NFMCLDA: Predicting miRNA-based lncRNA-disease associations by network fusion and matrix completion.

In recent years, emerging evidence has revealed a strong association between dysregulations of long non-coding RNAs (lncRNAs) and sophisticated human diseases. Biological experiments are adequate to identify such associations, but they are costly and time-consuming. Therefore, developing high-quality computational methods is a challenging and urgent task in the field of bioinformatics. This paper proposes a new lncRNA-disease association inference approach NFMCLDA (Network Fusion and Matrix Completion lncRNA-Disease Association), which can effectively integrate multi-source association data. In this approach, miRNA information is used as the transition path, and an unbalanced random walk method on three-layer heterogeneous network is adopted in the preprocessing. Therefore, more effective information between networks can be mined and the sparsity problem of the association matrix can be solved. Finally, the matrix completion method accurately predicts associations. The results show that NFMCLDA can provide more accurate lncRNA-disease associations than state-of-the-art methods. The areas under the receiver operating characteristic curves are 0.9648 and 0.9713, respectively, through the cross-validation of 5-fold and 10-fold. Data from published case studies on four diseases - lung cancer, osteosarcoma, cervical cancer, and colon cancer - have confirmed the reliable predictive potential of NFMCLDA model.

[Identifying spatial domains from spatial transcriptome by graph attention network].

Due to the high dimensionality and complexity of the data, the analysis of spatial transcriptome data has been a challenging problem. Meanwhile, cluster analysis is the core issue of the analysis of spatial transcriptome data. In this article, a deep learning approach is proposed based on graph attention networks for clustering analysis of spatial transcriptome data. Our method first enhances the spatial transcriptome data, then uses graph attention networks to extract features from nodes, and finally uses the Leiden algorithm for clustering analysis. Compared with the traditional non-spatial and spatial clustering methods, our method has better performance in data analysis through the clustering evaluation index. The experimental results show that the proposed method can effectively cluster spatial transcriptome data and identify different spatial domains, which provides a new tool for studying spatial transcriptome data.

Efficacy of Dupilumab in Children 6 Months to 11 Years Old With Atopic Dermatitis: A Retrospective Real-World Study in China.

Background: Atopic dermatitis (AD) is a common skin disease that affects patients' quality of life, especially in the pediatric population. Dupilumab has shown good efficacy and safety in the treatment of AD in adolescents and adults, but the real data on younger children using dupilumab are scarce. Objectives: We investigated the doses, efficacy, and safety of dupilumab in children with moderate-to-severe AD aged ≥6 months to 11 years. Methods: This single-center retrospective cohort analysis included dupilumab-treated patients with severe AD under 12 years of age. Primary endpoints included the proportion of Validated Investigator Global Assessment (vIGA) 0/1 achieved and the percentage change from baseline in eczema area and severity index (EASI) and SCORing Atopic Dermatitis (SCORAD) at week 24 (W24). Secondary endpoints were mean change in pruritus numerical rating score (P-NRS) and body surface area (BSA) after W24 of treatment, description of adverse events, and Children's Dermatology Life Quality Index (CDLQI) improvement from baseline in endpoints. Results: Fifty-seven patients were included (mean age 7.2 ± 3.0 years). The primary endpoint (vIGA = 0/1) was achieved by 51 of 57 (89.5%) patients at W24. Significant improvements in EASI, SCORAD, P-NRS, and CDLQI scores were observed from baseline to W24 with dupilumab treatment and remained until W40. In different age groups, the endpoint vIGA achieved 0/1: 95.2% (20/21) of younger children and 88.9% (32/36) of older children. No serious adverse drug reactions were reported. Conclusions: This study aimed to describe the safety and efficacy of dupilumab in pediatric patients and examined differences of efficacy with various doses. The outcomes are comparable with those of existing clinical trials. Phase III Clinical Trial: NCT03346434.

Characterization of natural co-infection with goose astrovirus genotypes I and II in gout affected goslings.

RESEARCH HIGHLIGHTS: Urate tophi were found in the kidneys, liver, spleen and lungs.IFA confirmed the co-expression of GoAstV-I and II antigens in the same kidney.

scGAMNN: Graph Antoencoder-Based Single-Cell RNA Sequencing Data Integration Algorithm Using Mutual Nearest Neighbors.

