High-throughput Techniques Research Articles

Optimized design of composition and brazing process for Cu-Ag-Zn-Mn-Ni-Si-B-P alloy brazing material based on machine learning strategy to improve brazing properties

As brazing devices become more sophisticated and service environments become more demanding, Cu-Ag-Zn-Mn-Ni-Si-B-P brazing material are subjected to higher wettability and brazed joint strength requirements. In this paper, a machine learning strategy is proposed. Firstly, the composition-process-properties dataset is obtained based on high-throughput experimental techniques. Then, the composition features are constructed by elemental chemical and physical parameters instead of the direct input of alloy composition, and the proposed novel feature selection method is used to effectively identify the key composition feature combinations that affect brazing properties. Then, the support vector regression algorithm was used to establish a joint strength model with an error of less than 3.47 % and a spreading area model with an error of less than 8.37 % using the key alloy composition features and brazing process features as inputs, respectively. Finally, two multi-objective optimization strategies, Bayesian optimization and NSGA-II genetic algorithm, were used for the integrated optimization design of composition and brazing process. The experimental results show that the Cu-14Ag-4Zn-24Mn-13.5Ni-0.4Si-0.3B-0.3 P alloy and the matched brazing process (945 °C and 12 min) were optimally designed using NSGA-II genetic algorithm, corresponding to the measured joint strength and spread area of 356 MPa and 412 mm2, respectively, which outperforms the high-throughput experimentally optimized data as well as commercialized data. The machine learning strategy established in this work provides a new research idea for the comprehensive improvement of brazing properties of brazing alloys with small data sets.

Synthesis and Antibacterial Activity of Ultrasmall Silver Nanoparticles by Pulsed Laser Ablation in Deionized Water

Background: The main objective of this work is the synthesis and evaluation of silver nanoparticles (Ag NPs) by using pulsed laser ablation of a silver (Ag) target in deionized water and examining their antibacterial activity. Methods: Colloidal solutions of silver nanoparticles were prepared with different pulsed laser energies (620, 880, and 1000) mJ of wavelength 1064 nm and frequency 10 Hz. To determine their structure, optical, morphology, elemental composition, and infrared spectra, the synthesized Ag NPs were characterized using various high-throughput analytical techniques such as (UVVis) spectroscopy, transmission electron microgram (TEM), electron dispersive X-ray spectroscopy (EDX), Fourier transform infrared (FTIR) spectra, and Zeta potential. Results: The results show that the properties of synthesized Ag NPs depend much more on the laser energy. The laser energy can be used to control the properties of the prepared nanoparticles. Uniform distributions of spherical ultrasmall Ag NPs with an average size of (3) nm were obtained suspended in deionized water, which is the most effective size for antibacterial activity. However, the result indicated that the ablated Ag NPs were stable for 4 months in deionized water. The antibacterial activity of the colloidal solution of synthesized Ag NPs against Gramnegative Escherichia coli (E. coli) and Gram-positive Staphylococcus aureus (S. aureus) bacteria was then examined using the agar-well diffusion method. Conclusion: It was found that the prepared nanoparticles exhibited strong activity against E. coli and S. aureus bacteria growth. The average zones of inhibition of Ag NPs were found to be about (26) mm for E. coli and (32) mm for S. aureus bacteria.

Deciphering the environmental adaptation and functional trait of core and noncore bacterial communities in impacted coral reef seawater

