A new framework for exploratory network mediator analysis in omics data.

Omics methods are widely used in basic biology and translational medicine research. More and more omics data are collected to explain the impact of certain risk factors on clinical outcomes. To explain the mechanism of the risk factors, a core question is how to find the genes/proteins/metabolites that mediate their effects on the clinical outcome. Mediation analysis is a modeling framework to study the relationship between risk factors and pathological outcomes, via mediator variables. However, high-dimensional omics data are far more challenging than traditional data: (1) From tens of thousands of genes, can we overcome the curse of dimensionality to reliably select a set of mediators? (2) How do we ensure that the selected mediators are functionally consistent? (3) Many biological mechanisms contain nonlinear effects. How do we include nonlinear effects in the high-dimensional mediation analysis? (4) How do we consider multiple risk factors at the same time? To meet these challenges, we propose a new exploratory mediation analysis framework, medNet, which focuses on finding mediators through predictive modeling. We propose new definitions for predictive exposure, predictive mediator, and predictive network mediator, using a statistical hypothesis testing framework to identify predictive exposures and mediators. Additionally, two heuristic search algorithms are proposed to identify network mediators, essentially subnetworks in the genome-scale biological network that mediate the effects of single or multiple exposures. We applied medNet on a breast cancer data set and a metabolomics data set combined with food intake questionnaire data. It identified functionally consistent network mediators for the exposures' impact on the outcome, facilitating data interpretation.

Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes.

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.

Chromosome structure modeling tools and their evaluation in bacteria.

The three-dimensional (3D) structure of bacterial chromosomes is crucial for understanding chromosome function. With the growing availability of high-throughput chromosome conformation capture (3C/Hi-C) data, the 3D structure reconstruction algorithms have become powerful tools to study bacterial chromosome structure and function. It is highly desired to have a recommendation on the chromosome structure reconstruction tools to facilitate the prokaryotic 3D genomics. In this work, we review existing chromosome 3D structure reconstruction algorithms and classify them based on their underlying computational models into two categories: constraint-based modeling and thermodynamics-based modeling. We briefly compare these algorithms utilizing 3C/Hi-C datasets and fluorescence microscopy data obtained from Escherichia coli and Caulobacter crescentus, as well as simulated datasets. We discuss current challenges in the 3D reconstruction algorithms for bacterial chromosomes, primarily focusing on software usability. Finally, we briefly prospect future research directions for bacterial chromosome structure reconstruction algorithms.

Mid-Pleistocene links between Asian dust, Tibetan glaciers, and Pacific iron fertilization.

Increasing Asian dust fluxes, associated with late Cenozoic cooling and intensified glaciations, are conventionally thought to drive iron fertilization of phytoplankton productivity in the North Pacific, contributing to ocean carbon storage and drawdown of atmospheric CO2. During the early Pleistocene glaciations, however, productivity remained low despite higher Asian dust fluxes, only displaying glacial stage increases after the mid-Pleistocene climate transition (~800 ka B.P.). We solve this paradox by analyzing an Asian dust sequence, spanning the last 3.6 My, from the Tarim Basin, identifying a major switch in the iron composition of the dust at ~800 ka, associated with expansion of Tibetan glaciers and enhanced production of freshly ground rock minerals. This compositional shift in the Asian dust was recorded synchronously in the downwind, deep sea sediments of the central North Pacific. The switch from desert dust, containing stable, highly oxidized iron, to glacial dust, richer in reactive reduced iron, coincided with increased populations of silica-producing phytoplankton in the equatorial North Pacific and increased primary productivity in more northerly locations, such as the South China Sea. We calculate that potentially bioavailable Fe2+ flux to the North Pacific was more than doubled after the switch to glacially- sourced dust. These findings indicate a positive feedback between Tibetan glaciations, glaciogenic production of dust with enhanced iron bioavailability, and changes in North Pacific iron fertilization. Notably, this strengthened link between climate and eolian dust coincided with the mid-Pleistocene transition to increased storage of C in the glacial North Pacific and more intense northern hemisphere glaciations.

Role of the GLP2-Wnt1 axis in silicon-rich alkaline mineral water maintaining intestinal epithelium regeneration in piglets under early-life stress.

