scholarly journals Detection of Paratuberculosis in Dairy Herds by Analyzing the Scent of Feces, Alveolar Gas and Stable Air

Molecules ◽  
2021 ◽  
Vol 26 (10) ◽  
pp. 2854
Author(s):  
Michael Weber ◽  
Peter Gierschner ◽  
Anne Klassen ◽  
Elisa Kasbohm ◽  
Jochen K. Schubert ◽  
...  
Keyword(s):  
Cross Validation ◽  
Dairy Herds ◽  
Exhaled Breath ◽  
Biological Specimen ◽  
Random Forest Classification ◽  
Explorative Study ◽  
Forest Classification ◽  
Successful Infection ◽  
Volatile Organic ◽  
Important Disease

Paratuberculosis is an important disease of ruminants caused by Mycobacterium avium ssp. paratuberculosis (MAP). Early detection is crucial for successful infection control, but available diagnostic tests are still dissatisfying. Methods allowing a rapid, economic, and reliable identification of animals or herds affected by MAP are urgently required. This explorative study evaluated the potential of volatile organic compounds (VOCs) to discriminate between cattle with and without MAP infections. Headspaces above fecal samples and alveolar fractions of exhaled breath of 77 cows from eight farms with defined MAP status were analyzed in addition to stable air samples. VOCs were identified by GC–MS and quantified against reference substances. To discriminate MAP-positive from MAP-negative samples, VOC feature selection and random forest classification were performed. Classification models, generated for each biological specimen, were evaluated using repeated cross-validation. The robustness of the results was tested by predicting samples of two different sampling days. For MAP classification, the different biological matrices emitted diagnostically relevant VOCs of a unique but partly overlapping pattern (fecal headspace: 19, alveolar gas: 11, stable air: 4–5). Chemically, relevant compounds belonged to hydrocarbons, ketones, alcohols, furans, and aldehydes. Comparing the different biological specimens, VOC analysis in fecal headspace proved to be most reproducible, discriminatory, and highly predictive.

scholarly journals Analysis of Apple Flavours: The Use of Volatile Organic Compounds to Address Cultivar Differences and the Correlation between Consumer Appreciation and Aroma Profiling

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Gareth Roberts ◽  
Natasha D. Spadafora
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Cultivar Differences ◽  
Gas Chromatography Mass Spectrometry ◽  
Mass Spectral ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Principal Coordinates ◽  
Volatile Organic ◽  
Processing And Storage

In the selection of apple (Malus domestica Borkh) fruits, consumers predominantly use visual and organoleptic responses, such as aroma and texture, as quality/preference markers. Previous studies profiling the sensory properties and aroma profiles of apple varieties have provided a lexicon describing important attributes within the modalities of aroma, texture, and flavour. The range of volatile organic compounds (VOCs) produced by four apple cultivars was assessed by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). To facilitate automated dynamic headspace extraction of the VOCs from bulk samples, a small multichamber thermal extraction system was used. Forty compounds were detected and putatively identified by mass spectral comparison with NIST libraries. Eight compounds were not previously identified in apples. Permutational multivariate analysis of variance (PerMANOVA) and canonical analysis of principal coordinates (CAP) done on the whole volatile organic compound profiles show separation of the four cultivars. The use of random forest classification enabled the determination of a minimum set of fifteen potential VOC markers that successfully differentiate the cultivars under study. Trends across samples, VOCs, and sensory evaluation are revealed. The association between 4-hexen-1-ol acetate with texture/consistency, and crispiness, and correlation of 2-methylbut-2-en-1-yl acetate with juiciness and acidity is shown for the first time. These sensory correlations indicate that the compounds determined by this experiment could be used as objective markers for the consumer appreciation of fresh apples, enabling the optimum conditions for processing and storage of individual cultivars to be identified without recourse to expensive sensory panels in every case. Such tests could also be used as part of routine quality control by the producer and retailer, reducing costs and eliminating waste due to batches of inferior product.

scholarly journals Application of Monte Carlo cross-validation to identify pathway cross-talk in neonatal sepsis