Microorganisms play pivotal roles in different biogeochemical cycles within coral reef waters. Nevertheless, our comprehension of the microbially mediated processes following environmental perturbation is still limited. To gain a deeper insight into the environmental adaptation and nutrient cycling, particularly within core and noncore bacterial communities, it is crucial to understand reef ecosystem functioning. In this study, we delved into the microbial community structure and function of seawater in a coral reef under different degrees of anthropogenic disturbance. To achieve this, we harnessed the power of 16S rRNA gene high-throughput sequencing and metagenomics techniques. The results showed that a continuous temporal succession but little spatial heterogeneity in the bacterial communities of core and noncore taxa and functional profiles involved in nitrogen (N) and phosphorus (P) cycling. Eutrophication state (i.e., nutrient concentration and turbidity) and temperature played pivotal roles in shaping both the microbial community composition and functional traits of coral reef seawater. Within this context, the core subcommunity exhibited a remarkably broader habitat niche breadth, stronger phylogenetic signal and lower environmental sensitivity when compared to the noncore taxa. Null model analysis further revealed that the core subcommunity was governed primarily by stochastic processes, while deterministic processes played a more significant role in shaping the noncore subcommunity. Furthermore, our observations indicated that changes in function related to N cycling were correlated to the variations in noncore taxa, while core taxa played a more substantial role in critical processes such as P cycling. Collectively, these findings facilitated our knowledge about environmental adaptability of core and noncore bacterial taxa and shed light on their respective roles in maintaining diverse nutrient cycling within coral reef ecosystems.

Unmanned aerial vehicles (UAVs)-based crop lodging susceptibility and seed yield assessment during different growth stages of rapeseed (Brassica napus)

Lodging is a great challenge in rapeseed production that significantly affects seed yield and quality. Prediction of lodging susceptibility and seed yield through unmanned aerial vehicles (UAVs)-based framework offers remarkable prospects for higher applicability in agriculture. This study aims to explore the possibility of using a UAV-based framework for predicting the stem and root lodging susceptibility (represented by safety factor, SFs and SFr respectively) and seed yield at different growth stages of rapeseed. The Red-Green-Blue (RGB) and multispectral (MS) images were captured during various growth stages by UAV platforms to calculate 16 vegetation indices (VIs). Furthermore, the relationships of these VIs with lodging susceptibility and seed yield were also established using multiple linear regression (MLR) and four machine learning methods (including random forest machine (RFR), support vector machine, artificial neural network, and K-nearest neighbors). The results revealed that MS-VIs provided a good estimation of seed yield, and stem and root lodging susceptibilities. Among the 16 VIs analyzed, MS-VI SR85 emerged as the best predictor for both seed yield and lodging susceptibility, as is evident by its highest importance scores. Furthermore, when RGB-VIs were coupled with MS-VIs, the R2 values for estimating seed yield, stem lodging and root lodging resistance were enhanced by 150%, 69.6% and 106%, respectively, in comparison with RGB-VIs. Similarly, the RFR provided a more accurate machine learning method for predicting seed yield and lodging susceptibility compared to the other three models. Stem elongation stage was the optimum growth stage for the estimation of seed yield, and stem and root lodging susceptibilities due to its maximum prediction accuracy, as is suggested by the highest R2 values. It can be inferred that a UAV-based framework in combination with RFR could serve as a high-throughput technique for large-scale prediction of lodging susceptibility and seed yield, as early as at stem elongation stage and thus provides an opportunity for timely agronomic intervention.

Characterization and evaluation of cytotoxic and antimicrobial activities of cyclotides from Viola japonica

Cyclotides are a type of defense peptide most commonly found in the Violaceae family of plants, exhibiting various biological activities. In this study, we focused on the Viola japonica as our research subject and conducted transcriptome sequencing and analysis using high-throughput transcriptomics techniques. During this process, we identified 61 cyclotides, among which 25 were previously documented, while the remaining 36 were designated as vija 1 to vija 36. Mass spectrometry detection showed that 21 putative cyclotides were found in the extract of V. japonica. Through isolation, purification and tandem mass spectrometry, we characterized and investigated the activities of five cyclotides. Our results demonstrated inhibitory effects of these cyclotides on the growth of Acinetobacter baumannii and Bacillus subtilis, with minimum inhibitory concentrations (MICs) of 4.2 μM and 2.1 μM, respectively. Furthermore, time killing kinetic assays revealed that cyclotides at concentration of 4 MICs achieved completely bactericidal effects within 2 h. Additionally, fluorescence staining experiments confirmed that cyclotides disrupt microbial membranes. Moreover, cytotoxicity studies showed that cyclotides possess cytotoxic effects, with IC50 values ranging from 0.1 to 3.5 μM. In summary, the discovery of new cyclotide sequences enhances our understanding of peptide diversity and the exploration of their activity lays the foundation for a deeper investigation into the mechanisms of action of cyclotides.

