Spatial differentiation of carbon emissions from energy consumption based on machine learning algorithm: A case study during 2015-2020 in Shaanxi, China.

Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide. Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem. Previous studies relied on statistical regression models that failed to capture the complex nonlinear relationships between carbon emissions and characteristic variables. In this study, we propose a machine learning algorithm for carbon emissions, a Bayesian optimized XGboost regression model, using multi-year energy carbon emission data and nighttime lights (NTL) remote sensing data from Shaanxi Province, China. Our results demonstrate that the XGboost algorithm outperforms linear regression and four other machine learning models, with an R2 of 0.906 and RMSE of 5.687. We observe an annual increase in carbon emissions, with high-emission counties primarily concentrated in northern and central Shaanxi Province, displaying a shift from discrete, sporadic points to contiguous, extended spatial distribution. Spatial autocorrelation clustering reveals predominantly high-high and low-low clustering patterns, with economically developed counties showing high-emission clustering and economically relatively backward counties displaying low-emission clustering. Our findings show that the use of NTL data and the XGboost algorithm can estimate and predict carbon emissions more accurately and provide a complementary reference for satellite remote sensing image data to serve carbon emission monitoring and assessment. This research provides an important theoretical basis for formulating practical carbon emission reduction policies and contributes to the development of techniques for accurate carbon emission estimation using remote sensing data.

Enhancement of ε-poly-L-lysine production by Streptomyces albulus FQF-24 with feeding strategies using cassava starch as carbon source.

ε-Poly-L-lysine (ε-PL) is a natural and wide-spectrum antimicrobial additive. In this study, the production of ε-PL by Streptomyces albulus FQF-24 using cassava starch (CS) as carbon source and the effects of different feeding methods were investigated in a fermenter. The initial shake flask experiments demonstrated the efficient production of ε-PL with CS, achieving the ε-PL production of 1.18 g/L. Subsequent investigations in the fermenter identified that the ideal pH was 3.8 during the ε-PL synthesis phase. Under this condition, the production of ε-PL reached 1.35 g/L. When the pH was maintained at 3.8, the investigation of improvement of feeding composition was carried out in a 5 L fermenter. The intermittent feeding containing CS, inorganic and organic nitrogen sources resulted in the maximum ε-PL production and dry cell weight (DCW) reaching 17.17 g/L and 42.73 g/L. Additionally, continuous feeding with the composition of CS, organic and inorganic nitrogen sources, and inorganic salts further increased ε-PL production and DCW to 27.56 g/L and 38.5 g/L. Summarily, the above results indicate that the fermentation using low-cost CS and continuous feeding strategy with whole medium composition can provide a beneficial reference for the efficient production of ε-PL.

Microbial Diversity and Screening for Potential Pathogens and Beneficial Bacteria of Five Jellyfish Species-Associated Microorganisms Based on 16S rRNA Sequencing.

Jellyfish, microorganisms, and the marine environment collectively shape a complex ecosystem. This study aimed to analyze the microbial communities associated with five jellyfish species, exploring their composition, diversity, and relationships. Microbial diversity among the species was assessed using 16S rRNA gene sequencing and QIIME analysis. Significant differences in bacterial composition were found, with distinct dominant taxa in each species: Mycoplasmataceae (99.21%) in Aurelia coerulea, Sphingomonadaceae (22.81%) in Cassiopea andromeda, Alphaproteobacteria_unclassified (family level) (64.09%) in Chrysaora quinquecirrha, Parcubacteria_unclassified (family level) (93.11%) in Phacellophora camtschatica, and Chlamydiaceae (35.05%) and Alphaproteobacteria_unclassified (family level) (38.73%) in Rhopilema esculentum. C. andromeda showed the highest diversity, while A. coerulea exhibited the lowest. Correlations among dominant genera varied, including a positive correlation between Parcubacteria_unclassified (genus level) and Chlamydiaceae_unclassified (genus level). Genes were enriched in metabolic pathways and ABC transporters. The most abundant potential pathogens at the phylum level were Proteobacteria, Tenericutes, Chlamydiae, and Epsilonbacteraeota. The differing microbial compositions are likely influenced by species and their habitats. Interactions between jellyfish and microorganisms, as well as among microorganisms, showed interdependency or antagonism. Most microbial gene functions focused on metabolic pathways, warranting further study on the relationship between pathogenic bacteria and these pathways.

