Advances in Functional Near-Infrared Spectroscopy: Physical Principles and Expanding Applications in Neuroscience.

Functional near-infrared spectroscopy (fNIRS) is an imaging technique that uses near-infrared light to monitor blood oxygen level changes in the cerebral cortex and noninvasively study brain function. This review provides an overview of the expanding applications and physical principles of fNIRS to enhance understanding of its imaging process and promote awareness of its broad applicability and potential. We systematically searched PubMed, Web of Science, and Google Scholar, analyzing studies on fNIRS applications in psychiatry, neurology, education, and multimodal imaging.

PGDPNN: prior-information generation and distribution prediction neural network for fluorescence molecular tomography reconstruction.

Fluorescence molecular tomography (FMT) is a promising and high sensitivity imaging modality that reconstructs the three-dimensional distribution of interior fluorescent sources. However, FMT reconstruction suffers from limited spatial resolution due to the simplifications in the forward model and the severely ill-posed nature of the inverse problem. In this study, we perform a clustering analysis using the radiomic features of the surface signal distribution.

Effects of Syntactic Complexity on the Comprehension of Passive Clauses and wh-questions in Children with Developmental Language Disorder and Autism Plus Language Impairment.

There is considerable debate over the similarities and differences between developmental language disorder (DLD) and autism spectrum disorder plus language impairment (ALI). Few studies have compared these in terms of complex syntactic operations. This study aimed to explore the similarities and differences between children with DLD and children with ALI via investigating the effects of syntactic complexity operationalized in terms of movement and intervention in Mandarin passives and wh-questions.

Imaging-aided diagnosis and treatment based on artificial intelligence for pulmonary nodules: A review.

Background: Pulmonary nodules are critical indicators for the early detection of lung cancer; however, their diagnosis and management pose significant challenges due to the variability in nodule characteristics, reader fatigue, and limited clinical expertise, often leading to diagnostic errors. The rapid advancement of artificial intelligence (AI) presents promising solutions to address these issues.

Methods: This review compares traditional rule-based methods, handcrafted feature-based machine learning, radiomics, deep learning, and hybrid models incorporating Transformers or attention mechanisms.

Deep system prior based graph convolution network for NIR-II fluorescence molecular tomography.

Background And Objective: Fluorescence molecular tomography (FMT) is a promising imaging technique that can quantify the internal distribution of tumor in the early stage. However, due to the ill-posed inverse problem caused by the severe photon scattering effect, the promotion of efficiency and accuracy is still an issue for FMT and the reconstruction of the morphological performance is still difficult to meet the practical requirement.

Methods: In this paper, the second near-infrared (NIR-II) fluorescence imaging was adopted to mitigate tissue scattering to alleviated ill-posedness, and a deep system prior based graph convolution network (DSPGN) was proposed for FMT, which fully takes the morphology represented by graph-structure into the reconstruction process to improve the morphological performance of FMT.

[Factors influencing Nitrogen Removal in Different Plant Zones Along the Littoral Zone of a Typical Urban Lake in Nanjing].

Nitrous oxide （NO） is one of the important greenhouse gases contributing to the global warming trend. As important nitrogen removal pathways， microbially mediated denitrification and anaerobic ammonia oxidation （ANAMMOX） in the rhizospheres of wetland plants can reduce nitrogen load in freshwater wetlands， and the denitrification process is the major NO source. Littoral zone of urban lakes is an ecotone between terrestrial and aquatic ecosystems and an important area for nitrogen input and transformation.

Intraosseous versus intravenous fluid resuscitation in gastrointestinal tumor-related acute hemorrhage: impact on 30-day mortality and lactate clearance.

This retrospective study evaluated the impact of intraosseous infusion (IO) versus traditional intravenous infusion (IV) on 30-day mortality and clinical outcomes in 518 patients with acute gastrointestinal bleeding (AGIB) secondary to gastrointestinal tumors from January 2022 to July 2024. Patients were divided into IO (n=217) and IV (n=301) groups based on initial resuscitation strategy. Compared to IV group, the IO group demonstrated higher first-attempt catheterization success rate, shorter vascular access time, and faster blood pressure recovery (all P<0.

Exploring the neural mechanisms of ADHD in children: a multifeature cross-task fNIRS analysis.

