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https://www.readbyqxmd.com/read/29676064/machine-learning-based-in-line-holographic-sensing-of-unstained-malaria-infected-rbcs
#1
Taesik Go, Jun Ho Kim, Hyeokjun Byeon, Sang Joon Lee
Accurate and immediate diagnosis of malaria is important for medication of the infectious disease. Conventional methods for diagnosing malaria are time consuming and rely on the skill of experts. Therefore, an automatic and simple diagnostic modality is essential for healthcare in developing countries that lack the expertise of trained microscopists. In the present study, a new automatic sensing method using digital in-line holographic microscopy (DIHM) combined with machine learning algorithms was proposed to sensitively detect unstained malaria-infected red blood cells (RBCs)...
April 19, 2018: Journal of Biophotonics
https://www.readbyqxmd.com/read/29675975/iprotgly-ss-identifying-protein-glycation-sites-using-sequence-and-structure-based-features
#2
Md Mofijul Islam, Sanjay Saha, Md Mahmudur Rahman, Swakkhar Shatabda, Dewan Md Farid, Abdollah Dehzangi
Glycation is chemical reaction by which sugar molecule bonds with a protein without the help of enzymes. This is often cause to many diseases and therefore the knowledge about glycation is very important. In this paper, we present iProtGly-SS, a protein lysine glycation site identification method based on features extracted from sequence and secondary structural information. In the experiments, we found the best feature groups combination: Amino Acid Composition, Secondary Structure Motifs and Polarity. We used support vector machine classifier to train our model and used an optimal set of features using a group based forward feature selection technique...
April 20, 2018: Proteins
https://www.readbyqxmd.com/read/29675320/tissue-intrinsic-fluorescence-recovering-by-an-empirical-approach-based-on-the-pso-algorithm-and-its-application-in-type-2-diabetes-screening
#3
Yuanzhi Zhang, Huayi Hou, Yang Zhang, Yikun Wang, Ling Zhu, Meili Dong, Yong Liu
In order to reduce the influence of scattering and absorption on tissue fluorescence spectra, after tissue fluorescence and diffuse reflectance in different tissue optical properties were simulated by the Monte Carlo method, a tissue intrinsic fluorescence recovering algorithm making use of diffuse reflectance spectrum was developed. The empirical parameters in the tissue intrinsic fluorescence recovering algorithm were coded as a particle in the solution domain, the classification performance was defined as the fitness, and then a particle swarm optimization (PSO) algorithm was established for empirical parameters optimization...
April 1, 2018: Biomedical Optics Express
https://www.readbyqxmd.com/read/29675311/label-free-light-sheet-microfluidic-cytometry-for-the-automatic-identification-of-senescent-cells
#4
Meiai Lin, Qiao Liu, Chao Liu, Xu Qiao, Changshun Shao, Xuantao Su
Label-free microfluidic cytometry is of increasing interest for single cell analysis due to its advantages of high-throughput, miniaturization, as well as noninvasive detection. Here we develop a next generation label-free light-sheet microfluidic cytometer for single cell analysis by two-dimensional (2D) light scattering measurements. Our cytometer integrates light sheet illumination with a disposable hydrodynamic focusing unit, which can achieve 3D hydrodynamic focusing of a sample fluid to a diameter of 19 micrometer without microfabrication...
April 1, 2018: Biomedical Optics Express
https://www.readbyqxmd.com/read/29675310/machine-learning-approach-for-single-molecule-localisation-microscopy
#5
Silvia Colabrese, Marco Castello, Giuseppe Vicidomini, Alessio Del Bue
Single molecule localisation (SML) microscopy is a fundamental tool for biological discoveries; it provides sub-diffraction spatial resolution images by detecting and localizing "all" the fluorescent molecules labeling the structure of interest. For this reason, the effective resolution of SML microscopy strictly depends on the algorithm used to detect and localize the single molecules from the series of microscopy frames. To adapt to the different imaging conditions that can occur in a SML experiment, all current localisation algorithms request, from the microscopy users, the choice of different parameters...
