scholarly journals Low Cost Optical-electronic Sensor Development Based on Raman Spectroscopy for Liquid

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Luqman Aji Kusumo ◽  
Totok Mujiono ◽  
Hendra Kusuma
Keyword(s):  
Raman Spectroscopy ◽  
Low Cost ◽  
Spectral Response ◽  
Scattered Light ◽  
Test Tube ◽  
Training Data ◽  
Validation Data ◽  
Data Set ◽  
Electronic Sensor ◽  
Cmos Sensor

Spectroscopy is a method that used to identifychemical structure of substances using its spectral patterncharacteristics. Optical spectroscopy term can be applied to anykind of optical photon interactions with matter. Ramanspectroscopy essentially shows spectral response like thewavelength of scattered light is shifted regarding initializingexcitation wavelength. In this paper, we propose a design of lowcost optical-electronic sensor based on Raman spectroscopy.This low cost optical-electronic sensor employs a violet-blue 405nm wavelength laser diode, a biconvex lens with 5 cm diameterand focus point, a test tube, and a Complementary Metal OxideSemiconductor (CMOS) sensor. We tested this low cost opticalelectronic sensor based on Raman spectroscopy in darkcondition. Combination of these hardware and components canprovide measurement result to any liquid sample. From thisexperiment, even all liquid samples that used to test thiscombination of hardware and components are transparent, theystill have different Raman spectra. This combination ofhardware and components can be implemented into someapplication for instance body liquid measurement such as blood.In specific application, we need to employ data analysis and abunch of data set which are organized into three different groupsuch as training data, validation data, and test data group,combined with this developed instrumentation.

scholarly journals A novel ferroptosis related gene signature is associated with prognosis in patients with ovarian serous cystadenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhixiang Yu ◽  
Haiyan He ◽  
Yanan Chen ◽  
Qiuhe Ji ◽  
Min Sun
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
Critical Role ◽  
Gene Signature ◽  
Cancer Genome ◽  
Training Data ◽  
Hub Genes ◽  
Validation Data ◽  
Data Set ◽  
Cox Regression Analysis

AbstractOvarian cancer (OV) is a common type of carcinoma in females. Many studies have reported that ferroptosis is associated with the prognosis of OV patients. However, the mechanism by which this occurs is not well understood. We utilized Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) to identify ferroptosis-related genes in OV. In the present study, we applied Cox regression analysis to select hub genes and used the least absolute shrinkage and selection operator to construct a prognosis prediction model with mRNA expression profiles and clinical data from TCGA. A series of analyses for this signature was performed in TCGA. We then verified the identified signature using International Cancer Genome Consortium (ICGC) data. After a series of analyses, we identified six hub genes (DNAJB6, RB1, VIMP/ SELENOS, STEAP3, BACH1, and ALOX12) that were then used to construct a model using a training data set. The model was then tested using a validation data set and was found to have high sensitivity and specificity. The identified ferroptosis-related hub genes might play a critical role in the mechanism of OV development. The gene signature we identified may be useful for future clinical applications.

Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms

Heart ◽  
2018 ◽  
Vol 104 (23) ◽  
pp. 1921-1928 ◽  
Author(s):  
Ming-Zher Poh ◽  
Yukkee Cheung Poh ◽  
Pak-Hei Chan ◽  
Chun-Ka Wong ◽  
Louise Pun ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Test Data ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Performance Comparison ◽  
Learning System ◽  
Training Data ◽  
Validation Data ◽  
Data Set

