new neural

Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments...

BACKGROUND: Intranasal transplantation of ANGE-S003 human neural stem cells showed therapeutic effects and were safe in preclinical models of Parkinson's disease (PD). We investigated the safety and tolerability of this treatment in patients with PD and whether these effects would be apparent in a clinical trial. METHODS: This was a 12-month, single-centre, open-label, dose-escalation phase 1 study of 18 patients with advanced PD assigned to four-time intranasal transplantation of 1 of 3 doses: 1...

In the natural world, animals make decisions on an ongoing basis, continuously selecting which action to undertake next. In the lab, however, the neural bases of decision processes have mostly been studied using artificial trial structures. New experimental tools based on the genetic toolkit of model organisms now make it experimentally feasible to monitor and manipulate neural activity in small subsets of neurons during naturalistic behaviors. We thus propose a new approach to investigating decision processes, termed reverse neuroethology...

Holistic and analytic thinking are two distinct modes of thinking used to interpret the world with relative preferences varying across cultures. While most research on these thinking styles has focused on behavioral and cognitive aspects, a few studies have utilized functional magnetic resonance imaging (fMRI) to explore the correlations between brain metrics and self-reported scale scores. Other fMRI studies used single holistic and analytic thinking tasks. As a single task may involve processing in spurious low-level regions, we used two different holistic and analytic thinking tasks, namely the frame-line task and the triad task, to seek convergent brain regions to distinguish holistic and analytic thinking using multivariate pattern analysis (MVPA)...

Recognizing talkers' identity via speech is an important social skill in interpersonal interaction. Behavioral evidence has shown that listeners can identify better the voices of their native language than those of a non-native language, which is known as the language familiarity effect (LFE). However, its underlying neural mechanisms remain unclear. This study therefore investigated how the LFE occurs at the neural level by employing functional near-infrared spectroscopy (fNIRS). Late unbalanced bilinguals were first asked to learn to associate strangers' voices with their identities and then tested for recognizing the talkers' identities based on their voices speaking a language either highly familiar (i...

Blood vessel segmentation is a crucial stage in extracting morphological characteristics of vessels for the clinical diagnosis of fundus and coronary artery disease. However, traditional convolutional neural networks (CNNs) are confined to learning local vessel features, making it challenging to capture the graph structural information and fail to perceive the global context of vessels. Therefore, we propose a novel graph neural network-guided vision transformer enhanced network (G2ViT) for vessel segmentation...

In recent years, self-supervised learning has emerged as a powerful approach to learning visual representations without requiring extensive manual annotation. One popular technique involves using rotation transformations of images, which provide a clear visual signal for learning semantic representation. However, in this work, we revisit the pretext task of predicting image rotation in self-supervised learning and discover that it tends to marginalise the perception of features located near the centre of an image...

Contrastive learning-based deep multi-view clustering methods have become a mainstream solution for unlabeled multi-view data. These methods usually utilize a basic structure that combines autoencoder, contrastive learning, or/and MLP projectors to generate more representative latent representations for the final clustering stage. However, existing deep contrastive multi-view clustering ignores two key points: (i) the latent representations projecting from one or more layers of MLP or new representations directly obtained from autoencoder fail to mine inherent relationship inner-view or cross-views; (ii) more existing frameworks only employ a one or dual-contrastive learning module, i...

The decision of when to add a new hidden unit or layer is a fundamental challenge for constructive algorithms. It becomes even more complex in the context of multiple hidden layers. Growing both network width and depth offers a robust framework for leveraging the ability to capture more information from the data and model more complex representations. In the context of multiple hidden layers, should growing units occur sequentially with hidden units only being grown in one layer at a time or in parallel with hidden units growing across multiple layers simultaneously? The effects of growing sequentially or in parallel are investigated using a population dynamics-inspired growing algorithm in a multilayer context...

Competitive opinion maximization (COM) aims to determine some individuals (i.e., seed nodes) from social networks, propagating the desired opinions toward a target entity to their neighbors through social relationships when facing with its competitors (components) and maximize the opinion spread after the specific time. Current studies on COM are still in its infancy, while the only work merely considers the scenario that the strategy of competitors is known but ignores the unknown scenario. In addition, previous studies on COM cannot easily address the situation where some users might dynamically change their opinions...