2018 ◽  
Vol 243 (5) ◽  
pp. 444-450 ◽  
Author(s):  
Yuxia Zhang ◽  
Cui Liu ◽  
Jingna Wang ◽  
Xingxia Li
Keyword(s):  
Monte Carlo ◽  
Cross Talk ◽  
Cross Validation ◽  
Integrated Approach ◽  
Dendritic Cell Maturation ◽  
Extensive Literature ◽  
Genetic Profiling ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Monte Carlo Cross Validation

To explore genetic pathway cross-talk in neonates with sepsis, an integrated approach was used in this paper. To explore the potential relationships between differently expressed genes between normal uninfected neonates and neonates with sepsis and pathways, genetic profiling and biologic signaling pathway were first integrated. For different pathways, the score was obtained based upon the genetic expression by quantitatively analyzing the pathway cross-talk. The paired pathways with high cross-talk were identified by random forest classification. The purpose of the work was to find the best pairs of pathways able to discriminate sepsis samples versus normal samples. The results found 10 pairs of pathways, which were probably able to discriminate neonates with sepsis versus normal uninfected neonates. Among them, the best two paired pathways were identified according to analysis of extensive literature. Impact statement To find the best pairs of pathways able to discriminate sepsis samples versus normal samples, an RF classifier, the DS obtained by DEGs of paired pathways significantly associated, and Monte Carlo cross-validation were applied in this paper. Ten pairs of pathways were probably able to discriminate neonates with sepsis versus normal uninfected neonates. Among them, the best two paired pathways ((7) IL-6 Signaling and Phospholipase C Signaling (PLC); (8) Glucocorticoid Receptor (GR) Signaling and Dendritic Cell Maturation) were identified according to analysis of extensive literature.

Volatile Organic Compounds in Exhaled Breath of Idiopathic Pulmonary Fibrosis for Discrimination from Healthy Subjects

Lung ◽  
2017 ◽  
Vol 195 (2) ◽  
pp. 247-254 ◽  
Author(s):  
Yu-ichi Yamada ◽  
Gen Yamada ◽  
Mitsuo Otsuka ◽  
Hirotaka Nishikiori ◽  
Kimiyuki Ikeda ◽  
...  
Keyword(s):  
Volatile Organic Compounds ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Organic Compounds ◽  
Healthy Subjects ◽  
Exhaled Breath ◽  
Volatile Organic

scholarly journals Statistical Evaluation of Total Expiratory Breath Samples Collected throughout a Year: Reproducibility and Applicability toward Olfactory Sensor-Based Breath Diagnostics

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4742
Author(s):  
Katsushige Inada ◽  
Hiroshi Kojima ◽  
Yukiko Cho-Isoda ◽  
Ryo Tamura ◽  
Gaku Imamura ◽  
...  
Keyword(s):  
Statistical Evaluation ◽  
Target Material ◽  
Health Condition ◽  
Exhaled Breath ◽  
Humidity Effects ◽  
Signal Features ◽  
Volatile Organic ◽  
Olfactory Systems ◽  
Membrane Type ◽  
Robust Protocol

The endogenous volatile organic compounds (VOCs) in exhaled breath can be promising biomarkers for various diseases including cancers. An olfactory sensor has a possibility for extracting a specific feature from collective variations of the related VOCs with a certain health condition. For this approach, it is important to establish a feasible protocol for sampling exhaled breath in practical conditions to provide reproducible signal features. Here we report a robust protocol for the breath analysis, focusing on total expiratory breath measured by a Membrane-type Surface stress Sensor (MSS), which possesses practical characteristics for artificial olfactory systems. To assess its reproducibility, 83 exhaled breath samples were collected from one subject throughout more than a year. It has been confirmed that the reduction of humidity effects on the sensing signals either by controlling the humidity of purging room air or by normalizing the signal intensities leads to reasonable reproducibility verified by statistical analyses. We have also demonstrated the applicability of the protocol for detecting a target material by discriminating exhaled breaths collected from different subjects with pre- and post-alcohol ingestion on different occasions. This simple yet reproducible protocol based on the total expiratory breath measured by the MSS olfactory sensors will contribute to exploring the possibilities of clinical applications of breath diagnostics.

scholarly journals MRI-based radiomic feature analysis of end-stage liver disease for severity stratification

