Data-driven electrical conductivity brain imaging using 3 T MRI

Magnetic resonance electrical properties tomography (MR-EPT) is a non-invasive measurement technique that derives the electrical properties (EPs, e.g., conductivity or permittivity) of tissues in the radiofrequency range (64 MHz for 1.5 T and 128 MHz for 3 T MR systems). Clinical studies have shown the potential of tissue conductivity as a biomarker. To date, model-based conductivity reconstructions rely on numerical assumptions and approximations, leading to inaccuracies in the reconstructed maps. To address such limitations, we propose an artificial neural network (ANN)-based non-linear conductivity estimator trained on simulated data for conductivity brain imaging. Network training was performed on 201 synthesized T2-weighted spin-echo (SE) data obtained from the finite-difference time-domain (FDTD) electromagnetic (EM) simulation. The dataset was composed of an approximated T2-w SE magnitude and transceive phase information. The proposed method was tested three in-silico and in-vivo on two volunteers and three patients' data. For comparison purposes, various conventional phase-based EPT reconstruction methods were used that ignore (Figure presented.) magnitude information, such as Savitzky–Golay kernel combined with Gaussian filter (S-G Kernel), phase-based convection-reaction EPT (cr-EPT), magnitude-weighted polynomial-fitting phase-based EPT (Poly-Fit), and integral-based phase-based EPT (Integral-based). From the in-silico experiments, quantitative analysis showed that the proposed method provides more accurate and improved quality (e.g., high structural preservation) conductivity maps compared to conventional reconstruction methods. Representatively, in the healthy brain in-silico phantom experiment, the proposed method yielded mean conductivity values of 1.97 ± 0.20 S/m for CSF, 0.33 ± 0.04 S/m for WM, and 0.52 ± 0.08 S/m for GM, which were closer to the ground-truth conductivity (2.00, 0.30, 0.50 S/m) than the integral-based method (2.56 ± 2.31, 0.39 ± 0.12, 0.68 ± 0.33 S/m). In-vivo ANN-based conductivity reconstructions were also of improved quality compared to conventional reconstructions and demonstrated network generalizability and robustness to in-vivo data and pathologies. The reported in-vivo brain conductivity values were in agreement with literatures. In addition, the proposed method was observed for various SNR levels (SNR levels = 10, 20, 40, and 58) and repeatability conditions (the eight acquisitions with the number of signal averages = 1). The preliminary investigations on brain tumor patient datasets suggest that the network trained on simulated dataset can generalize to unforeseen in-vivo pathologies, thus demonstrating its potential for clinical applications

The Influence of Climate Change on Forest Fires in Yunnan Province, Southwest China Detected by GRACE Satellites

Yunnan province in China has rich forest resources but high forest fire frequency. Therefore, a better understanding of the relationship between climate change and forest fires in this region is important for forest fire prevention. This study used the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage change (TWSC) data to analyze the influence of climate change on forest fires in the region during 2003–2016. To improve the accuracy and reliability of GRACE TWSC data, we used the generalized three-cornered hat (GTCH) and the least square method to fuse TWSC data from six GRACE solutions. The spatiotemporal variation of forest fires during 2003–2016 was investigated using burned area data. Then, the relationship between burned area and hydrological and climatic factors was analyzed. The results indicate that more than 90% of burned areas are located in northwestern and southern Yunnan (NW and S). On the seasonal scale, forest fires are mainly concentrated in January–April (dry season) and the burned area is negatively correlated with precipitation (correlation coefficient r = −0.83 (NW) and −0.51 (S)), relative humidity (r = −0.79 (NW) and −0.92 (S)), GRACE TWSC (r = −0.57 (NW) and −0.73 (S)) and evapotranspiration (r = −0.90 (NW) and −0.35 (S)). However, the burned area has no significant correlations with the above four factors on the interannual scale. The composite analysis suggests that the extreme climate affects precipitation, evapotranspiration and TWSC in this region, thereby changing water storage of the air in this region, leading to the formation of an environment prone to forest fires. Such conditions have led to an increase in the burned area in the above region. We also found that the difference between TWSC in high- and low-fire years is much greater than the precipitation in the same period. The above results show that GRACE satellites can detect the influence of climate change on forest fires in Yunnan province

