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نتیجه جستجو - فنوتیپ

تعداد مقالات یافته شده: 24
ردیف عنوان نوع
1 Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks
پدیده های گیاهی در زمان واقعی تحت راه اندازی رباتیک کشاورزی: یک پلت فرم مبتنی بر دید برای کارهای پیچیده فنوتیپ سازی گیاهان-2021
Plant phenotyping in general refers to quantitative estimation of the plant’s anatomical, ontogenetical, physiological and biochemical properties. Analyzing big data is challenging, and non-trivial given the different complexities involved. Efficient processing and analysis pipelines are the need of the hour with the increasing popularity of phenotyping technologies and sensors. Through this work, we largely address the overlapping object segmentation & localization problem. Further, we dwell upon multi-plant pipelines that pose challenges as detection and multi-object tracking becomes critical for single frame/set of frames aimed towards uniform tagging & visual features extraction. A plant phenotyping tool named RTPP (Real-Time Plant Phenotyping) is presented that can aid in the detection of single/multi plant traits, modeling, and visualization for agricultural settings. We compare our system with the plantCV platform. The relationship of the digital estimations, and the measured plant traits are discussed that plays a vital roadmap towards precision farming and/or plant breeding.
Keywords: Phenotype | Image processing | Spectral | Robotics | Object localization | Precision agriculture | Plant science | Pattern recognition | Computer vision | Automation | Perception
مقاله انگلیسی
2 Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery
رویکرد بینایی رایانه ای برای توصیف فنوتیپ های اندازه و شکل محصولات باغی با استفاده از تصاویر با توان بالا-2021
For many horticultural crops, variation in quality (e.g., shape and size) contributes significantly to the crop’s market value. Metrics characterizing less subjective harvest quantities (e.g., yield and total biomass) areroutinely monitored. In contrast, metrics quantifying more subjective crop quality characteristics such as ideal size and shape remain difficult to characterize objectively at the production-scale due to the lack of modular technologies for high-throughput sensing and computation. Several horticultural crops are sent to packing facilities after having been harvested, where they are sorted into boxes and containers using high-throughput scanners. These scanners capture images of each fruit or vegetable being sorted and packed, but the images are typically used solely for sorting purposes and promptly discarded. With further analysis, these images could offer unparalleled insight on how crop quality metrics vary at the industrial production-scale and provide further insight into how these characteristics translate to overall market value. At present, methods for extracting and quantifying quality characteristics of crops using images generated by existing industrial infrastructure have not been developed. Furthermore, prior studies that investigated horticultural crop quality metrics, specifically of size and shape, used a limited number of samples, did not incorporate deformed or non-marketable samples, and did not use images captured from high-throughput systems. In this work, using sweetpotato (SP) as a use case, we introduce a computer vision algorithm for quantifying shape and size characteristics in a high-throughput manner. This approach generates 3D model of SPs from two 2D images captured by an industrial sorter 90 degrees apart and extracts 3D shape features in a few hundred milliseconds. We applied the 3D reconstruction and feature extraction method to thousands of image samples to demonstrate how variations in shape features across SP cultivars can be quantified. We created a SP shape dataset containing SP images, extracted shape features, and qualitative shape types (U.S. No. 1 or Cull). We used this dataset to develop a neural network-based shape classifier that was able to predict Cull vs. U.S. No. 1 SPs with 84.59% accuracy. In addition, using univariate Chi-squared tests and random forest, we identified the most important features for determining qualitative shape type (U.S. No. 1 or Cull) of the SPs. Our study serves as a key step towards enabling big data analytics for industrial SP agriculture. The methodological framework is readily transferable to other horticultural crops, particularly those that are sorted using commercial imaging equipment.
