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The Zhujiang Formation in the Baiyun Sag, Pearl River Mouth Basin, China, is formed primarily in a deep-water continental slope environment. Its chronostratigraphic framework is based on biostratigraphy and sequence stratigraphy, and its geological dating is based on micropaleontological data. This makes it difficult to obtain precise absolute ages for various geological events. In this study, gamma ray (GR) well log data from Wells Y1, Y2, and Y3 were used as paleoclimate proxies, and spectral and wavelet analyses were used to conduct cyclostratigraphic research. The results show that the Milankovitch cycles were preserved in the Zhujiang Formation in the Baiyun Sag. Stratigraphic cycles controlled by 405 and 95 ka orbital eccentricity, 40.4 ka orbital obliquity, and 23.5 ka orbital precession cycles can be identified; the signal of stratigraphic cycles controlled by the 405 ka long eccentricity cycle is the strongest. The floating astronomical time scale is constructed based on 405 ka orbital eccentricity cycle tuning of the GR series. The precise durations of the Zhujiang Formation in Wells Y1, Y2, and Y3 are 7.13, 6.93, and 7.18 Ma, and the average deposition rates are 4.68, 5.91, and 5.33 cm/ka, respectively. The Zhujiang Formation was divided into 17 fourth-, 76 fifth-, and 174 sixth-order cycles using the 405, 95, and 40.4 ka orbital periods as the dividing scales, respectively. This study provides a quantitative method for high-precision isochronous stratigraphic division and correlation in deep-water sedimentary systems.

Background: Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by three neurobiological factors beta‐amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for AD at a late stage, urging for early detection and prevention. However, existing statistical inference approaches in neuroimaging studies of AD subtype identification do not take into account the pathological domain knowledge, which could lead to ill‐posed results that are sometimes inconsistent with the essential neurological principles. Purpose: Integrating systems biology modeling with machine learning, the study aims to assist clinical AD prognosis by providing a subpopulation classification in accordance with essential biological principles, neurological patterns, and cognitive symptoms. Methods: We propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction‐diffusion model, where we consider non‐linear interactions between major biomarkers and diffusion along the brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long‐term evolution trajectories that capture individual characteristic progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations. Results: Our stratification achieves superior performance in both inter‐cluster heterogeneity and intra‐cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. Conclusions: The proposed PSSN (i) reduces neuroimage data to low‐dimensional feature vectors, (ii) combines AT[N]‐Net based on real pathological pathways, (iii) predicts long‐term biomarker trajectories, and (iv) stratifies subjects into fine‐grained subtypes with distinct neurological underpinnings. PSSN provides insights into pre‐symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

A high Schottky barrier height (ΦB) is one of the essential prerequisites for achieving high‐performance self‐powered Schottky‐barrier diode (SBD)‐based photodetector. The ΦB value is predominantly determined by the metal function and interface quality of the metal/semiconductor contact. 2D MXenes with adjustable work functions and dangling bond‐free properties are promising building blocks for constructing self‐powered SBD with high ΦB. Herein, a novel Ti3C2Tx MXene/Si hexagonal microhole array (SiHMA) van der Waals SBD is developed for the first time via a feasible solution process. Significantly, the device possesses a large ΦB up to ≈1.07 eV, which is among the highest for the Si‐based SBD. In consequence, the Ti3C2Tx/SiHMA SBD yields a large responsivity up to 302 mA W−1 and detectivity as high as 5.4 × 1013 Jones in a self‐powered model, surpassing the performance of most 2D material/Si photodiodes reported to date. Furthermore, it is demonstrated that featured and reliable fingertip photoplethysmogram (PPG) signals can be detected using the self‐powered SBD, enabling us to further accurately extract the heart rate (HR), and blood pressures (BP) using the PPG‐only method. This work paves the way for the construction of high‐performance MXenes‐based self‐powered SBDs for health monitoring. [ABSTRACT FROM AUTHOR]