Search Results (694)

Red clover (Trifolium pratense L.) is a well-appreciated grassland crop in temperate climates but suffers from increasingly frequent and severe drought periods. Molecular markers for drought resilience (DR) would benefit breeding initiatives for red clover, as would a better understanding of the genes involved in DR. Two previous studies, as follows, have: (1) identified phenotypic DR traits in a diverse set of red clover accessions; and (2) produced genotypic data using a pooled genotyping-by-sequencing (GBS) approach in the same collection. In the present study, we performed genome-wide association studies (GWAS) for DR using the available phenotypic and genotypic data. Single nucleotide polymorphism (SNP) calling was performed using GBS data and the following two red clover genome assemblies: the recent HEN-17 assembly and the Milvus assembly. SNP positions with significant associations were used to delineate flanking regions in both genome assemblies, while functional annotations were retrieved from Medicago truncatula orthologs. GWAS revealed 19 significant SNPs in the HEN-17-derived SNP set, explaining between 5.3 and 23.2% of the phenotypic variation per SNP–trait combination for DR traits. Among the genes in the SNP-flanking regions, we identified candidate genes related to cell wall structuring, genes encoding sugar-modifying proteins, an ureide permease gene, and other genes linked to stress metabolism pathways. GWAS revealed 29 SNPs in the Milvus-derived SNP set that explained substantially more phenotypic variation for DR traits, between 5.3 and 42.3% per SNP–trait combination. Candidate genes included a DEAD-box ATP-dependent RNA helicase gene, a P-loop nucleoside triphosphate hydrolase gene, a Myb/SANT-like DNA-binding domain protein, and an ubiquitin–protein ligase gene. Most accessions in this study are genetically more closely related to the Milvus genotype than to HEN-17, possibly explaining how the Milvus-derived SNP set yielded more robust associations. The Milvus-derived SNP set pinpointed 10 genomic regions that explained more than 25% of the phenotypic variation for DR traits. A possible next step could be the implementation of these SNP markers in practical breeding programs, which would help to improve DR in red clover. Candidate genes could be further characterized in future research to unravel drought stress resilience in red clover in more detail. Full article

We present the reconstruction of palaeoenvironmental conditions in the Gdansk, Bornholm, and Arkona Basins of the Baltic Sea over the last millennium. A multiproxy study (including geochemical, XRF, grain size, AMS, and micropalaeontological analyses) of five short sediment cores was performed. The relative age of the sediments was determined based on the Pb distribution along the sediment sequences, as radiocarbon dating has resulted in an excessively old age. The retrieved cores cover two comparable warm periods, the Medieval Climate Anomaly and the Modern Warm Period, for which the increase in surface water productivity was reconstructed. Notably, the production of diatoms was higher during the colder periods (the Dark Ages and Little Ice Age), but this was also the case within the Modern Warm Period. In the Gdansk Basin, the initial salinity increase during the Littorina transgression started after 7.7 cal. a BP. The increased inflow activity was reconstructed during the Medieval Climate Anomaly, even in the Gdansk Basin, despite, in general, very low foraminiferal amounts and diversity. The strongly positive North Atlantic Oscillation Index during this period led to the prevalence of westerly winds over the Baltic region and stronger saltwater intrusions. In the recent sediments, the reconstructed inflow frequency demonstrates a variability against the reduction trend, and a general decline compared to the Medieval Climate Anomaly is seen. Full article

Elevation static correction is an important step in seismic data processing used to eliminate distortions in events caused by surface fluctuations. There is a well-established processing sequence for active-source seismic data, with static correction applied first, followed by subsequent processing. For passive-source data, static correction can be carried out using two methods. The first method is similar to the static correction of active-source seismic data and involves performing static correction on virtual shot gathers. The second method involves directly applying static correction to ambient noise and then using the corrected ambient noise to retrieve the virtual shot gathers. In this study, we derived and compared these two methods to perform static correction on passive-source data. We compared the above two methods through the forward modeling of passive-source data and field data from the Daqiao Gold Mine. The results showed that applying static correction to ambient noise and virtual shot gathers can correct passive-source data from an undulating surface to a flat surface. The equivalence of static correction on virtual shot gathers, and ambient noise offers more choices for conducting static correction of passive-source seismic reflection exploration on undulating surfaces. Full article

