A distinct subset of urothelial cells with enhanced EMT features promotes chemotherapy resistance and cancer recurrence by increasing COL4A1-ITGB1 mediated angiogenesis.

Drug resistance and tumor recurrence remain clinical challenges in the treatment of urothelial carcinoma (UC). However, the underlying mechanism is not fully understood. Here, we performed single-cell RNA sequencing and identified a subset of urothelial cells with epithelial-mesenchymal transition (EMT) features (EMT-UC), which is significantly correlated with chemotherapy resistance and cancer recurrence. To validate the clinical significance of EMT-UC, we constructed EMT-UC like cells by introducing overexpression of two markers, Zinc Finger E-Box Binding Homeobox 1 (ZEB1) and Desmin (DES), and examined their histological distribution characteristics and malignant phenotypes. EMT-UC like cells were mainly enriched in UC tissues from patients with adverse prognosis and exhibited significantly elevated EMT, migration and gemcitabine tolerance in vitro. However, EMT-UC was not specifically identified from tumorous tissues, certain proportion of them were also identified in adjacent normal tissues. Tumorous EMT-UC highly expressed genes involved in malignant behaviors and exhibited adverse prognosis. Additionally, tumorous EMT-UC was associated with remodeled tumor microenvironment (TME), which exhibited high angiogenic and immunosuppressive potentials compared with the normal counterparts. Furthermore, a specific interaction of COL4A1 and ITGB1 was identified to be highly enriched in tumorous EMT-UC, and in the endothelial component. Targeting the interaction of COL4A1 and ITGB1 with specific antibodies significantly suppressed tumorous angiogenesis and alleviated gemcitabine resistance of UC. Overall, our findings demonstrated that the driven force of chemotherapy resistance and recurrence of UC was EMT-UC mediated COL4A1-ITGB1 interaction, providing a potential target for future UC treatment.

Accelerating Molecular Vibrational Spectra Simulations with a Physically Informed Deep Learning Model.

In recent years, machine learning (ML) surrogate models have emerged as an indispensable tool to accelerate simulations of physical and chemical processes. However, there is still a lack of ML models that can accurately predict molecular vibrational spectra. Here, we present a highly efficient multitask ML surrogate model termed Vibrational Spectra Neural Network (VSpecNN), to accurately calculate infrared (IR) and Raman spectra based on dipole moments and polarizabilities obtained on-the-fly via ML-enhanced molecular dynamics simulations. The methodology is applied to pyrazine, a prototypical polyatomic chromophore. The VSpecNN-predicted energies are well within the chemical accuracy (1 kcal/mol), and the errors for VSpecNN-predicted forces are only half of those obtained from a popular high-performance ML model. Compared to the ab initio reference, the VSpecNN-predicted frequencies of IR and Raman spectra differ only by less than 5.87 cm-1, and the intensities of IR spectra and the depolarization ratios of Raman spectra are well reproduced. The VSpecNN model developed in this work highlights the importance of constructing highly accurate neural network potentials for predicting molecular vibrational spectra.

Triaxial-mechanical and permeability properties of coal body under varying CO2 pressures.

The sequestration of CO2 in coal seams has become an effective way to curb greenhouse-gas emissions. Coal mechanics and permeability properties are key factors affecting the safe sequestration of CO2 in coal seams, and both are significantly affected by the CO2 injection pressure. In this study, triaxial compression-permeability tests were conducted on coal under varying CO2 and limited confining pressures using a measurement system to determine the coupled mechanical properties and adsorption permeability of coal. The effects of CO2 pressure on the mechanical properties and evolution of coal permeability were investigated. A three-dimensional statistical damage constitutive model of coal that considers CO2 adsorption damage was established. The results showed that the stress-strain curve of the coal was divided into four stages. During the first two stages, the amplitudes of the permeability changes were small, whereas at the peak stress point, a permeability "jump" phenomenon occurred, after which an increase in stress resulted in a slower amplitude increase in permeability. The CO2 pressure had an evident damage-deterioration effect on the mechanical properties of the coal samples, and the primary failure characteristic was shearing damage. The higher the CO2 pressure, the higher the degree of internal fragmentation and fracture network complexity of the coal body. During the triaxial compression-permeability experiment, the normalized permeability of the coal samples tended to increase slowly, followed by a rapid increase and back to a slow increase. However, with increasing CO2 pressure, the initial permeability of the coal samples decreased, whereas the normalized permeability increased sharply. The rationality and accuracy of the constitutive model were verified by comparing the established constitutive model and test stress-strain curves of the coal samples. The results of this study can provide theoretical references for CO2 geological storage and facilitate the selection of appropriate CO2 injection pressures.