Development of a fluorescence-based excipient screening for improved stability and shelf-life of recombinant chitin deacetylase

Chitin deacetylase (CDA) modifies chitin into chitosan by removing acetyl groups, but its inherent instability poses a challenge for successful crystallisation. Despite limited successes in crystallizing CDAs, prior attempts with recombinant chitin deacetylase (BaCDA) failed due to poor stability. To address this, we propose an enzyme buffer formulation as a cost-effective strategy to enhance stability, prolong shelf life, and increase the likelihood of crystallisation. Utilizing the high-throughput screening technique FTSA, we developed a screening method correlating BaCDA stability with its activity. The optimised formulation comprises 50 mM Tris-HCl buffer pH 7, 1 M NaCl, 20 % glycerol, and 1 mM Mg2+ as excipients. This formulation significantly improves BaCDA's thermostability (140.47 % increase) and enzyme activity (2.9-fold enhancement). BaCDA remains stable in the formulated buffer at −20 °C and −80 °C for 30 days and at 4 °C for 15 days. The current study has designed a high-throughput screening method approach to assess the stability of CDA enzyme formulations. The results of this study could contribute to the exploration of formulation elements that enhance the structural stability of CDA, thereby facilitating investigations into the enzyme's structure-function relationships.

Unraveling the Properties of Phage Display Fab Libraries and Their Use in the Selection of Gliadin-Specific Probes by Applying High-Throughput Nanopore Sequencing.

Directed evolution is a pivotal strategy for new antibody discovery, which allowed the generation of high-affinity Fabs against gliadin from two antibody libraries in our previous studies. One of the libraries was exclusively derived from celiac patients' mRNA (immune library) while the other was obtained through a protein engineering approach (semi-immune library). Recent advances in high-throughput DNA sequencing techniques are revolutionizing research across genomics, epigenomics, and transcriptomics. In the present work, an Oxford Nanopore in-lab sequencing device was used to comprehensively characterize the composition of the constructed libraries, both at the beginning and throughout the phage-mediated selection processes against gliadin. A customized analysis pipeline was used to select high-quality reads, annotate chain distribution, perform sequence analysis, and conduct statistical comparisons between the different selection rounds. Some immunological attributes of the most representative phage variants after the selection process were also determined. Sequencing results revealed the successful transfer of the celiac immune response features to the immune library and the antibodies derived from it, suggesting the crucial role of these features in guiding the selection of high-affinity recombinant Fabs against gliadin. In summary, high-throughput DNA sequencing has improved our understanding of the selection processes aimed at generating molecular binders against gliadin.

Toward Self-Driven Autonomous Material and Device Acceleration Platforms (AMADAP) for Emerging Photovoltaics Technologies.

