Traditional Phenotypic Methods Research Articles

Of buds and bits: a meta-QTL study identifies stable QTL for berry quality and yield traits in cranberry mapping populations (Vaccinium macrocarpon Ait.).

For nearly two centuries, cranberry (Vaccinium macrocarpon Ait.) breeders have improved fruit quality and yield by selecting traits on fruiting stems, termed "reproductive uprights." Crop improvement is accelerating rapidly in contemporary breeding programs due to modern genetic tools and high-throughput phenotyping methods, improving selection efficiency and accuracy. We conducted genotypic evaluation on 29 primary traits encompassing fruit quality, yield, and chemical composition in two full-sib cranberry breeding populations-CNJ02 (n = 168) and CNJ04 (n = 67)-over 3 years. Genetic characterization was further performed on 11 secondary traits derived from these primary traits. For CNJ02, 170 major quantitative trait loci (QTL; R 2 ≥ 0.10) were found with interval mapping, 150 major QTL were found with model mapping, and 9 QTL were found to be stable across multiple years. In CNJ04, 69 major QTL were found with interval mapping, 81 major QTL were found with model mapping, and 4 QTL were found to be stable across multiple years. Meta-QTL represent stable genomic regions consistent across multiple years, populations, studies, or traits. Seven multi-trait meta-QTL were found in CNJ02, one in CNJ04, and one in the combined analysis of both populations. A total of 22 meta-QTL were identified in cross-study, cross-population analysis using digital traits for berry shape and size (8 meta-QTL), digital images for berry color (2 meta-QTL), and three-study cross-analysis (12 meta-QTL). Together, these meta-QTL anchor high-throughput fruit quality phenotyping techniques to traditional phenotyping methods, validating state-of-the-art methods in cranberry phenotyping that will improve breeding accuracy, efficiency, and genetic gain in this globally significant fruit crop.

Efficient Absorbance-Based Assay for Rapid Antibiotic Susceptibility Testing of Enterobacterales.

It is increasingly important to rapidly receive information on the antimicrobial susceptibility of bacteria due to the rise in antimicrobial resistance worldwide. However, traditional phenotypic methods are time-consuming. Thus, the objective of this study was to develop a rapid assay that can detect antibiotic-resistant bacterial isolates phenotypically in less than 2 h. The microplate assay used in this study is based on absorbance measurements of pure bacterial isolates grown in liquid media with and without antibiotics. Absorbance was measured at the beginning of the assay and after 90 min of incubation at 37 °C. Susceptibility was calculated for bacterial isolates that, in the absence of antibiotics, exhibited more than a 50% growth increase by comparing the absorbance value of the culture in the presence of an antibiotic at 90 min with its initial value. Here, we show that it is possible to phenotypically screen the antibiotic susceptibility of Enterobacterales and Acinetobacter spp. isolates to three different antibiotics in 90 min. The test demonstrated an accuracy of 98.8%, sensitivity of 97.6%, and specificity of 99.6%. The false susceptibility rates were 0.2% and false resistance rates were 1.0%. This rapid and simple absorbance test has proven suitable for the screening of antibiotic susceptibility for a large number of strains with high accuracy and sensitivity. This method can be performed without specialized and costly materials and/or equipment, which makes it highly suitable for laboratories with limited resources.

Evaluation of Whole Genome Sequencing-Based Predictions of Antimicrobial Resistance to TB First Line Agents: A Lesson from 5 Years of Data.

Phenotypic susceptibility testing of the Mycobacterium tuberculosis complex (MTBC) isolate requires culture growth, which can delay rapid detection of resistant cases. Whole genome sequencing (WGS) and data analysis pipelines can assist in predicting resistance to antimicrobials used in the treatment of tuberculosis (TB). This study compared phenotypic susceptibility testing results and WGS-based predictions of antimicrobial resistance (AMR) to four first-line antimicrobials-isoniazid, rifampin, ethambutol, and pyrazinamide-for MTBC isolates tested between the years 2018-2022. For this 5-year retrospective analysis, the WGS sensitivity for predicting resistance for isoniazid, rifampin, ethambutol, and pyrazinamide using Mykrobe was 86.7%, 100.0%, 100.0%, and 47.8%, respectively, and the specificity was 99.4%, 99.5%, 98.7%, and 99.9%, respectively. The predictive values improved slightly using Mykrobe corrections applied using TB Profiler, i.e., the WGS sensitivity for isoniazid, rifampin, ethambutol, and pyrazinamide was 92.31%, 100%, 100%, and 57.78%, respectively, and the specificity was 99.63%. 99.45%, 98.93%, and 99.93%, respectively. The utilization of WGS-based testing addresses concerns regarding test turnaround time and enables analysis for MTBC member identification, antimicrobial resistance prediction, detection of mixed cultures, and strain genotyping, all through a single laboratory test. WGS enables rapid resistance detection compared to traditional phenotypic susceptibility testing methods using the WHO TB mutation catalog, providing an insight into lesser-known mutations, which should be added to prediction databases as high-confidence mutations are recognized. The WGS-based methods can support TB elimination efforts in Canada and globally by ensuring the early start of appropriate treatment, rapidly limiting the spread of TB outbreaks.