Stress-induced intestinal epithelial injury (IEI) and a delay in repair in infancy are predisposing factors for refractory gut diseases in adulthood, such as irritable bowel syndrome (IBS). Hence, it is necessary to develop appropriate mitigation methods for mammals when experiencing early-life stress (ELS). Weaning, as we all know, is a vital procedure that all mammalian newborns, including humans, must go through. Maternal separation (MS) stress in infancy (regarded as weaning stress in animal science) is a commonly used ELS paradigm. Drinking silicon-rich alkaline mineral water (AMW) has a therapeutic effect on enteric disease, but the specific mechanisms involved have not been reported. Herein, we discover the molecular mechanism by which silicon-rich AMW repairs ELS-induced IEI by maintaining intestinal stem cell (ISC) proliferation and differentiation through the glucagon-like peptide (GLP)2-Wnt1 axis. Mechanistic study showed that silicon-rich AMW activates GLP2-dependent Wnt1/ß-catenin pathway, and drives ISC proliferation and differentiation by stimulating Lgr5+ ISC cell cycle passage through the G1-S-phase checkpoint, thereby maintaining intestinal epithelial regeneration and IEI repair. Using GLP2 antagonists (GLP23-33) and small interfering RNA (SiWnt1) in vitro, we found that the GLP2-Wnt1 axis is the target of silicon-rich AMW to promote intestinal epithelium regeneration. Therefore, silicon-rich AMW maintains intestinal epithelium regeneration through the GLP2-Wnt1 axis in piglets under ELS. Our research contributes to understanding the mechanism of silicon-rich AMW promoting gut epithelial regeneration and provides a new strategy for the alleviation of ELS-induced IEI.

Impact of Using Pre- and Postbronchodilator Spirometry Reference Values in a Chinese Population.

Rationale: Spirometry reference equations that are derived from a large, nationally representative general population are warranted in China, and the impact of using prebronchodilator (pre-BD) and post-BD spirometry reference values has yet to be assessed in Chinese populations. Objectives: To present the pre-BD and post-BD spirometry reference values for Chinese adults using the China Pulmonary Health (CPH) Study. Methods: A reference population of 17,969 healthy, nonsmoking participants in the CPH Study was used to calculate the pre- and post-BD reference values for FEV1, FVC, and FEV1/FVC ratio. Pre- and post-BD reference values were applied to the entire CPH population (N = 50,991) to illustrate the divergence between the use of different references in determining disease prevalence and severity grading. Measurements and Main Results: The prevalences of airflow limitation were 5.36% using the pre-BD reference and 8.02% using the post-BD reference. Individuals who had a post-BD FEV1/FVC ratio lower than the post-BD reference value but higher than the pre-BD reference value were found to have significantly higher rates of self-reported respiratory symptoms and significantly lower values on spirometry indicators than those whose post-BD FEV1/FVC ratio was greater than the post-BD reference value. An additional 3.51% of participants were identified as having grade II-IV chronic obstructive pulmonary disease using the post-BD FEV1 predicted values. Conclusions: This study generated and applied pre- and post-BD spirometry reference values in a nationally representative Chinese adult population. Post-BD reference values may serve as an additional criterion in identifying individuals at risk for obstructive pulmonary diseases, and their diagnostic and prognostic values should be further investigated.

Dynamic Recruitment of the Feedforward and Recurrent Mechanism for Black-White Asymmetry in the Primary Visual Cortex.

Black and white information is asymmetrically distributed in natural scenes, evokes asymmetric neuronal responses, and causes asymmetric perceptions. Recognizing the universality and essentiality of black-white asymmetry in visual information processing, the neural substrates for black-white asymmetry remain unclear. To disentangle the role of the feedforward and recurrent mechanisms in the generation of cortical black-white asymmetry, we recorded the V1 laminar responses and LGN responses of anesthetized cats of both sexes. In a cortical column, we found that black-white asymmetry starts at the input layer and becomes more pronounced in the output layer. We also found distinct dynamics of black-white asymmetry between the output layer and the input layer. Specifically, black responses dominate in all layers after stimulus onset. After stimulus offset, black and white responses are balanced in the input layer, but black responses still dominate in the output layer. Compared with that in the input layer, the rebound response in the output layer is significantly suppressed. The relative suppression strength evoked by white stimuli is notably stronger and depends on the location within the ON-OFF cortical map. A model with delayed and polarity-selective cortical suppression explains black-white asymmetry in the output layer, within which prominent recurrent connections are identified by Granger causality analysis. In addition to black-white asymmetry in response strength, the interlaminar differences in spatial receptive field varied dynamically. Our findings suggest that the feedforward and recurrent mechanisms are dynamically recruited for the generation of black-white asymmetry in V1.SIGNIFICANCE STATEMENT Black-white asymmetry is universal and essential in visual information processing, yet the neural substrates for cortical black-white asymmetry remain unknown. Leveraging V1 laminar recordings, we provided the first laminar pattern of black-white asymmetry in cat V1 and found distinct dynamics of black-white asymmetry between the output layer and the input layer. Comparing black-white asymmetry across three visual hierarchies, the LGN, V1 input layer, and V1 output layer, we demonstrated that the feedforward and recurrent mechanisms are dynamically recruited for the generation of cortical black-white asymmetry. Our findings not only enhance our understanding of laminar processing within a cortical column but also elucidate how feedforward connections and recurrent connections interact to shape neuronal response properties.

