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الأدوات البرونزية للصين
يعتبر ظهور الأدوات البرونزية قفزة مهمة في تاريخ الحضارة الإنسانية. وعلي الرغم من أن العصر البرونزي في الصين لم يكن الأول في تاريخ البشرية، ولكن الأدوات البرونزية في الصين القديمة قد احتلت مكانة فريدة في تاريخ الحضارة العالمية اعتمادا علي أنواعها المتنوعة وأنماطها الغنية وعملية السبك الدقيقة والدلالة التاريخية والثقافية العميقة التي يحملها كل عمل برونزي في الصين القديمة. وهذا الكتاب الذي بين يدي القارئ الكريم يعرفن بلغة واضحة وحية وبالشرح والصور على أحد اوجه الثقافة والفنون الصينية الشهيرة والتي هي ثقافة البرونز الرائعة في الصين القديمة، فمن خلال قطعة من القطع البرونزية الثمينة يمكننا أن نستمع إلي صوت العصر البرونزي من أماكن بعيدة وتجربة أسلوب فريد من نوعه في ذلك العصر الذي مازالت أسراره بعيدة عن متناول القارئ العربي والتي نحاول من خلال هذا الكتاب أن نقدمها بشكل موجز وواضح لننقل للقارئ الكريم وجها جديدا عليه من أوجه الحضارة الصينية المتميزة.
Book
Application of Chaos Mutation Adaptive Sparrow Search Algorithm in Edge Data Compression
In view of the large amount of data collected by an edge server, when compression technology is used for data compression, data classification accuracy is reduced and data loss is large. This paper proposes a data compression algorithm based on the chaotic mutation adaptive sparrow search algorithm (CMASSA). Constructing a new fitness function, CMASSA optimizes the hyperparameters of the Convolutional Auto-Encoder Network (CAEN) on the cloud service center, aiming to obtain the optimal CAEN model. The model is sent to the edge server to compress the data at the lower level of edge computing. The effectiveness of CMASSA performance is tested on ten high-dimensional benchmark functions, and the results show that CMASSA outperforms other comparison algorithms. Subsequently, experiments are compared with other literature on the Multi-class Weather Dataset (MWD). Experiments show that under the premise of ensuring a certain compression ratio, the proposed algorithm not only has better accuracy in classification tasks than other algorithms but also maintains a high degree of data reconstruction.
Journal Article
DNA methylation mediates differentiation in thermal responses of Pacific oyster (Crassostrea gigas) derived from different tidal levels
Epigenetic mechanisms such as DNA methylation have the potential to affect organism acclimatization and adaptation to environmental changes by influencing their phenotypic plasticity; however, little is known about the role of methylation in the adaptive phenotypic divergence of marine invertebrates. Therefore, in this study, a typical intertidal species, the Pacific oyster (Crassostrea gigas), was selected to investigate the epigenetic mechanism of phenotypic plasticity in marine invertebrates. Intertidal and subtidal oysters subjected to one-generation common garden experiments and exhibited phenotypic divergence were used. The methylation landscape of both groups of oysters was investigated under temperate and high temperature. The two tidal oysters exhibited divergent methylation patterns, regardless of the temperature, which was mainly original environment-induced. Intertidal samples exhibited significant hypomethylation and more plasticity of methylation in response to heat shock, while subtidal samples showed hypermethylation and less plasticity. Combined with RNA-seq data, a positive relationship between methylation and expression in gene bodies was detected on a genome-wide scale. In addition, approximately 11% and 7% of differentially expressed genes showed significant methylation variation under high temperatures in intertidal and subtidal samples, respectively. Genes related to apoptosis and organism development may be regulated by methylation in response to high temperature in intertidal oysters, whereas oxidation-reduction and ion homeostasis-related genes were involved in subtidal oysters. The results also suggest that DNA methylation mediates phenotypic divergence in oysters adapting to different environments. This study provides new insight into the epigenetic mechanisms underlying phenotypic plasticity in adaptation to rapid climate change in marine organisms.