Differential QTL underlie wheat grain physical quality when measured using image‐based versus traditional laboratory methods

AbstractBackgroundThe marketing value of wheat (Triticum aestivum L.) is determined, in parts, by the grain's physical characteristics, owing to which they directly (or indirectly) influence milling performance and baking quality. These characteristics have been manually measured in the past, but now, digital image analysis (DIA) is being increasingly used to replace the slow phenotyping system. Here, we asked whether this could lead to the identification of the same or different genes when compared to the traditional phenotyping methods.ResultsWe measured grain physical quality on 142 wheat doubled haploids grown in the field over 2 years, and in using the quantitative trait locus (QTL) mapping approach we found that (1) for wheat grain weight, the use of DIA provided genetic information that mostly conformed to those obtained using the traditional phenotyping methods, with heritability estimates that were identical across both methods. Majority of the QTL detected were consistent between the traditional versus digital phenotyping methods; (2) a more complex architecture, however, arose from QTL analyses of hectoliter mass (HLM) and percentage of shriveled grains (SCR). The estimates for heritability varied by as much as 0.24 across methods and, more significantly, many of the detected QTL for both traits were method‐specific; (3) though method‐specific, identified QTL was mapped to genomic regions known to harbor genes for grain physical traits.ConclusionsThousand‐grain weight (TGW) is robust to a phenotyping method, but a different genetic system underlies HLM and SCR, when these were measured using traditional versus digital image analysis. For these traits, heritability estimates were larger when phenotyped using traditional methods relative to digital image analysis, suggesting that further refinements are required to better correlate digital image analysis with the traditional phenotyping methods.

Comparing Regression and Classification Models to Estimate Leaf Spot Disease in Peanut (Arachis hypogaea L.) for Implementation in Breeding Selection

Late leaf spot (LLS) is an important disease of peanut, causing global yield losses. Developing resistant varieties through breeding is crucial for yield stability, especially for smallholder farmers. However, traditional phenotyping methods used for resistance selection are laborious and subjective. Remote sensing offers an accurate, objective, and efficient alternative for phenotyping for resistance. The objectives of this study were to compare between regression and classification for breeding, and to identify the best models and indices to be used for selection. We evaluated 223 genotypes in three environments: Serere in 2020, and Nakabango and Nyankpala in 2021. Phenotypic data were collected using visual scores and two handheld sensors: a red–green–blue (RGB) camera and GreenSeeker. RGB indices derived from the images, along with the normalized difference vegetation index (NDVI), were used to model LLS resistance using statistical and machine learning methods. Both regression and classification methods were also evaluated for selection. Random Forest (RF), the artificial neural network (ANN), and k-nearest neighbors (KNNs) were the top-performing algorithms for both regression and classification. The ANN (R2: 0.81, RMSE: 22%) was the best regression algorithm, while the RF was the best classification algorithm for both binary (90%) and multiclass (78% and 73% accuracy) classification. The classification accuracy of the models decreased with the increase in classification classes. NDVI, crop senescence index (CSI), hue, and greenness index were strongly associated with LLS and useful for selection. Our study demonstrates that the integration of remote sensing and machine learning can enhance selection for LLS-resistant genotypes, aiding plant breeders in managing large populations effectively.

High-throughput UAV-based rice panicle detection and genetic mapping of heading-date-related traits.