It is critical to correctly assemble high-dimensional single-cell RNA sequencing (scRNA-seq) datasets and downscale them for downstream analysis. However, given the complex relationships between cells, it remains a challenge to simultaneously eliminate batch effects between datasets and maintain the topology between cells within each dataset. Here, we propose scGAMNN, a deep learning model based on graph autoencoder, to simultaneously achieve batch correction and topology-preserving dimensionality reduction. The low-dimensional integrated data obtained by scGAMNN can be used for visualization, clustering and trajectory inference.By comparing it with the other five methods, multiple tasks show that scGAMNN consistently has comparable data integration performance in clustering and trajectory conservation.

Predicting potential microbe-disease associations based on multi-source features and deep learning.

Studies have confirmed that the occurrence of many complex diseases in the human body is closely related to the microbial community, and microbes can affect tumorigenesis and metastasis by regulating the tumor microenvironment. However, there are still large gaps in the clinical observation of the microbiota in disease. Although biological experiments are accurate in identifying disease-associated microbes, they are also time-consuming and expensive. The computational models for effective identification of diseases related microbes can shorten this process, and reduce capital and time costs. Based on this, in the paper, a model named DSAE_RF is presented to predict latent microbe-disease associations by combining multi-source features and deep learning. DSAE_RF calculates four similarities between microbes and diseases, which are then used as feature vectors for the disease-microbe pairs. Later, reliable negative samples are screened by k-means clustering, and a deep sparse autoencoder neural network is further used to extract effective features of the disease-microbe pairs. In this foundation, a random forest classifier is presented to predict the associations between microbes and diseases. To assess the performance of the model in this paper, 10-fold cross-validation is implemented on the same dataset. As a result, the AUC and AUPR of the model are 0.9448 and 0.9431, respectively. Furthermore, we also conduct a variety of experiments, including comparison of negative sample selection methods, comparison with different models and classifiers, Kolmogorov-Smirnov test and t-test, ablation experiments, robustness analysis, and case studies on Covid-19 and colorectal cancer. The results fully demonstrate the reliability and availability of our model.

Three-dimensional spheroid formation of adipose-derived stem cells improves the survival of fat transplantation by enhance their therapeutic effect.

Adipose-derived stem cells (ADSCs) have important applications in basic research, especially in fat transplantation. Some studies have found that three-dimensional (3D) spheroids formed by mesenchymal stem cells have enhanced therapeutic potential. However, the fundamental basics of this effect are still being discussed. ADSCs were harvested from subcutaneous adipose tissues and 3D spheroids were formed by the automatic aggregation of ADSCs in a non-adhesive 6-well plate. Oxygen glucose deprivation (OGD) was used to simulate the transplantation microenvironment. We found that 3D culture of ADSCs triggered cell autophagy. After inhibiting autophagy by Chloroquine, the rates of apoptosis were increased. When the 3D ADSC-spheroids were re-planked, the number of senescent ADSCs decreased, and the proliferation ability was promoted. In addition, there were more cytokines secreted by 3D ADSC-spheroids including VEGF, IGF-1, and TGF-ß. After adding the conditioned medium with human umbilical vein endothelial cells (HUVECs), 3D ADSC-spheroids were more likely to promote migration, and tube formation, stimulating the formation of new blood vessels. Fat grafting experiments in nude mice also showed that 3D ADSC-spheroids enhanced survival and neovascularization of fat grafts. These results suggested that 3D spheroids culturing of ADSCs can increase the therapeutic potential in fat transplantation.

P-CSN: single-cell RNA sequencing data analysis by partial cell-specific network.