Reprogrammed mRNA translation drives resistance to therapeutic targeting of ribosome biogenesis.

Elevated ribosome biogenesis in oncogene-driven cancers is commonly targeted by DNA-damaging cytotoxic drugs. Our previous first-in-human trial of CX-5461, a novel, less genotoxic agent that specifically inhibits ribosome biogenesis via suppression of RNA polymerase I (Pol I) transcription, revealed single-agent efficacy in refractory blood cancers. Despite this clinical response, patients were not cured. In parallel, we demonstrated a marked improvement in the in vivo efficacy of CX-5461 in combination with PI3K/AKT/mTORC1 pathway inhibitors. Here, we reveal the molecular basis for this improved efficacy observed in vivo, which is associated with specific suppression of translation of mRNAs encoding regulators of cellular metabolism. Importantly, acquired resistance to this cotreatment is driven by translational rewiring that results in dysregulated cellular metabolism and induction of a cAMP-dependent pathway critical for the survival of blood cancers including lymphoma and acute myeloid leukemia. Our studies thus identify key molecular mechanisms underpinning the response of blood cancers to selective inhibition of ribosome biogenesis and define metabolic vulnerabilities that will facilitate the rational design of more effective regimens for Pol I-directed therapies.

Ultrathin Porous Carbon Nitride Anchored with Pt Nanoclusters for Synergistic Enhancement of Hydrogen Production in Alkaline Photocatalytic Polyester Reforming.

Photocatalytic reforming (PR) of polyester waste, fueled by renewable sources like solar energy, offers a sustainable method for producing clean H2 and valuable by-products under mild conditions. The design of high-performance photocatalyst plays a pivotal role in determining the efficacy of an alkaline polyester PR system, influencing H2 generation activity and selectivity. Here, ultrathin porous carbon nitride nanosheets (UP-CN) loaded with Pt nanoclusters (Pt NCs, average diameter of 1.7 nm) with uniform Pt NCs distribution are introduced. The resulting Pt NCs/UP-CN catalyst can accelerate charge and mass transfer while providing additional active sites, achieving superior H2 generation rates of 11.69 mmol gcat -1 h-1 and 2923 mmol gPt -1 h-1 under AM 1.5 light, which nine times higher than that of Pt nanoparticles-bulk graphitic carbon nitride composite (1.29 mmol gcat -1 h-1 and 258 mmol gPt -1 h-1) as counterpart. This performance also surpasses that of previously reported carbon nitride-based and TiO2-based photocatalysts. Moreover, the density functional theory calculations reveal a significant reduction in the energy barrier for the water dissociation step (H2O + * → *H + OH) at the interface between UP-CN and anchored Pt NCs, showcasing the synergistic effect between Pt NCs and UP-CN. This catalytic system also exhibits universality across various polyester plastics.

Effects of organic fertilizers on plant growth and the rhizosphere microbiome.

Application of organic fertilizers is an important strategy for sustainable agriculture. The biological source of organic fertilizers determines their specific functional characteristics, but few studies have systematically examined these functions or assessed their health risk to soil ecology. To fill this gap, we analyzed 16S rRNA gene amplicon sequencing data from 637 soil samples amended with plant- and animal-derived organic fertilizers (hereafter plant fertilizers and animal fertilizers). Results showed that animal fertilizers increased the diversity of soil microbiome, while plant fertilizers maintained the stability of soil microbial community. Microcosm experiments verified that plant fertilizers were beneficial to plant root development and increased carbon cycle pathways, while animal fertilizers enriched nitrogen cycle pathways. Compared with animal fertilizers, plant fertilizers harbored a lower abundance of risk factors such as antibiotic resistance genes and viruses. Consequently, plant fertilizers might be more suitable for long-term application in agriculture. This work provides a guide for organic fertilizer selection from the perspective of soil microecology and promotes sustainable development of organic agriculture.IMPORTANCEThis study provides valuable guidance for use of organic fertilizers in agricultural production from the perspective of the microbiome and ecological risk.

