Validation Sample Set Research Articles

Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm

Annual land use land cover (LULC) change information at medium spatial resolution (i.e. at 30 m) is required in numerous subjects, such as biophysical modelling, land management and global change studies. Annual LULC information, however, is usually not available at continental or national scale due to reasons such as insufficient remote sensing data coverage or lack of computational capabilities. Here we integrate high temporal resolution and coarse spatial resolution satellite images (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Inventory Modelling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI)) with high spatial resolution datasets (China’s Land-Use/cover Datasets (CLUDs) derived from 30-meter Landsat TM/ETM+/OLI) to generate reliable annual nominal 30 m LULC maps for the whole of China between 1980 and 2015. We also test the performance of a statistical based change detection algorithm (Breaks for Additive Seasonal and Trend), originally designed for tracking forest change, in classifying all-type LULC change. As a result, a nominal 30 m annual land use/land cover datasets (CLUD-A) from 1980 to 2015 was developed for the whole China. The mapping results were assessed with a change sample dataset, a regional annual validation sample set and a three-year China sample set. Of the detected change years, 75.61% matched the exact time of conversion within ±1 year. Annual mapping results provided a detail process of urbanization, deforestation, afforestation, water and cropland dynamics over the past 36 years. The consistent characterization of land change dynamics for China can be further used in scientific research and to support land management for policy-makers.

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

BackgroundCommunity-acquired pneumonia (CAP) requires urgent and specific antimicrobial therapy. However, the causal pathogen is typically unknown at the point when anti-infective therapeutics must be initiated. Physicians synthesize information from diverse data streams to make appropriate decisions. Artificial intelligence (AI) excels at finding complex relationships in large volumes of data. We aimed to evaluate the abilities of experienced physicians and AI to answer this question at patient admission: is it a viral or a bacterial pneumonia?MethodsWe included patients hospitalized for CAP and recorded all data available in the first 3-h period of care (clinical, biological and radiological information). For this proof-of-concept investigation, we decided to study only CAP caused by a singular and identified pathogen. We built a machine learning model prediction using all collected data. Finally, an independent validation set of samples was used to test the pathogen prediction performance of: (i) a panel of three experts and (ii) the AI algorithm. Both were blinded regarding the final microbial diagnosis. Positive likelihood ratio (LR) values > 10 and negative LR values < 0.1 were considered clinically relevant.ResultsWe included 153 patients with CAP (70.6% men; 62 [51–73] years old; mean SAPSII, 37 [27–47]), 37% had viral pneumonia, 24% had bacterial pneumonia, 20% had a co-infection and 19% had no identified respiratory pathogen. We performed the analysis on 93 patients as co-pathogen and no-pathogen cases were excluded. The discriminant abilities of the AI approach were low to moderate (LR+ = 2.12 for viral and 6.29 for bacterial pneumonia), and the discriminant abilities of the experts were very low to low (LR+ = 3.81 for viral and 1.89 for bacterial pneumonia).ConclusionNeither experts nor an AI algorithm can predict the microbial etiology of CAP within the first hours of hospitalization when there is an urgent need to define the anti-infective therapeutic strategy.

Serum lipidomic biomarkers for non-small cell lung cancer in nonsmoking female patients

Lung cancer (Lca) is one of the malignant tumors with the fastest morbidity and mortality increase and the greatest threat to human health and life. The incidence of non-small cell lung cancer (NSCLC) in the nonsmoking female has increased recently. However, its pathogenesis is still unclear, and there is an urgent need for clinical diagnostic biomarkers, especially for early diagnosis. A nontargeted lipidomic approach based on ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS), as well as two machine learning approaches (genetic algorithm and binary logistic regression) was used to screen candidate discriminating lipids and define a combinational lipid biomarker in serum samples to distinguish female patients with NSCLC from healthy controls. Moreover, the candidate biomarkers were verified by using an external validation sample set. Our result revealed that fatty acid (FA) (20:4), FA (22:0) and LPE (20:4) can serve as a combinational biomarker for distinguishing female patients with NSCLC from healthy control with good sensitivity and specificity. Furthermore, this combinational biomarker also showed good performance in distinguishing early-stage NSCLC female patients from a healthy control. We observed that levels of unsaturated fatty acids clearly decreased, while saturated fatty acids and lysophosphatidylethanolamines pronouncedly increased in Lca patients, compared with the healthy controls, which revealed significant disturbance of lipid metabolism in NSCLC females. Our results not only provide hints to the pathological mechanism of NSCLC in nonsmoking female but also supply a combinational lipid biomarker to aid the diagnosis of NSCLC at early-stage.