Although many single-cell computational methods proposed use gene expression as input, recent studies show that replacing 'unstable' gene expression with 'stable' gene-gene associations can greatly improve the performance of downstream analysis. To obtain accurate gene-gene associations, conditional cell-specific network method (c-CSN) filters out the indirect associations of cell-specific network method (CSN) based on the conditional independence of statistics. However, when there are strong connections in networks, the c-CSN suffers from false negative problem in network construction. To overcome this problem, a new partial cell-specific network method (p-CSN) based on the partial independence of statistics is proposed in this paper, which eliminates the singularity of the c-CSN by implicitly including direct associations among estimated variables. Based on the p-CSN, single-cell network entropy (scNEntropy) is further proposed to quantify cell state. The superiorities of our method are verified on several datasets. (i) Compared with traditional gene regulatory network construction methods, the p-CSN constructs partial cell-specific networks, namely, one cell to one network. (ii) When there are strong connections in networks, the p-CSN reduces the false negative probability of the c-CSN. (iii) The input of more accurate gene-gene associations further optimizes the performance of downstream analyses. (iv) The scNEntropy effectively quantifies cell state and reconstructs cell pseudo-time.

Iris lactea var. chinensis plant drought tolerance depends on the response of proline metabolism, transcription factors, transporters and the ROS-scavenging system.

BACKGROUND: Iris lactea var. chinensis, a perennial herbaceous species, is widely distributed and has good drought tolerance traits. However, there is little information in public databases concerning this herb, so it is difficult to understand the mechanism underlying its drought tolerance. RESULTS: In this study, we used Illumina sequencing technology to conduct an RNA sequencing (RNA-seq) analysis of I. lactea var. chinensis plants under water-stressed conditions and rehydration to explore the potential mechanisms involved in plant drought tolerance. The resulting de novo assembled transcriptome revealed 126,979 unigenes, of which 44,247 were successfully annotated. Among these, 1187 differentially expressed genes (DEGs) were identified from a comparison of the water-stressed treatment and the control (CK) treatment (T/CK); there were 481 upregulated genes and 706 downregulated genes. Additionally, 275 DEGs were identified in the comparison of the rehydration treatment and the water-stressed treatment (R/T). Based on Quantitative Real-time Polymerase Chain Reaction (qRT-PCR) analysis, the expression levels of eight randomly selected unigenes were consistent with the transcriptomic data under water-stressed and rehydration treatment, as well as in the CK. According to Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, proline metabolism-related DEGs, including those involved in the 'proline catabolic process', the 'proline metabolic process', and 'arginine and proline metabolism', may play important roles in plant drought tolerance. Additionally, these DEGs encoded 43 transcription factors (TFs), 46 transporters, and 22 reactive oxygen species (ROS)-scavenging system-related proteins. Biochemical analysis and histochemical detection showed that proline and ROS were accumulated under water-stressed conditions, which is consistent with the result of the transcriptomic analysis. CONCLUSIONS: In summary, our transcriptomic data revealed that the drought tolerance of I. lactea var. chinensis depends on proline metabolism, the action of TFs and transporters, and a strong ROS-scavenging system. The related genes found in this study could help us understand the mechanisms underlying the drought tolerance of I. lactea var. chinensis.

Goose surfactant protein A inhibits the growth of avian pathogenic Escherichia coli via an aggregation-dependent mechanism that decreases motility and increases membrane permeability.

Pulmonary collectins have been reported to bind carbohydrates on pathogens and inhibit infection by agglutination, neutralization, and opsonization. In this study, surfactant protein A (SP-A) was identified from goose lung and characterized at expression- and agglutination-functional levels. The deduced amino acid sequence of goose surfactant protein A (gSP-A) has two characteristic structures: a shorter, collagen-like region and a carbohydrate recognition domain. The latter contains two conserved motifs in its Ca2+-binding site: EPN (Glu-Pro-Asn) and WND (Trp-Asn-Asp). Expression analysis using qRT-PCR and fluorescence IHC revealed that gSP-A was highly expressed in the air sac and present in several other tissues, including the lung and trachea. We went on to produce recombinant gSP-A (RgSP-A) using a baculovirus/insect cell system and purified using a Ni2+ affinity column. A biological activity assay showed that all bacterial strains tested in this study were aggregated by RgSP-A, but only Escherichia coli AE17 (E. coli AE17, O2) and E. coli AE158 (O78) were susceptible to RgSP-A-mediated growth inhibition at 2-6 h. Moreover, the swarming motility of the two bacterial strains were weakened with increasing RgSP-A concentration, and their membrane permeability was compromised at 3 h, as determined by flow cytometry and laser confocal microscopy. Therefore, RgSP-A is capable of reducing bacterial viability of E. coli O2 and O78 via an aggregation-dependent mechanism which involves decreasing motility and increasing the bacterial membrane permeability. These data will facilitate detailed studies into the role of gSP-A in innate immune defense as well as for development of antibacterial agents.