High luminous efficiency and excellent thermal performance in rod-shaped YAG:Ce phosphor ceramics for laser lighting.

High power and high brightness laser lighting puts forward new requirements for phosphor converters such as high luminous efficiency, high thermal conductivity and high saturation threshold due to the severe thermal effect. The structure design of phosphor converters is proposed as what we believe to be a novel strategy for less heat production and more heat conduction. In this work, the rod-shaped YAG:Ce phosphor ceramics (PCs) and disc-shaped YAG:Ce PCs as control group were fabricated by the gel casting and vacuum sintering, to comparatively study the luminescence performance for LD lighting, on the premise that the total number of transverse Ce3+ ions and the volume of samples from two comparison groups were same. All rod YAG:Ce PCs with low Ce3+ concentration exhibited the high luminous efficiency and better thermal stability than YAG:Ce discs with high Ce3+ concentration. Under the laser power density of 47.8 W/mm2, the luminous saturation was never observed in all rod-shaped YAG:Ce PCs. The high luminous efficacy of 245∼274 lm/W, CRI of 56.3∼59.5 and CCT of 4509∼4478 K were achieved. More importantly, due to the extremely low Ce3+ doping concentration (0.01 at%), rod-shaped ceramics based LDs devices showed the excellent thermal performance and their surface temperatures were even below 30.5 °C surprisingly under the laser power density of 20.3 W·mm-2 (2 W). These results indicate that the rod shape of phosphor converter is a promising structure engineering for high power laser lighting.

High luminous efficiency and high saturation threshold in highly transparent LuAG:Ce phosphor ceramics for laser diodes lighting.

Lu3Al5O12:Ce (LuAG:Ce) phosphor ceramics (PCs) with the excellent thermal stability and high saturation threshold are considered as the best green-fluorescent converters for high-power laser diodes (LDs) lighting. In this study, the effects of sintering additives and sintering processes on the transmittance and microstructure of LuAG:Ce PCs were systematically studied, and the luminescence performance of ceramics with different transmittance was compared. LuAG:Ce PCs with the transmittance of 80% (@800 nm, 1.5 mm) were obtained by using 0.1 wt.% MgO and 0.5 wt.% TEOS as sintering additives, combined with optimized vacuum pre-sintering and hot isostatic pressing. Compared to the non-HIP samples, the transmittance had increased by 11%. The microstructure of ceramics indicated that high transparency was closely related to the decrease in intergranular pores. Notably, the luminous efficiency of 253 lm/W and its saturation thresholds of > 46 W/mm2 were obtained simultaneously in green-emitting LDs devices. Moreover, under 3W laser irradiation, highly transparent ceramics had the low surface temperature of 66.4 °C, indicating the good heat dissipation performance. The observed high luminous efficiency and high saturation threshold of LuAG:Ce PCs were attributed to fewer pores and oxygen vacancies. Therefore, this work proves that highly transparent LuAG:Ce PCs are promising green-fluorescent converters for high-power LDs lighting.

Effect of vitrification on protein O-GlcNAcylation in mouse metaphase II oocytes.