April 1, 2018: Biomedical Optics Express
https://www.readbyqxmd.com/read/29673165/a-novel-detection-model-and-its-optimal-features-to-classify-falls-from-low-and-high-acceleration-activities-of-daily-life-using-an-insole-sensor-system
#6
Benjamin Cates, Taeyong Sim, Hyun Mu Heo, Bori Kim, Hyunggun Kim, Joung Hwan Mun
In order to overcome the current limitations in current threshold-based and machine learning-based fall detectors, an insole system and novel fall classification model were created. Because high-acceleration activities have a high risk for falls, and because of the potential damage that is associated with falls during high-acceleration activities, four low-acceleration activities, four high-acceleration activities, and eight types of high-acceleration falls were performed by twenty young male subjects. Encompassing a total of 800 falls and 320 min of activities of daily life (ADLs), the created Support Vector Machine model’s Leave-One-Out cross-validation provides a fall detection sensitivity (0...
April 17, 2018: Sensors
https://www.readbyqxmd.com/read/29672669/crispr-cas9-cleavage-efficiency-regression-through-boosting-algorithms-and-markov-sequence-profiling
#7
Hui Peng, Yi Zheng, Michael Blumenstein, Dacheng Tao, Jinyan Li
Motivation: CRISPR/Cas9 system is a widely used genome editing tool. A prediction problem of great interests for this system is: how to select optimal single guide RNAs (sgRNAs) such that its cleavage efficiency is high meanwhile the off-target effect is low. Results: This work proposed a two-step averaging method (TSAM) for the regression of cleavage efficiencies of a set of sgRNAs by averaging the predicted efficiency scores of a boosting algorithm and those by a support vector machine (SVM)...
April 16, 2018: Bioinformatics
https://www.readbyqxmd.com/read/29672639/computer-aided-diagnosis-of-lung-nodule-using-gradient-tree-boosting-and-bayesian-optimization
#8
Mizuho Nishio, Mitsuo Nishizawa, Osamu Sugiyama, Ryosuke Kojima, Masahiro Yakami, Tomohiro Kuroda, Kaori Togashi
We aimed to evaluate a computer-aided diagnosis (CADx) system for lung nodule classification focussing on (i) usefulness of the conventional CADx system (hand-crafted imaging feature + machine learning algorithm), (ii) comparison between support vector machine (SVM) and gradient tree boosting (XGBoost) as machine learning algorithms, and (iii) effectiveness of parameter optimization using Bayesian optimization and random search. Data on 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images...
2018: PloS One
https://www.readbyqxmd.com/read/29671781/development-of-noninvasive-classification-methods-for-different-roasting-degrees-of-coffee-beans-using-hyperspectral-imaging
#9
Bingquan Chu, Keqiang Yu, Yanru Zhao, Yong He
This study aimed to develop an approach for quickly and noninvasively differentiating the roasting degrees of coffee beans using hyperspectral imaging (HSI). The qualitative properties of seven roasting degrees of coffee beans (unroasted, light, moderately light, light medium, medium, moderately dark, and dark) were assayed, including moisture, crude fat, trigonelline, chlorogenic acid, and caffeine contents. These properties were influenced greatly by the respective roasting degree. Their hyperspectral images (874⁻1734 nm) were collected using a hyperspectral reflectance imaging system...
April 19, 2018: Sensors
https://www.readbyqxmd.com/read/29668729/using-machine-learning-on-cardiorespiratory-fitness-data-for-predicting-hypertension-the-henry-ford-exercise-testing-fit-project
#10
Sherif Sakr, Radwa Elshawi, Amjad Ahmed, Waqas T Qureshi, Clinton Brawner, Steven Keteyian, Michael J Blaha, Mouaz H Al-Mallah
This study evaluates and compares the performance of different machine learning techniques on predicting the individuals at risk of developing hypertension, and who are likely to benefit most from interventions, using the cardiorespiratory fitness data. The dataset of this study contains information of 23,095 patients who underwent clinician- referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 10-year follow-up. The variables of the dataset include information on vital signs, diagnosis and clinical laboratory measurements...
