Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinoma.

OBJECTIVE: To screen the risk factors affecting the recurrence risk of patients with ampullary carcinoma (AC)after radical resection, and then to construct a model for risk prediction based on Lasso-Cox regression and visualize it. METHODS: Clinical data were collected from 162 patients that received pancreaticoduodenectomy treatment in Hebei Provincial Cancer Hospital from January 2011 to January 2022. Lasso regression was used in the training group to screen the risk factors for recurrence. The Lasso-Cox regression and Random Survival Forest (RSF) models were compared using Delong test to determine the optimum model based on the risk factors. Finally, the selected model was validated using clinical data from the validation group. RESULTS: The patients were split into two groups, with a 7:3 ratio for training and validation. The variables screened by Lasso regression, such as CA19-9/GGT, AJCC 8th edition TNM staging, Lymph node invasion, Differentiation, Tumor size, CA19-9, Gender, GPR, PLR, Drinking history, and Complications, were used in modeling with the Lasso-Cox regression model (C-index = 0.845) and RSF model (C-index = 0.719) in the training group. According to the Delong test we chose the Lasso-Cox regression model (P = 0.019) and validated its performance with time-dependent receiver operating characteristics curves(tdROC), calibration curves, and decision curve analysis (DCA). The areas under the tdROC curves for 1, 3, and 5 years were 0.855, 0.888, and 0.924 in the training group and 0.841, 0.871, and 0.901 in the validation group, respectively. The calibration curves performed well, as well as the DCA showed higher net returns and a broader range of threshold probabilities using the predictive model. A nomogram visualization is used to display the results of the selected model. CONCLUSION: The study established a nomogram based on the Lasso-Cox regression model for predicting recurrence in AC patients. Compared to a nomogram built via other methods, this one is more robust and accurate.

Fiber-Optic Photoacoustic Gas Sensor with Multiplexed Fabry-Pérot Interferometric Cantilevers.

A fiber-optic photoacoustic (PA) gas sensor with multiplexed Fabry-Pérot (F-P) interferometric cantilevers is demonstrated. A compact cylindrical nonresonant PA tube with a volume of only 0.45 mL is designed. The PA signal is measured by two symmetrically installed fiber-optic interferometric cantilever microphones (FOICMs) to improve the signal-to-noise ratio (SNR). For multiplexing the two cantilevers by a single demodulation system, a dual cavity length synchronous measurement method based on total-phase demodulation algorithm with ultrahigh resolution is developed. The PA signal detection is realized by the second-harmonic wavelength modulation spectroscopy (2f-WMS) technique. The sensor performance is verified by conducting the detection of trace acetylene (C2H2). The normalized noise equivalent absorption (NNEA) coefficient is 2.5 × 10-9 cm-1·W·Hz-1/2, and the minimum detection limit (MDL) downs to about 0.2 ppm with an averaging time of 1 s. The fiber-optic PA gas sensor has characteristics of high resolution and immunity to electromagnetic and vibration interference. Furthermore, the technical scheme of the multiplexed cantilever demodulation shows great potential for remote multipoint monitoring of gases in harsh environments.

Multi-pass Differential Photoacoustic Sensor for Real-Time Measurement of SF6 Decomposition Component H2S at the ppb Level.

We designed and implemented a photoacoustic (PA) sensor for H2S detection in SF6 background gas based on a multi-pass differential photoacoustic cell (MDPC) and a near-infrared distributed feedback (DFB) laser. In the MDPC apparatus, two resonators with identical geometric parameters were vertically and symmetrically embedded. The differential processing algorithm of two phase-reversed signals realized the effective enhancement of the PA signal and suppressed the flow noise in the dynamic sampling process. In addition, the λ/4 buffer chamber in the MDPC was utilized as a muffler to further reduce the flow noise and realize the dynamic detection of H2S. The collimated excitation light was reflected 30 times in a multi-pass structure constituted of two gold-plated concave mirrors, and an absorption path length of 4.92 m was achieved. Due to the high gas density of SF6, the relationship between the signal-to-noise ratio (SNR) and the gas flow was different between SF6 and N2 background gases. The maximum flow rate of the characteristic gas components detected in the SF6 background is 150 standard cubic centimeters per minute (SCCM), which is lower than 350 SCCM in N2. The linearity property was analyzed, and the results show that the sensitivity of the sensor to H2S in the SF6 background was 27.3 µV/ppm. With the structure, parameters, temperature, gas flow, and natural frequency of the MDPC been optimized, a minimum detection limit (MDL) of 11 ppb was reached with an averaging time of 1000 s, which furnished an effective preventive implement for the safe operation of gas insulation equipment.

