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1.
Sci Total Environ ; 751: 141418, 2021 Jan 10.
Artigo em Inglês | MEDLINE | ID: mdl-33181989

RESUMO

Uptake of seven organic contaminants including bisphenol A, estriol, 2,4-dinitrotoluene, N,N-diethyl-meta-toluamide (DEET), carbamazepine, acetaminophen, and lincomycin by tomato (Solanum lycopersicum L.), corn (Zea mays L.), and wheat (Triticum aestivum L.) was measured. The plants were grown in a growth chamber under recommended conditions and dosed by these chemicals for 19 days. The plant samples (stem transpiration stream) and solution in the exposure media were taken to measure transpiration stream concentration factor (TSCF). The plant samples were analyzed by a freeze-thaw centrifugation technique followed by high performance liquid chromatography-tandem mass spectrometry detection. Measured average TSCF values were used to test a neural network (NN) model previously developed for predicting plant uptake based on physicochemical properties. The results indicated that moderately hydrophobic compounds including carbamazepine and lincomycin have average TSCF values of 0.43 and 0.79, respectively. The average uptake of DEET, estriol, acetaminophen, and bisphenol A was also measured as 0.34, 0.29, 0.22, and 0.1, respectively. The 2,4-dinitrotoluene was not detected in the stem transpiration stream and it was shown to degrade in the root zone. Based on these results together with plant physiology measurements, we concluded that physicochemical properties of the chemicals did predict uptake, however, the role of other factors should be considered in the prediction of TSCF. While NN model could predict TSCF based on physicochemical properties with acceptable accuracies (mean squared error less than 0.25), the results for 2,4-dinitrotoluene and other compounds confirm the needs for considering other parameters related to both chemicals (stability) and plant species (role of lipids, lignin, and cellulose).


Assuntos
Lycopersicon esculentum , Redes Neurais de Computação , Transporte Biológico , Raízes de Plantas , Transpiração Vegetal , Triticum , Zea mays
2.
Sci Total Environ ; 751: 142293, 2021 Jan 10.
Artigo em Inglês | MEDLINE | ID: mdl-33181995

RESUMO

The harm done to the environment by coal gangue was very serious, and it is urgent to adopt effective methods to dispose of coal gangue in order to prevent further environmental damage. Co-pyrolysis experiments of coal gangue (CG) and peanut shell (PS) were carried out using thermogravimetry-Fourier transform infrared spectroscopy (TG-FTIR) under nitrogen atmosphere. The heavy metal was detected using the inductively coupled plasma-optical emission spectroscopy (ICP-OES). CG and PS were mixed according to the mass ratio of 1:0, 3:1, 1:1, 1:3 and 0:1. The samples were heated to 1000 °C at the heating rate of 10 °C/min, 20 °C/min and 30 °C/min. The comprehensive pyrolysis index (CPI) of CG, C3P1, C1P1, C1P3 and PS is 0.17 × 10-8, 9.75 × 10-8, 35.47 × 10-8, 100.94 × 10-8 and 192.72 × 10-8%2 ·min-2·°C-3. The kinetic parameters were calculated by model-free methods (Flynn-Wall-Ozawa and Kissinger-Akahira-Sunose). The gas products generated at different temperatures during the pyrolysis experiment were detected by Fourier transform infrared spectrometer. The heating rate, temperature and mixing ratio are the input parameters of artificial neural network (ANN), and the remaining mass percentage of sample during the pyrolysis is the output parameter. The ANN model was established and used to predict thermogravimetric experimental data. The ANN18 model is the best model for predicting the co-pyrolysis of CG and PS.