MLatom Software Ecosystem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine Learning Methods.

We present an open-source MLatom@XACS software ecosystem for on-the-fly surface hopping nonadiabatic dynamics based on the Landau-Zener-Belyaev-Lebedev algorithm. The dynamics can be performed via Python API with a wide range of quantum mechanical (QM) and machine learning (ML) methods, including ab initio QM (CASSCF and ADC(2)), semiempirical QM methods (e.g., AM1, PM3, OMx, and ODMx), and many types of ML potentials (e.g., KREG, ANI, and MACE). Combinations of QM and ML methods can also be used. While the user can build their own combinations, we provide AIQM1, which is based on Δ-learning and can be used out-of-the-box. We showcase how AIQM1 reproduces the isomerization quantum yield of trans-azobenzene at a low cost. We provide example scripts that, in dozens of lines, enable the user to obtain the final population plots by simply providing the initial geometry of a molecule. Thus, those scripts perform geometry optimization, normal mode calculations, initial condition sampling, parallel trajectories propagation, population analysis, and final result plotting. Given the capabilities of MLatom to be used for training different ML models, this ecosystem can be seamlessly integrated into the protocols building ML models for nonadiabatic dynamics. In the future, a deeper and more efficient integration of MLatom with Newton-X will enable a vast range of functionalities for surface hopping dynamics, such as fewest-switches surface hopping, to facilitate similar workflows via the Python API.

Finite temperature dynamics in a polarized sub-Ohmic heat bath: A hierarchical equations of motion-tensor train study.

The dynamics of the sub-Ohmic spin-boson model under polarized initial conditions at finite temperatures is investigated by employing both analytical tools and the numerically accurate hierarchical equations of motion-tensor train method. By analyzing the features of nonequilibrium dynamics, we discovered a bifurcation phenomenon, which separates two regimes of the dynamics. It is found that before the bifurcation time, increasing temperature slows down the population dynamics, while the opposite effect occurs after the bifurcation time. The dynamics is highly sensitive to both initial preparation of the bath and thermal effects.

A Review of Intelligent Walking Support Robots: Aiding Sit-to-Stand Transition and Walking.

Nowadays, numerous countries are facing the challenge of aging population. Additionally, the number of people with reduced mobility due to physical illness is increasing. In response to this issue, robots used for walking assistance and sit-to-stand (STS) transition have been introduced in nursing to assist these individuals with walking. Given the shared characteristics of these robots, this paper collectively refers to them as Walking Support Robots (WSR). Additionally, service robots with assisting functions have been included in the scope of this review. WSR are a crucial element of modern nursing assistants and have received significant research attention. Unlike passive walkers that require much user's strength to move, WSR can autonomously perceive the state of the user and environment, and select appropriate control strategies to assist the user in maintaining balance and movement. This paper offers a comprehensive review of recent literature on WSR, encompassing an analysis of structure design, perception methods, control strategies and safety & comfort features. In conclusion, it summarizes the key findings, current challenges and discusses potential future research directions in this field.

Unraveling quantum coherences mediating primary charge transfer processes in photosystem II reaction center.

Photosystem II (PSII) reaction center (RC) is a unique complex that is capable of efficiently separating electronic charges across the membrane. The primary energy- and charge-transfer (CT) processes occur on comparable ultrafast timescales, which makes it extremely challenging to understand the fundamental mechanism responsible for the near-unity quantum efficiency of the transfer. Here, we elucidate the role of quantum coherences in the ultrafast energy and CT in the PSII RC by performing two-dimensional (2D) electronic spectroscopy at the cryogenic temperature of 20 kelvin, which captures the distinct underlying quantum coherences. Specifically, we uncover the electronic and vibrational coherences along with their lifetimes during the primary ultrafast processes of energy and CT. We construct an excitonic model that provides evidence for coherent energy and CT at low temperature in the 2D electronic spectra. The principles could provide valuable guidelines for creating artificial photosystems with exploitation of system-bath coupling and control of coherences to optimize the photon conversion efficiency to specific functions.

Contrast-enhanced CT in the differential diagnosis of bladder cancer and paraganglioma.