Journal Article
Noncoding Variation and Transcriptional Plasticity Promote Thermal Adaptation in Oysters by Altering Energy Metabolism
Abstract
Genetic variation and phenotypic plasticity are both important to adaptive evolution. However, how they act together on particular traits remains poorly understood. Here, we integrated phenotypic, genomic, and transcriptomic data from two allopatric but closely related congeneric oyster species, Crassostrea angulata from southern/warm environments and Crassostrea gigas from northern/cold environments, to investigate the roles of genetic divergence and plasticity in thermal adaptation. Reciprocal transplantation experiments showed that both species had higher fitness in their native habitats than in nonnative environments, indicating strong adaptive divergence. The southern species evolved higher transcriptional plasticity, and the plasticity was adaptive, suggesting that increased plasticity is important for thermal adaptation to warm climates. Genome-wide comparisons between the two species revealed that genes under selection tended to respond to environmental changes and showed higher sequence divergence in noncoding regions. All genes under selection and related to energy metabolism exhibited habitat-specific expression with genes involved in ATP production and lipid catabolism highly expressed in warm/southern habitats, and genes involved in ATP consumption and lipid synthesis were highly expressed in cold/northern habitats. The gene for acyl-CoA desaturase, a key enzyme for lipid synthesis, showed strong selective sweep in the upstream noncoding region and lower transcription in the southern species. These results were further supported by the lower free fatty acid (FFA) but higher ATP content in southern species and habitat, pointing to significance of ATP/FFA trade-off. Our findings provide evidence that noncoding variation and transcriptional plasticity play important roles in shaping energy metabolism for thermal adaptation in oysters.
Journal Article
Morphology and mechanical properties of nanocrystalline Cu/Ag alloy
Hybrid Monte Carlo/molecular dynamics (MD) simulations are conducted to study the microstructures of nanocrystalline (nc) Cu/Ag alloys with various Ag concentrations. When the Ag concentration is below 50 Ag atoms/nm
2
, an increase in Ag concentration leads to a gradual growth of monolayer grain boundary (GB) complexions into nanolayer complexions. Above the concentration of 50 Ag atoms/nm
2
, wetting layers with a bulk crystalline phase are observed. The effects of Ag on mechanical properties and deformation mechanisms of nc Cu/Ag alloys are investigated in MD simulations of uniaxial tension. GB sliding resistance is found to first increase and then decrease with an increase in Ag concentration. Surprisingly, we also find that the dislocation density decreases monotonically with an increase in Ag concentration, which suggests that the grain interiors are softened by the introduction of Ag dopants at GBs. In addition, there is a critical Ag concentration that maximizes flow stress of nc Cu/Ag alloys. The flow stress, GB sliding resistance, and the intragranular dislocation densities become less sensitive to Ag dopants when the grain diameter increases from 5 to 40 nm.
Journal Article
Cis- and Trans-variations of Stearoyl-CoA Desaturase Provide New Insights into the Mechanisms of Diverged Pattern of Phenotypic Plasticity for Temperature Adaptation in Two Congeneric Oyster Species
Abstract
The evolution of phenotypic plasticity plays an essential role in adaptive responses to climate change; however, its regulatory mechanisms in marine organisms which exhibit high phenotypic plasticity still remain poorly understood. The temperature-responsive trait oleic acid content and its major gene stearoyl-CoA desaturase (Scd) expression have diverged in two allopatric congeneric oyster species, cold-adapted Crassostrea gigas and warm-adapted Crassostrea angulata. In this study, genetic and molecular methods were used to characterize fatty acid desaturation and membrane fluidity regulated by oyster Scd. Sixteen causative single-nucleotide polymorphisms (SNPs) were identified in the promoter/cis-region of the Scd between wild C. gigas and C. angulata. Further functional experiments showed that an SNP (g.-333C [C. gigas allele] >T [C. angulata allele]) may influence Scd transcription by creating/disrupting the binding motif of the positive trans-factor Y-box factor in C. gigas/C. angulata, which mediates the higher/lower constitutive expression of Scd in C. gigas/C. angulata. Additionally, the positive trans-factor sterol-regulatory element–binding proteins (Srebp) were identified to specifically bind to the promoter of Scd in both species, and were downregulated during cold stress in C. gigas compared to upregulated in C. angulata. This partly explains the relatively lower environmental sensitivity (plasticity) of Scd in C. gigas. This study serves as an experimental case to reveal that both cis- and trans-variations shape the diverged pattern of phenotypic plasticity, which provides new insights into the formation of adaptive traits and the prediction of the adaptive potential of marine organisms to future climate change.
Journal Article
TPGLDA: Novel prediction of associations between lncRNAs and diseases via lncRNA-disease-gene tripartite graph
Accumulating evidences have indicated that lncRNAs play an important role in various human complex diseases. However, known disease-related lncRNAs are still comparatively small in number, and experimental identification is time-consuming and labor-intensive. Therefore, developing a useful computational method for inferring potential associations between lncRNAs and diseases has become a hot topic, which can significantly help people to explore complex human diseases at the molecular level and effectively advance the quality of disease diagnostics, therapy, prognosis and prevention. In this paper, we propose a novel prediction of lncRNA-disease associations via lncRNA-disease-gene tripartite graph (TPGLDA), which integrates gene-disease associations with lncRNA-disease associations. Compared to previous studies, TPGLDA can be used to better delineate the heterogeneity of coding-non-coding genes-disease association and can effectively identify potential lncRNA-disease associations. After implementing the leave-one-out cross validation, TPGLDA achieves an AUC value of 93.9% which demonstrates its good predictive performance. Moreover, the top 5 predicted rankings of lung cancer, hepatocellular carcinoma and ovarian cancer are manually confirmed by different relevant databases and literatures, affording convincing evidence of the good performance as well as potential value of TPGLDA in identifying potential lncRNA-disease associations. Matlab and R codes of TPGLDA can be found at following:
https://github.com/USTC-HIlab/TPGLDA
.
