Cost-effective genomic prediction of critical economic traits in sturgeons through low-coverage sequencing.

Low-coverage whole-genome sequencing (LCS) offers a cost-effective alternative for sturgeon breeding, especially given the lack of SNP chips and the high costs associated with whole-genome sequencing. In this study, the efficiency of LCS for genotype imputation and genomic prediction was assessed in 643 sequenced Russian sturgeons (∼13.68×). The results showed that using BaseVar+STITCH at a sequencing depth of 2× with a sample size larger than 300 resulted in the highest genotyping accuracy. In addition, when the sequencing depth reached 0.5× and SNP density was reduced to 50 K through linkage disequilibrium pruning, the prediction accuracy was comparable to that of whole sequencing depth. Furthermore, an incremental feature selection method has the potential to improve prediction accuracy. This study suggests that the combination of LCS and imputation can be a cost-effective strategy, contributing to the genetic improvement of economic traits and promoting genetic gains in aquaculture species.

A review of spatial distribution of typical antibiotic resistance genes in marine environment surrounding China.

Antibiotic resistance genes (ARGs) have been steadily increasing due to the extensive overuse of antibiotics in the marine environment. Currently, the research considering ARGs distribution in marine ecosystems gains more interest. As the coastal sea has been regarded as one of the most polluted areas by antibiotic contaminants in China. However, no comprehensive review of the spatial distribution of ARGs in marine environment surrounding China. The main objective of this review is to investigate the level, characteristic, and spatial distribution of ARGs in the marine environment (seawater and sediments) surrounding China. Key sea areas, such as Bohai Sea, Yellow Sea, East China Sea, and South China Sea were selected in this review. The marine environment was the reservoir of ARGs, and ARGs in seawater were generally 1 to 2 orders of magnitude higher than that in sediments. Total ARGs were more abundant in the Yellow Sea, followed by the Bohai Sea, the East China Sea, and the South China Sea. This study raises questions regarding the spread and distribution for antibiotic resistance in marine environments.

polyGBLUP: a modified genomic best linear unbiased prediction improved the genomic prediction efficiency for autopolyploid species.

Given the universality of autopolyploid species in nature, it is crucial to develop genomic selection methods that consider different allele dosages for autopolyploid breeding. However, no method has been developed to deal with autopolyploid data regardless of the ploidy level. In this study, we developed a modified genomic best linear unbiased prediction (GBLUP) model (polyGBLUP) through constructing additive and dominant genomic relationship matrices based on different allele dosages. polyGBLUP could carry out genomic prediction for autopolyploid species regardless of the ploidy level. Through comprehensive simulations and analysis of real data of autotetraploid blueberry and guinea grass and autohexaploid sweet potato, the results showed that polyGBLUP achieved higher prediction accuracy than GBLUP and its superiority was more obvious when the ploidy level of autopolyploids is high. Furthermore, when the dominant effect was added to polyGBLUP (polyGDBLUP), the greater the dominance degree, the more obvious the advantages of polyGDBLUP over the diploid models in terms of prediction accuracy, bias, mean squared error and mean absolute error. For real data, the superiority of polyGBLUP over GBLUP appeared in blueberry and sweet potato populations and a part of the traits in guinea grass population due to the high correlation coefficients between diploid and polyploidy genomic relationship matrices. In addition, polyGDBLUP did not produce higher prediction accuracy than polyGBLUP for most traits of real data as dominant genetic variance was not captured for these traits. Our study will be a significant promising method for genomic prediction of autopolyploid species.

Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven N2O mitigation effect in constructed wetlands.