ObjectiveTo evaluate the diagnostic performance of a deep learning system for automated detection of atrial fibrillation (AF) in photoplethysmographic (PPG) pulse waveforms.MethodsWe trained a deep convolutional neural network (DCNN) to detect AF in 17 s PPG waveforms using a training data set of 149 048 PPG waveforms constructed from several publicly available PPG databases. The DCNN was validated using an independent test data set of 3039 smartphone-acquired PPG waveforms from adults at high risk of AF at a general outpatient clinic against ECG tracings reviewed by two cardiologists. Six established AF detectors based on handcrafted features were evaluated on the same test data set for performance comparison.ResultsIn the validation data set (3039 PPG waveforms) consisting of three sequential PPG waveforms from 1013 participants (mean (SD) age, 68.4 (12.2) years; 46.8% men), the prevalence of AF was 2.8%. The area under the receiver operating characteristic curve (AUC) of the DCNN for AF detection was 0.997 (95% CI 0.996 to 0.999) and was significantly higher than all the other AF detectors (AUC range: 0.924–0.985). The sensitivity of the DCNN was 95.2% (95% CI 88.3% to 98.7%), specificity was 99.0% (95% CI 98.6% to 99.3%), positive predictive value (PPV) was 72.7% (95% CI 65.1% to 79.3%) and negative predictive value (NPV) was 99.9% (95% CI 99.7% to 100%) using a single 17 s PPG waveform. Using the three sequential PPG waveforms in combination (<1 min in total), the sensitivity was 100.0% (95% CI 87.7% to 100%), specificity was 99.6% (95% CI 99.0% to 99.9%), PPV was 87.5% (95% CI 72.5% to 94.9%) and NPV was 100% (95% CI 99.4% to 100%).ConclusionsIn this evaluation of PPG waveforms from adults screened for AF in a real-world primary care setting, the DCNN had high sensitivity, specificity, PPV and NPV for detecting AF, outperforming other state-of-the-art methods based on handcrafted features.

SaltSeg: Automatic 3D salt segmentation using a deep convolutional neural network

2019 ◽  
Vol 7 (3) ◽  
pp. SE113-SE122 ◽  
Author(s):  
Yunzhi Shi ◽  
Xinming Wu ◽  
Sergey Fomel
Keyword(s):  
Large Scale ◽  
Model Building ◽  
Ground Truth ◽  
Velocity Model ◽  
Training Data ◽  
Data Sets ◽  
Validation Data ◽  
Data Set ◽  
Seismic Image ◽  
Data Generator

Salt boundary interpretation is important for the understanding of salt tectonics and velocity model building for seismic migration. Conventional methods consist of computing salt attributes and extracting salt boundaries. We have formulated the problem as 3D image segmentation and evaluated an efficient approach based on deep convolutional neural networks (CNNs) with an encoder-decoder architecture. To train the model, we design a data generator that extracts randomly positioned subvolumes from large-scale 3D training data set followed by data augmentation, then feed a large number of subvolumes into the network while using salt/nonsalt binary labels generated by thresholding the velocity model as ground truth labels. We test the model on validation data sets and compare the blind test predictions with the ground truth. Our results indicate that our method is capable of automatically capturing subtle salt features from the 3D seismic image with less or no need for manual input. We further test the model on a field example to indicate the generalization of this deep CNN method across different data sets.

scholarly journals Equations To Predict Antimicrobial MICs in Neisseria gonorrhoeae Using Molecular Antimicrobial Resistance Determinants

2019 ◽  
Vol 64 (3) ◽  
Author(s):  
Walter Demczuk ◽  
Irene Martin ◽  
Pam Sawatzky ◽  
Vanessa Allen ◽  
Brigitte Lefebvre ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Neisseria Gonorrhoeae ◽  
Training Data ◽  
Sequencing Analysis ◽  
Regression Equations ◽  
Validation Data ◽  
Data Set ◽  
Content Type ◽  
Resistance Determinants ◽  
Extended Spectrum

ABSTRACT The emergence of Neisseria gonorrhoeae strains that are resistant to azithromycin and extended-spectrum cephalosporins represents a public health threat, that of untreatable gonorrhea infections. Multivariate regression modeling was used to determine the contributions of molecular antimicrobial resistance determinants to the overall antimicrobial MICs for ceftriaxone, cefixime, azithromycin, tetracycline, ciprofloxacin, and penicillin. A training data set consisting of 1,280 N. gonorrhoeae strains was used to generate regression equations which were then applied to validation data sets of Canadian (n = 1,095) and international (n = 431) strains. The predicted MICs for extended-spectrum cephalosporins (ceftriaxone and cefixime) were fully explained by 5 amino acid substitutions in PenA, A311V, A501P/T/V, N513Y, A517G, and G543S; the presence of a disrupted mtrR promoter; and the PorB G120 and PonA L421P mutations. The correlation of predicted MICs within one doubling dilution to phenotypically determined MICs of the Canadian validation data set was 95.0% for ceftriaxone, 95.6% for cefixime, 91.4% for azithromycin, 98.2% for tetracycline, 90.4% for ciprofloxacin, and 92.3% for penicillin, with an overall sensitivity of 99.9% and specificity of 97.1%. The correlations of predicted MIC values to the phenotypically determined MICs were similar to those from phenotype MIC-only comparison studies. The ability to acquire detailed antimicrobial resistance information directly from molecular data will facilitate the transition to whole-genome sequencing analysis from phenotypic testing and can fill the surveillance gap in an era of increased reliance on nucleic acid assay testing (NAAT) diagnostics to better monitor the dynamics of N. gonorrhoeae.