Porcine circovirus type 2 (PCV2) is strongly linked to a group of syndromes referred to as porcine-circovirus-associated diseases (PCVADs), which are controlled through vaccination; however, this does not induce sterilizing immunity but is instead involved in the evolution of the virus and is considered a factor in vaccine failure. This study sampled 84 herds (167 pigs) vaccinated against PCV2 and with clinical signs of PCVADs in five provinces across Colombia. PCV2 was identified and further characterized at the molecular level via genotyping and phylogenetic reconstructions. In addition, PCV2-associated lesions were examined via histopathology. Furthermore, the PCV2-Cap sequences retrieved were compared with three vaccines via the EpiCC tool and T cell epitope coverage. The prevalence of PCV2 was 82% in pigs and 92.9% in herds. The highest viral loads were identified in lymphoid tissue, and PCV2d emerged as the most predominant in pigs and herds (93.4% and 92.3%). Sequences for PCV2-ORF2 (n = 57; 55 PCV2d and 2 PCV2a) were determined, and PCV2d sequences were highly similar. The most common pneumonia pattern was suppurative bronchopneumonia, while the most common lung lesion was exudation in the airways; in lymphoid tissue, there was lymphoid depletion. The bivalent vaccine (PCV2a and PCVb) exhibited a higher EpiCC score (8.36) and T cell epitope coverage (80.6%) than monovalent PCV2a vaccines. In conclusion, PCV2d currently circulates widely in Colombia. Despite vaccination, there are clinical cases of PCV2, and immunoinformatic analyses demonstrate that bivalent vaccines improved the average coverage. Full article

Giardia duodenalis, Cryptosporidium parvum, Blastocystis spp. and Enterocytozoon bieneusi are four common zoonotic parasites associated with severe diarrhea and enteric diseases. In this study, we developed a multiplex PCR assay for the simultaneous detection of these four zoonotic protozoans in goat stool samples and assessed its detection efficiency. Specific primers were designed from conserved gene sequences retrieved from GenBank, and the PCR conditions were optimized. Genomic DNA from 130 samples was subjected to both single-target PCR and multiplex PCR. The multiplex PCR assay successfully amplified specific gene fragments (G. duodenalis, 1400 bp; C. parvum, 755 bp; Blastocystis spp., 573 bp; E. bieneusi, 314 bp). The assay sensitivity was ≥10² copies of pathogenic DNA clones with high specificity confirmed by negative results for other intestinal parasites. The detection rates were 23.08% (30/130) for G. duodenalis, 24.62% (32/130) for C. parvum, 41.54% (54/130) for Blastocystis spp., and 12.31% (16/130) for E. bieneusi, matching the single-target PCR results. The sensitivity and predictive values were 100.00%. This multiplex PCR provided a rapid, sensitive, specific, and cost-effective approach for detecting these four parasites. It also provided essential technical support for the rapid detection and epidemiological investigation of G. duodenalis, C. parvum, Blastocystis spp., and E. bieneusi infections in goat fecal samples. Full article

The leaf is not only the main site of photosynthesis, but also an important organ reflecting plant salt tolerance. Discovery of salt-stress-responding genes in the leaf is of great significance for the molecular improvement of salt tolerance in wheat varieties. In this study, transcriptome sequencing was conducted on the leaves of salt-tolerant wheat germplasm CH7034 seedlings at 0, 1, 6, 24, and 48 h after NaCl treatment. Based on weighted gene correlation network analysis of differentially expressed genes (DEGs) under salt stress, 12 co-expression modules were obtained, of which, 9 modules containing 4029 DEGs were related to the salt stress time-course. These DEGs were submitted to the Wheat Union database, and a total of 904,588 SNPs were retrieved from 114 wheat germplasms, distributed on 21 wheat chromosomes. Using the R language package and GAPIT program, association analysis was performed between 904,588 SNPs and leaf salt injury index of 114 wheat germplasms. The results showed that 30 single nucleotide polymorphisms (SNPs) from 15 DEGs were associated with salt tolerance. Then, nine candidate genes, including four genes (TaBAM, TaPGDH, TaGluTR, and TaAAP) encoding enzymes as well as five genes (TaB12D, TaS40, TaPPR, TaJAZ, and TaWRKY) encoding functional proteins, were identified by converting salt tolerance-related SNPs into Kompetitive Allele-Specifc PCR (KASP) markers for validation. Finally, interaction network prediction was performed on TaBAM and TaAAP, both belonging to the Turquoise module. Our results will contribute to a further understanding of the salt stress response mechanism in plant leaves and provide candidate genes and molecular markers for improving salt-tolerant wheat varieties. Full article