Biochar is a carbon-neutral tool for combating climate change. Artificial intelligence applications to estimate the biochar mitigation effect on greenhouse gases (GHGs) can assist scientists in making more informed solutions. However, there is also evidence indicating that biochar promotes, rather than reduces, N2O emissions. Thus, the effect of biochar on N2O remains uncertain in constructed wetlands (CWs), and there is not a characterization metric for this effect, which increases the difficulty and inaccuracy of biochar-driven alleviation effect projections. Here, we provide new insight by utilizing machine learning-based, tree-structured Parzen Estimator (TPE) optimization assisted by a meta-analysis to estimate the potency of biochar-driven N2O mitigation. We first synthesized datasets that contained 80 studies on global biochar-amended CWs. The mitigation effect size was then calculated and further introduced as a new metric. TPE optimization was then applied to automatically tune the hyperparameters of the built extreme gradient boosting (XGBoost) and random forest (RF), and the optimum TPE-XGBoost obtained adequately achieved a satisfactory prediction accuracy for N2O flux (R2 = 91.90%, RPD = 3.57) and the effect size (R2 = 92.61%, RPD = 3.59). Results indicated that a high influent chemical oxygen demand/total nitrogen (COD/TN) ratio and the COD removal efficiency interpreted by the Shapley value significantly enhanced the effect size contribution. COD/TN ratio made the most and the second greatest positive contributions among 22 input variables to N2O flux and to the effect size that were up to 18% and 14%, respectively. By combining with a structural equation model analysis, NH4+-N removal rate had significant negative direct effects on the N2O flux. This study implied that the application of granulated biochar derived from C-rich feedstocks would maximize the net climate benefit of N2O mitigation driven by biochar for future biochar-based CWs.

A comprehensive analysis of the prognostic and immunotherapeutic characteristics of KIFC1 in pan-cancer and its role in the malignant phenotype of pancreatic cancer.

BACKGROUND: Kinesin family member C1 (KIFC1) is an essential member of the motor protein family, which is critically involved in various cellular events, such as mitosis, meiosis, and macromolecular transport, but also in carcinogenesis, malignant progression, and tumor recurrence. METHODS: The analysis determined the relationship between KIFC1 expression, prognosis significance, immune characteristics landscape, and genetic alterations in pan-cancer with the data extracted from web-based platforms and databases, including but not limited to UCSC, NCBI, GEPIA2, HPA, cBioPortal, SangerBox, UALCAN, GEO and TCGA. Additionally, the expression of KIFC1 in pancreatic cancer tumor tissues and adjacent normal tissues was evaluated through immunohistochemistry. In vitro Edu, colony formation, wound healing, and Transwell assay were done to elucidate the biological functions of KIFC1 in pancreatic cancer cells. RESULTS: The analysis revealed that KIFC1 is upregulated in most cancers, and its increased expression is significantly associated with reduced overall survival and disease-free survival in multiple cancer types. Additionally, strong correlations between KIFC1 expression and tumor immunotherapy were observed across various malignancies. Through univariate and multivariate Cox regression analyses using TCGA data, KIFC1 was identified as an independent predictor of prognosis in pancreatic cancer cases. Furthermore, cellular experiments demonstrated that knockdown of KIFC1 resulted in the suppression of cell proliferation, migration, and invasive ability. CONCLUSIONS: Our study indicated that KIFC1 harbors the potential to be a prognostic and immunotherapeutic biomarker of tumors, and it can have an impact on the metastasis and the cell cycle of pancreatic cancer cells.

Current status of Fe-based MOFs in biomedical applications.

Recently nanoparticle-based platforms have gained interest as drug delivery systems and diagnostic agents, especially in cancer therapy. With their ability to provide preferential accumulation at target sites, nanocarrier-constructed antitumor drugs can improve therapeutic efficiency and bioavailability. In contrast, metal-organic frameworks (MOFs) have received increasing academic interest as an outstanding class of coordination polymers that combine porous structures with high drug loading via temperature modulation and ligand interactions, overcoming the drawbacks of conventional drug carriers. FeIII-based MOFs are one of many with high biocompatibility and good drug loading capacity, as well as unique Fenton reactivity and superparamagnetism, making them highly promising in chemodynamic and photothermal therapy, and magnetic resonance imaging. Given this, this article summarizes the applications of FeIII-based MOFs in three significant fields: chemodynamic therapy, photothermal therapy and MRI, suggesting a logical route to new strategies. This article concludes by summarising the primary challenges and development prospects in these promising research areas.