ConspectusIn the ever-increasing renewable-energy demand scenario, developing new photovoltaic technologies is important, even in the presence of established terawatt-scale silicon technology. Emerging photovoltaic technologies play a crucial role in diversifying material flows while expanding the photovoltaic product portfolio, thus enhancing security and competitiveness within the solar industry. They also serve as a valuable backup for silicon photovoltaic, providing resilience to the overall energy infrastructure. However, the development of functional solar materials poses intricate multiobjective optimization challenges in a large multidimensional composition and parameter space, in some cases with millions of potential candidates to be explored. Solving it necessitates reproducible, user-independent laboratory work and intelligent preselection of innovative experimental methods.Materials acceleration platforms (MAPs) seamlessly integrate robotic materials synthesis and characterization with AI-driven data analysis and experimental design, positioning them as enabling technologies for the discovery and exploration of new materials. They are proposed to revolutionize materials development away from the Edisonian trial-and-error approaches to ultrashort cycles of experiments with exceptional precision, generating a reliable and highly qualitative data situation that allows training machine learning algorithms with predictive power. MAPs are designed to assist the researcher in multidimensional aspects of materials discovery, such as material synthesis, precursor preparation, sample processing and characterization, and data analysis, and are drawing escalating attention in the field of energy materials. Device acceleration platforms (DAPs), however, are designed to optimize functional films and layer stacks. Unlike MAPs, which focus on material discovery, a central aspect of DAPs is the identification and refinement of ideal processing conditions for a predetermined set of materials. Such platforms prove especially invaluable when dealing with "disordered semiconductors," which depend heavily on the processing parameters that ultimately define the functional properties and functionality of thin film layers. By facilitating the fine-tuning of processing conditions, DAPs contribute significantly to the advancement and optimization of disordered semiconductor devices, such as emerging photovoltaics.In this Account, we review the recent advancements made by our group in automated and autonomous laboratories for advanced material discovery and device optimization with a strong focus on emerging photovoltaics, such as solution-processing perovskite solar cells and organic photovoltaics. We first introduce two MAPs and two DAPs developed in-house: a microwave-assisted high-throughput synthesis platform for the discovery of organic interface materials, a multipurpose robot-based pipetting platform for the synthesis of new semiconductors and the characterization of thin film semiconductor composites, the SPINBOT system, which is a spin-coating DAP with the potential to optimize complex device architectures, and finally, AMANDA, a fully integrated and autonomously operating DAP. Notably, we underscore the utilization of a robot-based high-throughput experimentation technique to address the common optimization challenges encountered in extensive multidimensional composition and parameter spaces pertaining to organic and perovskite photovoltaics materials. Finally, we briefly propose a holistic concept and technology, a self-driven autonomous material and device acceleration platform (AMADAP) laboratory, for autonomous functional solar materials discovery and development. We hope to discover how AMADAP can be further strengthened and universalized with advancing development of hardware and software infrastructures in the future.

Explainable machine learning to enable high-throughput electrical conductivity optimization and discovery of doped conjugated polymers

The combination of high-throughput experimentation techniques and machine learning (ML) has recently ushered in a new era of accelerated material discovery, enabling the identification of materials with cutting-edge properties. However, the measurement of certain physical quantities remains challenging to automate. Specifically, meticulous process control, experimentation and laborious measurements are required to achieve optimal electrical conductivity in doped polymer materials. We propose a ML approach, which relies on readily measured absorbance spectra, to accelerate the workflow associated with measuring electrical conductivity. The classification model accurately classifies samples with a conductivity >∼25 to 100 S/cm, achieving a maximum of 100 % accuracy rate. For the subset of highly conductive samples, we employed a regression model to predict their conductivities, yielding an impressive test R2 value of 0.984. We tested the models with samples of the two highest conductivities (498 and 506 S/cm) and showed that they were able to correctly classify and predict the two extrapolative conductivities at satisfactory levels of errors. The proposed ML-assisted workflow results in an improvement in the efficiency of the conductivity measurements by 89 % of the maximum achievable using our experimental techniques. Furthermore, our approach addressed the common challenge of the lack of explainability in ML models by exploiting bespoke mathematical properties of the descriptors and ML model, allowing us to gain corroborated insights into the spectral influences on conductivity. Through this study, we offer an accelerated pathway for optimizing the properties of doped polymer materials while showcasing the valuable insights that can be derived from purposeful utilization of ML in experimental science.

Solution-Processable and Printable Two-Dimensional Transition Metal Dichalcogenide Inks.

