Construction of Multi-Modal Transcriptome-Small Molecule Interaction Networks from High-Throughput Measurements to Study Human Complex Traits.

Small molecules (SMs) are integral to biological processes, influencing metabolism, homeostasis, and regulatory networks. Despite their importance, a significant knowledge gap exists regarding their downstream effects on biological pathways and gene expression, largely due to differences in scale, variability, and noise between untargeted metabolomics and sequencing-based technologies. To address these challenges, we developed a multi-omics framework comprising a machine learning-based protocol for data processing, a semi-supervised network inference approach, and network-guided analysis of complex traits.

Accelerating Brain MR Imaging With Multisequence and Convolutional Neural Networks.

Purpose: Magnetic resonance imaging (MRI) refers to one of the critical image modalities for diagnosis, whereas its long acquisition time limits its application. In this study, the aim was to investigate whether deep learning-based techniques are capable of using the common information in different MRI sequences to reduce the scan time of the most time-consuming sequences while maintaining the image quality.

Method: Fully sampled T1-FLAIR, T2-FLAIR, and T2WI brain MRI raw data originated from 217 patients and 105 healthy subjects.

CCL2 promotes EGFR-TKIs resistance in non-small cell lung cancer via the AKT-EMT pathway.

Acquired resistance to EGFR tyrosine kinase inhibitors (EGFR-TKIs) represents a primary cause of treatment failure in non-small cell lung cancer (NSCLC) patients. Chemokine (C-C motif) ligand 2 (CCL2) is recently found to play a pivotal role in determining anti-cancer treatment response. However, the role and mechanism of CCL2 in the development of EGFR-TKIs resistance have not been fully elucidated.

Multi-omics Integration Identifies Genes Influencing Traits Associated with Cardiovascular Risks: The Long Life Family Study.

The Long Life Family Study (LLFS) enrolled 4,953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that affect cardiovascular risks in the LLFS population, we developed a multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from rare-variant analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA).

CC Chemokine Ligand-2: A Promising Target for Overcoming Anticancer Drug Resistance.

CC chemokine ligand-2 (CCL2), a proinflammatory chemokine that mediates chemotaxis of multiple immune cells, plays a crucial role in the tumor microenvironment (TME) and promotes tumorigenesis and development. Recently, accumulating evidence has indicated that CCL2 contributes to the development of drug resistance to a broad spectrum of anticancer agents, including chemotherapy, hormone therapy, targeted therapy, and immunotherapy. It has been reported that CCL2 can reduce tumor sensitivity to drugs by inhibiting drug-induced apoptosis, antiangiogenesis, and antitumor immunity.

Nomograms predict prognosis and hospitalization time using non-contrast CT and CT perfusion in patients with ischemic stroke.

Background: Stroke is a major disease with high morbidity and mortality worldwide. Currently, there is no quantitative method to evaluate the short-term prognosis and length of hospitalization of patients.

Purpose: We aimed to develop nomograms as prognosis predictors based on imaging characteristics from non-contrast computed tomography (NCCT) and CT perfusion (CTP) and clinical characteristics for predicting activity of daily living (ADL) and hospitalization time of patients with ischemic stroke.

The Effects of Different Induction Chemotherapy Cycles and Adjuvant Chemotherapy on the Survival Outcomes of Patients With Locally Advanced Nasopharyngeal Carcinoma.

Objective: This study investigated whether differences in the induction chemotherapy (IC) cycle number and adjuvant chemotherapy (AC) affect survival outcomes in patients with locally advanced nasopharyngeal carcinoma (LA-NPC).

Methods: The survival outcomes of 386 consecutive LA-NPC patients treated between January 2015 and March 2018 were retrospectively analyzed. Univariate and multivariate analyses were used to compare treatment groups defined by IC< 3 or ≥3 IC cycles followed by radiotherapy with or without AC (i.

Multimodal MRI Reconstruction Assisted With Spatial Alignment Network.