Current status and prospects of MIL-based MOF materials for biomedicine applications.

This article mainly reviews the biomedicine applications of two metal-organic frameworks (MOFs), MIL-100(Fe) and MIL-101(Fe). These MOFs have advantages such as high specific surface area, adjustable pore size, and chemical stability, which make them widely used in drug delivery systems. The article first introduces the properties of these two materials and then discusses their applications in drug transport, antibacterial therapy, and cancer treatment. In cancer treatment, drug delivery systems based on MIL-100(Fe) and MIL-101(Fe) have made significant progress in chemotherapy (CT), chemodynamic therapy (CDT), photothermal therapy (PTT), photodynamic therapy (PDT), immunotherapy (IT), nano-enzyme therapy, and related combined therapy. Overall, these MIL-100(Fe) and MIL-101(Fe) materials have tremendous potential and diverse applications in the field of biomedicine.

Current status and prospect of ZIF-based materials for breast cancer treatment.

Breast cancer, one of the three most life-threatening cancers in modern times, must be explored for treatments with low side effects and practical efficacy. Metal organic framework materials (MOFs) is made by metal ions as the center for point and organic ligands as a bridge connecting a new type of porous nano-materials, among them, the zinc base zeolite imidazole skeleton material series (ZIFs) because of its excellent biocompatibility and pH slow controlled release ability, is widely used in the tumor microenvironment in basic research and achieved remarkable curative effect. Inspired by this, in this review, we focus on the recent research progress on the application of ZIFs in the treatment of breast cancer, mainly studying the structure of ZIFs such as ZIF-8, ZIF-90 and ZIF-67 and their application in novel therapies for breast cancer treatment, such as targeted drug delivery, photothermal therapy, immunotherapy and gene therapy.We will more fully demonstrate the potential of zif in breast cancer treatment, hoping to provide an avenue for exploring breast cancer treatment.

Biofilm electrode reactor coupled manganese ore substrate up-flow microbial fuel cell-constructed wetland system: High removal efficiencies of antibiotic, zinc (II), and the corresponding antibiotic resistance genes.

A coupled system comprised of a biofilm electrode reactor (BER) and a manganese ore substrate microbial fuel cell-constructed wetland (MFC-CW) system was used to remove co-exposed antibiotic and Zn (II), as well as simultaneously reduce copies of antibiotic resistance genes (ARGs) in the current study. In this system, BER primarily reduced the concentrations of antibiotics and Zn (II), and the effluent was used as the input to the MFC-CW, thereby providing electricity to BER. Co-exposure to a high concentration of Zn (II) decreased the relative abundances (RAs) of ARGs in the BER effluent, whereas the remaining sub-lethal concentration of Zn (II) increased the RAs of ARGs in the MFC-CW effluent. Even though the absolute copies of ARGs in the effluents increased during co-exposure, the total number of target ARG copies in the effluent of MFC-CW was significantly lower than that of BER. Moreover, BER pre-treatment eliminated most of Zn (II), which improved the electrical power generation characteristic of the MFC-CW unit. Correspondingly, the bacterial community and the ARGs hosts were analyzed to demonstrate the mechanism. In conclusion, the coupled system demonstrates significant potential to reduce antibiotics, Zn (II) and environmental risks posed by ARGs.

Impacts of draw solutes on the fate of tetracycline in an osmotic membrane bioreactor: Role of the combination between membrane fouling and microorganisms.