Rapid Nondestructive Analysis of Intact Canola Seeds Using a Handheld Near‐Infrared Spectrometer

AbstractThe near‐infrared (NIR) models for canola quality were developed with samples from Canadian canola seeds harvested in 2016 and 2017. All calibration models were first tested on a 2017 external validation sample set. The handheld NIR spectrometer used in this study has a limited wavelength range 908.1–1676.2 nm; however, the validation results showed that it could be used to predict several important parameters that defined canola seed quality. Final testing was performed using calibration models with the least number of factors on a second external canola validation sample set (2018 harvest). Some calibration models showed excellent stability and predictive powers with R2val values of 0.94–0.99 (i.e., oil, protein, oleic acid and iodine value) and low SEPs for both external validation sample sets. The α‐linolenic acid model had an R2val of 0.93 when applied to the 2017 external validation set, the correlation fell slightly to 0.88 when applied to the 2018 external validation sample set, potentially indicating a slight instability in the model. The prediction model for total glucosinolate was not very good, but still could be used to segregate the samples into low or high glucosinolate samples. Finally, the predictive models for chlorophyll and total saturates were unusable. The chlorophyll model was very unstable, likely due to the instrument's limited wavelength range.

Altered serum microRNA expression profile in subjects with heroin and methamphetamine use disorder

ObjectivesDrug abuse is one of the most severe global social and public health problems, especially in China. However, objective blood biomarkers that are easy to detect are still in great need. This study was aim to explore the expression pattern of circulating microRNAs (miRNAs) in subjects with drug addiction and test the potential of altered serum miRNAs as noninvasive diagnostic tools for drug abuse. MethodsSerum samples were obtained from 42 heroin abusers, 42 methamphetamine (MA) abusers and 42 controls. Microarray-based miRNA analysis was first applied to screen unique serum miRNA profiles in drug abusers on a training set of serum samples from 12 heroin abusers, 12 MA abusers and 12 control subjects. The expression levels of selected candidate miRNAs were subsequently verified in individual samples of the training set and further confirmed independently in a validation set of samples from 30 heroin abusers, 30 MA abusers and 30 controls using real-time quantitative polymerase chain reaction (RT-qPCR). ResultsMicroarray analysis identified 116 and 109 significantly altered miRNAs in heroin abusers and MA abusers, respectively. Three miRNAs, including let-7b-5p, miR-206 and miR-486-5p, were verified to be significantly and steadily increased in heroin abusers, and miR-9-3p was significantly increased in MA abusers compared with normal controls. The areas under the curve (AUCs) of the ROC curve of these miRNAs ranged from 0.718 to 0.867. ConclusionsOur study raises the possibility that the altered serum miRNAs could potentially be used as an auxiliary tool to identify individuals in drug abuse and addiction.

Pattern recognition-based Raman spectroscopy for non-destructive detection of pomegranates during maturity.

In this study, the feasibility of utilizing Fourier transform Raman spectroscopy, combined with supervised and unsupervised pattern recognition methods was considered, to distinguish the maturity stage of pomegranate "Ashraf variety" during four distinct maturity stages between 88 and 143days after full bloom. Principal component analysis (PCA) as an unsupervised pattern recognition method was performed to verify the possibility of clustering of the pomegranate samples into four groups. Two supervised pattern recognition techniques namely, partial least squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA) were also used as powerful supervised pattern recognition methods to classify the samples. The results showed that in all groups of samples, the Raman spectra of the samples were correctly clustered using PCA. The accuracy of the SIMCA classification for differentiation of four pomegranate groups was 82%. Also, the overall discriminant power of PLS-DA classes was about 96%, and 95% for calibration and validation sample sets, respectively. Due to the misclassification among different classes of immature pomegranates, that was lower than the expected, it was not possible to discriminate all the immature samples in individual classes. However, when considering only the two main categories of "immature" and "mature", a reasonable separation between the classes were obtained using supervised pattern recognition methods of SIMCA and PLS-DA. The SIMCA based on PCA modeling could correctly categorize the samples in two classes of immature and mature with classification accuracy of 100%.

