DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models

Performing magnetic resonance imaging (MRI) reconstruction from under-sampled k-space data can accelerate the procedure to acquire MRI scans and reduce patients' discomfort. The reconstruction problem is usually formulated as a denoising task that removes the noise in under-sampled MRI image slices. Although previous GAN-based methods have achieved good performance in image denoising, they are difficult to train and require careful tuning of hyperparameters. In this paper, we propose a novel MRI denoising framework DiffCMR by leveraging conditional denoising diffusion probabilistic models. Specifically, DiffCMR perceives conditioning signals from the under-sampled MRI image slice and generates its corresponding fully-sampled MRI image slice. During inference, we adopt a multi-round ensembling strategy to stabilize the performance. We validate DiffCMR with cine reconstruction and T1/T2 mapping tasks on MICCAI 2023 Cardiac MRI Reconstruction Challenge (CMRxRecon) dataset. Results show that our method achieves state-of-the-art performance, exceeding previous methods by a significant margin. Code is available at https://github.com/xmed-lab/DiffCMR.Comment: MICCAI 2023 STACOM-CMRxReco

Unsupervised Feature Selection Via Orthogonal Basis Clustering and Local Structure Preserving

Due to the "curse of dimensionality" issue, how to discard redundant features and select informative features in high-dimensional data has become a critical problem, hence there are many research studies dedicated to solving this problem. Unsupervised feature selection technique, which does not require any prior category information to conduct with, has gained a prominent place in preprocessing high-dimensional data among all feature selection techniques, and it has been applied to many neural networks and learning systems related applications, e.g., pattern classification. In this article, we propose an efficient method for unsupervised feature selection via orthogonal basis clustering and reliable local structure preserving, which is referred to as OCLSP briefly. Our OCLSP method consists of an orthogonal basis clustering together with an adaptive graph regularization, which realizes the functionality of simultaneously achieving excellent cluster separation and preserving the local information of data. Besides, we exploit an efficient alternative optimization algorithm to solve the challenging optimization problem of our proposed OCLSP method, and we perform a theoretical analysis of its computational complexity and convergence. Eventually, we conduct comprehensive experiments on nine real-world datasets to test the validity of our proposed OCLSP method, and the experimental results demonstrate that our proposed OCLSP method outperforms many state-of-the-art unsupervised feature selection methods in terms of clustering accuracy and normalized mutual information, which indicates that our proposed OCLSP method has a strong ability in identifying more important features

The impact on the soil microbial community and enzyme activity of two earthworm species during the bioremediation of pentachlorophenol-contaminated soils

The ecological effect of earthworms on the fate of soil pentachlorophenol (PCP) differs with species. This study addressed the roles and mechanisms by which two earthworm species (epigeic Eisenia fetida and endogeic Amynthas robustus E. Perrier) affect the soil microbial community and enzyme activity during the bioremediation of PCP-contaminated soils. A. robustus removed more soil PCP than did E. foetida. A. robustus improved nitrogen utilisation efficiency and soil oxidation more than did E. foetida, whereas the latter promoted the organic matter cycle in the soil. Both earthworm species significantly increased the amount of cultivable bacteria and actinomyces in soils, enhancing the utilisation rate of the carbon source (i.e. carbohydrates, carboxyl acids, and amino acids) and improving the richness and evenness of the soil microbial community. Additionally, earthworm treatment optimized the soil microbial community and increased the amount of the PCP-4-monooxygenase gene. Phylogenic classification revealed stimulation of indigenous PCP bacterial degraders, as assigned to the families Flavobacteriaceae, Pseudomonadaceae and Sphingobacteriacea, by both earthworms. A. robustus and E. foetida specifically promoted Comamonadaceae and Moraxellaceae PCP degraders, respectively

Replication of a Gene-Diet Interaction at CD36, NOS3 and PPARG in Response to Omega-3 Fatty Acid Supplements on Blood Lipids: A Double-Blind Randomized Controlled Trial.

