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

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

Quantifying Methane Emissions from Aquaculture Ponds in China.

Small ponds are important methane (CH4) sources. However, current estimates of CH4 emissions from aquaculture ponds are largely uncertain due to data paucity, especially in China─the largest aquaculture producer in the world. Here, we present a nationwide metadata analysis with a database of 55 field observations to examine total CH4 emissions from aquaculture ponds in China. We found that the annual CH4 fluxes from aquaculture ponds are much larger than those from reservoirs and lakes. The total CH4 emission from aquaculture ponds is 1.60 ± 0.62 Tg CH4 yr-1, with an average growth rate of ∼0.03 Tg CH4 yr-2 during the period 2008-2019. Compared with global major protein-producing livestocks, aquaculture species have a lower (63%) emission intensity, defined by the amount of CH4 emitted per unit of animal proteins. Our study highlights the essential contribution of China's aquaculture ponds to national CH4 emissions and the lower environmental cost of the aquaculture sector for future animal protein production. More field measurements with multi-scale observations are urgently needed to reduce the uncertainty of CH4 emissions from aquaculture ponds.

Investigation of Potential Drivers of Elevated Uranium Prevalence in Indian Groundwaters with a Unified Speciation Model.

Elevated uranium (U) (>WHO limit of 30 µg L-1) in Indian groundwaters is primarily considered geogenic, but the specific mineralogical sources and mechanisms for U mobilization are poorly understood. In this contribution, statistical and geochemical analyses of well-constrained metadata of Indian groundwater quality (n = 342 of 8543) were performed to identify key parameters and processes that influence U concentrations. For geochemical predictions, a unified speciation model was developed from a carefully compiled and updated thermodynamic database of inorganic, organic (Stockholm Humic model), and surface complexation reactions and associated constants. Critical U contamination was found at shallow depths (<100 m) within the Indo-Gangetic plain, as determined by bivariate nonparametric Kendall's Taub and probability-based association tests. Analysis of aquifer redox states, multivariate hierarchical clusters, and principal components indicated that U contamination was predominant not just in oxic but mixed (oxic-anoxic) aquifers under high Fe, Mn, and SO4 concentrations, presumably due to U release from dissolution of Fe/Mn oxides or Fe sulfides and silicate weathering. Most groundwaters were undersaturated with respect to relevant U-bearing solids despite being supersaturated with respect to atmospheric CO2 (average pCO2 of reported dissolved inorganic carbonate (DIC) data = 10-1.57 atm). Yet, dissolved U did not appear to be mass limited, as predicted solubilities from reported sediment concentrations of U were ∼3 orders of magnitude higher. Integration of surface complexation models of U on typical aquifer adsorbents, ferrihydrite, goethite, and manganese dioxide, was necessary to explain dissolved U concentrations. Uranium contamination probabilities with increasing dissolved Ca and Mn exhibited minima at equilibrium solubilities of calcite [∼50 mg L-1] and rhodochrosite [∼0.14 mg L-1], respectively, at an average groundwater pH of ∼7.5. A potential indirect control of such U-free carbonate solids on U mobilization was suggested. For locations (n = 37) where dissolved organic carbon was also reported, organic complexes of U contributed negligibly to dominant U speciation at the groundwater pH. Overall, the unified model suggested competitive dissolution-precipitation and adsorption-desorption controls on U speciation. The model provides a quantitative framework that can be extended to understand dominant mobilization mechanisms of geogenic U in aquifers worldwide after suitable modifications to the relevant aquifer parameters.

Hydrophobic Surface Coating Can Reduce Toxicity of Zinc Oxide Nanoparticles to the Marine Copepod Tigriopus japonicus.

Coated zinc oxide nanoparticles (ZnO-NPs) are more commonly applied in commercial products but current risk assessments mostly focus on bare ZnO-NPs. To investigate the impacts of surface coatings, this study examined acute and chronic toxicities of six chemicals, including bare ZnO-NPs, ZnO-NPs with three silane coatings of different hydrophobicity, zinc oxide bulk particles (ZnO-BKs), and zinc ions (Zn-IONs), toward a marine copepod, Tigriopus japonicus. In acute tests, bare ZnO-NPs and hydrophobic ZnO-NPs were less toxic than hydrophilic ZnO-NPs. Analyses of the copepod's antioxidant gene expression suggested that such differences were governed by hydrodynamic size and ion dissolution of the particles, which affected zinc bioaccumulation in copepods. Conversely, all test particles, except the least toxic hydrophobic ZnO-NPs, shared similar chronic toxicity as Zn-IONs because they mostly dissolved into zinc ions at low test concentrations. The metadata analysis, together with our test results, further suggested that the toxicity of coated metal-associated nanoparticles could be predicted by the hydrophobicity and density of their surface coatings. This study evidenced the influence of surface coatings on the physicochemical properties, toxicity, and toxic mechanisms of ZnO-NPs and provided insights into the toxicity prediction of coated nanoparticles from their coating properties to improve their future risk assessment and management.