An rapid nondestructive testing method for distinguishing rice producing areas based on Raman spectroscopy and support vector machine

This study reports method for rapid and nondestructive identification of single-grain rice produced in neighboring areas. Four types of representative rice from different areas were selected as experimental samples. A total of 284 single-grain rice Raman spectra were acquired and spectral information in the 400–1700 cm−1 spectral area was extracted for analysis. First, the samples were divided into 190 calibration sets and 94 validation sets by the Kennard–Stone method, and the raw spectra were pretreated using smoothing and differential methods. Next, principal component analysis (PCA) and a successive projections algorithm (SPA) were used to extract the optimal principal components and effective wavelengths, which were used as the input variables for k-NearestNeighbor (KNN) and least-squares support vector machine (LS-SVM) algorithms. The PCA-KNN,SPA-KNN,PCA-LS-SVM and SPA-LS-SVM models were established based on information from the rice spectra. Finally, the model was applied to classify the validation set samples. The recognition accuracies of the PCA-KNN,SPA-KNN, PCA-LS-SVM and SPA-LS-SVM models for the validation set are 91.43 %,93.62 %,91.49 and 94.68 %, respectively. These results indicate that single-grain rice from different producing areas can be identified by using Raman spectroscopy combined with KNN and LS-SVM. This method can achieve rapid and completely non-destructive testing.

The application of near infrared spectroscopy to predict faecal nitrogen and phosphorus in multiple ruminant herbivore species

Near infrared spectroscopy (NIRS) was applied to determine faecal nitrogen and phosphorus using a temporo-spatially diverse dataset derived from multiple ruminant herbivore species (i.e. cattle, bison, deer, elk, goats, and sheep). Single-species NIRS calibrations have previously been developed to predict faecal constituents. Multi-species NIRS calibrations have previously been developed for herbivore faecal nitrogen but not for faecal phosphorus. Faecal samples representing a herd or flock composite were analysed via NIRS (400–2498nm). Calibration sets for faecal nitrogen and phosphorus were developed from: (1) all samples from all six species, (2) all cattle samples only, (3) all samples except those from bison, (4) all samples except those from deer, (5) all samples except those from elk, (6) all samples except those from goats, and (7) all samples except those from sheep. Validation sample sets included: (1) each of the individual species (predicted with a cattle only-derived calibration), and (2) each of the individual species (other than cattle) predicted with a multi-species calibration constructed from all cattle samples plus those samples from the remaining four species (i.e. ‘leave-one-out’). All multiple coefficient of determination (R2) values for faecal nitrogen calibrations were ≥0.97. Corresponding standard error of cross validation (SECV) values were ≤0.13. Validation simple coefficient of determination (r2) and standard error of prediction (SEP) of each alternate species using the cattle-derived calibration ranged from 0.76 to 0.84, and 0.28 to 0.5 respectively. Similar values for the sequential species leave-one-out validation for faecal nitrogen were 0.67 to 0.89, and 0.17 to 0.47 respectively. All R2 values for faecal phosphorus calibrations were ≥0.79; corresponding SECV were ≤0.14. Validation r2 and SEP of each alternate species using the cattle-derived phosphorus calibration were ≤0.63 and ≥0.13 respectively. Similar values for the sequential species leave-one-out validation were ≤0.66 and ≥0.22 respectively for faecal phosphorus. Multi-species faecal NIRS calibrations can be developed for monitoring applications in which determination of faecal nitrogen is appropriate, e.g. free-ranging herbivore nutrition, nitrogen deposition from animal faeces on rangelands with declining forage quality, or runoff from confined animal feeding operations. Similar calibrations for faecal phosphorus require additional research to ascertain their applicability.

Evaluation of NIR and Raman spectroscopies for the quality analytical control of a solid pharmaceutical formulation with three active ingredients.

The manufacture of pharmaceutical products is one of the most regulated industrial sectors due to the importance of its products on human health. These regulations require a high number of physical and chemical determinations. These last are commonly performed by using chromatographic techniques coupled to different detectors, even though the use of spectroscopic techniques is growing.This study was aimed to evaluate the performance of a NIR and Raman spectroscopy methodologies for the quantitative determination of three active ingredients (APIs) with different concentrations (6.8%w/w, 26.3%w/w and 26.3%w/w) in a solid pharmaceutical product. Calibration models and prediction results were compared in terms of precision and prediction accuracy with those obtained through HPLC reference method. Different spectral pretreatments have been used to reduce the spectral variability associated with the physical characteristics of samples. Standard Normal Variate (SNV) and 2nd derivative (2D) were used as spectral treatments to correct scattering effect in NIR spectra. The best pretreatments for Raman were a baseline correction and a normalization. Each model was developed and evaluated with full subsets cross-validation and external validation sample set. For each technique, the best model was selected based on conventional criteria (R2, RMSEPCV).Results show highly predictive models based on NIR and Raman spectroscopy with root mean square error of prediction values between 0.26%w/w and 2.60%w/w depending on the active ingredient and the analytical technique. The methodologies have been applied for an adequate determination of the uniformity of the dosing units of two commercial lots. In addition, Raman spectroscopy application allowed the detection of an inappropriate distribution of active ingredients in pharmaceutical products.