Keywords: Crop phenotyping | Machine learning | Computer vision
مقاله انگلیسی
3 A deep learning approach to measure stress level in plants due to Nitrogen deficiency
یک روش یادگیری عمیق برای اندازه گیری سطح تنش در گیاهان به دلیل کمبود نیتروژن-2021
Stress due to nutrients deficiency in plants can reduce the agricultural yield significantly. Nitrogen, an essential nutrient, is a crucial growth-limiting factor and is the prime component of amino acids, proteins, nucleic acids, and chlorophyll. Nitrogen deficiency affects certain visible plant traits such as area, color, the number of leaves and plant height, etc. With the recent advancements in imaging technology, computer vision-based plant phenomics has become a promising field of plant research and management. Such imaging-based techniques are non-destructive and much faster with higher levels of automation. In this work, we have proposed an automatic image-based plant phenotyping approach for stress classification in plant shoot images. In this proposed phenotyping approach, a 23-layered deep learning technique is proposed and compared with traditional Machine Learning techniques and few other deep architectures. Results reveal that a simple 23-layered deep learning architecture is comparable to the established state of art deep learning architectures like ResNet18 and NasNet Large (having millions of trainable parameters) in yielding ceiling level stress classification from plant shoot images. In addition, the proposed model also outperforms traditional Machine Learning techniques by achieving an average of 8.25% better accuracy.
Keywords: Computer vision | Deep learning | Nitrogen stress | Plant phenotyping
مقاله انگلیسی
4 Plant trait estimation and classification studies in plant phenotyping using machine vision – A review
برآورد و طبقه بندی صفات گیاهی در فنوتیپ سازی گیاهان با استفاده از بینایی ماشین - مرور-2021
Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques. Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field. Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red, green and blue (RGB) imaging, thermal imaging, chlorophyll fluorescence imaging (CFIM), hyperspectral imaging, 3-dimensional (3-D) imaging or high resolution volumetric imaging. This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping. This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification. In this paper, information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods. This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural (2-D and 3-D), physiological and temporal trait estimation, and classification studies in plants.
Keywords: Plant phenotyping | Machine vision | Plant trait estimation | Imaging techniques | Leaf segmentation and counting | Plant classification studies
مقاله انگلیسی
5 Land cover and weather jointly predict biometric indicators of phenotypic quality in a large herbivore
پوشش زمین و آب و هوا به طور مشترک شاخص های بیومتریک با کیفیت فنوتیپی را در یک گیاهخوار بزرگ پیش بینی می کنند-2021
Body size and body mass are key indicators of individual phenotypic quality and predictors of important life- history traits such as survival and reproductive success. In wild herbivores, individual responses to changing environmental conditions influence morphometric traits over temporal scales and between populations. How- ever, little research has assessed joint effects of weather and land use on body size/mass at finer, intra-population scale. We used data collected on female and juvenile red deer Cervus elaphus shot over a 17-year period (2001–2017) along two sides of a mountainous ridge largely differing in land cover and habitat productivity, to investigate how fine-scale land use patterns and weather influenced multiple morphometric indicators of phenotypic quality. Accounting for weather, body mass of all sex/age classes increased with increasing pro- portion of cultivated areas in the landscape and, for young females and calves, that increase was stronger or occurred only in the “low-quality” site. Other biometric traits such as mandible length and hind foot length showed the same pattern in young and calves, suggesting that body mass/size reflects individual responses especially in the early life-stage. Accounting for land use, body mass of adult females and calves was enhanced by increasing rainfall and decreasing temperature in spring-summer, i.e. favourable conditions for vegetation growth. This result also supports late gestation- and lactation-mediated effects of vegetation productivity on offspring quality. Additionally, in male calves, body mass and several other traits increased with decreasing severity of the previous winter, suggesting that quality of male offspring - but not that of females - could depend on winter conditions experienced in utero, likely due to higher maternal costs. Our findings emphasise how land cover and weather jointly affect indicators of phenotypic quality in a large mammal, helping to predict size responses of herbivores under the ongoing climatic- and anthropogenic land use-changes.