Rice (Oryza sativa) serves as a vital staple crop that feeds over half the world's population. Optimizing rice breeding for increasing grain yield is critical for global food security. Heading-date-related or Flowering-time-related traits, is a key factor determining yield potential. However, traditional manual phenotyping methods for these traits are time-consuming and labor-intensive. Here we show that aerial imagery from unmanned aerial vehicles (UAVs), when combined with deep learning-based panicle detection, enables high-throughput phenotyping of heading-date-related traits. We systematically evaluated various state-of-the-art object detectors on rice panicle counting and identified YOLOv8-X as the optimal detector. Applying YOLOv8-X to UAV time-series images of 294 rice recombinant inbred lines (RILs) allowed accurate quantification of six heading-date-related traits. Utilizing these phenotypes, we identified quantitative trait loci (QTL), including verified loci and novel loci, associated with heading date. Our optimized UAV phenotyping and computer vision pipeline may facilitate scalable molecular identification of heading-date-related genes and guide enhancements in rice yield and adaptation.

Identification of Invasive Filamentous Mold Isolates Using DNA Sequencing: Experience of a Clinical Laboratory in a Resource-limited Setting

Background: Correct identification of clinically significant mold is becoming critical and cannot be relied only on phenotypic methods; hence, there is a dire need to develop an algorithm of workflows and capacity for molecular identification. This study shares the experience of DNA sequencing for invasive mold identification isolated and compared with phenotypic identification. Methods: This study was conducted at the microbiology laboratory, Aga Khan University, Karachi, Pakistan, and the Mycotic Disease Branch, Centers for Disease Control and Prevention, USA. Filamentous molds isolated from clinical specimens during January 2012–April 2013 were initially identified through phenotypic characteristics. Pan-fungal polymerase chain reaction targeting the internal transcribed spacer region of the ribosomal cistron and the D1/D2 domains of the 28S ribosomal cistron was performed. Sequencer version was utilized to edit and align the DNA sequences, and then sequences were identified using BLAST. The correlation between phenotypic and molecular identification was evaluated. Results: Gene sequencing identified 50% of clinical isolates as one of the Aspergillus species, followed by Mucorales 29%, Fusarium species 17%, and Cladosporium spp. 4%. Overall, 50% of clinical isolates were identified correctly till the species level by conventional methods. Phenotypic correlation with genotype till genus was 42%, while two isolates were wrongly identified phenotypically. Excellent agreement (100%) till species level between phenotypic and molecular identification for Aspergillus, while Mucorales had an agreement of 71%. Conclusion: Traditional phenotypic identification methods for filamentous molds had a good correlation with sequencing up to genus level identification; however, were not reliable up to species level.

Cotton morphological traits tracking through spatiotemporal registration of terrestrial laser scanning time-series data.

Understanding the complex interactions between genotype-environment dynamics is fundamental for optimizing crop improvement. However, traditional phenotyping methods limit assessments to the end of the growing season, restricting continuous crop monitoring. To address this limitation, we developed a methodology for spatiotemporal registration of time-series 3D point cloud data, enabling field phenotyping over time for accurate crop growth tracking. Leveraging multi-scan terrestrial laser scanning (TLS), we captured high-resolution 3D LiDAR data in a cotton breeding field across various stages of the growing season to generate four-dimensional (4D) crop models, seamlessly integrating spatial and temporal dimensions. Our registration procedure involved an initial pairwise terrain-based matching for rough alignment, followed by a bird's-eye view adjustment for fine registration. Point clouds collected throughout nine sessions across the growing season were successfully registered both spatially and temporally, with average registration errors of approximately 3cm. We used the generated 4D models to monitor canopy height (CH) and volume (CV) for eleven cotton genotypes over two months. The consistent height reference established via our spatiotemporal registration process enabled precise estimations of CH (R 2=0.95, RMSE = 7.6cm). Additionally, we analyzed the relationship between CV and the interception of photosynthetically active radiation (IPAR f ), finding that it followed a curve with exponential saturation, consistent with theoretical models, with a standard error of regression (SER) of 11%. In addition, we compared mathematical models from the Richards family of sigmoid curves for crop growth modeling, finding that the logistic model effectively captured CH and CV evolution, aiding in identifying significant genotype differences. Our novel TLS-based digital phenotyping methodology enhances precision and efficiency in field phenotyping over time, advancing plant phenomics and empowering efficient decision-making for crop improvement efforts.