BACKGROUND: Modulation of genetic variants on the effect of omega-3 fatty acid supplements on blood lipids is still unclear. METHODS: In a double-blind randomized controlled trial, 150 patients with type 2 diabetes (T2D) were randomized into omega-3 fatty acid group (n = 56 for fish oil and 44 for flaxseed oil) and control group (n = 50) for 180 days. All patients were genotyped for genetic variants at CD36 (rs1527483), NOS3 (rs1799983) and PPARG (rs1801282). Linear regression was used to examine the interaction between omega-3 fatty acid intervention and CD36, NOS3 or PPARG variants for blood lipids. FINDINGS: Significant interaction with omega-3 fatty acid supplements was observed for CD36 on triglycerides (p-interaction = 0.042) and PPAGR on low-density lipoprotein-cholesterol (p-interaction = 0.02). We also found a significant interaction between change in erythrocyte phospholipid omega-3 fatty acid composition and NOS3 genotype on triglycerides (p-interaction = 0.042), total cholesterol (p-interaction = 0.013) and ratio of total cholesterol to high-density lipoprotein cholesterol (p-interaction = 0.015). The T2D patients of CD36-G allele, PPARG-G allele and NOS3-A allele tended to respond better to omega-3 fatty acids in improving lipid profiles. The interaction results of the omega-3 fatty acid group were mainly attributed to the fish oil supplements. INTERPRETATION: This study suggests that T2D patients with different genotypes at CD36, NOS3 and PPARG respond differentially to intervention of omega-3 supplements in blood lipid profiles

Effects of two ecological earthworm species on atrazine degradation performance and bacterial community structure in red soil

Vermicomposting is an effective and environmentally friendly approach for eliminating soil organic contamination. Atrazine is one of the most commonly applied triazinic herbicides and frequently detected in agricultural soils. This study investigated the roles and mechanisms of two earthworm species (epigeic Eisenia foetida and endogeic Amynthas robustus) in microbial degradation of atrazine. Both earthworms accelerated atrazine degradation performance from 39.0% in sterile soils to 94.9%–95.7%, via neutralizing soil pH, consuming soil humus, altering bacterial community structure, enriching indigenous atrazine degraders and excreting the intestinal atrazine-degrading bacteria. Rhodoplanes and Kaistobacter were identified as soil indigenous degraders for atrazine mineralization and stimulated by both earthworm species. A. robustus excreted the intestinal Cupriavidus and Pseudomonas, whereas Flavobacterium was released by E. foetida. This study provides a comprehensive understanding of the distinct effects of two earthworm species on soil microbial community and atrazine degradation, offering technical supports to apply vermicomposting in effective soil bioremediation

Acid-Base Clusters during Atmospheric New Particle Formation in Urban Beijing

Molecular clustering is the initial step of atmospheric new particle formation (NPF) that generates numerous secondary particles. Using two online mass spectrometers with and without a chemical ionization inlet, we characterized the neutral clusters and the naturally charged ion clusters during NPF periods in urban Beijing. In ion clusters, we observed pure sulfuric acid (SA) clusters, SA-amine clusters, SA-ammonia (NH3) clusters, and SA-amine-NH3 clusters. However, only SA clusters and SA-amine clusters were observed in the neutral form. Meanwhile, oxygenated organic molecule (OOM) clusters charged by a nitrate ion and a bisulfate ion were observed in ion clusters. Acid-base clusters correlate well with the occurrence of sub-3 nm particles, whereas OOM clusters do not. Moreover, with the increasing cluster size, amine fractions in ion acid-base clusters decrease, while NH3 fractions increase. This variation results from the reduced stability differences between SA-amine clusters and SA-NH3 clusters, which is supported by both quantum chemistry calculations and chamber experiments. The lower average number of dimethylamine (DMA) molecules in atmospheric ion clusters than the saturated value from controlled SA-DMA nucleation experiments suggests that there is insufficient DMA in urban Beijing to fully stabilize large SA clusters, and therefore, other basic molecules such as NH3 play an important role.Peer reviewe

Reveal a hidden highly toxic substance in biochar to support its effective elimination strategy