In brief: Oocyte vitrification leads to DNA hypomethylation, which results in defect in early embryo development. This study reveals that oocyte vitrification impairs the DNA methylation pattern by influencing protein O-GlcNAcylation. Abstract: Oocyte vitrification leads to decreased DNA methylation levels, which impairs the quality and the developmental potential of oocytes. However, the underlying molecular mechanism still need to be further revealed. In this study, mouse metaphase II (M II) oocytes were frozen by vitrification technology, while fresh oocytes were used as the control group. The effect of oocyte vitrification on protein O-GlcNAcylation and its impact on the developmental potential of oocytes were elucidated. We found that the protein O-GlcNAcylation levels were significantly increased in vitrified oocytes. Increase of protein O-GlcNAcylation levels in control oocytes by PUGNAc (an O-GlcNAcase inhibitor) decreases blastocyst rate after parthenogenetic activation (20.82% in PUGNAc-treated group; 53.82% in control group, P < 0.05). We also discovered that DNA methylation was disrupted in two-cell embryos derived from vitrified oocytes, resulting in decreased 5mC and increased 5hmC, showing similar phenotypes to that derived from PUGNAc-treated oocytes. In vitrified and PUGNAc-treated oocytes, O-GlcNAcylated TET3 was significantly increased. Notably, by inhibiting protein O-GlcNAcylation in vitrified oocytes using OSMI1 (an O-GlcNAc transferase inhibitor) we restored the DNA methylation in two-cell embryos and ameliorated the developmental defects in early embryo. Thus, elevated protein O-GlcNAcylation in vitrified oocytes is an essential contributor to their declining embryonic developmental potential. Modulation of protein O-GlcNAcylation improves the developmental potential of vitrified oocytes.

Rod-shaped LuAG:Ce transparent ceramic enabling ultrahigh forward efficiency for laser lighting in a transmissive mode.

Ceramic phosphors have high thermal conductivity and high thermal stability, showing great potential for use in laser lighting. However, it is difficult to further improve the forward efficiency in transmissive mode because of the arbitrarily emitting ceramic phosphors and light loss by secondary optical components. Here, an effective design of rod-shaped LuAG:Ce transparent ceramics was proposed, and the silicone encapsulated ceramic-based devices could operate stably under 3.5 W laser excitation, possessing a luminous efficiency of 150-180 lm/W, far exceeding the level of existing commercial transmissive mode. Besides, because of the gradual absorption of blue light and the gradient distribution of heat, the rod-shaped LuAG:Ce transparent ceramics could bear a power density of 46 W/mm2 without luminous saturation, and the thermal-induced luminous degradation only accounted for 7% under a 15 min operation. The ceramic-based laser lighting sources with low divergence angle (∼4°) and uniform spatial distribution were obtained. Our optimized transparent ceramic rod and encapsulation scheme provided a solution to improve the efficiency of a transmissive mode for laser lighting.

Multiple gamma rhythms carry distinct spatial frequency information in primary visual cortex.

Gamma rhythms in many brain regions, including the primary visual cortex (V1), are thought to play a role in information processing. Here, we report a surprising finding of 3 narrowband gamma rhythms in V1 that processed distinct spatial frequency (SF) signals and had different neural origins. The low gamma (LG; 25 to 40 Hz) rhythm was generated at the V1 superficial layer and preferred a higher SF compared with spike activity, whereas both the medium gamma (MG; 40 to 65 Hz), generated at the cortical level, and the high gamma HG; (65 to 85 Hz), originated precortically, preferred lower SF information. Furthermore, compared with the rates of spike activity, the powers of the 3 gammas had better performance in discriminating the edge and surface of simple objects. These findings suggest that gamma rhythms reflect the neural dynamics of neural circuitries that process different SF information in the visual system, which may be crucial for multiplexing SF information and synchronizing different features of an object.

High-dimensional multisubject time series transition matrix inference with application to brain connectivity analysis.

Brain-effective connectivity analysis quantifies directed influence of one neural element or region over another, and it is of great scientific interest to understand how effective connectivity pattern is affected by variations of subject conditions. Vector autoregression (VAR) is a useful tool for this type of problems. However, there is a paucity of solutions when there is measurement error, when there are multiple subjects, and when the focus is the inference of the transition matrix. In this article, we study the problem of transition matrix inference under the high-dimensional VAR model with measurement error and multiple subjects. We propose a simultaneous testing procedure, with three key components: a modified expectation-maximization (EM) algorithm, a test statistic based on the tensor regression of a bias-corrected estimator of the lagged auto-covariance given the covariates, and a properly thresholded simultaneous test. We establish the uniform consistency for the estimators of our modified EM, and show that the subsequent test achieves both a consistent false discovery control, and its power approaches one asymptotically. We demonstrate the efficacy of our method through both simulations and a brain connectivity study of task-evoked functional magnetic resonance imaging.

Bayesian inference for group-level cortical surface image-on-scalar regression with Gaussian process priors.

