Browse Articles

Li2ZrF6 protective layer enabled high-voltage LiCoO2 positive electrode in sulfide all-solid-state batteries

Optimal design of tilt integral derivative controller for a boost converter based on swarm-inspired algorithms

Correlation of triglyceride glucose index with all cause mortality in acute myocardial infarction patients following percutaneous coronary intervention

Programming scheduled self-assembly of circadian materials

A deep learning method based on multi-scale fusion for noise-resistant coal-gangue recognition

The multi-target mechanism of action of Selaginella doederleinii Hieron in the treatment of nasopharyngeal carcinoma: a network pharmacology and multi-omics analysis

Reconfigurable and nonvolatile ferroelectric bulk photovoltaics based on 3R-WS2 for machine vision

An automated privacy-preserving self-supervised classification of COVID-19 from lung CT scan images minimizing the requirements of large data annotation

Logical reasoning for human activity recognition based on multisource data from wearable device

Spin polarization induced by atomic strain of MBene promotes the ·O2– production for groundwater disinfection

Generative adversarial synthetic neighbors-based unsupervised anomaly detection

Ascorbic acid-immobilized zinc selenide for electrochemical monitoring of hydrogen peroxide in liver cancer samples

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy

AbstractOptical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained ‘de-aberration’ networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

Noninvasive diagnosis of significant liver fibrosis in patients with chronic hepatitis B using nomogram and machine learning models

Assessing inflammatory protein factors in inflammatory bowel Disease using multivariable mendelian randomization

NIR-II-excited off-on-off fluorescent nanoprobes for sensitive molecular imaging in vivo

The effect of temperature constraints on the treatment of tumors using focused ultrasound-induced acoustic streaming

AbstractThe transport of drugs into tumor cells near the center of the tumor is known to be severely hindered due to the high interstitial pressure and poor vascularization. The aim of this work is to investigate the possibility to induce acoustic streaming in a tumor. Two tumor cases (breast and abdomen) are simulated to find the acoustic streaming and temperature rise, while varying the focused ultrasound transducer radius, frequency, and power for a constant duty cycle (1%). In the absence of perfusion, the simulated rise in temperature, despite the low duty cycle, never reaches a steady state and is fitted to a logarithmic equation, enabling predictions of the temperature for long treatment times. Higher frequencies and larger probe radii are found to result in shorter treatment times relative to the temperature rise, at the cost of a smaller treated area. Results from the simulations indicate that it may be possible to achieve reasonable acoustic streaming values in tumor without the temperature exceeding 50 °C. Treatment times for streaming a distance of 50 μm in the breast case are shown to range from less than one and a half hour to 93 h, depending on the probe settings.

Hybrid in-situ and ex-situ hydrolysis of catalytic epoxidation neem oil via a peracid mechanism

Functional protein mining with conformal guarantees

Abstract Molecular structure prediction and homology detection offer promising paths to discovering protein function and evolutionary relationships. However, current approaches lack statistical reliability assurances, limiting their practical utility for selecting proteins for further experimental and in-silico characterization. To address this challenge, we introduce a statistically principled approach to protein search leveraging principles from conformal prediction, offering a framework that ensures statistical guarantees with user-specified risk and provides calibrated probabilities (rather than raw ML scores) for any protein search model. Our method (1) lets users select many biologically-relevant loss metrics (i.e. false discovery rate) and assigns reliable functional probabilities for annotating genes of unknown function; (2) achieves state-of-the-art performance in enzyme classification without training new models; and (3) robustly and rapidly pre-filters proteins for computationally intensive structural alignment algorithms. Our framework enhances the reliability of protein homology detection and enables the discovery of uncharacterized proteins with likely desirable functional properties.

Crystal structure of Au-pseudocarbyne(C6)