Assessment of the Diagnostic Utility of Serum MicroRNA Classification in Patients With Diffuse Glioma

A blood-based screening tool for detecting diffuse glioma is necessary to improve clinical outcomes. To establish models using serum microRNAs to distinguish patients with diffuse glioma from control individuals without cancer (the Glioma Index) and to differentiate glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastatic brain tumors (the 3-Tumor Index). This retrospective, case-control diagnostic study included 157 patients with diffuse glioma and 109 patients with central nervous system (CNS) diseases other than diffuse glioma diagnosed from August 1, 2008, through May 1, 2016, and 314 sex- and age-matched controls without cancer. Samples of patients with diffuse glioma and controls were randomly divided into training and validation set 1, and those of patients with CNS diseases other than diffuse glioma were allocated to an exploratory set. Samples of patients with GBM, PCNSL, and metastatic brain tumors were randomly divided into training and validation set 2. Data were analyzed from April 1, 2018, to March 31, 2019. The expression of 2565 microRNAs was assessed, and the diagnostic performance was evaluated by calculating the area under the receiver operating characteristics curve (AUC), sensitivity, specificity, and accuracy. A total of 580 patients were included in the analysis (309 [53.3%] male; median age, 57 years [range, 10-87 years]). In training set 1, 100 patients with diffuse glioma (median age, 56 years [range, 14-87 years]; 55 male [55.0%]) were compared with 200 control patients (median age, 56 years [range, 14-87 years]; 105 male [52.5%]), and the Glioma Index was constructed using 3 microRNAs (miR-4763-3p, miR-1915-3p, and miR-3679-5p). In validation set 1, the AUC was 0.99 (95% CI, 0.99-1.00); sensitivity, 0.95 (95% CI, 0.89-1.00); and specificity, 0.97 (95% CI, 0.93-1.00). The Glioma Index classified 39 of 42 PCNSL samples (92.9%) and 25 of 28 metastatic brain tumor samples (89.3%) as positive and 2 of 2 spinal tumors (100%) as negative in the exploratory set. In training set 2, 68 patients with GBM, 34 with PCNSL, and 23 with metastatic brain tumor were compared, and the 3-Tumor Index was constructed using 48 microRNAs. The 3-Tumor Index had an accuracy of 0.80, positively detecting 16 of 17 GBM samples (94.1%), 4 of 5 metastatic brain tumor samples (80.0%), and 4 of 8 PCNSL samples (50.0%) in validation set 2. This study appears to have identified promising serum microRNA combinations for detecting diffuse glioma and for assessing histologic features of brain tumors.

Preliminary Study on the Determination of ppm-Level Concentration of Histamine in Tuna Fish Using a Dry Extract System for Infrared Coupled with Near-Infrared Spectroscopy.

Rapid and simple methods to determine histamine in tuna fish have been examined. A dry extract system for infrared (DESIR) was coupled with near-infrared spectroscopy in order to obtain the absorption of histamine in tuna fish at the ppm level. The result showed that the optimal extraction solvent for preparing DESIR samples was 75% methanol and boiling water (100 °C). Calibration equations were developed and tested by independent validation set samples. The calibration equation developed from boiling water as solvent extraction was slightly better than the equation developed from 75% methanol solvent with a coefficient of determination (R2) of 0.79, a standard error of calibration of 2.45 ppm, a standard error of prediction of 2.94 ppm, and a bias of 0.10 ppm. Furthermore, the predicted values from both equations were not significantly different from the reference values obtained from the standard method at the 95% confidence interval. Compared to the current AOAC fluorometric official method, the proposed technique simplified and reduced the preparation time.