Development of a model for early differentiation of adenovirus pneumonia from Mycoplasma pneumoniae pneumonia.

Background: Adenovirus pneumonia (AVP) and Mycoplasma pneumoniae pneumonia (MPP) have similar clinical manifestations such as a high prevalence of lung consolidation, making the differential diagnosis difficult before the etiology is reported. This study aimed to compare AVP and MPP, and to build a predictive model to differentiate them early. Methods: We selected 198 cases of AVP and 876 cases of MPP. Clinical manifestations, computed tomography (CT) features, and biomarkers were compared. A logistic regression model was built to predict AVP. The area under the curve (AUC) of the receiver-operating characteristic was calculated to evaluate the discriminant ability of the prediction model. Results: Patients in the AVP group were mainly infants and toddlers, while the MPP group had more pre-school age children. The rate of hypoxemia and severe pneumonia was 3- and 11-times higher, respectively, in the AVP group than in the MPP group (5.6% vs. 1.8%, 27.8% vs. 2.5%, P<0.01). The proportion of patients with a Pediatric Logistic Organ Dysfunction-2 score ≥2 was 10 times higher in the AVP group than in the MPP group (17.4% vs. 1.7%, P<0.01). Bilateral pneumonia was present in 90.2% of the AVP group. Biomarkers, such as interleukin (IL)-2 receptor, IL-10 and lactic dehydrogenase (LDH), were considerably higher in the AVP group than in the MPP group (P<0.01). The predictive model included eight variables, namely: age, severe pneumonia, bilateral pneumonia, ground-glass attenuation, consolidation, atelectasis, C-reactive protein, and LDH. The AUC was 86.6%. Conclusions: Compared with MPP, AVP affects younger children, presents a more severe respiratory tract involvement, results in a larger range of lung lesions, and is associated with higher inflammatory biomarkers. Our predictive model includes a combination of clinical features, imaging findings, and biomarkers. It may help pediatricians in the early differentiation of AVP from MPP.

scBPGRN: Integrating single-cell multi-omics data to construct gene regulatory networks based on BP neural network.

The deterioration and metastasis of cancer involve various aspects of genomic changes, including genomic DNA changes, epigenetic modifications, gene expression, and other complex interactions. Therefore, integrating single-cell multi-omics data to construct gene regulatory networks containing more omics information is of great significance for understanding the pathogenesis of cancer. In this article, an algorithm integrating single-cell RNA sequencing data and DNA methylation data to construct a gene regulatory network based on the back-propagation (BP) neural network (scBPGRN) is proposed. This algorithm uses biweight extreme correlation coefficients to measure the correlation between factors and uses neural networks to calculate generalized weights to construct gene regulation networks. Finally, the node strength is calculated to identify the genes associated with cancer. We apply the scBPGRN algorithm to hepatocellular carcinoma (HCC) data. We construct a regulatory network and identify top-ranked genes, such as MYCBP, KLHL35, PRKCZ, and SERPINA6, as the key HCC-related genes. We analyze the top 100 genes, and the HCC-related genes are concentrated in the top 20. In addition, the single cell data is found to consist of two subpopulations. We also apply scBPGRN to two subpopulations. We analyze the top 50 genes in them, and the HCC-related genes are concentrated in the top 20. The consequences of functional enrichment analysis indicate that the gene regulatory network we have constructed is valid. Our results have been verified in several pieces of literature. This study provides a reference for the integration of single-cell multi-omics data to construct gene regulatory networks.

Lung infection of avian pathogenic Escherichia coli co-upregulates the expression of cSP-A and cLL in chickens.

