Search Results (880)

Accurate estimation of chlorophyll content is essential for understanding the growth status and optimizing the cultivation practices of Ginkgo, a dominant multi-functional tree species in China. Traditional methods based on chemical analysis for determining chlorophyll content are labor-intensive and time-consuming, making them unsuitable for large-scale dynamic monitoring and high-throughput phenotyping. To accurately quantify chlorophyll content in Ginkgo seedlings under different nitrogen levels, this study employed a hyperspectral imaging camera to capture canopy hyperspectral images of seedlings throughout their annual growth periods. Reflectance derived from pure leaf pixels of Ginkgo seedlings was extracted to construct a set of spectral parameters, including original reflectance, logarithmic reflectance, and first derivative reflectance, along with spectral index combinations. A one-dimensional convolutional neural network (1D-CNN) model was then developed to estimate chlorophyll content, and its performance was compared with four common machine learning methods, including Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest (RF). The results demonstrated that the 1D-CNN model outperformed others with the first derivative spectra, achieving higher CV-R² and lower RMSE values (CV-R² = 0.80, RMSE = 3.4). Furthermore, incorporating spectral index combinations enhanced the model’s performance, with the 1D-CNN model achieving the best performance (CV-R² = 0.82, RMSE = 3.3). These findings highlight the potential of the 1D-CNN model in strengthening the chlorophyll estimations, providing strong technical support for the precise cultivation and the fertilization management of Ginkgo seedlings. Full article

The Nao-zhou stock large yellow croaker (Larimichthys crocea) is a unique economic seawater fish species in China and exhibits significant dimorphism in both male and female phenotypes. Cultivating all-female seedlings can significantly improve breeding efficiency. To accelerate the cultivation process of all female seedlings of this species, it is necessary to deeply understand the regulatory mechanisms of sexual differentiation and gonadal development. This study used Illumina high-throughput sequencing to sequence the transcriptome of the testes and ovaries of Nao-zhou stock large yellow croaker to identify genes and molecular functions related to sex determination. A total of 10,536 differentially expressed genes were identified between males and females, including 5682 upregulated and 4854 downregulated genes. Functional annotation screened out 70 important candidate genes related to sex, including 34 genes highly expressed in the testis (including dmrt1, foxm1, and amh) and 36 genes highly expressed in the ovary (including gdf9, hsd3b1, and sox19b). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis found that differentially expressed genes were significantly enriched in nine signaling pathways related to sex determination and gonadal development, including steroid hormone biosynthesis, MAPK signaling pathway, and the TGF-beta signaling pathway. By screening sex-related differentially expressed genes and mapping protein–protein interaction networks, hub genes such as dmrt1, amh, and cyp19a1a were found to be highly connected. The expression levels of 15 sex-related genes, including amh, dmrt1, dmrt2a, foxl1, and zp3b, were determined by qRT–PCR and RNA sequencing. This study screened for differentially expressed genes related to sex determination and differentiation of Nao-zhou stock large yellow croaker and revealed the signaling pathways involved in gonad development of male and female individuals. The results provide important data for future research on sex determination and differentiation mechanisms, thereby providing a scientific basis for the cultivation of all-female seedlings. Full article

Background: Frailty increases the risk of needing nursing care and significantly affects the life and functional prognosis of older individuals. Early detection and tailored interventions are crucial for maintaining and enhancing their life functions. Recognizing distinct clinical phenotypes is essential for devising appropriate interventions. This study aimed to explore diverse frailty phenotypes, focusing on poor nutrition in older Japanese individuals through observational research. Methods: Twenty-one nursing home residents underwent a comprehensive survey covering physical, blood, dietary, cardiac, cognitive, nutritional, nursing care, frailty, agitated behavior, and gut microbiome assessments (high-throughput 16S rRNA gene sequencing). Using clustering analysis with 239 survey items (excluding gut microbiome), participants were classified into subgroups based on clinical phenotypes, and group characteristics were compared through analysis. Results: Individuals with moderate or severe frailty and suspected dementia formed subgroups with distinct clinical phenotypes based on nutritional, defecation, and nursing care statuses. The gut microbiome significantly varied among these groups (p = 0.007), indicating its correlation with changes in clinical phenotype. Nutritional status differences suggested poor nutrition as a differentiating factor in the core clinical phenotype. Conclusions: This study proposes that the gut microbiome differs based on the clinical phenotype of Japanese older individuals with frailty, and targeted interventions addressing the gut microbiome may contribute to preventing frailty in this population. Full article

