The contribution of microbial shikimic acid to humus formation during organic wastes composting: a review.

Microbial shikimic acid is an important intermediate metabolite in the synthesis of aromatic amino acids which are precursors for forming humus during composting process. Generally, the pathways producing shikimic acid and its downstream products are collectively referred as shikimic acid pathway (SKP). Microbial SKP can produce phenols, and tyrosine. Pyrogallol is the precursor of phenols. And, tyrosine can form an ammoniated monomer. Therefore, regulation of SKP can promote shikimic acid production, which is beneficial in promoting humus production and humification. However, SKP present in microbial cells is distinctive because of providing precursors for humification process, which needs to be recognized during composting. Due to the different structures of various organic wastes, it is difficult to control the SKP efficiency and shikimic acid production. Therefore, it is valuable to review the synthesis of shikimic acid by microorganisms and propose how to promote SKP during different materials composting. Furthermore, we have attempted to illustrate the application of metabolites from SKP in forming humus during organic waste composting. Finally, a series of regulating methods has been outlined to enhance microbial SKP, which are effective to promote humus aromatization and to improve humus formation during different materials composting.

Diversity in the Mechanisms of Humin Formation during Composting with Different Materials.

Humins (HMs) play a very important role in various environmental processes and are crucial for regulating global carbon and nitrogen cycles in various ecosystems. Composting is a controlled decomposition process accompanied by the stabilization of organic solid waste materials. During composting, active fractions of organic substances can be transformed into HMs containing stable and complex macromolecules. However, the structural heterogeneity and formation mechanisms of HMs during composting with various substrates have not been clarified. Here, the structure and composition of HMs extracted from livestock manure (LM) and straw (SW) during composting were investigated by excitation-emission matrices spectroscopy and Fourier transform infrared spectroscopy. The results showed that the stability and humification of LM-HM were lower than that of SW-HM. The parallel factor analysis components of the HM in LM composting contained the same fluorescent unit, and the intermediate of cellulose degradation affected the structure of the HM from SW composting. Structural equation modeling demonstrated that low-molecular-weight compounds were key factors in humification. On the basis of the structure and key factors impacting HM, we constructed two mechanisms for the formation of HM from different composting processes. The LM-HMs from different humification processes have multiple identical fluorescent structural units, and the high humification of SW is affected by its polysaccharide constituents, which contains a fluorescent component in their skeleton, providing a basis for studying HM in composting.

Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction.

Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. For all 13 data sets, Chemi-Net resulted in higher R2 values compared with the Cubist benchmark. The median R2 increase rate over Cubist was 26.7%. We expect that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery.

MeSHLabeler: improving the accuracy of large-scale MeSH indexing by integrating diverse evidence.

MOTIVATION: Medical Subject Headings (MeSHs) are used by National Library of Medicine (NLM) to index almost all citations in MEDLINE, which greatly facilitates the applications of biomedical information retrieval and text mining. To reduce the time and financial cost of manual annotation, NLM has developed a software package, Medical Text Indexer (MTI), for assisting MeSH annotation, which uses k-nearest neighbors (KNN), pattern matching and indexing rules. Other types of information, such as prediction by MeSH classifiers (trained separately), can also be used for automatic MeSH annotation. However, existing methods cannot effectively integrate multiple evidence for MeSH annotation. METHODS: We propose a novel framework, MeSHLabeler, to integrate multiple evidence for accurate MeSH annotation by using 'learning to rank'. Evidence includes numerous predictions from MeSH classifiers, KNN, pattern matching, MTI and the correlation between different MeSH terms, etc. Each MeSH classifier is trained independently, and thus prediction scores from different classifiers are incomparable. To address this issue, we have developed an effective score normalization procedure to improve the prediction accuracy. RESULTS: MeSHLabeler won the first place in Task 2A of 2014 BioASQ challenge, achieving the Micro F-measure of 0.6248 for 9,040 citations provided by the BioASQ challenge. Note that this accuracy is around 9.15% higher than 0.5724, obtained by MTI. AVAILABILITY AND IMPLEMENTATION: The software is available upon request.

Bioavailability of riverine dissolved organic carbon and nitrogen in the Heilongjiang watershed of northeastern China.

