A Comparison of Stacking Methods to Estimate Survival Using Residual Lifetime Data from Prevalent Cohort Studies

Prevalent cohort studies are widely used for their cost-efficiency and convenience. However, in such studies, only the residual lifetime can be observed. Traditionally, researchers rely on self-reported onset times to infer the underlying survival distribution, which may introduce additional bias that confounds downstream analysis. This study compares two stacking procedures and one mixture model approach that uses only residual lifetime data while leveraging the strengths of different estimators. Our simulation results show that the two stacked estimators outperform the nonparametric maximum likelihood estimator (NPMLE) and the mixture model, allowing robust and accurate estimations for underlying survival distributions

ElasticNotebook: Enabling Live Migration for Computational Notebooks (Technical Report)

Computational notebooks (e.g., Jupyter, Google Colab) are widely used for interactive data science and machine learning. In those frameworks, users can start a session, then execute cells (i.e., a set of statements) to create variables, train models, visualize results, etc. Unfortunately, existing notebook systems do not offer live migration: when a notebook launches on a new machine, it loses its state, preventing users from continuing their tasks from where they had left off. This is because, unlike DBMS, the sessions directly rely on underlying kernels (e.g., Python/R interpreters) without an additional data management layer. Existing techniques for preserving states, such as copying all variables or OS-level checkpointing, are unreliable (often fail), inefficient, and platform-dependent. Also, re-running code from scratch can be highly time-consuming. In this paper, we introduce a new notebook system, ElasticNotebook, that offers live migration via checkpointing/restoration using a novel mechanism that is reliable, efficient, and platform-independent. Specifically, by observing all cell executions via transparent, lightweight monitoring, ElasticNotebook can find a reliable and efficient way (i.e., replication plan) for reconstructing the original session state, considering variable-cell dependencies, observed runtime, variable sizes, etc. To this end, our new graph-based optimization problem finds how to reconstruct all variables (efficiently) from a subset of variables that can be transferred across machines. We show that ElasticNotebook reduces end-to-end migration and restoration times by 85%-98% and 94%-99%, respectively, on a variety (i.e., Kaggle, JWST, and Tutorial) of notebooks with negligible runtime and memory overheads of <2.5% and <10%.Comment: Accepted to VLDB 202

Number 2 Feibi Recipe Reduces PM2.5-Induced Lung Injury in Rats

Air pollution is the main cause of respiratory diseases. Fine particulates with the diameter below 2.5 μm can get into the alveoli and then enter the blood circulation through the lung tissue ventilation function and cause multiple systemic diseases especially the respiratory diseases. This study investigated the pathological mechanism of the lungs injury in rats induced by PM2.5 and the effect and mechanism of the Chinese herbal medicine number 2 Feibi Recipe (number 2 FBR) on lungs injury. In this experiment, Wistar rats were used. Lungs injury was induced by PM2.5. Number 2 FBR was used to treat the rats. The result showed that number 2 FBR could improve the lung injury in the rats. Meanwhile, it significantly reduced pathological response and inflammatory mediators including interleukin-6 (IL-6), interleukin-13 (IL-13), interleukin-17 (IL17), monocyte chemotactic protein-1 (MCP-1), and transforming growth factor-α (TNF-α) and upregulated glutathione peroxidase (GSH-Px) in the PM2.5 induced lung injury in the rats. Collectively, number 2 FBR appears to attenuate the lungs injury in rats induced by PM2.5

Bond-Slip Behavior of Basalt Fiber Reinforced Polymer Bar in Concrete Subjected to Simulated Marine Environment: Effects of BFRP Bar Size, Corrosion Age, and Concrete Strength

Basalt Fiber Reinforced Polymer (BFRP) bars have bright potential application in concrete structures subjected to marine environment due to their superior corrosion resistance. Available literatures mainly focused on the mechanical properties of BFRP concrete structures, while the bond-slip behavior of BFRP bars, which is a key factor influencing the safety and service life of ocean concrete structures, has not been clarified yet. In this paper, effects of BFRP bars size, corrosion age, and concrete strength on the bond-slip behavior of BFRP bars in concrete cured in artificial seawater were investigated, and then an improved Bertero, Popov, and Eligehausen (BPE) model was employed to describe the bond-slip behavior of BFRP bars in concrete. The results indicated that the maximum bond stress and corresponding slip decreased gradually with the increase of corrosion age and size of BFRP bars, and ultimate slip also decreased sharply. The ascending segment of bond-slip curve tends to be more rigid and the descending segment tends to be softer after corrosion. A horizontal end in bond-slip curve indicates that the friction between BFRP bars and concrete decreased sharply

Metabolomics analysis of stool in rats with type 2 diabetes mellitus after single-anastomosis duodenal–ileal bypass with sleeve gastrectomy

