HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest

HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest

Combination of thymosin α1 with conventional therapy improves APC and IL-1R1 levels in children with severe pneumonia

Purpose: To investigate the effect of a combination of thymosin α1 with  conventional regimen on APC and IL-1R1 levels in children with severe pneumonia, and to provide a reference for its clinical treatment.Methods: A total of 96 children with severe pneumonia over a period of two years were divided into control and observation groups (48 cases per  group) by random number table method. The patients consisted of 46 males and 50 females within the age range of 0.1 - 6 years (mean age = 3.0 ± 1.2 years), and mean duration of 13.4 ± 2.2 days). Their acute physiology and chronic health evaluation II (APACHE II) was 20.3 ± 3.2 points. Patients in the control group were placed on conventional treatment programs, while those in the observation group, in addition to the  conventional treatments, received thymosin α1 (subcutaneous injection of thymosin α1 at a dose of 1.5 mg per injection) twice daily for the first 3 days, then once daily until the 7th day, after which the adverse reactions were observed and treated. Pulmonary function indices, and levels of APC, and IL-1R1 were also determined in the patients’ sera before and after the treatment regime.Results: The clinical efficacy of the observation group was significantly better (p < 0.05) than that of control. There were no significant differences (p > 0.05) in the indices of pulmonary function (maximal inspiratory and expiratory pressure, and peak expiratory flow) between the two groups before treatment. However, after treatment they were significantly higher (p < 0.05) in the observation group than in control. There were no significant differences (p > 0.05) in APC and IL-1R1 levels between the two groups before treatment, but after treatment, while the level of APC in the observation group was significantly increased (p < 0.05), the IL-1R1 level was significantly decreased (p < 0.05), when compared to the control group. Both groups showed no obvious adverse reactions during the treatmentregime.Conclusion: Combining thymosin α1 with routine treatment in the  management of children with severe pneumonia can significantly alleviate the symptoms of patients, greatly stimulate recovery of pulmonary function, improve APC and IL-1R1 levels, and prevent inflammation.Keywords: Pneumonia, Thymosin α1, Routine treatment, Inflammatory factors, Pulmonary functio

PI‑Plat: a high‑resolution image‑based 3D reconstruction method to estimate growth dynamics of rice inflorescence traits

Background: Recent advances in image-based plant phenotyping have improved our capability to study vegetative stage growth dynamics. However, more complex agronomic traits such as inflorescence architecture (IA), which predominantly contributes to grain crop yield are more challenging to quantify and hence are relatively less explored. Previous efforts to estimate inflorescence-related traits using image-based phenotyping have been limited to destructive end-point measurements. Development of non-destructive inflorescence phenotyping platforms could accelerate the discovery of the phenotypic variation with respect to inflorescence dynamics and mapping of the underlying genes regulating critical yield components. Results: The major objective of this study is to evaluate post-fertilization development and growth dynamics of inflorescence at high spatial and temporal resolution in rice. For this, we developed the Panicle Imaging Platform (PI-Plat) to comprehend multi-dimensional features of IA in a non-destructive manner. We used 11 rice genotypes to capture multi-view images of primary panicle on weekly basis after the fertilization. These images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity. We found that the voxel count of developing panicles is positively correlated with seed number and weight at maturity. The voxel count from developing panicles projected overall volumes that increased during the grain filling phase, wherein quantification of color intensity estimated the rate of panicle maturation. Our 3D based phenotyping solution showed superior performance compared to conventional 2D based approaches. Conclusions: For harnessing the potential of the existing genetic resources, we need a comprehensive understanding of the genotype-to-phenotype relationship. Relatively low-cost sequencing platforms have facilitated high-throughput genotyping, while phenotyping, especially for complex traits, has posed major challenges for crop improvement. PI-Plat offers a low cost and high-resolution platform to phenotype inflorescence-related traits using 3D reconstruction-based approach. Further, the non-destructive nature of the platform facilitates analyses of the same panicle at multiple developmental time points, which can be utilized to explore the genetic variation for dynamic inflorescence traits in cereals

Semaphorin 4C accelerates disease progression and enables disease detection in breast cancer

Semaphorins constitute a diverse family of widely expressed transmembrane, diffusible, and GPI-linked proteins with versatile physiologic functions in orchestrating nerve system development, immune homeostasis, angiogenesis, and cell metabolism. Accumulating evidence highlights semaphorins as essential regulators of tumorigenesis by coordinating the cell-cell communications in the tumor microenvironment. Semaphorin 4C (SEMA4C) is a member of the fourth class of semaphorins with high affinity to Plexin-B2 and its interplay with cancer has long been a significant knowledge gap. Here, this perspective summarizes the recent progress in the understanding of SEMA4C in cancer and comprehensively delineates the discovery of SEMA4C in lymphatic vessels of breast cancer, the mechanisms by which SEMA4C promotes the invasiveness, proliferation, metastasis, and drug resistance of breast cancer, and the explorations of leveraging serum SEMA4C in breast cancer detection, highlighting SEMA4C as a critical driver of breast cancer progression, an effective biomarker for breast cancer diagnosis, and potential therapeutic target for breast cancer treatment

SIRT1-mediated downregulation of p27(Kip1) is essential for overcoming contact inhibition of Kaposi's sarcoma-associated herpesvirus transformed cells

