The In-depth Comparative Analysis of Four Large Language AI Models for Risk Assessment and Information Retrieval from Multi-Modality Prostate Cancer Work-up Reports.

Purpose: Information retrieval (IR) and risk assessment (RA) from multi-modality imaging and pathology reports are critical to prostate cancer (PC) treatment. This study aims to evaluate the performance of four general-purpose large language model (LLMs) in IR and RA tasks.

Materials And Methods: We conducted a study using simulated text reports from computed tomography, magnetic resonance imaging, bone scans, and biopsy pathology on stage IV PC patients.

Joint variation and ZhuYin dataset for Traditional Chinese document enhancement.

Digital documents play a crucial role in contemporary information management. However, their quality can be significantly impacted by various factors such as hand-drawn annotations, image distortion, watermarks, stains, and degradation. Deep learning-based methods have emerged as powerful tools for document enhancement.

A comprehensive study of Z-DNA density and its evolutionary implications in birds.

Background: Z-DNA, a left-handed helical form of DNA, plays a significant role in genomic stability and gene regulation. Its formation, associated with high GC content and repetitive sequences, is linked to genomic instability, potentially leading to large-scale deletions and contributing to phenotypic diversity and evolutionary adaptation.

Results: In this study, we analyzed the density of Z-DNA-prone motifs of 154 avian genomes using the non-B DNA Motif Search Tool (nBMST).

Predictions of Lattice Parameters in NiTi High-Entropy Shape-Memory Alloys Using Different Machine Learning Models.

This work applied three machine learning (ML) models-linear regression (LR), random forest (RF), and support vector regression (SVR)-to predict the lattice parameters of the monoclinic B19' phase in two distinct training datasets: previously published ZrO-based shape-memory ceramics (SMCs) and NiTi-based high-entropy shape-memory alloys (HESMAs). Our findings showed that LR provided the most accurate predictions for a, a, b, and c in NiTi-based HESMAs, while RF excelled in computing β for both datasets. SVR disclosed the largest deviation between the predicted and actual values of lattice parameters for both training datasets.

LYNSU: automated 3D neuropil segmentation of fluorescent images for brains.

The brain atlas, which provides information about the distribution of genes, proteins, neurons, or anatomical regions, plays a crucial role in contemporary neuroscience research. To analyze the spatial distribution of those substances based on images from different brain samples, we often need to warp and register individual brain images to a standard brain template. However, the process of warping and registration may lead to spatial errors, thereby severely reducing the accuracy of the analysis.

Integrating In Silico and In Vitro Approaches to Identify Natural Peptides with Selective Cytotoxicity against Cancer Cells.

Anticancer peptides (ACPs) are bioactive compounds known for their selective cytotoxicity against tumor cells via various mechanisms. Recent studies have demonstrated that in silico machine learning methods are effective in predicting peptides with anticancer activity. In this study, we collected and analyzed over a thousand experimentally verified ACPs, specifically targeting peptides derived from natural sources.

The genomic evolution of visual opsin genes in amphibians.

Among tetrapod (terrestrial) vertebrates, amphibians remain more closely tied to an amphibious lifestyle than amniotes, and their visual opsin genes may be adapted to this lifestyle. Previous studies have discussed physiological, morphological, and molecular changes in the evolution of amphibian vision. We predicted the locations of the visual opsin genes, their neighboring genes, and the tuning sites of the visual opsins, in 39 amphibian genomes.

Conserved regulatory switches for the transition from natal down to juvenile feather in birds.

The transition from natal downs for heat conservation to juvenile feathers for simple flight is a remarkable environmental adaptation process in avian evolution. However, the underlying epigenetic mechanism for this primary feather transition is mostly unknown. Here we conducted time-ordered gene co-expression network construction, epigenetic analysis, and functional perturbations in developing feather follicles to elucidate four downy-juvenile feather transition events.

Using U-Net convolutional neural network to model pixel-based electrostatic potential distributions in GaN power MIS-HEMTs.

This study demonstrates a novel use of the U-Net convolutional neural network (CNN) for modeling pixel-based electrostatic potential distributions in GaN metal-insulator-semiconductor high-electron mobility transistors (MIS-HEMTs) with various gate and source field plate designs and drain voltages. The pixel-based images of the potential distribution are successfully modeled from the developed U-Net CNN with an error of less than 1% error relative to a TCAD simulated reference of a 500-V electrostatic potential distribution in the AlGaN/GaN interface. Furthermore, the modeling time of potential distributions by U-Net takes about 80 ms.

Assessing Indoor Climate Control in a Water-Pad System for Small-Scale Agriculture in Taiwan: A CFD Study on Fan Modes.

Heat stress poses a significant challenge to egg production in layer hens. High temperatures can disrupt the physiological functions of these birds, leading to reduced egg production and lower egg quality. This study evaluated the microclimate of laying hen houses using different management systems to determine the impact of heat stress on productivity and hen health.

Towards a Controllable and Reversible Privacy Protection System for Facial Images through Enhanced Multi-Factor Modifier Networks.