Deep-Red Amplified Spontaneous Emission from <i>cis</i>-Configured Squaraine

We report deep-red amplified spontaneous emission (ASE) from <i>cis</i>-configured squaraine (<i>cis</i>-DCSQ1) under optical pumping. Doping of <i>cis</i>-DCSQ1 into a 4,4′-bis­(9-carbazolyl)-2,2′-biphenyl (CBP) host film at an appropriate concentration (0.5 wt % for this system) led to a marked increase of photoluminescence quantum yield because of efficient CBP-to-<i>cis</i>-DCSQ1 Förster resonance energy transfer and suppression of aggregation-caused quenching. Thus, this doped film showed clear ASE with a threshold of around 10 μJ cm^–2 and peak wavelength of 687 nm. These results proved that <i>cis</i>-configured squaraine derivatives are promising as gain materials for future applications as deep-red organic lasers

MOESM1 of Nitrate decreases methane production also by increasing methane oxidation through stimulating NC10 population in ruminal culture

Additional file 1. Unpublished sequencing data

Transcriptome in Liver of Periparturient Dairy Cows Differs between Supplementation of Rumen-Protected Niacin and Rumen-Protected Nicotinamide

To investigate the difference between rumen-protected niacin (RPN) and rumen-protected nicotinamide (RPM) in the transcriptome of genes relating to the lipid metabolism of the liver of periparturient dairy cows, 10 healthy Chinese Holstein cows were randomly divided into two groups and fed diets supplemented with 18.4 g/d RPN or 18.7 g/d RPM, respectively. The experiment lasted from 14 days before to 21 days after parturition. Liver biopsies were taken 21 days postpartum for transcriptomic sequencing. In addition, human LO2 cells were cultured in a medium containing 1.6 mmol/L of non-esterified fatty acids and 1 mmol/L niacin (NA) or 2 mmol/L nicotinamide (NAM) to verify the expression of the 10 genes selected from the transcriptomic analysis of the liver biopsies. The expression of a total of 9837 genes was detected in the liver biopsies, among which 1210 differentially expressed genes (DEGs) were identified, with 579 upregulated and 631 downregulated genes. These DEGs were associated mainly with lipid metabolism, oxidative stress, and some inflammatory pathways. Gene ontology (GO) enrichment analysis showed that 355 DEGs were enriched in 38 GO terms. The differences in the expression of these DEGs between RPN and RPM were predominantly related to the processes of steroid catabolism, steroid hydroxylase, monooxygenase activity, oxidoreductase activity, hemoglobin binding, and ferric iron binding, which are involved mainly in lipid anabolism and redox processes. The expressions of FADS2, SLC27A6, ARHGAP24, and THRSP in LO2 cells were significantly higher (p BCO2, MARS1, GARS1, S100A12, AGMO, and OSBPL11 were significantly lower (p < 0.05) on the NA treatment compared to the NAM treatment, indicating that NA played a role in liver metabolism by directly regulating fatty acid anabolism and transport, inflammatory factor expression, and oxidative stress; and NAM functioned more as a precursor of nicotinamide adenine dinucleotide (NAD, coenzyme I) and nicotinamide adenine dinucleotide phosphate (NADP, coenzyme II) to participate indirectly in biological processes such as ether lipid metabolism, cholesterol metabolism, energy metabolism, and other processes

Improving phase-based conductivity reconstruction by means of deep learning-based denoising of B 1 + phase data for 3T MRI

Purpose: To denoise (Formula presented.) phase using a deep learning method for phase-based in vivo electrical conductivity reconstruction in a 3T MR system. Methods: For (Formula presented.) phase deep-learning denoising, a convolutional neural network (U-net) was chosen. Training was performed on data sets from 10 healthy volunteers. Input data were the real and imaginary components of single averaged spin-echo data (SNR = 45), which was used to approximate the (Formula presented.) phase. For label data, multiple signal-averaged spin-echo data (SNR = 128) were used. Testing was performed on in silico and in vivo data. Reconstructed conductivity maps were derived using phase-based conductivity reconstructions. Additionally, we investigated the usability of the network to various SNR levels, imaging contrasts, and anatomical sites (ie, T 1, T 2, and proton density–weighted brain images and proton density–weighted breast images. In addition, conductivity reconstructions from deep learning–based denoised data were compared with conventional image filters, which were used for data denoising in electrical properties tomography (ie, the Gaussian filtering and the Savitzky-Golay filtering). Results: The proposed deep learning–based denoising approach showed improvement for (Formula presented.) phase for both in silico and in vivo experiments with reduced quantitative error measures compared with other methods. Subsequently, this resulted in an improvement of reconstructed conductivity maps from the denoised (Formula presented.) phase with deep learning. Conclusion: The results suggest that the proposed approach can be used as an alternative preprocessing method to denoise (Formula presented.) maps for phase-based conductivity reconstruction without relying on image filters or signal averaging