Deep block

Radiation therapy treatment planning requires balancing the delivery of the target dose while sparing normal tissues, making it a complex process. To streamline the planning process and enhance its quality, there is a growing demand for knowledge-based planning (KBP). Ensemble learning has shown impressive power in various deep learning tasks, and it has great potential to improve the performance of KBP. However, the effectiveness of ensemble learning heavily depends on the diversity and individual accuracy of the base learners...

Alzheimer's disease (AD) is a neurodegenerative illness that impairs cognition, function, and behavior by causing irreversible damage to multiple brain areas, including the hippocampus. The suffering of the patients and their family members will be lessened with an early diagnosis of AD. The automatic diagnosis technique is widely required due to the shortage of medical experts and eases the burden of medical staff. The automatic artificial intelligence (AI)-based computerized method can help experts achieve better diagnosis accuracy and precision rates...

We present a deep learning-based method that corrects motion artifacts and thus accelerates data acquisition and reconstruction of magnetic resonance images. The novel model, the Motion Artifact Correction by Swin Network (MACS-Net), uses a Swin transformer layer as the fundamental block and the Unet architecture as the neural network backbone. We employ a hierarchical transformer with shifted windows to extract multiscale contextual features during encoding. A new dual upsampling technique is employed to enhance the spatial resolutions of feature maps in the Swin transformer-based decoder layer...

BACKGROUND: Influenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance...

The steric mass-action (SMA) model has been widely reported to describe the adsorption of proteins in different types of chromatographic adsorbents. Here in the present work, a pore-blocking steric mass-action model (PB-SMA) was developed for the adsorption of large-size bioparticles, which usually exhibit the unique pore-blocking characteristic on the adsorbent and thus lead to a fraction of ligands in the deep channels physically inaccessible to bioparticles adsorption, instead of being shielded due to steric hindrance by adsorbed bioparticles...

KEY CLINICAL MESSAGE: Forced inspiration during mechanical ventilation risks self-inflicted lung injury. However, controlling it with sedation or paralysis may cause polyneuropathy and myopathy. We tested bilateral phrenic nerve paralysis with local anesthetic in a patient, showing reduced inspiratory force. This offers an alternative to drug-induced muscle paralysis. ABSTRACT: Mechanical ventilation, although a life-saving measure, can also pose a risk of causing lung injury known as "ventilator-induced lung injury" or VILI...

BACKGROUND: The presence of noise in medical ultrasound images significantly degrades image quality and affects the accuracy of disease diagnosis. The convolutional neural network-denoising autoencoder (CNN-DAE) model extracts feature information by stacking regularly sized kernels. This results in the loss of texture detail, the over-smoothing of the image, and a lack of generalizability for speckle noise. METHODS: A lightweight attention denoise-convolutional neural network (LAD-CNN) is proposed in the present study...

OBJECTIVE: One of the main causes of death worldwide among young people are car crashes, and most of these fatalities occur to children who are seated in the front passenger seat and who, at the time of an accident, receive a direct impact from the airbags, which is lethal for children under 13 years of age. The present study seeks to raise awareness of this risk by interior monitoring with a child face detection system that serves to alert the driver that the child should not be sitting in the front passenger seat...

Botryosphaeria dieback is a grapevine trunk disease caused by fungi of the Botryosphaeriaceae family, which attacks more specifically the woody tissues. The infection leads to different symptoms including a severe form with a leaf drop as well as premature plant death. Botryosphaeria dieback causes major economic losses, since no effective treatment is yet available. A better understanding is necessary to find solutions to fight this disease. In this study, our objective was to characterize the "leaf drop" form by (1) looking for the presence of pathogens in the basal internodes of stems, (2) quantifying blocked vessels by tylosis and/or gummosis, and (3) describing the impact of the disease on vine physiology (gene expression and metabolome) and development (establishment and functioning of the cambium and phellogen) at the level of xylem and phloem of basal stem internodes...

Achieving the extreme balance of the key performance requirements is the crucial to breakthrough the application bottleneck for nonlinear optical (NLO) materials. Herein, by assembly of the π-conjugated [B3O6] functional species with the aid of structure-directing property of mer-isomer [YO3F3] octahedra, a new ultraviolet (UV) NLO material, Cs2YB3O6F2 with aligned arrangement of coplanar [B3O6] groups has been synthesized. The polar material exhibits the rare coexistence of the largest second harmonic generation response of 5...