Dynamic detection of ppb-level SO2 based on a differential photoacoustic cell coupled with UV-LED.

We design a photoacoustic (PA) SO2 sensor based on the coupling of a differential photoacoustic cell (DPAC) and cost-effective UV-LED, which realized the dynamic monitoring of SO2 gas at the ppb level. Aiming at the limitation of UV-LED divergence, a light source combination module with high condensing efficiency was devised based on a lens through theoretical derivation and experimental analysis. The PA signal with the optimum matching of the lens was 20-times larger than the direct coupling of the UV-LED. Due to the excellent beam collimation effect of the lens assembly, the background interference was only 1 ppm. In addition, the DPAC gathered the merits of doubling the PA signal and reducing the flow noise interference. The analysis of Allan-Werle deviation showed that the detection limit of SO2 was 1.3 ppb with the averaging time of 100 s.

Postoperative survival of pulmonary invasive mucinous adenocarcinoma versus non-mucinous invasive adenocarcinoma.

PURPOSE: In 2015, the World Health Organization renamed mucinous bronchioloalveolar adenocarcinoma as pulmonary invasive mucinous adenocarcinoma (IMA). Due to its low incidence and unclear prognosis with surgical treatment, previous studies have presented opposing survival outcomes. We aimed to investigate the differences in surgical prognosis and prognosis-related risk factors by comparing IMA with non-mucinous invasive adenocarcinoma (NMA). METHODS: A total of 20,914 patients diagnosed with IMA or NMA from 2000 to 2014 were screened from the Surveillance, Epidemiology, and End Results database. The screened patients were subjected to propensity score matching (PSM) in a 1:4 ratio to explore the survival differences between patients with IMA and NMA and the factors influencing prognosis. RESULTS: For all patients, IMA was prevalent in the lower lobes of the lungs (p < 0.0001), well-differentiated histologically (p < 0.0001), less likely to have lymph node metastases (94.4% vs. 72.0%, p < 0.0001) and at an earlier pathological stage (p = 0.0001). After PSM, the IMA cohort consisted of 303 patients, and the NMA cohort consisted of 1212 patients. Kaplan‒Meier survival analysis showed no difference in overall survival (OS) between patients in the IMA cohort and those in the NMA cohort (p = 0.7). Cox proportional hazards analysis showed that differences in tumor pathological type did not influence OS between the two cohorts (p = 0.65). Age (HR: 1.98, 95% CI 1.7-2.31, p < 0.0001), gender (HR: 0.64, 95% CI 0.55-0.75, p < 0.0001), and radiation treatment (HR: 2.49, 95% CI 1.84-3.37, p < 0.0001) were independent predictors of patient OS. CONCLUSION: There was no significant difference in OS between patients with IMA and those with NMA after surgical treatment. Age, sex, and radiation treatment can independently predict OS.

Toxicogenomics scoring system: TGSS, a novel integrated risk assessment model for chemical carcinogenicity prediction.

BACKGROUND: Given the increasing exposure of humans to environmental chemicals and the limitations of conventional toxicity test, there is an urgent need to develop next-generation risk assessment methods. OBJECTIVES: This study aims to establish a novel computational system named Toxicogenomics Scoring System (TGSS) to predict the carcinogenicity of chemicals coupling chemical-gene interactions with multiple cancer transcriptomic datasets. METHODS: Chemical-related gene signatures were derived from chemical-gene interaction data from the Comparative Toxicogenomics Database (CTD). For each cancer type in TCGA, genes were ranked by their effects on tumorigenesis, which is based on the differential expression between tumor and normal samples. Next, we developed carcinogenicity scores (C-scores) using pre-ranked GSEA to quantify the correlation between chemical-related gene signatures and ranked gene lists. Then we established TGSS by systematically evaluating the C-scores in multiple chemical-tumor pairs. Furthermore, we examined the performance of our approach by ROC curves or prognostic analyses in TCGA and multiple independent cancer cohorts. RESULTS: Forty-six environmental chemicals were finally included in the study. C-score was calculated for each chemical-tumor pair. The C-scores of IARC Group 3 chemicals were significantly lower than those of chemicals in Group 1 (P-value = 0.02) and Group 2 (P-values = 7.49 ×10-5). ROC curves analysis indicated that C-score could distinguish "high-risk chemicals" from the other compounds (AUC = 0.67) with a specificity and sensitivity of 0.86 and 0.57. The results of survival analysis were also in line with the assessed carcinogenicity in TGSS for the chemicals in Group 1. Finally, consistent results were further validated in independent cancer cohorts. CONCLUSION: TGSS highlighted the great potential of integrating chemical-gene interactions with gene-cancer relationships to predict the carcinogenic risk of chemicals, which would be valuable for systems toxicology.