Molecular Imaging of Endometriosis Tissues using Desorption Electrospray Ionization Mass Spectrometry

Endometriosis is a pathologic condition affecting approximately 10% of women in their reproductive years. Characterized by abnormal growth of uterine endometrial tissue in other body areas, endometriosis can cause severe abdominal pain and/or infertility. Despite devastating consequences to patients’ quality of life, the causes of endometriosis are not fully understood and validated diagnostic markers for endometriosis have not been identified. Molecular analyses of ectopic and eutopic endometrial tissues could lead to enhanced understanding of the disease. Here, we apply desorption electrospray ionization (DESI) mass spectrometry (MS) imaging to chemically and spatially characterize the molecular profiles of 231 eutopic and ectopic endometrial tissues from 89 endometriosis patients. DESI-MS imaging allowed clear visualization of endometrial glandular and stromal regions within tissue samples. Statistical models built from DESI-MS imaging data allowed classification of endometriosis lesions with overall accuracies of 89.4%, 98.4%, and 98.8% on training, validation, and test sample sets, respectively. Further, molecular markers that are significantly altered in ectopic endometrial tissues when compared to eutopic tissues were identified, including fatty acids and glycerophosphoserines. Our study showcases the value of MS imaging to investigate the molecular composition of endometriosis lesions and pinpoints metabolic markers that may provide new knowledge on disease pathogenesis.

Study of a noninvasive blood glucose detection model using the near-infrared light based on SA-NARX

Accumulated attempts have been made to develop models for near-infrared noninvasive measurement of human blood glucose concentration. Most of them focus on the relationship between near-infrared absorbance and blood glucose concentration, but do not consider the fluctuation regularity of blood glucose concentration and the influence of environmental factors and human physiological state on near-infrared absorption. In order to improve the performance of prediction model, a hybrid method is proposed in this paper. The nonlinear autoregressive model with exogenous input (NARX) was introduced as prediction model. 7 variables, including 1550 nm near-infrared absorbance, ambient temperature, ambient humidity, systolic pressure, diastolic pressure, pulse rate and body temperature were introduced as initial input variables. The sensitivity analysis (SA) method was employed to select the relative important input variables for NARX model. Based on the result of SA, a robust and accurate NARX model with 4 input variables (1550 nm near-infrared absorbance, systolic pressure, pulse rate and body temperature) was derived. Compared with the back propagation neural network (BPNN) with the same selected 4 input variables and the BPNN with initial 7 input variables, the NARX model developed there showed better prediction performance, of which the root mean square error and correlation coefficients were 0.72 mmol/L and 0.85 respectively for the 10-fold cross validation set. The percentages of the 10-fold cross validation set samples falling in region A and B of the Clarke error grid analysis were 90.27% and 9.73% respectively. These results demonstrate the potential of our model for noninvasive measurement of blood glucose concentration.

Identification of PBMC-expressed miRNAs for rheumatoid arthritis

ABSTRACTPost-transcriptional regulation by miRNAs plays an important role in the pathogenesis of rheumatoid arthritis (RA), however, the roles of specific miRNAs in RA pathogenesis remain largely unclear. This study performed dual-omics (miRNA and mRNA) integration analysis and in-depth cellular and molecular functional exploration to identify novel RA-associated miRNAs and to understand their underlying pathogenic mechanism. Based on the miRNA and mRNA expression profiles in peripheral blood mononuclear cells (PBMCs) from a discovery sample set (25 RA cases and 18 healthy controls), 18 differentially expressed miRNAs (DEMIRs) (|Fold-change|>2 and P < 0.05) were identified and corresponding interaction networks of DEMIRs and mRNA were constructed. After the expression validation of the DEMIRs in a validation sample set (35 RA cases and 35 healthy controls), miR-99b-5p was highlighted. The over-expression of newly discovered miR-99b-5p is able to suppress T cell apoptosis, promote cell proliferation and activation, increase expression of proinflammatory cytokines (IL-2, IL-6, TNF-α, and IFN-γ), and inhibit expression of its target genes mTOR and RASSF4. This study comprehensively identified PBMC-expressed miRNAs along with corresponding regulatory networks significant for RA and discovered miR-99b-5p as a novel post-transcriptional mediator involved in RA pathogenesis. The findings improved our understanding of RA pathogenesis and provided novel insights into the molecular mechanisms underlying RA pathogenesis.