Assuntos
Carvão Mineral , Pirólise , Biomassa , Carvão Mineral/análise , Cinética , Redes Neurais de Computação , Espectroscopia de Infravermelho com Transformada de Fourier
3.
Food Chem ; 335: 127640, 2021 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-32738536

RESUMO

In order to distinguish different vegetable oils, adulterated vegetable oils, and to identify and quantify counterfeit vegetable oils, a method based on a small sample size of total synchronous fluorescence (TSyF) spectra combined with convolutional neural network (CNN) was proposed. Four typical vegetable oils were classified by three ways of fine-tuning the pre-trained CNN, the pre-trained CNN as a feature extractor, and traditional chemometrics. The pre-trained CNN was combined with support vector machines to distinguish adulterated sesame oil and counterfeit sesame oil separately with 100% correct classification rates. The pre-trained CNN combined with partial least square regression was used to predict the level of counterfeit sesame oil. The coefficient of determination for calibration (Rc2) values were all greater than 0.99, and the root mean square errors of validation were 0.81% and 1.72%, respectively. These results show that it is feasible to combine TSyF spectra with CNN for vegetable oil identification.


Assuntos
Redes Neurais de Computação , Óleos Vegetais/química , Espectrometria de Fluorescência/métodos , Qualidade dos Alimentos , Fraude , Análise dos Mínimos Quadrados , Óleo de Gergelim/química , Máquina de Vetores de Suporte
4.
Environ Monit Assess ; 192(12): 761, 2020 Nov 14.
Artigo em Inglês | MEDLINE | ID: mdl-33188607

RESUMO

Hourly river flow pattern monitoring and simulation is the indispensable precautionary task for river engineering sustainability, water resource management, flood risk mitigation, and impact reduction. Reliable river flow forecasting is highly emphasized to support major decision-makers. This research paper adopts a new implementation approach for the application of a river flow prediction model for hourly prediction of the flow of Mary River in Australia; a novel data-intelligent model called emotional neural network (ENN) was used for this purpose. A historical dataset measured over a 4-year period (2011-2014) at hourly timescale was used in building the ENN-based predictive model. The results of the ENN model were validated against the existing approaches such as the minimax probability machine regression (MPMR), relevance vector machine (RVM), and multivariate adaptive regression splines (MARS) models. The developed models are evaluated against each other for validation purposes. Various numerical and graphical performance evaluators are conducted to assess the predictability of the proposed ENN and the competitive benchmark models. The ENN model, used as an objective simulation tool, revealed an outstanding performance when applied for hourly river flow prediction in comparison with the other benchmark models. However, the order of the model, performance wise, is ENN > MARS > RVM > MPMR. In general, the present results of the proposed ENN model reveal a promising modeling strategy for the hourly simulation of river flow, and such a model can be explored further for its ability to contribute to the state-of-the-art of river engineering and water resources monitoring and future prediction at near real-time forecast horizons.


Assuntos
Monitoramento Ambiental , Rios , Austrália , Previsões , Aprendizado de Máquina , Redes Neurais de Computação
5.
Brain Nerve ; 72(11): 1255-1262, 2020 Nov.
Artigo em Japonês | MEDLINE | ID: mdl-33191303

RESUMO

Focusing on the developmental process of the brain, we propose a neural network model of functional differentiation including functional parcellation. We explain the emerging process of functional elements, of the system through the constraints, which act on the whole network system. We explain several kinds of differentiation, such as the differentiation of neuronal cells, functional modules, and the sensory neurons, thereby proposing three hypotheses.


Assuntos
Encéfalo , Redes Neurais de Computação , Humanos , Células Receptoras Sensoriais
6.
Phys Rev Lett ; 125(17): 178301, 2020 Oct 23.
Artigo em Inglês | MEDLINE | ID: mdl-33156685

RESUMO

Deep learning has achieved impressive prediction accuracies in a variety of scientific and industrial domains. However, the nested nonlinear feature of deep learning makes the learning highly nontransparent, i.e., it is still unknown how the learning coordinates a huge number of parameters to achieve decision-making. To explain this hierarchical credit assignment, we propose a mean-field learning model by assuming that an ensemble of subnetworks, rather than a single network, is trained for a classification task. Surprisingly, our model reveals that apart from some deterministic synaptic weights connecting two neurons at neighboring layers, there exists a large number of connections that can be absent, and other connections can allow for a broad distribution of their weight values. Therefore, synaptic connections can be classified into three categories: very important ones, unimportant ones, and those of variability that may partially encode nuisance factors. Therefore, our model learns the credit assignment leading to the decision and predicts an ensemble of sub-networks that can accomplish the same task, thereby providing insights toward understanding the macroscopic behavior of deep learning through the lens of distinct roles of synaptic weights.


