the modified intention-to-treat population, of whom 345 (34%) patients were assigned to the adjuvant-CapOx, 340 (33%) patients to the adjuvant-SOX group, and 337 (33%) patients to the perioperative-SOX group. 3-year disease-free survival was 51·1% (95% CI 45·5–56·3) in the adjuvant-CapOx group, 56·5% (51·0–61·7) in the adjuvant-SOX group, and 59·4% (53·8–64·6) in the perioperative-SOX group. The hazard ratio (HR) was 0·77 (95% CI 0·61–0·97; Wald p=0·028) for the perioperative-SOX group compared with the adjuvant-CapOx group and 0·86 (0·68–1·07; Wald p=0·17) for the adjuvant-SOX group compared with the adjuvant-CapOx group. The most common grade 3–4 adverse events was neutropenia (32 [12%] of 258 patients in the adjuvant-CapOx group, 21 [8%] of 249 patients in the adjuvant-SOX group, and 30 [10%] of 310 patients in the perioperative-SOX group). Serious adverse events were reported in seven (3%) of 258 patients in adjuvant-CapOx group, two of which were related to treatment; eight (3%) of 249 patients in adjuvant-SOX group, two of which were related to treatment; and seven (2%) of 310 patients in perioperative-SOX group, four of which were related to treatment. No treatment-related deaths were reported. Perioperative-SOX showed a clinically meaningful improvement compared with adjuvant-CapOx in patients with locally advanced gastric cancer who had D2 gastrectomy; adjuvant-SOX was non-inferior to adjuvant-CapOx in these patients. Perioperative-SOX could be considered a new treatment option for patients with locally advanced gastric cancer. National Key Research and Development Program of China, Beijing Scholars Program 2018–2024, Peking University Clinical Scientist Program, Taiho, Sanofi-Aventis, and Hengrui Pharmaceutical. For the Chinese translation of the abstract see Supplementary Materials section.

\"Underwater robots play a significant role in ocean exploration. This book provides full coverage of the theoretical and practical aspects of bionic gliding underwater robots, including system design, modeling control and motion planning. To overcome the inherent shortcomings of traditional underwater robots that can simultaneously lack maneuverability and endurance, a new type of robot, the bionic gliding underwater robot, has attracted much attention from scientists and engineers. On the one hand, by imitating the appearance and swimming mechanisms of natural creatures, bionic gliding underwater robots achieve high maneuverability, swimming efficiency, and strong concealment. On the other hand, borrowing from the buoyancy adjustment systems of underwater gliders, bionic gliding underwater robots can obtain strong endurance, which is significant in practical applications. Taking gliding robotic dolphin and fish as examples, the designed prototypes and proposed methods are discussed, offering valuable insights into the development of next-generation underwater robots that are well-suited for various oceanic applications. This book will be of great interest to students and professionals alike in the field of robotics or intelligent control. It will also be a great reference for engineers or technicians who deal with the development of underwater robots\"-- Provided by publisher.

Long-distance entanglement distribution is essential for both foundational tests of quantum physics and scalable quantum networks. Owing to channel loss, however, the previously achieved distance was limited to ~100 kilometers. Here we demonstrate satellite-based distribution of entangled photon pairs to two locations separated by 1203 kilometers on Earth, through two satellite-to-ground downlinks with a summed length varying from 1600 to 2400 kilometers. We observed a survival of two-photon entanglement and a violation of Bell inequality by 2.37 ± 0.09 under strict Einstein locality conditions. The obtained effective link efficiency is orders of magnitude higher than that of the direct bidirectional transmission of the two photons through telecommunication fibers.

Quantum key distribution (QKD) uses individual light quanta in quantum superposition states to guarantee unconditional communication security between distant parties. However, the distance over which QKD is achievable has been limited to a few hundred kilometres, owing to the channel loss that occurs when using optical fibres or terrestrial free space that exponentially reduces the photon transmission rate. Satellite-based QKD has the potential to help to establish a global-scale quantum network, owing to the negligible photon loss and decoherence experienced in empty space. Here we report the development and launch of a low-Earth-orbit satellite for implementing decoy-state QKD—a form of QKD that uses weak coherent pulses at high channel loss and is secure because photon-number-splitting eavesdropping can be detected. We achieve a kilohertz key rate from the satellite to the ground over a distance of up to 1,200 kilometres. This key rate is around 20 orders of magnitudes greater than that expected using an optical fibre of the same length. The establishment of a reliable and efficient space-to-ground link for quantum-state transmission paves the way to global-scale quantum networks. Decoy-state quantum key distribution from a satellite to a ground station is achieved with much greater efficiency than is possible over the same distance using optical fibres. Quantum security in orbit The laws of quantum physics give rise to protocols for ultra-secure cryptography and quantum communications. However, to be useful in a global network, these protocols will have to function with satellites. Extending existing protocols to such long distances poses a tremendous experimental challenge. Researchers led by Jian-Wei Pan present a pair of papers in this issue that take steps toward a global quantum network, using the low-Earth-orbit satellite Micius. They demonstrate satellite-to-ground quantum key distribution, an integral part of quantum cryptosystems, at kilohertz rates over 1,200 kilometres, and report quantum teleportation of a single-photon qubit over 1,400 kilometres. Quantum teleportation is the transfer of the exact state of a quantum object from one place to another, without physical travelling of the object itself, and is a central process in many quantum communication protocols. These two experiments suggest that Micius could become the first component in a global quantum internet.