PURPOSE: We sought to summarize the value of contrast-enhanced computed tomography (CECT) in the differential diagnosis of bladder paraganglioma (BPG) and bladder cancer. METHODS: The medical records of 19 patients with BPG (13 males, 6 females) and 56 patients with bladder cancer (49 males, 7 females) between November 2007 and June 2023 were retrospectively reviewed. All patients underwent unenhanced and contrast-enhanced CT scanning. RESULTS: Patient age (46.4 ± 11.1 years vs. 58.6 ± 16.0 years), tumor calcification (1/19 vs. 18/56), stalk (0/19 vs. 10/56), internal vessels (15/19 vs. 19/56) and the enlarged adjacent supplying artery (14/19 vs. 10/56) were significantly different between BPG and bladder cancer (P < 0.05). The CT value in the corticomedullary phase (92.4 ± 16.6 HU vs. 64.0 ± 14.5 HU) and the contrast-enhanced value in the corticomedullary phase (54.5 ± 17.4 HU vs. 28.5 ± 12.8 HU) were significantly greater in BPG patients than in bladder cancer patients (P < 0.001), with corresponding area under the curve values of 0.930 and 0.912, respectively. The optimal cutoff values were 83.2 HU and 38.5 HU, respectively. A CT value > 83.2 HU in the corticomedullary phase and a contrast-enhanced CT value > 38.5 HU in the corticomedullary phase were used to indicate BPG with sensitivities of 78.9% and 89.5%, respectively, and specificities of 94.6% and 75.0%, respectively. CONCLUSION: The corticomedullary phase of CECT plays an important role in the preoperative differential diagnosis of BPG and bladder cancer.

Artificial-Intelligence-Based Surrogate Solution of Dissipative Quantum Dynamics: Physics-Informed Reconstruction of the Universal Propagator.

The accurate (or even approximate) solution of the equations that govern the dynamics of dissipative quantum systems remains a challenging task in quantum science. While several algorithms have been designed to solve those equations with different degrees of flexibility, they rely mainly on highly expensive iterative schemes. Most recently, deep neural networks have been used for quantum dynamics, but current architectures are highly dependent on the physics of the particular system and usually limited to population dynamics. Here we introduce an artificial-intelligence-based surrogate model that solves dissipative quantum dynamics by parametrizing quantum propagators as Fourier neural operators, which we train using both data set and physics-informed loss functions. Compared with conventional algorithms, our quantum neural propagator avoids time-consuming iterations and provides a universal superoperator that can be used to evolve any initial quantum state for arbitrarily long times. To illustrate the wide applicability of the approach, we employ our quantum neural propagator to compute the population dynamics and time-correlation functions of the Fenna-Matthews-Olson complex.

Cardiovascular events reported in patients with B-cell malignancies treated with zanubrutinib.

ABSTRACT: First-generation Bruton tyrosine kinase (BTK) inhibitor, ibrutinib, has been associated with an increased risk of cardiovascular toxicities. Zanubrutinib is a more selective, next-generation BTK inhibitor. In this analysis, incidence rates of atrial fibrillation, symptomatic (grade ≥2) ventricular arrhythmia, and hypertension were evaluated in a pooled analysis of 10 clinical studies with zanubrutinib monotherapy in patients (N = 1550) with B-cell malignancies and a pooled analysis of head-to-head studies comparing zanubrutinib with ibrutinib (ASPEN cohort 1; ALPINE). Among the 10 studies, most patients (median age, 67 years) were male (66.3%) and had CLL/SLL (60.5%). Overall incidence and exposure-adjusted incidence rates (EAIR) for atrial fibrillation, symptomatic ventricular arrhythmia, and hypertension were lower with zanubrutinib than ibrutinib. Despite a similar prevalence of preexisting cardiovascular events in ASPEN and ALPINE, atrial fibrillation/flutter incidence rates (6.1% vs 15.6%) and EAIR (0.2 vs 0.64 persons per 100 person-months; P < .0001) were lower with zanubrutinib than with ibrutinib. Symptomatic ventricular arrhythmia incidence was low for both zanubrutinib (0.7%) and ibrutinib (1.7%) with numerically lower EAIR (0.02 vs 0.06 persons per 100 person-months, respectively) for zanubrutinib. The hypertension EAIR was lower with zanubrutinib than ibrutinib in ASPEN but similar between treatment arms in ALPINE. The higher hypertension EAIR in ALPINE was inconsistent with other zanubrutinib studies. However, fewer discontinuations (1 vs 14) and deaths (0 vs 6) due to cardiac disorders occurred with zanubrutinib versus ibrutinib in ALPINE. These data support zanubrutinib as a treatment option with improved cardiovascular tolerability compared with ibrutinib for patients with B-cell malignancies in need of BTK inhibitors. These trials were registered at www.ClinicalTrials.gov as # NCT03053440, NCT03336333, NCT03734016, NCT04170283, NCT03206918, NCT03206970, NCT03332173, NCT03846427, NCT02343120, and NCT03189524.