In clinical practice, multi-modal magnetic resonance imaging (MRI) with different contrasts is usually acquired in a single study to assess different properties of the same region of interest in the human body. The whole acquisition process can be accelerated by having one or more modalities under-sampled in the k -space. Recent research has shown that, considering the redundancy between different modalities, a target MRI modality under-sampled in the k -space can be more efficiently reconstructed with a fully-sampled reference MRI modality.

Deep learning-assisted PET imaging achieves fast scan/low-dose examination.

Purpose: This study aimed to investigate the impact of a deep learning (DL)-based denoising method on the image quality and lesion detectability of F-FDG positron emission tomography (PET) images.

Methods: Fifty-two oncological patients undergoing an F-FDG PET/CT imaging with an acquisition of 180 s per bed position were retrospectively included. The list-mode data were rebinned into four datasets: 100% (reference), 75%, 50%, and 33.

Deep Learning in Prostate Cancer Diagnosis Using Multiparametric Magnetic Resonance Imaging With Whole-Mount Histopathology Referenced Delineations.

Multiparametric magnetic resonance imaging (mpMRI) plays an important role in the diagnosis of prostate cancer (PCa) in the current clinical setting. However, the performance of mpMRI usually varies based on the experience of the radiologists at different levels; thus, the demand for MRI interpretation warrants further analysis. In this study, we developed a deep learning (DL) model to improve PCa diagnostic ability using mpMRI and whole-mount histopathology data.

Review and Prospect: Artificial Intelligence in Advanced Medical Imaging.

Artificial intelligence (AI) as an emerging technology is gaining momentum in medical imaging. Recently, deep learning-based AI techniques have been actively investigated in medical imaging, and its potential applications range from data acquisition and image reconstruction to image analysis and understanding. In this review, we focus on the use of deep learning in image reconstruction for advanced medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET).

A deep learning method for eliminating head motion artifacts in computed tomography.

Purpose: Involuntary patient movement results in data discontinuities during computed tomography (CT) scans which lead to a serious degradation in the image quality. In this paper, we specifically address artifacts induced by patient motion during a head scan.

Method: Instead of trying to solve an inverse problem, we developed a motion simulation algorithm to synthesize images with motion-induced artifacts.

Single-breath-hold T2WI liver MRI with deep learning-based reconstruction: A clinical feasibility study in comparison to conventional multi-breath-hold T2WI liver MRI.

Objective: To investigate the clinical feasibility of single-breath-hold (SBH) T2-weighted (T2WI) liver MRI with deep learning-based reconstruction in the evaluation of image quality and lesion delineation, compared with conventional multi-breath-hold (MBH) T2WI.

Methods: One hundred and fifty-two adult patients with suspected liver disease were prospectively enrolled. Two independent readers reviewed images acquired with conventional MBH-T2WI and SBH-T2WI at 3.

[Nitrogen and phosphorus resorption and stoichiometric characteristics of different tree species in a mid-subtropical common-garden, China.].

To understand the nutrient use strategies of 11 tree species in a subtropical common-garden, we measured the specific leaf area, nitrogen (N) and phosphorus (P) resorption and stoichiometric characteristics of leaves in August 2019. The results showed that the specific leaf area, N and P concentrations in mature and senescent leaves of evergreen broadleaved (, , , , and ) and coniferous species ( and ) were lower than those of deciduous broadleaved species (, and ). In contrast, C:N and C:P in mature leaves of evergreen broadleaved and coniferous species were significantly higher than those of deciduous broadleaved species.

Preparation of Anti Toll-Like Receptor-4 Nano-Antibody and Its Effect on Gram Negative Sepsis.

The essence of sepsis is the excessive immune response caused by infectious factors, which leads to the release of a large number of inflammatory factors and the injury of tissues and organs. Once the reaction is triggered, it will develop according to its own rules, and it does not depend on infection factors. The occurrence and severity of sepsis depend entirely on the reaction of the body.

α-Ketoglutarate attenuates Wnt signaling and drives differentiation in colorectal cancer.