Challenges on optimization of 3D-printed bone scaffolds.

Advances in biomaterials and the need for patient-specific bone scaffolds require modern manufacturing approaches in addition to a design strategy. Hybrid materials such as those with functionally graded properties are highly needed in tissue replacement and repair. However, their constituents, proportions, sizes, configurations and their connection to each other are a challenge to manufacturing. On the other hand, various bone defect sizes and sites require a cost-effective readily adaptive manufacturing technique to provide components (scaffolds) matching with the anatomical shape of the bone defect. Additive manufacturing or three-dimensional (3D) printing is capable of fabricating functional physical components with or without porosity by depositing the materials layer-by-layer using 3D computer models. Therefore, it facilitates the production of advanced bone scaffolds with the feasibility of making changes to the model. This review paper first discusses the development of a computer-aided-design (CAD) approach for the manufacture of bone scaffolds, from the anatomical data acquisition to the final model. It also provides information on the optimization of scaffold's internal architecture, advanced materials, and process parameters to achieve the best biomimetic performance. Furthermore, the review paper describes the advantages and limitations of 3D printing technologies applied to the production of bone tissue scaffolds.

Deciphering Microbial Community and Nitrogen Fixation in the Legume Rhizosphere.

Nitrogen is the most limiting factor in crop production. Legumes establish a symbiotic relationship with rhizobia and enhance nitrogen fixation. We analyzed 1,624 rhizosphere 16S rRNA gene samples and 113 rhizosphere metagenomic samples from three typical legumes and three non-legumes. The rhizosphere microbial community of the legumes had low diversity and was enriched with nitrogen-cycling bacteria (Sphingomonadaceae, Xanthobacteraceae, Rhizobiaceae, and Bacillaceae). Furthermore, the rhizosphere microbiota of legumes exhibited a high abundance of nitrogen-fixing genes, reflecting a stronger nitrogen-fixing potential, and Streptomycetaceae and Nocardioidaceae were the predominant nitrogen-fixing bacteria. We also identified helper bacteria and confirmed through metadata analysis and a pot experiment that the synthesis of riboflavin by helper bacteria is the key factor in promoting nitrogen fixation. Our study emphasizes that the construction of synthetic communities of nitrogen-fixing bacteria and helper bacteria is crucial for the development of efficient nitrogen-fixing microbial fertilizers.

BacSPaD: A Robust Bacterial Strains' Pathogenicity Resource Based on Integrated and Curated Genomic Metadata.

The vast array of omics data in microbiology presents significant opportunities for studying bacterial pathogenesis and creating computational tools for predicting pathogenic potential. However, the field lacks a comprehensive, curated resource that catalogs bacterial strains and their ability to cause human infections. Current methods for identifying pathogenicity determinants often introduce biases and miss critical aspects of bacterial pathogenesis. In response to this gap, we introduce BacSPaD (Bacterial Strains' Pathogenicity Database), a thoroughly curated database focusing on pathogenicity annotations for a wide range of high-quality, complete bacterial genomes. Our rule-based annotation workflow combines metadata from trusted sources with automated keyword matching, extensive manual curation, and detailed literature review. Our analysis classified 5502 genomes as pathogenic to humans (HP) and 490 as non-pathogenic to humans (NHP), encompassing 532 species, 193 genera, and 96 families. Statistical analysis demonstrated a significant but moderate correlation between virulence factors and HP classification, highlighting the complexity of bacterial pathogenicity and the need for ongoing research. This resource is poised to enhance our understanding of bacterial pathogenicity mechanisms and aid in the development of predictive models. To improve accessibility and provide key visualization statistics, we developed a user-friendly web interface.

Geographic patterns of microbial traits of river basins in China.