Construction of ecological security pattern in combination with landslide sensitivity: A case study of Yan'an City, China.

The ecological security pattern can harmonize the relationship between natural environmental protection and socio-economic development. This study proposes a regional ecological security pattern optimization framework by integrating theory and practice with landslide sensitivity and landscape structure. Using Yan'an City as an example, this study optimizes the landscape layout of preliminary ecological sources. The landslide sensitivity index is generated using the information value model and then used to adjust the ecological resistance surface. The Minimum Cumulative Resistance (MCR) approach is used to extract ecological corridors, locate ecological nodes utilizing circuit theory, and outline crucial ecological control areas. The results demonstrate: (1) the ecological sources are primarily composed of forestlands, with a total area of 2,352.2400 km2, concentrated in the southwest, central, and southeast regions. The optimal landscape granularity for the source patches is 600 m. (2) Yan'an is divided into four landslide sensitivity level zones: extremely high, high, medium, and low, with the overall landslide sensitivity of the region being high. (3) The highest ecological resistance is observed in built-up land and the lowest in forestland. The total number of ecological corridors is 26, avoiding most of the highly sensitive areas of landslides. (4) The number of ecological pinch points is 61, while the ecological barrier points amounted to 54. The critical ecological control areas consist mainly of cropland, forestland, and grassland, and differentiated restoration strategies are proposed to address their unique characteristics. The findings of the research can offer scientific guidance for the practice of ecological security protection in geohazard-prone areas.

Exploring Emotional Stimuli Detection in Artworks: A Benchmark Dataset and Baselines Evaluation.

We introduce an emotional stimuli detection task that targets extracting emotional regions that evoke people's emotions (i.e., emotional stimuli) in artworks. This task offers new challenges to the community because of the diversity of artwork styles and the subjectivity of emotions, which can be a suitable testbed for benchmarking the capability of the current neural networks to deal with human emotion. For this task, we construct a dataset called APOLO for quantifying emotional stimuli detection performance in artworks by crowd-sourcing pixel-level annotation of emotional stimuli. APOLO contains 6781 emotional stimuli in 4718 artworks for validation and testing. We also evaluate eight baseline methods, including a dedicated one, to show the difficulties of the task and the limitations of the current techniques through qualitative and quantitative experiments.

Biocontrol agents modulate phyllosphere microbiota interactions against pathogen Pseudomonas syringae.

The pathogen Pseudomonas syringae, responsible for a variety of diseases, poses a considerable threat to global crop yields. Emerging biocontrol strategies employ antagonistic microorganisms, utilizing phyllosphere microecology and systemic resistance to combat this disease. However, the interactions between phyllosphere microbial dynamics and the activation of the plant defense system remain poorly understood. Here we show significant alterations in phyllosphere microbiota structure and plant gene expression following the application of biocontrol agents. We reveal enhanced collaboration and integration of Sphingomonas and Methylobacterium within the microbial co-occurrence network. Notably, Sphingomonas inhibits P. syringae by disrupting pathogen chemotaxis and virulence. Additionally, both Sphingomonas and Methylobacterium activate plant defenses by upregulating pathogenesis-related gene expression through abscisic acid, ethylene, jasmonate acid, and salicylic acid signaling pathways. Our results highlighted that biocontrol agents promote plant health, from reconstructing beneficial microbial consortia to enhancing plant immunity. The findings enrich our comprehension of the synergistic interplays between phyllosphere microbiota and plant immunity, offering potential enhancements in biocontrol efficacy for crop protection.

Characterization of a novel ribose-5-phosphate isomerase B from Curtobacterium flaccumfaciens ZXL1 for D-allose production.