Genetic-driven deregulation of the Wnt pathway is crucial but not sufficient for colorectal cancer (CRC) tumourigenesis. Here, we show that environmental glutamine restriction further augments Wnt signaling in APC mutant intestinal organoids to promote stemness and leads to adenocarcinoma formation via decreasing intracellular alpha-ketoglutarate (aKG) levels. aKG supplementation is sufficient to rescue low-glutamine induced stemness and Wnt hyperactivation.

New Directions for Axion Searches via Scattering at Reactor Neutrino Experiments.

Searches for pseudoscalar axionlike-particles (ALPs) typically rely on their decay in beam dumps or their conversion into photons in haloscopes and helioscopes. We point out a new experimental direction for ALP probes via their production by the intense gamma ray flux available from megawatt-scale nuclear reactors at neutrino experiments through Primakoff-like or Compton-like channels. Low-threshold detectors in close proximity to the core will have visibility to ALP decays and inverse Primakoff and Compton scattering, providing sensitivity to the ALP-photon and ALP-electron couplings.

Dark Matter Signals from Timing Spectra at Neutrino Experiments.

We propose a novel strategy to search for new physics in timing spectra at low-energy neutrino experiments using a pulsed beam, envisioning the situation in which a new particle comes from the decay of its heavier partner with a finite particle width. The timing distribution of events induced by the dark matter (DM) candidate particle scattering at the detector may populate in a relatively narrow range, forming a "resonancelike" shape. Because of this structural feature, the signal may be isolated from the backgrounds, in particular when the backgrounds are uniformly distributed in energy and time.

Searching for Beyond the Standard Model Physics with COHERENT Energy and Timing Data.

We search for beyond the standard model physics by combining COHERENT Collaboration energy and timing data. Focusing on light, ≲GeV mediators, we find the data favor a ∼10-1000  MeV mediator, as compared to the standard model best fit at the ≲2σ level. The best-fit coupling range is g∼10^{-5}-10^{-3}.

Non-target Effects of Naphthalene on the Soil Microbial Biomass and Bacterial Communities in the Subalpine Forests of Western China.

Naphthalene is a biocide of soil fauna, particularly of soil arthropods, that has been widely applied to test the functional roles of soil fauna in soil processes. However, whether the use of naphthalene to expel soil fauna has a non-target effect on soil bacteria in subalpine forests remains unclear. We conducted a naphthalene treatment experiment to explore the effects of naphthalene on the soil bacterial community in subalpine forest soil.

Long non-coding RNA H19 promotes the proliferation and invasion of lung cancer cells and regulates the expression of E-cadherin, N-cadherin, and vimentin.

Background: This study aimed to explore the effect of long non-coding RNA (LncRNA) H19 on the proliferation and invasion of lung carcinoma cells A549, and to determine its molecular targets.

Methods: A549 cells were with either LncRNA H19 or LncRNA H19 shRNA, and the expression levels of LncRNA H19 were evaluated by quantitative real-time PCR (RT-PCR). We measured cell proliferation using the CCK-8 assay, cell counting assays, and colony formation assay in response to shLncRNA H19-2.

Phylogenetic relationships in the genus Avena based on the nuclear Pgk1 gene.

The phylogenetic relationships among 76 Avena taxa, representing 14 diploids, eight tetraploids, and four hexaploids were investigated by using the nuclear plastid 3-phosphoglycerate kinase gene (Pgk1). A significant deletion (131 bp) was detected in all the C genome homoeologues which reconfirmed a major structural divergence between the A and C genomes. Phylogenetic analysis indicated the Cp genome is more closely related to the polyploid species than is the Cv genome.

Interplay between membrane curvature and protein conformational equilibrium investigated by solid-state NMR.

Many membrane proteins sense and induce membrane curvature for function, but structural information about how proteins modulate their structures to cause membrane curvature is sparse. We review our recent solid-state NMR studies of two virus membrane proteins whose conformational equilibrium is tightly coupled to membrane curvature. The influenza M2 proton channel has a drug-binding site in the transmembrane (TM) pore.