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تدور أحداث الرواية حول الطالب \"مايكل\" عبقري الفيزياء، الذي أسس جمعية للسفر عبر الزمن لإنقاذ عباقرة التاريخ من المصاعب والأزمات التي تعترض اكتشافاتهم العلمية ومسار حياتهم الطبيعية، وتساعده في ذلك صديقته الوحيدة الطالبة \"تشياو تشياو\" والإنسان الآلي \"ريكي\". والذين تتعدد مغامراتهم ما بين الصين القديمة واليونان القديمة وإنجلترا وسويسرا في العصور الوسطى، ويقابلون كلا من: عالم الرياضيات الإغريقي أرشميدس، والفيزيائي البريطاني إسحاق نيوتن، والبطل الشعبي السويسري ويليام تل.‪

The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer (PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation tasks.

Qingfeiyin (QFY) is a common Chinese herbal formula for the treatment of acute lung injury (ALI). However, its mechanisms of action are unclear. In this study, we systematically explored the effects and mechanism of action of QFY in ALI using network pharmacology and molecular docking. Active compounds and targets of QFY were obtained from TCMSP and TCMID. ALI-related targets were retrieved from GEO datasets combined with GeneCards, OMIM, and TTD databases. A protein–protein interaction (PPI) network was built to screen the core targets. DAVID was used for GO and KEGG pathway enrichment analyses. The tissue and organ distribution of targets was evaluated. Interactions between potential targets and active compounds were assessed by molecular docking. A molecular dynamics simulation was conducted for the optimal core protein–compound complexes obtained by molecular docking. In total, 128 active compounds and 121 targets of QFY were identified. A topological analysis of the PPI network revealed 13 core targets. GO and KEGG pathway enrichment analyses indicated that the effects of QFY are mediated by genes related to inflammation, apoptosis, and oxidative stress as well as the MAPK and PI3K-Akt signaling pathways. Molecular docking and molecular dynamics simulations revealed good binding ability between the active compounds and screened targets. This study successfully predict the effective components and potential targets and pathways involved in the treatment of ALI for QFY. We provided a novel strategy for future research of molecular mechanisms of QFY in ALI treatment. Moreover, the potential active ingredients provide a reliable source for drug screening for ALI. •Qingfeiyin (QFY),a traditional Chinese herbal decoction, has shown significant clinical efficacy against acute lung injury (ALI).•We found that QFY works through the PI3K-Akt and MAPK signaling pathways in ALI treatment.•QFY interacts with targets related to inflammation, apoptosis, and oxidative stress.•The present study made a comprehensive analysis to elucidate the active ingredients and the mechanisms of action of QFY by a bioinformatics approach for the first time.

In the Arctic Ocean (AO), net community production (NCP $NCP$) has displayed spatially heterogeneous responses to sea ice reduction and associated environmental changes. Using a random forest machine learning model trained with >42,000 in situ measurements and concurrent, collocated environmental predictors, we reconstructed 19 years of 8‐day, 6‐km NCP $NCP$ maps. During 2015–2021, the integrated NCP $NCP$ between late‐May and early‐September (NCPint _{\\mathit{int}}NCP$) over the western AO was 10.95±3.30TgC $10.95\\pm 3.30\\,\\text{Tg}\\,\\mathrm{C}$ per year, with interannual variations positively tracking open water area. While the relationship between NCPint _{\\mathit{int}}NCP$ and open water area was quasi‐linear at high latitudes, strong nonlinearity was detected on the inflow shelf. The nonlinearity highlights that the NCPint _{\\mathit{int}}NCP$ increase resulted from area gain could be compounded by sea‐ice loss induced ecosystem adjustments. Additional retrospective analysis for 2003–2014 suggests a potential long‐term increase of export production and efficiency in the western AO with sea ice loss. Plain Language Summary Net community production (NCP $NCP$) refers to the portion of phytoplankton production that remains unused by consumers and can be exported to the deeper part of the ocean. In the western Arctic Ocean (AO), NCP $NCP$ patterns are uneven due to complex interactions between the physical environment and the ecosystem. In this study, we developed a machine learning model of NCP $NCP$ in the western AO. The model used publicly available underway measurements and the associated environmental variables to create long‐term, high‐resolution maps of NCP $NCP$. For the period of 2015–2021, we found that the integrated NCP $NCP$ between late‐May and early‐September (NCPint _{\\mathit{int}}NCP$) was 10.95±3.30TgC $10.95\\pm 3.30\\,\\text{Tg}\\,\\mathrm{C}$ per year in the western AO. NCPint _{\\mathit{int}}NCP$ varied from year to year and was higher when the open water area was larger. Notably, on the inflow shelf, NCPint _{\\mathit{int}}NCP$ increased at a faster rate than a linear relationship would suggest, due to both area expansion and ecosystem adjustments induced by sea ice loss. Our findings indicate that with long‐term sea ice loss, the western AO is likely to export more phytoplankton production to deeper ocean waters. Key Points A multiyear, gap‐free net community production (NCP $NCP$) product was constructed using a machine learning model for the western Arctic Ocean Seasonally and regionally integrated NCP $NCP$ responded to sea ice loss quasi‐linearly at high latitudes but nonlinearly on the inflow shelf Compared with the 2010s, carbon export production has increased in recent years, accompanying sea ice loss in the western Arctic Ocean

