Computer Vision and Machine Learning Based Grape Fruit Cluster Detection and Yield Estimation Robot

Estimation and detection of fruits plays a crucial role in harvesting. Traditionally, fruit growers rely on manual methods but nowadays they are facing problems of rapidly increasing labor costs and labour shortage. Earlier various techniques were developed using hyper spectral cameras, 3D images, clour based segmentation where it was difficult to find and distinguish grape bunches. In this research computer vision based novel approach is implemented using Open Source Computer Vision Library (OpenCV) and Random Forest machine learning algorithm for counting, detecting and segmentation of blue grape bunches. Here, fruit object segmentation is based on a binary threshold and Otsu method. For training and testing, classification based on pixel intensities were taken by a single image related to grape and non-grape fruit. The validation of developed technique represented by random forest algorithm achieved a good result with an accuracy score of 97.5% and F1-Score of 90.7% as compared to Support Vector Machine (SVM). The presented research pipeline for grape fruit bunch detection with noise removal, training, segmentation and classification techniques exhibit improved accuracy

NUCLEOTIDE SEQUENCE VARIATION IN LEPTIN GENE OF MURRAH BUFFALO (BUBALUS BUBALIS)

Leptin is a 16 kD protein, synthesized by adipose tissue and is involved in regulation of feed intake, energy balance, fertility and immune functions. Present study was undertaken with the objectives of sequence characterization and studying the nucleotide variation in leptin gene in Murrah buffalo. The leptin gene consists of three exons and two introns which spans about 18.9kb, of which the first exon is not transcribed into protein. In buffaloes, the leptin gene is located on chromosome eight and maps to BBU 8q32. The leptin gene was amplified by PCR using oligonucleotide primers to obtain 289 bp fragment comprising of exon 2 and 405 bp fragment containing exon 3 of leptin gene. The amplicons were sequenced to identify variation at nucleotide level. Sequence comparison of buffalo with cattle reveals variation at five nucleotide sequences at positions 983, 1083, 1147, 1152, 1221 and all the SNPs are synonymous resulting no in change in amino acids. Three of these eight nucleotide variations have been reported for the first time in buffalo. The results indicate conservation of DNA sequence between cattle and buffalo. Nucleotide sequence variations observed at leptin gene between Bubalus bubalis and Bos taurus species revealed 97% nucleotide identity

Prophylactic and Therapeutic Role of Human Breast Milk Proteins and Bioactive Peptides against Neonatal Bacterial Infections

Breast milk represents nature’s best mechanism to provide complete nourishment and protection to the newborn. Human breast milk acts as a store house of an array of bioactive factors, which includes antimicrobial proteins and antimicrobial peptides that confer early protection while lowering the incidence of developing various infections and exhibiting immune modulation property to activate the immune cells to fight against the invading pathogens. Among the bioactive peptides, endogenous peptides present in breast milk have opened a new window of research on studying their unique mechanisms of action. This will help in incorporating these peptides in formula milk for meeting special needs where breastfeeding is not possible. The present chapter aims to give a deep insight into the various antimicrobial peptides and the newly reported endogenous peptides in human breast milk with emphasis on their levels and activity in preterm milk as data related to this is lacking and preterm newborns are highly vulnerable to acquire infections. Further, the chapter focuses on highlighting the antibacterial mechanisms adopted by the bioactive peptides for protection against the neonatal bacterial pathogens with special emphasis on the infections caused by resistant bacterial strains in hospital settings (neonatal wards) and their future implications

Global Retinoblastoma Presentation and Analysis by National Income Level.

Importance: Early diagnosis of retinoblastoma, the most common intraocular cancer, can save both a child's life and vision. However, anecdotal evidence suggests that many children across the world are diagnosed late. To our knowledge, the clinical presentation of retinoblastoma has never been assessed on a global scale. Objectives: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis. Design, Setting, and Participants: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017. Main Outcomes and Measures: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis. Results: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- and middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis was more common in LMICs even after adjusting for age (odds ratio for low-income countries vs upper-middle-income countries and HICs, 17.92 [95% CI, 12.94-24.80], and for lower-middle-income countries vs upper-middle-income countries and HICs, 5.74 [95% CI, 4.30-7.68]). Conclusions and Relevance: This study is estimated to have included more than half of all new retinoblastoma cases worldwide in 2017. Children from LMICs, where the main global retinoblastoma burden lies, presented at an older age with more advanced disease and demonstrated a smaller proportion of familial history of retinoblastoma, likely because many do not reach a childbearing age. Given that retinoblastoma is curable, these data are concerning and mandate intervention at national and international levels. Further studies are needed to investigate factors, other than age at presentation, that may be associated with advanced disease in LMICs

