Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data

Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem in decision-making across various industrial scenarios. However, existing time-series forecasting methods often overlook two important characteristics of cumulative data, namely monotonicity and irregularity, which limit their practical applicability. To address this limitation, we propose a principled approach called Monotonic neural Ordinary Differential Equation (MODE) within the framework of neural ordinary differential equations. By leveraging MODE, we are able to effectively capture and represent the monotonicity and irregularity in practical cumulative data. Through extensive experiments conducted in a bonus allocation scenario, we demonstrate that MODE outperforms state-of-the-art methods, showcasing its ability to handle both monotonicity and irregularity in cumulative data and delivering superior forecasting performance.Comment: Accepted as CIKM'23 Applied Research Trac

Accelerating Relaxation Dynamics in Open Quantum System with Liouvillian Skin Effect

We investigate a non-Hermitian model featuring non-reciprocal gradient hoppings. Through an in-depth analysis of the Liouvillian spectrum and dynamics, we confirm the emergence of the Liouvillian skin effect resulting from the non-reciprocal nature of hoppings in this model. Furthermore, we observe that the presence of gradient hopping strength leads to an accelerated relaxation time for the system. Through numerical investigations of the Liouvillian gap, relaxation time, and steady-state localization length, we discover that the relaxation time in this model cannot be explained by the currently established relationship associated with the Liouvillian skin effect. This discrepancy highlights the need for further exploration and theoretical advancements to fully comprehend the intricate mechanisms underlying quantum relaxation processes. Motivated by these findings, we propose a theoretical approach to realize this non-Hermitian model in an atomic system with a sideband structure by employing adiabatic elimination technique. These results contribute to our deeper comprehension of quantum relaxation dynamics and provide theoretical backing for the development of techniques aimed at controlling quantum relaxation processes.Comment: 9 pages, 6 figures, To be published in PR

An update on Ym1 and its immunoregulatory role in diseases

Ym1 is a rodent-specific chitinase-like protein (CLP) lacking catalytic activity, whose cellular origins are mainly macrophages, neutrophils and other cells. Although the detailed function of Ym1 remains poorly understood, Ym1 has been generally recognized as a fundamental feature of alternative activation of macrophages in mice and hence one of the prevalent detecting targets in macrophage phenotype distinguishment. Studies have pointed out that Ym1 may have regulatory effects, which are multifaceted and even contradictory, far more than just a mere marker. Allergic lung inflammation, parasite infection, autoimmune diseases, and central nervous system diseases have been found associations with Ym1 to varying degrees. Thus, insights into Ym1’s role in diseases would help us understand the pathogenesis of different diseases and clarify the genuine roles of CLPs in mammals. This review summarizes the information on Ym1 from the gene to its expression and regulation and focuses on the association between Ym1 and diseases

Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration

Whole brain segmentation with magnetic resonance imaging (MRI) enables the non-invasive measurement of brain regions, including total intracranial volume (TICV) and posterior fossa volume (PFV). Enhancing the existing whole brain segmentation methodology to incorporate intracranial measurements offers a heightened level of comprehensiveness in the analysis of brain structures. Despite its potential, the task of generalizing deep learning techniques for intracranial measurements faces data availability constraints due to limited manually annotated atlases encompassing whole brain and TICV/PFV labels. In this paper, we enhancing the hierarchical transformer UNesT for whole brain segmentation to achieve segmenting whole brain with 133 classes and TICV/PFV simultaneously. To address the problem of data scarcity, the model is first pretrained on 4859 T1-weighted (T1w) 3D volumes sourced from 8 different sites. These volumes are processed through a multi-atlas segmentation pipeline for label generation, while TICV/PFV labels are unavailable. Subsequently, the model is finetuned with 45 T1w 3D volumes from Open Access Series Imaging Studies (OASIS) where both 133 whole brain classes and TICV/PFV labels are available. We evaluate our method with Dice similarity coefficients(DSC). We show that our model is able to conduct precise TICV/PFV estimation while maintaining the 132 brain regions performance at a comparable level. Code and trained model are available at: https://github.com/MASILab/UNesT/wholebrainSeg

Benefits and risks of antihypertensive medication in adults with different systolic blood pressure: A meta-analysis from the perspective of the number needed to treat