Two-dimensional (2D) transition metal dichalcogenides (TMDs) with layered crystal structures have been attracting enormous research interest for their atomic thickness, mechanical flexibility, and excellent electronic/optoelectronic properties for applications in diverse technological areas. Solution-processable 2D TMD inks are promising for large-scale production of functional thin films at an affordable cost, using high-throughput solution-based processing techniques such as printing and roll-to-roll fabrications. This paper provides a comprehensive review of the chemical synthesis of solution-processable and printable 2D TMD ink materials and the subsequent assembly into thin films for diverse applications. We start with the chemical principles and protocols of various synthesis methods for 2D TMD nanosheet crystals in the solution phase. The solution-based techniques for depositing ink materials into solid-state thin films are discussed. Then, we review the applications of these solution-processable thin films in diverse technological areas including electronics, optoelectronics, and others. To conclude, a summary of the key scientific/technical challenges and future research opportunities of solution-processable TMD inks is provided.

Current advances in human-induced pluripotent stem cell-based models and therapeutic approaches for congenital heart disease.

Congenital heart disease (CHD) represents a significant risk factor with profound implications for neonatal survival rates and the overall well-being of adult patients. The emergence of induced pluripotent stem cells (iPSCs) and their derived cells, combined with CRISPR technology, high-throughput experimental techniques, and organoid technology, which are better suited to contemporary research demands, offer new possibilities for treating CHD. Prior investigations have indicated that the paracrine effect of exosomes may hold potential solutions for therapeutic intervention. This review provides a summary of the advancements in iPSC-based models and clinical trials associated with CHD while elucidating potential therapeutic mechanisms and delineating clinical constraints pertinent to iPSC-based therapy, thereby offering valuable insights for further deliberation.

Abdominal CT metrics in 17,646 patients reveal associations between myopenia, myosteatosis, and medical phenotypes: a phenome-wide association study

BackgroundDeep learning facilitates large-scale automated imaging evaluation of body composition. However, associations of body composition biomarkers with medical phenotypes have been underexplored. Phenome-wide association study (PheWAS) techniques search for medical phenotypes associated with biomarkers. A PheWAS integrating large-scale analysis of imaging biomarkers and electronic health record (EHR) data could discover previously unreported associations and validate expected associations. Here we use PheWAS methodology to determine the association of abdominal CT-based skeletal muscle metrics with medical phenotypes in a large North American cohort. MethodsAn automated deep learning pipeline was used to measure skeletal muscle index (SMI; biomarker of myopenia) and skeletal muscle density (SMD; biomarker of myosteatosis) from abdominal CT scans of adults between 2012-2018. A PheWAS was performed with logistic regression using patient sex and age as covariates to assess for associations between CT-derived muscle metrics and 611 common EHR-derived medical phenotypes. PheWAS P values were considered significant at a Bonferroni corrected threshold (α=0.05/1222). Findings17,646 adults (mean age, 56 years +/- 19 [SD]; 57.5% women) were included. CT-derived SMI was significantly associated with 268 medical phenotypes; SMD with 340 medical phenotypes. Previously unreported associations with the highest magnitude of significance included higher SMI with decreased cardiac dysrhythmias (OR [95%CI], 0.59 [0.55-0.64]; P<0.0001), decreased epilepsy (OR, 0.59 [0.50-0.70]; P<0.0001), and increased elevated prostate-specific antigen (OR, 1.84 [1.47-2.31]; P<0.0001), and higher SMD with decreased decubitus ulcers (OR, 0.36 [0.31-0.42]; P<0.0001), sleep disorders (OR, 0.39 [0.32-0.47]; P<0.0001), and osteomyelitis (OR, 0.43 [0.36-0.52]; P<0.0001). InterpretationPheWAS methodology reveals previously unreported associations between CT-derived biomarkers of myopenia and myosteatosis and EHR medical phenotypes. The high-throughput PheWAS technique applied on a population scale can generate research hypotheses related to myopenia and myosteatosis and can be adapted to research possible associations of other imaging biomarkers with hundreds of EHR medical phenotypes.