Kaposi's sarcoma-associated herpesvirus (KSHV) is an oncogenic virus associated with Kaposi's sarcoma (KS), a malignancy commonly found in AIDS patients. Despite intensive studies in the last two decades, the mechanism of KSHV-induced cellular transformation and tumorigenesis remains unclear. In this study, we found that the expression of SIRT1, a metabolic sensor, was upregulated in a variety of KSHV-infected cells. In a model of KSHV-induced cellular transformation, SIRT1 knockdown with shRNAs or knockout by CRISPR/Cas9 gene editing dramatically suppressed cell proliferation and colony formation in soft agar of KSHV-transformed cells by inducing cell cycle arrest and contact inhibition. SIRT1 knockdown or knockout induced the expression of cyclin-dependent kinase inhibitor 1B (p27(Kip1)). Consequently, p27 knockdown rescued the inhibitory effect of SIRT1 knockdown or knockout on cell proliferation and colony formation. Furthermore, treatment of KSHV-transformed cells with a SIRT1 inhibitor, nicotinamide (NAM), had the same effect as SIRT1 knockdown and knockout. NAM significantly inhibited cell proliferation in culture and colony formation in soft agar, and induced cell cycle arrest. Significantly, NAM inhibited the progression of tumors and extended the survival of mice in a KSHV-induced tumor model. Collectively, these results demonstrate that SIRT1 suppression of p27 is required for KSHV-induced tumorigenesis and identify a potential therapeutic target for KS.

Wild emmer introgression alters root-to-shoot growth dynamics in durum wheat in response to water stress

Water deficit during the early vegetative growth stages of wheat (Triticum) can limit shoot growth and ultimately impact grain productivity. Introducing diversity in wheat cultivars to enhance the range of phenotypic responses to water limitations during vegetative growth can provide potential avenues for mitigating subsequent yield losses. We tested this hypothesis in an elite durum wheat background by introducing a series of introgressions from a wild emmer (Triticum turgidum ssp. dicoccoides) wheat. Wild emmer populations harbor rich phenotypic diversity for drought-adaptive traits. To determine the effect of these introgressions on vegetative growth under water-limited conditions, we used image-based phenotyping to catalog divergent growth responses to water stress ranging from high plasticity to high stability. One of the introgression lines exhibited a significant shift in root-to-shoot ratio in response to water stress. We characterized this shift by combining genetic analysis and root transcriptome profiling to identify candidate genes (including a root-specific kinase) that may be linked to the root-to-shoot carbon reallocation under water stress. Our results highlight the potential of introducing functional diversity into elite durum wheat for enhancing the range of water stress adaptation

A non-destructive approach for measuring rice panicle-level photosynthetic responses using 3D-image reconstruction

Our understanding of the physiological response of rice inflorescence (panicle) to environmental stresses is limited by the challenge of accurately determining panicle photosynthetic parameters and their impact on grain yield. This is primarily due to lack of a suitable gas exchange methodology for panicles, as well as non-destructive methods to accurately determine panicle surface area. To address these challenges, we have developed a custom panicle gas exchange cylinder compatible with the LiCor 6800 Infra-red Gas Analyzer. Accurate surface area measurements were determined with a 3D panicle imaging platform to normalize the panicle-level photosynthetic measurements. We observed differential responses in both panicle and flag leaf for two temperate Japonica rice genotypes (accessions, TEJ-1 and TEJ-2) exposed to heat stress during early grain filling. There was a notable divergence in relative photosynthetic contribution of flag leaf and panicles for the genotype tolerant to heat stress (TEJ-2) compared to the less tolerant accession. The novelty of this approach is that it is non-destructive and more accurately determines panicle area and photosynthetic parameters, enabling researchers to monitor temporal changes in panicle physiology during the reproductive development. The method is useful for panicle-level measurements under diverse environmental stresses, and for evaluating genotypic variation for panicle physiology and architecture in other cereals with compact inflorescences

Rice \u3ci\u3eChalky\u3c/i\u3e Grain 5 regulates natural variation for grain quality under heat stress

Heat stress occurring during rice (Oryza sativa) grain development reduces grain quality, which often manifests as increased grain chalkiness. Although the impact of heat stress on grain yield is well-studied, the genetic basis of rice grain quality under heat stress is less explored as quantifying grain quality is less tractable than grain yield. To address this, we used an image-based colorimetric assay (Red, R; and Green, G) for genome-wide association analysis to identify genetic loci underlying the phenotypic variation in rice grains exposed to heat stress. We found the R to G pixel ratio (RG) derived from mature grain images to be effective in distinguishing chalky grains from translucent grains derived from control (28/24°C) and heat stressed (36/32°C) plants. Our analysis yielded a novel gene, rice Chalky Grain 5 (OsCG5) that regulates natural variation for grain chalkiness under heat stress. OsCG5 encodes a grain-specific, expressed protein of unknown function. Accessions with lower transcript abundance of OsCG5 exhibit higher chalkiness, which correlates with higher RG values under stress. These findings are supported by increased chalkiness of OsCG5 knock-out (KO) mutants relative to wildtype (WT) under heat stress. Grains from plants overexpressing OsCG5 are less chalky than KOs but comparable to WT under heat stress. Compared to WT and OE, KO mutants exhibit greater heat sensitivity for grain size and weight relative to controls. Collectively, these results show that the natural variation at OsCG5 may contribute towards rice grain quality under heat stress