Genome-wide codon usage pattern analysis reveals the correlation between codon usage bias and gene expression in Cuscuta australis.

Simple Summary The integration of fish in rice fields can influence the diversity and structural composition of soil microbial communities. Therefore, soil microorganisms between rice–fish co-culture (RF) and rice monoculture (MC) were compared. The key findings revealed that Actinobacteria, Chloroflexi, Proteobacteria, Acidobacteria, and Planctomycetes were the most dominant taxa across both paddy fields. The most abundant genus in MC belonged to Anaeromyxobacter, whereas that in RF was Bacillus. Nitrogen fixation, aromatic compound degradation, and hydrocarbon degradation were more abundant in RF. Phosphatase, β-glucosidase, cellulase, and urease enzymes were detected in both paddy fields. However, a 2-year conversion from organic rice to rice–fish co-culture may not be long enough to significantly alter alpha diversity indices. Abstract Soil microorganisms play an important role in determining nutrient cycling. The integration of fish into rice fields can influence the diversity and structural composition of soil microbial communities. However, regarding the rice–fish co-culture (RF) farming system in Thailand, the study of the diversity and composition of soil microbes is still limited. Here, we aim to compare the microbial diversity, community composition, and functional structure of the bacterial communities between RF and rice monoculture (MC) farming systems and identify the environmental factors shaping bacterial community composition. Bacterial taxonomy was observed using 16s rRNA gene amplicon sequencing, and the functional structures of the bacterial communities were predicted based on their taxonomy and sequences. The results showed that soil organic carbon, total nitrogen (TN), organic matter, available phosphorous, and clay content were significantly higher in RF than in MC. The most dominant taxa across both paddy rice fields belonged to Actinobacteria, Chloroflexi, Proteobacteria, Acidobacteria, and Planctomycetes. The taxa Nitrosporae, Rokubacteria, GAL15, and Elusimicrobia were significantly different between both rice fields. At the genus level, Bacillus, Anaeromyxobacter, and HSB OF53-F07 were the predominant genera in both rice fields. The most abundant genus in MC was Anaeromyxobacter, whereas RF belonged to Bacillus. The community composition in MC was positively correlated with magnesium and sand content, while in RF was positively correlated with pH, TN, and clay content. Nitrogen fixation, aromatic compound degradation, and hydrocarbon degradation were more abundant in RF, while cellulolysis, nitrification, ureolysis, and phototrophy functional groups were more abundant in MC. The enzymes involved in paddy soil ecosystems included phosphatase, β-glucosidase, cellulase, and urease. These results provide novel insights into integrated fish in the paddy field as an efficient agricultural development strategy for enhancing soil microorganisms that increase soil fertility.

/pdf/soil-microbial-diversity-and-community-composition-in-rice-1x941hep.pdf

Soil Microbial Diversity and Community Composition in Rice–Fish Co-Culture and Rice Monoculture Farming System

Bt proteins are crystal proteins produced by Bacillus thuringiensis (Bt) in the early stage of spore formation that exhibit highly specific insecticidal activities. The application of Bt proteins primarily includes Bt transgenic plants and Bt biopesticides. Transgenic crops with insect resistance (via Bt)/herbicide tolerance comprise the largest global area of agricultural planting. After artificial modification, Bt insecticidal proteins expressed from Bt can be released into soils through root exudates, pollen, and plant residues. In addition, the construction of Bt recombinant engineered strains through genetic engineering has become a major focus of Bt biopesticides, and the expressed Bt proteins will also remain in soil environments. Bt proteins expressed and released by Bt transgenic plants and Bt recombinant strains are structurally and functionally quite different from Bt prototoxins naturally expressed by B. thuringiensis in soils. The former can thus be regarded as an environmentally exogenous substance with insecticidal toxicity that may have potential ecological risks. Consequently, biosafety evaluations must be conducted before field tests and production of Bt plants or recombinant strains. This review summarizes the adsorption, retention, and degradation behavior of Bt insecticidal proteins in soils, in addition to their impacts on soil physical and chemical properties along with soil microbial diversity. The review provides a scientific framework for evaluating the environmental biosafety of Bt transgenic plants, Bt transgenic microorganisms, and their expression products. In addition, prospective research targets, research methods, and evaluation methods are highlighted based on current research of Bt proteins.