Journal Article
Leveraging machine learning for enhanced and interpretable risk prediction of venous thromboembolism in acute ischemic stroke care
Venous thromboembolism (VTE) is a life-threatening complication commonly occurring after acute ischemic stroke (AIS), with an increased risk of mortality. Traditional risk assessment tools lack precision in predicting VTE in AIS patients due to the omission of stroke-specific factors.
We developed a machine learning model using clinical data from patients with acute ischemic stroke (AIS) admitted between December 2021 and December 2023. Predictive models were developed using machine learning algorithms, including Gradient Boosting Machine (GBM), Random Forest (RF), and Logistic Regression (LR). Feature selection involved stepwise logistic regression and LASSO, with SHapley Additive exPlanations (SHAP) used to enhance model interpretability. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
Among the 1,632 AIS patients analyzed, 4.17% developed VTE. The GBM model achieved the highest predictive accuracy with an AUC of 0.923, outperforming other models such as Random Forest and Logistic Regression. The model demonstrated strong sensitivity (90.83%) and specificity (93.83%) in identifying high-risk patients. SHAP analysis revealed that key predictors of VTE risk included elevated D-dimer levels, premorbid mRS, and large vessel occlusion, offering clinicians valuable insights for personalized treatment decisions.
This study provides an accurate and interpretable method to predict VTE risk in patients with AIS using the GBM model, potentially improving early detection rates and reducing morbidity. Further validation is needed to assess its broader clinical applicability.
Journal Article
Temporal shifts in prognostic factors for 90- and 180-day outcomes after stroke thrombolysis: A machine learning analysis
Prognostication at 90 and 180 days after thrombolysis for acute ischemic stroke (AIS) is critical, yet the temporal evolution of key predictors remains inadequately understood. The utility of machine learning for systematically comparing prognostic factors across these distinct time points remains to be fully established.
We retrospectively analyzed consecutive AIS patients undergoing intravenous thrombolysis from October 2020 to December 2024. Features were selected from pre-therapy baseline data via univariable analysis and LASSO regression to develop five machine learning models. Model performance and clinical utility were assessed by AUC-ROC and Decision Curve Analysis (DCA), respectively. The primary endpoint was a modified Rankin Scale > 2 at 90 and 180 days.
A total of 432 patients were included. At 90 days, 81 patients (18.8%) had an unfavorable outcome. By the 180-day follow-up, 86 patients (19.9%) were lost to follow-up. Among the remaining 346 patients, 48 (13.9%) had an unfavorable outcome. On the holdout test set, Logistic Regression (AUC = 0.757) and Random Forest (AUC = 0.833) were the optimal models for 90- and 180-day outcomes, respectively. While baseline NIHSS score and age were dominant predictors for both endpoints, a notable temporal shift in biomarker significance emerged: admission fibrinogen was a key predictor at 90 days, but was supplanted by white blood cell count for the 180-day prognosis.
Our study reveals a crucial temporal evolution in prognostic biomarkers after thrombolysis, shifting from fibrinogen at 90 days to white blood cell count at 180 days. This dynamic landscape of predictors, identified through machine learning, underscores the necessity of developing time-specific models to accurately forecast patient recovery.
Journal Article
Nanocellulose Composite Films in Food Packaging Materials: A Review
Owing to the environmental pollution caused by petroleum-based packaging materials, there is an imminent need to develop novel food packaging materials. Nanocellulose, which is a one-dimensional structure, has excellent physical and chemical properties, such as renewability, degradability, sound mechanical properties, and good biocompatibility, indicating promising applications in modern industry, particularly in food packaging. This article introduces nanocellulose, followed by its extraction methods and the preparation of relevant composite films. Meanwhile, the performances of nanocellulose composite films in improving the mechanical, barrier (oxygen, water vapor, ultraviolet) and thermal properties of food packaging materials and the development of biodegradable or edible packaging materials in the food industry are elaborated. In addition, the excellent performances of nanocellulose composites for the packaging and preservation of various food categories are outlined. This study provides a theoretical framework for the development and utilization of nanocellulose composite films in the food packaging industry.
Journal Article