Far-Infrared Therapy Based on Graphene Ameliorates High-Fat Diet-Induced Anxiety-Like Behavior in Obese Mice via Alleviating Intestinal Barrier Damage and Neuroinflammation.

The consumption of a high-fat diet (HFD) has been implicated in the etiology of obesity and various neuropsychiatric disturbances, including anxiety and depression. Compelling evidence suggests that far-infrared ray (FIR) possesses beneficial effects on emotional disorders. However, the efficacy of FIR therapy in addressing HFD-induced anxiety and the underlying mechanisms remain to be elucidated. Here, we postulate that FIR emitted from a graphene-based therapeutic device may mitigate HFD-induced anxiety behaviors. The graphene-FIR modify the gut microbiota in HFD-mice, particularly by an enriched abundance of beneficial bacteria Clostridiaceae and Erysipelotrichaceae, coupled with a diminution of harmful bacteria Lachnospiraceae, Anaerovoracaceae, Holdemania and Marvinbryantia. Graphene-FIR also improved intestinal barrier function, as evidenced by the augmented expression of the tight junction protein occludin and G protein-coupled receptor 43 (GPR43). In serum level, we observed the decreased free fatty acids (FFA), lipopolysaccharides (LPS), diamine oxidase (DAO) and D-lactate, and increased the glucagon-like peptide-2 (GLP-2) levels in graphene-FIR mice. Simultaneously, inflammatory cytokines IL-6, IL-1ß, and TNF-α manifested a decrease subsequent to graphene-FIR treatment in both peripheral and central system. Notably, graphene-FIR inhibited over expression of astrocytes and microglia. We further noticed that the elevated the BDNF and decreased TLR4 and NF-κB expression in graphene-FIR group. Overall, our study reveals that graphene-FIR rescued HFD-induced anxiety via improving the intestine permeability and the integrity of blood-brain barrier, and reduced inflammatory response by down regulating TLR4/NF-κB inflammatory pathway.

Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy.

BACKGROUND: Quantification of metabolites concentrations in institutional unit (IU) is important for inter-subject and long-term comparisons in the applications of magnetic resonance spectroscopy (MRS). Recently, deep learning (DL) algorithms have found a variety of applications on the process of MRS data. A quantification strategy compatible to DL base MRS spectral processing method is, therefore, useful. MATERIALS AND METHODS: This study aims to investigate whether metabolite concentrations quantified using a convolutional neural network (CNN) based method, coupled with a scaling procedure that normalizes spectral signals for CNN input and linear regression, can effectively reflect variations in metabolite concentrations in IU across different brain regions with varying signal-to-noise ratios (SNR) and linewidths (LW). An error index based on standard error (SE) is proposed to indicate the confidence levels associated with metabolite predictions. In vivo MRS spectra were acquired from three brain regions of 43 subjects using a 3T system. RESULTS: The metabolite concentrations in IU of five major metabolites, quantified using CNN and LCModel, exhibit similar ranges with Pearson's correlation coefficients ranging from 0.24 to 0.78. The SE of the metabolites shows a positive correlation with Cramer-Rao lower bound (CRLB) (r=0.46) and  absolute CRLB (r=0.81), calculated by multiplying CRLBs with the quantified metabolite content. CONCLUSION: In conclusion, the CNN based method with the proposed scaling procedures can be employed to quantify in vivo MRS spectra and derive metabolites concentrations in IU. The SE can be used as error index, indicating predicted uncertainties for metabolites and sharing information similar to the absolute CRLB.

Analysis of microbial diversity in apple vinegar fermentation process through 16s rDNA sequencing.

Based on SPME-GC-MS analysis, it could be found that the production of acetic acid, phenethyl acetate, and isoamyl acetate gradually increased in the apple vinegar fermentation broth with the fermentation time. Consequently, in order to systematically explore the dynamic changes of microbial diversity and metabolites in the process of apple vinegar fermentation, 16S rDNA were sequenced and analyzed in this work. The present results showed that bacterial diversity was rich and exhibited a certain variation during the dynamic fermentation process of apple vinegar. Furthermore, Lactococcus and Oenococcus were the predominant bacteria in the pre-fermentation (alcoholic fermentation) of apple vinegar, while the dominant bacteria in the middle and late fermentation stages (acetic acid fermentation) were Lactococcus and Acetobacter. In addition, during the whole fermentation process of apple vinegar, Lactococcus was the most dominant bacteria, Oenococcus was the unique species in the stage of alcohol fermentation, and Acetobacter increased rapidly in the stage of acetic acid fermentation. In conclusion, our finding provided a theoretical basis for the processing technology of apple vinegar fermentation, and a theory evidence for the safety and health assessment of apple vinegar.