Medical named entity recognition (NER) focuses on extracting and classifying key entities from medical texts. Through automated medical information extraction, NER can effectively improve the efficiency of electronic medical record analysis, medical literature retrieval, and intelligent medical question–answering systems, enabling doctors and researchers to obtain the required medical information more quickly and thereby helping to improve the accuracy of diagnosis and treatment decisions. The current methods have certain limitations in dealing with contextual dependencies and entity memory and fail to fully consider the contextual relevance and interactivity between entities. To address these issues, this paper proposes a Chinese medical named entity recognition model that combines contextual dependency perception and a new memory unit. The model combines the BERT pre-trained model with a new memory unit (GLMU) and a recall network (RMN). The GLMU can efficiently capture long-distance dependencies, while the RMN enhances multi-level semantic information processing. The model also incorporates fully connected layers (FC) and conditional random fields (CRF) to further optimize the performance of entity classification and sequence labeling. The experimental results show that the model achieved F1 values of 91.53% and 64.92% on the Chinese medical datasets MCSCSet and CMeEE, respectively, surpassing other related models and demonstrating significant advantages in the field of medical entity recognition. Full article

Optimizing urban spatial morphology is one of the most effective methods for improving the urban thermal environment. Some studies have used the local climate zones (LCZ) classification system to examine the relationship between urban spatial morphology and Surface Urban Heat Islands (SUHIs). However, these studies often rely on single-time-point data, failing to consider the changes in urban space and the time-series LCZ mapping relationships. This study utilized remote sensing data from Landsat 5, 7, and 8–9 to retrieve land surface temperatures in Changsha from 2005 to 2020 using the Mono-Window Algorithm. The spatial-temporal evolution of the LCZ and the Surface Urban Heat Island Intensity (SUHII) was then examined and analyzed. This study aims to (1) propose a localized, long-time LCZ mapping method, (2) investigate the spatial-temporal relationship between the LCZ and the SUHII, and (3) develop a more convenient SUHI assessment method for urban planning and design. The results showed that the spatial-temporal evolution of the LCZ reflects the sequence of urban expansion. In terms of quantity, the number of built-type LCZs maintaining their original types is low, with each undergoing at least one type change. The open LCZs increased the most, followed by the sparse and the composite LCZs. Spatially, the LCZs experience reverse transitions due to urban expansion and quality improvements in central urban areas. Seasonal changes in the LCZ types and the SUHI vary, with differences not only among the LCZ types but also in building heights within the same type. The relative importance of the LCZ parameters also differs between seasons. The SUHI model constructed using Boosted Regression Trees (BRT) demonstrated high predictive accuracy, with R² values of 0.911 for summer and 0.777 for winter. In practical case validation, the model explained 97.86% of the data for summer and 96.77% for winter. This study provides evidence-based planning recommendations to mitigate urban heat and create a comfortable built environment. Full article

Computer-aided analysis of proteins or nucleic acids seems like a matter of course nowadays; however, the history of Bioinformatics and Computational Biology is quite recent. The advent of high-throughput sequencing has led to the production of “big data”, which has also affected the field of virology. The collaboration between the communities of bioinformaticians and virologists already started a few decades ago and it was strongly enhanced by the recent SARS-CoV-2 pandemics. In this article, which is the first in a series on how bioinformatics can enhance virus research, we show that highly useful information is retrievable from selected general and dedicated databases. Indeed, an enormous amount of information—both in terms of nucleotide/protein sequences and their annotation—is deposited in the general databases of international organisations participating in the International Nucleotide Sequence Database Collaboration (INSDC). However, more and more virus-specific databases have been established and are progressively enriched with the contents and features reported in this article. Since viruses are intracellular obligate parasites, a special focus is given to host-pathogen protein-protein interaction databases. Finally, we illustrate several phylogenetic and phylodynamic tools, combining information on algorithms and features with practical information on how to use them and case studies that validate their usefulness. Databases and tools for functional inference will be covered in the next article of this series: Bioinformatics goes viral: II. Sequence-based and structure-based functional analyses for boosting virus research. Full article