In regression-based analyses of group-level neuroimage data, researchers typically fit a series of marginal general linear models to image outcomes at each spatially referenced pixel. Spatial regularization of effects of interest is usually induced indirectly by applying spatial smoothing to the data during preprocessing. While this procedure often works well, the resulting inference can be poorly calibrated. Spatial modeling of effects of interest leads to more powerful analyses; however, the number of locations in a typical neuroimage can preclude standard computing methods in this setting. Here, we contribute a Bayesian spatial regression model for group-level neuroimaging analyses. We induce regularization of spatially varying regression coefficient functions through Gaussian process priors. When combined with a simple non-stationary model for the error process, our prior hierarchy can lead to more data-adaptive smoothing than standard methods. We achieve computational tractability through a Vecchia-type approximation of our prior that retains full spatial rank and can be constructed for a wide class of spatial correlation functions. We outline several ways to work with our model in practice and compare performance against standard vertex-wise analyses and several alternatives. Finally, we illustrate our methods in an analysis of cortical surface functional magnetic resonance imaging task contrast data from a large cohort of children enrolled in the adolescent brain cognitive development study.

Penalized deep partially linear cox models with application to CT scans of lung cancer patients.

Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective patient-centered therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis, which provides objective measurements of texture patterns on CT scans, to quantify the mortality risks of lung cancer patients. Partially linear Cox models have gained popularity for survival analysis by dissecting the hazard function into parametric and nonparametric components, allowing for the effective incorporation of both well-established risk factors (such as age and clinical variables) and emerging risk factors (eg, image features) within a unified framework. However, when the dimension of parametric components exceeds the sample size, the task of model fitting becomes formidable, while nonparametric modeling grapples with the curse of dimensionality. We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the smoothly clipped absolute deviation (SCAD) penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model. We prove the convergence and asymptotic properties of the estimator and compare it to other methods through extensive simulation studies, evaluating its performance in risk prediction and feature selection. The proposed method is applied to the NLST study dataset to uncover the effects of key clinical and imaging risk factors on patients' survival. Our findings provide valuable insights into the relationship between these factors and survival outcomes.

Characterizations of gut bacteriome, mycobiome, and virome of healthy individuals living in sea-level and high-altitude areas.

BACKGROUND: The contribution of gut microbiota to human high-altitude adaptation remains inadequately understood. METHODS: Here a comparative analysis of gut microbiota was conducted between healthy individuals living at sea level and high altitude using deep whole-metagenome shotgun sequencing, to investigate the adaptive mechanisms of gut microbiota in plateau inhabitants. RESULTS: The results showed the gut bacteriomes in high-altitude individuals exhibited greater within-sample diversity and significant alterations in both bacterial compositional and functional profiles when compared to those of sea-level individuals, indicating the potential selection of unique bacteria associated with high-altitude environments. The strain-level investigation revealed enrichment of Collinsella aerofaciens and Akkermansia muciniphila in high-altitude populations. The characteristics of gut virome and gut mycobiome were also investigated. Compared to sea-level subjects, high-altitude subjects exhibited a greater diversity in their gut virome, with an increased number of viral operational taxonomic units (vOTUs) and unique annotated genes. Finally, correlation analyses revealed 819 significant correlations between 42 bacterial species and 375 vOTUs, while no significant correlations were observed between bacteria and fungi or between fungi and viruses. CONCLUSION: The findings have significantly contributed to an enhanced comprehension of the mechanisms underlying the high-altitude geographic adaptation of the human gut microbiota.

A first-principles study of organic Lewis bases for passivating tin-based perovskite solar cells.

Tin-based perovskite solar cells (PSCs) are potential light absorbers for solar cell applications since they are less toxic compared to commonly used lead-based alternatives. Retaining the less stable Sn2+ state is key to improving the efficiency of tin-based PSCs. Organic Lewis base molecules have demonstrated potential to achieve this purpose. However, the critical factors influencing the performance of Lewis bases are largely unknown. In this study, we applied density functional theory (DFT) to investigate seven Lewis base materials, including methanol (MeOH), dimethyl ether (DME), ethyl methyl ether (EME), methyl acetate (MeOAc), methyl ammonium (MA), methyl sulfonic acid (MSA), and methyl phosphonic acid (MPA). Our results show that the effectiveness of passivation is linked to the gap between the HOMO and the LUMO (Egap). These findings provide theoretical guidance to screen Lewis base additives for enhancing energy conversion efficiencies of tin-based PSCs.