Enzymatic preparation of rare sugars as an alternative to traditional sweeteners is an effective strategy to achieve a low-calorie healthy diet. Ribose-5-phosphate isomerase B (RpiB) is a key enzyme in the non-oxidative branch of the catalytic pentose phosphate pathway. Here, we investigated the potential of Curtobacterium flaccumfaciens ZXL1 (C. flaccumfaciens ZXL1) derived RpiB (CfRpiB) in D-allose preparation. The optimal reaction conditions for recombinant CfRpiB were found experimentally to be pH 7.0, 55 °C, and no metal ions. The kinetic parameters Km, kcat, and catalytic efficiency kcat/Km were 320 mM, 4769 s-1, and 14.9 mM-1 s-1 respectively. The conversion of D-allulose by purified enzyme (1 g L-1 ) to D-allose was 13% within 1 h. In addition, homology modeling and molecular docking were used to predict the active site residues: Asp13, Asp14, Cys72, Gly73, Thr74, Gly77, Asn106, and Lys144.

Phyllosphere bacterial community dynamics in response to bacterial wildfire disease: succession and interaction patterns.

Plants interact with complex microbial communities in which microorganisms play different roles in plant development and health. While certain microorganisms may cause disease, others promote nutrient uptake and resistance to stresses through a variety of mechanisms. Developing plant protection measures requires a deeper comprehension of the factors that influence multitrophic interactions and the organization of phyllospheric communities. High-throughput sequencing was used in this work to investigate the effects of climate variables and bacterial wildfire disease on the bacterial community's composition and assembly in the phyllosphere of tobacco (Nicotiana tabacum L.). The samples from June (M1), July (M2), August (M3), and September (M4) formed statistically separate clusters. The assembly of the whole bacterial population was mostly influenced by stochastic processes. PICRUSt2 predictions revealed genes enriched in the M3, a period when the plant wildfire disease index reached climax, were associated with the development of the wildfire disease (secretion of virulence factor), the enhanced metabolic capacity and environmental adaption. The M3 and M4 microbial communities have more intricate molecular ecological networks (MENs), bursting with interconnections within a densely networked bacterial population. The relative abundances of plant-beneficial and antagonistic microbes Clostridiales, Bacillales, Lactobacillales, and Sphingobacteriales, showed significant decrease in severally diseased sample (M3) compared to the pre-diseased samples (M1/M2). Following the results of MENs, we further test if the correlating bacterial pairs within the MEN have the possibility to share functional genes and we have unraveled 139 entries of such horizontal gene transfer (HGT) events, highlighting the significance of HGT in shaping the adaptive traits of plant-associated bacteria across the MENs, particularly in relation to host colonization and pathogenicity.

Metagenomic insights into the response of soil microbial communities to pathogenic Ralstonia solanacearum.

Understanding the response of soil microbial communities to pathogenic Ralstonia solanacearum is crucial for preventing bacterial wilt outbreaks. In this study, we investigated the soil physicochemical and microbial community to assess their impact on the pathogenic R.solanacearum through metagenomics. Our results revealed that certain archaeal taxa were the main contributors influencing the health of plants. Additionally, the presence of the pathogen showed a strong negative correlation with soil phosphorus levels, while soil phosphorus was significantly correlated with bacterial and archaeal communities. We found that the network of microbial interactions in healthy plant rhizosphere soils was more complex compared to diseased soils. The diseased soil network had more linkages, particularly related to the pathogen occurrence. Within the network, the family Comamonadaceae, specifically Ramlibacter_tataouinensis, was enriched in healthy samples and showed a significantly negative correlation with the pathogen. In terms of archaea, Halorubrum, Halorussus_halophilus (family: Halobacteriaceae), and Natronomonas_pharaonis (family: Haloarculaceae) were enriched in healthy plant rhizosphere soils and showed negative correlations with R.solanacearum. These findings suggested that the presence of these archaea may potentially reduce the occurrence of bacterial wilt disease. On the other hand, Halostagnicola_larseniia and Haloterrigena_sp._BND6 (family: Natrialbaceae) had higher relative abundance in diseased plants and exhibited significantly positive correlations with R.solanacearum, indicating their potential contribution to the pathogen's occurrence. Moreover, we explored the possibility of functional gene sharing among the correlating bacterial pairs within the Molecular Ecological Network. Our analysis revealed 468 entries of horizontal gene transfer (HGT) events, emphasizing the significance of HGT in shaping the adaptive traits of plant-associated bacteria, particularly in relation to host colonization and pathogenicity. Overall, this work revealed key factors, patterns and response mechanisms underlying the rhizosphere soil microbial populations. The findings offer valuable guidance for effectively controlling soil-borne bacterial diseases and developing sustainable agriculture practices.