Neural decoding is still a challenging and a hot topic in neurocomputing science. Recently, many studies have shown that brain network patterns containing rich spatiotemporal structural information represent the brain's activation information under external stimuli. In the traditional method, brain network features are directly obtained using the standard machine learning method and provide to a classifier, subsequently decoding external stimuli. However, this method cannot effectively extract the multidimensional structural information hidden in the brain network...

This study investigates the electrochemical behavior of GelMA-based hydrogels and their interactions with PC12 neural cells under electrical stimulation in the presence of conducting substrates. Focusing on indium tin oxide (ITO), platinum, and gold mylar substrates supporting conductive scaffolds composed of hydrogel, graphene oxide, and gold nanorods, we explored how the substrate materials affect scaffold conductivity and cell viability. We examined the impact of an optimized electrical stimulation protocol on the PC12 cell viability...

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India...

OBJECTIVE: Electroencephalography (EEG) has been widely used in motor imagery (MI) research by virtue of its high temporal resolution and low cost, but its low spatial resolution is still a major criticism. The EEG source localization (ESL) algorithm effectively improves the spatial resolution of the signal by inverting the scalp EEG to extrapolate the cortical source signal, thus enhancing the classification accuracy. APPROACH: To address the problem of poor spatial resolution of EEG signals, this paper proposed a sub-band source chaotic entropy (SSCE) feature extraction method based on sub-band ESL...

OBJECTIVE: In the specific use of electromyogram (EMG) driven prosthetics, the user's disability reduces the space available for the electrode array. We propose a framework for EMG decomposition adapted to the condition of a few channels (less than 30 observations), which can elevate the potential of prosthetics in terms of cost and applicability. APPROACH: The new framework contains a peel-off approach, a refining strategy for motor unit spike train (MUST) and motor unit action potential (MUAP) and a re-subtracting strategy to adapt the framework to few channels environments...

Determining binding affinities in protein-protein and protein-peptide complexes is a challenging task that directly impacts the development of peptide and protein pharmaceuticals. Although several models have been proposed to predict the value of the dissociation constant and the Gibbs free energy, they are currently not capable of making stable predictions with high accuracy, in particular for complexes consisting of more than two molecules. In this work, we present ProBAN, a new method for predicting binding affinity in protein-protein complexes based on a deep convolutional neural network...

BACKGROUND: Marsupials are a diverse and unique group of mammals, but remain underutilized in developmental biology studies, hindering our understanding of mammalian diversity. This study focuses on establishing the fat-tailed dunnart (Sminthopsis crassicaudata) as an emerging laboratory model, providing reproductive monitoring methods and a detailed atlas of its embryonic development. RESULTS: We monitored the reproductive cycles of female dunnarts and established methods to confirm pregnancy and generate timed embryos...

The dynamics of neurite extension and synaptic connections are central issues in neural circuit research. The development of technologies for labeling purified cytoskeletal proteins with fluorescent dyes and introducing them into living neurons using microinjection greatly facilitated our understanding of cytoskeletal dynamics in neuronal axons. Imaging data showed that the cytoskeleton repeatedly polymerized and depolymerized within the axon, and elongation was driven by the new cytoskeleton formed at the axon tip...

BACKGROUND: Acupuncture is a widely used clinical treatment method, and studies have confirmed its therapeutic effects on stroke patients. It can also reduce the burden on patients and society. Acupuncture treatment is a complementary and preventive treatment for stroke. However, there has yet to be a visual bibliometric analysis of the field of acupuncture for stroke rat models. This study explores future trends, research hotspots, and frontiers in acupuncture for stroke rat models over the past 20 years through investigation and visualization...

BACKGROUND: The prevalence of Type 2 Diabetes Mellitus (T2D) and its significant role in increasing Coronary Heart Disease (CHD) risk highlights the urgent need for effective CHD screening within this population. Despite current advancements in T2D management, the complexity of cardiovascular complications persists. Our study aims to develop a comprehensive CHD screening model for T2D patients, employing multimodal data to improve early detection and management, addressing a critical gap in clinical practice...