Unraveling the diversity of sedimentary sulfate-reducing prokaryotes (SRP) across Tibetan saline lakes using epicPCR

Sulfate reduction is an important biogeochemical process in the ecosphere; however, the major taxa of sulfate reducers have not been fully identified. Here, we used epicPCR (Emulsion, Paired Isolation, and Concatenation PCR) technology to identify the phylogeny of sulfate-reducing prokaryotes (SRP) in sediments from Tibetan Plateau saline lakes. A total of 12,519 OTUs and 883 SRP-OTUs were detected in ten lakes by sequencing of 16S rRNA gene PCR amplicons and epicPCR products of fused 16S rRNA plus dsrB gene, respectively, with Proteobacteria, Firmicutes, and Bacteroidetes being the dominant phyla in both datasets. The 120 highly abundant SRP-OTUs (>1% in at least one sample) were affiliated with 17 described phyla, only 7 of which are widely recognized as SRP phyla. The majority of OTUs from both the whole microbial communities and the SRPs were not detected in more than one specific lake, suggesting high levels of endemism. The -diversity of the entire microbial community and SRP sub-community showed significant positive correlations. The pH value and mean water temperature of the month prior to sampling were the environmental determinants for the whole microbial community, while the mean water temperature and total nitrogen were the major environmental drivers for the SRP sub-community. This study revealed there are still many undocumented SRP in Tibetan saline lakes, many of which could be endemic and adapted to specific environmental conditions.Peer reviewe

When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks

In-context learning (ICL) has become the default method for using large language models (LLMs), making the exploration of its limitations and understanding the underlying causes crucial. In this paper, we find that ICL falls short of handling specification-heavy tasks, which are tasks with complicated and extensive task specifications, requiring several hours for ordinary humans to master, such as traditional information extraction tasks. The performance of ICL on these tasks mostly cannot reach half of the state-of-the-art results. To explore the reasons behind this failure, we conduct comprehensive experiments on 18 specification-heavy tasks with various LLMs and identify three primary reasons: inability to specifically understand context, misalignment in task schema comprehension with humans, and inadequate long-text understanding ability. Furthermore, we demonstrate that through fine-tuning, LLMs can achieve decent performance on these tasks, indicating that the failure of ICL is not an inherent flaw of LLMs, but rather a drawback of existing alignment methods that renders LLMs incapable of handling complicated specification-heavy tasks via ICL. To substantiate this, we perform dedicated instruction tuning on LLMs for these tasks and observe a notable improvement. We hope the analyses in this paper could facilitate advancements in alignment methods enabling LLMs to meet more sophisticated human demands.Comment: Under revie

Design and Simulation of 3-DOF Reconfigurable Planar Parallel Robot

This paper presents the design approach for 3-DOF (degrees-of-freedom) reconfigurable planar parallel mechanism (RPPM) with modular kinematic limbs and hybrid platform. Using the proposed approach, a series of kinematic models with rotatability are obtained. At first, the main models of PPMs are constructed. Then a series of configuration models of PPM modules are reconstructed. And the new configuration families of 3-DOF PPMs are obtained. Finally, the motion trajectories of three driven layout configurations are simulated, and the results show that the hybrid-loop platform can expand the workspace of PPM. Especially, the configurations of the PPM can transform from the two operations modes to the original mode

Design and Simulation of 3-DOF Reconfigurable Planar Parallel Robot

This paper presents the design approach for 3-DOF (degrees-of-freedom) reconfigurable planar parallel mechanism (RPPM) with modular kinematic limbs and hybrid platform. Using the proposed approach, a series of kinematic models with rotatability are obtained. At first, the main models of PPMs are constructed. Then a series of configuration models of PPM modules are reconstructed. And the new configuration families of 3-DOF PPMs are obtained. Finally, the motion trajectories of three driven layout configurations are simulated, and the results show that the hybrid-loop platform can expand the workspace of PPM. Especially, the configurations of the PPM can transform from the two operations modes to the original mode