Osmotic membrane bioreactors (OMBRs) are considered a suitable technology for treating wastewater containing tetracycline due to their high rejection and biodegradation efficiency. However, the impact of membrane fouling layer (i.e., chemical composition, microbial composition, and formation) on the filtration and biodegradation of tetracycline is still unclear. Herein, the effects of draw solute concentration and type on the formation of a membrane fouling layer for tetracycline filtration and its relationship with microbial activity were investigated. The results showed that over 99% of tetracycline was retained on the feed side by membrane rejection, and the fouling layer played an important role in tetracycline filtration. Specifically, membrane foulants resulted in a more hydrophilic membrane facilitating tetracycline filtration, while the tetracycline-degrading genera from the fouled membrane promoted tetracycline degradation. The structure equation model showed that tetracycline filtration dominated by electrostatic repulsion between tetracycline and the fouled membrane was more important than tetracycline degradation for tetracycline removal (path coefficient of 0.655 vs. 0.395). This study provided insights into the combined effect of membrane foulants and microorganisms on tetracycline removal.

Enhanced removal of antibiotic and antibiotic resistance genes by coupling biofilm electrode reactor and manganese ore substrate up-flow microbial fuel cell constructed wetland system.

Manganese ore substrate up-flow microbial fuel cell constructed wetland (UCW-MFC(Mn)) as an innovative wastewater treatment technology for purifying antibiotics and electricity generation with few antibiotic resistance genes (ARGs) generation has attracted attention. However, antibiotic purifying effects should be further enhanced. In this study, a biofilm electrode reactor (BER) that needs direct current driving was powered by a Mn ore anode (UCW-MFC(Mn)) to form a coupled system without requiring direct-current source. Removal efficiencies of sulfadiazine (SDZ), ciprofloxacin (CIP) and the corresponding ARGs in the coupled system were compared with composite (BER was powered by direct-current source) and anaerobic systems (both of BER and UCW-MFC were in open circuit mode). The result showed that higher antibiotic removal efficiency (94% for SDZ and 99.1% for CIP) in the coupled system was achieved than the anaerobic system (88.5% for SDZ and 98.2% for CIP). Moreover, electrical stimulation reduced antibiotic selective pressure and horizontal gene transfer potential in BER, and UCW-MFC further reduced ARG abundances by strengthening the electro-adsorption of ARG hosts determined by Network analysis. Bacterial community diversity continuously decreased in BER while it increased in UCW-MFC, indicating that BER mitigated the toxicity of antibiotic. Degree of modularity, some functional bacteria (antibiotic degrading bacteria, fermentative bacteria and EAB), and P450 enzyme related to antibiotic and xenobiotics biodegradation genes were enriched in electric field existing UCW-MFC, accounting for the higher degradation efficiency. In conclusion, this study provided an effective strategy for removing antibiotics and ARGs in wastewater by operating a BER-UCW-MFC coupled system.

Towards bio-utilization and energy recovery potential exploration of membrane foulant from membrane bioreactor by using microbial fuel cell-centered technology.

The utilization of membrane foulant is expected to push forward the developments of membrane bioreactor (MBR). In this study, the combination of microbial fuel cell (MFC) with bio-electrochemical enhanced hydrolysis process was proposed, and three systems were conducted to utilize the membrane foulant and simultaneously harvest electricity. Polysaccharides (PS), proteins (PN) and humic acid (HA) concentration variations and the fluorescent compound changes in different chambers revealed the biodegradability of membrane foulant. Optimized HRT improved the hydrolysis of membrane foulant while allowing MFC to utilize the biodegradable components efficiently. MFC-MFC system had the highest voltage and satisfactory effluent quality at HRT of 1 d. Microbial community structure analysis indicated that Proteobacteria, Planctomycetes and Bacteroidetes were the majority phyla and network analysis further revealed that Proteobacteria played a key role in membrane foulant utilization. This study suggests that MFC hybrid systems has potential application for synchronous membrane foulant reuse and energy recovery.

Shedding light on the transfer of tetracycline in forward osmosis through experimental investigation and machine learning modeling.