What role do biocontrol agents with Mg2+ play in the fate of antibiotic resistome and pathogenic bacteria in the phyllosphere?

The contamination of the plant phyllosphere with antibiotics and antibiotic resistance genes (ARGs), caused by application of antibiotics, is a significant environmental issue in agricultural management. Alternatively, biocontrol agents are environmentally friendly and have attracted a lot of interest. However, the influence of biocontrol agents on the phyllosphere resistome remains unknown. In this study, we applied biocontrol agents to control the wildfire disease in the Solanaceae crops and investigated their effects on the resistome and the pathogen in the phyllosphere by using metagenomics. A total of 250 ARGs were detected from 15 samples, which showed a variation in distribution across treatments of biocontrol agents (BA), BA with Mg2+ (T1), BA with Mn2+ (T2), and kasugamycin (T3) and nontreated (CK). The results showed that the abundance of ARGs under the treatment of BA-Mg2+ was lower than that in the CK group. The abundance of cphA3 (carbapenem resistance), PME-1 (carbapenem resistance), tcr3 (tetracycline antibiotic resistance), and AAC (3)-VIIIa (aminoglycoside antibiotic resistance) in BA-Mg2+ was significantly higher than that in BA-Mn2+ (P < 0.05). The abundance of cphA3, PME_1, and tcr3 was significantly negatively related to the abundance of the phyllosphere pathogen Pseudomonas syringae (P < 0.05). We also found that the upstream and downstream regions of cphA3 were relatively conserved, in which rpl, rpm, and rps gene families were identified in most sequences (92%). The Ka/Ks of cphA3 was 0 in all observed sequences, indicating that under the action of purifying selection, nonsynonymous substitutions are often gradually eliminated in the population. Overall, this study clarifies the effect of biocontrol agents with Mg2+ on the distribution of the phyllosphere resistome and provides evolutionary insights into the biocontrol process. IMPORTANCE: Our study applied metagenomics analysis to examine the impact of biocontrol agents (BAs) on the phyllosphere resistome and the pathogen. Irregular use of antibiotics has led to the escalating dissemination of antibiotic resistance genes (ARGs) in the environment. The majority of BA research has focused on the effect of monospecies on the plant disease control process, the role of the compound BA with nutrition elements in the phyllosphere disease, and the resistome is still unknown. We believe BAs are eco-friendly alternatives for antibiotics to combat the transfer of ARGs. Our results revealed that BA-Mg2+ had a lower relative abundance of ARGs compared to the CK group, and the phyllosphere pathogen Pseudomonas syringae was negatively related to three specific ARGs, cphA3, PME-1, and tcr3. These three genes also present different Ka/Ks. We believe that the identification of the distribution and evolution modes of ARGs further elucidates the ecological role and facilitates the development of BAs, which will attract general interest in this field.

Joint superpixel and Transformer for high resolution remote sensing image classification.

Deep neural networks combined with superpixel segmentation have proven to be superior to high-resolution remote sensing image (HRI) classification. Currently, most HRI classification methods that combine deep learning and superpixel segmentation use stacking on multiple scales to extract contextual information from segmented objects. However, this approach does not take into account the contextual dependencies between each segmented object. To solve this problem, a joint superpixel and Transformer (JST) framework is proposed for HRI classification. In JST, HRI is first segmented into superpixel objects as input, and Transformer is used to model the long-range dependencies. The contextual relationship between each input superpixel object is obtained and the class of analyzed objects is output by designing an encoding and decoding Transformer. Additionally, we explore the effect of semantic range on classification accuracy. JST is also tested by using two HRI datasets with overall classification accuracy, average accuracy and Kappa coefficients of 0.79, 0.70, 0.78 and 0.91, 0.85, 0.89, respectively. The effectiveness of the proposed method is compared qualitatively and quantitatively, and the results achieve competitive and consistently better than the benchmark comparison method.

