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1.
Sensors (Basel) ; 21(12)2021 Jun 10.
Artigo em Inglês | MEDLINE | ID: mdl-34200575

RESUMO

Industrial Internet of Things (IIoT) applications are being used more and more frequently. Data collected by various sensors can be used to provide innovative digital services supporting increasing efficiency or cost reduction. The implementation of such applications requires the integration and analysis of heterogeneous data coming from a broad variety of sensors. To support these steps, this paper introduces OPAL, a software toolbox consolidating several software components for the semantically annotated integration and analysis of IoT-data. Data storage is realized in a standardized and INSPIRE-compliant way utilizing the SensorThings API. Supporting a broad variety of use cases, OPAL provides several import adapters to access data sources with various protocols (e.g., the OPC UA protocol, which is often used in industrial environments). In addition, a unified management and execution environment, called PERMA, is introduced to allow the programming language independent integration of algorithms.


Assuntos
Internet das Coisas , Software , Algoritmos
2.
Sensors (Basel) ; 21(12)2021 Jun 10.
Artigo em Inglês | MEDLINE | ID: mdl-34200635

RESUMO

An annotated photoplethysmogram (PPG) is required when evaluating PPG algorithms that have been developed to detect the onset and systolic peaks of PPG waveforms. However, few publicly accessible PPG datasets exist in which the onset and systolic peaks of the waveforms are annotated. Therefore, this study developed a MATLAB toolbox that stitches predetermined annotated PPGs in a random manner to generate a long, annotated PPG signal. With this toolbox, any combination of four annotated PPG templates that represent regular, irregular, fast rhythm, and noisy PPG waveforms can be stitched together to generate a long, annotated PPG. Furthermore, this toolbox can simulate real-life PPG signals by introducing different noise levels and PPG waveforms. The toolbox can implement two stitching methods: one based on the systolic peak and the other on the onset. Additionally, cubic spline interpolation is used to smooth the waveform around the stitching point, and a skewness index is used as a signal quality index to select the final signal output based on the stitching method used. The developed toolbox is free and open-source software, and a graphical user interface is provided. The method of synthesizing by stitching introduced in this paper is a data augmentation strategy that can help researchers significantly increase the size and diversity of annotated PPG signals available for training and testing different feature extraction algorithms.


Assuntos
Algoritmos , Fotopletismografia , Frequência Cardíaca , Processamento de Sinais Assistido por Computador , Software
3.
Talanta ; 233: 122538, 2021 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-34215041

RESUMO

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is the most common enzymopathy in humans. More than 400 million people worldwide are affected by this genetic condition. Testing for G6PD deficiency before drug administration is essential for patient safety. Rapidly ascertaining the G6PD status of a person is desirable for proper treatment. The device described in this study, the G6PD diaxBOX, was developed to quantify G6PD deficiency using paper-based analytical devices (PADs) and a colorimetric assay. The G6PD diaxBOX is a straightforward, affordable, portable, and instrument-free analytical system. The major components of the G6PD diaxBox are a banknote-checking UV fluorescent lamp and camera that are easy to access and analysis software. When NADPH is generated, it absorbs at UV 340 nm and emits colored light that is detected with the camera. The determined Pearson's coefficient shows that the color intensity measured from the G6PD diaxBOX correlated with G6PD activity level. Also, a Bland-Altman analysis indicated that more than 95% of the measurement error was in the upper and lower boundaries (±2 SD) and the error from the severe and moderate deficiency group was less than ± 1 SD. Therefore, the error from G6PD diaxBOX was within the limit boundary and the overall accuracy was more than 80%. The G6PD diaxBOX facilitates the effective and efficient quantification of G6PD deficiency and as such represents a clinically well-suited, rapid point-of-care test.


Assuntos
Deficiência de Glucosefosfato Desidrogenase , Glucosefosfato Desidrogenase , Colorimetria , Humanos , Testes Imediatos , Software
4.
Talanta ; 233: 122605, 2021 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-34215092

RESUMO

Bridging the gap between complex signal data output and clear interpretation by non-expert end-users is a major challenge many scientists face when converting their scientific technology into a real-life application. Currently, pattern recognition algorithms are the most frequently encountered signal data interpretation algorithms to close this gap, not in the least because of their straight-forward implementation via convenient software packages. Paradoxically, just because their implementation is so straight-forward, it becomes cumbersome to integrate the expert's domain-specific knowledge. In this work, a novel signal data interpretation approach is presented that uses this domain-specific knowledge as its fundament, thereby fully exploiting the unique expertise of the scientist. The new approach applies data preprocessing in an innovative way that transcends its usual purpose and is easy to translate into a software application. Multiple case studies illustrate the straight-forward application of the novel approach. Ultimately, the approach is highly suited for integration in various (bio)analytical applications that require interpretation of signal data.