/pdf/environmental-behaviors-of-bacillus-thuringiensis-bt-9b101pi5.pdf

Environmental Behaviors of Bacillus thuringiensis (Bt) Insecticidal Proteins and Their Effects on Microbial Ecology

Ecosystem sustainability of rice and aquatic animal co-culture systems and a synthesis of its underlying mechanisms.

Abstract Food fraud is a major concern worldwide, and the majority of cases include meat adulteration or fraud. Many incidences of food fraud have been identified for meat products both in China and abroad over the last decade. We created a meat food fraud risk database compiled from 1987 pieces of information recorded by official circular information and media reports in China from 2012 to 2021. The data covered livestock, poultry, by-products, and various processed meat products. We conducted a summary analysis of meat food fraud incidents by researching fraud types, regional distribution, adulterants and categories involved, categories and sub-categories of foods, risk links and locations, etc. The findings can be used not only to analyze meat food safety situations and study the burden of food fraud but also help to promote the efficiency of detection and rapid screening, along with improving prevention and regulation of adulteration in the meat supply chain markets. 

/pdf/meat-food-fraud-risk-in-chinese-markets-2012-2021-3dlsroeo.pdf

Meat food fraud risk in Chinese markets 2012–2021

Long-term rice-crayfish-turtle co-culture maintains high crop yields by improving soil health and increasing soil microbial community stability

Fusarium wilt is a severe and worldwide disease in potato cultivation. In this study, Fusarium foetens was first identified as the pathogen of potato wilt. Bacillus subtilis SF1 has the potential for controlling potato wilt induced by F. foetens, resulting in a mycelium growth inhibition of 52.50 ± 2.59% in vitro and a significant decrease in incidence rate by 45.56% in vivo. This research highlighted the antifungal activity of surfactin from B. subtilis SF1 and attempted to reveal the unknown antifungal mechanisms. Surfactin inhibited F. foetens mycelium growth beyond the concentration of 20 μg/μL. Surfactin-treated mycelium appeared to have morphological malformation. Surfactin enhanced reduced glutathione production and caused the increase in values of the extracellular fluids in OD260 and OD280. Surfactin induced differential protein expression and changed the genes’ transcription levels. Surfactin binds to fungal DNA via groove-binding mode, with a binding constant of Kb 2.97 × 104 M−1. Moreover, B. subtilis SF1 harbored genes encoding plant-promoting determinants, making potato seedlings grow vigorously. The results will help provide a comprehensive understanding of the mechanisms of surfactin against filamentous fungi and the application of surfactin-producing microbial in the biocontrol of plant pathogenic fungi.

https://www.mdpi.com/2309-608X/9/3/367/pdf?version=1679042326

Mechanisms of Surfactin from Bacillus subtilis SF1 against Fusarium foetens: A Novel Pathogen Inducing Potato Wilt