scholarly journals Cerebrospinal Fluid Treponema pallidum Particle Agglutination Assay for Neurosyphilis Diagnosis

2017 ◽  
Vol 55 (6) ◽  
pp. 1865-1870 ◽  
Author(s):  
Christina M. Marra ◽  
Clare L. Maxwell ◽  
Shelia B. Dunaway ◽  
Sharon K. Sahi ◽  
Lauren C. Tantalo
Keyword(s):  
Cerebrospinal Fluid ◽  
Vision Loss ◽  
Treponema Pallidum ◽  
Training Data ◽  
Validation Data ◽  
Data Set ◽  
Convenience Sample ◽  
Content Type ◽  
Agglutination Assay ◽  
Particle Agglutination

ABSTRACT Limited data suggest that the cerebrospinal fluid Treponema pallidum particle agglutination assay (CSF-TPPA) is sensitive and a CSF Treponema pallidum hemagglutination assay (CSF-TPHA) titer of ≥1:640 is specific for neurosyphilis diagnosis. CSF-TPPA reactivity and titer were determined for a convenience sample of 191 CSF samples from individuals enrolled in a study of CSF abnormalities in syphilis (training data set). The sensitivity of a reactive test and the specificity for reactivity at serial higher CSF dilutions were determined. Subsequently, CSF-TPPA reactivity at a 1:640 dilution was determined for all available samples from study participants enrolled after the last training sample was collected (validation data set, n = 380). Neurosyphilis was defined as (i) a reactive CSF Venereal Disease Research Laboratory test (CSF-VDRL), (ii) detection of T. pallidum in CSF by reverse transcriptase PCR, or (iii) new vision loss or hearing loss. In the training data set, the diagnostic sensitivities of a reactive CSF fluorescent treponemal antibody absorption test (CSF-FTA-ABS) and a reactive CSF-TPPA did not differ significantly (67 to 98% versus 76 to 95%). The specificity of a CSF-TPPA titer of ≥1:640 was significantly higher than that of lower dilutions and was not significantly different from that of CSF-VDRL. In the validation data set, the diagnostic specificity of a CSF-TPPA titer of ≥1:640 was high and did not differ significantly from that of CSF-VDRL (93 to 94% versus 90 to 91%). Ten CSF samples with a nonreactive CSF-VDRL had a CSF-TPPA titer of ≥1:640. If a CSF-TPPA titer of ≥1:640 was used in addition to a reactive CSF-VDRL, the number of neurosyphilis diagnoses would have increased from 47 to 57 (21.3%). A CSF-TPPA titer cutoff of ≥1:640 may be useful in identifying patients with neurosyphilis when CSF-VDRL is nonreactive.

scholarly journals PS1-STRM: neural network source classification and photometric redshift catalogue for PS1 3π DR1

2020 ◽  
Vol 500 (2) ◽  
pp. 1633-1644
Author(s):  
Róbert Beck ◽  
István Szapudi ◽  
Heather Flewelling ◽  
Conrad Holmberg ◽  
Eugene Magnier ◽  
...  
Keyword(s):  
Neural Network ◽  
Broad Band ◽  
Monte Carlo Sampling ◽  
Training Data ◽  
Space Telescopes ◽  
Absolute Deviation ◽  
Self Organizing Maps ◽  
Validation Data ◽  
Data Set ◽  
Photometric Redshift

