Nanjing Foreign Language School

Alzheimer's disease (AD) is a severe degenerative neurological disease which leads to progressive cognitive decline and memory impairment. We have affirmed amyloid- (A) plaques and tau tangles as the primary pathological hallmarks for a long time. However, many therapeutic approaches have found out that both A and tau alone have achieved little success. Increasing evidence indicates that the neuroinflammation, which primarily related with microglia and astrocytes, takes the central role of causing and exacerbating the disease. Microglia and astrocytes not only respond to A and tau but also control their accumulation, propagation, and associated neurotoxicity. They are also influenced by a kind ofrisky gene related to late-onset AD calls ApoE, which can further modulate them by regulating lipid metabolism and glial responses. This review synthesizes current knowledge on the roles of microglia, astrocytes, A, tau, and ApoE in AD, emphasizing their interconnected contributions to pathogenesis and therapeutic opportunities.

This study examines the emerging role of threedimensional (3D) bioprinting i n constructing physiologically relevant tumor microenvironment (TME) models. By synthesizing recent advances in bioprinting strategies, bioink development, immune–tumor integration, and vascularization techniques, the analysis highlights how engineered spatial precision and controlled multicellular organization enhance the accuracy of cancer modeling. The findings indicate that 3D bioprinted TMEs provide improved platforms for studying tumor heterogeneity, therapy resistance, and drug response dynamics. Despite these strengths, challenges remain in achieving stable perfusion, standardizing bioink properties, and reproducing dynamic in vivo conditions. Future work should focus on integrating microfluidic systems, optimizing bioink formulations, and advancing interdisciplinary collaboration. Overall, this study underscores the value of 3D bioprinting as a transformative tool for oncology research and the development of more predictive preclinical models.

Using immunotherapy to treat breast cancer, one of the most common cancers that affects women and the second most common cancer among women in the United States, has achieved many successes. However, how to solve the problem of cancer resistance to therapies caused by myeloid-derived suppressor cells and immunosuppressive tumor microenvironment remains a major challenge. To improve the performance of immunotherapy, we chose to explore the three putative therapeutic approaches including anti IL 8 antibody injection, PDH inhibition, and the use of tasquinimod, then combining them to observe anti-tumor effects in a murine model. We anticipate the greatest decrease of tumor volume in triple combination, concluding the feasibility of our method on treating breast cancer.

From initial laboratory explorations to large-scale applications, robots have not only achieved significant technological breakthroughs but have also demonstrated extensive application potential across various sectors of society. Among these advancements, the robotic arm plays a crucial role, necessitating in-depth analysis of both forward and inverse kinematics via solving nonlinear equations. Numerous methods exist for analyzing multi-degree-of-freedom robotic arms, including the construction of coordinate systems using Denavit-Hartenberg (D-H) notation [1]. This paper examines various solution methods for nonlinear equations in robotic arms kinematics, including Newton's method, the secant method, the parallel line method, and the homotopy method. Additionally, MATLAB is utilized for analysis to provide a clearer understanding of the specific steps, advantages, and disadvantages associated with each method. This study offers valuable insights into the application of robotic arms in solving systems of nonlinear equations.

Hydrological time series forecasting faces significant challenges in balancing prediction accuracy and timeliness due to the inherent nonlinearity and non-stationarity of water level data. While Long Short-Term Memory (LSTM) networks are widely employed for their capability to capture long-term dependencies, they frequently exhibit prediction delays, particularly in complex hydrological scenarios. To address this limitation, we propose a hybrid model, CS-VMDLSTM, which integrates Variational Mode Decomposition (VMD) with Cuckoo Search (CS) optimization. The core innovation involves the adaptive decomposition of water level sequences into multiscale intrinsic mode functions (IMFs) via VMD, where its critical parameters (number of modes <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex>, penalty factor <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex>) are optimized using the CS algorithm. Each IMF subcomponent is subsequently processed by a dedicated LSTM sub-model. The final prediction is obtained by aggregating the outputs of all sub-models. Experimental evaluation on the Tunxi Basin dataset (2016-2018) demonstrates that the proposed model reduces prediction delays by an average of 1 hour across 2-6 hour lead times and improves the Nash-Sutcliffe Efficiency (NSE) by 7.6 % compared to the baseline LSTM. The CS-VMD-LSTM framework effectively mitigates peak lagging and enhances temporal responsiveness, providing a robust solution for real-time flood forecasting and water resource management.

Few institutions are more important to economic development than the commercial bank; and yet its actual form and function are continually changing. This paper describes the history of the latter-day commercial bank. It traces the evolution of the commercial bank into a technological and algorithmic entity in the 21st century, analyzing its dual character: a facilitator of commerce and a potential instrument of risk. The primary theme in the history of the commercial bank is the technical and institutional means of creating and organizing “trust.” The study examines this development at the various stages encompassing the invention of double-entry bookkeeping, and state-chartered public banks (the Bank of England, etc.), the globalization of money and finance, and the latter placing risk management in the hands of banks (under the Basel Accords). Finally, the paper examines today’s turning point, wherein the competitive forces of Financial Technology (Fintech) and its “weaponization” by warring nation states (especially the UK and USA), require that the banks define themselves ironically as brand-names who can be trusted.

This paper presents an empirical analysis and case study of the 2021 - 2023 U.S. inflation cycle. Leveraging monthly data from FRED, it examines the dynamic interplay between the Federal Reserve's monetary policy tools (federal funds rate and balance - sheet policy) and key macroeconomic indicators. The study reveals that delayed policy tightening in 2021, due to factors like the Average Inflation Targeting framework and labor market concerns, led to a rapid rate - hike cycle in 2022 - 2023. While inflation control was achieved, significant costs emerged, including financial fragility and recession risks. The findings emphasize the importance of timely policy diagnosis, policy coordination, and balancing inflation control with financial stability and employment goals in a shock - prone world.

Three body problem is one of the most complicated dynamical problems in classical mechanics; the difficulty comes from the nonlinear equations and sensitive initial conditions. In this paper, it reviews the disintegration process of Three body system in long-term evolution and discuss the application of statistical mechanics in explaining this phenomenon. Based on the theoretical model, numerical simulation, and observational results, it draws the following conclusions: The most frequent final state of a three-body system is the disintegration of a binary star and an escape star. This kind of mechanism comes from the energy and angular momentum’s homogenization, and it has been proved by the BN/KL region in Orion. Besides, the statistical model based on the microcanonical distribution and ergodicity assumption can predict the final state parameter’s distribution, which is consistent with the simulation results and provides a new way to explore chaotic systems. In the future, it will combine Gaia, gravitational wave observation, and high-precision numerical simulation to further complete the theory and application in the dynamics of compact objects.