BackgroundSingle-anastomosis duodenal-ileal bypass with sleeve gastrectomy (SADI-S) is one of the most effective bariatric procedures in the treatment of type 2 diabetes mellitus (T2DM). However, the mechanisms by which SADI-S improves T2DM are not well-known.ObjectiveTo explore the effects of SADI-S on metabolites in the stool of rats with T2DM.MethodsTwenty rats were fed on high-fat diet and administered with a low-dose (30mg/kg) of streptozotocin to establish T2DM models. The rats were then randomly assigned to the SADI-S group (n=10) and sham operation group (n=9). Stool samples were collected from all rats at 8 weeks after surgery and stored at -80 °C. Metabolomics analysis was performed to identify differential metabolites through ultra- performance liquid chromatography-mass spectrometry.ResultsAt 8-week after surgery, rats of the SADI-S group showed significantly decreased fasting blood glucose, glucose tolerance test 2-hour, glycated haemoglobin, and body weight compared with those of the sham group. A total of 245 differential metabolites were identified between the two groups, among which 8 metabolites were detectable under both the positive ion model and negative ion model. Therefore, a total of 237 differential metabolites were identified in our study which were mainly involved in tryptophan metabolism; cysteine and methionine metabolism; phenylalanine metabolism; phenylalanine; tyrosine and tryptophan biosynthesis; arginine biosynthesis; alanine, aspartate and glutamate metabolism; Arginine and proline metabolism; glyoxylate and dicarboxylate metabolism; alpha-Linolenic acid metabolism; Linoleic acid metabolism; riboflavin metabolism; nicotinate and nicotinamide metabolism; pyrimidine metabolism; porphyrin and chlorophyll metabolism.ConclusionSADI-S significantly improved the glucose metabolism in T2DM rats. In addition, SADI-S significantly changed the composition of metabolites in T2DM rats which were involved in tryptophan metabolism pathway, linoleic acid metabolism pathway and so on. This may be the mechanism by which SADI-S improved T2DM

Deep Learning in Single-Cell Analysis

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning through different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.Comment: 77 pages, 11 figures, 15 tables, deep learning, single-cell analysi

Highly Viscous States Affect the Browning of Atmospheric Organic Particulate Matter

Initially transparent organic particulate matter (PM) can become shades of light-absorbing brown via atmospheric particle-phase chemical reactions. The production of nitrogen-containing compounds is one important pathway for browning. Semisolid or solid physical states of organic PM might, however, have sufficiently slow diffusion of reactant molecules to inhibit browning reactions. Herein, organic PM of secondary organic material (SOM) derived from toluene, a common SOM precursor in anthropogenically affected environments, was exposed to ammonia at different values of relative humidity (RH). The production of light-absorbing organonitrogen imines from ammonia exposure, detected by mass spectrometry and ultraviolet–visible spectrophotometry, was kinetically inhibited for RH < 20% for exposure times of 6 min to 24 h. By comparison, from 20% to 60% RH organonitrogen production took place, implying ammonia uptake and reaction. Correspondingly, the absorption index k across 280 to 320 nm increased from 0.012 to 0.02, indicative of PM browning. The k value across 380 to 420 nm increased from 0.001 to 0.004. The observed RH-dependent behavior of ammonia uptake and browning was well captured by a model that considered the diffusivities of both the large organic molecules that made up the PM and the small reactant molecules taken up from the gas phase into the PM. Within the model, large-molecule diffusivity was calculated based on observed SOM viscosity and evaporation. Small-molecule diffusivity was represented by the water diffusivity measured by a quartz-crystal microbalance. The model showed that the browning reaction rates at RH < 60% could be controlled by the low diffusivity of the large organic molecules from the interior region of the particle to the reactive surface region. The results of this study have implications for accurate modeling of atmospheric brown carbon production and associated influences on energy balance

Machine learning to improve prognosis prediction of metastatic clear-cell renal cell carcinoma treated with cytoreductive nephrectomy and systemic therapy

Cytoreductive nephrectomy (CN) combined with systemic therapy is commonly used to treat metastatic clear-cell renal cell carcinoma (mccRCC). However, prognostic models for these patients are limited. In the present study, the clinical data of 782 mccRCC patients who received both CN and systemic therapy were obtained from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2016), and patients were divided into training and internal test cohorts. A total of 144 patients who met the same criteria from our center (Peking Union Medical College Hospital) were placed in the external test cohort. The cancer-specific survival rate (CSS) at 1, 3, and 5 years was set as the research outcome. Then, four ML models, i.e., a gradient boosting machine (GBM), support vector machine (SVM), random forest (RF), and logistic regression (LR), were established. Fifteen potential independent features were included in this study.  Model performance was evaluated using the area under the receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis (DCA). Seven clinical features, namely pathological grade, T stage, N stage, number of metastatic sites, brain or liver metastases, and metastasectomy were selected for subsequent analysis via the recursive feature elimination (RFE) algorithm. In conclusion, the GBM model performed best at 1-, 3- and 5-year CSS prediction (0.836, 0.819 and 0.808, respectively in the internal test cohort and 0.819, 0.805 and 0.786, respectively in the external cohort). Furthermore, we divided the patients into three strata (high-, intermediate- and low-risk) via X-tile analysis and concluded that clinically individualized treatment can be aided by these practical prognostic models

Attosecond Delays in X-ray Molecular Ionization

The photoelectric effect is not truly instantaneous, but exhibits attosecond delays that can reveal complex molecular dynamics. Sub-femtosecond duration light pulses provide the requisite tools to resolve the dynamics of photoionization. Accordingly, the past decade has produced a large volume of work on photoionization delays following single photon absorption of an extreme ultraviolet (XUV) photon. However, the measurement of time-resolved core-level photoionization remained out of reach. The required x-ray photon energies needed for core-level photoionization were not available with attosecond tabletop sources. We have now measured the x-ray photoemission delay of core-level electrons, and here report unexpectedly large delays, ranging up to 700 attoseconds in NO near the oxygen K-shell threshold. These measurements exploit attosecond soft x-ray pulses from a free-electron laser (XFEL) to scan across the entire region near the K-shell threshold. Furthermore, we find the delay spectrum is richly modulated, suggesting several contributions including transient trapping of the photoelectron due to shape resonances, collisions with the Auger-Meitner electron that is emitted in the rapid non-radiative relaxation of the molecule, and multi-electron scattering effects. The results demonstrate how x-ray attosecond experiments, supported by comprehensive theoretical modelling, can unravel the complex correlated dynamics of core-level photoionization