The host innate defense-pathogen interaction in the lung has always been a topic of concern. The respiratory tract is a common entry route for Avian pathogenic Escherichia coli (APEC). Chicken surfactant protein A (cSP-A) and chicken lung lectin (cLL) can bind to the carbohydrate moieties of various microorganisms. Despite their detection in chickens, their role in the innate immune response is largely unknown. This study aimed to examine whether the expression levels of cSP-A and cLL in the chicken respiratory system were affected by APEC infection. A lung colonization model was established in vivo using 5-day-old specific-pathogen-free chickens infected intratracheally with APEC. The chickens were euthanized 12 h post-infection (hpi) and 1-3 days post-infection (dpi) to detect various indicators. The results of quantitative reverse transcription-polymerase chain reaction and fluorescence multiplex immunohistochemical staining showed that the mRNA and protein expression levels of cSP-A and cLL in the lung and trachea were significantly co-upregulated at 2dpi.Transcriptome RNA-sequencing analysis indicated that the inoculation with APEC AE17 at 2 dpi resulted in differential gene expression of approximately 810 genes compared with control birds, but only a few genes were expressed with astatistically significant ≧2-fold difference. cLL and cSP-A were among the significantly upregulated genes involved in innate immunity. These findings indicated that cSP-A and cLL might play an important role in lung innate host defense against APEC infection at the early stage.

Complete chloroplast genome of Serrated Tussock, Nassella trichotoma (Poaceae: Stipeae).

Nassella trichotoma is one of the most serious weed species in Australia. It is often confused with other Nassella and stipoid species, especially at the young seedling stage, adding another layer of complexity in effective weed management. We report here the complete chloroplast genome of N. trichotoma (137,568 bp, GenBank accession number KX792500.2) sequenced using Next Generation Sequencing technology (Illumina). The N. trichotoma was grouped closely with other Nassella species and separated from other Stipeae species in the phylogenetic tree constructed based on the complete chloroplast genome sequences. The sequence information could be used for further identification of novel DNA barcodes for correct weed identification and subsequently improve management of this invasive grass.

Sandbur Drought Tolerance Reflects Phenotypic Plasticity Based on the Accumulation of Sugars, Lipids, and Flavonoid Intermediates and the Scavenging of Reactive Oxygen Species in the Root.

The perennial grass Cenchrus spinifex (common sandbur) is an invasive species that grows in arid and semi-arid regions due to its remarkable phenotypic plasticity, which confers the ability to withstand drought and other forms of abiotic stress. Exploring the molecular mechanisms of drought tolerance in common sandbur could lead to the development of new strategies for the protection of natural and agricultural environments from this weed. To determine the molecular basis of drought tolerance in C. spinifex, we used isobaric tags for relative and absolute quantitation (iTRAQ) to identify proteins differing in abundance between roots growing in normal soil and roots subjected to moderate or severe drought stress. The analysis of these proteins revealed that drought tolerance in C. spinifex primarily reflects the modulation of core physiological activities such as protein synthesis, transport and energy utilization as well as the accumulation of flavonoid intermediates and the scavenging of reactive oxygen species. Accordingly, plants subjected to drought stress accumulated sucrose, fatty acids, and ascorbate, shifted their redox potential (as determined by the NADH/NAD ratio), accumulated flavonoid intermediates at the expense of anthocyanins and lignin, and produced less actin, indicating fundamental reorganization of the cytoskeleton. Our results show that C. spinifex responds to drought stress by coordinating multiple metabolic pathways along with other adaptations. It is likely that the underlying metabolic plasticity of this species plays a key role in its invasive success, particularly in semi-arid and arid environments.

Pathologic Characterization of Coinfection with Histomonas meleagridis, Marek's Disease Virus, and Subtype J Avian Leukosis Virus in Chickens.

