AIMC Topic: Machine Learning

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Advancing Whole-Cell Biosensors: Kinetics-Dependent Metabolic SERS Analytics for Pollutant Differentiation and Quantification.

Analytical chemistry
Whole-cell biosensors (WCBs), which detect targeting analytes through cellular responses, have become powerful tools for environmental monitoring. However, existing WCBs often rely on the single-channel low-dimension signal outputs (e.g., fluorescenc...

Predicting fracture toughness of human cortical bone from donors with and without type 2 diabetes using Raman spectroscopy and machine learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Type 2 diabetes mellitus (T2DM) is associated with increased skeletal fragility, yet standard clinical assessments often fail to detect diabetes-induced changes in bone quality. Raman spectroscopy (RS), a label-free and non-destructive technique, off...

Machine Learning-Assisted Prediction of Mercury Removal Efficiency of Carbon-Based Adsorbents.

Environmental science & technology
Adsorbent injection is the most promising technology for solving anthropogenic mercury (mainly Hg) emission from stationary sources. Carbon-based adsorbents have strong potential for Hg removal due to their high specific surface area and abundant fun...

Automated Determination of the Molecular Substructure from Nuclear Magnetic Resonance Spectra Using Neural Networks.

Journal of chemical information and modeling
Nuclear magnetic resonance (NMR) spectroscopy is an indispensable tool for determining the structural characteristics of a molecule by analyzing its chemical shifts. A wealth of NMR spectra therefore exists and continues to amass on a daily basis, at...

A Machine-Learning-Algorithm Enhanced Multi-Functional Gas Sensor for Self-Humidity Compensation and Partial Discharge Detection.

ACS sensors
Gas-Insulated switchgear (GIS) is prone to partial discharges (PDs) in high electric field environments, and the concentration of generated NO is an essential indicator for determining the PD types and severity of faults. Notably, environmental humid...

Active Learning of Atomic Size Gas/Solid Potential Energy Surfaces via Physics Aware Models.

Journal of chemical information and modeling
We propose an active learning (AL) framework to develop classical force fields (FFs) that accurately model the potential energy surfaces (PES) of gas/solid atomic-scale complexes. A central challenge is integrating AL with flexible, computationally e...

Screening, Validation, and Machine Learning-Based Evaluation of Serum Protein Biomarkers for Esophageal Squamous Cell Carcinoma Based on Single-Cell Subtype-Specific Genes.

Journal of proteome research
Cellular heterogeneity of epithelial cells and fibroblasts is critical in esophageal squamous cell carcinoma development (ESCC). Identifying dysregulated subtype-specific genes in these cells is essential for early diagnosis and treatment. In this st...

Machine learning-based ensemble of Global climate models and trend analysis for projecting extreme precipitation indices under future climate scenarios.

Environmental monitoring and assessment
Monitoring changes in climatic extremes is vital, as they influence current and future climate while significantly impacting ecosystems and society. This study examines trends in extreme precipitation indices over an Indian tropical river basin, anal...

Current imaging applications, radiomics, and machine learning modalities of CNS demyelinating disorders and its mimickers.

Journal of neurology
Distinguishing among neuroinflammatory demyelinating diseases of the central nervous system can present a significant diagnostic challenge due to substantial overlap in clinical presentations and imaging features. Collaboration between specialists, n...

Domain Knowledge Inclusive Monotonic Neural Network Guides Patient-Specific Induction of General Anesthesia Dosing.

A&A practice
BACKGROUND: Postinduction hypotension is a well-known risk factor for adverse postoperative outcomes. Anesthesiologists estimate anesthetic dosages based on a patient's chart and domain knowledge. Machine learning is increasingly applied in predictin...