Ultrasensitive Electrochemiluminescence Biosensor to Detect Ampicillin Resistance Gene (ARGAMP) Based on a Novel Near-Infrared Ruthenium Carbene Complex/TPrA/PEI Ternary ECL System.

The establishment of rapid target identification and analysis methods for antibiotic resistance genes (ARGs) is urgently needed. In this study, we unprecedently designed a target-catalyzed hairpin assembly (CHA) electrochemiluminescent (ECL) biosensor for the ultrasensitive detection of ampicillin resistance genes (ARGAMP) based on a novel, efficient near-infrared ruthenium carbene complex/TPrA/PEI ternary ECL system with low oxidation potential. The ternary NIR-ECL system illustrated in this work displayed double ECL intensity in comparison with their corresponding traditional binary ECL system. The as-prepared ECL biosensor illustrated in this work demonstrates highly selective and sensitive determination of ARGAMP from 1 fM to 1 nM and a low detection limit of 0.23 fM. Importantly, it also exhibits good accuracy and stabilities to identify ARGAMP in plasmid and bacterial genome DNA, which demonstrates its excellent reliability and great potential in detecting ARGAMP in real environmental samples.

Production of mannooligosaccharides from orange peel waste with ß-mannanase expressed in Trichosporonoides oedocephalis.

A large quantity of orange peel waste (OPW) is generated per year, yet effective biorefinery methods are lacking. In this study, Trichosporonoides oedocephalis ATCC 16958 was employed for hydrolyzing OPW to produce soluble sugars. Glycosyl hydrolases from Paenibacillussp.LLZ1 which can hydrolyze cellulose and hemicellulose were mined and characterized, with the highest ß-mannanase activity of 39.1 U/mg at pH 6.0 and 50 ℃. The enzyme was overexpressed in T. oedocephalis and the sugar production was enhanced by 16 %. The accumulated sugar contains 57 % value-added mannooligosaccharides by the hydrolysis of mannans. The process was intensified by a pretreatment combining H2O2 submergence and steam explosion to remove potential inhibitors. The mannooligosaccharides yield of 6.5 g/L was achieved in flask conversion and increased to 9.7 g/L in a 5-L fermenter. This study improved the effectiveness of orange peel waste processing, and provided a hydrolysis-based methodology for the utilization of fruit wastes.

Enhancing plant growth in biofertilizer-amended soil through nitrogen-transforming microbial communities.

Biofertilizers have immense potential for enhancing agricultural productivity. However, there is still a need for clarification regarding the specific mechanisms through which these biofertilizers improve soil properties and stimulate plant growth. In this research, a bacterial agent was utilized to enhance plant growth and investigate the microbial modulation mechanism of soil nutrient turnover using metagenomic technology. The results demonstrated a significant increase in soil fast-acting nitrogen (by 46.7%) and fast-acting phosphorus (by 88.6%) upon application of the bacterial agent. This finding suggests that stimulated soil microbes contribute to enhanced nutrient transformation, ultimately leading to improved plant growth. Furthermore, the application of the bacterial agent had a notable impact on the accumulation of key genes involved in nitrogen cycling. Notably, it enhanced nitrification genes (amo, hao, and nar), while denitrification genes (nir and nor) showed a slight decrease. This indicates that ammonium oxidation may be the primary pathway for increasing fast-acting nitrogen in soils. Additionally, the bacterial agent influenced the composition and functional structure of the soil microbial community. Moreover, the metagenome-assembled genomes (MAGs) obtained from the soil microbial communities exhibited complementary metabolic processes, suggesting mutual nutrient exchange. These MAGs contained widely distributed and highly abundant genes encoding plant growth promotion (PGP) traits. These findings emphasize how soil microbial communities can enhance vegetation growth by increasing nutrient availability and regulating plant hormone production. This effect can be further enhanced by introducing inoculated microbial agents. In conclusion, this study provides novel insights into the mechanisms underlying the beneficial effects of biofertilizers on soil properties and plant growth. The significant increase in nutrient availability, modulation of key genes involved in nitrogen cycling, and the presence of MAGs encoding PGP traits highlight the potential of biofertilizers to improve agricultural practices. These findings have important implications for enhancing agricultural sustainability and productivity, with positive societal and environmental impacts.