Influenza A viruses (IAVs) circulate among different species and have the potential to cause significant pandemics in humans. This study focuses on reassortment events in the H5N8 subtype of IAV, which poses a serious threat to public health due to its high pathogenicity in birds and potential for cross-species transmission. We retrieved 2359 H5N8 IAV sequences from GISAID, and filtered and analyzed 442 complete genomic sequences for reassortment events using pairwise distance deviation matrices (PDDMs) and pairwise distance correspondence plots (PDCPs). This detailed case study of specific H5N8 viruses revealed previously undescribed reassortment events, highlighting the complex evolutionary history and potential pandemic threat of H5N8 IAVs. Full article

PE/PPE proteins secreted by the ESX-5 type VII secretion system constitute a major protein repertoire in pathogenic mycobacteria and are essential for bacterial survival, pathogenicity, and host–pathogen interaction; however, little is known about their expression and secretion. The scarcity of arginine and lysine residues in PE/PPE protein sequences and the high homology of their N-terminal domains limit protein identification using classical trypsin-based proteomic methods. This study used endoproteinase AspN and trypsin to characterize the proteome of Mycobacterium marinum. Twenty-seven PE/PPE proteins were uniquely identified in AspN digests, especially PE_PGRS proteins. These treatments allowed the identification of approximately 50% of the PE/PPE pool encoded in the genome. Moreover, EspG5 pulldown assays retrieved 44 ESX-5-associated PPE proteins, covering 85% of the PPE pool in the identified proteome. The identification of PE/PE_PGRS proteins in the EspG5 interactome suggested the presence of PE–PPE pairs. The correlation analysis between protein abundance and phylogenetic relationships found potential PE/PPE pairs, indicating the presence of multiple PE/PE_PGRS partners in one PPE. We validated that EspG5 interacted with PPE31 and PPE32 and mapped critical residues for complex formation. The modified proteomic platform increases the coverage of PE/PPE proteins and elucidates the expression and localization of these proteins. Full article

Dengue virus poses a significant global health challenge, particularly in tropical and subtropical regions. Despite the urgent demand for vaccines in the control of the disease, the two approved vaccines, Dengvaxia and TV003/TV005, there are current questions regarding their effectiveness due to an increased risk of antibody-dependent enhancement (ADE) and reduced protection. These challenges have underscored the need for further development of improved vaccines for Dengue Virus. This study presents a new design using an in silico approach to generate a more effective dengue vaccine. Initially, our design process began with the collection of Dengue polyprotein sequences from 10 representative countries worldwide. And then conserved fragments of viral proteins were retrieved as the bases for epitope screening. The selection of epitopes was then carried out with criteria such as antigenicity, immunogenicity, and binding affinity with MHC molecules, while the exclusion criteria were according to their allergenicity, toxicity, and potential for antibody-dependent enhancement. We then constructed a core antigen with the selected epitopes and linked the outcomes with distinct adjuvant proteins, resulting in three candidate vaccines: PSDV-1, PSDV-2, and PSDV-3. Among these, PSDV-2 was selected for further validation due to its superior physicochemical and structural properties. Extensive simulations demonstrated that PSDV-2 exhibited strong binding to pattern recognition receptors, high stability, and robust immune induction, confirming its potential as a high-quality vaccine candidate. For its recombinant expression, a plasmid was subsequently designed. Our new vaccine design offers a promising additional option for Dengue virus protection. Further experimental validations will be conducted to confirm its protective efficacy and safety. Full article

