scholarly journals Changes in Gut Microbiome Associated With Co-Occurring Symptoms Development During Chemo-Radiation for Rectal Cancer: A Proof of Concept Study

2020 ◽  
Vol 23 (1) ◽  
pp. 31-41
Author(s):  
Velda J. González-Mercado ◽  
Wendy A. Henderson ◽  
Anujit Sarkar ◽  
Jean Lim ◽  
Leorey N. Saligan ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Gut Microbiome ◽  
Learning Algorithm ◽  
Descriptive Statistics ◽  
Proof Of Concept ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Symptom Ratings ◽  
Stool Samples ◽  
Microbiome Data

Purpose: To examine a) whether there are significant differences in the severity of symptoms of fatigue, sleep disturbance, or depression between patients with rectal cancer who develop co-occurring symptoms and those with no symptoms before and at the end of chemotherapy and radiation therapy (CRT); b) differences in gut microbial diversity between those with co-occurring symptoms and those with no symptoms; and c) whether before-treatment diversity measurements and taxa abundances can predict co-occurrence of symptoms. Methods: Stool samples and symptom ratings were collected from 31 patients with rectal cancer prior to and at the end of (24–28 treatments) CRT. Descriptive statistics were computed and the Mann-Whitney U test was performed for symptoms. Gut microbiome data were analyzed using R’s vegan package software. Results: Participants with co-occurring symptoms reported greater severity of fatigue at the end of CRT than those with no symptoms. Bacteroides and Blautia2 abundances differed between participants with co-occurring symptoms and those with no symptoms. Our random forest classification (unsupervised learning algorithm) predicted participants who developed co-occurring symptoms with 74% accuracy, using specific phylum, family, and genera abundances as predictors. Conclusion: Our preliminary results point to an association between the gut microbiota and co-occurring symptoms in rectal cancer patients and serves as a first step in potential identification of a microbiota-based classifier.

scholarly journals Developing a Reproducible Microbiome Data Analysis Pipeline Using the Amazon Web Services Cloud for a Cancer Research Group: Proof-of-Concept Study (Preprint)

2019 ◽  
Author(s):  
Jinbing Bai ◽  
Ileen Jhaney ◽  
Jessica Wells
Keyword(s):  
Data Analysis ◽  
Data Storage ◽  
Gut Microbiome ◽  
Data Sets ◽  
Proof Of Concept ◽  
Analysis Pipeline ◽  
Amazon Web Services ◽  
Microbiome Data ◽  
Data Analysis Pipeline ◽  
Microbiome Data Analysis

BACKGROUND Cloud computing for microbiome data sets can significantly increase working efficiencies and expedite the translation of research findings into clinical practice. The Amazon Web Services (AWS) cloud provides an invaluable option for microbiome data storage, computation, and analysis. OBJECTIVE The goals of this study were to develop a microbiome data analysis pipeline by using AWS cloud and to conduct a proof-of-concept test for microbiome data storage, processing, and analysis. METHODS A multidisciplinary team was formed to develop and test a reproducible microbiome data analysis pipeline with multiple AWS cloud services that could be used for storage, computation, and data analysis. The microbiome data analysis pipeline developed in AWS was tested by using two data sets: 19 vaginal microbiome samples and 50 gut microbiome samples. RESULTS Using AWS features, we developed a microbiome data analysis pipeline that included Amazon Simple Storage Service for microbiome sequence storage, Linux Elastic Compute Cloud (EC2) instances (ie, servers) for data computation and analysis, and security keys to create and manage the use of encryption for the pipeline. Bioinformatics and statistical tools (ie, Quantitative Insights Into Microbial Ecology 2 and RStudio) were installed within the Linux EC2 instances to run microbiome statistical analysis. The microbiome data analysis pipeline was performed through command-line interfaces within the Linux operating system or in the Mac operating system. Using this new pipeline, we were able to successfully process and analyze 50 gut microbiome samples within 4 hours at a very low cost (a c4.4xlarge EC2 instance costs $0.80 per hour). Gut microbiome findings regarding diversity, taxonomy, and abundance analyses were easily shared within our research team. CONCLUSIONS Building a microbiome data analysis pipeline with AWS cloud is feasible. This pipeline is highly reliable, computationally powerful, and cost effective. Our AWS-based microbiome analysis pipeline provides an efficient tool to conduct microbiome data analysis.

scholarly journals The Role of Gut Microbiome Perturbation in Fatigue Induced by Repeated Stress from Chemoradiotherapy: A Proof of Concept Study

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Velda J. González-Mercado ◽  
Josué Pérez-Santiago ◽  
Debra Lyon ◽  
Israel Dilán-Pantojas ◽  
Wendy Henderson ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Alpha Diversity ◽  
Total Sample ◽  
Fecal Microbiota ◽  
Proof Of Concept ◽  
Fatigue Score ◽  
Microbiome Composition ◽  
Concept Study ◽  
Symptom Ratings ◽  
Rectal Cancers