Rapid Detection of Live and Dead Escherichia coli in a Suspension Using Spectroscopy and Chemometrics

Viability assessment of bacteria is critical in monitoring of food or environmental samples. Existing methods are time-consuming, labour-intensive or require trained manpower and costly chemicals. Potential of commonly used UV-visual spectrometer was explored for rapid viability detection of Escherichia coli (ATCC 8739). Spectra of samples (live and dead cells) mixed in different proportion revealed clear differences. Live bacterial suspension showed absorption peak at 260 nm with decreasing amplitude as the proportion of live bacteria was reduced in the suspension and vice-versa. Principal component analyses (PCA) of spectral data showed clear clustering of samples based on the level of live bacterial cells (5 % significance level). Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Square Discriminant Analysis (PLSDA) yielded 100 % correct classification with test samples. The percentage of live and dead bacteria in a suspension could be predicted with coefficient of determination (R2 ) of 0.980 and 0.977 for calibration and validation sample sets, respectively, in the range of 259-261 nm using Multiple Linear Regression (MLR). Low standard errors of calibration (4.5), prediction (4.8) and high R2 (0.98) indicated the potential of UV visual spectrometer to detect and predict live and dead cells of E. coli in a suspension.

Determination of glucose concentration in aqueous solution using FT NIR spectroscopy

NIR spectroscopy is widely used due to its capability to measure a large number of solid and liquid samples, including water-soluble constituents. The measurements of glucose concentrations in aqueous solutions are useful to examine how low concentrations of glucose in water can be measured using near-infrared spectroscopy and its potential applications for non-invasive measurements of glucose level in the blood. This paper describes an alternative approach to the determination of glucose content in aqueous solutions below 1000 mg/dL using Fourier transform near-infrared spectroscopy. This technique has the advantage of being less intensive sample preparation and non-destructive. Glucose in aqueous solutions were carefully prepared with concentrations of 0 - 100 mg/dL at intervals of 5 mg/dL, 110 - 500 mg/dL at intervals of 10 mg/dL and 525 - 1000 mg/dL at intervals of 25 mg/dL. Thus, the total produces 81 samples of standard solutions for both calibration and validation sample sets. PLSR analysis to near-infrared spectra show that glucose content in aqueous solutions can be predicted accurately with a maximum deviation of 6 mg/dL, indicating that the near-infrared prediction model is sufficient to determine glucose content in the aqueous solutions below 1000 mg/dL. The ability of the NIR to detect glucose content below 1000 mg/dL is particularly important when designing a non-destructive glucose level measuring device using a near-infrared light source.

Avoiding non-contributive molecular results in cancer samples: proposal of a score-based approach for sample choice

Mutational analyses have become crucial for therapeutic choices in patients with advanced lung cancer, colorectal cancer and melanoma. Short turnaround times for molecular analyses are necessary to match the patient's therapeutic management. Non-contributive molecular analyses may increase the delay in reaching a relevant mutational status. We attempted to identify criteria in samples associated with non-contributive molecular results to better anticipate them and select samples with contributive analyses. We compared several criteria such as cancer type, sample type, organ of origin and percentage of tumour cells between samples with non-contributive or contributive EGFR, KRAS, NRAS and BRAF mutation analyses. Among two sets of 3367 and 554 tumour samples analysed in 2015-2017 and 2018, respectively, 11.7% and 15.7% of sample analyses were non-contributive for at least one oncogene. Lung cancer and melanoma cancer subtypes [odds ratio (OR)=7.2], cytological (OR=1.8) or bone samples (OR=8.5) and a percentage of tumour cells ≤20% (OR=41.4) were significantly associated with non-contributive results. By combining these parameters in a scoring system, we were able to predict the contributive or non-contributive result of a molecular analysis with sensitivity and specificity higher than 80% in a validation set of samples. Predicting the contributive or non-contributive result of a molecular analysis is feasible in samples on the basis of simple features. A combination of these features could be used to better choose samples to analyse in order to reduce the rate of non-contributive molecular results and related treatment delays and costs in patients with advanced cancers.