The bioavailabilities of dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) were examined in eight sampling stations of the Heilongjiang watershed, located in Heilongjiang Province, northeast China. Water samples were incubated for 55 days at 20 °C, and the decreases in the DOC and DON concentrations were measured during the laboratory incubations. The experiments showed that bioavailable DOC (BDOC) accounted for 15-30% of DOC and bioavailable DON (BDON) accounted for 29-57% of DON. DOM bioavailability was higher for DON compared to DOC, suggesting that DON was more bioavailable and had a faster turnover than DOC in the Heilongjiang watershed. Furthermore, the percent of bioavailable DOC (%BDOC) was significantly related to SUVA254, not the DOC concentration, suggesting that the chemistry composition of DOM played a more important role in affecting its bioavailability compared to the DOM concentration. In addition, significant negative correlations were observed between the initial DOC/DON ratios and the percent of bioavailable DOM fractions (%BDOC and %BDON), especially for %BDON, implying that low C/N molecules or N-rich compounds may be preferentially utilized by microbes. Graphical Abstract DOC concentrations of eight sampling sites, microbial decomposition of DOC over 55 days, % bioavailable DOC of eight sampling sites, DOM chemical composition of eight sampling sites, demonstrated chemical composition influence on DOM bioavailability.

Interaction of TLR-IFN and HLA polymorphisms on susceptibility of chronic HBV infection in Southwest Han Chinese.

BACKGROUND & AIMS: The toll-like receptor-interferon (TLR-IFN) signalling pathway plays a crucial role in HBV infection. Human leucocyte antigen (HLA) polymorphisms are associated with chronic HBV infection by genome wide association study (GWAS). We aimed to explore interaction between TLR-IFN and HLA gene polymorphisms in susceptibility of chronic HBV infection. METHODS: In the Chinese Southwest Han population, 1191 chronic HBV infection patients and 273 HBV clearance were selected. A total of 39 single nucleotide polymorphism loci in 23 genes of the TLR-IFN pathway and four HLA polymorphism loci associated with chronic HBV infection identified by GWAS were selected for genotyping. SNPStats, QVALUE, and multifactor dimensionality reduction were used for statistical analysis. RESULTS: A significant association was seen in several of the TLR-IFN pathway genes, TLR9 rs352140 (OR = 0.70, P = 0.0088), IL1B rs16944 (OR = 0.67, P = 0.016), IL12B rs3212227 (OR = 1.38, P = 0.021), IFNGR1 rs3799488 (OR = 1.48, P = 0.0048), IFNGR2 rs1059293 (OR = 0.27, P = 0.011), MX1 rs467960 (OR = 0.68, P = 0.022), as well as four loci in HLA, rs3077 (OR = 0.55, P < 0.0001), rs2856718 (OR = 0.60, P = 4e-04), rs9277535 (OR = 0.54, P < 0.0001) and rs7453920 (OR = 0.43, P < 0.0001). A synergistic relationship was seen between rs9277535 and rs16944 (0.13%), rs1143623 and rs6613 (0.10%). The combination of rs9277535 in HLA and rs16944 in IL1B was the best model to predict chronic HBV infection (testing accuracy = 0.6040, P = 0.0010, cross-validation consistency = 10/10). CONCLUSIONS: TLR-IFN pathway gene polymorphisms are associated with chronic HBV infection. Interactions with polymorphisms in these genes may be one mechanism by which HLA polymorphisms influence susceptibility to chronic HBV infection, as specific single nucleotide polymorphism combinations are highly predictive of chronic HBV infection.

Gaining insight into the effect of laccase expression on humic substance formation during lignocellulosic biomass composting.

The aim is to enhance lignin humification by promoting laccase activities which can promote lignin depolymerization and reaggregation during composting. 1-Hydroxybenzotriazole (HBT) is employed to conduct laccase mediator system (LMS), application of oxidized graphene (GO) in combination to strengthen LMS. Compared with control, the addition of GO, HBT, and GH (GO coupled with HBT) significantly improved laccase expression and activities (P < 0.05), with lignin humification efficiency also increased by 68.6 %, 36.7 %, and 107.8 %. GH treatment induces microbial expression of laccase by increasing the abundance and synergy of core microbes. The unsupervised learning model, vector autoregressive model and Mantel test function were combined to elucidate the mechanism of action of exogenous materials. The results showed that GO stabilized the composting environment on the one hand, and acted as a support vector to stabilize the LMS and promote the function of laccase on the other. In GH treatment, degradation of macromolecules and humification of small molecules were promoted simultaneously by activating the dual function of laccase. Additionally, it also reveals the GH enhances the humification of lignocellulosic compost by converting phenolic pollutants into aggregates. These findings provide a new way to enhance the dual function of laccase and promote lignin humification during composting. It could effectively achieve the resource utilization of organic solid waste and reduce composting pollution.