Children with attention-deficit hyperactivity disorder exhibit multidimensional abnormalities of brain function, and the identification of key brain regions is often inconsistent across studies due to the influence of specific cognitive demands and feature selection. We conducted multifeature cross-task analysis and correlation analysis of functional near-infrared spectroscopy-based functional activity and connectivity under both resting state and verbal fluency task. Results reveal that more pronounced brain activation differences were observed in the right hemisphere of attention-deficit hyperactivity disorder group compared with healthy controls, particularly in channels 7 and 13, with cross-task consistent activation patterns.

Near-Infrared Electroluminescence from Ambipolar Transistors Built on sp-Functionalized Carbon-Nanotube Networks.

We demonstrate room-temperature, near-infrared electroluminescence (EL) from networks of sp-functionalized (6,5) single-walled carbon nanotubes (SWCNTs) using ambipolar transistors with sub-10 μm channels. EL efficiency dependences on drain current and channel length are investigated, giving insights into the carrier recombination in SWCNT networks. EL from free and localized excitons are strongly limited by trap-assisted Shockley-Read-Hall (SRH) recombination; high emission efficiency can be achieved in short channels down to 2 μm with the alleviation of SRH recombination.

Differences in Language Impairment Between Developmental Language Disorder and Autism: Insights From Mandarin and Constructions.

Background And Aims: Both developmental language disorder (DLD) and autism spectrum disorder (ASD) are characterized by language and communication deficits, and the extent to which commonalities in syntactic difficulties are shared between DLD and autism plus language impairment (ALI), a subtype of ASD, is a matter of debate. Thus, this study aims to further explore the extent of overlapping vulnerabilities in the syntactic profiles of children with DLD and ALI.

Method: We investigated the comprehension and production of two complex syntactic structures, constructions and constructions in Mandarin by 18 children with DLD (mean age = 5;03) and 17 children with ALI (mean age = 5;05), compared to their 24 typically developing peers matched on chronological age.

SFPGCL: Specificity-preserving federated population graph contrastive learning for multi-site ASD identification using rs-fMRI data.

Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder that affects people's social communication and daily routine. Most existing imaging studies on ASD use single site resting-state functional magnetic resonance imaging (rs-fMRI) data, which may suffer from limited samples and geographic bias. Improving the generalization ability of the diagnostic models often necessitates a large-scale dataset from multiple imaging sites.

Psychological stress-induced ALKBH5 deficiency promotes tumour innervation and pancreatic cancer via extracellular vesicle transfer of RNA.

The pathological role and mechanism of psychological stress in cancer progression are little known. Here we show in a mouse model that psychological stress drives pancreatic ductal adenocarcinoma (PDAC) progression by stimulating tumour nerve innervation. We demonstrate that nociception and other stressors activate sympathetic nerves to release noradrenaline, downregulating RNA demethylase alkB homologue 5 (Alkbh5) in tumour cells.

Carbon Nanotube Intramolecular Junction Photodetector Via Strain Engineering.

In modern photonics, a semiconductor homojunction or heterojunction is the core of optoelectronic devices and photonic integrated circuits. Here, a strategy is demonstrated to create tunable bandgap carbon nanotube intramolecular junctions via uniaxial strain modulation. The fabrication of photodetectors based on this mechanism is controllable, reproducible, and scalable, which is completely superior to other pathways for forming intramolecular junctions.

Improved strong tracking Sage-Husa adaptive algorithm for multi-MEMS IMU data fusion.

A circuit array of 16 micro-electro-mechanical system inertial measurement unit (IMUs) is developed, and an improved multi-IMU data fusion method based on the strong tracking Sage-Husa adaptive Kalman filter (ST-SHAKF) is proposed to achieve high-precision inertial measurement at low cost. The traditional Sage-Husa adaptive (SHAKF) algorithm is simplified for adaptive parameterization, with improved measurement noise variance estimation to ensure positive-definiteness. Filter divergence is addressed by supplementing the SHAKF with a strong tracking filter to maintain convergence.

Lymph node ratio emerges as a pivotal prognostic determinant for cancer-specific survival amidst individuals diagnosed with stage N1 and N2 non-small cell lung carcinoma: A population-based retrospective cohort study.

Lung cancer is the leading cause of cancer-related deaths in the US, predominantly non-small cell lung cancer (NSCLC). Lymph node metastasis significantly impacts prognosis, yet current classification systems lack precision. Lymph node ratio (LNR), correlating metastatic to total lymph nodes, emerges as a superior prognostic tool.

Unveiling complex brain dynamics during movie viewing via deep recurrent autoencoder model.

