Crystal violet (CV) and malachite green (MG) are antibiotics used in aquaculture, regulated due to their teratogenicity and potential carcinogenic risk. Detecting them in seafood is challenging due to low concentrations and complex matrices, especially fat interference. In this study, low-temperature clean-up removed fat interference, and magnetic nanoparticles preconcentrated the target analytes, achieving high sensitivity, low cost, and easy operation. The proposed method achieved detection limits of 0.18 ng g-1 and 0.54 ng g-1 for CV and MG, respectively, with quantification limits of 0.21 ng g-1 and 0.60 ng g-1. The intra-and inter-day relative standard deviations were 1.45 %-4.81 % and 4.53 %-7.60 %, respectively, indicating good reproducibility. Recovery rates were 83.19 %-112.88 %, with stable adsorption capacity after 25 cycles. This method offers an ideal solution for rapid screening of CV and MG in seafood for batch samples in markets, stock houses, and on-site situations, adaptable to various analytes and matrices.

A low dose of ZnO NPs improved potato growth and quality. However, the short-term exposure of high-dose ZnO NPs poses certain ecological risks.

To effectively depict the results of population genetics studies, it is essential to present ancestry composition and genetic distance. The growing amount of genomic data prompted us to design AncestryPainter 1.0, a Perl program to display the ancestry composition of numerous individuals using a rounded graph. Motivated by the requests of users in practical applications, we updated AncestryPainter to version 2.0 by coding in an R package and improving the layout, providing more options and compatible statistical functions for graphing. In particular, AncestryPainter 2.0 implements a method admixture history graph (AHG) to infer the admixture sequence of multiple ancestry populations, and allows for multiple pie charts at the center of the graph to display the ancestry composition of more than one target population. We also introduced an additional graphing module to visualize genetic distance through radial bars of varying lengths surrounding a core. Visualization functions per se have been enhanced in this update as well. Furthermore, AncestryPainter 2.0 includes two statistical modules to 1) merge ancestry proportion matrices and 2) infer admixture sequences through correlation analyses. AncestryPainter 2.0 is publicly available at and . ### Competing Interest Statement The authors have declared no competing interest.

In the context of global population growth expected in the future, enhancing the agri-food yield is crucial. Plant diseases significantly impact crop production and food security. Modern microfluidics offers a compact and convenient approach for detecting these defects. Although this field is still in its infancy and few comprehensive reviews have explored this topic, practical research has great potential. This paper reviews the principles, materials, and applications of microfluidic technology for detecting plant diseases caused by various pathogens. Its performance in realizing the separation, enrichment, and detection of different pathogens is discussed in depth to shed light on its prospects. With its versatile design, microfluidics has been developed for rapid, sensitive, and low-cost monitoring of plant diseases. Incorporating modules for separation, preconcentration, amplification, and detection enables the early detection of trace amounts of pathogens, enhancing crop security. Coupling with imaging systems, smart and digital devices are increasingly being reported as advanced solutions.

The earlier version of AncestryPainter is a Perl program that displays the ancestry composition of numerous individuals using a rounded graph. Motivated by the requests of users in practical applications, we updated AncestryPainter to version 2.0 by coding in an R package and improving the layout, providing more options and compatible statistical functions for graphing. Apart from improving visualization functions per se in this update, we added an extra graphing module to visualize genetic distance through radial bars of varying lengths surrounding a core. Notably, AncestryPainter 2.0 allows for multiple pie charts at the center of the graph to display the ancestry composition of more than one target population and implements a method admixture history graph to infer the admixture sequence of multiple ancestry populations. We validated the six admixture history graph metrics using both simulated and real data and implemented a Pearson coefficient-based metric with the best performance in AncestryPainter 2.0. Furthermore, a statistical module was implemented to merge ancestry proportion matrices. AncestryPainter 2.0 is freely available at https://github.com/Shuhua-Group/AncestryPainterV2 and https://pog.fudan.edu.cn/#/Software.