River microbiotas contribute to critical geochemical processes and ecological functions of rivers but are sensitive to variations of environmental drivers. Understanding the geographic pattern of river microbial traits in biogeochemical processes can provide important insights into river health. Many studies have characterized river microbial traits in specific situations, but the geographic patterns of these traits and environmental drivers at a large scale are unknown. We reanalyzed 4505 raw 16S rRNA sequences samples for microbiota from river basins in China. The results indicated differences in the diversity, composition, and structure of microbiotas across diverse river basins. Microbial diversity and functional potential in the river basins decreased over time in northern China and increased in southern China due to niche differentiation, e.g., the Yangtze River basin was the healthiest ecosystem. River microbiotas were mainly involved in the cycling of carbon and nitrogen in the river ecosystems and participated in potential organic metabolic functions. Anthropogenic pollutants discharge was the most critical environmental driver for the microbial traits, e.g., antibiotic discharge, followed by climate change. The prediction by machine-learning models indicated that the continuous discharge of antibiotics and climate change led to high ecological risks for the rivers. Our study provides guidelines for improving the health of river ecosystems and for the formulation of strategies to restore the rivers.

Machine learning predicts ecological risks of nanoparticles to soil microbial communities.

With the rapid development of nanotechnology in agriculture, there is increasing urgency to assess the impacts of nanoparticles (NPs) on the soil environment. This study merged raw high-throughput sequencing (HTS) data sets generated from 365 soil samples to reveal the potential ecological effects of NPs on soil microbial community by means of metadata analysis and machine learning methods. Metadata analysis showed that treatment with nanoparticles did not have a significant impact on the alpha diversity of the microbial community, but significantly altered the beta diversity. Unfortunately, the abundance of several beneficial bacteria, such as Dyella, Methylophilus, Streptomyces, which promote the growth of plants, and improve pathogenic resistance, was reduced under the addition of synthetic nanoparticles. Furthermore, metadata demonstrated that nanoparticles treatment weakened the biosynthesis ability of cofactors, carriers, and vitamins, and enhanced the degradation ability of aromatic compounds, amino acids, etc. This is unfavorable for the performance of soil functions. Besides the soil heterogeneity, machine learning uncovered that a) the exposure time of nanoparticles was the most important factor to reshape the soil microbial community, and b) long-term exposure decreased the diversity of microbial community and the abundance of beneficial bacteria. This study is the first to use a machine learning model and metadata analysis to investigate the relationship between the properties of nanoparticles and the hazards to the soil microbial community from a macro perspective. This guides the rational use of nanoparticles for which the impacts on soil microbiota are minimized.

Compare the performance of multiple binary classification models in microbial high-throughput sequencing datasets.

The development of machine learning and deep learning provided solutions for predicting microbiota response on environmental change based on microbial high-throughput sequencing. However, there were few studies specifically clarifying the performance and practical of two types of binary classification models to find a better algorithm for the microbiota data analysis. Here, for the first time, we evaluated the performance, accuracy and running time of the binary classification models built by three machine learning methods - random forest (RF), support vector machine (SVM), logistic regression (LR), and one deep learning method - back propagation neural network (BPNN). The built models were based on the microbiota datasets that removed low-quality variables and solved the class imbalance problem. Additionally, we optimized the models by tuning. Our study demonstrated that dataset pre-processing was a necessary process for model construction. Among these 4 binary classification models, BPNN and RF were the most suitable methods for constructing microbiota binary classification models. Using these 4 models to predict multiple microbial datasets, BPNN showed the highest accuracy and the most robust performance, while the RF method was ranked second. We also constructed the optimal models by adjusting the epochs of BPNN and the n_estimators of RF for six times. The evaluation related to performances of models provided a road map for the application of artificial intelligence to assess microbial ecology.

The structural shift and collaboration capacity in GenBank Networks: A longitudinal study.

Metadata in scientific data repositories such as GenBank contain links between data submissions and related publications. As a new data source for studying collaboration networks, metadata in data repositories compensate for the limitations of publication-based research on collaboration networks. This paper reports the findings from a GenBank metadata analytics project. We used network science methods to uncover the structures and dynamics of GenBank collaboration networks from 1992-2018. The longitudinality and large scale of this data collection allowed us to unravel the evolution history of collaboration networks and identify the trend of flattening network structures over time and optimal assortative mixing range for enhancing collaboration capacity. By incorporating metadata from the data production stage with the publication stage, we uncovered new characteristics of collaboration networks as well as developed new metrics for assessing the effectiveness of enablers of collaboration-scientific and technical human capital, cyberinfrastructure, and science policy.