2021 ◽  
Author(s):  
Jennifer Nitsch ◽  
Jordan Sack ◽  
Michael W. Halle ◽  
Jan H. Moltz ◽  
April Wall ◽  
...  
Keyword(s):  
Disease Severity ◽  
Prediction Models ◽  
Blood Parameters ◽  
Meld Score ◽  
Poor Correlation ◽  
Discrimination Ability ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Academic Center ◽  
End Stage

Abstract Purpose We aimed to develop a predictive model of disease severity for cirrhosis using MRI-derived radiomic features of the liver and spleen and compared it to the existing disease severity metrics of MELD score and clinical decompensation. The MELD score is compiled solely by blood parameters, and so far, it was not investigated if extracted image-based features have the potential to reflect severity to potentially complement the calculated score. Methods This was a retrospective study of eligible patients with cirrhosis ($$n=90$$ n = 90 ) who underwent a contrast-enhanced MR screening protocol for hepatocellular carcinoma (HCC) screening at a tertiary academic center from 2015 to 2018. Radiomic feature analyses were used to train four prediction models for assessing the patient’s condition at time of scan: MELD score, MELD score $$\ge $$ ≥ 9 (median score of the cohort), MELD score $$\ge $$ ≥ 15 (the inflection between the risk and benefit of transplant), and clinical decompensation. Liver and spleen segmentations were used for feature extraction, followed by cross-validated random forest classification. Results Radiomic features of the liver and spleen were most predictive of clinical decompensation (AUC 0.84), which the MELD score could predict with an AUC of 0.78. Using liver or spleen features alone had slightly lower discrimination ability (AUC of 0.82 for liver and AUC of 0.78 for spleen features only), although this was not statistically significant on our cohort. When radiomic prediction models were trained to predict continuous MELD scores, there was poor correlation. When stratifying risk by splitting our cohort at the median MELD 9 or at MELD 15, our models achieved AUCs of 0.78 or 0.66, respectively. Conclusions We demonstrated that MRI-based radiomic features of the liver and spleen have the potential to predict the severity of liver cirrhosis, using decompensation or MELD status as imperfect surrogate measures for disease severity.

scholarly journals Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification

2016 ◽  
Vol 146 ◽  
pp. 370-385 ◽  
Author(s):  
Adam Hedberg-Buenz ◽  
Mark A. Christopher ◽  
Carly J. Lewis ◽  
Kimberly A. Fernandes ◽  
Laura M. Dutca ◽  
...  
Keyword(s):  
Random Forest ◽  
Ganglion Cell ◽  
Retinal Ganglion Cell ◽  
Quantitative Measurement ◽  
Cell Populations ◽  
Retinal Ganglion ◽  
Random Forest Classification ◽  
Forest Classification

scholarly journals Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery

2021 ◽  
Vol 11 (9) ◽  
pp. 4258
Author(s):  
Jordan R. Cissell ◽  
Steven W. J. Canty ◽  
Michael K. Steinberg ◽  
Loraé T. Simpson
Keyword(s):  
Google Earth ◽  
Mangrove Forests ◽  
Random Forest Classification ◽  
High Resolution Mapping ◽  
Mangrove Ecosystems ◽  
Forest Classification ◽  
Resolution Mapping ◽  
Google Earth Engine ◽  
Terrestrial Biodiversity ◽  
Sentinel 2

In this paper, we present the highest-resolution-available (10 m) national map of the mangrove ecosystems of Belize. These important ecosystems are increasingly threatened by human activities and climate change, support both marine and terrestrial biodiversity, and provide critical ecosystem services to coastal communities in Belize and throughout the Mesoamerican Reef ecoregion. Previous national- and international-level inventories document Belizean mangrove forests at spatial resolutions of 30 m or coarser, but many mangrove patches and loss events may be too small to be accurately mapped at these resolutions. Our 10 m map addresses this need for a finer-scale national mangrove inventory. We mapped mangrove ecosystems in Belize as of 2020 by performing a random forest classification of Sentinel-2 Multispectral Instrument imagery in Google Earth Engine. We mapped a total mangrove area of 578.54 km2 in 2020, with 372.04 km2 located on the mainland and 206.50 km2 distributed throughout the country’s islands and cayes. Our findings are substantially different from previous, coarser-resolution national mangrove inventories of Belize, which emphasizes the importance of high-resolution mapping efforts for ongoing conservation efforts.

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