Background: As a common pollutant, the carcinogenic properties of polycyclic aromatic hydrocarbons have garnered considerable attention. Trace metabolites of polycyclic aromatic hydrocarbons can be detected in urine as a non-invasively approach to monitor the exposure level. Nonetheless, the urine samples have the disadvantages of being large in volume and containing numerous impurities. Given the growing demand to study metabolites with low abundance and potential biomarkers, there is a pressing need for a preconcentration and high-throughput technique for effectively handling complex liquid samples. Results: Polystyrene-coated magnetic nanoparticles were used to establish a novel magnetic extraction method for monohydroxy polycyclic aromatic hydrocarbons in urine samples. Polystyrene magnetic nanoparticles are an ideal absorbent for solid-phase extraction. After the material was mixed with the sample and adsorbed the target analyte, the analytes on the material were eluted and quantified using high-performance liquid chromatography. Influencing factors were optimized, and the proposed method achieved desirable sensitivity in analyzing lowabundance metabolites in large volumes of complex urine samples. The recoveries of intra-day and inter-day were 78.0-118.0 % and 81.0 %-115.0 %, respectively. The intra-day and inter-day reproducibility were less than 4.5 % and 8.6 %, respectively. The limits of detection were in the range of 0.009-0.041 ng mL-1, and the limits of quantification were in the range of 0.030-0.135 ng mL-1. Significance and novelty: The application of reusable polystyrene-coated magnetic solid-phase nanoparticles as adsorbents makes the extraction of monohydroxy polycyclic aromatic hydrocarbons from urine samples economical and environmentally benign. The proposed method is simple, sensitive, and efficient compared to existing techniques. The nanoparticles are easy to prepare, showing potential for rapid screening of complex bulk bio-samples in batches with high efficiency and low budget.

Microplastics (MPs), especially polyethylene MPs (PE MPs), which are the primary component of mulch, have attracted increasing attention in recent years. ZnO nanoparticles (NPs), which constitute a metal-based nanomaterial commonly used in agricultural production, co-converge with PE MPs in the soil. However, studies revealing the behavior and fate of ZnO NPs in soil-plant systems in the presence of MPs are limited. In this study, a pot experiment was used to evaluate the effects of maize co-exposure to PE MPs (0.5 % and 5 % w/w) and ZnO NPs (500 mg/kg) on growth, element distribution, speciation, and adsorption mechanism. The results demonstrate that individual exposure to PE MPs posed no significant toxicity; however, it significantly decreased maize grain yield (essentially 0). ZnO NP-exposure treatments significantly increased the Zn concentration and distribution intensity in maize tissues. Among them, the Zn concentration in the maize root exceeded 200 mg/kg, compared with 40 mg/kg in the grain. Moreover, the Zn concentrations in various tissues decreased in the following order: stem, leaf, cob, bract, and grain. Reassuringly, ZnO NPs still could not be transported to the maize stem under co-exposure to PE MPs. ZnO NPs had been biotransformed (64 % of the Zn was associated with histidine, with the remainder being associated with P [phytate] and cysteine) in maize stem. This study provides new insights into the plant physiological risks of PE MP and ZnO NP co-exposure in the soil-plant system and assesses the fate of ZnO NPs.

Structural variations (SVs) play important roles in human evolution and diseases, but there is a lack of data resources concerning representative samples, especially for East Asians. Taking advantage of both next-generation sequencing and third-generation sequencing data at the whole-genome level, we developed the database PGG.SV to provide a practical platform for both regionally and globally representative structural variants. In its current version, PGG.SV archives 584 277 SVs obtained from whole-genome sequencing data of 6048 samples, including 1030 long-read sequencing genomes representing 177 global populations. PGG.SV provides (i) high-quality SVs with fine-scale and precise genomic locations in both GRCh37 and GRCh38, covering underrepresented SVs in existing sequencing and microarray data; (ii) hierarchical estimation of SV prevalence in geographical populations; (iii) informative annotations of SV-related genes, potential functions and clinical effects; (iv) an analysis platform to facilitate SV-based case-control association studies and (v) various visualization tools for understanding the SV structures in the human genome. Taken together, PGG.SV provides a user-friendly online interface, easy-to-use analysis tools and a detailed presentation of results. PGG.SV is freely accessible via https://www.biosino.org/pggsv.