Assuntos
Aprendizado Profundo , Redes Neurais de Computação , Dinâmica não Linear
7.
BMC Bioinformatics ; 21(1): 501, 2020 Nov 04.
Artigo em Inglês | MEDLINE | ID: mdl-33148191

RESUMO

BACKGROUND: The use of predictive gene signatures to assist clinical decision is becoming more and more important. Deep learning has a huge potential in the prediction of phenotype from gene expression profiles. However, neural networks are viewed as black boxes, where accurate predictions are provided without any explanation. The requirements for these models to become interpretable are increasing, especially in the medical field. RESULTS: We focus on explaining the predictions of a deep neural network model built from gene expression data. The most important neurons and genes influencing the predictions are identified and linked to biological knowledge. Our experiments on cancer prediction show that: (1) deep learning approach outperforms classical machine learning methods on large training sets; (2) our approach produces interpretations more coherent with biology than the state-of-the-art based approaches; (3) we can provide a comprehensive explanation of the predictions for biologists and physicians. CONCLUSION: We propose an original approach for biological interpretation of deep learning models for phenotype prediction from gene expression data. Since the model can find relationships between the phenotype and gene expression, we may assume that there is a link between the identified genes and the phenotype. The interpretation can, therefore, lead to new biological hypotheses to be investigated by biologists.


Assuntos
Regulação Neoplásica da Expressão Gênica , Neoplasias/patologia , Redes Neurais de Computação , Bases de Dados Genéticas , Humanos , Neoplasias/metabolismo
8.
BMC Bioinformatics ; 21(1): 507, 2020 Nov 07.
Artigo em Inglês | MEDLINE | ID: mdl-33160328

RESUMO

BACKGROUND: Enhancer-promoter interactions (EPIs) play key roles in transcriptional regulation and disease progression. Although several computational methods have been developed to predict such interactions, their performances are not satisfactory when training and testing data from different cell lines. Currently, it is still unclear what extent a across cell line prediction can be made based on sequence-level information. RESULTS: In this work, we present a novel Sequence-based method (called SEPT) to predict the enhancer-promoter interactions in new cell line by using the cross-cell information and Transfer learning. SEPT first learns the features of enhancer and promoter from DNA sequences with convolutional neural network (CNN), then designing the gradient reversal layer of transfer learning to reduce the cell line specific features meanwhile retaining the features associated with EPIs. When the locations of enhancers and promoters are provided in new cell line, SEPT can successfully recognize EPIs in this new cell line based on labeled data of other cell lines. The experiment results show that SEPT can effectively learn the latent import EPIs-related features between cell lines and achieves the best prediction performance in terms of AUC (the area under the receiver operating curves). CONCLUSIONS: SEPT is an effective method for predicting the EPIs in new cell line. Domain adversarial architecture of transfer learning used in SEPT can learn the latent EPIs shared features among cell lines from all other existing labeled data. It can be expected that SEPT will be of interest to researchers concerned with biological interaction prediction.


Assuntos
Redes Neurais de Computação , Regiões Promotoras Genéticas/genética , Sequências Reguladoras de Ácido Nucleico/genética , Área Sob a Curva , Linhagem Celular , Humanos , Curva ROC
9.
BMC Bioinformatics ; 21(1): 512, 2020 Nov 10.
Artigo em Inglês | MEDLINE | ID: mdl-33167861