Histomonas meleagridis is a trichomonad protozoan parasite that can cause an important poultry disease known as histomoniasis; Marek's disease virus (MDV) and subtype J avian leukosis virus (ALV-J) usually cause avian oncogenic diseases. Although these diseases have been reported in a single pathogen infection, information about their coinfection is scarce. This study reports a naturally occurring case of coinfection with H. meleagridis, MDV, and ALV-J in a local chicken flock at the age of 150 days. Necropsy revealed necrosis and swelling in the liver and spleen. Histologic analysis showed large areas of mild to severe necrosis of hepatocytes, with numerous intralesional trophozoites of H. meleagridis by H&E and periodic acid-Schiff staining; H&E staining showed pleomorphic and neoplastic lymphoid tumor cells in the liver and myeloid cells with eosinophilic cytoplasmic granules in the spleen. Coexpression of MDV and ALV-J antigens was detected in the liver by fluorescence multiplex immunohistochemistry staining. The 18S rRNA gene of H. meleagridis, meq gene of MDV, and gp85 gene of ALV-J were identified in mixed liver and spleen tissues by PCR and sequencing, respectively.

Reporte de caso­Caracterización patológica de la coinfección con Histomonas meleagridis, el virus de la enfermedad de Marek y el virus de la leucosis aviar subtipo J en pollos Histomonas meleagridis es un parásito protozoario tricomonial que puede causar una enfermedad avícola importante conocida como histomoniasis; El virus de la enfermedad de Marek (MDV) y el virus de la leucosis aviar subtipo J (ALV-J) suelen causar enfermedades oncogénicas aviares. Aunque estas enfermedades se han reportado como infecciones patógenas separadas, la información sobre coinfección es escasa. Este estudio reporta un caso natural de coinfección con H. meleagridis, el virus de la enfermedad de Marek y el virus de la leucosis aviar subtipo J en una parvada de pollos local a la edad de 150 días. La necropsia reveló necrosis e inflamación del hígado y el bazo. El análisis histológico mostró grandes áreas de necrosis de hepatocitos de leve a severa, con numerosos trofozoítos intralesionales de H. meleagridis por tinción de hematoxilina y eosina y por tinción de ácido periódico-Schiff. La tinción de hematoxilina y eosina mostró células linfoides neoplásicas y pleomórficas en el hígado y en el bazo presencia de células mieloides con gránulos citoplásmicos eosinofílicos. La coexpresión de antígenos del virus de Marek y de la leucosis aviar subtipo J se detectó en el hígado mediante tinción inmunohistoquímica de fluorescencia múltiple. El gene de ARNr 18S de H. meleagridis, el gene meq del virus de Marek y el gene gp85 del virus de la leucosis aviar subtipo J se identificaron en tejidos mixtos de hígado y bazo mediante PCR y secuenciación, respectivamente.

The Remarkable Journey of a Weed: Biology and Management of Annual Ryegrass (Lolium rigidum) in Conservation Cropping Systems of Australia.

Annual ryegrass (Lolium rigidum Gaud.), traditionally utilised as a pasture species, has become the most problematic and difficult-to-control weed across grain production regions in Australia. Annual ryegrass has been favoured by the adoption of conservation tillage systems due to its genetic diversity, prolific seed production, widespread dispersal, flexible germination requirements and competitive growth habit. The widespread evolution of herbicide resistance in annual ryegrass has made its management within these systems extremely difficult. The negative impacts of this weed on grain production systems result in annual revenue losses exceeding $93 million (AUD) for Australian grain growers. No single method of management provides effective and enduring control hence the need of integrated weed management programs is widely accepted and practiced in Australian cropping. Although annual ryegrass is an extensively researched weed, a comprehensive review of the biology and management of this weed in conservation cropping systems has not been conducted. This review presents an up-to-date account of knowledge on the biology, ecology and management of annual ryegrass in an Australian context. This comprehensive account provides pragmatic information for further research and suitable management of annual ryegrass.

Resistance Mechanism to Metsulfuron-Methyl in Polypogon fugax.