2018: PloS One
https://www.readbyqxmd.com/read/29667885/radiomics-approach-to-prediction-of-occult-mediastinal-lymph-node-metastasis-of-lung-adenocarcinoma
#11
Yan Zhong, Mei Yuan, Teng Zhang, Yu-Dong Zhang, Hai Li, Tong-Fu Yu
OBJECTIVE: The purpose of this study was to evaluate the prognostic impact of radiomic features from CT scans in predicting occult mediastinal lymph node (LN) metastasis of lung adenocarcinoma. MATERIALS AND METHODS: A total of 492 patients with lung adenocarcinoma who underwent preoperative unenhanced chest CT were enrolled in the study. A total of 300 radiomics features quantifying tumor intensity, texture, and wavelet were extracted from the segmented entire-tumor volume of interest of the primary tumor...
April 18, 2018: AJR. American Journal of Roentgenology
https://www.readbyqxmd.com/read/29667323/clades-a-classification-based-machine-learning-method-for-species-delimitation-from-population-genetic-data
#12
Jingwen Pei, Chong Chu, Xin Li, Bin Lu, Yufeng Wu
Species are considered to be the basic unit of ecological and evolutionary studies. Since multi-locus genomic data are increasingly available there has been considerable interests in the use of DNA sequence data to delimit species. In this paper, we show that machine learning can be used for species delimitation. Our method treats the species delimitation problem as a classification problem for identifying the category of a new observation on the basis of training data. Extensive simulation is first conducted over a broad range of evolutionary parameters for training purposes...
April 18, 2018: Molecular Ecology Resources
https://www.readbyqxmd.com/read/29666463/towards-a-generalized-toxicity-prediction-model-for-oxide-nanomaterials-using-integrated-data-from-different-sources
#13
Jang-Sik Choi, My Kieu Ha, Tung Xuan Trinh, Tae Hyun Yoon, Hyung-Gi Byun
A generalized toxicity classification model for 7 different oxide nanomaterials is presented in this study. A data set extracted from multiple literature sources and screened by physicochemical property based quality scores were used for model development. Moreover, a few more preprocessing techniques, such as synthetic minority over-sampling technique, were applied to address the imbalanced class problem in the data set. Then, classification models using four different algorithms, such as generalized linear model, support vector machine, random forest, and neural network, were developed and their performances were compared to find the best performing preprocessing methods as well as algorithms...
April 17, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29666460/cortical-classification-with-rhythm-entropy-for-error-processing-in-cocktail-party-environment-based-on-scalp-eeg-recording
#14
Yin Tian, Wei Xu, Li Yang
Using single-trial cortical signals calculated by weighted minimum norm solution estimation (WMNE), the present study explored a feature extraction method based on rhythm entropy to classify the scalp electroencephalography (EEG) signals of error response from that of correct response during performing auditory-track tasks in cocktail party environment. The classification rate achieved 89.7% with single-trial (≈700 ms) when using support vector machine(SVM) with the leave-one-out-cross-validation (LOOCV)...
April 17, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29666413/texture-analysis-and-support-vector-machine-assisted-diffusional-kurtosis-imaging-may-allow-in-vivo-gliomas-grading-and-idh-mutation-status-prediction-a-preliminary-study
#15
Sotirios Bisdas, Haocheng Shen, Steffi Thust, Vasileios Katsaros, George Stranjalis, Christos Boskos, Sebastian Brandner, Jianguo Zhang
We sought to investigate, whether texture analysis of diffusional kurtosis imaging (DKI) enhanced by support vector machine (SVM) analysis may provide biomarkers for gliomas staging and detection of the IDH mutation. First-order statistics and texture feature extraction were performed in 37 patients on both conventional (FLAIR) and mean diffusional kurtosis (MDK) images and recursive feature elimination (RFE) methodology based on SVM was employed to select the most discriminative diagnostic biomarkers. The first-order statistics demonstrated significantly lower MDK values in the IDH-mutant tumors...