Artificial-Intelligence-Enhanced On-the-Fly Simulation of Nonlinear Time-Resolved Spectra.

Time-resolved spectroscopy is an important tool for unraveling the minute details of structural changes in molecules of biological and technological significance. The nonlinear femtosecond signals detected for such systems must be interpreted, but it is a challenging task for which theoretical simulations are often indispensable. Accurate simulations of transient absorption or two-dimensional electronic spectra are, however, computationally very expensive, prohibiting the wider adoption of existing first-principles methods. Here, we report an artificial-intelligence-enhanced protocol to drastically reduce the computational cost of simulating nonlinear time-resolved electronic spectra, which makes such simulations affordable for polyatomic molecules of increasing size. The protocol is based on the doorway-window approach for the on-the-fly surface-hopping simulations. We show its applicability for the prototypical molecule of pyrazine for which it produces spectra with high precision with respect to ab initio reference while cutting the computational cost by at least 95% compared to pure first-principles simulations.

Association of miR-203 Expression with Prognostic Value in Patients with Esophageal Cancer: A Systematic Review and Meta-Analysis.

OBJECTIVE: This study aims to investigate the association between miR-203 expression and the prognostic value in patients with esophageal cancer by the method of systematic review and meta-analysis. METHODS: We searched PubMed, Web of Science, Embase, and Cochrane Library to collect studies on the relationship between miR-203 expression and the prognostic value of esophageal cancer up to July 2023. Stata 15.0 statistical software was used for data analysis. Hazard ratio (HR) and 95% confidence interval (CI) were used as effect sizes. RESULTS: A total of 6 studies were included in this review, including 476 patients with esophageal cancer. The results showed that miR-203 low expression was associated with worse overall survival (OS) in patients with esophageal cancer compared with miR-203 high expression (HR = 2.80, 95%CI: 1.99 ∼ 3.93, p < 0.001). The results of Egger's (p = 0.154) and Begg's Tests (p = 0.221) indicated no obvious publication bias. Sensitivity analysis verified the robustness of the results obtained in this study. CONCLUSION: The expression of miR-203 is significantly correlated with the prognostic value in patients with esophageal cancer. Esophageal cancer patients with high expression of miR-203 had better prognosis than those with low expression of miR-203. Due to the limited studies included in this meta-analysis, more trials are needed to confirm the conclusions of this study in the future.

Identification of a 24-gene panel and a novel marker of PODXL2 essential for the pathological diagnosis of early prostate cancer.

Precise diagnosis of early prostate cancer (PCa) is critical for preventing tumor progression. However, the diagnostic outcomes of currently used markers are far from satisfactory due to the low sensitivity or specificity. Here, we identified a diagnostic subpopulation in PCa tissue with the integrating analysis of single-cell and bulk RNA-seq. The representative markers of this subpopulation were extracted to perform intersection analysis with early-PCa-related gene module generated from weighted correlation network analysis (WGCNA). A total of 24 overlapping genes were obtained, the diagnostic roles of which were validated by distinguishing normal and tumorous prostate samples from the public dataset. A least absolute shrinkage and selection operator (LASSO) model was constructed based on these genes and the obtained 24-gene panel showed high sensitivity and specificity for PCa diagnosis, with better identifying capability of PCa than the commercially used gene panel of Oncotype DX. The top two risk factors, TRPM4 and PODXL2, were verified to be highly expressed in early PCa tissues by multiplex immunostaining, and PODXL2 was more sensitive and specific compared to TRPM4 and the pathologically used marker AMACR for early PCa diagnosis, suggesting a novel and promising pathology marker.

Gut microbiome in men with chronic prostatitis/chronic pelvic pain syndrome: profiling and its predictive significance.

