Browse Articles

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)

Resting-state functional magnetic resonance imaging study on cerebrovascular reactivity changes in the precuneus of Alzheimer’s disease and mild cognitive impairment patients

Mitigating CaCO3 crystal nucleation and growth through continuous ion displacement via alternating electric fields

AbstractMineral crystal formation poses a challenge on surfaces (e.g., heat exchangers, pipes, membranes, etc.) in contact with super-saturated fluids. Applying alternating currents (AC) to such surfaces can prevent surface crystallization under certain conditions. Here, we demonstrate that ion displacement induced by periodic charging and discharging of the electrical double layer (EDL) inhibits both heterogeneous and homogeneous nucleation (and crystal growth) of CaCO3. Titanium sheets (meant to simulate metallic heat exchanger surfaces) are immersed in super-saturated CaCO3 solutions with a saturation index >11. We show that at relatively high AC frequencies, incomplete EDL formation leads to an alternating electric field that propagates far into the bulk solution, inducing rapid ion migration that overwhelms the Brownian motion of ions. Electrochemical characterization reveals EDL charging/discharging under AC conditions that greatly inhibits precipitation. Operating at 4 Vpp, 0.1–10 Hz reduces turbidity by over 96% and reduces CaCO3 coverage on the metal plates by over 92%. Based on electrokinetic and crystallization models, the ion displacement velocity (exceeding the mean Brownian velocity) and displacement length disrupts ion collision and crystal nucleation. Overall, the technique has potential for preventing mineral crystal formation in heat exchangers and many other industrially relevant systems.

Circulating resistin levels and mutation burden of the RETN gene variants predict long-term mortality in a Taiwanese population

The diabetes mellitus comorbidity index in European Union member states based on the 2019 European Health Interview Survey

Essential and dual effects of Notch activity on a natural transdifferentiation event

AbstractCell identity can be reprogrammed, naturally or experimentally, albeit with low frequency. Why some cells, but not their neighbours, undergo a cell identity conversion remains unclear. We find that Notch signalling plays a key role to promote natural transdifferentiation in C. elegans hermaphrodites. Endogenous Notch signalling endows a cell with the competence to transdifferentiate by promoting plasticity factors expression (hlh-16/Olig and sem-4/Sall). Strikingly, ectopic Notch can trigger additional transdifferentiation in vivo. However, Notch signalling can both promote and block transdifferentiation depending on its activation timing. Notch only promotes transdifferentiation during an early precise window of opportunity and signal duration must be tightly controlled in time. Our findings emphasise the importance of temporality and dynamics of the underlying molecular events preceding the initiation of natural cell reprogramming. Finally, our results support a model where both an extrinsic signal and the intrinsic cellular context combine to empower a cell with the competence to transdifferentiate.

Muscle activity and lower body kinematics change when performing motor imagery of gait