Novel Electrochemiluminescent Biosensor to Ultrasensitively Detect U94 Gene in Human Herpesvirus 6 Using Metal-Organic Framework-Based Nanoemitters Comprising Iridium(III) Complexes via One-Pot Coordination Reaction Strategy.

The detection of the U94 gene in human herpesvirus 6 is crucial for early diagnosis of HHV-6 infections, which could induce acute febrile illness in infants. In this work, the first ultrasensitive electrochemiluminescence (ECL) biosensor for detecting U94 gene in Human Herpesvirus 6 was successfully designed by utilizing efficient novel metal-organic framework (MOF)-based ECL nanoemitters comprising iridium(III) complexes (Ir-ZIF-8-NH2) synthesized via one-pot coordination reaction strategy as an ECL indicator and a target-catalyzed hairpin assembly (CHA) signal amplification strategy. The as-prepared ECL indicator Ir-ZIF-8-NH2 exhibited an approximately 2.7-fold ECL intensity compared with its small molecular analogue of emissive iridium(III) complex named IrppymIM formed by in situ coordination reaction between iridium(III) solvent complex and imidazole ligands. In addition, a target-catalyzed hairpin assembly (CHA) strategy was employed to further improve the sensitivity of the proposed ECL biosensor, which demonstrated a wide linear range from 1 fM to 1 µM and the limit of detection as low as 0.113 fM (S/N = 3). Significantly, this biosensor was successfully applied to detect U94 gene in plasmids and real virus samples. The recoveries were in the range of 97.0-109.0% for plasmids and 95.7-107.5% for real virus samples with a relative standard deviation (RSD) of 1.87-2.53%. These satisfactory experimental results from the proposed ECL biosensor in this work would inevitably promote the development of new time/cost-effective and sensitive methods to detect HHV-6 with a major global health threat and substantial burden on healthcare in the future.

Transcriptomics of Lactobacillus paracasei: metabolism patterns and cellular responses under high-density culture conditions.

Lactobacillus paracasei has significant potential for development and application in the environmental field, particularly in addressing malodor pollution. This study aims to investigate the cellular response of L. paracasei B1 under high-density culture conditions. The selected strain has previously shown effective deodorizing and bacteriostatic abilities. Transcriptomics techniques are employed to dissect the nutrient metabolism pattern of L. paracasei B1 and its response mechanism under environmental stress. The study characterizes the functions of key differentially expressed genes during growth before and after optimizing the culture conditions. The optimization of fermentation culture conditions provides a suitable growth environment for L. paracasei B1, inducing an enhancement of its phosphotransferase system for sugar source uptake and maintaining high levels of glycolysis and pyruvate metabolism. Consequently, the strain is able to grow and multiply rapidly. Under acid stress conditions, glycolysis and pyruvate metabolism are inhibited, and L. paracasei B1 generates additional energy through aerobic respiration to meet the energy demand. The two-component system and quorum sensing play roles in the response and regulation of L. paracasei B1 to adverse environments. The strain mitigates oxygen stress damage through glutathione metabolism, cysteine and methionine metabolism, base excision repair, and purine and pyrimidine metabolism. Additionally, the strain enhances lysine synthesis, the alanine, aspartate, and glutamate metabolic pathways, and relies on the ABC transport system to accumulate amino acid-compatible solutes to counteract acid stress and osmotic stress during pH regulation. These findings establish a theoretical basis for the further development and application of L. paracasei B1 for its productive properties.

Construction of a xylose metabolic pathway in Trichosporonoides oedocephalis ATCC 16958 for the production of erythritol and xylitol.