B-149 Next-generation Sequencing Via Targeted Metagenomics for Broad Antimicrobial Resistance Surveillance

Abstract Background Antimicrobial-resistant infections secondary to the overuse of antibiotics are a major public health threat. The standard of care for identifying resistance is antimicrobial susceptibility testing, the phenotypic characterization of minimum inhibitory concentrations (mic) for antibiotics in the presence of bacterial growth, but these methods are labor intensive, have long turnaround times, and face challenges with non-cultivable bacteria. Molecular methods for antimicrobial resistance (AMR) ascertainment are high-throughput, robust, and sensitive and have gained interest as a genotypic complement to traditional phenotypic methods. Since the COVID-19 pandemic, next-generation sequencing has emerged as the leading technology for tracking and surveillance of infectious diseases and antimicrobial resistance, emphasizing the need for flexible sequencing panels that are easily interchangeable and capable of detecting pathogens and AMR genes. Methods In a collaboration between Illumina, a national retail pharmacy, and Aegis Sciences Corporation, residual, self-collected nasal swab samples (n = 4390) submitted for COVID-19 testing from patients across the U.S. were sequenced using the Respiratory Pathogen ID/AMR Panel (RPIP, Illumina – For Research Use Only), a target capture-based next-generation sequencing panel targeting 282 respiratory pathogens and 2097 relevant AMR markers. Sequencing data was analyzed using the Explify RPIP Analysis app (BaseSpace, Illumina) and paired with self-reported patient data submitted via an online questionnaire. Data including pathogens and anti-microbial resistance (AMR) genes detected, age, race, gender, residing state of the patient, collection month, self-reported symptoms, and health risks were collated, imported into Microsoft PowerBI software, and used to create interactive visuals for a surveillance dashboard. Results In this cohort of sequenced specimens, 85.1% (n = 3576) had one or more viral, bacterial, or fungal pathogens present, 41% (n = 1816) had one or more AMR genes present, and 38.4% (n = 1685) had one or more pathogens and AMR genes co-detected. Twenty-six different AMR genes were identified with the most prevalent being Erm 23S ribosomal RNA methyltransferase (65.5%; n = 1115). Erm was identified in samples from all 50 US states and was co-detected with staphylococcus aureus (18.2%; n = 216) and streptococcus pneumoniae (4.9%; n = 57), among others. Following Erm in prevalence, this cohort of samples harbored genes for trimethoprim resistant dihydrofolate reductase (dfr = 27.9%), tetracycline-resistant ribosomal protection protein (tet = 21.0%), and macrolide phosphotransferase (MPH = 14.2%). The data also revealed AMR gene-pathogen co-detections listed as CDC serious threats, such as the mecA gene responsible for methicillin resistance in staphylococcal species. The mecA gene was co-detected in 5.3% (n = 28) of staphylococcus aureus positive samples in this cohort. Finally, although not classified as a serious threat, neuraminidase inhibitor resistance, associated with less susceptibility to anti-virals, was found in 12% (n = 15) of all influenza A (H3N2) infected samples (n = 112). Conclusions Completion of these studies demonstrated that next-generation sequencing using targeted metagenomics with RPIP is a cost effective, high-throughput option for large-scale surveillance of AMR genes in potential respiratory infections.

Enhancing Crop Improvement through Synergistic Integration of Advanced Plant Breeding and Proximal Remote Sensing Techniques: A Review

Accelerating crop improvement with enhanced adaptability to changing climatic conditions and meeting the ever-increasing global food demand requires urgent action. To achieve this, we must employ advanced molecular breeding techniques, such as marker-assisted selection, marker-assisted backcrossing, genomic selection, genome editing, and targeted mutation. However, these approaches demand the screening of large populations to identify potential genes and genotypes. Unfortunately, a significant bottleneck lies in the absence of high-throughput plant phenotyping methods that can rapidly and cost-effectively facilitate data-driven genotype selection in plant breeding. Traditional phenotyping methods, reliant on trained experts, are slow, expensive, labour-intensive, subjective, and often require destructive sampling. Proximal remote sensing technologies, including RGB imaging, thermal imaging, hyperspectral imaging, multispectral imaging, and fluorescence imaging, offer a non-destructive and rapid collection of detailed phenotypic data, providing valuable insights into various plant traits at different growth stages. High-throughput phenotyping platforms, such as Conveyor-Type Indoor, Benchtop-Type Indoor, Unmanned Aerial Platform (UAP), and Manned Aerial Platform (MAP), utilize a combination of the aforementioned remote sensing technologies. This review article aims to explore the integration of proximal remote sensing and molecular breeding approaches, showcasing how this synergistic approach can expedite crop improvement efforts. By emphasizing the benefits, challenges, and future prospects of this integrative approach, we hope to pave the way for sustainable and productive agriculture, ensuring food security in the face of changing environmental conditions.