Betula luminifera, an indigenous hardwood tree in South China, possesses significant economic and ecological value. In view of the current severe drought situation, it is urgent to enhance this tree’s drought tolerance. However, traditional artificial methods fall short of meeting the demands of breeding efforts due to their inefficiency. To monitor drought situations in a high-throughput and automatic approach, a deep learning model based on phenotype characteristics was proposed to identify and classify drought stress in B. luminifera seedlings. Firstly, visible-light images were obtained from a drought stress experiment conducted on B. luminifera shoots. Considering the images’ characteristics, we proposed an SAM-CNN architecture by incorporating spatial attention modules into classical CNN models. Among the four classical CNNs compared, ResNet50 exhibited superior performance and was, thus, selected for the construction of the SAM-CNN. Subsequently, we analyzed the classification performance of the SAM-ResNet50 model in terms of transfer learning, training from scratch, model robustness, and visualization. The results revealed that SAM-ResNet50 achieved an accuracy of 1.48% higher than that of ResNet50, at 99.6%. Furthermore, there was a remarkable improvement of 18.98% in accuracy, reaching 82.31% for the spatial transform images generated from the test set images by applying movement and rotation for robustness testing. In conclusion, the SAM-ResNet50 model achieved outstanding performance, with 99.6% accuracy and realized high-throughput automatic monitoring based on phenotype, providing a new perspective for drought stress classification and technical support for B. luminifera-related breeding work. Full article

Rare and ultra-rare diseases constitute a significant medical challenge due to their low prevalence and the limited understanding of their origin and underlying mechanisms. These disorders often exhibit phenotypic diversity and molecular complexity that represent a challenge to biomedical research. There are more than 6000 different rare diseases that affect nearly 300 million people worldwide. However, the prevalence of each rare disease is low, and in consequence, the biomedical resources dedicated to each rare disease are limited and insufficient to effectively achieve progress in the research. The use of animal models to investigate the mechanisms underlying pathogenesis has become an invaluable tool. Among the animal models commonly used in research, Drosophila melanogaster has emerged as an efficient and reliable experimental model for investigating a wide range of genetic disorders, and to develop therapeutic strategies for rare and ultra-rare diseases. It offers several advantages as a research model including short life cycle, ease of laboratory maintenance, rapid life cycle, and fully sequenced genome that make it highly suitable for studying genetic disorders. Additionally, there is a high degree of genetic conservation from Drosophila melanogaster to humans, which allows the extrapolation of findings at the molecular and cellular levels. Here, I examine the role of Drosophila melanogaster as a model for studying rare and ultra-rare diseases and highlight its significant contributions and potential to biomedical research. High-throughput next-generation sequencing (NGS) technologies, such as whole-exome sequencing and whole-genome sequencing (WGS), are providing massive amounts of information on the genomic modifications present in rare diseases and common complex traits. The sequencing of exomes or genomes of individuals affected by rare diseases has enabled human geneticists to identify rare variants and identify potential loci associated with novel gene–disease relationships. Despite these advances, the average rare disease patient still experiences significant delay until receiving a diagnosis. Furthermore, the vast majority (95%) of patients with rare conditions lack effective treatment or a cure. This scenario is enhanced by frequent misdiagnoses leading to inadequate support. In consequence, there is an urgent need to develop model organisms to explore the molecular mechanisms underlying these diseases and to establish the genetic origin of these maladies. The aim of this review is to discuss the advantages and limitations of Drosophila melanogaster, hereafter referred as Drosophila, as an experimental model for biomedical research, and the applications to study human disease. The main question to address is whether Drosophila is a valid research model to study human disease, and in particular, rare and ultra-rare diseases. Full article