A retrospective comparison of the modified tension band technique and the parallel titanium cannulated lag screw technique in transverse patella fracture.

OBJECTIVE: To compare efficacy between the modified tension band technique and the parallel titanium cannulated lag screw technique for the transverse patella fracture. METHODS: Seventy-two patients were retrospectively analyzed aged 22 to 79 years (mean, 55.6 years) with transverse patella fractures, among whom 37 patients underwent the modified tension band and 35 patients received the titanium cannulated lag screw. Patients were followed up for 1-3 years. We analyzed the difference of operation time, complications, fracture reduction, fracture healing time, and the Iowa score for knee function between both groups. RESULTS: In modified tension band group, five patients had skin irritation and seven suffered wire migration, two of whom required a second operation. In comparison, there were no complications in the titanium cannulated lag screw group, which also had a higher fracture reduction rate and less operation time. CONCLUSION: The parallel titanium cannulated lag screw technique has superior results and should be considered as an alternative method to treat transverse patella fracture.

Monitoring and identification of spatiotemporal landscape changes in multiple remote sensing images by using a stratified conditional Latin hypercube sampling approach and geostatistical simulation.

This study develops a stratified conditional Latin hypercube sampling (scLHS) approach for multiple, remotely sensed, normalized difference vegetation index (NDVI) images. The objective is to sample, monitor, and delineate spatiotemporal landscape changes, including spatial heterogeneity and variability, in a given area. The scLHS approach, which is based on the variance quadtree technique (VQT) and the conditional Latin hypercube sampling (cLHS) method, selects samples in order to delineate landscape changes from multiple NDVI images. The images are then mapped for calibration and validation by using sequential Gaussian simulation (SGS) with the scLHS selected samples. Spatial statistical results indicate that in terms of their statistical distribution, spatial distribution, and spatial variation, the statistics and variograms of the scLHS samples resemble those of multiple NDVI images more closely than those of cLHS and VQT samples. Moreover, the accuracy of simulated NDVI images based on SGS with scLHS samples is significantly better than that of simulated NDVI images based on SGS with cLHS samples and VQT samples, respectively. However, the proposed approach efficiently monitors the spatial characteristics of landscape changes, including the statistics, spatial variability, and heterogeneity of NDVI images. In addition, SGS with the scLHS samples effectively reproduces spatial patterns and landscape changes in multiple NDVI images.

Detecting the land-cover changes induced by large-physical disturbances using landscape metrics, spatial sampling, simulation and spatial analysis.

The objectives of the study are to integrate the conditional Latin Hypercube Sampling (cLHS), sequential Gaussian simulation (SGS) and spatial analysis in remotely sensed images, to monitor the effects of large chronological disturbances on spatial characteristics of landscape changes including spatial heterogeneity and variability. The multiple NDVI images demonstrate that spatial patterns of disturbed landscapes were successfully delineated by spatial analysis such as variogram, Moran'I and landscape metrics in the study area. The hybrid method delineates the spatial patterns and spatial variability of landscapes caused by these large disturbances. The cLHS approach is applied to select samples from Normalized Difference Vegetation Index (NDVI) images from SPOT HRV images in the Chenyulan watershed of Taiwan, and then SGS with sufficient samples is used to generate maps of NDVI images. In final, the NDVI simulated maps are verified using indexes such as the correlation coefficient and mean absolute error (MAE). Therefore, the statistics and spatial structures of multiple NDVI images present a very robust behavior, which advocates the use of the index for the quantification of the landscape spatial patterns and land cover change. In addition, the results transferred by Open Geospatial techniques can be accessed from web-based and end-user applications of the watershed management.

[Determination of trace arsenic and mercury in antimony concentrates by hydride generation atomic fluorescence spectrometry].

A method for the determination of trace arsenic and mercury in antimony concentrates by hydride generation atomic fluorescence spectrometry was investigated. The method of dissolving sample and the effect of concomitant ion were studied. It was found that aqua regia can be used to dissolve the sample completely, with maximum quantities of dissolved arsenic and mercury. The interference of chemical elements in antimony concentrates was avoided by adding thiourea-Vc. The matrix of antimony was vaporized with tartaric acid during sample pre-treatment. Trace arsenic and mercury were determined in real samples. The detection limits of 0.220 ng x mL(-1) and 0.002 ng x mL(-1) were obtained, the relative standard deviations(RSD% = 11) are 1.47% and 0.52%, and the recoveries are 94.0%-103.0% and 98.7%-102.8% for As and Hg respectively. This method has proved rapid, accurate and highly sensitive.