Comamonas-dominant microbial community in carbon poor aquitard sediments revealed by metagenomic-based growth rate investigation.

The microbiological ecology of a low-nutrient shallow aquifer with high arsenic content in the Yinchuan Plain was investigated in this study. Amplicon sequencing data from five samples (depths: 1.5 m, 3.5 m, 11.2 m, 19.3 m, and 25.5 m) revealed diverse and adaptable microbial community. Among the microbial community, Comamonas was the most prominent, accounting for 10.52 % of the total. This genus displayed high growth rates, with a maximum growth rate of 12.06 d-1 and a corresponding doubling time of 1.38 days, as determined through an analysis of codon usage bias. Functional annotation of Metagenome-Assembled Genomes (MAGs) for samples at 1.5 m and 11.2 m depths revealed Comamonas' metabolic versatility, including various carbon pathways, assimilative sulfate reduction (ASR), and dissimilatory reduction to ammonium (DNRA). The TPM (Transcripts Per Kilobase of exon model per Million mapped reads) of MAGs at 11.2 m sample was 15.7 and 12.3. The presence of arsenic resistance genes in Comamonas aligns with sediment arsenic levels (65.8 mg/kg for 1.5 m depth, 32.8 mg/kg for 11.2 m depth). This study highlights the role of Comamonas as a 'generalist' bacteria in challenging oligotrophic sediments, emphasizing the significance of such organisms in community stability and ecological functions. ENVIRONMENTAL IMPLICATION: Low-biomass limits the microbial activity and biogeochemical study in oligotrophic environments, which is the typical condition for underground aquatic ecosystems. Facilitated by growth rate estimation, our research focuses on active functional microorganisms and their biogeochemical metabolic in oligotrophic aquifer sediments, revealing their impact on the environment and response to arsenic threats. Findings illuminate the metabolic advantage of a 'generalist life-style' in carbon-scarce environments and contribute to a broader understanding of bacterial ecosystems and environmental impacts in oligotrophic aquifer sediments worldwide.

Learning Energy-Based Models in High-Dimensional Spaces with Multiscale Denoising-Score Matching.

Energy-based models (EBMs) assign an unnormalized log probability to data samples. This functionality has a variety of applications, such as sample synthesis, data denoising, sample restoration, outlier detection, Bayesian reasoning and many more. But, the training of EBMs using standard maximum likelihood is extremely slow because it requires sampling from the model distribution. Score matching potentially alleviates this problem. In particular, denoising-score matching has been successfully used to train EBMs. Using noisy data samples with one fixed noise level, these models learn fast and yield good results in data denoising. However, demonstrations of such models in the high-quality sample synthesis of high-dimensional data were lacking. Recently, a paper showed that a generative model trained by denoising-score matching accomplishes excellent sample synthesis when trained with data samples corrupted with multiple levels of noise. Here we provide an analysis and empirical evidence showing that training with multiple noise levels is necessary when the data dimension is high. Leveraging this insight, we propose a novel EBM trained with multiscale denoising-score matching. Our model exhibits a data-generation performance comparable to state-of-the-art techniques such as GANs and sets a new baseline for EBMs. The proposed model also provides density information and performs well on an image-inpainting task.

Virus impacted community adaptation in oligotrophic groundwater environment revealed by Hi-C coupled metagenomic and viromic study.

Viruses play a crucial role in microbial mortality, diversity and biogeochemical cycles. Groundwater is the largest global freshwater and one of the most oligotrophic aquatic systems on Earth, but how microbial and viral communities are shaped in this special habitat is largely unexplored. In this study, we collected groundwater samples from 23 to 60 m aquifers at Yinchuan Plain, China. In total, 1920 non-reductant viral contigs were retrieved from metagenomes and viromes constructed by Illumina and Nanopore hybrid sequencing. Only 3% of them could be clustered with known viruses, most of which were Caudoviricetes. Coupling 1.2 Tb Hi-C sequencing with CRISPR matching and homology search, we connected 469 viruses with their hosts while some viral clusters presented a broad-host-range trait. Meanwhile, a large proportion of biosynthesis related auxiliary metabolism genes were identified. Those characteristics might benefit viruses for a better survival in this special oligotrophic environment. Additionally, the groundwater virome showed genomic features distinct from those of the open ocean and wastewater treatment facilities in GC distribution and unannotated gene compositions. This paper expands the current knowledge of the global viromic records and serves as a foundation for a more thorough understanding of viruses in groundwater.

Carbon Coated Metal-Based Composite Electrode Materials for Lithium Sulfur Batteries: A Review.