Classification and Identification of Archaea Using Single-Cell Raman Ejection and Artificial Intelligence: Implications for Investigating Uncultivated Microorganisms.

Archaea can produce special cellular components such as polyhydroxyalkanoates, carotenoids, rhodopsin, and ether lipids, which have valuable applications in medicine and green energy production. Most of the archaeal species are uncultivated, posing challenges to investigating their biomarker components and biochemical properties. In this study, we applied Raman spectroscopy to examine the biological characteristics of nine archaeal isolates, including halophilic archaea (Haloferax larsenii, Haloarcula argentinensis, Haloferax mediterranei, Halomicrobium mukohataei, Halomicrobium salinus, Halorussus sp., Natrinema gari), thermophilic archaea (Sulfolobus acidocaldarius), and marine group I (MGI) archaea (Nitrosopumilus maritimus). Linear discriminant analysis of the Raman spectra allowed visualization of significant separations among the nine archaeal isolates. Machine-learning classification models based on support vector machine achieved accuracies of 88-100% when classifying the nine archaeal species. The predicted results were validated by DNA sequencing analysis of cells isolated from the mixture by Raman-activated cell sorting. Raman spectra of uncultured archaea (MGII) were also obtained based on Raman spectroscopy and fluorescence in situ hybridization. The results combining multiple Raman-based techniques indicated that MGII may have the ability to produce lipids distinct from other archaeal species. Our study provides a valuable approach for investigating and classifying archaea, especially uncultured species, at the single-cell level.

Insights into Adsorption of Humic Substances on Graphitic Carbon Nitride.

Graphitic carbon nitride (CN) has been widely used in environmental pollution remediation. However, the adsorption of organic compounds on CNs, which has practical significance for the environmental application of CNs, is poorly understood. For the first time, this study systematically investigated the adsorption behaviors and mechanisms of humic substances (HSs), i.e., humic acid (HA) and fulvic acid (FA), on CNs derived from four typical precursors. Intriguingly, CN derived from urea (CN-U) showed a great capacity for HS adsorption due to its porous structure and large surface area, with maximum adsorption amounts of 73.24 and 51.62 mgC/g for HA and FA, respectively. The formation, influencing factors, and relative contributions of multiple interactions to HS adsorption on CNs were thoroughly elucidated. HS adsorption on CNs was mainly mediated by electrostatic interactions, π-π interactions, and H-bonding. The dominance of electrostatic interactions resulted in HS adsorption being highly dependent on pH and ionic strength. HS components with high aromaticity and high molecular weight were preferentially adsorbed due to π-π interactions. These multiple interactions were largely affected by amino groups and tri-s-triazine units of CNs, as well as the moieties of aromatic rings and oxygen-containing groups of HSs.

Application of single-cell Raman-deuterium isotope probing to reveal the resistance of marine ammonia-oxidizing archaea SCM1 against common antibiotics.

