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1 |
Quantitative study of starch swelling capacity during gelatinization with an efficient automatic segmentation methodology
Quantitative study of starch swelling capacity during gelatinization with an efficient automatic segmentation methodology-2021 A novel image segmentation methodology combined with optical microscopy observation was developed for qualifying starch swelling. Starch granules in the micrograph were successfully segmented based on high- precision edges extraction achieved by Canny edge detection together with mathematical morphology operation. Granules were automatically identified by computer vision and characterized by giving quantifiable area of these granules. The evolved swelling process could be generally divided into two phases. During the first phase, starch granules were only swollen up by 2.56 %, which is hard to be identified by conventional naked eye. During the following narrow temperature interval (60–66 ℃), these starch granules were detected to swell up significantly by 9.08 %. Through the granule area variable, swelling capacity was high-throughput characterized, which allows for the whole evaluation to be completed within a couple of minutes. The proposed methodology showed a high accuracy and potential as a novel technique for characterizing gelatinization. Keywords: Gelatinization | Computer vision | Quantification | Canny detection | Mathematical morphology |
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 |
Synaptic Specificity, Recognition Molecules, and Assembly of Neural Circuits
ویژگی سیناپسی ، مولکولهای تشخیص و مونتاژ مدارهای عصبی-2020 Developing neurons connect in specific and stereotyped ways to form the complex circuits that underlie
brain function. By comparison to earlier steps in neural development, progress has been slow
in identifying the cell surface recognition molecules that mediate these synaptic choices, but new
high-throughput imaging, genetic, and molecular methods are accelerating progress. Over the past
decade, numerous large and small gene families have been implicated in target recognition,
including members of the immunoglobulin, cadherin, and leucine-rich repeat superfamilies. We review
these advances and propose ways in which combinatorial use of multifunctional recognition
molecules enables the complex neuron-neuron interactions that underlie synaptic specificity. |
مقاله انگلیسی |
4 |
Downstream of the bioreactor: advancements in recovering fuels and commodity chemicals
پایین دست از بیورآکتور: پیشرفت در بازیابی سوخت ها و مواد شیمیایی کالایی-2020 Downstream processing aims at recovering the target product
at the required specifications from the bioreactor effluent.
Research and development in this field relies on experimental
and mathematical tools at the levels of chemical components,
unit operations and processes. Recently, advances have been
made in addressing the broth mixture complexity early on, in
incorporating high-throughput experimentation for data
generation and mechanistic understanding of the separation
processes, in improving the materials and scalability of specific
unit operations, as well as establishing the potential of process
integration concepts. Further developments are expected
considering the variety of (non-sugar) feedstocks currently
under research, the need to transition to renewable energy
sources, and the opportunities for improved scale-up through
initiatives as Big Data and digital manufacturing |
مقاله انگلیسی |
5 |
Food allergomics based on high-throughput and bioinformatics technologies
آلرژیک مواد غذایی بر اساس فن آوری های توان بالا و بیوانفورماتیک-2020 Food allergy is a serious food safety problem worldwide, and the investigation of food allergens is the foundation
of preventing and treating them, but relevant knowledge is far from sufficient. With the advent of the “big data
era”, it has been possible to investigate food allergens by high-throughput methods, proposing the concept of
allergomics. Allergomics is the discipline studying the repertoire of allergens, which has relatively higher
throughput and is faster and more sensitive than conventional methods. This review introduces the basis of
allergomics and summarizes its major strategies and applications. Particularly, strategies based on immunoblotting,
phage display, allergen microarray, and bioinformatics are reviewed in detail, and the advantages
and limitations of each strategy are discussed. Finally, further development of allergomics is predicted. This
provides basic theories and recent advances in food allergomics research, which could be insightful for both food
allergy research and practical applications. Keywords: High-throughput | Bioinformatics | Allergome | Allergomics | Food allergy |
مقاله انگلیسی |
6 |
Individualizing Care
مراقبت شخصی-2020 The forthcoming availability of several novel drugs in primary biliary cholangitis (PBC)
coupled with the rise of high-throughput omics technologies prompt changing the paradigm
of the management of the disease.
Precision medicine (PM), through the application of omics-based approaches, should enable
identifying disease variants, stratifying patients according to disease trajectory, risk of disease
progression, and likelihood of response to different therapeutic options in PBC.
The development of PM needs specific interventions, such as sequencing more genomes,
creating bigger biobanks, and linking biological information to health data in electronic
medical record.