Accurate and timely prediction of oilseed rape yield is crucial in precision agriculture and field remote sensing. We explored the feasibility and potential for predicting oilseed rape yield through the utilization of a UAV-based platform equipped with RGB and multispectral cameras. Genetic algorithm–partial least square was employed and evaluated for effective wavelength (EW) or vegetation index (VI) selection. Additionally, different machine learning algorithms, i.e., multiple linear regression (MLR), partial least squares regression (PLSR), least squares support vector machine (LS-SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and radial basis function neural network (RBFNN), were developed and compared. With multi-source data fusion by combining vegetation indices (color and narrow-band VIs), robust prediction models of yield in oilseed rape were built. The performance of prediction models using the combination of VIs (RBFNN: R_pre = 0.8143, RMSEP = 171.9 kg/hm²) from multiple sensors manifested better results than those using only narrow-band VIs (BPNN: R_pre = 0.7655, RMSEP = 188.3 kg/hm²) from a multispectral camera. The best models for yield prediction were found by applying BPNN (R_pre = 0.8114, RMSEP = 172.6 kg/hm²) built from optimal EWs and ELM (R_pre = 0.8118, RMSEP = 170.9 kg/hm²) using optimal VIs. Taken together, the findings conclusively illustrate the potential of UAV-based RGB and multispectral images for the timely and non-invasive prediction of oilseed rape yield. This study also highlights that a lightweight UAV equipped with dual-image-frame snapshot cameras holds promise as a valuable tool for high-throughput plant phenotyping and advanced breeding programs within the realm of precision agriculture. Full article

Maize (Zea mays L.) has been shown to be sensitive to temperature deviations, influencing its yield potential. The development of new maize hybrids resilient to unfavourable weather is a desirable aim for crop breeders. In this paper, we showcase the development of a multimodal deep learning model using RGB images, phenotypic, and weather data under temporal effects to predict the yield potential of maize before or during anthesis and silking stages. The main objective of this study was to assess if the inclusion of historical weather data, maize growth captured through imagery, and important phenotypic traits would improve the predictive power of an established multimodal deep learning model. Evaluation of the model performance when training from scratch showed its ability to accurately predict ~89% of hybrids with high-yield potential and demonstrated enhanced explanatory power compared with previously published models. Shapley Additive explanations (SHAP) analysis indicated the top influential features include plant density, hybrid placement in the field, date to anthesis, parental line, temperature, humidity, and solar radiation. Including weather historical data was important for model performance, significantly enhancing the predictive and explanatory power of the model. For future research, the use of the model can move beyond maize yield prediction by fine-tuning the model on other crop data, serving as a potential decision-making tool for crop breeders to determine high-performing individuals from diverse crop types. Full article

This study aimed to assess the fermentation characteristics, bacterial community structure, co-occurrence networks, and their predicted functionality and pathogenic risk in high-moisture Italian ryegrass (IR; Lolium multiflorum Lam.) silage. The IR harvested at heading stage (208 g dry matter (DM)/kg fresh weight) was spontaneously ensiled in plastic silos (10 L scale). Triplicated silos were opened after 1, 3, 7, 15, 30, and 60 days of fermentation, respectively. The bacterial community structure on days 3 and 60 were investigated using high-throughput sequencing technology, and 16S rRNA-gene predicted functionality and phenotypes were determined by PICRUSt2 and BugBase tools, respectively. After 60 days, the IR silage exhibited good ensiling characteristics indicated by large amounts of acetic acid (~58.7 g/kg DM) and lactic acid (~91.5 g/kg DM), relatively low pH (~4.20), acceptable levels of ammonia nitrogen (~87.0 g/kg total nitrogen), and trace amounts of butyric acid (~1.59 g/kg DM). Psychrobacter was prevalent in fresh IR, and Lactobacillus became the most predominant genus after 3 and 60 days. The ensilage process reduced the complexity of the bacterial community networks in IR silage. The bacterial functional pathways in fresh and ensilaged IR are primarily characterized by the metabolism of carbohydrate and amino acid. The pyruvate kinase and 1-phosphofructokinase were critical in promoting lactic acid fermentation. A greater (p < 0.01) abundance of the “potentially pathogenic” label was noticed in the bacterial communities of ensiled IR than fresh IR. Altogether, the findings indicated that the high-moisture IR silage exhibited good ensiling characteristics, but the potential for microbial contamination and pathogens still remained after ensiling. Full article