Antimicrobial resistance (AMR) in oceans poses a significant threat to human health through the seafood supply chain. Ammonia-oxidizing archaea (AOA) are important marine microorganisms and play a key role in the biogeochemical nitrogen cycle around the world. However, the AMR of marine AOA to aquicultural antibiotics is poorly explored. Here, Raman-deuterium isotope probing (Raman-DIP), a single-cell tool, was developed to reveal the AMR of a typical marine species of AOA, Nitrosopumilus maritimus (designated SCM1), against six antibiotics, including erythromycin, tetracycline, novobiocin, neomycin, bacitracin, and vancomycin. The D2O concentration (30% v/v) and culture period (9 days) were optimized for the precise detection of metabolic activity in SCM1 cells through Raman-DIP. The relative metabolic activity of SCM1 upon exposure to antibiotics was semi-quantitatively calculated based on single-cell Raman spectra. SCM1 exhibited high resistance to erythromycin, tetracycline, novobiocin, neomycin, and vancomycin, with minimum inhibitory concentration (MIC) values between 100 and 400 mg/L, while SCM1 is very sensitive to bacitracin (MIC: 0.8 mg/L). Notably, SCM1 cells were completely inactive under the metabolic activity minimum inhibitory concentration conditions (MA-MIC: 1.6-800 mg/L) for the six antibiotics. Further genomic analysis revealed the antibiotic resistance genes (ARGs) of SCM1, including 14 types categorized into 33 subtypes. This work increases our knowledge of the AMR of marine AOA by linking the resistant phenome to the genome, contributing to the risk assessment of AMR in the underexplored ocean environment. As antibiotic resistance in marine microorganisms is significantly affected by the concentration of antibiotics in coastal environments, we encourage more studies concentrating on both the phenotypic and genotypic antibiotic resistance of marine archaea. This may facilitate a comprehensive evaluation of the capacity of marine microorganisms to spread AMR and the implementation of suitable control measures to protect environmental safety and human health.

RSPSSL: A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization.

Raman spectroscopy has tremendous potential for material analysis with its molecular fingerprinting capability in many branches of science and technology. It is also an emerging omics technique for metabolic profiling to shape precision medicine. However, precisely attributing vibration peaks coupled with specific environmental, instrumental, and specimen noise is problematic. Intelligent Raman spectral preprocessing to remove statistical bias noise and sample-related errors should provide a powerful tool for valuable information extraction. Here, we propose a novel Raman spectral preprocessing scheme based on self-supervised learning (RSPSSL) with high capacity and spectral fidelity. It can preprocess arbitrary Raman spectra without further training at a speed of ~1 900 spectra per second without human interference. The experimental data preprocessing trial demonstrated its excellent capacity and signal fidelity with an 88% reduction in root mean square error and a 60% reduction in infinite norm ([Formula: see text]) compared to established techniques. With this advantage, it remarkably enhanced various biomedical applications with a 400% accuracy elevation (ΔAUC) in cancer diagnosis, an average 38% (few-shot) and 242% accuracy improvement in paraquat concentration prediction, and unsealed the chemical resolution of biomedical hyperspectral images, especially in the spectral fingerprint region. It precisely preprocessed various Raman spectra from different spectroscopy devices, laboratories, and diverse applications. This scheme will enable biomedical mechanism screening with the label-free volumetric molecular imaging tool on organism and disease metabolomics profiling with a scenario of high throughput, cross-device, various analyte complexity, and diverse applications.

Ultrastructural insights into cellular organization, energy storage and ribosomal dynamics of an ammonia-oxidizing archaeon from oligotrophic oceans.

Introduction: Nitrososphaeria, formerly known as Thaumarchaeota, constitute a diverse and widespread group of ammonia-oxidizing archaea (AOA) inhabiting ubiquitously in marine and terrestrial environments, playing a pivotal role in global nitrogen cycling. Despite their importance in Earth's ecosystems, the cellular organization of AOA remains largely unexplored, leading to a significant unanswered question of how the machinery of these organisms underpins metabolic functions. Methods: In this study, we combined spherical-chromatic-aberration-corrected cryo-electron tomography (cryo-ET), scanning transmission electron microscopy (STEM), and energy dispersive X-ray spectroscopy (EDS) to unveil the cellular organization and elemental composition of Nitrosopumilus maritimus SCM1, a representative member of marine Nitrososphaeria. Results and Discussion: Our tomograms show the native ultrastructural morphology of SCM1 and one to several dense storage granules in the cytoplasm. STEM-EDS analysis identifies two types of storage granules: one type is possibly composed of polyphosphate and the other polyhydroxyalkanoate. With precise measurements using cryo-ET, we observed low quantity and density of ribosomes in SCM1 cells, which are in alignment with the documented slow growth of AOA in laboratory cultures. Collectively, these findings provide visual evidence supporting the resilience of AOA in the vast oligotrophic marine environment.