A rapid and efficient method using an alkyl-functionalized magnetic nanoparticles-based extraction technique combined with Ultra-High Performance Liquid Chromatography was developed for the detection of trace amounts of polycyclic aromatic hydrocarbons in tea leaves. As a popular coating for chromatographic column packing materials, C-18-alkyl has been demonstrated to be effective in separating polycyclic aromatic hydrocarbons. Additionally, the magnetism of the nanomaterials accelerates the extraction process while their high surface ratio enables desirable dispersity in the sample matrix. Meanwhile, the adsorbents can be washed and reused 30 times without compromising recovery, which greatly reduces the budget. The effects of various parameters were investigated and optimized, and the recoveries for five analytes were in the range of 84.8-105.4%. The RSD of intra-day and inter-day were below 11.9% and 6.8%, respectively. The limits of detection and limits of quantification ranged from 1.69-9.97 ng g(-1) and 5.12-30.21 ng g(-1), indicating satisfactory sensitivity. Thus, the proposed methodology is rapid, highly efficient, and economical, and it expands the application of magnetic cleanup approaches in complex food matrices.

This review provides a comprehensive conclusion of the relationship between the intake of various polycyclic aromatic hydrocarbons (PAHs) and different dietary patterns, pointing to the accompanying potential health risks. To achieve this, existing pertinent research was collected and analyzed. The collation revealed that the concentration of PAHs in food and their dietary patterns were diverse in different regions. Specifically, the concentration of PAHs in food was found to be related to the level of pollution in the area, including soil, air, and water pollution, which is then accumulated through the food chain into food that can be ingested directly by the human body, resulting in malformations in offspring, increased risk of cancer, and gene mutation. Guidebooks and dietary surveys were consulted to uncover disparities in dietary patterns, which indicated regional variations in taste preferences, traditional foods, and eating habits. Different regions are spatially categorized in this assessment by cities, countries, and continents. Notably, smoking and grilling are two of the food processing methods most likely to produce high levels of PAHs. To prevent excessive intake of PAHs from food items and attain a higher quality of life, more health education is urgently needed to promote healthy eating patterns.

Human genomics is witnessing an ongoing paradigm shift from a single reference sequence to a pangenome form, but populations of Asian ancestry are underrepresented. Here we present data from the first phase of the Chinese Pangenome Consortium, including a collection of 116 high-quality and haplotype-phased de novo assemblies based on 58 core samples representing 36 minority Chinese ethnic groups. With an average 30.65× high-fidelity long-read sequence coverage, an average contiguity N50 of more than 35.63 megabases and an average total size of 3.01 gigabases, the CPC core assemblies add 189 million base pairs of euchromatic polymorphic sequences and 1,367 protein-coding gene duplications to GRCh38. We identified 15.9 million small variants and 78,072 structural variants, of which 5.9 million small variants and 34,223 structural variants were not reported in a recently released pangenome reference1. The Chinese Pangenome Consortium data demonstrate a remarkable increase in the discovery of novel and missing sequences when individuals are included from underrepresented minority ethnic groups. The missing reference sequences were enriched with archaic-derived alleles and genes that confer essential functions related to keratinization, response to ultraviolet radiation, DNA repair, immunological responses and lifespan, implying great potential for shedding new light on human evolution and recovering missing heritability in complex disease mapping.

The human leukocyte antigen (HLA) system, or the human version of the major histocompatibility complex (MHC), is known for its extreme polymorphic nature and high heterogeneity. Taking advantage of whole-genome and whole-exome sequencing data, we developed PGG. MHC to provide a platform to explore the diversity of the MHC in Asia as well as in global populations. PGG. MHC currently archives high-resolution HLA alleles of 53 254 samples representing 190 populations spanning 66 countries. PGG. MHC provides: (i) high-quality allele frequencies for eight classical HLA loci (HLA-A, -B, -C, -DQA1, -DQB1, -DRB1, -DPA1 and -DPB1); (ii) visualization of population prevalence of HLA alleles on global, regional, and country-wide levels; (iii) haplotype structure of 134 populations; (iv) two online analysis tools including 'HLA imputation' for inferring HLA alleles from SNP genotyping data and 'HLA association' to perform case/control studies for HLA-related phenotypes and (v) East Asian-specific reference panels for HLA imputation. Equipped with high-quality frequency data and user-friendly computer tools, we expect that the PGG. MHC database can advance the understanding and facilitate applications of MHC genomic diversity in both evolutionary and medical studies. The PGG. MHC database is freely accessible via https://pog.fudan.edu.cn/pggmhc or https://www.pggmhc.org/pggmhc.