Developing an integrated genomic selection approach beyond biomass for varietal protection and nutritive traits in perennial ryegrass (Lolium perenne L.)

Key messageBreeding target traits can be broadened to include nutritive value and plant breeder’s rights traits in perennial ryegrass by using in-field regression-based spectroscopy phenotyping and genomic selection.Perennial ryegrass breeding has focused on biomass yield, but expansion into a broader set of traits is needed to benefit livestock industries whilst also providing support for intellectual property protection of cultivars. Numerous breeding objectives can be targeted simultaneously with the development of sensor-based phenomics and genomic selection (GS). Of particular interest are nutritive value (NV), which has been difficult and expensive to measure using traditional phenotyping methods, resulting in limited genetic improvement to date, and traits required to obtain varietal protection, known as plant breeder’s rights (PBR) traits. In order to assess phenotyping requirements for NV improvement and potential for genetic improvement, in-field reflectance-based spectroscopy was assessed and GS evaluated in a single population for three key NV traits, captured across four timepoints. Using three prediction approaches, the possibility of targeting PBR traits using GS was evaluated for five traits recorded across three years of a breeding program. Prediction accuracy was generally low to moderate for NV traits and moderate to high for PBR traits, with heritability highly correlated with GS accuracy. NV did not show significant or consistent correlation between timepoints highlighting the need to incorporate seasonal NV into selection indexes and the value of being able to regularly monitor NV across seasons. This study has demonstrated the ability to implement GS for both NV and PBR traits in perennial ryegrass, facilitating the expansion of ryegrass breeding targets to agronomically relevant traits while ensuring necessary varietal protection is achieved.

Nondestructive 3D phenotyping method of passion fruit based on X-ray micro-computed tomography and deep learning.

Passion fruit is a tropical liana of the Passiflora family that is commonly planted throughout the world due to its abundance of nutrients and industrial value. Researchers are committed to exploring the relationship between phenotype and genotype to promote the improvement of passion fruit varieties. However, the traditional manual phenotyping methods have shortcomings in accuracy, objectivity, and measurement efficiency when obtaining large quantities of personal data on passion fruit, especially internal organization data. This study selected samples of passion fruit from three widely grown cultivars, which differed significantly in fruit shape, size, and other morphological traits. A Micro-CT system was developed to perform fully automated nondestructive imaging of the samples to obtain 3D models of passion fruit. A designed label generation method and segmentation method based on U-Net model were used to distinguish different tissues in the samples. Finally, fourteen traits, including fruit volume, surface area, length and width, sarcocarp volume, pericarp thickness, and traits of fruit type, were automatically calculated. The experimental results show that the segmentation accuracy of the deep learning model reaches more than 0.95. Compared with the manual measurements, the mean absolute percentage error of the fruit width and length measurements by the Micro-CT system was 1.94% and 2.89%, respectively, and the squares of the correlation coefficients were 0.96 and 0.93. It shows that the measurement accuracy of external traits of passion fruit is comparable to manual operations, and the measurement of internal traits is more reliable because of the nondestructive characteristics of our method. According to the statistical data of the whole samples, the Pearson analysis method was used, and the results indicated specific correlations among fourteen phenotypic traits of passion fruit. At the same time, the results of the principal component analysis illustrated that the comprehensive quality of passion fruit could be scored using this method, which will help to screen for high-quality passion fruit samples with large sizes and high sarcocarp content. The results of this study will firstly provide a nondestructive method for more accurate and efficient automatic acquisition of comprehensive phenotypic traits of passion fruit and have the potential to be extended to more fruit crops. The preliminary study of the correlation between the characteristics of passion fruit can also provide a particular reference value for molecular breeding and comprehensive quality evaluation.