Morel mushroom (Morchella spp.) is a rare edible fungus with high nutritional and medicinal value. In China they are cultivated in sandy soils in greenhouses and production of fresh mushrooms reached 10,000 tons in 2019. However, from 2019 to 2020, a serious rot disease with 30% natural incidence was observed on M. sextelata at a mushroom farm in Pinghu (N30°39', E121°2'), Zhejiang province of China. The symptoms mainly occurred after the first flush in the early February. First, a small white mold-like symptoms appeared on the surface or the pinnacle of pileus. Then the lesion developed to encircle the pileus and spread gradually to the stipe. The lesions expanded rapidly at high temperature (>20 °C) and humidity (>70%). In the final stages of infection, the fruiting bodies became soft with white molds. The pathogen was isolated from the margin of the lesions by plating onto potato dextrose agar (PDA) and incubated at 25 °C in the dark. Colonies on PDA grew fast, reaching 60 mm in 7 days at 25 °C, and were white to cream in color, while the back of colonies appeared red to brick-red gradually. Conidiogenous cell was solitary or in whorls of 2-4, flask-shaped in the beginning, and tapered into a thread-like neck. Conidia were borne at the tips of conidiogenous cells, were oval to sub-globose, and ranged from 1.2-2.0 µm in width and 3.2-4.3 µm in length. All these characteristics were consistent with those of Lecanicillium aphanocladii (Zare R and Gams W. 2001). To confirm the identity of the pathogen (L. aphanocladii strain G1), the genomic fragments for the internal transcribed spacer (ITS) and RNA polymerase II second largest subunit (RPB2) gene of the isolate were amplified by PCR (White et al. 1990; Zhou et al. 2020). The resulting sequence was deposited in GenBank with accession No. OL629617 and No. ON005041, respectively. BLAST results showed >99% identity with those of L. aphanocladii (MG593981.1 and KM283853.1, respectively). Concatenated sequences of the two genes in L. aphanocladii strain G1 were used to conduct a phylogenetic analysis using Bayesian inference (BI) and maxium likelihood (ML) methods in MEGA6 (Tamura et al. 2013). The pathogen was grown in PDB medium at 25 °C, 200 r/min for 14 days, and after which conidial suspension (1×107conidia/mL) was prepared by filtration with four layers of sterile gauze. A pathogenicity test was performed by spraying on ten fruit bodies of M. sextelata and cultured in 20 °C and 90 to 95% relative humidity for 7 days. The test results showed that the pathogen infected the pileus and developed into white mold-like lesion, gradually spread to the stipe, and eventually the whole fruiting body became soft with white molds. The pathogen was re-isolated from infected fruiting bodies and was confirmed to be L. aphanocladii, based on morphological characteristics and the ITS, RPB2 sequence. Meanwhile,the control M. sextelata was sprayed with PDB medium and grew normally without any symptoms. L. aphanocladii has been reported on cultivated fungi such as Agaricus bisporus and A. bitorquis in Europe (Zare & Gams 2001) as well as more recently on Tremella fuciformis in China (Liu et al 2018). To our knowledge, this is the first report of L. aphanocladii causing rot of M. sextelata. According to the disease observation in the farm of Pinghu, this rot disease breaks out and spreads fast, and is getting worse ever year, resulting in a huge loss of yield and commodity value. It is a big concern to producers of this edible fungus.

First Report of Lecanicillium aphanocladii causing rot of Morchella sextelata in China.

Rapid, accurate and visual point-of-care testing (POCT) methods for pathogenic bacteria detection are essential for avoiding foodborne diseases caused by pathogens or their toxins. In this study, we proposed a rapid and visual detection method that we named “Cas12aVIP”. By combining recombinase polymerase amplification (RPA), a CRISPR/Cas12a system and a cationic-conjugated polythiophene derivative (poly[3-(3′-N,N,N-triethylamino-1′-propyloxy)-4-methyl-2,5-thiophene hydrochloride] (PMNT) mixed with single-stranded DNA (ssDNA)), the solution turned red in the absence of the target DNA based on conformational modifications of the conjugated backbone of PMNT, whereas it displayed yellow, thus realizing the colorimetric detection of DNA. The Cas12aVIP method yielded high specificity and no interference from other nontargeted bacteria. The detection was accomplished in 40 min and the signal could be observed by the naked eye under natural light, presenting great potential for a variety of rapid nucleic acid detection applications without requiring technical expertise or ancillary equipment.

/pdf/a-rapid-and-visual-method-for-nucleic-acid-detection-of-h2ljaryl.pdf

A Rapid and Visual Method for Nucleic Acid Detection of Escherichia coli O157:H7 Based on CRISPR/Cas12a-PMNT

In order to seek high profit, businesses mix beef and mutton with cheap meat, such as duck, pork, and chicken. Five pairs of primers were designed for quintuple droplet digital PCR (qddPCR) of specific genomic regions from five selected species and specificity and amplification efficiency were determined. The mixed DNA template with an equal copy number was used for detecting the accuracy and limit of multiplex PCR. The results showed that the primers and probes of the five selected species had good specificity with the minimum number of detection copies: 0.15 copies/µL beef (Bos taurus), 0.28 copies/μL duck (Anas platyrhynchos), 0.37 copies/μL pork (Sus scrofa), 0.39 copies/μL chicken (Gallus gallus), and 0.41 copies/μL mutton (Ovis aries), respectively. The five sets of primers and probes could quickly judge whether the specified meat components existed in the food commodities.

/pdf/detection-and-quantification-of-adulterated-beef-and-mutton-3bqjljqy.pdf

Detection and Quantification of Adulterated Beef and Mutton Products by Multiplex Droplet Digital PCR

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Beibei Lv

Author Tools

Chat about Author