Tetracycline in wastewater can pose adverse impacts on the environment and human health. Forward osmosis (FO) is a promising method to reject antibiotics due to its low energy demand and high rejection rate. Tetracycline rejection during FO is a complicated process. Mechanistic models have been developed to describe antibiotic rejection by the FO membrane under ideal conditions but cannot be applied to real wastewater. Herein, the effects of draw concentration, pH, and solute type on the fate of tetracycline during FO were investigated by combining experimentation, factor analysis, and artificial neural network (ANN) modeling. High draw concentrations led to high convection that favored tetracycline diffusion. Low draw pH helped reject antibiotics potentially due to the decreased tortuosity and pore size of the FO membrane. When different draw solutes were tested, both convection and electrostatic interaction exerted effects on tetracycline retention on the FO membrane surface, and steric hindrance could further affect the amount of tetracycline in the draw solution. Exploratory factor analysis (EFA) showed that tetracycline rejection was a combined result of convection, steric hindrance, and electrostatic interactions. Path analysis revealed the significant roles of initial conductivity and draw pH in tetracycline rejection. Eight representative input variables were selected from 13 observed explanatory variables using redundancy analysis (RDA), based on which an ANN was trained and successfully predicted tetracycline diffusion and transfer through the FO membrane. These results have provided practical and predictive insights in the development of FO processes for efficient treatment of pharmaceutical wastewater.

Quantifying biochar-induced greenhouse gases emission reduction effects in constructed wetlands and its heterogeneity: A multi-level meta-analysis.

Zero-waste biochar is an emerging tool for carbon neutralization, but the role of biochar in reducing greenhouse gases (GHGs) emissions from CWs were controversy and uncertainty. Yet, no previous study has integrated multiple research systems to quantitatively examine biochar-mediated GHGs emission reduction potential in CWs. Here we synthesized 114 studies to quantify biochar-induced declines ability of GHGs in the CWs by using the multi-level meta-analysis, reveal the variation of GHGs emission effect in different biochar-CWs and its response relationship with biochar, and identify the moderating variables that had a strong explanatory effect on the emission reduction effect of biochar. We showed that biochar remarkably affect CO2 mitigation (p < 0.05), but has insignificant and heterogeneous effects on CH4 and N2O. Pyrolysis time, influent dissolved oxygen (DO), influent NO3--N concentration, hydraulic retention time (HRT) and wetland type can significantly affect the effect of biochar on CH4 emission reduction. Particularly, the importance of HRT and wetland type was 0.89 and 0.85, respectively. Specially, the surface batch CWs modified by biochar could significantly promote the emission of CH4 (p < 0.001), and the effect size was up to 89.59. For N2O, biochar diameter, biochar addition ratio, influent COD/TN ratio, plant name, and removal efficiency of NO3--N/TN/COD were significant moderators. Among them, influent COD/TN ratio and plant name showed a stronger explanation. Planting Cyperus alternifolius L. significantly enhanced the N2O emission reduction capacity by biochar (p < 0.001), and effect size was as low as -24.32. 700-900 °C biochar can promote CH4 flux but inhibit N2O flux. This study provides an important theoretical basis and valuable strategic guidance for more accurate estimation and improvement of synergistic emission reduction benefits between CH4 and N2O of biochar in CWs.

G × EBLUP: A novel method for exploring genotype by environment interactions and genomic prediction.