Zelus renardii Roaming in Southern Italy.

This study collects data from the literature and updates our Zelus renardii Kolenati, 1856 (Leafhopper Assassin Bug, LAB) prey knowledge. The literature consists of ca. 170 entries encompassing the years 1856 to 2021. This reduviid originated in the Nearctic region, but has entered and acclimatised in many Mediterranean countries. Our quantitative predation experiments-in the laboratory on caged plants plus field or environmental observations-confirm that LAB prefers a selected array of prey. Laboratory predation tests on living targets (Hemiptera, Coleoptera, Diptera, and Hymenoptera) agree with the literature. Zelus renardii prefers comparatively large, highly mobile, and readily available prey. LAB preferences on available hemipterans targets suggest that Zelus renardii is a good inundative biocontrol agent for Xylella fastidiosapauca ST53 infections. LAB also prey on other important olive pests, such as Bactrocera oleae. Therefore, Zelus renardii is a major integrated pest management (IPM) component to limit Xylella fastidiosa pandemics and other pest invasions.

A comparative study on HIPAA technical safeguards assessment of android mHealth applications.

Protecting personal health records is becoming increasingly important as more people use Mobile Health applications (mHealth apps) to improve their health outcomes. These mHealth apps enable consumers to monitor their health-related problems, store, manage, and share health records, medical conditions, treatment, and medication. With the increase of mHealth apps accessibility and usability, it is crucial to create, receive, maintain or transmit protected health information (PHI) on behalf of a covered entity or another business associate. The Health Insurance Portability and Accountability Act (HIPAA) provides guidelines to the app developers so that the apps must be compliant with required and addressable Technical Safeguards. However, most mobile app developers, including mHealth apps are not aware of HIPAA security and privacy regulations. Therefore, a research opportunity has emerged to develop an analytical framework to assist the developer to maintain a secure and HIPAA-compliant source code and raise awareness among consumers about the privacy and security of sensitive and personal health information. We proposed an Android source code analysis framework that evaluates twelve HIPAA Technical Safeguards to check whether a mHealth application is HIPAA compliant or not. The implemented meta-analysis and data-flow analysis algorithms efficiently identify the risk and safety features of mHealth apps that violate HIPAA regulations. Furthermore, we addressed API level checking for secure data communication mandated by recent CMS guidelines between third-party mobile health apps and EHR systems. Experimentally, a web-based tool has been developed for evaluating the efficacy of analysis techniques and algorithms. We have investigated 200 top popular Medical and Health & Fitness category Android apps collected from Google Play Store. We identified from the comparative analysis of the HIPAA rules assessment results that authorization to access sensitive resources, data encryption-decryption, and data transmission security is the most vulnerable features of the investigated apps. We provided recommendations to app developers about the most common mistake made at the time of app development and how to avoid these mistakes to implement secure and HIPAA-compliant apps. The proposed framework enables us to develop an IDE plugin for mHealth app developers and a web-based interface for mHealth app consumers.

A comprehensive quality assessment framework for scientific events.

Systematic assessment of scientific events has become increasingly important for research communities. A range of metrics (e.g., citations, h-index) have been developed by different research communities to make such assessments effectual. However, most of the metrics for assessing the quality of less formal publication venues and events have not yet deeply investigated. It is also rather challenging to develop respective metrics because each research community has its own formal and informal rules of communication and quality standards. In this article, we develop a comprehensive framework of assessment metrics for evaluating scientific events and involved stakeholders. The resulting quality metrics are determined with respect to three general categories-events, persons, and bibliometrics. Our assessment methodology is empirically applied to several series of computer science events, such as conferences and workshops, using publicly available data for determining quality metrics. We show that the metrics' values coincide with the intuitive agreement of the community on its "top conferences". Our results demonstrate that highly-ranked events share similar profiles, including the provision of outstanding reviews, visiting diverse locations, having reputed people involved, and renowned sponsors.

Maize specialized metabolome networks reveal organ-preferential mixed glycosides.