Lithium-sulfur battery is one of the most promising secondary battery systems due to their high energy density and low material cost. During the past decade, great progress has been achieved in promoting the performances of Li-S batteries by addressing the challenges at the laboratory-level model systems. With growing attention paid to the application of Li-S batteries, new challenges at practical cell scales emerge as the bottleneck. However, challenges remain for the commercialization of lithium-sulfur batteries. The current review mainly focused on metal-based catalysts decorated-carbon materials for enhanced lithium sulfur battery performance. Firstly, the synthesis methods of various carbon-sulfur composites are discussed, as well as the influence of different material structures on the electrochemical performance. Secondly, a variety of catalysts, including metal atoms, metal oxides, sulfides, phosphides, nitrides, and carbide-decorated carbon nanomaterials, are systematically introduced to determine how lithium can be enhanced by suppressing polysulfides and promoting redox conversion reactions. Also, analyzed the multi-step electrochemical reaction mechanism of the battery during the charging and discharging process, and provide a feasible path for the practical application of high energy density lithium-sulfur batteries.

A Neural Network MCMC Sampler That Maximizes Proposal Entropy.

Markov Chain Monte Carlo (MCMC) methods sample from unnormalized probability distributions and offer guarantees of exact sampling. However, in the continuous case, unfavorable geometry of the target distribution can greatly limit the efficiency of MCMC methods. Augmenting samplers with neural networks can potentially improve their efficiency. Previous neural network-based samplers were trained with objectives that either did not explicitly encourage exploration, or contained a term that encouraged exploration but only for well structured distributions. Here we propose to maximize proposal entropy for adapting the proposal to distributions of any shape. To optimize proposal entropy directly, we devised a neural network MCMC sampler that has a flexible and tractable proposal distribution. Specifically, our network architecture utilizes the gradient of the target distribution for generating proposals. Our model achieved significantly higher efficiency than previous neural network MCMC techniques in a variety of sampling tasks, sometimes by more than an order magnitude. Further, the sampler was demonstrated through the training of a convergent energy-based model of natural images. The adaptive sampler achieved unbiased sampling with significantly higher proposal entropy than a Langevin dynamics sample. The trained sampler also achieved better sample quality.

Reduction of iron (hydr)oxide-bound arsenate: Evidence from high depth resolution sampling of a reducing aquifer in Yinchuan Plain, China.

Sediment in fluvial-deltaic plains with high-As groundwater is heterogenous but its characterization of As and Fe oxidation states lacks resolution, and is rarely attempted for aqueous and solid phases simultaneously. Here, we pair high-resolution (> 1 sample/meter) Fe extended fine-structure spectroscopy (EXAFS, n = 40) and As X-ray absorption near-edge spectroscopy (XANES, n = 49) with groundwater composition and metagenomics measurements for two sediment cores and their associated wells (n = 8) from the Yinchuan Plain in northwest China. At shallower depths, nitrate and Mn/Fe reducing sediment zones are fine textured, contain 9.6 ± 5.6 mg kg-1 of As(V) and 2.3 ± 2.7 mg kg-1 of As(III) with 9.1 ± 8.1 g kg-1 of Fe(III) (hydr)oxides, with bacterial genera capable of As and Fe reduction identified. In four deeper 10-m sections, sulfate-reducing sediments are coarser and contain 2.6 ± 1.3 mg kg-1 of As(V) and 1.1 ± 1.0 mg kg-1 of As(III) with 3.2 ± 2.6 g kg-1 of Fe(III) (hydr)oxides, even though groundwater As concentrations can exceed 200 µg/L, mostly as As(III). Super-enrichment of sediment As (42-133 mg kg-1, n = 7) at shallower depth is due to redox trapping during past groundwater discharge. Active As and Fe reduction is supported by the contrast between the As(III)-dominated groundwater and the As(V)-dominated sediment, and by the decreasing sediment As(V) and Fe(III) (hydr)oxides concentrations with depth.

Direct wavefront sensing enables functional imaging of infragranular axons and spines.

We advance two-photon microscopy for near-diffraction-limited imaging up to 850 µm below the pia in awake mice. Our approach combines direct wavefront sensing of light from a guidestar (formed by descanned fluorescence from Cy5.5-conjugated dextran in brain microvessels) with adaptive optics to compensate for tissue-induced aberrations in the wavefront. We achieve high signal-to-noise ratios in recordings of glutamate release from thalamocortical axons and calcium transients in spines of layer 5b basal dendrites during active tactile sensing.

Deoxyribonucleic acid telomere length shortening can predict the incidence of non-alcoholic fatty liver disease in patients with type 2 diabetes mellitus.