Improved analysis of 230 pesticide residues in three fermented soy products by using automated one-step accelerated solvent extraction coupled with GC–MS/MS

Consumer concerns over healthy diets are increasing as a result of the toxicity and persistence of pesticide residues in foodstuffs. Developing sensitive and high-throughput monitoring techniques for these trace residues is seen as an essential step in ensuring food safety. An automatic and sensitive multi-residue analytical method was developed and validated for the simultaneous determination of 230 compounds, including pesticides and their hazardous metabolites, in fermented soy products. The method included preparing the sample using on-line extraction and clean-up system based on accelerated solvent extraction (ASE), then determining the analytes using GC–MS/MS techniques. The homogenized samples (soy sauce, douchi, and sufu) were automatically extracted at 80 °C and 10.3 MPa and at the same time, in situ cleaned by 300 mg of primary secondary amine (PSA) combined with 20 mg of hydroxylated multi-walled carbon nanotubes in an extraction cell. The method obtained excellent calibration linearity (r > 0.9220) and a satisfactory analysis of the targeted compounds, which were evaluated with matrix-matched calibration standards over the range of 5–500 μg L−1. The limit of detections (LODs) of analytes were in the range of 0.01–1.29 μg kg−1, 0.01–1.39 μg kg−1, and 0.01–1.34 μg kg−1 in soy sauce, douchi, and sufu, respectively. The limit of quantifications (LOQs), which defined as the lowest spiking level, were set at 5.0 μg kg−1. The recoveries were within 70–120 % for over 95 % of the analytes, and the relative standard deviations (RSDs) were below 13.6 %. Moreover, a positive detection rate of 47 % were obtained when the proposed method was used on 15 real fermented soy products. These results suggested that the developed high-throughput method is highly feasible for monitoring of these target analytes in trace level.

TFTG: A comprehensive database for human transcription factors and their targets

Transcription factors (TFs) are major contributors to gene transcription, especially in controlling cell-specific gene expression and disease occurrence and development. Uncovering the relationship between TFs and their target genes is critical to understanding the mechanism of action of TFs. With the development of high-throughput sequencing techniques, a large amount of TF-related data has accumulated, which can be used to identify their target genes. In this study, we developed TFTG (Transcription Factor and Target Genes) database (http://tf.liclab.net/TFTG), which aimed to provide a large number of available human TF-target gene resources by multiple strategies, besides performing a comprehensive functional and epigenetic annotations and regulatory analyses of TFs. We identified extensive available TF-target genes by collecting and processing TF-associated ChIP-seq datasets, perturbation RNA-seq datasets and motifs. We also obtained experimentally confirmed relationships between TF and target genes from available resources. Overall, the target genes of TFs were obtained through integrating the relevant data of various TFs as well as fourteen identification strategies. Meanwhile, TFTG was embedded with user-friendly search, analysis, browsing, downloading and visualization functions. TFTG is designed to be a convenient resource for exploring human TF-target gene regulations, which will be useful for most users in the TF and gene expression regulation research.

A hybrid RNA-protein biosensor for high-throughput screening of adenosylcobalamin biosynthesis

Genetically encoded circuits have been successfully utilized to assess and characterize target variants with desirable traits from large mutant libraries. Adenosylcobalamin is an essential coenzyme that is required in many intracellular physiological reactions and is widely used in the pharmaceutical and food industries. High-throughput screening techniques capable of detecting adenosylcobalamin productivity and selecting superior adenosylcobalamin biosynthesis strains are critical for the creation of an effective microbial cell factory for the production of adenosylcobalamin at an industrial level. In this study, we developed an RNA-protein hybrid biosensor whose input part was an endogenous RNA riboswitch to specifically respond to adenosylcobalamin, the inverter part was an orthogonal transcriptional repressor to obtain signal inversion, and the output part was a fluorescent protein to be easily detected. The hybrid biosensor could specifically and positively correlate adenosylcobalamin concentrations to green fluorescent protein expression levels in vivo. This study also improved the operating concentration and dynamic range of the hybrid biosensor by systematic optimization. An individual cell harboring the hybrid biosensor presented over 20-fold higher fluorescence intensity than the negative control. Then, using such a biosensor combined with fluorescence-activated cell sorting, we established a high-throughput screening platform for screening adenosylcobalamin overproducers. This study demonstrates that this platform has significant potential to quickly isolate high-productive strains to meet industrial demand and that the framework is acceptable for various metabolites.