Stress phenotyping in plants using artificial intelligence and machine learning

The global population is rapidly increasing and is expected to exceed 9 billion by 2050, resulting in significant challenges for agriculture due to factors such as industrialization, reduced farmland, and biotic and abiotic stresses. To address these challenges and ensure future sustainability, the agriculture system needs to become more productive, efficient, and resilient. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools to transform the agricultural sector. Agricultural productivity is greatly influenced by biotic and abiotic stresses, and developing climate-smart crops through conventional breeding techniques is time-consuming and challenging. Plant phenotyping, which involves measuring specific plant features related to function, is crucial in breeding for target traits. However, traditional phenotyping methods are laborious, error-prone, and less accurate, particularly under stress conditions. To overcome these limitations, researchers have focused on developing high-throughput phenotyping technologies. State-of-the-art imaging techniques, such as light detection and ranging (LIDAR), remote sensing, and RGB imaging, combined with autonomous carriers like unmanned aerial vehicles (UAVs) and ground robots, enable real-time and high-throughput phenotyping of morphological, physiological, and stress-related traits. ML tools can compartmentalize big data, identify related traits, classify them, quantify their expression, and predict their function within the plant system. AI and ML offer multidisciplinary approaches for analyzing big data accumulated over time, leading to the discovery of patterns and systematic data of interest, such as stress phenotypes. Using these technologies, researchers worldwide can expedite agricultural research and develop climate-smart crops. The future of AI and ML in agriculture is promising, as they can lead to new scientific discoveries and help overcome the challenges of limited resources in food production.

Molecular study among burn patients infected with bacterial pseudomonas species

The Invade of the Burns infaction by potentially pathogenic bacteria constitutes threatens its transmission to different human tissues and organs,.A total number of 120 swabs in patient about 96/120 (80%) show positive culture of bacterial growth versus. 24/120 (20 %) show negative results for (No growth of bacteria) .A total of 96 sample were divided on the basis of( 61/96)sample were Gram-negative bacteria and (35/96)sample were Gram-positive bacteria. this study provides isolation and identification of 37 isolates bacteria pseudomonas species for the 120 specimen(20/37) pseudomonas.aeruginosa.10/37Pseudomonas. fluorescent. 7/37Pseuomonas. putadia. recovered from burn infections.In 120 patients according to Sex the specimens were collected from patients he suffer burns, to sex: female 64/120(53.34% samples) and male 56/120 (46.66 % samples) with age groups from 10-60 years. In this study, some of virulence factors were detected by traditional phenotypic methods. The results Bacterial Adhesion: showed P. aeruginosa 17/20 (85%). P fluorescens 7/10(70%). P putadia 4/7(57.14). most of bacteria isolate demonstrated Bacterial Adhesion:- The results showed that most isolates were able to adhere to surface epithelial cells .

Immunity study among burn patients infected with bacterial pseudomonas species

The Invade of the Burns infaction by potentially pathogenic bacteria constitutes threatens its transmission to different human tissues and organs, this study provides isolation and identification of 37 isolates bacteria pseudomonas species for the 120 specimen (20 pseudomonas.aeruginosa.10 Pseudomonas. fluorescens.7 Pseuomonas.putadia) recovered from burn infections. In this study, some of virulence factors were detected by traditional phenotypic methods. The results Bacterial gelatinase . The results of the study demonstrated that the high production of gelatinase was released from P.aeruginosa 20/20(100%, P.fluorescens10/10(100%).p putedia 5/7(71.42%) . the total pseudomonas spp 35/37(94.59%) isolates . The results haemolysin production In the blood agar plate method, 30/37 (80.08%) of 20/20(100%) P. aeruginosa.9/10(90%) p. fluorescens. and 1/7(14.28%) p.putedia of all isolates demonstrate beta-hemolytic activity. Total and Deferential Count of White Blood Cells the results of WBC and differential cells. The results of WBC and differential count of neutrophils and monocytes in survivors burn patients have shown the significant increase (P<0.05) compared to control group. But the lymphocytes,eosinophils and basophils showed non-significant (P>0.05). Cytokines Detections Interleukin-6 the concentration of IL-6 in burn patients were of significant elevation (P<0.01) in compared to control group, yet the concentration of IL-6 showed a highly elevation burn patients.

Genome-based analyses reveal heterotypic synonyms of Streptomyces species and associated subspecies.