AIMS/INTRODUCTION: To investigate the effect of telomere shortening and other predictive factors of non-alcoholic fatty liver disease (NAFLD) in type 2 diabetes mellitus patients in a 6-year prospective cohort study. MATERIALS AND METHODS: A total of 70 type 2 diabetes mellitus (mean age 57.8 ± 6.7 years) patients without NAFLD were included in the study, and 64 of them were successfully followed up 6 years later, excluding four cases with significant alcohol consumption. NAFLD was diagnosed by the hepatorenal ratio obtained by a quantitative ultrasound method using NIH image analysis software. The 39 individuals that developed NAFLD were allocated to group A, and the 21 individuals that did not develop NAFLD were allocated to group B. Fluorescent real-time quantitative polymerase chain reaction was used to measure telomere length. RESULTS: There was no significant difference between the two groups in baseline telomere length; however, at the end of the 6th year, telomere length had become shorter in group A compared with group B. There were significant differences between these two groups in baseline body mass index, waistline, systolic blood pressure, glycated hemoglobin and fasting C-peptide level. In addition, the estimated indices of baseline insulin resistance increased in group A. Fasting insulin level, body mass index, systolic blood pressure at baseline and the shortening of telomere length were independent risk factors of NAFLD in type 2 diabetes mellitus patients. CONCLUSIONS: Telomere length became shorter in type 2 diabetes mellitus patients who developed NAFLD over the course of 6 years. Type 2 diabetes mellitus patients who developed NAFLD had more serious insulin resistance compared with those who did not develop NAFLD a long time ago.

Analysis of association among clinical features and shorter leukocyte telomere length in mitochondrial diabetes with m.3243A>G mitochondrial DNA mutation.

BACKGROUND: Mitochondrial diabetes is a kind of rare diabetes caused by monogenic mutation in mitochondria. The study aimed to summarize the clinical phenotype profiles in mitochondrial diabetes with m.3243 A>G mitochondrial DNA mutation and to investigate the mechanism in this kind of diabetes by analyzing the relationship among clinical phenotypes and peripheral leukocyte DNA telomere length. METHODS: Fifteen patients with maternally inherited diabetes in five families were confirmed as carrying the m.3243 A>G mitochondrial DNA mutation. One hundred patients with type 2 diabetes and one hundred healthy control subjects were recruited to participate in the study. Sanger sequencing was used to detect the m.3243 A>G mitochondrial DNA mutation. The peak height G/A ratio in the sequence diagram was calculated. Real-time polymerase chain reaction (PCR) was used to measure telomere length. RESULTS: The patients with mitochondrial diabetes all had definite maternally inherited history, normal BMI (19.5 ± 2.36 kg/m(2)), early onset of diabetes (35.0 ± 14.6 years) and deafness. The peak height G/A ratio correlated significantly and negatively with the age at onset of diabetes (≦ 25 years, 61.6 ± 20.17%; 25-45 years, 16.59 ± 8.64%; >45 years, 6.37 ± 0.59%; p = 0.000). Telomere length was significantly shorter among patients with mitochondrial diabetes and type 2 diabetes than in the control group (1.28 ± 0.54 vs. 1.14 ± 0.43 vs. 1.63 ± 0.61; p = 0.000). However, there was no significant difference between patients with mitochondrial diabetes and those with type 2 diabetes. There was no correlation between telomere length and the peak height G/A ratio. CONCLUSION: Deafness with definite maternal inheritance and normal BMI, associated with elevated blood lactic acid and encephalomyopathy, for the most part, suggest the diagnosis of mitochondrial diabetes . The peak height G/A ratio could reflect the spectrum of age at onset of the disease. Telomere length was shorter in patients with mitochondrial diabetes and those with type 2 diabetes, which suggests that the shorter telomere length is likely involved in the pathogenesis of diabetes but is not specific for this kind of diabetes.

Log (TG)/HDL-C ratio as a predictor of decreased islet beta cell function in patients with type 2 diabetes: 6-year cohort study.

BACKGROUND: The aim of the present study was to explore whether the triglyceride to high density lipoprotein cholesterol ratio [log (TG)/HDL-C] and peripheral blood leukocytes DNA telomere length could predict future islet beta cell function decreased in Chinese type 2 diabetes mellitus (T2DM) during a 6-year cohort. METHODS: Sixty T2DM patients (without insulin treatment at baseline) were included in the 6-year cohort study. Peripheral blood leukocytes DNA telomere length, HbA1c, blood lipid profile, fatty fat acid, glucose, insulin and C peptide (3 h after a mixed meal) were determined. Delta C peptide area under curve (Delta CP AUC) was used to reflect change in beta cell secretion function (Delta CP AUC = baseline CP AUC - CP AUC after 6 years). Subjects were divided into slow decrease of beta cell function group (Delta CP AUCslow group) and fast decrease group (Delta CP AUCfast group) according to median of Delta CP AUC. Baseline demographic characteristics, clinical variables between two groups were compared. Correlations between baseline data and Delta CP AUC were analyzed. RESULTS: Baseline log (TG)/HDL-C was positively correlated with Delta CP AUC (r = 0.306, P = 0.027); log (TG)/HDL-C in Delta CP AUCfast group was higher than that in Delta CP AUCslow group (0.103 ± 0.033 vs 0.083 ± 0.030, P = 0.027). There was no significant difference in DNA telomere length between the two groups. Change in DNA telomere length over 6 years was not significantly correlated with baseline blood lipid. CONCLUSIONS: In Chinese T2DM patients, high baseline log (TG)/HDL-C ratio predicts fast progression of islet beta cell dysfunction. It may be a simple index to predict progression speed of islet beta cell dysfunction.