Assuntos
Algoritmos , Software
5.
Sensors (Basel) ; 21(12)2021 Jun 13.
Artigo em Inglês | MEDLINE | ID: mdl-34199208

RESUMO

Drones are being increasingly used in conservation to tackle the illegal poaching of animals. An important aspect of using drones for this purpose is establishing the technological and the environmental factors that increase the chances of success when detecting poachers. Recent studies focused on investigating these factors, and this research builds upon this as well as exploring the efficacy of machine-learning for automated detection. In an experimental setting with voluntary test subjects, various factors were tested for their effect on detection probability: camera type (visible spectrum, RGB, and thermal infrared, TIR), time of day, camera angle, canopy density, and walking/stationary test subjects. The drone footage was analysed both manually by volunteers and through automated detection software. A generalised linear model with a logit link function was used to statistically analyse the data for both types of analysis. The findings concluded that using a TIR camera improved detection probability, particularly at dawn and with a 90° camera angle. An oblique angle was more effective during RGB flights, and walking/stationary test subjects did not influence detection with both cameras. Probability of detection decreased with increasing vegetation cover. Machine-learning software had a successful detection probability of 0.558, however, it produced nearly five times more false positives than manual analysis. Manual analysis, however, produced 2.5 times more false negatives than automated detection. Despite manual analysis producing more true positive detections than automated detection in this study, the automated software gives promising, successful results, and the advantages of automated methods over manual analysis make it a promising tool with the potential to be successfully incorporated into anti-poaching strategies.


Assuntos
Aprendizado de Máquina , Software , Animais , Humanos
6.
Molecules ; 26(12)2021 Jun 08.
Artigo em Inglês | MEDLINE | ID: mdl-34201410

RESUMO

In-process control (IPC) is an important task during chemical syntheses in pharmaceutical industry. Despite the fact that each chemical reaction is unique, the most common analytical technique used for IPC analysis is high performance liquid chromatography (HPLC). Today, the so-called "Quality by Design" (QbD) principle is often being applied rather than "Trial and Error" approach for HPLC method development. The QbD approach requires only for a very few experimental measurements to find the appropriate stationary phase and optimal chromatographic conditions such as the composition of mobile phase, gradient steepness or time (tG), temperature (T), and mobile phase pH. In this study, the applicability of a multifactorial liquid chromatographic optimization software was studied in an extended knowledge space. Using state-of-the-art ultra-high performance liquid chromatography (UHPLC), the analysis time can significantly be shortened. By using UHPLC, it is possible to analyse the composition of the reaction mixture within few minutes. In this work, a mixture of route of synthesis of apixaban was analysed on short narrow bore column (50 × 2.1 mm, packed with sub-2 µm particles) resulting in short analysis time. The aim of the study was to cover a relatively narrow range of method parameters (tG, T, pH) in order to find a robust working point (zone). The results of the virtual (modeled) robustness testing were systematically compared to experimental measurements and Design of Experiments (DoE) based predictions.


Assuntos
Cromatografia Líquida de Alta Pressão/métodos , Pirazóis/química , Piridonas/química , Concentração de Íons de Hidrogênio , Projetos de Pesquisa , Software , Temperatura
7.
Molecules ; 26(13)2021 Jun 30.
Artigo em Inglês | MEDLINE | ID: mdl-34209011