ABSTRACT The Pan-STARRS1 (PS1) 3π survey is a comprehensive optical imaging survey of three quarters of the sky in the grizy broad-band photometric filters. We present the methodology used in assembling the source classification and photometric redshift (photo-z) catalogue for PS1 3π Data Release 1, titled Pan-STARRS1 Source Types and Redshifts with Machine learning (PS1-STRM). For both main data products, we use neural network architectures, trained on a compilation of public spectroscopic measurements that has been cross-matched with PS1 sources. We quantify the parameter space coverage of our training data set, and flag extrapolation using self-organizing maps. We perform a Monte Carlo sampling of the photometry to estimate photo-z uncertainty. The final catalogue contains 2902 054 648 objects. On our validation data set, for non-extrapolated sources, we achieve an overall classification accuracy of $98.1{{\ \rm per\ cent}}$ for galaxies, $97.8{{\ \rm per\ cent}}$ for stars, and $96.6{{\ \rm per\ cent}}$ for quasars. Regarding the galaxy photo-z estimation, we attain an overall bias of 〈Δznorm〉 = 0.0005, a standard deviation of σ(Δznorm) = 0.0322, a median absolute deviation of MAD(Δznorm) = 0.0161, and an outlier fraction of $P\left(|\Delta z_{\mathrm{norm}}|\gt 0.15\right)=1.89{{\ \rm per\ cent}}$. The catalogue will be made available as a high-level science product via the Mikulski Archive for Space Telescopes.

scholarly journals MOBILE LASER SCAN DATA FOR ROAD SURFACE DAMAGE DETECTION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1141-1148 ◽  
Author(s):  
B. B. van der Horst ◽  
R. C. Lindenbergh ◽  
S. W. J. Puister
Keyword(s):  
Road Surface ◽  
Training Data ◽  
Morphological Operations ◽  
Road Maintenance ◽  
Damage Classification ◽  
Validation Data ◽  
Data Set ◽  
On The Road ◽  
Scan Data

<p><strong>Abstract.</strong> Road surface anomalies affect driving conditions, such as driving comfort and safety. Examples for such anomalies are potholes, cracks and ravelling. Automatic detection and localisation of these anomalies can be used for targeted road maintenance. Currently road damage is detected by road inspectors who drive slowly on the road to look out for surface anomalies, which can be dangerous. For improving the safety road inspectors can evaluate road images. However, results may be different as this evaluation is subjective. In this research a method is created for detecting road damage by using mobile profile laser scan data. First features are created, based on a sliding window. Then K-means clustering is used to create training data for a Random Forest algorithm. Finally, mathematical morphological operations are used to clean the data and connect the damage points. The result is an objective and detailed damage classification. The method is tested on a 120 meters long road data set that includes different types of damage. Validation is done by comparing the results to a classification of a human road inspector. However, the damage classification of the proposed method contains more details which makes validation difficult. Nevertheless does this method result in 79% overlap with the validation data. Although the results are already promising, developments such as pre-processing the data could lead to improvements.</p>

scholarly journals Comparison of Metamodel Performances on an Electronic Circuit Problem

2021 ◽  
pp. 1-11
Author(s):  
Muzaffer Balaban
Keyword(s):  
Linear Regression ◽  
Radial Basis Function ◽  
Basis Function ◽  
Ordinary Kriging ◽  
Electronic Circuit ◽  
Training Data ◽  
Validation Data ◽  
Data Set ◽  
Simple Kriging ◽  
Radial Basis

Aims: Investigation of building and validation of metamodels which of linear regression, simple kriging, ordinary kriging and radial basis function for an electronic circuit problem are the main aim of this study. Study Design: An electronic circuit problem was considered to compare the performances of the metamodels. Latin hypercube design was used for experimental design of five input variables of the considered problem. Methodology: A training data set consisting of 45 experiments and a validation data set consisting of 500 experiments were obtained using Latin hypercube design. Input variables were used by coded to calculate the spatial distances between observation points more consistently. Then using training data set linear regression, simple kriging, ordinary kriging and radial basis function metamodels were built. And, performance measures were calculated for the validation data set. Results: It has been shown that simple kriging which are applied to outputs the differences from the mean, and ordinary kriging metamodels, produce superior solutions compared to the linear regression and radial basis function metamodels for the electronic circuit problem considered in this study. Prediction superiority of SK and OK than RBF on five-dimensional problem is another important result of the study. Conclusion: Kriging metamodels are considered to be strong alternatives to the other metamodels for the problems that are considered in this study and have a similar nature. Since the superiority of metamodel methods to each other may vary from problem to problem, it is another important issue to compare their performance by considering more than one method in problem solving stage.

scholarly journals Development and Validation of a Score for Screening Suicide of Patients With Neuroendocrine Neoplasms