High-throughput first-principles study on the effect of Ni segregation on the formation of Cr-depleted zone and corrosion resistance of S32205 duplex stainless steels

Recent studies have found that in the absence of Cr carbide precipitation, co-segregation of Cr and other elements at the interface between α-Fe and γ-Fe can also lead to the formation of Cr-depleted zones, leading to intergranular corrosion. The study employed three-dimensional atomic probes, focused ion beams, and high-throughput first-principles calculation techniques to investigate the impact of Ni atoms on the α-Fe/γ-Fe interface of UNS S32205 duplex stainless steels. The goal was to analyze the formation of Cr-depleted zones and interface corrosion resistance. The results indicate that both Ni and Cr atoms are segregated at the interface between α-Fe and γ-Fe phases; The strong interaction between Ni and Cr atoms hinders Cr segregation at the interface and slows down the formation of Cr-depleted zones; co-segregation of Ni and Cr can improve the stability of the α-Fe/γ-Fe phase interface, reduce the generation of microcracks, and reduce the stability of Cl atom adsorption in the cracks. This establishes the theoretical basis for further suppressing the formation of Cr-depleted zones and preventing intergranular corrosion.

Characteristics of the oral microbiota in patients with primary Sjögren's syndrome.

Primary Sjögren's syndrome (pSS) is an autoimmune disease with unknown etiology that is considered to be related to environmental and genetic factors. The aim of this study was to clarify the oral microflora characteristics of pSS patients and to reveal the connection between oral bacterial composition and dental caries using a high-throughput sequencing technique. Thirty-five pSS patients and 20 healthy controls were enrolled in this study. We collected saliva and plaque samples from pSS patients and saliva samples from healthy controls. We used 16S ribosomal DNA (16S rDNA) high-throughput sequencing targeting the V3-V4 hypervariable region to determine the composition and structure of the microbiota in the three sample sets. Finally, bioinformatics analyses, including the diversity of the microbiota, species differences, and functional prediction were performed. In the alpha diversity and beta diversity analysis, the Chao1 (P < 0.01), observed species (P < 0.01), and PD whole tree indices (P < 0.01) were significantly lower in the saliva and plaque samples of pSS patients than in the saliva samples of healthy controls, but the Shannon (P < 0.01) and Simpson indices (P < 0.01) were significantly higher in the healthy controls, and their total diversity significantly differed. In the main flora composition at the genus level (top 10), we identified Prevotella and Veillonella as more enriched in the saliva of pSS patients and Fusobacterium, Actinomyces, and Leptotrichia as more enriched in the plaque of pSS patients. Predictive functional analysis showed that the oral microbiota of pSS patients was related to translation, metabolism of cofactors and vitamins, and nucleotide metabolism. The oral microbial ecology of patients with pSS is dysregulated, resulting in a decrease in overall diversity. Prevotella and Veillonella may be related to pSS, while Fusobacterium, Actinomyces, and Leptotrichia may be related to dental caries in pSS patients. Key Points • This study revealed differences in the oral microbial composition of patients with pSS compared to healthy controls. • We included a plaque group of pSS patients to identify the microbiota related to pSS and dental caries. • Prevotella and Veillonella may contribute to pSS, and Fusobacterium, Actinomyces, and Leptotrichia are associated with dental caries in pSS patients.

Bacterial and fungal community structures in Hulun Lake are regulated by both stochastic processes and environmental factors.