RESUMO

In the current study, a simple in silico approach using free software was used with the experimental studies to optimize the antiproliferative activity and predict the potential mechanism of action of pyrrolizine-based Schiff bases. A compound library of 288 Schiff bases was designed based on compound 10, and a pharmacophore search was performed. Structural analysis of the top scoring hits and a docking study were used to select the best derivatives for the synthesis. Chemical synthesis and structural elucidation of compounds 16a-h were discussed. The antiproliferative activity of 16a-h was evaluated against three cancer (MCF7, A2780 and HT29, IC50 = 0.01-40.50 µM) and one normal MRC5 (IC50 = 1.27-24.06 µM) cell lines using the MTT assay. The results revealed the highest antiproliferative activity against MCF7 cells for 16g (IC50 = 0.01 µM) with an exceptionally high selectivity index of (SI = 578). Cell cycle analysis of MCF7 cells treated with compound 16g revealed a cell cycle arrest at the G2/M phase. In addition, compound 16g induced a dose-dependent increase in apoptotic events in MCF7 cells compared to the control. In silico target prediction of compound 16g showed six potential targets that could mediate these activities. Molecular docking analysis of compound 16g revealed high binding affinities toward COX-2, MAP P38α, EGFR, and CDK2. The results of the MD simulation revealed low RMSD values and high negative binding free energies for the two complexes formed between compound 16g with EGFR, and CDK2, while COX-2 was in the third order. These results highlighted a great potentiality for 16g to inhibit both CDK2 and EGFR. Taken together, the results mentioned above highlighted compound 16g as a potential anticancer agent.


Assuntos
Antineoplásicos , Pontos de Checagem do Ciclo Celular/efeitos dos fármacos , Modelos Biológicos , Simulação de Acoplamento Molecular , Pirróis , Software , Antineoplásicos/química , Antineoplásicos/farmacologia , Ensaios de Seleção de Medicamentos Antitumorais , Células HT29 , Humanos , Células MCF-7 , Pirróis/química , Pirróis/farmacologia , Bases de Schiff/química , Bases de Schiff/farmacologia
8.
Bioinformatics ; 37(Suppl_1): i349-i357, 2021 07 12.
Artigo em Inglês | MEDLINE | ID: mdl-34252956

RESUMO

MOTIVATION: Recent advances in single-cell RNA-sequencing (scRNA-seq) technologies promise to enable the study of gene regulatory associations at unprecedented resolution in diverse cellular contexts. However, identifying unique regulatory associations observed only in specific cell types or conditions remains a key challenge; this is particularly so for rare transcriptional states whose sample sizes are too small for existing gene regulatory network inference methods to be effective. RESULTS: We present ShareNet, a Bayesian framework for boosting the accuracy of cell type-specific gene regulatory networks by propagating information across related cell types via an information sharing structure that is adaptively optimized for a given single-cell dataset. The techniques we introduce can be used with a range of general network inference algorithms to enhance the output for each cell type. We demonstrate the enhanced accuracy of our approach on three benchmark scRNA-seq datasets. We find that our inferred cell type-specific networks also uncover key changes in gene associations that underpin the complex rewiring of regulatory networks across cell types, tissues and dynamic biological processes. Our work presents a path toward extracting deeper insights about cell type-specific gene regulation in the rapidly growing compendium of scRNA-seq datasets. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. AVAILABILITY AND IMPLEMENTATION: The code for ShareNet is available at http://sharenet.csail.mit.edu and https://github.com/alexw16/sharenet.


Assuntos
Perfilação da Expressão Gênica , Análise de Célula Única , Teorema de Bayes , Disseminação de Informação , Análise de Sequência de RNA , Software
9.
Bioinformatics ; 37(Suppl_1): i317-i326, 2021 07 12.
Artigo em Inglês | MEDLINE | ID: mdl-34252968

RESUMO

MOTIVATION: Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) provides new opportunities to dissect epigenomic heterogeneity and elucidate transcriptional regulatory mechanisms. However, computational modeling of scATAC-seq data is challenging due to its high dimension, extreme sparsity, complex dependencies and high sensitivity to confounding factors from various sources. RESULTS: Here, we propose a new deep generative model framework, named SAILER, for analyzing scATAC-seq data. SAILER aims to learn a low-dimensional nonlinear latent representation of each cell that defines its intrinsic chromatin state, invariant to extrinsic confounding factors like read depth and batch effects. SAILER adopts the conventional encoder-decoder framework to learn the latent representation but imposes additional constraints to ensure the independence of the learned representations from the confounding factors. Experimental results on both simulated and real scATAC-seq datasets demonstrate that SAILER learns better and biologically more meaningful representations of cells than other methods. Its noise-free cell embeddings bring in significant benefits in downstream analyses: clustering and imputation based on SAILER result in 6.9% and 18.5% improvements over existing methods, respectively. Moreover, because no matrix factorization is involved, SAILER can easily scale to process millions of cells. We implemented SAILER into a software package, freely available to all for large-scale scATAC-seq data analysis. AVAILABILITY AND IMPLEMENTATION: The software is publicly available at https://github.com/uci-cbcl/SAILER. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Sequenciamento de Cromatina por Imunoprecipitação , Análise de Célula Única , Epigenômica , Análise de Sequência de RNA , Software , Transposases
10.
Nat Commun ; 12(1): 4242, 2021 07 13.
Artigo em Inglês | MEDLINE | ID: mdl-34257289