With the aim to develop optimized biochar with minimal contaminants, it is important significance to broaden the understanding of biochar. Here, we disclose for the first time, a highly toxic substance (metal cyanide, MCN, such as KCN or NaCN) in biochar. The cyanide ion (CN−) content in biochar can be up to 85,870 mg/kg, which is determined by the inherent metal content and type in the biomass with K and Na increasing and Ca, Mg and Fe decreasing its formation. Density functional theory (DFT) analysis shows that unstable alkali oxygen-containing metal salts such as K2CO3 can induce an N rearrangement reaction to produce for example, KOCN. The strong reducing character of the carbon matrix further converts KOCN to KCN, thus resulting biochar with high risk. However, the stable Mg, Ca and Fe salts in biomass cannot induce an N rearrangement reaction due to their high binding energies. We therefore propose that high valent metal chloride salts such as FeCl3 and MgCl2 could be used to inhibit the production of cyanide via metal interactive reaction. These findings open a new point of view on the potential risk of biochar and provide a mitigation solution for biochar’s sustainable application

Lipid profiles in the cerebrospinal fluid of rats with 6-hydroxydopamine-induced lesions as a model of Parkinson’s disease

BackgroundParkinson’s disease (PD) is a progressive neurodegenerative disease with characteristic pathological abnormalities, including the loss of dopaminergic (DA) neurons, a dopamine-depleted striatum, and microglial activation. Lipid accumulation exhibits a close relationship with these pathologies in PD.MethodsHere, 6-hydroxydopamine (6-OHDA) was used to construct a rat model of PD, and the lipid profile in cerebrospinal fluid (CSF) obtained from model rats was analyzed using lipidomic approaches.ResultsEstablishment of this PD model was confirmed by apomorphine-induced rotation behaviors, loss of DA neurons, depletion of dopamine in the striatum, and microglial activation after 6-OHDA-induced lesion generation. Unsupervised and supervised methods were employed for lipid analysis. A total of 172 lipid species were identified in CSF and subsequently classified into 18 lipid families. Lipid families, including eicosanoids, triglyceride (TG), cholesterol ester (CE), and free fatty acid (FFA), and 11 lipid species exhibited significantly altered profiles 2 weeks after 6-OHDA administration, and significant changes in eicosanoids, TG, CE, CAR, and three lipid species were noted 5 weeks after 6-OHDA administration. During the period of 6-OHDA-induced lesion formation, the lipid families and species showed concentration fluctuations related to the recovery of behavior and nigrostriatal abnormalities. Correlation analysis showed that the levels of eicosanoids, CE, TG families, and TG (16:0_20:0_18:1) exhibited positive relationships with apomorphine-induced rotation behaviors and negative relationships with tyrosine hydroxylase (TH) expression in the midbrain.ConclusionThese results revealed that non-progressive nigrostriatal degeneration induced by 6-OHDA promotes the expression of an impairment-related lipidomic signature in CSF, and the level of eicosanoids, CE, TG families, and TG (16:0_20:0_18:1) in CSF may reveal pathological changes in the midbrain after 6-OHDA insult

Five-Frame Variable Phase-Shifting Method for Full-Range Spectral-Domain Optical Coherence Tomography

In order to achieve a better complex conjugate artifacts (CCA) suppression, we propose a five-frame variable phase-shifting (FVP) method for spectral domain optical coherence tomography (SD-OCT). The traditional five-frame invariant phase-shifting (FIP) method employs five phase shifts correlate with the center wavelength. However, due to the effects of polychromatic errors, the FIP method cannot get excellent CCA suppression. In the present work, we employ FVP method using variable phase shifts which is dependent on all the wavelengths and therefore, theoretically, the system would have no effects of polychromatic errors. This is the reason why the FVP method would achieve better CCA suppression than the FIP method. Comparative studies between FIP and FVP methods are investigated in the work. Subsequently, we develop a homemade SD-OCT system involving a homemade spectrometer, by which the anterior segment of a rat’s eyeball is measured. The experimental results demonstrate that the quality of OCT images is significantly improved by using FVP method with an increase by a factor of 1.7 on the CCA suppression of SD-OCT. FVP provides a new strategy for complex conjugate artifacts suppression for spectral domain optical coherence tomography