Multi-mechanism collaboration enhanced photoacoustic analyzer for trace H2S detection.

To realize the real-time highly sensitive detection of SF6 decomposition product H2S, a multi-mechanism collaboration enhancement photoacoustic spectroscopy analyzer (MCEPA) based on acoustic resonance enhancement, cantilever enhancement and excitation light enhancement is proposed. An SF6 background gas-induced photoacoustic cell (PAC) was used for acoustic resonance (AR) enhancement of the photoacoustic signals. A fiber-optic acoustic sensor based on a silicon cantilever is optimized and fabricated. The narrow-band acoustic signal enhancement based on cantilever mechanical resonance (MR) is realized in the optimal working frequency band of the PAC. A fiber-coupled DFB cascaded an Erbium-doped fiber amplifier (EDFA) realized the light power enhancement (LPE) of the photoacoustic signals excitation source. Experimental results show that the MR of the fiber-optic silicon cantilever acoustic sensor (FSCAS) is matched with the AR of the PAC and combined with the LPE, which realizes the multi-mechanism collaboration enhancement of weak photoacoustic signals. The Allan-Werle deviation evaluation showed that the minimum detection limit of H2S in the SF6 background is 10.96 ppb when the average time is 200 s. Benefiting from the all-optimization of photoacoustic excitation and detection, the MCEPA has near-field high-sensitivity gas detection capability immune to electromagnetic interference.

In situ identification of environmental microorganisms with Raman spectroscopy.

Microorganisms in natural environments are crucial in maintaining the material and energy cycle and the ecological balance of the environment. However, it is challenging to delineate environmental microbes' actual metabolic pathways and intraspecific heterogeneity because most microorganisms cannot be cultivated. Raman spectroscopy is a culture-independent technique that can collect molecular vibration profiles from cells. It can reveal the physiological and biochemical information at the single-cell level rapidly and non-destructively in situ. The first part of this review introduces the principles, advantages, progress, and analytical methods of Raman spectroscopy applied in environmental microbiology. The second part summarizes the applications of Raman spectroscopy combined with stable isotope probing (SIP), fluorescence in situ hybridization (FISH), Raman-activated cell sorting and genomic sequencing, and machine learning in microbiological studies. Finally, this review discusses expectations of Raman spectroscopy and future advances to be made in identifying microorganisms, especially for uncultured microorganisms.

Reduction capacity of humic acid and its association with the evolution of redox structures during composting.

The reducing capacity (RC) of compost-derived humic acid (HA) is related to the type and number of redox-active functional moieties in its structure and has a considerable environmental influence on its geochemical redox cycle. Composting treatment can affect the redox-active fractions of organic substances through microbial transformation and degradation. However, the relationship between the RC of compost-derived HA and its fluorescence component and infrared spectra remains unclear. In this study, we assessed the response of the organic reducing capacity (ORC) and inorganic reducing capacity (IRC) of compost-derived HA to the stabilization of organic solid waste materials by analyzing the redox-active functional groups of HA extracted at different composting times. The results demonstrated that the RC of compost-derived HA continuously increased during composting because of the formation of fulvic- and humic-like fluorescent components, which consist of amide, phenolic hydroxyl, quinone, and aromatic groups. Adsorption occurred between HA and FeCit by aliphatic and out-of-plane aromatic CH, which released free hydrogen and increased the Fe-binding site; consequently, ORC was obviously higher than IRC. The results of this study could provide an understanding of the transformation of the fluorescent substances and functional groups that affect redox properties during composting; therefore, this study has considerable significance for exploring the application of compost products.

Identification of metastasis-related long non-coding RNAs in lung cancer through a novel tumor mesenchymal score.