RESUMO

Humankind is generating digital data at an exponential rate. These data are typically stored using electronic, magnetic or optical devices, which require large physical spaces and cannot last for a very long time. Here we report the use of peptide sequences for data storage, which can be durable and of high storage density. With the selection of suitable constitutive amino acids, designs of address codes and error-correction schemes to protect the order and integrity of the stored data, optimization of the analytical protocol and development of a software to effectively recover peptide sequences from the tandem mass spectra, we demonstrated the feasibility of this method by successfully storing and retrieving a text file and the music file Silent Night with 40 and 511 18-mer peptides respectively. This method for the first time links data storage with the peptide synthesis industry and proteomics techniques, and is expected to stimulate the development of relevant fields.


Assuntos
Bases de Dados de Proteínas , Software , Algoritmos , Animais , Humanos , Proteômica/métodos
11.
Sensors (Basel) ; 21(13)2021 Jul 04.
Artigo em Inglês | MEDLINE | ID: mdl-34283140

RESUMO

The sparse data in PM2.5 air quality monitoring systems is frequently happened on large-scale smart city sensing applications, which is collected via massive sensors. Moreover, it could be affected by inefficient node deployment, insufficient communication, and fragmented records, which is the main challenge of the high-resolution prediction system. In addition, data privacy in the existing centralized air quality prediction system cannot be ensured because the data which are mined from end sensory nodes constantly exposed to the network. Therefore, this paper proposes a novel edge computing framework, named Federated Compressed Learning (FCL), which provides efficient data generation while ensuring data privacy for PM2.5 predictions in the application of smart city sensing. The proposed scheme inherits the basic ideas of the compression technique, regional joint learning, and considers a secure data exchange. Thus, it could reduce the data quantity while preserving data privacy. This study would like to develop a green energy-based wireless sensing network system by using FCL edge computing framework. It is also one of key technologies of software and hardware co-design for reconfigurable and customized sensing devices application. Consequently, the prototypes are developed in order to validate the performances of the proposed framework. The results show that the data consumption is reduced by more than 95% with an error rate below 5%. Finally, the prediction results based on the FCL will generate slightly lower accuracy compared with centralized training. However, the data could be heavily compacted and securely transmitted in WSNs.


Assuntos
Poluição do Ar , Privacidade , Cidades , Material Particulado , Software
12.
Nat Commun ; 12(1): 4188, 2021 07 07.
Artigo em Inglês | MEDLINE | ID: mdl-34234121

RESUMO

Klebsiella pneumoniae is a leading cause of antimicrobial-resistant (AMR) healthcare-associated infections, neonatal sepsis and community-acquired liver abscess, and is associated with chronic intestinal diseases. Its diversity and complex population structure pose challenges for analysis and interpretation of K. pneumoniae genome data. Here we introduce Kleborate, a tool for analysing genomes of K. pneumoniae and its associated species complex, which consolidates interrogation of key features of proven clinical importance. Kleborate provides a framework to support genomic surveillance and epidemiology in research, clinical and public health settings. To demonstrate its utility we apply Kleborate to analyse publicly available Klebsiella genomes, including clinical isolates from a pan-European study of carbapenemase-producing Klebsiella, highlighting global trends in AMR and virulence as examples of what could be achieved by applying this genomic framework within more systematic genomic surveillance efforts. We also demonstrate the application of Kleborate to detect and type K. pneumoniae from gut metagenomes.