Inhibition of Hepatocyte Apoptosis: An Important Mechanism of Corn Peptides Attenuating Liver Injury Induced by Ethanol

In this study, the effects of mixed corn peptides and synthetic pentapeptide (QLLPF) on hepatocyte apoptosis induced by ethanol were investigated in vivo. QLLPF, was previously characterized from corn protein hydrolysis, which had been shown to exert good facilitating alcohol metabolism activity. Mice were pre-treated with the mixed corn peptides and the pentapeptide for 1 week and then treated with ethanol. After treatment of three weeks, the biochemical indices and the key ethanol metabolizing enzymes, the serum TNF-α, liver TGF-β1 concentrations and the protein expressions related to apoptosis were determined. We found that the Bcl-2, Bax and cytochrome c expressions in the intrinsic pathway and the Fas, FasL and NF-κB expressions in the extrinsic pathway together with higher TNF-α and TGF-β1 concentrations were reversed compared with the model group by both the mixed corn peptides and the pentapeptide. The activation of caspase3 was also suppressed. Additionally, apoptosis was further confirmed with terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) and the TUNEL assay demonstrated peptides suppressed hepatocyte apoptosis. Our results suggest that apoptosis induced by ethanol is alleviated in response to the treatment of corn peptides, potentially due to reversing the related protein expression

A Debye dispersion model of a two-layered material

Debye formulas are widely used to describe the electrical dispersion characteristics of a uniform lossy material. Debye model uses some empirical coefficients to control the shape and position of spectroscopy curves. It can fit most of the data from experiments. A two-layered model is investigated through its equivalent Debye circuit model. A Finite Difference Method (FDM) is developed to extract the effective permittivity and conductivity of a two-layered model as the verification of the analytical derivation. The computation results indicate the results obtained from FDM and the layered Debye formulas agree very well, which shows the validity of the layered Debye formulas in terms of the original circuit parameters. The derived formulas are used to analyze the relationship between the effective electrical spectra and the electrical parameters of each layer. A few examples are given in the discussions. And it can be concluded that: (1) with the increase of the average value of the conductivity of the two layers, the spectra of effective permittivity transition area shifts to higher frequencies; while the spectra of effective conductivity transition kept the same with the values decrease; (2) with the increase of the average value of the relative permittivity of the two layers, the transition area of the spectra of effective permittivity kept the same with values increase; and the transition area of the spectra of effective conductivity shifts to the lower frequencies; (3) the effective permittivity enhances at the lower frequency region as the ratio between the conductivity of two layers increase

A Nomogram Model for Mortality Risk Prediction in Pulmonary Tuberculosis Patients Subjected to Directly Observed Treatment Shortcourse (DOTS)

We analyzed the risk factors of mortality for patients with pulmonary tuberculosis under the Directly Observed Treatment Shortcourse (DOTS) and established a predictive nomogram for the risk of mortality. The retrospective cohort analysis was conducted on the treatment outcomes of 11207 tuberculosis patients in the tuberculosis management information system in Tianjin from 2014 to 2019. Based on the multivariable unconditional logistic regression, we analyzed the risk factors of mortality in patients with pulmonary TB and established the death risk prediction nomogram. We further applied cross-validation and the receiver operating characteristic (ROC) curve to explore the efficiency of the nomogram. There were 10,697 patients in the survival group and 510 in the mortality group who had successfully initiated DOTS, and the mortality rate was 4.55%. Multivariable logistic regression analysis showed that age, male, relapse cases, first sputum positivity, patient delay, and HIV-positive were independent risk factors for pulmonary TB death. The calibration curve shows that the average absolute error between the predicted mortality risk and the actual death risk is 0.003. The ROC curve shows that the area under the curve where the line-up model predicts the risk of death is 0.816 (95% CI: 0.799∼0.832). The nomogram model based on independent risk factors of mortality in TB patients shows good discrimination and accuracy, with potentially high clinical value in screening patients with a high risk of death, which could be useful for setting the interventional strategies in patients with tuberculosis who had successfully initiated DOTS