Computer Vision and Machine Learning Based Grape Fruit Cluster Detection and Yield Estimation Robot

866-872Estimation and detection of fruits plays a crucial role in harvesting. Traditionally, fruit growers rely on manual methods but nowadays they are facing problems of rapidly increasing labor costs and labour shortage. Earlier various techniques were developed using hyper spectral cameras, 3D images, clour based segmentation where it was difficult to find and distinguish grape bunches. In this research computer vision based novel approach is implemented using Open Source Computer Vision Library (OpenCV) and Random Forest machine learning algorithm for counting, detecting and segmentation of blue grape bunches. Here, fruit object segmentation is based on a binary threshold and Otsu method. For training and testing, classification based on pixel intensities were taken by a single image related to grape and non-grape fruit. The validation of developed technique represented by random forest algorithm achieved a good result with an accuracy score of 97.5% and F1-Score of 90.7% as compared to Support Vector Machine (SVM). The presented research pipeline for grape fruit bunch detection with noise removal, training, segmentation and classification techniques exhibit improved accuracy

Green synthesis of TiO2-Al2O3-ZnFe2O4 nanocomposite using the Hibiscus rosa sinesis and evaluation of its photocatalytic applications

Use of the green approach for the synthesis of nanoparticles has gained significant interest in the field of nanotechnology. The applicability of greener nanomaterials in environmental remediation has unveiled their tremendous properties and potentiality to eradicate the pollutants from nature in ecofriendly manner. In the present paper, we proposed synthesis of TiO2-Al2O3-ZnFe2O4 nanocomposites using flower extract of Hibiscus rosa sinesis via hydrothermal approach. The synthesized product was characterized by several optical and morphological techniques i.e. Field emission scanning electron microscope, X ray diffraction, energy dispersive spectroscopy, Fourier transform infrared spectroscopy and Brunauer Emmett Teller. To conclude, Field emission scanning electron microscope reveals the nanostructures are of 40-130 nm size. X ray diffraction reveals the presence of rutile TiO2 and hexagonal Al2O3 along with ZnFe2O4 crystallites. Barrett-Joyner-Halenda model reveals that the pore volume and pore diameter were found to be 38.206 m2/g, 0.048 cc/g and 3.640 nm, respectively. TiO2-Al2O3-ZnFe2O4 have shown photocatalytic degradation of methylene blue (96.54 ± 1.24%.), industrial sample (88.52 ± 0.64), and heavy metal hexavalent chromium (94.7 ± 3.20%) in the presence of sunlight. Besides, the synthesized product exhibited good antioxidant properties (84 ± 2.1%) against 2,2-diphenyl-1-picrylhydrazyl

Microstructural and metabolic variations induced by Bipolaris oryzae inciting brown spot disease of rice

Brown spot disease, caused by Bipolaris oryzae, is a dominant lethal rice disease that causes qualitative and quantitative crop damage. The current study sought to identify various histological and metabolic changes that occur during brown spot development in susceptible rice plants. We present a conceptual framework that shows B. oryzae suppresses the production of immune-related metabolites in a susceptible cultivar, PR 124 using a comparative metabolomics approach. Un-inoculated rice leaves have an epidermis followed by cortex parenchyma with large intercellular spaces, and no fungal hyphae or distortion were found. Following pathogen inoculation, fungus hyphae grow intercellularly in photosynthetic areas and intracellularly in the bundle sheath, resulting in the microcracks on the surface of the rice leaf. Cellular depositions could have produced the clogging, which disrupted water channels and induced distortion of vascular bundles, ultimately leading to cellular collapse and the withering of rice plants under field conditions. Silica on an infected leaf surface suggests a more robust defence response, thus providing some degree of endurance at the later stages of infection. A significant decline in the total chlorophyll and lignin content was observed in the inoculated leaves compared to the un-inoculated ones. Higher relative injury was recorded post-inoculation. Early oxidative responses like malondialdehyde, proline and hydrogen peroxide accumulation occurred in the flag leaves at various intervals after inoculation. Reduced salicylic acid, phenol and lignin content post-inoculation could be attributed to lowered phenylalanine ammonia lyase activity. Significant declines in the activities of catalase, peroxidase, chitinase and glucanase suggest that immune suppression by this biotrophic pathogen impacts specialised plant metabolism. Thus, these findings form the basis for additional studies focussed on the characterisation of metabolic components involved in pathogen perception during the early stages of intracellular signal transduction