Assuntos
Proteínas de Bactérias/genética , Infecção Hospitalar/microbiologia , Infecções por Klebsiella/microbiologia , Klebsiella pneumoniae/genética , Tipagem Molecular/métodos , Antibacterianos/farmacologia , Antibacterianos/uso terapêutico , Infecção Hospitalar/diagnóstico , Infecção Hospitalar/tratamento farmacológico , Infecção Hospitalar/epidemiologia , Conjuntos de Dados como Assunto , Farmacorresistência Bacteriana Múltipla/genética , Monitoramento Epidemiológico , Microbioma Gastrointestinal/genética , Genoma Bacteriano , Humanos , Lactente , Recém-Nascido , Infecções por Klebsiella/diagnóstico , Infecções por Klebsiella/tratamento farmacológico , Infecções por Klebsiella/epidemiologia , Klebsiella pneumoniae/isolamento & purificação , Klebsiella pneumoniae/patogenicidade , Metagenoma/genética , Epidemiologia Molecular/métodos , Mutação , Filogenia , Software , Virulência/genética , Fatores de Virulência/genética , Sequenciamento Completo do Genoma , beta-Lactamases/genética
13.
Nat Commun ; 12(1): 4175, 2021 07 07.
Artigo em Inglês | MEDLINE | ID: mdl-34234126

RESUMO

Although we can now measure single-cell signaling responses with multivariate, high-throughput techniques our ability to interpret such measurements is still limited. Even interpretation of dose-response based on single-cell data is not straightforward: signaling responses can differ significantly between cells, encompass multiple signaling effectors, and have dynamic character. Here, we use probabilistic modeling and information-theory to introduce fractional response analysis (FRA), which quantifies changes in fractions of cells with given response levels. FRA can be universally performed for heterogeneous, multivariate, and dynamic measurements and, as we demonstrate, quantifies otherwise hidden patterns in single-cell data. In particular, we show that fractional responses to type I interferon in human peripheral blood mononuclear cells are very similar across different cell types, despite significant differences in mean or median responses and degrees of cell-to-cell heterogeneity. Further, we demonstrate that fractional responses to cytokines scale linearly with the log of the cytokine dose, which uncovers that heterogeneous cellular populations are sensitive to fold-changes in the dose, as opposed to additive changes.


Assuntos
Ensaios de Triagem em Larga Escala/métodos , Interferon Tipo I/metabolismo , Leucócitos Mononucleares/metabolismo , Modelos Imunológicos , Células 3T3 , Animais , Voluntários Saudáveis , Humanos , Interferon Tipo I/imunologia , Leucócitos Mononucleares/imunologia , Camundongos , Modelos Estatísticos , Cultura Primária de Células , Transdução de Sinais/imunologia , Análise de Célula Única , Software
14.
Nat Commun ; 12(1): 4197, 2021 07 07.
Artigo em Inglês | MEDLINE | ID: mdl-34234139

RESUMO

Single cell RNA sequencing (scRNA-seq) is a powerful tool in detailing the cellular landscape within complex tissues. Large-scale single cell transcriptomics provide both opportunities and challenges for identifying rare cells playing crucial roles in development and disease. Here, we develop GapClust, a light-weight algorithm to detect rare cell types from ultra-large scRNA-seq datasets with state-of-the-art speed and memory efficiency. Benchmarking on diverse experimental datasets demonstrates the superior performance of GapClust compared to other recently proposed methods. When applying our algorithm to an intestine and 68 k PBMC datasets, GapClust identifies the tuft cells and a previously unrecognised subtype of monocyte, respectively.


Assuntos
Algoritmos , RNA-Seq/métodos , Análise de Célula Única/métodos , Conjuntos de Dados como Assunto , Células HEK293 , Humanos , Mucosa Intestinal/citologia , Células Jurkat , Software
15.
Nat Commun ; 12(1): 4192, 2021 07 07.
Artigo em Inglês | MEDLINE | ID: mdl-34234142

RESUMO

Most existing tools for constructing genetic prediction models begin with the assumption that all genetic variants contribute equally towards the phenotype. However, this represents a suboptimal model for how heritability is distributed across the genome. Therefore, we develop prediction tools that allow the user to specify the heritability model. We compare individual-level data prediction tools using 14 UK Biobank phenotypes; our new tool LDAK-Bolt-Predict outperforms the existing tools Lasso, BLUP, Bolt-LMM and BayesR for all 14 phenotypes. We compare summary statistic prediction tools using 225 UK Biobank phenotypes; our new tool LDAK-BayesR-SS outperforms the existing tools lassosum, sBLUP, LDpred and SBayesR for 223 of the 225 phenotypes. When we improve the heritability model, the proportion of phenotypic variance explained increases by on average 14%, which is equivalent to increasing the sample size by a quarter.