Climate change has intensified droughts, severely impacting crops like oats and highlighting the need for effective adaptation strategies. In this context, the implementation of IoT-based climate control systems in greenhouses emerges as a promising solution for optimizing microclimates. These systems allow for the precise monitoring and adjustment of critical variables such as temperature, humidity, vapor pressure deficit (VPD), and photosynthetically active radiation (PAR), ensuring optimal conditions for crop growth. During the experiment, the average daytime temperature was 22.6 °C and the nighttime temperature was 15.7 °C. The average relative humidity was 60%, with a VPD of 0.46 kPa during the day and 1.26 kPa at night, while the PAR reached an average of 267 μmol m⁻² s⁻¹. Additionally, the use of high-throughput gravimetric phenotyping platforms enabled precise data collection on the plant–soil–atmosphere relationship, providing exhaustive control over water balance and irrigation. This facilitated the evaluation of the physiological response of plants to abiotic stress. Inoculation with microbial consortia (PGPB) was used as a tool to mitigate water stress. In this 69-day study, irrigation was suspended in specific treatments to simulate drought, and it was observed that inoculated plants maintained chlorophyll b and carotenoid levels akin to those of irrigated plants, indicating greater tolerance to water deficit. These plants also exhibited greater efficiency in dissipating light energy and rapid recovery after rehydration. The results underscore the potential of combining IoT monitoring technologies, advanced phenotyping platforms, and microbial consortia to enhance crop resilience to climate change. Full article

In production activities and breeding programs, large-scale investigations of crop high-throughput phenotype information are needed to help improve management and decision-making. The development of UAV (unmanned aerial vehicle) remote sensing technology provides a new means for the large-scale, efficient, and accurate acquisition of crop phenotypes, but its practical application and popularization are hindered due to the complicated data processing required. To date, there is no automated system that can utilize the canopy images acquired through UAV to conduct a phenotypic character analysis. To address this bottleneck, we developed a new scalable software called CimageA. CimageA uses crop canopy images obtained by UAV as materials. It can combine machine vision technology and machine learning technology to conduct the high-throughput processing and phenotyping of crop remote sensing data. First, zoning tools are applied to draw an area-of-interest (AOI). Then, CimageA can rapidly extract vital remote sensing information such as the color, texture, and spectrum of the crop canopy in the plots. In addition, we developed data analysis modules that estimate and quantify related phenotypes (such as leaf area index, canopy coverage, and plant height) by analyzing the association between measured crop phenotypes and CimageA-derived remote sensing eigenvalues. Through a series of experiments, we confirmed that CimageA performs well in extracting high-throughput remote sensing information regarding crops, and verified the reliability of retrieving LAI (R² = 0.796) and estimating plant height (R² = 0.989) and planting area using CimageA. In short, CimageA is an efficient and non-destructive tool for crop phenotype analysis, which is of great value for monitoring crop growth and guiding breeding decisions. Full article