April 17, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29666336/investigating-multiple-dysregulated-pathways-in-rheumatoid-arthritis-based-on-pathway-interaction-network
#16
Xian-Dong Song, Xian-Xu Song, Gui-Bo Liu, Chun-Hui Ren, Yuan-Bo Sun, Ke-Xin Liu, Bo Liu, Shuang Liang, Zhu Zhu
The traditional methods of identifying biomarkers in rheumatoid arthritis (RA) have focussed on the differentially expressed pathways or individual pathways, which however, neglect the interactions between pathways. To better understand the pathogenesis of RA, we aimed to identify dysregulated pathway sets using a pathway interaction network (PIN), which considered interactions among pathways. Firstly, RA-related gene expression profile data, protein-protein interactions (PPI) data and pathway data were taken up from the corresponding databases...
March 2018: Journal of Genetics
https://www.readbyqxmd.com/read/29665725/epileptic-eeg-identification-via-lbp-operators-on-wavelet-coefficients
#17
Qi Yuan, Weidong Zhou, Fangzhou Xu, Yan Leng, Dongmei Wei
The automatic identification of epileptic electroencephalogram (EEG) signals can give assistance to doctors in diagnosis of epilepsy, and provide the higher security and quality of life for people with epilepsy. Feature extraction of EEG signals determines the performance of the whole recognition system. In this paper, a novel method using the local binary pattern (LBP) based on the wavelet transform (WT) is proposed to characterize the behavior of EEG activities. First, the WT is employed for time-frequency decomposition of EEG signals...
March 19, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29664902/a-general-prediction-model-for-the-detection-of-adhd-and-autism-using-structural-and-functional-mri
#18
Bhaskar Sen, Neil C Borle, Russell Greiner, Matthew R G Brown
This work presents a novel method for learning a model that can diagnose Attention Deficit Hyperactivity Disorder (ADHD), as well as Autism, using structural texture and functional connectivity features obtained from 3-dimensional structural magnetic resonance imaging (MRI) and 4-dimensional resting-state functional magnetic resonance imaging (fMRI) scans of subjects. We explore a series of three learners: (1) The LeFMS learner first extracts features from the structural MRI images using the texture-based filters produced by a sparse autoencoder...
2018: PloS One
https://www.readbyqxmd.com/read/29660789/evaluation-of-chilling-injury-in-mangoes-using-multispectral-imaging
#19
Norhashila Hashim, Daniel I Onwude, Muhamad Syafiq Osman
Commodities originating from tropical and subtropical climes are prone to chilling injury (CI). This injury could affect the quality and marketing potential of mango after harvest. This will later affect the quality of the produce and subsequent consumer acceptance. In this study, the appearance of CI symptoms in mango was evaluated non-destructively using multispectral imaging. The fruit were stored at 4 °C to induce CI and 12 °C to preserve the quality of the control samples for 4 days before they were taken out and stored at ambient temperature for 24 hr...
April 16, 2018: Journal of Food Science
https://www.readbyqxmd.com/read/29660675/improvements-in-event-related-desynchronization-and-classification-performance-of-motor-imagery-using-instructive-dynamic-guidance-and-complex-tasks
#20
Yan Bian, Hongzhi Qi, Li Zhao, Dong Ming, Tong Guo, Xing Fu
BACKGROUND AND OBJECTIVE: The motor-imagery based brain-computer interface supplies a potential approach for motor-impaired patients, not only to control rehabilitation facilities but also to promote recovery from motor dysfunctions. To improve event-related desynchronization during motor imagery and obtain improved brain-computer interface classification accuracy, we introduce dynamic video guidance and complex motor tasks to the motor imagery paradigm. METHODS: Eleven participants were included in the experiment; 64-channel electroencephalographic data were collected and analyzed during four motor imagery tasks with different guidance...
March 30, 2018: Computers in Biology and Medicine
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