RESUMO

BACKGROUND: An enzyme activity is influenced by the external environment. It is important to have an enzyme remain high activity in a specific condition. A usual way is to first determine the optimal condition of an enzyme by either the gradient test or by tertiary structure, and then to use protein engineering to mutate a wild type enzyme for a higher activity in an expected condition. RESULTS: In this paper, we investigate the optimal condition of an enzyme by directly analyzing the sequence. We propose an embedding method to represent the amino acids and the structural information as vectors in the latent space. These vectors contain information about the correlations between amino acids and sites in the aligned amino acid sequences, as well as the correlation with the optimal condition. We crawled and processed the amino acid sequences in the glycoside hydrolase GH11 family, and got 125 amino acid sequences with optimal pH condition. We used probabilistic approximation method to implement the embedding learning method on these samples. Based on these embedding vectors, we design a computational score to determine which one has a better optimal condition for two given amino acid sequences and achieves the accuracy 80% on the test proteins in the same family. We also give the mutation suggestion such that it has a higher activity in an expected environment, which is consistent with the previously professional wet experiments and analysis. CONCLUSION: A new computational method is proposed for the sequence based on the enzyme optimal condition analysis. Compared with the traditional process that involves a lot of wet experiments and requires multiple mutations, this method can give recommendations on the direction and location of amino acid substitution with reference significance for an expected condition in an efficient and effective way.


Assuntos
Biologia Computacional/métodos , Glicosídeo Hidrolases/metabolismo , Sequência de Aminoácidos , Glicosídeo Hidrolases/química , Glicosídeo Hidrolases/genética , Concentração de Íons de Hidrogênio , Redes Neurais de Computação
10.
PLoS One ; 15(11): e0242301, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33180877

RESUMO

Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, their use in medical computer vision tasks faces several limitations, viz., (i) adapting to visual characteristics that are unlike natural images; (ii) modeling random noise during training due to stochastic optimization and backpropagation-based learning strategy; (iii) challenges in explaining DL black-box behavior to support clinical decision-making; and (iv) inter-reader variability in the ground truth (GT) annotations affecting learning and evaluation. This study proposes a systematic approach to address these limitations through application to the pandemic-caused need for Coronavirus disease 2019 (COVID-19) detection using chest X-rays (CXRs). Specifically, our contribution highlights significant benefits obtained through (i) pretraining specific to CXRs in transferring and fine-tuning the learned knowledge toward improving COVID-19 detection performance; (ii) using ensembles of the fine-tuned models to further improve performance over individual constituent models; (iii) performing statistical analyses at various learning stages for validating results; (iv) interpreting learned individual and ensemble model behavior through class-selective relevance mapping (CRM)-based region of interest (ROI) localization; and, (v) analyzing inter-reader variability and ensemble localization performance using Simultaneous Truth and Performance Level Estimation (STAPLE) methods. We find that ensemble approaches markedly improved classification and localization performance, and that inter-reader variability and performance level assessment helps guide algorithm design and parameter optimization. To the best of our knowledge, this is the first study to construct ensembles, perform ensemble-based disease ROI localization, and analyze inter-reader variability and algorithm performance for COVID-19 detection in CXRs.


Assuntos
Infecções por Coronavirus/diagnóstico por imagem , Aprendizado Profundo , Processamento de Imagem Assistida por Computador/métodos , Variações Dependentes do Observador , Pneumonia Viral/diagnóstico por imagem , Radiografia Torácica/normas , Algoritmos , Betacoronavirus , Humanos , Redes Neurais de Computação , Pandemias
11.
Environ Monit Assess ; 192(12): 770, 2020 Nov 19.
Artigo em Inglês | MEDLINE | ID: mdl-33215263

RESUMO

In the current research, the efficiency of a solar flat plate collector (SFPC) was examined experimentally, while the system was modeled with an artificial neural network (ANN) under semi-arid weather conditions of Rafsanjan, Iran. Based on the backpropagation algorithm, a feedforward neural network was established to estimate and forecast the outlet flow temperature of SFPC. To identify the most appropriate model, the ANN topology hidden layer, the number of hidden neurons, iteration, and statistical indicators were analyzed. In the first ANN modeling (CASE I), five parameters, including solar radiation, inlet flow temperature, flow rate, ambient temperature, and wind speed, were applied in the input layer of the network, while the output flow temperature (subsequently efficiency) was in the output layer. In the second artificial neural network modeling (CASE II), the wind speed was omitted from the input of the ANN model. Results showed that the ANN with four inputs yields more accurate results for both estimation and prediction of outlet flow temperature.