PURPOSE: Erythritol is a valuable compound as sweetener and chemical material however cannot be fermented from the abundant substrate xylose. METHODS: The strain Trichosporonoides oedocephalis ATCC 16958 was employed to produce polyols including xylitol and erythritol by metabolic engineering approaches. RESULTS: The introduction of a substrate-specific ribose-5-phosphate isomerase endowed T. oedocephalis with xylose-assimilation activity to produce xylitol, and eliminated glycerol production simultaneously. A more value-added product, erythritol was produced by further introducing a homologous xylulose kinase. The carbon flux was redirected from xylitol to erythritol by adding high osmotic pressure. The production of erythritol was improved to 46.5 g/L in flasks by fermentation adjustment, and the process was scaled up in a 5-L fermentor, with a 40 g/L erythritol production after 120 h, and a time-space yield of 0.56 g/L/h. CONCLUSION: This study demonstrated the potential of T. oedocephalis in the synthesis of multiple useful products from xylose.

A novel efficient intramolecular self-enhanced water-soluble iridium-based electrochemiluminescence reagent and its analytical application in aptamer sensor.

In order to overcome the poor solubilities of iridium-based ECL luminophores and explore self-enhanced ECL luminophores, polyethyleneimine (PEI) covalently linked with iridium complex via amide bonds (abbreviated as Ir-PEI) as a new novel intramolecular self-enhanced water-soluble ECL reagent has been unprecedently designed and successfully synthesized in this work. The chemical structure data, FT-IR spectra, photophysical, electrochemical and electrochemiluminescence of this new ECL reagent have been well characterized. In addition, in order to investigate its properties in the real applications, a corresponding new sensitive and specific ECL-based aptasensor to monitor tetracycline (TET) residues in honey and lake water has been further constructed based on this novel self-enhanced reagent of Ir-PEI in this work. This as-prepared intramolecular self-enhanced water-soluble of Ir-PEI illustrated in this work would pave a new avenue to promote the analytical applications of iridium-based ECL luminophores in the future.

AI-driven pan-proteome analyses reveal insights into the biohydrometallurgical properties of Acidithiobacillia.

Microorganism-mediated biohydrometallurgy, a sustainable approach for metal recovery from ores, relies on the metabolic activity of acidophilic bacteria. Acidithiobacillia with sulfur/iron-oxidizing capacities are extensively studied and applied in biohydrometallurgy-related processes. However, only 14 distinct proteins from Acidithiobacillia have experimentally determined structures currently available. This significantly hampers in-depth investigations of Acidithiobacillia's structure-based biological mechanisms pertaining to its relevant biohydrometallurgical processes. To address this issue, we employed a state-of-the-art artificial intelligence (AI)-driven approach, with a median model confidence of 0.80, to perform high-quality full-chain structure predictions on the pan-proteome (10,458 proteins) of the type strain Acidithiobacillia. Additionally, we conducted various case studies on de novo protein structural prediction, including sulfate transporter and iron oxidase, to demonstrate how accurate structure predictions and gene co-occurrence networks can contribute to the development of mechanistic insights and hypotheses regarding sulfur and iron utilization proteins. Furthermore, for the unannotated proteins that constitute 35.8% of the Acidithiobacillia proteome, we employed the deep-learning algorithm DeepFRI to make structure-based functional predictions. As a result, we successfully obtained gene ontology (GO) terms for 93.6% of these previously unknown proteins. This study has a significant impact on improving protein structure and function predictions, as well as developing state-of-the-art techniques for high-throughput analysis of large proteomic data.

Sequence similarity network and protein structure prediction offer insights into the evolution of microbial pathways for ferrous iron oxidation.

IMPORTANCE: Microbial Fe(II) oxidation is a crucial process that harnesses and converts the energy available in Fe, contributing significantly to global element cycling. However, there are still many aspects of this process that remain unexplored. In this study, we utilized a combination of comparative genomics, sequence similarity network analysis, and artificial intelligence-driven structure modeling methods to address the lack of structural information on Fe(II) oxidation proteins and offer a comprehensive perspective on the evolution of Fe(II) oxidation pathways. Our findings suggest that several microbial Fe(II) oxidation pathways currently known may have originated within classes Gammaproteobacteria and Betaproteobacteria.