Prey metabarcoding has become a popular tool in molecular ecology for resolving trophic interactions at high resolution, from various sample types and animals. To date, most predator-prey studies of small-sized animals (<1 mm) have met the problem of overabundant predator DNA in dietary samples by adding blocking primers/peptide nucleic acids. These primers aim to limit the PCR amplification and detection of the predator DNA but may introduce bias to the prey composition identified by interacting with sequences that are similar to those of the predator...

BACKGROUND: Preclinical low-count positron emission tomography (LC-PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long- and short-lived isotopes as well as increasing scanner throughput. However, LC-PET is characterized by reduced photon-count levels resulting in low signal-to-noise ratio (SNR), segmentation difficulties, and quantification uncertainties. PURPOSE: We developed and evaluated a novel deep-learning (DL) architecture-Attention based Residual-Dilated Net (ARD-Net)-to generate standard-count PET (SC-PET) images from LC-PET images...

Efforts to engineer high-performance protein-based materials inspired by nature have mostly focused on altering naturally occurring sequences to confer the desired functionalities, whereas de novo design lags significantly behind and calls for unconventional innovative approaches. Here, using partially disordered elastin-like polypeptides (ELPs) as initial building blocks we show that de novo engineering of protein materials can be accelerated through hybrid biomimetic design, which we achieve by integrating computational modeling, deep neural network, and recombinant DNA technology...

Developing new pharmaceuticals is a costly and time-consuming endeavor fraught with significant safety risks. A critical aspect of drug research and disease therapy is discerning the existence of interactions between drugs and proteins. The evolution of deep learning (DL) in computer science has been remarkably aided in this regard in recent years. Yet, two challenges remain: (i) balancing the extraction of profound, local cohesive characteristics while warding off gradient disappearance and (ii) globally representing and understanding the interactions between the drug and target local attributes, which is vital for delivering molecular level insights indispensable to drug development...

BACKGROUND AND PURPOSE: Radiation-induced erectile dysfunction (RiED) commonly affects prostate cancer patients, prompting clinical trials across institutions to explore dose-sparing to internal-pudendal-arteries (IPA) for preserving sexual potency. IPA, challenging to segment, isn't conventionally considered an organ-at-risk (OAR). This study proposes a deep learning (DL) auto-segmentation model for IPA, using Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) or CT alone to accommodate varied clinical practices...

Genomic profiling, or molecular profiling of the tumor, is becoming a key component of therapeutic decision making in clinical oncology, and is typically carried out via next generation sequencing. However, the interpretation of the results and evaluation of rationale for targeting the uncovered alterations is challenging and requires a deep understanding of cancer biology, genetics, genomics and oncology. Multidisciplinary molecular tumor boards represent a promising strategy in the facilitation of molecularly-informed therapeutic decisions, and usually consist of specialists with various fields of expertise...

There is an increasing demand for efficient and precise plant disease detection methods that can quickly identify disease outbreaks. For this, researchers have developed various machine learning and image processing techniques. However, real-field images present challenges due to complex backgrounds, similarities between different disease symptoms, and the need to detect multiple diseases simultaneously. These obstacles hinder the development of a reliable classification model. The attention mechanisms emerge as a critical factor in enhancing the robustness of classification models by selectively focusing on relevant regions or features within infected regions in an image...

High intraocular pressure (IOP) is one of the high-risk pathogenic factors of glaucoma. Existing methods of IOP measurement are based on the direct interaction with the cornea. Commercial ophthalmic tonometers based on snapshot measurements are expensive, bulky, and their operation requires trained personnel. Theranostic contact lenses are easy to use, but they may block vision and cause infection. Here, we report a sensory system for IOP assessment that uses a soft indentor with two asymmetrically deployed iontronic flexible pressure sensors to interact with the eyelid-eyeball in an eye-closed situation...

BACKGROUND: Protein residue-residue distance maps are used for remote homology detection, protein information estimation, and protein structure research. However, existing prediction approaches are time-consuming, and hundreds of millions of proteins are discovered each year, necessitating the development of a rapid and reliable prediction method for protein residue-residue distances. Moreover, because many proteins lack known homologous sequences, a waiting-free and alignment-free deep learning method is needed...

The segmentation of the fetal head (FH) and pubic symphysis (PS) from intrapartum ultrasound images plays a pivotal role in monitoring labor progression and informing crucial clinical decisions. Achieving real-time segmentation with high accuracy on systems with limited hardware capabilities presents significant challenges. To address these challenges, we propose the real-time segmentation network (RTSeg-Net), a groundbreaking lightweight deep learning model that incorporates innovative distribution shifting convolutional blocks, tokenized multilayer perceptron blocks, and efficient feature fusion blocks...