Keywords: Phenotypic quality | Biometric indicators | Deer | Land use | Body mass | Cervus elaphus
مقاله انگلیسی
6 Reinforcement learning as an intermediate phenotype in psychosis? Deficits sensitive to illness stage but not associated with polygenic risk of schizophrenia in the general population
یادگیری تقویتی به عنوان یک فنوتیپ متوسط در روان پریشی؟ کمبودهای حساس به مرحله بیماری اما با خطر پلی ژنیک اسکیزوفرنی در جمعیت عمومی ارتباط ندارد-2020
Background: Schizophrenia is a complex disorder in which the causal relations between risk genes and observed clinical symptoms are not well understood and the explanatory gap is too wide to be clarified without considering an intermediary level. Thus, we aimed to test the hypothesis of a pathway frommolecular polygenic influence to clinical presentation occurring via deficits in reinforcement learning. Methods: We administered a reinforcement learning task (Go/NoGo) that measures reinforcement learning and the effect of Pavlovian bias on decision making. We modelled the behavioural data with a hierarchical Bayesian approach (hBayesDM) to decompose task performance into its underlying learning mechanisms. Study 1 included controls (n = 29, F|M = 0.81), At Risk Mental State for psychosis (ARMS, n = 23, F|M= 0.35) and FEP (First-episode psychosis, n = 26, F|M = 0.18). Study 2 included healthy adolescents (n = 735, F|M = 1.06), 390 of whom had their polygenic risk scores for schizophrenia (PRSs) calculated. Results: Patients with FEP showed significant impairments in overriding Pavlovian conflict, a lower learning rate and a lower sensitivity to both reward and punishment. Less widespread deficits were observed in ARMS. PRSs did not significantly predict performance on the task in the general population, which only partially correlated with measures of psychopathology. Conclusions: Reinforcement learning deficits are observed in first episode psychosis and, to some extent, in those at clinical risk for psychosis, and were not predicted by molecular genetic risk for schizophrenia in healthy individuals. The study does not support the role of reinforcement learning as an intermediate phenotype in psychosis.
Keywords: Psychosis | Schizophrenia | PRS | Bayesian | Reinforcement learning | Go/NoGo task | Computational psychiatry
مقاله انگلیسی
7 Development and validation of the VISAGE AmpliSeq basic tool to predict appearance and ancestry from DNA
توسعه و اعتبارسنجی ابزار اصلی VISAGE AmpliSeq برای پیش بینی ظاهر و نسب از DNA-2020
Forensic DNA phenotyping is gaining interest as the number of applications increases within the forensic genetics community. The possibility of providing investigative leads in addition to conventional DNA profiling for human identification provides new insights into otherwise “cold” police investigations. The ability of reporting on the bio-geographical ancestry (BGA), appearance characteristics and age based on DNA obtained from a crime scene sample of an unknown donor makes the exploration of such markers and the development of new methods meaningful for criminal investigations. The VISible Attributes through GEnomics (VISAGE) Consortium aims to disseminate and broaden the use of predictive markers and develop fully optimized and validated prototypes for forensic casework implementation. Here, the first VISAGE appearance and ancestry tool development, performance and validation is reported. A total of 153 SNPs (96.84 % assay conversion rate) were successfully incorporated into a single multiplex reaction using the AmpliSeq™ design pipeline, and applied for massively parallel sequencing with the Ion S5 platform. A collaborative effort involving six VISAGE laboratory partners was devised to perform all validation tests. An extensive validation plan was carefully organized to explore the assay’s overall performance with optimum and low-input samples, as well as with challenging and casework mock samples. In addition, forensic validation studies such as concordance and mixture tests recurring to the Coriell sample set with known genotypes were performed. Finally, inhibitor tolerance and specificity were also evaluated. Results showed a robust, highly sensitive assay with good overall concordance between laboratories.
Keywords: Forensic DNA phenotyping | Appearance and bio-geographical ancestry | prediction | MPS Ion S5 | AmpliSeq | SNP multiplex
مقاله انگلیسی
8 Distinct Pathogenic Genes Causing Intellectual Disability and Autism Exhibit a Common Neuronal Network Hyperactivity Phenotype
ژنهای پاتوژن مشخص متمایز کننده ناتوانی ذهنی و اوتیسم از فنوتیپ بیش فعالی شبکه عصبی مشترک-2020
Pathogenic mutations in either one of the epigenetic modifiers EHMT1, MBD5, MLL3, or SMARCB1 have been identified to be causative for Kleefstra syndrome spectrum (KSS), a neurodevelopmental disorder with clinical features of both intellectual disability (ID) and autism spectrum disorder (ASD). To understand how these variants lead to the phenotypic convergence in KSS, we employ a loss-of-function approach to assess neuronal network development at the molecular, single-cell, and network activity level. KSS-gene-deficient neuronal networks all develop into hyperactive networks with altered network organization and excitatory-inhibitory balance. Interestingly, even though transcriptional data reveal distinct regulatory mechanisms, KSS target genes share similar functions in regulating neuronal excitability and synaptic function, several of which are associated with ID and ASD. Our results show that KSS genes mainly converge at the level of neuronal network communication, providing insights into the pathophysiology of KSS and phenotypically congruent disorders.