In the genus Streptomyces, several validly described species have been reduced to synonyms of earlier described species though additional synonyms remain to be detected given the previous dependence on traditional phenotypic methods. In this study, genome-based procedures, including DNA-DNA hybridization analyses, overall genome-related indices, such as ANI, dDDH and AAI, revealed that certain strains recorded genomic indices above the threshold values used to define species boundaries. The results of phylogenetic and phylogenomic trees based on concatenated and phylogenomic analyses showed that 33 out of 364 tested species could be assigned to 15 species groups and that 18 Streptomyces species names be reclassified as later heterotypic synonyms of earlier validly published species. Consequently, it is proposed that S. albaduncus is a later heterotypic synonym of S. griseoloalbus; S. bellus is a synonym of S. coeruleorubidus; S. gancidicus and S. rubiginosus are synonyms of S. pseudogriseolus; S. niveoruber is a synonym of S. griseoviridis; S. griseomycini is a synonym of S. griseostramineus; S. jietaisiensis is a synonym of S. griseoaurantiacus; S. pluricolorescens is a synonym of S. rubiginosohelvolus; S. nashvillensis is a synonym of S. tanashiensis; S. yerevanensis is a synonym of S. flaveus; S. durhamensis is a synonym of S. filipinensis; S. recifensis is a synonym of S. griseoluteus; S. canaries and S. olivaceoviridis are synonyms of S. corchorusii; S. melanosporofaciens is a synonym of S. antimycoticus; S. albulus is a synonym of S. noursei; and S. janthinus and S. violarus are synonyms of S. violaceus. Additionally, seven of these 18 Streptomyces species have been designated subspecies.

Evaluation of the U.S. Peanut Germplasm Mini-Core Collection in the Virginia-Carolina Region Using Traditional and New High-Throughput Methods

Peanut (Arachis hypogaea L.) is an important food crop for the U.S. and the world. The Virginia-Carolina (VC) region (Virginia, North Carolina, and South Carolina) is an important peanut-growing region of the U.S and is affected by numerous biotic and abiotic stresses. Identification of stress-resistant germplasm, along with improved phenotyping methods, are important steps toward developing improved cultivars. Our objective in 2017 and 2018 was to assess the U.S. mini-core collection for desirable traits, a valuable source for resistant germplasm under limited water conditions. Accessions were evaluated using traditional and high-throughput phenotyping (HTP) techniques, and the suitability of HTP methods as indirect selection tools was assessed. Traditional phenotyping methods included stand count, plant height, lateral branch growth, normalized difference vegetation index (NDVI), canopy temperature depression (CTD), leaf wilting, fungal and viral disease, thrips rating, post-digging in-shell sprouting, and pod yield. The HTP method included 48 aerial vegetation indices (VIs), which were derived using red, blue, green, and near-infrared reflectance; color space indices were collected using an octocopter drone at the same time, with traditional phenotyping. Both phenotypings were done 10 times between 4 and 16 weeks after planting. Accessions had yields comparable to high yielding checks. Correlation coefficients up to 0.8 were identified for several Vis, with yield indicating their suitability for indirect phenotyping. Broad-sense heritability (H2) was further calculated to assess the suitability of particular VIs to enable genetic gains. VIs could be used successfully as surrogates for the physiological and agronomic trait selection in peanuts. Further, this study indicates that UAV-based sensors have potential for measuring physiologic and agronomic characteristics measured for peanut breeding, variable rate input application, real time decision making, and precision agriculture applications.

Estimation of Maize Yield and Flowering Time Using Multi-Temporal UAV-Based Hyperspectral Data

Maize (Zea mays L.) is one of the most consumed grains in the world. Within the context of continuous climate change and the reduced availability of arable land, it is urgent to breed new maize varieties and screen for the desired traits, e.g., high yield and strong stress tolerance. Traditional phenotyping methods relying on manual assessment are time-consuming and prone to human errors. Recently, the application of uncrewed aerial vehicles (UAVs) has gained increasing attention in plant phenotyping due to their efficiency in data collection. Moreover, hyperspectral sensors integrated with UAVs can offer data streams with high spectral and spatial resolutions, which are valuable for estimating plant traits. In this study, we collected UAV hyperspectral imagery over a maize breeding field biweekly across the growing season, resulting in 11 data collections in total. Multiple machine learning models were developed to estimate the grain yield and flowering time of the maize breeding lines using the hyperspectral imagery. The performance of the machine learning models and the efficacy of different hyperspectral features were evaluated. The results showed that the models with the multi-temporal imagery outperformed those with imagery from single data collections, and the ridge regression using the full band reflectance achieved the best estimation accuracies, with the correlation coefficients (r) between the estimates and ground truth of 0.54 for grain yield, 0.91 for days to silking, and 0.92 for days to anthesis. In addition, we assessed the estimation performance with data acquired at different growth stages to identify the good periods for the UAV survey. The best estimation results were achieved using the data collected around the tasseling stage (VT) for the grain yield estimation and around the reproductive stages (R1 or R4) for the flowering time estimation. Our results showed that the robust phenotyping framework proposed in this study has great potential to help breeders efficiently estimate key agronomic traits at early growth stages.