Slow-release fertilizers (MSRFs) are effective in improving fertilizer utilization and reducing environmental fertilizer pollution. In this study, polyvinyl alcohol (PVA) was modified with kitchen waste oil (KWO) and grafted with urea to prepare a urea MSRF. FTIR, Scanning electron microscopy, X-ray diffraction and water contact angle were used to study the MSRF structure and properties. The effects of the nitrogen content on its properties were also investigated through release performance and degradation behavior evaluation and plant experiments. The results showed that the oil-based polyalcohol-modified PVA (OPMPVA) solution prepared from KWO had good compatibility with the urea-modified compound hydroxylurea, and the prepared MSRF had good hydrophobicity, degradation and slow-release properties. The nutrient release period reached more than 20 days, and the degradation rate after 30 days reached 10.2%. Plant experiments showed that the MSRF prepared in this research has good application potential, and the utilization of nitrogen nutrients was improved.

In this paper, graphene-like carbon (KW-GO) extracted from kitchen waste (KW) is used to reduce the agglomeration of V2O5 and improve the separation rate of photogenerated electron-hole pairs from V2O5. We found that the V2O5-KW-GO composite material (VKW-GO) could significantly enhance the photocatalytic activity and H-2 production rate under visible light irradiation compared to pure V2O5. To analyze the composition and morphology of the materials, XRD, SEM, BET, UV-Vis, XPS, and Raman were measured. The results showed that the addition of KW-GO reduced the aggregation of V2O5 powder. At the same time, the specific surface area of the composite sample increased providing more active sites for photocatalytic hydrogen production. In addition, the visible absorption range of the composite sample also increased. As a result, the hydrogen production rate of V2O5 increased from 247.52 mol h(-1) g(-1) to 354.15 mol h(-1) g(-1). The method using V2O5 and VKW-GO as a catalyst for H-2 production is innovative, and the conclusion may provide important theoretical guidance for photocatalytic hydrogen production. (C) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

This review (with 126 references) aims at providing an updated overview of the recent developments and innovations of the preparation and application of magnetic nanoparticles for food safety analysis. During the past two decades, various magnetic nanoparticles with different sizes, shapes, and surface modifications have been designed, synthesized, and characterized with the prospering development of material science. Analytical scientists and food scientists are among the ones who bring these novel materials from laboratories to commercial applications. Powerful and versatile surface functional groups and high surface to mass ratios make these magnetic nanoparticles useful tools for high-efficiency capture and preconcentration of certain molecules, even when they exist in trace levels or complicated food matrices. This is why more and more methods for sensitive detection and quantification of hazards in foods are developed based on these magic magnetic tools. In this review, the principles and superiorities of using magnetic nanoparticles for food pollutant analysis are first introduced, like the mechanism of magnetic solid phase extraction, a most commonly used method for food safety-related sample pretreatment. Their design and preparation are presented afterward, alongside the mechanisms underlying their application for different analytical purposes. After that, recently developed magnetic nanoparticle-based methods for dealing with food pollutants such as organic pollutants, heavy metals, and pathogens in different food matrices are summarized in detail. In the end, some humble outlooks on future directions for work in this field are provided.

Microplastic (MP) pollution is increasing and several extensive global studies have demonstrated its ubiquity. Information on the summary of the sources of microplastics in all areas of the environment at the global level is insufficient, thus, the need for this study. Through this study, we aim to find answers to the questions: (1) What are the sources, distribution, and nature of microplastics in the environment from a global perspective? (2) What are the knowledge gaps for future microplastic studies? To achieve these objectives, the PRISMA guidelines for reviews were employed to gather similar but relevant peer-reviewed articles, book chapters, and scientific reports on this topic. The main search engines used were: Google Scholar, ScienceDirect, Web of Science, and PubMed. The study focused on environmental compartments such as the marine environment, soil, and the atmosphere. Two other relevant components including food and drinking water were also considered. Microplastics are found everywhere ranging from remote areas such as deserts and the Arctic Sea to indoor environments. Fragmentation of plastic debris, synthetic textiles, road markings, personal care products, tire wear, indoor furnishes, illegal dumps, and vessels lost at sea remain the ultimate sources of microplastics in the environment. It revealed that studies on this potential deleterious contaminant are concentrated in some regions (e.g., Asia, Europe) with just a few studies in other areas (e.g., Africa, South America). Microplastics will continue to increase as plastic debris enters the environment each day and with the aid of photodegradation, mechanical, thermal, animal predation, microbial degradation, and biochemical mechanisms. Polyethylene, polypropylene, polyethylene terephthalate, polyvinyl chloride, polystyrene, and polyester are the major types of polymers reported by studies. Polyethylene, for example, was found in almost all studies on personal care products, which are widely used. We conclude that, although studies on the confirmation of the effects of microplastics on human health are still in the early stages, measures to contain microplastics contamination should be strengthened and adopted globally.

