Leveraging Prototype Patient Representations with Feature-Missing-Aware Calibration to Mitigate EHR Data Sparsity

Electronic Health Record (EHR) data frequently exhibits sparse characteristics, posing challenges for predictive modeling. Current direct imputation such as matrix imputation approaches hinge on referencing analogous rows or columns to complete raw missing data and do not differentiate between imputed and actual values. As a result, models may inadvertently incorporate irrelevant or deceptive information with respect to the prediction objective, thereby compromising the efficacy of downstream performance. While some methods strive to recalibrate or augment EHR embeddings after direct imputation, they often mistakenly prioritize imputed features. This misprioritization can introduce biases or inaccuracies into the model. To tackle these issues, our work resorts to indirect imputation, where we leverage prototype representations from similar patients to obtain a denser embedding. Recognizing the limitation that missing features are typically treated the same as present ones when measuring similar patients, our approach designs a feature confidence learner module. This module is sensitive to the missing feature status, enabling the model to better judge the reliability of each feature. Moreover, we propose a novel patient similarity metric that takes feature confidence into account, ensuring that evaluations are not based merely on potentially inaccurate imputed values. Consequently, our work captures dense prototype patient representations with feature-missing-aware calibration process. Comprehensive experiments demonstrate that designed model surpasses established EHR-focused models with a statistically significant improvement on MIMIC-III and MIMIC-IV datasets in-hospital mortality outcome prediction task. The code is publicly available at \url{https://github.com/yhzhu99/SparseEHR} to assure the reproducibility

Bioelectrochemical Sensor Using Living Biofilm To in Situ Evaluate Flocculant Toxicity

Flocculants have been used to clarify water for thousands of years. However, the in situ evaluation of flocculant toxicity is difficult because flocculants usually exist as growing complex flocs which is hard to monitor. With alum (KAl­(SO₄)₂·12H₂O) as the typical flocculant, a bioelectrochemical sensor is designed to in situ detect its bacterial toxicity. The attenuation ratio of current densities linearly increased with alum concentration (<i>R</i>² > 0.98) with a slope of 0.0054 A m^–2 drop per mg L^–1 of alum, indicating a typical toxic response of alum. Turnover and nonturnover cyclic voltammetries (CVs) revealed that the alum inhibited the electrochemical activity of bacteria rather than changing the electron transfer pathways. Alum also hindered the diffusion by flocculation at a concentration larger than 100 mg L^–1, which was further confirmed by the linear decrease in viability when biofilm thickness increased. It was revealed that both inactivation of biofilm and influences on diffusion by alum can be detected by bioelectrochemical sensors, which provided a new platform to in situ investigate the biological toxicity of new flocculants

Single-cell Transcriptomic Analysis Reveals the Cellular Heterogeneity of Mesenchymal Stem Cells

Ex vivo-expanded mesenchymal stem cells (MSCs) have been demonstrated to be a heterogeneous mixture of cells exhibiting varying proliferative, multipotential, and immunomodulatory capacities. However, the exact characteristics of MSCs remain largely unknown. By single-cell RNA sequencing of 61,296 MSCs derived from bone marrow and Wharton’s jelly, we revealed five distinct subpopulations. The developmental trajectory of these five MSC subpopulations was mapped, revealing a differentiation path from stem-like active proliferative cells (APCs) to multipotent progenitor cells, followed by branching into two paths: 1) unipotent preadipocytes or 2) bipotent prechondro-osteoblasts that were subsequently differentiated into unipotent prechondrocytes. The stem-like APCs, expressing the perivascular mesodermal progenitor markers CSPG4/MCAM/NES, uniquely exhibited strong proliferation and stemness signatures. Remarkably, the prechondrocyte subpopulation specifically expressed immunomodulatory genes and was able to suppress activated CD3+ T cell proliferation in vitro, supporting the role of this population in immunoregulation. In summary, our analysis mapped the heterogeneous subpopulations of MSCs and identified two subpopulations with potential functions in self-renewal and immunoregulation. Our findings advance the definition of MSCs by identifying the specific functions of their heterogeneous cellular composition, allowing for more specific and effective MSC application through the purification of their functional subpopulations

Swift Acid Rain Sensing by Synergistic Rhizospheric Bioelectrochemical Responses

Acid rain poses significant threats to crops and causes a decline in food production, but current monitoring and response to acid rain damage is either slow or expensive. The direct damage observation on plants can take several hours to days when the damage is irreversible. This study presents a real time bioelectrochemical monitoring approach that can detect acid rain damage within minutes. The rhizospheric bioelectrochemical sensor (RBS) takes advantage of the fast chain responses from leaves to roots, and then to the microbial electrochemical reactions in the rhizosphere. Immediate and repeatable current fluctuations were observed within 2 min after acid rain, and such changes were found to correspond well to the changes in rhizospheric organic concentration and electrochemical responses. Such correlation not only can be observed during acid rain events that can be remedied via rinsing, but it was also validated when such damage is irreversible, resulted in zero current, photosynthetic efficiency, and electrochemical signals. The alanine, aspartate, and glutamate metabolism and galactose metabolism in leaves and roots were inhibited by the acid rain, which resulted in the decrease of rhizodeposits such as fumaric acid, d-galactose, and d-glucose. These changes resulted in reduced electroactivity of anodic microorganisms, which was confirmed by a reduced redox current, a narrower spectrum in differential pulse voltammetry, and the loss of peak in the Bode plot. These findings indicate that the RBS process can be a simple, swift, and low-cost monitoring tool for acid rain that allows swift remediation measures, and its potential may be broadened to other environmental monitoring applications