Proteomics analysis discovers biomarkers in serum months to years before small cell lung cancer: The HUNT study.

e20095 Background: The high incidence and high mortality rate of small-cell lung cancer (SCLC) calls for identification of methods for early diagnosis. Searching of cancer-related proteins and proteins signature in biofluids is an emerging approach in early diagnostic of malignancies. In the present study we have used proteomics-based profiling of serum collected 2 months to 5 years before SCLC diagnosis to search for early diagnostic biomarkers. Methods: All serum samples in this study were obtained from the Nord-Trøndelag Health Study (HUNT) Research Centre’s Biobank. Discovery sample set (Cohort I): Serum samples from 12 individuals that subsequently developed SCLC and 12 matched controls were obtained. Validation sample set (Cohort II): Serum samples from 5 future SCLC patients and 5 matched controls were obtained. The serum samples in both cohorts were collected in a time frame of 2 months to 5 years before diagnosis. All controls included in the study were matched to the cases for smoking status (pack years and quit time), gender and age, and were cancer-free at least 5 years before blood sampling. All subjects were smokers or ex-smokers. Twenty (20) µl of serum was depleted of its high-abundant proteins and the cleaned-up peptides were analysed by LC- MS with an Orbitrap Elite mass spectrometer. Data were processed/analysed using MaxQuant software. Using Cohort I as training set, proteins most related to diagnosis were identified at first with limma univariate analysis. Reference and equivalent signatures were identified using JADbio with SES as feature selection algorithm. The JADbio matched pipeline with 50 repetitions for performance assessment (AUC with 95% CI) was used. Results: In each serum sample the analysis detected 435 proteins in Cohort I and 609 proteins in Cohort II. Reference signature for 12 SCLC future cases vs 12 matched controls had a conservative AUC of 0.686 (95% CI: 0.557, 0.765) in the discovery cohort. Three proteins in the reference and equivalent signatures were also found to be differentially expressed in validation Cohort II; all three proteins discriminated small-cell lung cancer patients from their respective controls (AUC 0.757-0.826 in the discovery Cohort I and AUC 0.68-0.84 in the validation Cohort II). Conclusions: The results indicate that differential levels of a few proteins in serum may help detecting SCLC 2 months to 5 years prior to clinical diagnosis. This is one of the first large-scale proteomics screening studies of pre-diagnostic serum of future SCLC patients. Further validation studies are in progress.

Study on principal component analysis and multi-class discrimination against the combination to identify easy-confused fur

In this paper, the principal component analysis was performed on infrared spectral data, which included 60 groups of cattle and horse hair surfaces and 60 groups of cattle and horse flesh surfaces, respectively, by using SPSS 22. Using 100 sets of data as modelling samples (50 sets of cattle fur and horse fur), the multi-class discriminant analysis was carried out by using SPSS, the typical discriminant function and class function of cattle fur and horse fur were established and the back substitution verification was carried out. The typical discriminant function and the class function of cattle fur and horse fur were verified by 20 sets of data validation samples (10 groups of cattle fur and horse fur). The results show that the principal component analysis can reduce the dimension effectively, reduce the hair surfaces spectra from 2696 wavelength variables to nine wavelength variables and decrease the flesh surfaces spectra from 2696 wavelength variables to 13 wavelength variables. The cumulative contribution rate of the new wavelength variables is up to 99.89 and 99.88%, respectively. The back substitution accuracy rate of the typical discriminant function is 100%, and the verification accuracy rate is 100%. From the clustering graph, the established class function of cattle fur and horse fur back substitution clustering is well, and the correct clustering rate of the verification clustering graph is 100%.

Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds

The inspection of rice grain that may be infected by seedborne disease is important for ensuring uniform plant stands in production fields as well as preventing proliferation of some seedborne diseases. The goal of this study was to use a hyperspectral imaging (HSI) technique to find optimal wavelengths and develop a model for detecting discolored, diseased rice seed infected by bacterial panicle blight (Burkholderia glumae), a seedborne pathogen. For this purpose, the HSI data spanning the visible/near-infrared wavelength region between 400 and 1000 nm were collected for 500 sound and discolored rice seeds. For selecting optimal wavelengths to use for detecting diseased seed, a sequential forward selection (SFS) method combined with various spectral pretreatments was employed. To evaluate performance based on optimal wavelengths, support vector machine (SVM) and linear and quadratic discriminant analysis (LDA and QDA) models were developed for detection of discolored seeds. As a result, the violet and red regions of the visible spectrum were selected as key wavelengths reflecting the characteristics of the discolored rice seeds. When using only two or only three selected wavelengths, all of the classification methods achieved high classification accuracies over 90% for both the calibration and validation sample sets. The results of the study showed that only two to three wavelengths are needed to differentiate between discolored, diseased and sound rice, instead of using the entire HSI wavelength regions. This demonstrates the feasibility of developing a low cost multispectral imaging technology based on these selected wavelengths for non-destructive and high-throughput screening of diseased rice seed.