The authors envisage that a diagnostic work-up of PBC patients will include information
on genetic variants and molecular signature that may define a particular subtype of disease
and provide an estimate of treatment response and survival KEYWORDS : Primary biliary cholangitis | Precision medicine | Risk-stratification | Autoimmune liver disease | Individualized care | Novel therapies | Omics |
مقاله انگلیسی |
7 |
Discovering protein-binding RNA motifs with a generative model of RNA sequences
کشف نقوش RNA پروتئین اتصال با یک مدل تولیدی از توالی RNA-2020 Recent advances in high-throughput experimental technologies have generated a huge amount of data on interactions
between proteins and nucleic acids. Motivated by the big experimental data, several computational
methods have been developed either to predict binding sites in a sequence or to determine if an interaction exists
between protein and nucleic acid sequences. However, most of the methods cannot be used to discover new
nucleic acid sequences that bind to a target protein because they are classifiers rather than generators. In this
paper we propose a generative model for constructing protein-binding RNA sequences and motifs using a long
short-term memory (LSTM) neural network. Testing the model for several target proteins showed that RNA
sequences generated by the model have high binding affinity and specificity for their target proteins and that the
protein-binding motifs derived from the generated RNA sequences are comparable to the motifs from experimentally
validated protein-binding RNA sequences. The results are promising and we believe this approach will
help design more efficient in vitro or in vivo experiments by suggesting potential RNA aptamers for a target
protein Keywords: Protein-RNA interaction | Binding motif | Generator | Long short-term memory network |
مقاله انگلیسی |
8 |
Petri-net-based dynamic scheduling of flexible manufacturing system via deep reinforcement learning with graph convolutional network
زمانبندی پویا مبتنی بر Petri-Net سیستم تولید انعطاف پذیر از طریق یادگیری تقویتی عمیق با شبکه کانولوشن نمودار-2020 To benefit from the accurate simulation and high-throughput data contributed by advanced digital twin technologies
in modern smart plants, the deep reinforcement learning (DRL) method is an appropriate choice to
generate a self-optimizing scheduling policy. This study employs the deep Q-network (DQN), which is a successful
DRL method, to solve the dynamic scheduling problem of flexible manufacturing systems (FMSs) involving
shared resources, route flexibility, and stochastic arrivals of raw products. To model the system in
consideration of both manufacturing efficiency and deadlock avoidance, we use a class of Petri nets combining
timed-place Petri nets and a system of simple sequential processes with resources (S3PR), which is named as the
timed S3PR. The dynamic scheduling problem of the timed S3PR is defined as a Markov decision process (MDP)
that can be solved by the DQN. For constructing deep neural networks to approximate the DQN action-value
function that maps the timed S3PR states to scheduling rewards, we innovatively employ a graph convolutional
network (GCN) as the timed S3PR state approximator by proposing a novel graph convolution layer called a
Petri-net convolution (PNC) layer. The PNC layer uses the input and output matrices of the timed S3PR to
compute the propagation of features from places to transitions and from transitions to places, thereby reducing
the number of parameters to be trained and ensuring robust convergence of the learning process. Experimental
results verify that the proposed DQN with a PNC network can provide better solutions for dynamic scheduling
problems in terms of manufacturing performance, computational efficiency, and adaptability compared with
heuristic methods and a DQN with basic multilayer perceptrons. Keywords: Dynamic scheduling | Petri nets | Deep reinforcement learning | Graph convolutional networks | Digital twin |
مقاله انگلیسی |
9 |
Discovering unusual structures from exception using big data and machine learning techniques
کشف ساختارهای غیر معمول از استثناء با استفاده از داده های بزرگ و تکنیک های یادگیری ماشین-2019 Recently, machine learning (ML) has become a widely used technique in materials science study. Most
work focuses on predicting the rule and overall trend by building a machine learning model. However,
new insights are often learnt from exceptions against the overall trend. In this work, we demonstrate that
how unusual structures are discovered from exceptions when machine learning is used to get the relationship
between atomic and electronic structures based on big data from high-throughput calculation
database. For example, after training an ML model for the relationship between atomic and electronic
structures of crystals, we find AgO2F, an unusual structure with both Ag3+ and O2 2 , from structures whose
band gap deviates much from the prediction made by our model. A further investigation on this structure
might shed light into the research on anionic redox in transition metal oxides of Li-ion batterie. Keywords: Machine learning | Gradient boosting decision tree | Band gap | Unusual structures |
مقاله انگلیسی |
10 |
Utilizing supervised machine learning to identify microglia and astrocytes in situ: implications for large-scale image analysis and quantification
استفاده از یادگیری ماشین نظارت شده برای شناسایی میکروگلیا و آستروسیت ها درجا: پیامدهای آنالیز و اندازه گیری تصویر در مقیاس بزرگ-2019 Background: The evaluation of histological tissue samples plays a crucial role in deciphering preclinical disease
and injury mechanisms. High-resolution images can be obtained quickly however data acquisition are often
bottlenecked by manual analysis methodologies.
New Method: We describe and validate a pipeline for a novel machine learning-based analytical method, using
the Opera High-Content Screening system and Harmony software, allowing for detailed image analysis of cellular
markers in histological samples.
Results: To validate the machine learning pipeline, analyses of single proteins in mouse brain sections were
utilized. To demonstrate adaptability of the pipeline for multiple cell types and epitopes, the percent brain
coverage of microglial cells, identified by ionized calcium binding adaptors molecule 1 (Iba1), and of astrocytes,
by glial fibrillary acidic protein (GFAP) demonstrated no significant differences between automated and manual
analyses protocols. Further to examine the robustness of this protocol for multiple proteins simultaneously labeling
of rat brain sections were utilized; co-localization of astrocytic endfeet on blood vessels, using aquaporin-
4 and tomato lectin respectively, were efficiently identified and quantified by the novel pipeline and were not
significantly different between the two analyses protocols.
Comparison with Existing Methods: The automated platform maintained the sensitivity and accuracy of
manual analysis, while accomplishing the analyses in 1/200th of the time.
Conclusions: We demonstrate the benefits and potential of adapting an automated high-throughput machinelearning
analytical approach for the analysis ofin situ tissue samples, show effectiveness across different animal
models, while reducing analysis time and increasing productivity. Keywords: Supervised machine learning | Artificial intelligence | Histology | Immunofluorescence | Astrocytes | Microglia |
مقاله انگلیسی |