Assuntos
Previsões/métodos , Modelos Genéticos , Herança Multifatorial , Medicina de Precisão/métodos , Característica Quantitativa Herdável , Estudos de Casos e Controles , Conjuntos de Dados como Assunto , Estudo de Associação Genômica Ampla , Humanos , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas , Tamanho da Amostra , Software
16.
Int J Mol Sci ; 22(11)2021 Jun 02.
Artigo em Inglês | MEDLINE | ID: mdl-34199677

RESUMO

The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins' 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug-target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.


Assuntos
Aprendizado Profundo , Aprendizado de Máquina , Conformação Proteica , Software , Sequência de Aminoácidos , Biologia Computacional , Bases de Dados de Proteínas , Evolução Molecular , Humanos , Redes Neurais de Computação , Alinhamento de Sequência/métodos
17.
Sensors (Basel) ; 21(13)2021 Jun 25.
Artigo em Inglês | MEDLINE | ID: mdl-34202052

RESUMO

The design and implementation of an electronic system that involves head movements to operate a prototype that can simulate future movements of a wheelchair was developed here. The controller design collects head-movements data through a MEMS sensor-based motion capture system. The research was divided into four stages: First, the instrumentation of the system using hardware and software; second, the mathematical modeling using the theory of dynamic systems; third, the automatic control of position, speed, and orientation with constant and variable speed; finally, system verification using both an electronic controller test protocol and user experience. The system involved a graphical interface for the user to interact with it by executing all the controllers in real time. Through the System Usability Scale (SUS), a score of 78 out of 100 points was obtained from the qualification of 10 users who validated the system, giving a connotation of "very good". Users accepted the system with the recommendation to improve safety by using laser sensors instead of ultrasonic range modules to enhance obstacle detection.


Assuntos
Cadeiras de Rodas , Computadores , Movimentos da Cabeça , Movimento (Física) , Software
18.
Sensors (Basel) ; 21(13)2021 Jun 25.
Artigo em Inglês | MEDLINE | ID: mdl-34202291

RESUMO

Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.


Assuntos
Inteligência Artificial , Fenômica , Aprendizado de Máquina , Fenótipo , Software
19.
Sensors (Basel) ; 21(13)2021 Jun 29.
Artigo em Inglês | MEDLINE | ID: mdl-34209748

RESUMO

Cryosurgery is a technique of growing popularity involving tissue ablation under controlled freezing. Technological advancement of devices along with surgical technique improvements have turned cryosurgery from an experimental to an established option for treating several diseases. However, cryosurgery is still limited by inaccurate planning based primarily on 2D visualization of the patient's preoperative images. Several works have been aimed at modelling cryoablation through heat transfer simulations; however, most software applications do not meet some key requirements for clinical routine use, such as high computational speed and user-friendliness. This work aims to develop an intuitive platform for anatomical understanding and pre-operative planning by integrating the information content of radiological images and cryoprobe specifications either in a 3D virtual environment (desktop application) or in a hybrid simulator, which exploits the potential of the 3D printing and augmented reality functionalities of Microsoft HoloLens. The proposed platform was preliminarily validated for the retrospective planning/simulation of two surgical cases. Results suggest that the platform is easy and quick to learn and could be used in clinical practice to improve anatomical understanding, to make surgical planning easier than the traditional method, and to strengthen the memorization of surgical planning.


Assuntos
Realidade Aumentada , Criocirurgia , Simulação por Computador , Humanos , Estudos Retrospectivos , Software
20.
BMC Genomics ; 22(1): 513, 2021 Jul 07.
Artigo em Inglês | MEDLINE | ID: mdl-34233619

RESUMO

BACKGROUND: Direct-sequencing technologies, such as Oxford Nanopore's, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data. RESULT: Here, we present Sequoia, a visual analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization. CONCLUSIONS: Sequoia's interactive features complement existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available at https://github.com/dnonatar/Sequoia .


Assuntos
Sequenciamento por Nanoporos , Nanoporos , Sequoia , Células HeLa , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Análise de Sequência de DNA , Software
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