Assuntos
Monitoramento Ambiental , Redes Neurais de Computação , Algoritmos , Irã (Geográfico) , Temperatura
12.
Environ Monit Assess ; 192(12): 752, 2020 Nov 07.
Artigo em Inglês | MEDLINE | ID: mdl-33159587

RESUMO

The aim of this study was to model the surface water quality of the Broad River Basin, South Carolina. The most suitable two monitoring stations numbered as USGS 02156500 (Near Carlisle) and USGS 02160991 (Near Jenkinsville) were selected for the reason that the river water temperature (WT), pH, and specific conductance (SC), as well as dissolved oxygen (DO) concentration, were simultaneously monitored and recorded at these sites. The monitoring period from September 2016 to August 2017 was taken into account for the modeling studies. The electrical conductivity (EC) values corresponding to the river SC values were calculated. First, the conventional regression analysis (CRA) was applied to three regression forms, i.e., linear, power, and exponential functions, to estimate the river DO concentration. Then, the multivariate adaptive regression splines (MARS) and TreeNet gradient boosting machine (TreeNet) techniques were employed. Three performance statistics, i.e., root means square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe coefficient of efficiency (NS), were used to compare the estimation capabilities of these techniques. The TreeNet technique, which was used for the first time in the modeling of DO concentration, had higher estimation success with the RMSE, MAE, and NS values of 0.182 mg/L, 0.123 mg/L, and 0.990, respectively, for the Carlisle station and 0.313 mg/L, 0.233 mg/L, and 0.965, respectively, for the Jenkinsville station in the training phase. The MARS technique, which had limited availability of its application in the modeling of DO concentration, had higher estimation success with the RMSE, MAE, and NS values of 0.240 mg/L, 0.195 mg/L, and 0.981, respectively, for the Carlisle station and 0.527 mg/L, 0.432 mg/L, and 0.980, respectively, for the Jenkinsville station in the testing phase. Considering the RMSE and MAE values being lower, as well as NS values being higher for the model having an input combination of WT, pH, and EC, the Carlisle station came into prominence. It was concluded that international researchers, who have engaged in the river water quality modeling studies, can favor the MARS and TreeNET techniques without any hesitation and estimate the river DO concentration successfully. The models developed for the Carlisle station were tested with the data sets for the monitoring period from September 2017 to August 2018 at the same station. Similarly, the models developed for the Jenkinsville station were tested with the data sets for the monitoring period from September 2017 to August 2018 at the same station. It was concluded that the models could estimate the river DO concentrations very close to in situ measurements at the same site but for the different monitoring periods, too. Furthermore, the models developed for the Carlisle station were tested with the data sets from the Jenkinsville station for the same monitoring period. Similarly, the models developed for the Jenkinsville station were tested with the data sets from the Carlisle station for the same monitoring period. It was also concluded that the developed models could estimate the river DO concentrations very close to in situ measurements at different monitoring sites but for the same monitoring period on the same river, too. It can be asserted that the models developed for any monitoring site on a river can be employed for another monitoring site on the same river, too, as in the case of the Broad River, South Carolina.


Assuntos
Rios , Qualidade da Água , Monitoramento Ambiental , Análise Multivariada , Redes Neurais de Computação , Oxigênio , Análise de Regressão , South Carolina
13.
Zhonghua Bing Li Xue Za Zhi ; 49(11): 1120-1125, 2020 Nov 08.
Artigo em Chinês | MEDLINE | ID: mdl-33152815