The density dependence of nuclear symmetry energy is among the most uncertain parts of the Equation of State (EOS) of dense neutron-rich nuclear matter. It is currently poorly known especially at suprasaturation densities partially because of our poor knowledge about isovector nuclear interactions at short distances. Because of its broad impacts on many interesting issues, pinning down the density dependence of nuclear symmetry energy has been a longstanding and shared goal of both astrophysics and nuclear physics. New observational data of neutron stars including their masses, radii, and tidal deformations since GW170817 have helped improve our knowledge about nuclear symmetry energy, especially at high densities. Based on various model analyses of these new data by many people in the nuclear astrophysics community, while our brief review might be incomplete and biased unintentionally, we learned in particular the following: (1) The slope parameter L of nuclear symmetry energy at saturation density ρ0 of nuclear matter from 24 new analyses of neutron star observables was about L≈57.7±19 MeV at a 68% confidence level, consistent with its fiducial value from surveys of over 50 earlier analyses of both terrestrial and astrophysical data within error bars. (2) The curvature Ksym of nuclear symmetry energy at ρ0 from 16 new analyses of neutron star observables was about Ksym≈−107±88 MeV at a 68% confidence level, in very good agreement with the systematics of earlier analyses. (3) The magnitude of nuclear symmetry energy at 2ρ0, i.e., Esym(2ρ0)≈51±13 MeV at a 68% confidence level, was extracted from nine new analyses of neutron star observables, consistent with the results from earlier analyses of heavy-ion reactions and the latest predictions of the state-of-the-art nuclear many-body theories. (4) While the available data from canonical neutron stars did not provide tight constraints on nuclear symmetry energy at densities above about 2ρ0, the lower radius boundary R2.01=12.2 km from NICER’s very recent observation of PSR J0740+6620 of mass 2.08±0.07M⊙ and radius R=12.2–16.3 km at a 68% confidence level set a tight lower limit for nuclear symmetry energy at densities above 2ρ0. (5) Bayesian inferences of nuclear symmetry energy using models encapsulating a first-order hadron–quark phase transition from observables of canonical neutron stars indicated that the phase transition shifted appreciably both L and Ksym to higher values, but with larger uncertainties compared to analyses assuming no such phase transition. (6) The high-density behavior of nuclear symmetry energy significantly affected the minimum frequency necessary to rotationally support GW190814’s secondary component of mass (2.50–2.67) M⊙ as the fastest and most massive pulsar discovered so far. Overall, thanks to the hard work of many people in the astrophysics and nuclear physics community, new data of neutron star observations since the discovery of GW170817 have significantly enriched our knowledge about the symmetry energy of dense neutron-rich nuclear matter.

It has been speculated that the partial pressure of carbon dioxide ( p CO 2 ) in shelf waters may lag the rise in atmospheric CO 2 . Here, we show that this is the case across many shelf regions, implying a tendency for enhanced shelf uptake of atmospheric CO 2 . This result is based on analysis of long-term trends in the air–sea p CO 2 gradient (Δ p CO 2 ) using a global surface ocean p CO 2 database spanning a period of up to 35 years. Using wintertime data only, we find that Δ p CO 2 increased in 653 of the 825 0.5° cells for which a trend could be calculated, with 325 of these cells showing a significant increase in excess of +0.5 μatm yr −1 ( p  < 0.05). Although noisier, the deseasonalized annual data suggest similar results. If this were a global trend, it would support the idea that shelves might have switched from a source to a sink of CO 2 during the last century. It remains unclear whether surface water partial pressure of CO 2 ( p CO 2 ) in continental shelves tracks with increasing atmospheric p CO 2 . Here, the authors show that p CO 2 in shelf waters lags behind rising atmospheric CO 2 in a number of shelf regions, suggesting shelf uptake of atmospheric CO 2 .