Naturalistic stimuli have become an effective tool to uncover the dynamic functional brain networks triggered by cognitive and emotional real-life experiences through multimodal and dynamic stimuli. However, current research predominantly focused on exploring dynamic functional connectivity generated via chosen templates under resting-state paradigm, with relatively limited investigation into the dynamic functional interactions among large-scale brain networks. Moreover, these studies might overlook the longer time-scale adaptability and information transmission that occur over extended periods during naturalistic stimuli.

FMT-ReconNet: Fluorescence Molecular Tomography Reconstruction using Prior Knowledge and Deformation Neural Network.

Fluorescence molecular tomography (FMT) is a powerful imaging technique for 3D reconstruction of internal fluorescent sources. However, its spatial resolution is limited by a simplified forward model and an ill-posed inverse problem. To address this, we introduce FMT-ReconNet, a deep neural network comprising a spatial transformer network (STN) for source transformation and a V-Net for reconstruction.

SSN: Monitoring liver injure through signal separation network in dynamic fluorescence molecular tomography.

Dynamic fluorescence molecular tomography (DFMT) is a promising molecular imaging technique that offers the potential to monitor fast kinetic behaviors within small animals in three dimensions. Early monitoring of liver disease requires the ability to distinguish and analyze normal and injured liver tissues. However, the inherent ill-posed nature of the problem and energy signal interference between the normal and injured liver regions limit the practical application of liver injury monitoring.

The pseudo-siamese framework combines Transformer and CNN for medical image generation.

Multi-phase medical imaging can provide significant improvement in disease multi-modal diagnosis. However, medical image data often suffer from modality missing issues. Therefore, synthesizing missing phases using available phases is of great clinical significance.

Monotone Accelerated Proximal Gradient Network For Bioluminescence Tomography Reconstruction.

Bioluminescence tomography (BLT) is a noninvasive technique designed to enable three-dimensional (3D) visualization and quantification of viable tumor cells in living organisms. However, despite the excellent sensitivity and specificity of bioluminescence imaging (BLI), BLT is limited by the photon scattering effect and ill-posed inverse problem. To overcome this problem, regularization algorithms have been widely studied and achieved impressive results.

3D System matrix recovery based on iterative up-and-down sampling super-resolution network in magnetic particle imaging.

Magnetic Particle Imaging (MPI) is a promising technique for mapping magnetic nanoparticle distributions within biological tissues. The reconstruction process, which relies on the system matrix (SM), is crucial for accurate MPI imaging. However, the time-consuming nature of SM measurements often requires repetition whenever there are changes in scan parameters, particle types, or environmental conditions.

Dimension Reduction Based on Grouped Feature Selection Strategy for Fluorescence Molecular Tomography.

Fluorescence Molecular Tomography (FMT) is an optical molecular imaging technique with high sensitivity. Multi-point excitation and multi-view measurement have improved the recovered results while also resulting in high computation and memory requirements. In this work, we proposed a grouped feature selection strategy (GFS) based on correlation and information entropy to reduce the size of the system matrix.

The Combine of GLCM and Group, Focuses on the Grayscale of Medical Images.

Medical image classification is very important in the diagnosis of hepatocellular carcinoma, which can provide more accurate computer-aided diagnosis, and accurate extraction of key semantic information from medical data is crucial to improve classification performance. However, many studies focus on the feature extraction module of natural image design, and do not carry out targeted design from the perspective of medical images, resulting in limited improvement in classification performance and mediocre performance. Therefore, we designed a plug and play attention module from the perspective of Grey-Level Cooccurrence Matrix and groups to improve the performance of backbone network for medical image classification.

BrainFTFCN: Synergistic feature fusion of temporal dynamics and network connectivity for brain age prediction.

Using neuroimaging-derived data for age estimation serves as a prominent approach in comprehending the normal pace of brain development and mechanisms underlying cognitive declines due to aging and neurological diseases. Despite the promise of resting-state functional magnetic resonance imaging (rs-fMRI) for brain age prediction, previous deep learning models have prioritized capturing either the temporal dynamics via time courses (TCs) or the inherent network topology revealed by functional network connectivity (FNC). These fragmented models neglect the complementary information available by synergistically integrating both.

GAICN: Graph Attention Iterative Contraction Network for Bioluminescence Tomography.

Bioluminescence tomography (BLT) can provide non-invasive quantitative three-dimensional tumor information which has been widely applied in pre-clinical studies. Meanwhile, in recent years, deep learning methods have significantly improved the reconstruction resolution and speed by establishing a non-linear mapping relationship between surface-measured bioluminescence and light source distribution. However, this mapping relationship only works for specific biological tissues and light transmission processes under fixed wavelengths, resulting in poor stability and generalizability.