مقاله انگلیسی
9 عملکرد اجرایی، مهارت های تطبیقی، مشخصات عاطفی و رفتاری: مقایسه بین اختلال طیف اوتیسم و فنیل کتونوری
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 26
نظریه های تأثیرگذار پذیرفته اند که برخی از علائم اصلی اختلال طیف اوتیسم (ASD) ممکن است ناشی از کسری در عملکردهای اجرایی (EF) باشد. نقص EF همچنین یک علامت عصبی در افراد تحت درمان اولیه با فنیل کتونوری (PKU) محسوب می شود. اهداف این مطالعه: بررسی صحت وقایع و الگوهای اختلالات خاص EF در هر دو گروه بالینی بود تا همزیستی تغییرات EF با مشکلات سازگاری، رفتاری و عاطفی در هر شرایط بالینی را بررسی کند.
مواد و روش ها: ما EF ، مشخصات سازگار، رفتاری و عاطفی را در 21 شرکت کننده با ASD ارزیابی کردیم، 15 فرد مبتلا به PKU زودرس درمان شده، قابل مقایسه با سن و ضریب هوشی و 14 نفر از گروه کنترل، از نظر سن با گروههای بالینی قابل مقایسه هستند (دامنه سنی: 7 تا 14 سال).
یافته ها: شرکت کنندگان ASD و PKU دو مورد متفاوت ارائه دادند، اما الگوهای اختلال EF با هم همپوشانی دارند. در حالی که شرکت کنندگان در ASD فقط در انعطاف پذیری شناختی کسری خاص را نشان دادند، افراد PKU دارای اختلال گسترده تر در EF با عملکرد ضعیف تر در دو حوزه EF هسته ای (مهار، انعطاف پذیری شناختی) نسبت به گروه کنترل سالم بودند. مشخصات روانشناختی و سازگاری در شرکت کنندگان PKU معمولی بود، در حالی که شرکت کنندگان در ASD رفتاری (علائم بیرونی)، عاطفی (علائم درونی سازی) و اختلالات سازگاری (حوزه های عمومی، عملی، اجتماعی) را تجربه کردند.
نتیجه گیری: نتایج حاضر از نمایشی برای تفکیک نسبی مشخصات تطبیقی و عاطفی- رفتاری با توجه به مهارت های EF پشتیبانی می کند و نشان می دهد که اختلالات دیگر به فنوتیپ چند بعدی شرکت کنندگان در ASD کمک می کند.
کلید واژه ها: اختلال طیف اوتیسم | فنیل کتونوری | عملکرد اجرایی | رفتار سازشی | درونی و بیرونی کردن علائم
مقاله ترجمه شده
10 Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning
شناسایی و تجزیه و تحلیل فنوتیپ های رفتاری در اختلال طیف اوتیسم از طریق یادگیری ماشین بدون نظارت-2019
Background and objective: Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored potential ASD subgroups with preliminary evidence supporting the existence of behaviorally and genetically distinct subgroups; however, research has yet to leverage machine learning to identify phenotypes on a scale large enough to robustly examine treatment response across such subgroups. The purpose of the present study was to apply Gaussian Mixture Models and Hierarchical Clustering to identify behavioral phenotypes of ASD and examine treatment response across the learned phenotypes. Materials and methods: The present study included a sample of children with ASD (N = 2400), the largest of its kind to date. Unsupervised machine learning was applied to model ASD subgroups as well as their taxonomic relationships. Retrospective treatment data were available for a portion of the sample (n =1034). Treatment response was examined within each subgroup via regression. Results: The application of a Gaussian Mixture Model revealed 16 subgroups. Further examination of the subgroups through Hierarchical Agglomerative Clustering suggested 2 overlying behavioral phenotypes with unique deficit profiles each composed of subgroups that differed in severity of those deficits. Furthermore, differentiated response to treatment was found across subtypes, with a substantially higher amount of variance accounted for due to the homogenization effect of the clustering. Discussion: The high amount of variance explained by the regression models indicates that clustering provides a basis for homogenization, and thus an opportunity to tailor treatment based on cluster memberships. These findings have significant implications on prognosis and targeted treatment of ASD, and pave the way for personalized intervention based on unsupervised machine learning.
Keywords: Machine learning | Autism spectrum disorder | Behavioral phenotypes | Cluster analysis | Treatment response
مقاله انگلیسی
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