ينطلق هذا المجلد من العلاقة الهيكلية بين \"الفضاء\" و\"الثقافة\"، ويجمع بين مفهوم \"الفضاء\" في الجغرافيا ومفهوم \"السياق\" في الدراسات الثقافية وغيرهم من المفاهيم الأخرى، ويشرح نماذج ودلالات وأهداف مبادرة \"الحزام والطريق\" في سياق الحضارة الحديثة، ويصف صورة امتداد الحضارة على المحور التاريخي لـ \"الحزام والطريق\"، ويحلل الدلالة الثقافية في كل من اتجاه القيمة وبناء القوة الناعمة وإنشاء السياق الشرقي الخاصين بمبادرة \"الحزام والطريق\"، ويفسر علاقة الارتباط بين الثقافة الوطنية لـ \"الحزام والطريق\" والفضاء الوطني، ويكشف عن السرد عبر الفضاء للرموز الثقافية لـ \"الحزام والطريق\" ويوضح التعبير الرقمي والمرئي لواقع \"الحزام والطريق\"

Background The treatment of complex diseases by taking multiple drugs becomes increasingly popular. However, drug-drug interactions (DDIs) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. DDI detection in the wet lab is expensive and time-consuming. Thus, it is highly desired to develop the computational methods for predicting DDIs. Generally, most of the existing computational methods predict DDIs by extracting the chemical and biological features of drugs from diverse drug-related properties, however some drug properties are costly to obtain and not available in many cases. Results In this work, we presented a novel method (namely DPDDI) to predict DDIs by extracting the network structure features of drugs from DDI network with graph convolution network (GCN), and the deep neural network (DNN) model as a predictor. GCN learns the low-dimensional feature representations of drugs by capturing the topological relationship of drugs in DDI network. DNN predictor concatenates the latent feature vectors of any two drugs as the feature vector of the corresponding drug pairs to train a DNN for predicting the potential drug-drug interactions. Experiment results show that, the newly proposed DPDDI method outperforms four other state-of-the-art methods; the GCN-derived latent features include more DDI information than other features derived from chemical, biological or anatomical properties of drugs; and the concatenation feature aggregation operator is better than two other feature aggregation operators (i.e., inner product and summation). The results in case studies confirm that DPDDI achieves reasonable performance in predicting new DDIs. Conclusion We proposed an effective and robust method DPDDI to predict the potential DDIs by utilizing the DDI network information without considering the drug properties (i.e., drug chemical and biological properties). The method should also be useful in other DDI-related scenarios, such as the detection of unexpected side effects, and the guidance of drug combination.

Expensive optimization problem (EOP) widely exists in various significant real-world applications. However, EOP requires expensive or even unaffordable costs for evaluating candidate solutions, which is expensive for the algorithm to find a satisfactory solution. Moreover, due to the fast-growing application demands in the economy and society, such as the emergence of the smart cities, the internet of things, and the big data era, solving EOP more efficiently has become increasingly essential in various fields, which poses great challenges on the problem-solving ability of optimization approach for EOP. Among various optimization approaches, evolutionary computation (EC) is a promising global optimization tool widely used for solving EOP efficiently in the past decades. Given the fruitful advancements of EC for EOP, it is essential to review these advancements in order to synthesize and give previous research experiences and references to aid the development of relevant research fields and real-world applications. Motivated by this, this paper aims to provide a comprehensive survey to show why and how EC can solve EOP efficiently. For this aim, this paper firstly analyzes the total optimization cost of EC in solving EOP. Then, based on the analysis, three promising research directions are pointed out for solving EOP, which are problem approximation and substitution, algorithm design and enhancement, and parallel and distributed computation. Note that, to the best of our knowledge, this paper is the first that outlines the possible directions for efficiently solving EOP by analyzing the total expensive cost. Based on this, existing works are reviewed comprehensively via a taxonomy with four parts, including the above three research directions and the real-world application part. Moreover, some future research directions are also discussed in this paper. It is believed that such a survey can attract attention, encourage discussions, and stimulate new EC research ideas for solving EOP and related real-world applications more efficiently.