Privacy protection data processing has been critical in recent years when pervasively equipped mobile devices could easily capture high-resolution personal images and videos that may disclose personal information. We propose a new controllable and reversible privacy protection system to address the concern in this work. The proposed scheme can automatically and stably anonymize and de-anonymize face images with one neural network and provide strong security protection with multi-factor identification solutions.

Hierarchical Image Transformation and Multi-Level Features for Anomaly Defect Detection.

Anomalies are a set of samples that do not follow the normal behavior of the majority of data. In an industrial dataset, anomalies appear in a very small number of samples. Currently, deep learning-based models have achieved important advances in image anomaly detection.

Device simulations with A U-Net model predicting physical quantities in two-dimensional landscapes.

Although Technology Computer-Aided Design (TCAD) simulation has paved a successful and efficient way to significantly reduce the cost of experiments under the device design, it still encounters many challenges as the semiconductor industry goes through rapid development in recent years, i.e. Complex 3D device structures, power devices.

mRNA lipid nanoparticle phase transition.

Crucial for mRNA-based vaccines are the composition, structure, and properties of lipid nanoparticles (LNPs) as their delivery vehicle. Using all-atom molecular dynamics simulations as a computational microscope, we provide an atomistic view of the structure of the Comirnaty vaccine LNP, its molecular organization, physicochemical properties, and insight in its pH-driven phase transition enabling mRNA release at atomistic resolution. At physiological pH, our simulations suggest an oil-like LNP core that is composed of the aminolipid ALC-0315 and cholesterol (ratio 72:28).

Rate-Distortion-Based Stego: A Large-Capacity Secure Steganography Scheme for Hiding Digital Images.

Steganography is one of the most crucial methods for information hiding, which embeds secret data on an ordinary file or a cover message for avoiding detection. We designed a novel rate-distortion-based large-capacity secure steganographic system, called rate-distortion-based Stego (RD-Stego), to effectively solve the above requirement. The considered effectiveness of our system design includes embedding capacity, adaptability to chosen cover attacks, and the stability of the trained model.

Artificial intelligence deep learning for 3D IC reliability prediction.

Three-dimensional integrated circuit (3D IC) technologies have been receiving much attention recently due to the near-ending of Moore's law of minimization in 2D IC. However, the reliability of 3D IC, which is greatly influenced by voids and failure in interconnects during the fabrication processes, typically requires slow testing and relies on human's judgement. Thus, the growing demand for 3D IC has generated considerable attention on the importance of reliability analysis and failure prediction.

Spontaneous local membrane curvature induced by transmembrane proteins.

The (local) curvature of cellular membranes acts as a driving force for the targeting of membrane-associated proteins to specific membrane domains, as well as a sorting mechanism for transmembrane proteins, e.g., by accumulation in regions of matching spontaneous curvature.

Engineer design process assisted by explainable deep learning network.

Engineering simulation accelerates the development of reliable and repeatable design processes in various domains. However, the computing resource consumption is dramatically raised in the whole development processes. Making the most of these simulation data becomes more and more important in modern industrial product design.

RNA-seq of peripheral blood mononuclear cells of congenital generalized lipodystrophy type 2 patients.

Illumina RNA-seq analysis was used to characterize the whole transcriptomes of peripheral blood mononuclear cells (PBMCs) from patients with congenital generalized lipodystrophy. RNA-seq information for seven patients with type 2 congenital generalized lipodystrophy (CGL2; Berardinelli-Seip congenital lipodystrophy, BSCL2) was obtained and compared with similar information for seven age- and sex-matched healthy control subjects. All seven CGL2 patients carried biallelic pathogenic mutations affecting the BSCL2 gene and had clinical symptoms of varying severity.

: Automatic Neuron Segmentation for Connectome Assembly.

Segmenting individual neurons from a large number of noisy raw images is the first step in building a comprehensive map of neuron-to-neuron connections for predicting information flow in the brain. Thousands of fluorescence-labeled brain neurons have been imaged. However, mapping a complete connectome remains challenging because imaged neurons are often entangled and manual segmentation of a large population of single neurons is laborious and prone to bias.

The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods.

Scour around bridge piers remains the leading cause of bridge failure induced in flood. Floods and torrential rains erode riverbeds and damage cross-river structures, causing bridge collapse and a severe threat to property and life. Reductions in bridge-safety capacity need to be monitored during flood periods to protect the traveling public.

Constraints on Cosmic Strings Using Data from the Third Advanced LIGO-Virgo Observing Run.

We search for gravitational-wave signals produced by cosmic strings in the Advanced LIGO and Virgo full O3 dataset. Search results are presented for gravitational waves produced by cosmic string loop features such as cusps, kinks, and, for the first time, kink-kink collisions. A template-based search for short-duration transient signals does not yield a detection.

A Layer-Arranged Meshless Method for the Simulation of Additive Manufacturing with Irregular Shapes.

Additive manufacturing (3D Printing) has become a promising manufacturing method as it can produce parts in a flexible and efficient way, especially for very irregular parts. However, during the printing process, the material experiences a great temperature change from the melting temperature to room temperature; this causes high thermal strains and induces distinct deformations which degrade the quality of the printed parts, especially in metal 3D printing. In order to reduce possible problems and find possible solutions, a prior evaluation by simulation is often adopted.