Despite the scientific and economic importance of maize, little is known about its specialized metabolism. Here, five maize organs were profiled using different reversed-phase liquid chromatography-mass spectrometry methods. The resulting spectral metadata, combined with candidate substrate-product pair (CSPP) networks, allowed the structural characterization of 427 of the 5,420 profiled compounds, including phenylpropanoids, flavonoids, benzoxazinoids, and auxin-related compounds, among others. Only 75 of the 427 compounds were already described in maize. Analysis of the CSPP networks showed that phenylpropanoids are present in all organs, whereas other metabolic classes are rather organ-enriched. Frequently occurring CSPP mass differences often corresponded with glycosyl- and acyltransferase reactions. The interplay of glycosylations and acylations yields a wide variety of mixed glycosides, bearing substructures corresponding to the different biochemical classes. For example, in the tassel, many phenylpropanoid and flavonoid-bearing glycosides also contain auxin-derived moieties. The characterized compounds and mass differences are an important step forward in metabolic pathway discovery and systems biology research. The spectral metadata of the 5,420 compounds is publicly available (DynLib spectral database, https://bioit3.irc.ugent.be/dynlib/).

Analysing the evolution of computer science events leveraging a scholarly knowledge graph: a scientometrics study of top-ranked events in the past decade.

The publish or perish culture of scholarly communication results in quality and relevance to be are subordinate to quantity. Scientific events such as conferences play an important role in scholarly communication and knowledge exchange. Researchers in many fields, such as computer science, often need to search for events to publish their research results, establish connections for collaborations with other researchers and stay up to date with recent works. Researchers need to have a meta-research understanding of the quality of scientific events to publish in high-quality venues. However, there are many diverse and complex criteria to be explored for the evaluation of events. Thus, finding events with quality-related criteria becomes a time-consuming task for researchers and often results in an experience-based subjective evaluation. OpenResearch.org is a crowd-sourcing platform that provides features to explore previous and upcoming events of computer science, based on a knowledge graph. In this paper, we devise an ontology representing scientific events metadata. Furthermore, we introduce an analytical study of the evolution of Computer Science events leveraging the OpenResearch.org knowledge graph. We identify common characteristics of these events, formalize them, and combine them as a group of metrics. These metrics can be used by potential authors to identify high-quality events. On top of the improved ontology, we analyzed the metadata of renowned conferences in various computer science communities, such as VLDB, ISWC, ESWC, WIMS, and SEMANTiCS, in order to inspect their potential as event metrics.

The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0.

OBJECTIVE: The population representativeness of a clinical study is influenced by how real-world patients qualify for the study. We analyze the representativeness of eligible patients for multiple type 2 diabetes trials and the relationship between representativeness and other trial characteristics. METHODS: Sixty-nine study traits available in the electronic health record data for 2034 patients with type 2 diabetes were used to profile the target patients for type 2 diabetes trials. A set of 1691 type 2 diabetes trials was identified from ClinicalTrials.gov, and their population representativeness was calculated using the published Generalizability Index of Study Traits 2.0 metric. The relationships between population representativeness and number of traits and between trial duration and trial metadata were statistically analyzed. A focused analysis with only phase 2 and 3 interventional trials was also conducted. RESULTS: A total of 869 of 1691 trials (51.4%) and 412 of 776 phase 2 and 3 interventional trials (53.1%) had a population representativeness of <5%. The overall representativeness was significantly correlated with the representativeness of the Hba1c criterion. The greater the number of criteria or the shorter the trial, the less the representativeness. Among the trial metadata, phase, recruitment status, and start year were found to have a statistically significant effect on population representativeness. For phase 2 and 3 interventional trials, only start year was significantly associated with representativeness. CONCLUSIONS: Our study quantified the representativeness of multiple type 2 diabetes trials. The common low representativeness of type 2 diabetes trials could be attributed to specific study design requirements of trials or safety concerns. Rather than criticizing the low representativeness, we contribute a method for increasing the transparency of the representativeness of clinical trials.

Chromothripsis Detection and Characterization Using the CTLPScanner Web Server.

Accurate detection of chromothripsis event is important to study the mechanisms underlying this phenomenon. CTLPScanner ( http://cgma.scu.edu.cn/CTLPScanner/ ) is a web-based tool for identification and annotation of chromothripsis-like pattern (CTLP) in genomic array data. In this chapter, we illustrate the utility of CTLPScanner for screening chromosome pulverization regions and give interpretation of the results. The web interface offers a set of parameters and thresholds for customized screening. We also provide practical recommendations for effective chromothripsis detection. In addition to the user data processing module, CTLPScanner contains more than 50,000 preprocessed oncogenomic arrays, which allow users to explore the presence of chromothripsis signatures from public data resources.