2021 ◽  
Vol 12 ◽  
Author(s):  
Lili Lu ◽  
Yuru Shang ◽  
Dietmar Zechner ◽  
Christina Susanne Mullins ◽  
Michael Linnebacher ◽  
...  
Keyword(s):  
Life Expectancy ◽  
Test Data ◽  
Characteristic Curve ◽  
Life Quality ◽  
Training Data ◽  
Neuroendocrine Neoplasm ◽  
Neuroendocrine Neoplasms ◽  
Validation Data ◽  
Data Set ◽  
Commit Suicide

Background: If the diagnosis of neuroendocrine neoplasm (NEN) increases the risk of patients to commit suicide has not been investigated so far. Identifying NEN patients at risk to commit suicide is important to increase their life quality and life expectancy.Methods and findings: Cancer cases were extracted from the Surveillance, Epidemiology, and End Results program and were divided into the NEN and the non-NEN cohorts. Subsequently, the NEN patients were randomly split into a training data set and a validation data set. Analyzing the training data set, we developed a score for assessing the risk to commit suicide for patients with NEN. In addition, we validated the score using the validation data set and evaluated, if this score could also be applied to other cancer entities by using the test data set, a non-NEN cohort. The odds ratio (OR) of suicide between NEN and non-NEN patients was determined. Moreover, the performance of a score was evaluated by the receiver operating characteristic curve and the area under the curve (AUC). Compared to non-NEN, NEN significantly increased the risk of suicide to 1.8-fold (NEN vs. non-NEN; OR, 1.832; P &lt; 0.001). In addition, we observed that age, gender, race, marital status, tumor stage, histologic grade, surgery, and chemotherapy were associated with suicide among NEN patients; and a synthesized score based on these factors could significantly distinguish suicide individuals from non-suicide individuals in the training data set (AUC, 0.829; P &lt; 0.001) and in the validation data set (AUC, 0.735; P &lt; 0.001). This score also had a good performance when it was assessed by the test data set (AUC, 0.690; P &lt; 0.001). This demonstrates that the score might also be applicable to other cancer entities.Conclusions: This population-based study suggests that NEN patients have a higher risk of suicide than non-NEN patients. In addition, this study provided a score, which can identify NEN patients at high-risk of committing suicide. Thus, this score in combination with current screening and prevention strategies for suicide may improve life quality and life expectancy of NEN patients.

scholarly journals Estimating the geoeffectiveness of halo CMEs from associated solar and IP parameters using neural networks

2012 ◽  
Vol 30 (6) ◽  
pp. 963-972 ◽  
Author(s):  
J. Uwamahoro ◽  
L. A. McKinnell ◽  
J. B. Habarulema
Keyword(s):  
Neural Network ◽  
Geomagnetic Storms ◽  
Solar Wind Speed ◽  
Training Data ◽  
Set Covering ◽  
Occurrence Rate ◽  
Angular Width ◽  
Validation Data ◽  
Data Set ◽  
Halo Cmes

Abstract. Estimating the geoeffectiveness of solar events is of significant importance for space weather modelling and prediction. This paper describes the development of a neural network-based model for estimating the probability occurrence of geomagnetic storms following halo coronal mass ejection (CME) and related interplanetary (IP) events. This model incorporates both solar and IP variable inputs that characterize geoeffective halo CMEs. Solar inputs include numeric values of the halo CME angular width (AW), the CME speed (Vcme), and the comprehensive flare index (cfi), which represents the flaring activity associated with halo CMEs. IP parameters used as inputs are the numeric peak values of the solar wind speed (Vsw) and the southward Z-component of the interplanetary magnetic field (IMF) or Bs. IP inputs were considered within a 5-day time window after a halo CME eruption. The neural network (NN) model training and testing data sets were constructed based on 1202 halo CMEs (both full and partial halo and their properties) observed between 1997 and 2006. The performance of the developed NN model was tested using a validation data set (not part of the training data set) covering the years 2000 and 2005. Under the condition of halo CME occurrence, this model could capture 100% of the subsequent intense geomagnetic storms (Dst ≤ −100 nT). For moderate storms (−100 < Dst ≤ −50), the model is successful up to 75%. This model's estimate of the storm occurrence rate from halo CMEs is estimated at a probability of 86%.

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