PURPOSE: To investigate the difference in gut microbiome composition between patients with chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) and healthy controls, and to assess the potential of gut microbiota as predictive markers for CP/CPPS risk. METHODS: The present study included 41 CP/CPPS patients and 43 healthy controls in China. Fecal specimen data were obtained and analysed using 16S rRNA gene sequencing. Alpha and beta-diversity indices, relative microbiome abundances, cluster analysis, and linear discriminant analysis effect size (LEfSe) were employed. Microbial biomarkers were selected for the development of a diagnostic classification model, and the functional prediction was conducted using PICRUSt2. RESULTS: Alpha-diversity measures revealed no statistically significant difference in bacterial community structure between CP/CPPS patients and controls. However, significant differences were observed in the relative abundances of several bacterial genera. Beta-diversity analysis revealed a distinct separation between the two groups. Significant inter-group differences were noted at various taxonomic levels, with specific bacterial genera being significantly different in abundance. The LEfSe analysis indicated that three bacterial species were highly representative and seven bacterial species were low in CP/CPPS patients as compared to the control group. A diagnostic model for CP/CPPS based on microbial biomarkers exhibited good performance. PICRUSt2 functional profiling indicated significant differences in the development and regeneration pathway. CONCLUSION: Significant differences in the gut microbiome composition were found between groups. The study provided a novel diagnostic model for CP/CPPS based on microbiota, presenting promising potential for future therapeutic targets and non-invasive diagnostic biomarkers for CP/CPPS patients.

Dynamic changes in macrophage subtypes during lung cancer progression and metastasis at single-cell resolution.

Background: Lung cancer remains a major global health challenge. Macrophages (Macs) are one important component of tumor microenvironments (TMEs); however, their prognostic relevance to lung cancer is currently unknown due to the complexity of their phenotypes. Methods: In the present study, reanalysis and atlas reconstruction of downloaded single-cell RNA sequencing (scRNAseq) data were used to systematically compare the component and transcriptional changes in Mac subtypes across different stages of lung cancer. Results: We found that with the progression of lung cancer, the proportion of alveolar macrophages (aMacs) gradually decreased, while the proportions of Macs and monocytes (Monos) gradually increased, suggesting a chemotaxis process followed by a Mono-Mac differentiation process. Meanwhile, through ligand-receptor (LR) screening, we identified 9 Mac-specific interactions that were enriched during the progression and metastasis of lung cancer, which could potential promote M2 polarization or the infiltration of M2 Macs. Moreover, we found that the expression of SPP1 in Macs increased with lung cancer progression, and identified 9 genes that were correlated with the expression of SPP1 in Macs, which might also contribute to the immunosuppression process in lung cancer. Conclusions: Our results revealed detailed changes in Macs at different stages of lung cancer progression and metastasis and provided potential therapeutic targets that could be used in future lung cancer treatments.

Adherence to the Mediterranean diet and risk of gastric cancer: a systematic review and dose-response meta-analysis.

Background: Despite growing evidence for the association of adherence to the Mediterranean diet with gastric cancer risk, the results remain inconclusive. The purpose of this systematic review and meta-analysis was to summarize the evidence from previous observational studies and assess the potential association between adherence to the Mediterranean diet and risk of gastric cancer using a dose-response meta-analysis. Methods: A comprehensive literature search for all observational studies published up to June 30, 2023 was conducted using the databases of PubMed, ISI Web of Science, EBSCO, China National Knowledge Infrastructure (CNKI) and Wanfang Data. The pooled relative risks (RRs) and 95% confidence intervals (CIs) were calculated for the highest versus the lowest categories of Mediterranean diet score in relation to gastric cancer risk, using random-effects models. The Cochran's Q test and I-squared (I2) statistic were used to detect the sources of heterogeneity among the included studies. Results: Overall, 11 studies (five cohort and six case-control studies) with a total number of 1,366,318 participants were included in the final analysis. Combining 14 effect sizes from 11 studies revealed that compared with the lowest category, the highest adherence to the Mediterranean diet was associated with a 29% reduction in the risk of gastric cancer (RR:0.71; 95%CI:0.59-0.84, p < 0.001). In addition, linear dose-response analysis showed that each 1-score increment in Mediterranean diet score was associated with a 5% lower risk of gastric cancer (RR:0.95; 95%CI: 0.94-0.96, p < 0.001). Stratified analysis showed a significant association between adherence to the Mediterranean diet and risk of gastric cancer in case-control studies (RR = 0.44;95%CI:0.32-0.61, p < 0.001), and a marginally significant association in prospective cohort studies (RR = 0.88; 95%CI: 0.79-0.98, p = 0.024), respectively. At the same time, a more significant association between Mediterranean diet and reduced risk of gastric cancer was observed in other countries (RR = 0.28; 95%CI:0.16-0.49, p < 0.001) than in Western countries (RR = 0.75; 95%CI:0.64-0.88, p = 0.001). Conclusion: Our results demonstrate that high adherence to the Mediterranean diet is associated with 29% reduced risk of gastric cancer. Further large prospective studies and randomized controlled trials are warranted to confirm our findings.