Solar-induced chlorophyll fluorescence (SIF) serves as a proxy indicator for vegetation photosynthesis and can directly reflect the growth status of vegetation. Using SIF for drought monitoring offers greater potential compared to traditional vegetation indices. This study aims to develop and validate a novel approach, the improved Temperature Fluorescence Dryness Index (iTFDI), for more accurate drought monitoring in Henan Province, China. However, the low spatial resolution, data dispersion, and short temporal sequence of SIF data hinder its direct application in drought studies. To overcome these challenges, this study constructs a random forest SIF downscaling model based on the TROPOspheric Monitoring Instrument SIF (TROPOSIF) and the Moderate-resolution Imaging Spectroradiometer (MODIS) data. Assuming an unchanging spatial scale relationship, an improved SIF (iSIF) product with a temporal resolution of 500 m over the period March to September, 2010–2022 was obtained for Henan Province. Subsequently, using the retrieved iSIF and the surface temperature difference data, the iTFDI was proposed, based on the assumption that under the same vegetation cover conditions, lower soil moisture and a greater diurnal temperature range of the surface indicate more severe drought. Results showed that: (1) The accuracy of the TROPOSIF downscaling model achieved coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) values of 0.847, 0.073 mW m⁻² nm⁻¹ sr⁻¹, and 0.096 mW m⁻² nm⁻¹ sr⁻¹, respectively. (2) The 2022 iTFDI drought monitoring results indicated favorable soil moisture in Henan Province during March, April, July, and August, while extensive droughts occurred in May, June, and September, accounting for 70.27%, 71.49%, and 43.61%, respectively. The monitored results were consistent with the regional water conditions measured at ground stations. (3) The correlation between the Standardized Precipitation Evapotranspiration Index (SPEI) and iTFDI at five stations was significantly stronger than the correlation with the Temperature Vegetation Dryness Index (TVDI), with the values −0.631, −0.565, −0.612, −0.653, and −0.453, respectively. (4) The annual Sen’s slope and Mann–Kendall significance test revealed a significant decreasing trend in drought severity in the southern and western regions of Henan Province (6.74% of the total area), while the eastern region showed a significant increasing trend (4.69% of the total area). These results demonstrate that the iTFDI offers a significant advantage over traditional indices, providing a more accurate reflection of regional drought conditions. This enhances the ability to identify drought trends and supports the development of targeted drought management strategies. In conclusion, the iTFDI constructed using the downscaled iSIF data and surface temperature differential data shows great potential for drought monitoring. Full article

Phytoplasmas are small, intracellular bacteria that infect a vast range of plant species, causing significant economic losses and impacting agriculture and farmers’ livelihoods. Early and rapid diagnosis of phytoplasma infections is crucial for preventing the spread of these diseases, particularly through early symptom recognition in the field by farmers and growers. A symptom database for phytoplasma infections can assist in recognizing the symptoms and enhance early detection and management. In this study, nearly 35,000 phytoplasma sequence entries were retrieved from the NCBI nucleotide database using the keyword “phytoplasma” and information on phytoplasma disease-associated plant hosts and symptoms was gathered. A total of 945 plant species were identified to be associated with phytoplasma infections. Subsequently, links to symptomatic images of these known susceptible plant species were manually curated, and the Phytoplasma Disease Symptom Database (iPhyDSDB) was established and implemented on a web-based interface using the MySQL Server and PHP programming language. One of the key features of iPhyDSDB is the curated collection of links to symptomatic images representing various phytoplasma-infected plant species, allowing users to easily access the original source of the collected images and detailed disease information. Furthermore, images and descriptive definitions of typical symptoms induced by phytoplasmas were included in iPhyDSDB. The newly developed database and web interface, equipped with advanced search functionality, will help farmers, growers, researchers, and educators to efficiently query the database based on specific categories such as plant host and symptom type. This resource will aid the users in comparing, identifying, and diagnosing phytoplasma-related diseases, enhancing the understanding and management of these infections. Full article

Lower respiratory tract infections (LRTIs) are the fourth leading cause of death worldwide, among which Escherichia coli (E. coli) pneumonia is considered a rare phenomenon. Treatment options for LRTIs have become limited, especially for extended-spectrum β-lactamase-producing E. coli (ESBL-EC), which are usually resistant to other groups of antimicrobials as well. The aim of our study was to compare the phenotypic resistance profiles and genotypes of ESBL-EC isolates associated with LRTIs before (pre-COVID-19) and during (COVID-19) the COVID-19 pandemic. All isolates were screened for antimicrobial resistance genes (ARGs) and virulence-associated genes (VAGs) and assigned to phylogenetic groups, sequence types and clonal groups by PCR. During the pandemic, a significantly lower proportion of ciprofloxacin-, levofloxacin- and trimethoprim-sulfamethoxazole-resistant ESBL-EC isolates was retrieved from lower respiratory tract (LRT) samples. PCR-based genotypization revealed greater clonal diversity and a significantly lower proportion of isolates with bla_TEM, aac(6′)-Ib-cr and qacEΔ1 genes. In addition, a higher proportion of isolates with the integrase gene int1 and virulence genes sat and tsh was confirmed. The lower prevalence of fluoroquinolone resistance and greater genetic diversity of ESBL-EC isolated during the COVID-19 period may have been due to the introduction of new bacterial strains into the hospital environment, along with changes in clinical establishment guidelines and practices. Full article