RESUMO

Objective: To establish an artificial intelligence (AI)-assisted diagnostic system for lung cancer via deep transfer learning. Methods: The researchers collected 519 lung pathologic slides from 2016 to 2019, covering various lung tissues, including normal tissues, adenocarcinoma, squamous cell carcinoma and small cell carcinoma, from the Beijing Chest Hospital, the Capital Medical University. The slides were digitized by scanner, and 316 slides were used as training set and 203 as the internal test set. The researchers labeled all the training slides by pathologists and establish a semantic segmentation model based on DeepLab v3 with ResNet-50 to detect lung cancers at the pixel level. To perform transfer learning, the researchers utilized the gastric cancer detection model to initialize the deep neural network parameters. The lung cancer detection convolutional neural network was further trained by fine-tuning of the labeled data. The deep learning model was tested by 203 slides in the internal test set and 1 081 slides obtained from TCIA database, named as the external test set. Results: The model trained with transfer learning showed substantial accuracy advantage against the one trained from scratch for the internal test set [area under curve (AUC) 0.988 vs. 0.971, Kappa 0.852 vs. 0.832]. For the external test set, the transferred model achieved an AUC of 0.968 and Kappa of 0.828, indicating superior generalization ability. By studying the predictions made by the model, the researchers obtained deeper understandings of the deep learning model. Conclusions: The lung cancer histopathological diagnostic system achieves higher accuracy and superior generalization ability. With the development of histopathological AI, the transfer learning can effectively train diagnosis models and shorten the learning period, and improve the model performance.


Assuntos
Aprendizado Profundo , Neoplasias Pulmonares , Inteligência Artificial , Bases de Dados Factuais , Humanos , Neoplasias Pulmonares/diagnóstico , Redes Neurais de Computação
14.
Water Sci Technol ; 82(9): 1921-1931, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-33201855

RESUMO

The optimal layout of low-impact development (LID) facilities satisfying annual runoff control for low rainfall expectation is not effective under extreme rainfall conditions and urban waterlogging may occur. In order to avoid the losses of urban waterlogging, it is particularly significant to establish a waterlogging early warning system. In this study, based on coupling RBF-NARX neural networks, we establish an early warning system that can predict the whole rainfall process according to the rainfall curve of the first 20 minutes. Using the predicted rainfall process curve as rainfall input to the rainfall-runoff calculation engine, the area at risk of waterlogging can be located. The results indicate that the coupled neural networks perform well in the prediction of the hypothetical verification rainfall process. Under the studied extreme rainfall conditions, the location of 25 flooding areas and flooding duration are well predicted by the early warning system. The maximum of average flooding depth and flooding duration is 16.5 cm and 99 minutes, respectively. By predicting the risk area and the corresponding flooding time, the early warning system is quite effective in avoiding and reducing the losses from waterlogging.


Assuntos
Inundações , Chuva , Redes Neurais de Computação
15.
Medicine (Baltimore) ; 99(43): e21518, 2020 Oct 23.
Artigo em Inglês | MEDLINE | ID: mdl-33120725

RESUMO

RATIONALE: Computer-assisted detection (CAD) systems based on artificial intelligence (AI) using convolutional neural network (CNN) have been successfully used for the diagnosis of unruptured cerebral aneurysms in experimental situations. However, it is yet unclear whether CAD systems can detect cerebral aneurysms effectively in real-life clinical situations. This paper describes the diagnostic efficacy of CAD systems for cerebral aneurysms and the types of cerebral aneurysms that they can detect. PATIENT CONCERNS: From March 7, 2017 to August 26, 2018 we performed brain magnetic resonance imaging (MRI) scans for 1623 subjects, to rule out intracranial diseases. We retrospectively reviewed the medical records including the history and images for each patient. DIAGNOSES, INTERVENTIONS AND OUTCOMES: Among them, we encountered 5 cases in whom the cerebral aneurysms had been overlooked in the first and second round of imaging, and were detected for the first time by CAD. All missed aneurysms were less than 2 mm in diameter. Of the 5 aneurysms, 2 were internal carotid artery (ICA) paraclinoid aneurysms, 2 were Internal carotid-posterior communicating artery (IC-PC) aneurysms and 1 was a distal middle cerebral artery (MCA) aneurysm. LESSONS: Our CAD system can detect very small aneurysms masked by the surrounding arteries and difficult for radiologists to detect. In the future, CAD systems might pave the way to substitute the workload of diagnostic radiologists and reduce the cost of human labor.