The reverse function of lignin-degrading enzymes: The polymerization ability to promote the formation of humic substances in domesticated composting.

This study aimed to confirm the ability of lignin peroxidase (LiP) and manganese peroxidase (MnP) in promoting the formation of humic substances (HS) during domesticated composting. Three raw materials with different lignin types were used for composting, including rice straw, tree branches, and pine needles. Results suggested that LiP and MnP activity increased during domesticated composting. But HS formation was only promoted by LiP. The effect of MnP was insignificant, which might be caused by the lack of enzyme cofactors like Mn2+. Meanwhile, bacteria highly associated with LiP and MnP production were identified as core bacteria. Function prediction of 16S-PICRUSt2 showed that the function of core bacteria was consistent with total bacterial functions which mainly promoted compost humification. Therefore, it speculated that LiP and MnP had the ability to promote HS formation during composting. Accordingly, it is a new understanding of the role of biological enzymes in composting.

Variation in russet sparrow (Passer cinnamomeus) breeding biology in relation to small-scale altitudinal differences in China.

Evolution of avian life histories is typically strongly influenced by both altitude and latitude. To date, most studies have investigated the effects of extreme differences in altitude and latitude on variation in reproductive traits. Studies based on small altitude and latitude spans are needed to better understand the resolution of selective pressures. We compared several aspects of russet sparrow (Passer cinnamomeus) breeding biology between a low-altitude (200 m) and a high-altitude (1,500 m) population in China, representing a relatively small altitudinal gradient (1,300 m). High-altitude birds initiated breeding significantly later compared to their low-altitude counterparts. Interestingly, breeding season was significantly longer in the high-altitude site (57 vs. 84 d). Lowland sparrows laid larger clutches (4.92 vs. 4.09 eggs) and showed greater fledging success (4.20 vs. 3.46 fledgings) than did upland birds. Variation in life history traits thus appears to occur even along a small scale altitudinal gradient. We suggest that the longer breeding season and smaller clutch size in the highland population may be an adaptation or acclimation to compensate for the reduced annual productivity resulting from unfavourable or sub-optimal habitats for these sparrows.

Microhabitat drive microbial anabolism to promote carbon sequestration during composting.

Transforming organic waste into stable carbon by composting is an eco-friendly way. However, the complex environment, huge microbial community and complicated metabolic of composting have limited the directional transformation of organic carbon, which is also not conducive to the fixation of organic carbon. Therefore, this review is based on the formation of humus, a stable by-product of composting, to expound how to promote carbon fixation by increasing the yield of humus. Firstly, we have clarified the transformation regularity of organic matter during composting. Meanwhile, the microhabitat factors affecting microbial catabolism and anabolism were deeply analyzed, in order to provide a theoretical basis for the micro habitat regulation of directional transformation of organic matter during composting. Given that, a method to adjust the directional humification and stabilization of organic carbon has been proposed. Hoping the rapid reduction and efficient stabilization of organic waste can be realized according to this method.

Effects of heavy metals stress on chicken manures composting via the perspective of microbial community feedback.

Heavy metal pollution was the main risk during livestock manures composting, in which microorganisms played a vital role. However, response strategies of microbial community to heavy metals stress (HMS) remained largely unclear. Therefore, the objective of this study was to reveal the ecological adaptation and counter-effect of bacterial community under HMS during chicken manures composting, and evaluating environmental implications of HMS on composting. The degradation of organic matters (more than 6.4%) and carbohydrate (more than 19.8%) were enhanced under intense HMS, suggesting that microorganisms could quickly adapt to the HMS to ensure smooth composting. Meanwhile, HMS increased keystone nodes and strengthened significant positive correlation relationships between genera (p < 0.05), indicating that bacteria resisted HMS through cooperating during composting. In addition, different bacterial groups performed various functions to cope with HMS. Specific bacterial groups responded to HMS, and certain groups regulated bacterial networks. Therefore, bacterial community had the extraordinary potential to deal with HMS and guarantee chicken manures composting even in the presence of high concentrations of heavy metals.