A 3D grape bunch reconstruction pipeline based on constraint-based optimisation and restricted reconstruction grammar

• Wine grape bunch architecture reconstruction from a 2D image. • Prediction of invisible bunch elements including their connectivity. • Element-wise comparison improves on existing methods. • Application on a larger scale would facilitate grapevine breeding trials. Phenotypic traits of grapevines are known to be closely related to grapevine yield, wine flavour and sensitivity to disease. Traditional phenotyping methods based on manual measurements face the bottleneck of intensely repetitive and time consuming measurement. As viticulturists turning their focus to the 3D automatic phenotyping domain, existing work on grape bunch phenotyping indicates deficiencies in incomplete reconstruction information, poor element coincidence with the ground truth and poor performance under field conditions. To this end, the proposed work introduces a novel reconstruction pipeline by dividing it into sub-problems of visible-berry-related reconstruction and invisible element prediction. By taking a 2D image of the target bunch as the only sensor input, visible berries are detected using image processing algorithms. With the detected berry information, their morphological positions are predicted, from which internodes that are associated with the detected berries are derived. Parameters of derived internodes are then estimated employing constraint-based optimisation theory, from which the visible-berry-related reconstruction is able to be achieved. Invisible element prediction is then conducted by filling elements according to a Restricted Reconstruction Grammar (RRG). A fully reconstructed bunch model is finally presented. Compared with existing work, the proposed grape bunch reconstruction pipeline achieved an improvement in quantity estimation of rachis internodes, tertiary internodes and pedicels, whose percentage errors were indicated as 21.2, 42.2 and 31.2% respectively. A better performance was also revealed in length estimations of secondary internodes and pedicels with percentage errors of 3.5 and 0.5%. This may largely facilitate related studies on disease control of grape bunches since internode numbers and lengths are closely related to bunch compactness which is the indicator of disease sensitivity. Especially, the proposed reconstruction pipeline shows an promising improvement in element coincidence with F 1 scores of 0.90, 0.77, 0.45, 0.43 for respective element types. Knowing that element coincidence may influence the inner space utilisation of a bunch, the proposed work provides a better option for 3D grape bunch phenotyping.

Prevalence of b-lactamase produced in Klebsiella pneumoniae and Enterobacter cloacae isolated from gingivitis in Al-Najaf Province, Iraq

Abstract. Motaweq ZY. 2022. Prevalence of ?-lactamase produced in Klebsiella pneumoniae and Enterobacter cloacae isolated from gingivitis in Al-Najaf Province, Iraq. Nusantara Bioscience 14: 78-83. This study provides phenotypic and genotypic ?-lactamase formation data on 26 isolates of Klebsiella pneumoniae and Enterobacter cloacae isolated from patients suffering from gingivitis checked at Al-Kafeel clinic and private clinic in Al-Najaf Province-Iraq during the period from September 2020 to February 2021. In this study, some of them were detected by traditional phenotypic methods, while others were detected by phenotypic and then genotypically by using monoplex-PCR technique. The results revealed that out of 14 ?-lactam resistance K. pneumoniae isolates, 8 isolates (57.1%) gave positive results with the direct capillary tubes method, while 12 ?-lactam resistance E. cloacae gave 6 (50%) positive results. This result indicated that the enzymatic resistance was prevalent among isolates. Furthermore, the results showed that most isolates were ESBL producers according to initial and confirmatory methods. Molecular amplification of the ?-lactamase enzyme blaSHV gene was detected in 8 (57.1%) and 5 (41.6%) for K. pneumoniae and E. cloacae, respectively. While all 100% of K. pneumoniae and E. cloacae isolates gave negative results for blaGES gene. This study aimed to investigate the ?-lactamase formation and detection of blaSHV and blaGES genes in K. pneumoniae and E. cloacae isolated from gingivitis diseases.