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Pseudogap in electron-doped cuprates as a thermal precursor to magnetism
Subpolar North Atlantic Ocean heat content drives 21st-century Arctic multi-decadal variability in CESM1 LE
Club-like receptors respond to light touch but not to whisking
Abstract Rodents explore their environment by actively whisking and contacting objects with their whiskers. Each whisker follicle contains hundreds of mechanoreceptors, but how specific afferents distinguish between self-motion and touch remains unclear. Here, using artificial whisking in male rats, intra-axonal recordings, and morphological reconstruction, we identify a distinct mechanoreceptor subtype—club-like endings—that respond exclusively to touch and remain silent during whisking. In contrast, Merkel and lanceolate endings exhibit mixed selectivity. Club-like endings are arranged in a single-layer circular array near the center of mass of the whisker-follicle unit, embedded in a collagen-rich structure called the ringwulst. Using scanning electron microscopy, we show that these endings are tightly anchored to the glassy membrane and collagen fibers, forming a mechanically isolated zone. This configuration minimizes activation during whisking while preserving sensitivity to touch. We propose that these features evolved to enhance tactile precision in whisking species, as supported by the absence of such specializations in non-whisking animals such as cats.
Numerical simulation and experimental research on the oil removal efficiency during the oily wastewater separation by hydrocyclone
Abstract Some oil and gas fields in China are located in the north, and in winter, oil fields face technical difficulties in separating oil and water from high viscosity condensate produced fluids. This article proposes a low-temperature oil-water separation scheme using a preheated hydrocyclone, with a focus on studying the separation effect of oil-water mixtures with different oil contents by varying the overflow diversion ratio and inlet flow rate of the hydrocyclone. The structure of the cyclone was optimized using response surface methodology (RSM) through three-dimensional modeling. The Euler multiphase flow model was used to study the distribution characteristics of the flow field and phase volume fraction of the oil-water two-phase medium inside the hydraulic cyclone under different injection parameters. The results show that when the oil content of the mixed liquid is 10% and the viscosity of the oil phase is 27-31mPa·s, an increase in the overflow diversion ratio is beneficial for the rapid separation of low-density phase media. If the diversion ratio is too high, it will cause the liquid flowing out of the overflow port to form an oily mixture again. In our experiment, the optimal overflow diversion ratio is 0.2, and the effective oil removal efficiency can reach 92%. The influence of the oil content in the mixed liquid on the oil-water separation efficiency of the cyclone is achieved through the interaction of viscous drag and centrifugal force on the radial partial pressure and tangential flow velocity. As the oil content and viscosity of the mixed liquid increase, the radial flow resistance experienced by the cylindrical and conical sections of the cyclone will increase. The radial partial pressure, pressure gradient, and radial flow velocity of the aqueous medium will also decrease, and the separation efficiency will also decrease accordingly.
Momentum space AC Josephson effect and intervalley coherence in multilayer graphene
Prevalence and factors associated with diabetes distress in northwest Ethiopia: a cross-sectional study
Demonstrating quantum error mitigation on logical qubits
Simulation of the additional damping characteristics of underwater heave plates for mitigating long-span bridges vibration
Reduced Ventral Tegmental Area GABA neuron output contributes to hyperactivity in the activity-based anorexia model in female mice
Machine learning of clinical and neural data predicts future homicide in high-risk youth
Roles of micro/nanoplastics in the spread of antimicrobial resistance through conjugative gene transfer
An open-source slicer for 3D mSLA printing of microfluidic chips
Abstract Microfluidic chips enable high-precision handling and automation of chemical and biological experiments by transporting and manipulating fluids through complex channel networks embedded on-chip. While polydimethylsiloxane (PDMS) soft lithography remains the standard for microfluidic device fabrication, it is labor-intensive and requires specialized expertise. Masked stereolithography (mSLA) 3D printing offers a rapid, high-resolution alternative, where the 3D chip geometry is fabricated layer-by-layer. These layers are cross-sections of the geometry and stacked on top of each other. However, all existing “off the shelf” slicing processes are optimized for speed and printing outside features rather than the small channels inside microfluidic chips. As a result, the fabrication of these chips still frequently leads to imperfect quality as manifested, e.g., in clogged channels or rough surfaces, which influence the flow through these channels or even render the result unusable. In this work, we propose a novel slicing tool, OpenSLAice , that is optimized for 3D printing of microfluidic chips. To this end, we present a slicing method that automatically detects microfluidic features within a microfluidic chip and, then, orients the chip as well as dynamically slices it so that the imperfections in the fabrication of those features are avoided. It rasterizes the sliced cross-layers and, if several parts should be printed at the same time, it automatically arranges them. Finally, a single print exposure calibration is presented to efficiently determine the required exposure times per layer thickness for resin-printer combinations. The modular, open-source tool is available at https://github.com/cda-tum/mmft-openSLAice and allows to pre-process microfluidic chips for mSLA fabrication.
A stationary phase-specific bacterial green light sensor for enhancing metabolite production
Abstract Genetically-encoded sensors are used to control protein and metabolite production in bacterial fermentations. However, these sensors are generally optimized for exponential growth rather than stationary phase where production occurs. Here, we find that our previously engineered E. coli green light sensor CcaSR, which functions robustly in exponential phase, fails in stationary phase due to spontaneous loss of an engineered chromophore biosynthetic pathway and accumulation of CcaS and CcaR. We optimize the genetic context and expression determinants of each component, resulting in a stable system named CcaSR stat that imposes little metabolic burden, exhibits low leakiness and an 80-fold green light response, and functions exclusively in stationary phase. We combine CcaSR stat -driven enzyme expression with varied static and periodic illumination patterns to achieve high titers of the industrially-relevant phenylpropanoid p -Coumaric acid and demonstrate that these optimizations scale to benchtop bioreactor conditions. Finally, we use CcaSR stat to optimize the expression level of a co-transcribed multi-enzyme metabolic pathway encoding production of plant-derived betaxanthin family pigments. Stationary phase-optimized bacterial sensors should enhance fermentation productivity by enabling rapid interrogation of the impact of enzyme expression level and induction dynamics.
Enhanced classification prostate cancer based on generative adversarial networks and integrated deep learning with vision transformer models
Abstract By eliminating the need to alter the source images, this paper introduces a secure technique for coverless image steganography that strengthens defense against steganalysis attacks. Our method makes use of a hybrid Generative Adversarial Network (GAN) with a Support Vector Machine (SVM), which is trained and validated on a Diffusion Weighted Imaging (DWI) dataset to retain visually indistinguishable steganographic representations while increasing security. A powerful feature extraction capability of several Deep Learning Models (DLMs), EfficientNet-B4, DenseNet121, and Residual Network-18 (ResNet-18), integrated with the Vision Transformer (ViT) is performed. With the highest Peak Signal-to-Noise Ratio (PSNR) of 45.87 dB and Structural Similarity Index (SSIM) of 0.98, the ViT-GAN-SVM model exceeds other suggested models in terms of steganographic quality. Additionally, the ViT-GAN-SVM system achieves 99.78% accuracy, 99.85% sensitivity, 98.99% precision, and 99.85% F1-Score in terms of diagnostic accuracy. The ViT-GAN-SVM model performs much better than other introduced models in all diagnostic performance metrics, with increases ranging from 5.55% to 6.36%. This shows that ViT-GAN-SVM is a superior choice for medical diagnostic tasks since it can correctly identify prostate cancer on the DWI prostate cancer dataset.