Slowed down nitrogen mineralization under bacterial community-driven conditions by adding inhibitors during rice straw composting.

The aim of this paper was to confirm the role of inhibitors addition, namely adenosine triphosphate (ATP) and malonic acid (MA), on nitrogen availability during rice straw (RS) composting. The results showed that inhibitors addition slowed down the mineralization of ammonium nitrogen and nitrate nitrogen compared to CK. Meanwhile, amino sugar nitrogen and hydrolysable unknown nitrogen contents in ATP and MA treatments were higher, indicating that their addition improved the retention of organic nitrogen components. Furthermore, inhibitors additions attenuated the responsive relationship between bacterial communities and nitrogen components. The main reason was that the addition changed the bacterial community structure of RS compost. The final structural equation verified that inhibitors addition enhanced conversion between nitrogen components, that was, to complex nitrogen components to improve the quality of compost, and the remodeling of bacterial community played an important role. Therefore, adding inhibitors had a driving effect on promoting nitrogen sequestration.

Identification of key drivers of the microbial shikimic acid pathway during different materials composting.

Metabolites of shikimic acid (SA) pathway can be used as humic substance (HS) precursors. Due to the complexity of SA anabolism, there were few studies on SA pathway during composting. The aim of this study was to identify the key drivers of SA pathway during different materials composting. During composting, the SA, protocatechuic acid (PA) and gallic acid (GA) decreased by 57.09%, 72.27% and 54.04% on average, respectively. The structural equation model showed that SA had key driving factors (organic matter and pH) during lawn waste composting. In addition, the complexity of material structure was the main factor affecting PA driving factors. The factors and degree of influence on GA varied with different materials. Accordingly, this study provided theoretical support for the improvement of SA metabolic intensity by single material and mixed material composting, and further provided a new direction for future HS research.

Identifying the role of fired clay minerals on reducing of nitrogen loss and immobilization of organic nitrogen during chicken manure composting.

This study compared effects of clay minerals before and after firing in immobilization of organic nitrogen and reducing of nitrogen loss during chicken manure composting. The clay minerals and fired clay minerals treatments increased organic nitrogen contents and significantly reduced nitrogen loss, the loss was in order CK (52.61%) > M (47.15%) > I (45.90%) > M- (42.58%) > I- (40.59%). Meanwhile, network analysis indicated that core bacterial community associated with nitrogen transformation were more abundant, and conversion effect of single core bacteria on nitrogen components was enhanced in fired clay minerals treatments. In addition, fired clay minerals strengthened correlation between environmental factors, bacterial community and organic nitrogen, and enhanced interaction of abiotic and biotic pathways, which verified by variance partitioning analysis and structural equation model. Therefore, fired clay minerals play a remarkable driving role in formation and immobilization of organic nitrogen.

Machine learning framework for intelligent prediction of compost maturity towards automation of food waste composting system.

Reactive composting is a promising technology for recovering valuable resources from food waste, while its manual regulation is laborious and time-consuming. In this study, machine learning (ML) technologies are adopted to enable automated composting by predicting compost maturity and providing process regulation. Four machine learning algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Multilayer Perceptron (MLP) are employed to predict the seed germination index (GI) and C/N ratio. Based on the best fusion model with the highest R2 of 0.977 and 0.986 for the multi-task prediction of GI and C/N ratio, the critical factors and their interactions with maturity are identified. Moreover, the ML model is validated on a composting reactor and the ML-based prediction application can provide regulation to ensure food waste decompose within the required time. In conclusion, this compost maturity prediction system automates the reactive composting, thus reducing labor costs.

Photodegradation, bacterial metabolism, and photosynthesis drive the dissolved organic matter cycle in the Heilongjiang River.

Dissolved organic matter (DOM) plays a vital role in the biogeochemistry of aquatic ecosystems. However, the mechanisms of DOM cycling in the water column during different seasons have not been fully elucidated to date. The differences in DOM degradation in summer, autumn, and winter water columns were evaluated in this study. The results showed that bacteria played an essential role in the degradation of DOM in the summer water column. Photochemical degradation was the primary degradation pathway of DOM in the autumn and winter water columns. Notably, while DOM is degraded, photosynthetic bacteria produce organic matter through photosynthesis to replenish the water column. EEM-PARAFAC analysis indicated more tryptophan component C1 in summer, but the contents of humic substance component C2 and terrestrial substance C3 were higher in autumn and winter. In summer, more tryptophan-like components were consumed by bacteria, and Cyanobacteria produced more organic matter through photosynthesis to replenish the water column. Moreover, a similar bacterial community structure and a more active tryptophan biosynthesis pathway were found in autumn and winter. Random forest models identified representative bacteria involved in the DOM transformation process in different seasons. The above findings may be helpful to explore the degradation characteristics of DOM in different seasons and predict the fate of DOM in the water column in the future.