Studies on the phenotypic traits and their associations in Chinese cabbage lack precise and objective digital evaluation metrics. Traditional assessment methods often rely on subjective evaluations and experience, compromising accuracy and reliability. This study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology, with the aim of enhancing the precision, reliability, and standardization of the comprehensive phenotypic traits of Chinese cabbage. By using multi-view image sequences and structure-from-motion algorithms, 3D point clouds of 50 plants from each of the 17 Chinese cabbage varieties were reconstructed. Color-based region growing and 3D convex hull techniques were employed to measure 30 agronomic traits. Comparisons between 3D point cloud-based measurements of the plant spread, plant height, leaf area, and leaf ball volume and traditional methods yielded R² values greater than 0.97, with root mean square errors of 1.27 cm, 1.16 cm, 839.77 cm³, and 59.15 cm², respectively. Based on the plant spread and plant height, a linear regression prediction of Chinese cabbage weights was conducted, yielding an R² value of 0.76. Integrated optimization algorithms were used to test the parameters, reducing the measurement time from 55 min when using traditional methods to 3.2 min. Furthermore, in-depth analyses including variation, correlation, principal component analysis, and clustering analyses were conducted. Variation analysis revealed significant trait variability, with correlation analysis indicating 21 pairs of traits with highly significant positive correlations and 2 pairs with highly significant negative correlations. The top six principal components accounted for 90% of the total variance. Using the elbow method, k-means clustering determined that the optimal number of clusters was four, thus classifying the 17 cabbage varieties into four distinct groups. This study provides new theoretical and methodological insights for exploring phenotypic trait associations in Chinese cabbage and facilitates the breeding and identification of high-quality varieties. Compared with traditional methods, this system provides significant advantages in terms of accuracy, speed, and comprehensiveness, with its low cost and ease of use making it an ideal replacement for manual methods, being particularly suited for large-scale monitoring and high-throughput phenotyping. Full article

For different broccoli materials, it used to be necessary to manually plant in a large area for the investigation of flower ball information, and this method is susceptible to subjective influence, which is not only time-consuming and laborious but may also cause some damage to the broccoli in the process of investigation. Therefore, the rapid and nondestructive monitoring of flower heads is key to acquiring high-throughput phenotypic information on broccoli crops. In this study, we used an unmanned aerial vehicle (UAV) to acquire hundreds of images of field-grown broccoli to evaluate their flower head development rate and sizes during growth. First, YOLOv5 and YOLOv8 were used to complete the position detection and counting statistics at the seedling and heading stages. Then, UNet, PSPNet, DeepLabv3+, and SC-DeepLabv3+ were used to segment the flower heads in the images. The improved SC-DeepLabv3+ model excelled in segmenting flower heads, showing Precision, reconciled mean F1-score, mean intersection over union, and mean pixel accuracy values of 93.66%, 95.24%, 91.47%, and 97.24%, respectively, which were 0.57, 1.12, 1.16, and 1.70 percentage points higher than the respective values achieved with the DeepLabv3+ model. Flower head sizes were predicted on the basis of the pixel value of individual flower heads and ground sampling distance, yielding predictions with an R² value of 0.67 and root-mean-squared error of 1.81 cm. Therefore, the development rate and sizes of broccoli flower heads during growth were successively estimated and calculated. Compared with the existing technology, it greatly improves work efficiency and can help to obtain timely information on crop growth in the field. Our methodology provides a convenient, fast, and reliable way for investigating field traits in broccoli breeding. Full article

Cellular therapies exhibit immense potential in treating complex diseases with sustained responses. The manufacture of cell therapies involves the purification and engineering of specific cells from a donor or patient to achieve a therapeutic response upon injection. Magnetic cell sorting targeting the presence or absence of surface markers is commonly used for upfront purification. However, emerging research shows that optimal therapeutic phenotypes are characterized not only by the presence or absence of specific antigens but also by antigen density. Unfortunately, current cell purification tools like magnetic or fluorescence-activated cell sorting (FACS) lack the resolution to differentiate populations based on antigen density while maintaining scalability. Utilizing a technique known as digital magnetic sorting (DMS), we demonstrate proof of concept for a scalable, magnetic-based approach to fractionate cell populations based on antigen density level. Targeting CD4 on human leukocytes, DMS demonstrated fractionation into CD4^Hi T cells and CD4^Low monocytes and neutrophils as quantified by flow cytometry and single-cell RNA seq. DMS also demonstrated high throughput processing at throughputs 3–10× faster than FACS. We believe DMS can be leveraged and scaled to enable antigen density-based sorting in cell therapy manufacturing, leading to the production of more potent and sustainable cellular therapies. Full article