Recent interest in the ocean's capacity to absorb atmospheric CO2 and buffer the accompanying “ocean acidification” has prompted discussions on the magnitude of ocean margin alkalinity production via anaerobic processes. However, available estimates are largely based on gross reaction rates or misconceptions regarding reaction stoichiometry. In this paper, we argue that net alkalinity gain does not result from the internal cycling of nitrogen and sulfur species or from the reduction of metal oxides. Instead, only the processes that involve permanent loss of anaerobic remineralization products, i.e., nitrogen gas from net denitrification and reduced sulfur (i.e., pyrite burial) from net sulfate reduction, could contribute to this anaerobic alkalinity production. Our revised estimate of net alkalinity production from anaerobic processes is on the order of 4–5 Tmol yr−1 in global ocean margins that include both continental shelves and oxygen minimum zones, significantly smaller than the previously estimated rate of 16–31 Tmol yr−1. In addition, pyrite burial in coastal habitats (salt marshes, mangroves, and seagrass meadows) may contribute another 0.1–1.1 Tmol yr−1, although their long‐term effect is not yet clear under current changing climate conditions and rising sea levels. Finally, we propose that these alkalinity production reactions can be viewed as “charge transfer” processes, in which negative charges of nitrate and sulfate ions are converted to those of bicarbonate along with a net loss of these oxidative anions. Key Points Ocean margin anaerobic alkalinity production is less than previously estimated Anaerobic alkalinity production can be viewed through a charge transfer process

During the summers of 2009 and 2013, seawater pH and concentrations of dissolved oxygen, inorganic carbon, and nutrients were measured off the Changjiang estuary in the East China Sea. The 2009 cruise captured the effects of Typhoon Morakot; the 2013 cruise sampled more typical conditions (no typhoon). Data from both years indicate a close correlation between high primary productivity in surface waters and hypoxia in bottom waters. Based on these observations, we developed a conceptual model to guide an exploration of processes contributing to the formation of summertime bottom hypoxia. A mixing-model analysis of the 2009 data identified a surface diatom bloom as the major (70–80%) source of the organic carbon that decomposed and ultimately led to bottom water hypoxia. Within the Changjiang River plume, depth-integrated net biological production in the water column was 1.8 g C m−2 d−1, indicating strong autotrophic production, which in turn led to a high respiration rate of 1.2 g C m−2 d−1 in the bottom water. During both cruises, strong surface-to-bottom physical and metabolic coupling was evident. In 2009, storm-driven inputs of nutrients from elevated river discharge and strong vertical mixing helped to fuel the rapid development of a surface diatom bloom. Afterwards, stratified conditions re-established, newly formed labile organic matter sank, and bottom water oxygen was quickly consumed to an extent that hypoxia and acidification developed. To our knowledge, the observed rate of hypoxia and acidification development (within 6 d) is the fastest yet reported for the Changjiang River plume.

Background Systemic inflammation and immune dysfunction have been proved to be associated with cancer progression and metastasis in various malignancies. The aim of this retrospective study was to evaluate the prognostic significance of pre-treatment systemic immune-inflammation index (SII) in patients with advanced pancreatic cancer. Methods In total, 419 patients diagnosed with advanced pancreatic cancer, between January 2011 and December 2015, were retrospectively enrolled. The SII was developed based on a training set of 197 patients from 2011 to 2013 and validated in an independent cohort of 222 patients from 2014 to 2015. Data on baseline clinicopathologic characteristics; pre-treatment laboratory variables such as absolute neutrophil, lymphocyte, and platelet counts; and carbohydrate antigen 19-9 (CA19-9), total bilirubin (TBIL), albumin (ALB), alkaline phosphatase (ALP), alanine transaminase (ALT), and aspartate transaminase (AST) levels were collected. The association between clinicopathologic characteristics and SII was assessed. The overall survival was calculated using the Kaplan–Meier survival curves and compared using the log-rank test. Univariate and multivariate Cox proportional hazard regression models were used to analyze the prognostic value of the SII. Result An optimal cutoff point for the SII of 440 stratified the patients with advanced pancreatic cancer into high (> 440) and low (≤ 440) SII groups in the training cohort. Univariate and multivariate analyses revealed that the SII was an independent predictor for overall survival. The prognostic significance of the SII was confirmed in both normal and elevated CA19-9 levels. Conclusion The baseline SII serves as an independent prognostic marker for patients with advanced pancreatic cancer and can be used in patients with both normal and elevated CA19-9 levels.