Long non-coding RNAs (lncRNAs) have been proven to play critical roles in epithelial-mesenchymal transition (EMT) and metastasis of lung cancer. However, the biological functions and related mechanisms of lncRNAs are unclear. In addition, the EMT-based prognosis prediction in lung cancer still lacks investigation. Here, we established the methodology of identifying critical metastasis-related lncRNAs using comprehensive datasets of cancer transcriptome, genome and epigenome, and also provided tools for prognosis prediction in lung cancer. Initially, important mesenchymal marker genes were identified to compose the tumor mesenchymal score, which predicted patient prognosis in lung cancer, especially lung adenocarcinoma (LUAD). The score was also correlated with several crucial biological and physiological processes, such as tumor immune and hypoxia. Based on the score, lung cancer patients was classified into epithelial and mesenchymal subtypes, and lncRNAs which exhibited expressional dysregulation, promotor methylation alteration and copy number variation between the two subtypes in LUAD were identified and underwent further prognostic analyses. Finally, we identified 14 lncRNAs as EMT-related and significant biomarkers in prognosis prediction of LUAD. As validation, lncRNA RBPMS-AS1 was proven to be co-expressed with epithelial biomarkers, suppressive for A549 cell migration, invasion and EMT, and also significantly associated with better outcomes of LUAD patients, suggesting the potential of RBPMS-AS1 to serve as a lncRNA epithelial biomarker in metastasis of LUAD. Based on the identified lncRNAs, an EMT-linked lncRNA prognostic signature was further established. Taken together, our study provides robust predictive tools, potential lncRNA targets and feasible screening strategies for future study of lung cancer metastasis.

Unusual Force Constants Guided Distortion-Triggered Loss of Long-Range Order in Phase Change Materials.

Unusual force constants originating from the local charge distribution in crystalline GeTe and Sb2Te3 are observed by using the first-principles calculations. The calculated stretching force constants of the second nearest-neighbor Sb-Te and Ge-Te bonds are 0.372 and -0.085 eV/Å2, respectively, which are much lower than 1.933 eV/Å2 of the first nearest-neighbor bonds although their lengths are only 0.17 Å and 0.33 Å longer as compared to the corresponding first nearest-neighbor bonds. Moreover, the bending force constants of the first and second nearest-neighbor Ge-Ge and Sb-Sb bonds exhibit large negative values. Our first-principles molecular dynamic simulations also reveal the possible amorphization of Sb2Te3 through local distortions of the bonds with weak and strong force constants, while the crystalline structure remains by the X-ray diffraction simulation. By identifying the low or negative force constants, these weak atomic interactions are found to be responsible for triggering the collapse of the long-range order. This finding can be utilized to guide the design of functional components and devices based on phase change materials with lower energy consumption.

Divergent response of heavy metal bioavailability in soil rhizosphere to agricultural land use change from paddy fields to various drylands.

The heavy metal pollution induced by agricultural land use change has attracted great attention. In this study, the divergent response of the bioavailability of heavy metals in rhizosphere soil to different agricultural land uses was analyzed using sequential extraction, and possible influence paths were constructed. The results show that land use change can affect the heavy metal bioavailability by influencing the soil organic matter and redox potential (Eh). The average concentrations of N, P, K, Ca, Mg, S, and Fe in the soil showed no significant differences. However, the conversion direction and extent of chemical speciation of heavy metals were different across land use changes from paddy fields to various drylands. After conversion from paddy to wheat field, the bioavailability of heavy metals decreased due to an increase in permanganate oxidizable carbon (KMnO4-C) and a decrease in Eh. The transformation from paddy to celery soil is accompanied by a change in the soil's KMnO4-C content, increasing the proportion of the bioavailable states of heavy metals. However, the response of bioavailability to changes in the soil KMnO4-C varied among heavy metals. In contrast, when land use changed to grapevine culture, the bioavailability of heavy metals increased due to a change in the KMnO4-C content. Moreover, the dissolved organic carbon (DOC) content increased, which positively affected the Eh and, in turn, increased the bioavailability of heavy metals. This research is of great significance for understanding the impact of land use change on the heavy metal migration and activity in the rhizosphere microenvironment of soil.

Insights into the effects of heavy metal pressure driven by long-term treated wastewater irrigation on bacterial communities and nitrogen-transforming genes along vertical soil profiles.