Polypogon fugax is a common winter weed in China and other Asia countries. We have previously found a P. fugax biotype (R) resistant to acetyl co-enzyme A carboxylase (ACCase) herbicides also cannot be effectively controlled by some acetolactate synthase (ALS) herbicides. This study evaluated the level of resistance to four ALS herbicides (metsulfuron-methyl, chlorsulfuron, monosulfuron, pyribambenz isopropyl) in the R biotype and the associated resistance mechanism. The R biotype exhibited moderate level of resistance to metsulfuron-methyl (6.0-fold) compared with the sensitive biotype (S). Sequence analysis of ALS gene revealed that two ALS genes existed in P. fugax. However, no substitution associated with ALS resistance mechanism were found in ALS genes between the S and R biotypes. The activity of ALS enzyme isolated from the R biotype was inherently higher and less sensitive to metsulfuron-methyl than the S biotype. Glutathione S-transferases (GST) activity was also less sensitive to metsulfuron-methyl in the R than as the S biotypes. Malathion, a cytochrome P450 (CYP) monooxygenase inhibitor, had much greater synergistic effect with metsulfuron-methyl on the R than as the S plants, reducing the ED50 value (herbicide dose to inhibit growth by 50%) of metsulfuron-methyl by 23- and 6-fold, respectively, suggesting that CYP mediated enhanced metabolism might contribute to the resistance to ALS herbicides. These results suggest that metsulfuron-methyl resistance in the R biotype was associated with the up-regulated ALS enzymatic activity and the GST and CYP-mediated enhanced herbicide metabolism.

Fate and adaptive plasticity of heterogeneous resistant population of Echinochloa colona in response to glyphosate.

Understanding the fate of heterogenous herbicide resistant weed populations in response to management practices can help towards overcoming the resistance issues. We selected one pair of susceptible (S) and resistant (R) phenotypes (2B21-R vs 2B21-S and 2B37-R vs 2B37-S) separately from two glyphosate resistant heterogeneous populations (2B21 and 2B37) of Echinochloa colona and their fate and adaptive plasticity were evaluated after glyphosate application. Our study revealed the glyphosate concentration required to cause a 50% plant mortality (LD50) was 1187, 200, 3064, and 192 g a. e. ha-1 for the four phenotypes 2B21-R, 2B21-S, 2B37-R, and 2B37-S respectively. Both S phenotypes accumulated more biomass than the R phenotypes at the lower application rates (34 and 67.5 g a. e. ha-1) of glyphosate. However, the R phenotypes generally produced more biomass at rates of glyphosate higher than 100 g a. e. ha-1 throughout the growth period. Plants from the R phenotypes of 2B21 and 2B37 generated 32% and 38% fewer spikesplant-1 than their respective S counterparts in the absence of glyphosate respectively. The spike and seed numbersplant-1 significantly higher in R than S phenotypes at increased rates of glyphosate and these relationships were significant. Our research suggests that glyphosate-resistant E. colona plants will be less fit than susceptible plants (from the same population) in the absence of glyphosate. But in the presence of glyphosate, the R plants may eventually dominate in the field. The use of glyphosate is widespread in field, would favour the selection towards resistant individuals.

Persistence of atrazine and trifluralin in a clay loam soil undergoing different temperature and moisture conditions.

Dissipation kinetics of atrazine and trifluralin in a clay loam soil was investigated in a laboratory incubation experiment under different temperature and moisture conditions. The soil was spiked with diluted atrazine and trifluralin concentrations at 4.50 and 4.25 mg/kg soil, respectively, the moisture content adjusted to 40, 70, and 100% of field capacity (FC) and then incubated in three climatic chambers at 10, 20, and 30 °C. For each of the herbicides, soil samples were collected at 0, 7, 21, 42, 70, and 105 days and analysed by Gas Chromatography-Electron Capture Detector (GC-ECD). A stochastic gamma model was used to model the dissipation of herbicides from the clay loam soil by incorporating environmental factors as covariates to determine half-life and days to complete dissipation. Results showed that temperature played a greater role on atrazine persistence than soil moisture; while the interaction effect of temperature and moisture was significant on the persistence of trifluralin over time. Atrazine dissipated more rapidly at 30 °C compared to 10 and 20 °C, with a half-life of 7.50 days and 326.23 days to reach complete dissipation. Rapid loss of trifluralin was observed at 70% moisture content when incubated at 30 °C, with a half-life of 5.80 days and 182.01 days to complete dissipation. It was observed that the half-life of both herbicides tended to double with every 10 °C decreases of temperature over the range tested. The model indicated that both atrazine and trifluralin have the potential to persist in clay loam soil for several years at temperature ≤20 °C; which could potentially affect following crops in rotation.