Enhancer-promoter interactions (EPIs) regulate the expression of specific genes in cells, which help facilitate understanding of gene regulation, cell differentiation and disease mechanisms. EPI identification approaches through wet experiments are often costly and time-consuming, leading to the design of high-efficiency computational methods is in demand. In this paper, we propose a deep neural network-based method named EPIHC to predict Enhancer-Promoter Interactions with Hybrid features and Communicative learning. EPIHC extracts enhancer and promoter sequence-derived features using convolutional neural networks (CNN), and then we design a communicative learning module to capture the communicative information between enhancer and promoter sequences. Besides, EPIHC takes the genomic features of enhancers and promoters into account, incorporating with the sequence-derived features to predict EPIs. The computational experiments show that EPIHC outperforms the existing state-of-the-art EPI prediction methods on the benchmark datasets and chromosome-split datasets, and the study reveals that the communicative learning module can bring explicit information about EPIs, which is ignored by CNN, and provide explainability about EPIs to some degree. Moreover, we consider two strategies to improve the performances of EPIHC in the cross-cell line prediction, and experimental results show that EPIHC constructed on some cell lines can exhibit good performances for other cell lines. The codes and data are available at https://github.com/BioMedicalBigDataMiningLab/EPIHC.

BACKGROUND:Determining drug-disease associations is an integral part in the process of drug development. However, the identification of drug-disease associations through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for predicting drug-disease associations is of great significance.RESULTS:In this paper, we propose a novel computational method named as layer attention graph convolutional network (LAGCN) for the drug-disease association prediction. Specifically, LAGCN first integrates the known drug-disease associations, drug-drug similarities and disease-disease similarities into a heterogeneous network, and applies the graph convolution operation to the network to learn the embeddings of drugs and diseases. Second, LAGCN combines the embeddings from multiple graph convolution layers using an attention mechanism. Third, the unobserved drug-disease associations are scored based on the integrated embeddings. Evaluated by 5-fold cross-validations, LAGCN achieves an area under the precision-recall curve of 0.3168 and an area under the receiver-operating characteristic curve of 0.8750, which are better than the results of existing state-of-the-art prediction methods and baseline methods. The case study shows that LAGCN can discover novel associations that are not curated in our dataset.CONCLUSION:LAGCN is a useful tool for predicting drug-disease associations. This study reveals that embeddings from different convolution layers can reflect the proximities of different orders, and combining the embeddings by the attention mechanism can improve the prediction performances.

In this study, GO and Fe2P were used as co-catalysts to improve the separation efficiency of photogenerated electron-hole pairs in an In2S3 photocatalyst. The metallic character of Fe2P provided a cheap substitute for traditional noble metal co-catalyst for H-2 production in aqueous media. The GO/Fe2P/In2S3 composite demonstrated significantly enhanced photocatalytic activity compared to pure In2S3, delivering a H-2 production rate of 483.35 mmol h(-1) g(-1) and a quantum yield was 22.68% under visible light irradiation. The design of the photocatalyst was optimized using "Design Expert" software. The analysis showed that a GO loading of 1.18 wt%, a Fe loading of 5.36 wt%, and a calcination temperature of 180 degrees C were optimal. (C) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Accurately and rapidly distinguishing long noncoding RNAs (lncRNAs) from transcripts is prerequisite for exploring their biological functions. In recent years, many computational methods have been developed to predict lncRNAs from transcripts, but there is no systematic review on these computational methods. In this review, we introduce databases and features involved in the development of computational prediction models, and subsequently summarize existing state-of-the-art computational methods, including methods based on binary classifiers, deep learning and ensemble learning. However, a user-friendly way of employing existing state-of-the-art computational methods is in demand. Therefore, we develop a Python package ezLncPred, which provides a pragmatic command line implementation to utilize nine state-of-the-art lncRNA prediction methods. Finally, we discuss challenges of lncRNA prediction and future directions.