Irrigation with treated wastewater (TWW) influences soil ecological function due to the accumulation of heavy metals (HMs) and nutrients in soils. However, the interaction between HMs and microbial processes in TWW-irrigated soil has not been fully explored. We investigated the effect of HMs on bacterial communities and nitrogen-transforming (N-transforming) genes along vertical soil profiles irrigated with domestic TWW (DTWW) and industrial TWW (ITWW) for more than 30 years. Results indicate that long-term TWW irrigation reshaped bacterial community structure and composition. Irrigation with ITWW led to increased accumulation of Cd, Cr, Cu, Pb, Zn, and Ni in soils than DTWW. Accumulation of inorganic N, soil organic carbon, and HMs in topsoil irrigated with ITWW contributed to the activities of Micrococcaceae. The effect of the activation of nutrient factors on Bacillus, which was the dominant species in DTWW-irrigated soils, was greater than that of HMs. HM pressure driven by ITWW irrigation changed the vertical distribution of N-transforming functional genes, increasing the abundance of amoA gene and decreasing that of nifH through soil depth. ITWW irrigation enhanced the denitrification capacity in topsoil; ammonia-oxidizing capacity in deeper soil was increased after long-term irrigation with DTWW and ITWW, suggesting a potential risk of nitrogen loss.

[Different Responses of Soil Dissolved Organic Matter to Different Types of Compost].

This study explored the responses of soil dissolved organic matter (DOM) to the application of different types of compost using a soil sample without compost as a control. Ultraviolet and fluorescence spectrum technology and EEM-PARAFAC was used to analyze DOM structure and driving factors in soil added with different proportion of cow dung compost (SCC), food and kitchen waste compost (SFC), and sludge compost (SCC). Compared with the control group, contents of AN, NH4+-N, DOC, and SOM in soil added with compost were significantly increased, and contents of SOM and DOC increased with the increasing of compost amount. When added compost in the same proportion, contents of AN, NO3--N, and DOC in SCC and SFC were significantly higher than those in SSC, while contents of NH4+-N and SOM were higher in SSC. The results of spectral analysis showed that the structure of conjugated benzene ring, hydrophobic component, quinone group, and chromogenic component in DOM of soil added with compost were significantly increased, the transition of unsaturated organic molecule (π→π*) was more active, the molecular weight of DOM increased, and the degree of humification was enhanced. When the amount of compost added is 5%, the influence of food and kitchen waste compost on DOM structure was greatest among three types of compost. At 10% and 20%, sludge compost had the greatest impact on DOM structure. The results of EEM-PARAFAC analysis showed that the relative content of fulvic acid-like substances with low molecular in DOM of soil added with compost was increased, while the relative content of proteoid-like substances decreased. 2D-COS analysis showed that compost affected the change order of fluorescence components in DOM. SCC and SFC were as follows:proteoid-like > fulvic acid-like > humus-like; in SSC, it was fulvic acid-like > proteoid-like > humus-like. The enhance of humification and the decrease of relative content of protein-like substances in DOM were related to increased DOC and AN, the relative content of humus-like in low molecular weight was positively correlated with the content of NO3--N, and the relative content of macromolecule fulvic acid-like was increased due to the input of SOM from compost.

Untangling the response of fungal community structure, composition and function in soil aggregate fractions to food waste compost addition.

Soil fungi are key drivers in regulating the ecosystem function, playing a vital role in protecting the plant from phytopathogens and other biotic and abiotic pressures. However, the potential impact of compost addition and soil aggregate size on the fungal community and functional ecological guild remains uncertain. This study investigated the structure, composition, and function of soil fungal communities across aggregate fractions under food waste compost addition using Miseq sequencing and FUNGuild. Compost addition exerted a negative impact on fungal α-diversity, and shifted the structure and changed the composition of fungal community. Compost addition rates exhibited more contributions to fungal α-diversity variations (R = 0.609, 0.895, and 0.501 for Sobs, Shannon, and Chao indices, respectively, P = 0.001) and the separation of community structure than soil aggregate size (R = 0.952, P = 0.001). Biomarkers, including Chaetomiaceae, Ascobolaceae, and Sordariomycete, displayed significant superiority in compost-added soils, whereas the populations of Nectriaceae and Clavicipitaceae were significantly decreased. The relative abundances of animal and plant pathogens were significantly decreased, whereas that of saprotrophs were increased. The abundances of pathogens correlated positively with pH and negatively with nutrients (soil organic matter, dissolved organic carbon, total nitrigen, NH4+, and NO3-), whereas those of saprotrophs showed an opposite trend. The dose of compost was the major driver for fungal functional guild variation, whereas carbon and nitrogen source exhibited more contributions to function variation than pH value. These results provide a reference for sustainable ecological agriculture by applying compost rationally under the conditions of soil health and agricultural performance.