Genotype by environment (G × E) interaction is fundamental in the biology of complex traits and diseases. However, most of the existing methods for genomic prediction tend to ignore G × E interaction (GEI). In this study, we proposed the genomic prediction method G × EBLUP by considering GEI. Meanwhile, G × EBLUP can also detect the genome-wide single nucleotide polymorphisms (SNPs) subject to GEI. Using comprehensive simulations and analysis of real data from pigs and maize, we showed that G × EBLUP achieved higher efficiency in mapping GEI SNPs and higher prediction accuracy than the existing methods, and its superiority was more obvious when the GEI variance was large. For pig and maize real data, compared with GBLUP, G × EBLUP showed improvement by 3% in the prediction accuracy for backfat thickness, while our findings indicated that the trait of days to 100 kg of pig was not affected by GEI and G × EBLUP did not improve the accuracy of genomic prediction for the trait. A significant advantage was observed for G × EBLUP in maize; the prediction accuracy was improved by ∼5.0 and 7.7% for grain weight and water content, respectively. Furthermore, G × EBLUP was not influenced by the number of environment levels. It could determine a favourable environment using SNP Bayes factors for each environment, implying that it is a robust and useful method for market-specific animal and plant breeding. We proposed G × EBLUP, a novel method for the estimation of genomic breeding value by considering GEI. This method identified the genome-wide SNPs that were susceptible to GEI and yielded higher genomic prediction accuracies and lower mean squared error compared with the GBLUP method.

Exploring the optimal strategy of imputation from SNP array to whole-genome sequencing data in farm animals.

Genotype imputation from BeadChip to whole-genome sequencing (WGS) data is a cost-effective method of obtaining genotypes of WGS variants. Beagle, one of the most popular imputation software programs, has been widely used for genotype inference in humans and non-human species. A few studies have systematically and comprehensively compared the performance of beagle versions and parameter settings of farm animals. Here, we investigated the imputation performance of three representative versions of Beagle (Beagle 4.1, Beagle 5.0, and Beagle 5.4), and the effective population size (Ne) parameter setting for three species (cattle, pig, and chicken). Six scenarios were investigated to explore the impact of certain key factors on imputation performance. The results showed that the default Ne (1,000,000) is not suitable for livestock and poultry in small reference or low-density arrays of target panels, with 2.47%-10.45% drops in accuracy. Beagle 5 significantly reduced the computation time (4.66-fold-13.24-fold) without an accuracy loss. In addition, using a large combined-reference panel or high-density chip provides greater imputation accuracy, especially for low minor allele frequency (MAF) variants. Finally, a highly significant correlation in the measures of imputation accuracy can be obtained with an MAF equal to or greater than 0.05.

Insight into the role of microbial community interactions on nitrogen removal facilitated by a bioelectrochemical system in an osmotic membrane bioreactor.

Insufficient nitrogen removal is a key challenge for the application of an osmotic membrane bioreactor (OMBR). The integration of a bioelectrochemical system (BES) and an OMBR was constructed to enhance nitrogen removal.To optimize the operation, five aeration intensities and three draw solutes (DSs) were applied in the proposed system. The results showed that the proposed system obtained the highest nitrogen removal efficiency of 77.36 ± 3.55 % with an aeration intensity of 0.6 L/min, and it was further increased to 94.99 ± 2.83 % and 99.92 ± 0.14 %with the NaOAc DS and the glucose DS, respectively.The analysis ofmetabolic pathways implied that species interactions existed,andthe following different mechanisms of enhanced nitrogen removal for the two organic DSs were proposed. The growth of denitrifying bacteria was enhanced by using reverse-fluxed organic NaOAc DS as a carbon source;glucoseDS stimulated electron transfer system activity to accelerate denitrification.

Enhanced soil erosion threatens fluvial tufa landscapes after an Ms 7.0 earthquake in the Jiuzhaigou World Heritage Site, southwestern China.