Assuntos
Aneurisma Intracraniano/diagnóstico por imagem , Diagnóstico Ausente , Redes Neurais de Computação , Adulto , Idoso , Feminino , Humanos , Imageamento Tridimensional , Achados Incidentais , Angiografia por Ressonância Magnética , Pessoa de Meia-Idade , Estudos Retrospectivos , Software
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1481-1484, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018271

RESUMO

Long scan duration remains a challenge for high-resolution MRI. Deep learning has emerged as a powerful means for accelerated MRI reconstruction by providing data-driven regularizers that are directly learned from data. These data-driven priors typically remain unchanged for future data in the testing phase once they are learned during training. In this study, we propose to use a transfer learning approach to fine-tune these regularizers for new subjects using a self-supervision approach. While the proposed approach can compromise the extremely fast reconstruction time of deep learning MRI methods, our results on knee MRI indicate that such adaptation can substantially reduce the remaining artifacts in reconstructed images. In addition, the proposed approach has the potential to reduce the risks of generalization to rare pathological conditions, which may be unavailable in the training data.


Assuntos
Algoritmos , Redes Neurais de Computação , Imagem por Ressonância Magnética , Física , Cintilografia
17.
J Med Life ; 13(3): 382-387, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33072212

RESUMO

By changing the lifestyle and increasing the cancer incidence, accurate diagnosis becomes a significant medical action. Today, DNA microarray is widely used in cancer diagnosis and screening since it is able to measure gene expression levels. Analyzing them by using common statistical methods is not suitable because of the high gene expression data dimensions. So, this study aims to use new techniques to diagnose acute myeloid leukemia. In this study, the leukemia microarray gene data, contenting 22283 genes, was extracted from the Gene Expression Omnibus repository. Initial preprocessing was applied by using a normalization test and principal component analysis in Python. Then DNNs neural network designed and implemented to the data and finally results cross-validated by classifiers. The normalization test was significant (P>0.05) and the results show the PCA gene segregation potential and independence of cancer and healthy cells. The results accuracy for single-layer neural network and DNNs deep learning network with three hidden layers are 63.33 and 96.67, respectively. Using new methods such as deep learning can improve diagnosis accuracy and performance compared to the old methods. It is recommended to use these methods in cancer diagnosis and effective gene selection in various types of cancer.


Assuntos
Aprendizado Profundo , Leucemia Mieloide Aguda/diagnóstico , Humanos , Redes Neurais de Computação , Análise de Componente Principal , Controle de Qualidade
20.
PLoS One ; 15(10): e0239960, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33017421

RESUMO

The outbreak of Corona Virus Disease 2019 (COVID-19) in Wuhan has significantly impacted the economy and society globally. Countries are in a strict state of prevention and control of this pandemic. In this study, the development trend analysis of the cumulative confirmed cases, cumulative deaths, and cumulative cured cases was conducted based on data from Wuhan, Hubei Province, China from January 23, 2020 to April 6, 2020 using an Elman neural network, long short-term memory (LSTM), and support vector machine (SVM). A SVM with fuzzy granulation was used to predict the growth range of confirmed new cases, new deaths, and new cured cases. The experimental results showed that the Elman neural network and SVM used in this study can predict the development trend of cumulative confirmed cases, deaths, and cured cases, whereas LSTM is more suitable for the prediction of the cumulative confirmed cases. The SVM with fuzzy granulation can successfully predict the growth range of confirmed new cases and new cured cases, although the average predicted values are slightly large. Currently, the United States is the epicenter of the COVID-19 pandemic. We also used data modeling from the United States to further verify the validity of the proposed models.


Assuntos
Infecções por Coronavirus/epidemiologia , Modelos Teóricos , Pneumonia Viral/epidemiologia , Probabilidade , Máquina de Vetores de Suporte , China/epidemiologia , Previsões , Lógica Fuzzy , Humanos , Redes Neurais de Computação , Pandemias , Estados Unidos/epidemiologia
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