Microorganisms are a crucial component of lake ecosystems and significant contributors to biogeochemical cycles. However, the understanding of how primary microorganism groups (e.g., bacteria and fungi) are distributed and constructed within different lake habitats is lacking. We investigated the bacterial and fungal communities of Hulun Lake using high-throughput sequencing techniques targeting 16S rRNA and Internal Transcribed Spacer 2 genes, including a range of ecological and statistical methodologies. Our findings reveal that environmental factors have high spatial and temporal variability. The composition and community structures vary significantly depending on differences in habitats. Variance partitioning analysis showed that environmental and geographical factors accounted for <20% of the community variation. Canonical correlation analysis showed that among the environmental factors, temperature, pH, and dissolved oxygen had strong control over microbial communities. However, the microbial communities (bacterial and fungal) were primarily controlled by the dispersal limitations of stochastic processes. This study offers fresh perspectives regarding the maintenance mechanism of bacterial and fungal biodiversity in lake ecosystems, especially regarding the responses of microbial communities under identical environmental stress.IMPORTANCELake ecosystems are an important part of the freshwater ecosystem. Lake microorganisms play an important role in material circulation and energy flow owing to their unique enzymatic and metabolic capacity. In this study, we observed that bacterial and fungal communities varied widely in the water and sediments of Hulun Lake. The primary factor affecting their formation was identified as dispersal limitation during stochastic processes. Environmental and geographical factors accounted for <20% of the variation in bacterial and fungal communities, with pH, temperature, and dissolved oxygen being important environmental factors. Our findings provide new insights into the responses of bacteria and fungi to the environment, shed light on the ecological processes of community building, and deepen our understanding of lake ecosystems. The results of this study provide a reference for lake management and conservation, particularly with respect to monitoring and understanding microbial communities in response to environmental changes.

Endogenous aldehyde-induced DNA–protein crosslinks are resolved by transcription-coupled repair

DNA–protein crosslinks (DPCs) induced by aldehydes interfere with replication and transcription. Hereditary deficiencies in DPC repair and aldehyde clearance processes cause progeria, including Ruijs–Aalfs syndrome (RJALS) and AMeD syndrome (AMeDS) in humans. Although the elimination of DPC during replication has been well established, how cells overcome DPC lesions in transcription remains elusive. Here we show that endogenous aldehyde-induced DPC roadblocks are efficiently resolved by transcription-coupled repair (TCR). We develop a high-throughput sequencing technique to measure the genome-wide distribution of DPCs (DPC-seq). Using proteomics and DPC-seq, we demonstrate that the conventional TCR complex as well as VCP/p97 and the proteasome are required for the removal of formaldehyde-induced DPCs. TFIIS-dependent cleavage of RNAPII transcripts protects against transcription obstacles. Finally, a mouse model lacking both aldehyde clearance and TCR confirms endogenous DPC accumulation in actively transcribed regions. Collectively, our data provide evidence that transcription-coupled DPC repair (TC-DPCR) as well as aldehyde clearance are crucial for protecting against metabolic genotoxin, thus explaining the molecular pathogenesis of AMeDS and other disorders associated with defects in TCR, such as Cockayne syndrome.

A Review on Unlocking Toxicity: How Zebrafish embryos Help Ensure Safe Chemicals

Aquatic ecotoxicity testing has benefited greatly from zebrafish embryo toxicity testing (ZFET) because of its high throughput, low cost, sensitivity to a variety of toxicants, and ethical issues. This study highlights the benefits of ZFET over conventional fish toxicity testing, such as its capacity to detect teratogenic defects and possible long-term impacts. The limitations of ZFET are also addressed, notably its emphasis on acute dosages and difficulties with interspecies extrapolation. In addition to the generation of in vitro alternatives utilizing zebrafish cell lines, recent developments in ZFET technology are also highlighted, including the use of transgenic zebrafish lines and high-throughput screening techniques. The paper's conclusion includes a discussion of potential prospects for ZFET research. These include improving interspecies extrapolation methods, optimizing chronic toxicity assessment procedures, and integrating ZFET with other ecotoxicity testing strategies. ZFET is a great instrument for environmental protection and a crucial component in avoiding chemical contamination of aquatic ecosystems, so long as it is acknowledged for what it is and is open to future improvement