Insights into enzyme activity and phosphorus conversion during kitchen waste composting utilizing phosphorus-solubilizing bacterial inoculation.

The main objective of this research was to investigate the effects of Phosphorus-Solubilizing Bacterial (PSB) inoculant on the bacterial structure and phosphorus transformation during kitchen waste composting. High throughput sequencing, topological roles, and multiple analysis methods were conducted to explain the links between phosphorus fractions, enzyme contents, and microbial community structure and function. The findings indicated that bacterial inoculant improved environmental parameters and increased the concentration of total phosphorus, Olsen phosphorus, citric acid phosphorus, OM decomposition, and bacterial diversity. Network analysis concluded that the inoculation treatment was more complex (nodes and edges) and contained more positive links than the control, implying the inoculation effect. The structural equation model also displayed that pH and enzyme activity directly enhanced the phosphorus conversion and bacterial structure. Overall, these results suggest that bacterial inoculation may considerably increase enzyme activity, thus improving biological phosphorus transformation and nutrient content in composting products.

δ-MnO2 changed the structure of humic-like acid during co-composting of chicken manure and rice straw.

Improving the structure and quantity of humus is important to reduce agriculture organic waste by composting. The present study was aimed to assess the role of δ-MnO2 on humus fractions formation during co-composting of chicken manure and rice straw. Two tests (control group (CK), the addition of δ-MnO2 (M)) were carried out. The results showed that organic matter content decreased by 34% and 29% at M and CK, suggesting the process of organic waste disposal was accelerated by adding δ-MnO2. The structures and quantity of fulvic acid (FA) and humic acid (HA) (as the main fractions of humus) were investigated. The δ-MnO2 had no significant effect on improving the concentration of FA and HA (p > 0.05). However, the addition of δ-MnO2 caused different effects on the FA and HA structure. The humification degree of FA improved, while bioavailability of HA increased through adding δ-MnO2. The addition of δ-MnO2 rephased the bacterial community structure, slowing down the succession rate of the bacterial community in M composting. After adding δ-MnO2, the structural equation modeling results showed that environmental factors could directly drive changes in FA and HA by modulating the bacterial community. Furthermore, the role of FA and HA in the soil amendment was also demonstrated. Therefore, the addition of MnO2 might be promising for agriculture organic waste treatment and environmental repair during composting.

Modified montmorillonite and illite adjusted the preference of biotic and abiotic pathways of humus formation during chicken manure composting.

The study aimed to identify the preference of pathways of humus formation. Five lab-scale composting experiments were established: the control (CK), montmorillonite addition (M), illite addition (I), thermal treatment montmorillonite addition (M-) and thermal treatment illite addition (I-). Results showed humus content was increased by 11.5%, 39.3%, 37.2%, 30.9% and 27.6% during CK, M-, M, I- and I composting. Meanwhile, Redundancy analysis indicated the bands of bacteria community related to humic acid (HA) were more abundant in the M- and I- treatments. Furthermore, structural equation model and variance partitioning analysis demonstrated that M- and I- treatments promoted precursors to synthesize HA by coordinated regulation of biotic pathway and abiotic pathway, the increase of HA in the M and I treatments mainly through the abiotic pathway. In summary, an effective method was proposed to improve humus production by adjusting the preference of biotic and abiotic pathways of humus formation.

Accurate Prediction of Hydration Sites of Proteins Using Energy Model With Atom Embedding.

We propose a method based on neural networks to accurately predict hydration sites in proteins. In our approach, high-quality data of protein structures are used to parametrize our neural network model, which is a differentiable score function that can evaluate an arbitrary position in 3D structures on proteins and predict the nearest water molecule that is not present. The score function is further integrated into our water placement algorithm to generate explicit hydration sites. In experiments on the OppA protein dataset used in previous studies and our selection of protein structures, our method achieves the highest model quality in terms of F1 score, compared to several previous studies.