Two-dimensional fluorescence excitation spectroscopy: A novel technique for monitoring excited-state photophysics of molecular species with high time and frequency resolution.

We propose a novel UV/Vis femtosecond spectroscopic technique, two-dimensional fluorescence-excitation (2D-FLEX) spectroscopy, which combines spectral resolution during the excitation process with exclusive monitoring of the excited-state system dynamics at high time and frequency resolution. We discuss the experimental feasibility and realizability of 2D-FLEX, develop the necessary theoretical framework, and demonstrate the high information content of this technique by simulating the 2D-FLEX spectra of a model four-level system and the Fenna-Matthews-Olson antenna complex. We show that the evolution of 2D-FLEX spectra with population time directly monitors energy transfer dynamics and can thus yield direct qualitative insight into the investigated system. This makes 2D-FLEX a highly efficient instrument for real-time monitoring of photophysical processes in polyatomic molecules and molecular aggregates.

Future self-continuity and depression among college students: The role of presence of meaning and perceived social support.

INTRODUCTION: Future self-continuity has been shown to have a protective effect against depression. This study aims to investigate the longitudinal relationship between future self-continuity and depression among college students, and to explore the mediating role of the presence of meaning and the moderating role of perceived social support. METHODS: We conducted two studies in China in 2022 and 2023. Study 1 was a longitudinal cross-lagged study that examined the relationship between future self-continuity and depression among 173 participants (49.13% females, Mage = 19.39, SD = 1.63). Study 2 was a cross-sectional study that explored the mediating role of the presence of meaning and the moderating role of perceived social support among 426 participants (48.59% females, Mage = 19.30, SD = 1.60). RESULTS: Study 1 showed that future self-continuity (T1) could significantly predict depression (T2), but depression (T1) could not predict future self-continuity (T2). Study 2 showed that after controlling for gender, the presence of meaning mediated the relationship between future self-continuity and depression, whereas perceived social support moderated the first half of the mediated model's pathway and the direct pathway. CONCLUSIONS: These findings suggest that enhancing the future self-continuity of college students and increasing the level of presence of meaning are effective measures for alleviating depression. Meanwhile, educators and families are called upon to provide more social support to college students.

A study on the sustainability assessment of China's basic medical insurance fund under the background of population aging-evidence from Shanghai.

Objective: As China's population aging process accelerates, the expenditure of China's basic medical insurance fund for employees may increase significantly, which may threaten the sustainability of China's basic medical insurance fund for employees. This paper aims to forecast the future development of China's basic medical insurance fund for employees in the context of the increasingly severe aging of the population. Methods: This paper taking an empirical study from Shanghai as an example, constructs an actuarial model to analyze the impact of changes in the growth rate of per capita medical expenses due to non-demographic factors and in the population structure on the sustainability of the basic medical insurance fund for employees. Results: Shanghai basic medical insurance fund for employees can achieve the goal of sustainable operation in 2021-2035, with a cumulative balance of 402.150-817.751 billion yuan in 2035. The lower the growth rate of per capita medical expenses brought about by non-demographic factors, the better the sustainable operation of the fund. Conclusion: Shanghai basic medical insurance fund for employees can operate sustainably in the next 15 years, which can further reduce the contribution burden of enterprises, which lays the foundation for improving the basic medical insurance treatment for employees.

Simulating optical linear absorption for mesoscale molecular aggregates: An adaptive hierarchy of pure states approach.

In this paper, we present dyadic adaptive HOPS (DadHOPS), a new method for calculating linear absorption spectra for large molecular aggregates. This method combines the adaptive HOPS (adHOPS) framework, which uses locality to improve computational scaling, with the dyadic HOPS method previously developed to calculate linear and nonlinear spectroscopic signals. To construct a local representation of dyadic HOPS, we introduce an initial state decomposition that reconstructs the linear absorption spectra from a sum over locally excited initial conditions. We demonstrate the sum over initial conditions can be efficiently Monte Carlo sampled and that the corresponding calculations achieve size-invariant [i.e., O(1)] scaling for sufficiently large aggregates while trivially incorporating static disorder in the Hamiltonian. We present calculations on the photosystem I core complex to explore the behavior of the initial state decomposition in complex molecular aggregates as well as proof-of-concept DadHOPS calculations on an artificial molecular aggregate inspired by perylene bis-imide to demonstrate the size-invariance of the method.