BackgroundThe blood pressure (BP) threshold for initial pharmacological treatment remains controversial. The number needed to treat (NNT) is a significant indicator. This study aimed to explore the benefits and risks of antihypertensive medications in participants with different systolic BPs (SBPs), and cardiovascular disease status from the perspective of the NNT.MethodsWe conducted a meta-analysis of 52 randomized placebo-controlled trials. The data were extracted from published articles and pooled to calculate NNTs. The participants were divided into five groups, based on the mean SBP at entry (120–129.9, 130–139.9, 140–159.9, 160–179.9, and ≥180 mmHg). Furthermore, we stratified patients into those with and without cardiovascular disease. The primary outcomes were the major adverse cardiovascular events (MACEs), and adverse events (AEs) leading to discontinuation.ResultsAntihypertensive medications were not associated with MACEs, however, it increased AEs, when the SBP was <140 mmHg. For participants with cardiovascular disease or at a high risk of heart failure and stroke, antihypertensive treatment reduced MACEs when SBP was ≥130 mmHg. Despite this, only 2–4 subjects had reduced MACEs per 100 patients receiving antihypertensive medications for 3.50 years. The number of individuals who needed to treat to avoid MACEs declined with an increased cardiovascular risk.ConclusionPharmacological treatment could be activated when SBP reaches 140 mmHg. For people with cardiovascular disease or at a higher risk of stroke and heart failure, 130 mmHg may be a better therapeutic threshold. It could be more cost-effective to prioritize antihypertensive medications for people with a high risk of developing cardiovascular disease

Electrochemical hydrogenation of mixed-phase TiO₂ nanotube arrays enables remarkably enhanced photoelectrochemical water splitting performance

We first report that photoelectrochemical (PEC) performance of electrochemically hydrogenated TiO2 nanotube arrays (TNTAs) as high-efficiency photoanodes for solar water splitting could be well tuned by designing and adjusting the phase structure and composition of TNTAs. Among various TNTAs annealed at different temperature ranging from 300 to 700 °C, well-crystallized single anatase (A) phase TNTAs-400 photoanode shows the best photoresponse properties and PEC performance due to the favorable crystallinity, grain size and tubular structures. After electrochemical hydrogenation (EH), anatase-rutile (A-R) mixed phase EH-TNTAs-600 photoanode exhibits the highest photoactivity and PEC performance for solar water splitting. Under simulated solar illumination, EH-TNTAs-600 achieves the best photoconversion efficiency of up to 1.52% and maximum H2 generation rate of 40.4 µmol h−1 cm−2, outstripping other EH-TNTAs photoanodes. Systematic studies reveal that the signigicantly enhanced PEC performance for A-R mixed phaes EH-TNTAs-600 photoanode could be attributed to the synergy of A-R mixed phases and intentionally introduced Ti3+ (oxygen vacancies) which enhances the photoactivity over both UV and visible-light regions, and boosts both charge separation and transfer efficiencies. These findings provide new insight and guidelines for the construction of highly efficient TiO2-based devices for the application of solar water splitting.This work was supported by the National Natural Science Foundation of China (51402078, 21702041, and 11674354), the National Basic Research Program of China (2014CB660815), and the Fundamental Research Funds for the Central Universities (JZ2016HGTB0711, JZ2016HGTB0719, and JZ2017HGPA0167)

Multi-Contrast Computed Tomography Atlas of Healthy Pancreas

With the substantial diversity in population demographics, such as differences in age and body composition, the volumetric morphology of pancreas varies greatly, resulting in distinctive variations in shape and appearance. Such variations increase the difficulty at generalizing population-wide pancreas features. A volumetric spatial reference is needed to adapt the morphological variability for organ-specific analysis. Here, we proposed a high-resolution computed tomography (CT) atlas framework specifically optimized for the pancreas organ across multi-contrast CT. We introduce a deep learning-based pre-processing technique to extract the abdominal region of interests (ROIs) and leverage a hierarchical registration pipeline to align the pancreas anatomy across populations. Briefly, DEEDs affine and non-rigid registration are performed to transfer patient abdominal volumes to a fixed high-resolution atlas template. To generate and evaluate the pancreas atlas template, multi-contrast modality CT scans of 443 subjects (without reported history of pancreatic disease, age: 15-50 years old) are processed. Comparing with different registration state-of-the-art tools, the combination of DEEDs affine and non-rigid registration achieves the best performance for the pancreas label transfer across all contrast phases. We further perform external evaluation with another research cohort of 100 de-identified portal venous scans with 13 organs labeled, having the best label transfer performance of 0.504 Dice score in unsupervised setting. The qualitative representation (e.g., average mapping) of each phase creates a clear boundary of pancreas and its distinctive contrast appearance. The deformation surface renderings across scales (e.g., small to large volume) further illustrate the generalizability of the proposed atlas template

Extrachromosomal DNA (ecDNA) in cancer: mechanisms, functions, and clinical implications

Extrachromosomal DNA (ecDNA) is circular DNA that plays an important role in the development and heterogeneity of cancer. The rapid evolution of methods to detect ecDNA, including microscopic and sequencing approaches, has greatly enhanced our knowledge of the role of ecDNA in cancer development and evolution. Here, we review the molecular characteristics, functions, mechanisms of formation, and detection methods of ecDNA, with a focus on the potential clinical implications of ecDNA in cancer. Specifically, we consider the role of ecDNA in acquired drug resistance, as a diagnostic and prognostic biomarker, and as a therapeutic target in the context of cancer. As the pathological and clinical significance of ecDNA continues to be explored, it is anticipated that ecDNA will have broad applications in the diagnosis, prognosis, and treatment of patients with cancer