224,250 research outputs found
A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials
Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in
the last few decades as the direct and indirect methods available are laborious and
time-consuming. The recent emergence of high-throughput plant phenotyping platforms has
increased the need to develop new phenotyping tools for better decision-making by breeders. In
this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue
(RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The
model mixes numerical data collected in a wheat breeding field and visual features extracted from
the images to make rapid and accurate LAI estimations. Model-based LAI estimations were
validated against LAI measurements determined non-destructively using an allometric
relationship obtained in this study. The model performance was also compared with LAI estimates
obtained by other classical indirect methods based on bottom-up hemispherical images and gaps
fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The
model performance was slightly better than that of the hemispherical image-based method, which
tended to underestimate LAI. These results show the great potential of the developed model for
near real-time LAI estimation, which can be further improved in the future by increasing the
dataset used to train the model
Deep Learning Approach for Large-Scale, Real-Time Quantification of Green Fluorescent Protein-Labeled Biological Samples in Microreactors
Absolute quantification of biological samples entails determining expression
levels in precise numerical copies, offering enhanced accuracy and superior
performance for rare templates. However, existing methodologies suffer from
significant limitations: flow cytometers are both costly and intricate, while
fluorescence imaging relying on software tools or manual counting is
time-consuming and prone to inaccuracies. In this study, we have devised a
comprehensive deep-learning-enabled pipeline that enables the automated
segmentation and classification of GFP (green fluorescent protein)-labeled
microreactors, facilitating real-time absolute quantification. Our findings
demonstrate the efficacy of this technique in accurately predicting the sizes
and occupancy status of microreactors using standard laboratory fluorescence
microscopes, thereby providing precise measurements of template concentrations.
Notably, our approach exhibits an analysis speed of quantifying over 2,000
microreactors (across 10 images) within remarkably 2.5 seconds, and a dynamic
range spanning from 56.52 to 1569.43 copies per micron-liter. Furthermore, our
Deep-dGFP algorithm showcases remarkable generalization capabilities, as it can
be directly applied to various GFP-labeling scenarios, including droplet-based,
microwell-based, and agarose-based biological applications. To the best of our
knowledge, this represents the first successful implementation of an all-in-one
image analysis algorithm in droplet digital PCR (polymerase chain reaction),
microwell digital PCR, droplet single-cell sequencing, agarose digital PCR, and
bacterial quantification, without necessitating any transfer learning steps,
modifications, or retraining procedures. We firmly believe that our Deep-dGFP
technique will be readily embraced by biomedical laboratories and holds
potential for further development in related clinical applications.Comment: 23 pages, 6 figures, 1 tabl
Microservices and Machine Learning Algorithms for Adaptive Green Buildings
In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings
RF Localization in Indoor Environment
In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained
QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology.
Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber cross-sectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the "patchy" distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigator-specific needs and provides novel analytical approaches for quantifying muscle morphology
Integrated process of images and acceleration measurements for damage detection
The use of mobile robots and UAV to catch unthinkable images together with on-site global automated acceleration measurements easy achievable by wireless sensors, able of remote data transfer, have strongly enhanced the capability of defect and damage evaluation in bridges. A sequential procedure is, here, proposed for damage monitoring and bridge condition assessment based on both: digital image processing for survey and defect evaluation and structural identification based on acceleration measurements. A steel bridge has been simultaneously inspected by UAV to acquire images using visible light, or infrared radiation, and monitored through a wireless sensor network (WSN) measuring structural vibrations. First, image processing has been used to construct a geometrical model and to quantify corrosion extension. Then, the consistent structural model has been updated based on the modal quantities identified using the acceleration measurements acquired by the deployed WSN. © 2017 The Authors. Published by Elsevier Ltd
Understanding Health and Disease with Multidimensional Single-Cell Methods
Current efforts in the biomedical sciences and related interdisciplinary
fields are focused on gaining a molecular understanding of health and disease,
which is a problem of daunting complexity that spans many orders of magnitude
in characteristic length scales, from small molecules that regulate cell
function to cell ensembles that form tissues and organs working together as an
organism. In order to uncover the molecular nature of the emergent properties
of a cell, it is essential to measure multiple cell components simultaneously
in the same cell. In turn, cell heterogeneity requires multiple cells to be
measured in order to understand health and disease in the organism. This review
summarizes current efforts towards a data-driven framework that leverages
single-cell technologies to build robust signatures of healthy and diseased
phenotypes. While some approaches focus on multicolor flow cytometry data and
other methods are designed to analyze high-content image-based screens, we
emphasize the so-called Supercell/SVM paradigm (recently developed by the
authors of this review and collaborators) as a unified framework that captures
mesoscopic-scale emergence to build reliable phenotypes. Beyond their specific
contributions to basic and translational biomedical research, these efforts
illustrate, from a larger perspective, the powerful synergy that might be
achieved from bringing together methods and ideas from statistical physics,
data mining, and mathematics to solve the most pressing problems currently
facing the life sciences.Comment: 25 pages, 7 figures; revised version with minor changes. To appear in
J. Phys.: Cond. Mat
Evaluation of Variability Concepts for Simulink in the Automotive Domain
Modeling variability in Matlab/Simulink becomes more and more important. We
took the two variability modeling concepts already included in Matlab/Simulink
and our own one and evaluated them to find out which one is suited best for
modeling variability in the automotive domain. We conducted a controlled
experiment with developers at Volkswagen AG to decide which concept is
preferred by developers and if their preference aligns with measurable
performance factors. We found out that all existing concepts are viable
approaches and that the delta approach is both the preferred concept as well as
the objectively most efficient one, which makes Delta-Simulink a good solution
to model variability in the automotive domain.Comment: 10 pages, 7 figures, 6 tables, Proceedings of 48th Hawaii
International Conference on System Sciences (HICSS), pp. 5373-5382, Kauai,
Hawaii, USA, IEEE Computer Society, 201
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