Tufa is a porous freshwater deposit comprising primarily calcite (CaCO3) and organic matter. Massive tufa depositions can spread for up to several kilometers, forming tufa landscapes that have been recognized as national parks and World Heritage Sites. Previous studies have suggested that enhanced soil erosion owing to human activities (e.g., deforestation and agriculture) is one of the major causes of fluvial tufa decline in many places worldwide. In 2017, an Ms 7.0 earthquake occurred in Jiuzhaigou, which greatly increased soil erosion in the catchment. We compared the water chemistry and tufa deposition before and after the earthquake to understand the impact of soil erosion on tufa landscapes in Jiuzhaigou. After the earthquake, we found that high turbidity greatly reduced the aesthetic value of the lakes. Enhanced soil erosion increased NO3-, dissolved organic carbon (DOC), and PO43- concentrations in surface water, which may worsen the problems of increased algal biomass and marsh development. Enhanced soil erosion reduced alkalinity, HCO3-, and the saturation index of calcite (SIc), thereby decreasing the potential to generate new calcite. Enhanced soil erosion may also increase the annual tufa deposition rates by increasing the soil and organic materials in the sediment. In addition, the tufa sediment affected by enhanced soil erosion was loose, highly porous, and contained numerous diatoms. This study provides observational data to explain the impact mechanisms of soil erosion on tufa landscapes and assess the necessity and achievements of artificial soil erosion control.

Strategies to improve the accuracy and reduce costs of genomic prediction in aquaculture species.

Genomic selection (GS) has great potential to increase genetic gain in aquaculture breeding; however, its implementation is hindered owing to high genotyping cost and the large number of individuals to genotype. This study investigated the efficiency of genomic prediction in four aquaculture species. In total, 749 to 1481 individuals with records for disease resistance and growth traits were genotyped using SNP arrays ranging from 12K to 40K. We compared the prediction accuracies and bias of breeding values obtained from BLUP, genomic BLUP (GBLUP), Bayesian mixture (BayesR), weighted GBLUP (WGBLUP), and genomic feature BLUP (GFBLUP). For GFBLUP, the genomic feature matrix was constructed based on prior information from genome-wide association studies. Fivefold cross-validation was performed with 20 replicates. Moreover, to reduce the cost of GS, we reduced the SNP density based on linkage disequilibrium as well as the reference population size. The results showed that the methods with marker information produced more accurate predictions than the pedigree-based BLUP method. For the genomic model, BayesR performed prediction with a similar or higher accuracy compared to GBLUP. For the four traits, WGBLUP yielded an average of 1.5% higher accuracy than GBLUP. However, the accuracy of genomic prediction decreased by an average of 6.2% for GFBLUP compared to GBLUP. When the density of SNP panels was reduced to 3K, which was sufficient to obtain accuracies similar to those using the whole dataset in the four species, the cost of GS was estimated to be 50% lower than that of genotyping all animals with high-density panels. In addition, when the reference population size was reduced by 10%, evenly from full-sib family, the accuracy of genomic prediction was almost unchanged, and the cost reduction was 8% in the four populations. Our results have important implications for translating the benefits of GS to most aquaculture species.

Enhanced removal of antibiotics and antibiotic resistance genes in a soil microbial fuel cell via in situ remediation of agricultural soils with multiple antibiotics.

Soil microbial fuel cells (MFCs) have been applied for the in situ remediation of soils polluted by single antibiotics. However, the investigation of only single antibiotic pollution has hindered MFC application in real-world soil remediation, where the effects of multiple antibiotics with similar chemical structures on the fate of antibiotics and their corresponding antibiotic resistance genes (ARGs) remain unknown. In this study, antibiotic removal rates, microbial community compositions, metabolite compositions, and ARG abundances were investigated in soil MFCs by adding two commonly used antibiotics (sulfadiazine, SDZ, and sulfamethoxazole, SMX), and comparing them with the addition of only a single antibiotic (SDZ). The antibiotic removal rate was higher in the soil MFC with addition of mixed antibiotics compared to the single antibiotic due to enhanced biodegradation efficiency in both the upper (57.24% of the initial antibiotic concentration) and lower layers (57.07% of the initial concentration) of the antibiotic-polluted soils. Bacterial community diversity in the mixed antibiotic conditions increased, and this likely resulted from the decreased toxicity of intermediates produced during antibiotic biodegradation. Moreover, the addition of mixed antibiotics led to lower risks of ARG release into soil environments, as reflected by higher abundances of host bacteria in the single antibiotic treatment. These results encourage the further development of soil MFC technology for in situ remediation of antibiotic-polluted soils.