Global, regional, and national trends in metabolic risk factor-associated mortality among the working-age population from 1990-2019: An age-period-cohort analysis of the Global Burden of Disease 2019 study.

BACKGROUND: Metabolic diseases contribute significantly to premature mortality worldwide, with increasing burdens observed among the working-age population (WAP). This study assessed global, regional, and national trends in metabolic disorders and associated mortality over three decades in WAP. METHODS: Data from the Global Burden of Disease 2019 study were leveraged to assess global metabolism-associated mortality and six key metabolic risk factors in WAP from 1990-2019. An age-period-cohort model was employed to determine the overall percentage change in mortality. RESULTS: The 2019 global metabolic risk-related mortality rate in WAP rose significantly by 50.73%, while the age-standardized mortality rate declined by 21.5%. India, China, Indonesia, the USA, and the Russian Federation were the top contributing countries to mortality in WAP, accounting for 51.01% of the total. High systolic blood pressure (HSBP), high body mass index (HBMI), and high fasting plasma glucose (HFPG) were the top metabolic risk factors for the highest mortality rates. Adverse trends in HBMI-associated mortality were observed, particularly in lower sociodemographic index (SDI) regions. HFPG-related mortality declined globally but increased in older age groups in lower SDI countries. CONCLUSIONS: Despite a general decline in metabolic risk-related deaths in WAP, increasing HBMI- and HFPG-related mortality in lower SDI areas poses ongoing public health challenges. Developing nations should prioritize interventions addressing HBMI and HFPG to mitigate mortality risks in WAP.

Visual boundary cues suffice to anchor place and grid cells in virtual reality.

The hippocampal formation contains neurons responsive to an animal's current location and orientation, which together provide the organism with a neural map of space.1,2,3 Spatially tuned neurons rely on external landmark cues and internally generated movement information to estimate position.4,5 An important class of landmark cue are the boundaries delimiting an environment, which can define place cell field position6,7 and stabilize grid cell firing.8 However, the precise nature of the sensory information used to detect boundaries remains unknown. We used 2-dimensional virtual reality (VR)9 to show that visual cues from elevated walls surrounding the environment are both sufficient and necessary to stabilize place and grid cell responses in VR, when only visual and self-motion cues are available. By contrast, flat boundaries formed by the edges of a textured floor did not stabilize place and grid cells, indicating only specific forms of visual boundary stabilize hippocampal spatial firing. Unstable grid cells retain internally coherent, hexagonally arranged firing fields, but these fields "drift" with respect to the virtual environment over periods >5 s. Optic flow from a virtual floor does not slow drift dynamics, emphasizing the importance of boundary-related visual information. Surprisingly, place fields are more stable close to boundaries even with floor and wall cues removed, suggesting invisible boundaries are inferred using the motion of a discrete, separate cue (a beacon signaling reward location). Subsets of place cells show allocentric directional tuning toward the beacon, with strength of tuning correlating with place field stability when boundaries are removed.

Synthesis, biological activities and mechanism studies of 1,3,4-oxadiazole analogues of petiolide A as anticancer agents.

In order to develop new natural product-based anticancer agents, a series of 1,3,4-oxadiazole analogues based on petiolide A were prepared and evaluated for their anticancer activities by MTT method. The structures of all analogues were characterized by various spectral analyses, and B9 was further confirmed by X-ray crystallography. Among all the synthesized compounds, B1 displayed the most promising growth inhibitory effect on colon cancer cells (HCT116) with the IC50 value of 8.53 µM. Flow cytometric analysis exhibited that B1 arrested the cell cycle at G2 phase and induced apoptosis. Additionally, network pharmacology analysis calculated that B1 might target several key proteins, including AKT serine/threonine kinase 1 (AKT1), SRC proto-oncogene, non-receptor tyrosine kinase (SRC) and epidermal growth factor receptor (EGFR). Furthermore, molecular docking study indicated that B1 had potentially high binding affinity to these three target proteins. Given these results, analogue B1 could be deeply developed as potential anticancer agents.

Enhancing Data Freshness in Air-Ground Collaborative Heterogeneous Networks through Contract Theory and Generative Diffusion-Based Mobile Edge Computing.

Mobile edge computing is critical for improving the user experience of latency-sensitive and freshness-based applications. This paper provides insights into the potential of non-orthogonal multiple access (NOMA) convergence with heterogeneous air-ground collaborative networks to improve system throughput and spectral efficiency. Coordinated resource allocation between UAVs and MEC servers, especially in the NOMA framework, is addressed as a key challenge. Under the unrealistic assumption that edge nodes contribute resources indiscriminately, we introduce a two-stage incentive mechanism. The model is based on contract theory and aims at optimizing the utility of the service provider (SP) under the constraints of individual rationality (IR) and incentive compatibility (IC) of the mobile user. The block coordinate descent method is used to refine the contract design and complemented by a generative diffusion model to improve the efficiency of searching for contracts. During the deployment process, the study emphasizes the positioning of UAVs to maximize SP effectiveness. An improved differential evolutionary algorithm is introduced to optimize the positioning of UAVs. Extensive evaluation shows our approach has excellent effectiveness and robustness in deterministic and unpredictable scenarios.

An Efficient Implementation Method for Distributed Fusion in Sensor Networks Based on CPHD Filters.

A highly efficient implementation method for distributed fusion in sensor networks based on CPHD filters is proposed to address the issues of unknown cross-covariance fusion estimation and long fusion times in multi-sensor distributed fusion. This method can effectively and efficiently fuse multi-node information in multi-target tracking applications. Discrete gamma cardinalized probability hypothesis density (DG-CPHD) can effectively reduce the computational burden while ensuring computational accuracy similar to that of CPHD filters. Parallel inverse covariance intersection (PICI) can effectively avoid solving high-dimensional weight coefficient convex optimization problems, reduce the computational burden, and efficiently implement filtering fusion strategies. The effectiveness of the algorithm is demonstrated through simulation results, which indicate that PICI-GM-DG-CPHD can substantially reduce the computational time compared to other algorithms and is more suitable for distributed sensor fusion.