As one of the world’s most crucial food crops, maize plays a pivotal role in ensuring food security and driving economic growth. The diversification of maize variety breeding is significantly enhancing the cumulative benefits in these areas. Precise measurement of phenotypic data is pivotal for the selection and breeding of maize varieties in cultivation and production. However, in outdoor environments, conventional phenotyping methods, including point cloud processing techniques based on region growing algorithms and clustering segmentation, encounter significant challenges due to the low density and frequent loss of point cloud data. These issues substantially compromise measurement accuracy and computational efficiency. Consequently, this paper introduces a Constrained Region Point Cloud Phenotyping (CRPCP) algorithm that proficiently detects the phenotypic traits of multiple maize plants in sparse outdoor point cloud data. The CRPCP algorithm consists primarily of three core components: (1) a constrained region growth algorithm for effective segmentation of maize stem point clouds in complex backgrounds; (2) a radial basis interpolation technique to bridge gaps in point cloud data caused by environmental factors; and (3) a multi-level parallel decomposition strategy leveraging scene blocking and plant instances to enable high-throughput real-time computation. The results demonstrate that the CRPCP algorithm achieves a segmentation accuracy of 96.2%. When assessing maize plant height, the algorithm demonstrated a strong correlation with manual measurements, evidenced by a coefficient of determination R² of 0.9534, a root mean square error (RMSE) of 0.4835 cm, and a mean absolute error (MAE) of 0.383 cm. In evaluating the diameter at breast height (DBH) of the plants, the algorithm yielded an R² of 0.9407, an RMSE of 0.0368 cm, and an MAE of 0.031 cm. Compared to the PointNet point cloud segmentation method, the CRPCP algorithm reduced segmentation time by more than 44.7%. The CRPCP algorithm proposed in this paper enables efficient segmentation and precise phenotypic measurement of low-density maize multi-plant point cloud data in outdoor environments. This algorithm offers an automated, high-precision, and highly efficient solution for large-scale field phenotypic analysis, with broad applicability in precision breeding, agronomic management, and yield prediction. Full article

Nitrogen use efficiency (NUE) is important for the growth and development of rice and is significant in reducing the costs of rice production. OsNRT1.1b is involved in nitrate assimilation, and the alleles at position 21,759,092 on chromosome 10 clearly separate indica (Pathum Thani 1 (PTT1) and Homcholasit (HCS)) and japonica (Azucena and Leum Pua (LP)) rice varieties. Rice morphological and physiological traits were collected at three nitrogen levels (N0 = 0 kg ha⁻¹, N7 = 43.75 kg ha⁻¹, and N14 = 87.5 kg ha⁻¹). Leaf and tiller numbers in PTT1 and HCS at N7 and N14 were two to three times higher than those at N0. At harvest, the biomass yield in PTT1 was the highest, while the total grain number in HCS was the maximum. The leaf widths and total chlorophyll contents (SPAD units) of Azucena and LP increased with nitrogen application as well as photosynthetic pigment parameters; for example, plant senescence reflectance indices (PSRIs), structure-insensitive pigment indices (SIPIs), and modified chlorophyll absorption ratio indices (MCARIs) were highly related in the japonica varieties. PTT1 and HCS, both carrying the A allele at OsNRT1.1b, had better NUE than Azucena and LP with the G allele. HCS, overall, had better NUE than PTT1. The translation to grain yield of assimilates was remarkable in PTT1 and HCS compared with Azucena and LP. In addition, HCS converted biomass for a 75% higher yield than PTT1. The ability of HCS to produce high yields was achieved even at N7 nitrogen fertilization, manifesting efficient use of nitrogen. Full article