Objectives. The objectives of this proof of concept study were to (a) examine the temporal changes in fatigue and diversity of the gut microbiome over the course of chemoradiotherapy (CRT) in adults with rectal cancers; (b) investigate whether there are differences in diversity of the gut microbiome between fatigued and nonfatigued participants at the middle and at the end of CRT; and (c) investigate whether there are differences in the relative abundance of fecal microbiota at the phylum and genus levels between fatigued and nonfatigued participants at the middle and at the end of CRT. Methods. Stool samples and symptom ratings were collected prior to the inception of CRT, at the middle (after 12–16 treatments) and at the end (after 24–28 treatments) of the CRT. Descriptive statistics and Mann–Whitney U test were computed for fatigue. Gut microbiome data were analyzed using the QIIME2 software. Results. Participants (N = 29) ranged in age from 37 to 80 years. The median fatigue score significantly changed at the end of CRT (median = 23.0) compared with the median score before the initiation of CRT for the total sample (median = 17.0; p≤0.05). At the middle of CRT, the alpha diversity (abundance of Operational Taxonomic Units) was lower for fatigued participants (149.30 ± 53.1) than for nonfatigued participants (189.15 ± 44.18, t(23) = 2.08, p≤0.05). A similar trend was observed for the Shannon and Faith diversity indexes at the middle of CRT. However, at the end of CRT, there were no significant differences for any alpha diversity indexes between fatigued and nonfatigued participants. Proteobacteria, Firmicutes, and Bacteroidetes were the dominant phyla for fatigued participants, and Escherichia, Bacteroides, Faecalibacterium, and Oscillospira were the most abundant genera for fatigued participants. Conclusion. CRT-associated perturbation of the gut microbiome composition may contribute to fatigue.

scholarly journals Developing a Reproducible Microbiome Data Analysis Pipeline Using the Amazon Web Services Cloud for a Cancer Research Group: Proof-of-Concept Study

10.2196/14667 ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. e14667 ◽  
Author(s):  
Jinbing Bai ◽  
Ileen Jhaney ◽  
Jessica Wells
Keyword(s):  
Data Analysis ◽  
Data Storage ◽  
Gut Microbiome ◽  
Data Sets ◽  
Proof Of Concept ◽  
Analysis Pipeline ◽  
Amazon Web Services ◽  
Microbiome Data ◽  
Data Analysis Pipeline ◽  
Microbiome Data Analysis

Background Cloud computing for microbiome data sets can significantly increase working efficiencies and expedite the translation of research findings into clinical practice. The Amazon Web Services (AWS) cloud provides an invaluable option for microbiome data storage, computation, and analysis. Objective The goals of this study were to develop a microbiome data analysis pipeline by using AWS cloud and to conduct a proof-of-concept test for microbiome data storage, processing, and analysis. Methods A multidisciplinary team was formed to develop and test a reproducible microbiome data analysis pipeline with multiple AWS cloud services that could be used for storage, computation, and data analysis. The microbiome data analysis pipeline developed in AWS was tested by using two data sets: 19 vaginal microbiome samples and 50 gut microbiome samples. Results Using AWS features, we developed a microbiome data analysis pipeline that included Amazon Simple Storage Service for microbiome sequence storage, Linux Elastic Compute Cloud (EC2) instances (ie, servers) for data computation and analysis, and security keys to create and manage the use of encryption for the pipeline. Bioinformatics and statistical tools (ie, Quantitative Insights Into Microbial Ecology 2 and RStudio) were installed within the Linux EC2 instances to run microbiome statistical analysis. The microbiome data analysis pipeline was performed through command-line interfaces within the Linux operating system or in the Mac operating system. Using this new pipeline, we were able to successfully process and analyze 50 gut microbiome samples within 4 hours at a very low cost (a c4.4xlarge EC2 instance costs $0.80 per hour). Gut microbiome findings regarding diversity, taxonomy, and abundance analyses were easily shared within our research team. Conclusions Building a microbiome data analysis pipeline with AWS cloud is feasible. This pipeline is highly reliable, computationally powerful, and cost effective. Our AWS-based microbiome analysis pipeline provides an efficient tool to conduct microbiome data analysis.

scholarly journals Predicting Crop yield and Effective use of Fertilizers using Machine Learning Techniques

2020 ◽  
Vol 9 (7) ◽  
pp. 1288-1292
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Crop Yield ◽  
Soil Type ◽  
Ph Value ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Random Forest Classification ◽  
Crop Selection ◽  
Forest Classification

In-season crop yield estimation has various applications such as the farmer taking corrective measures to increase the yield. We are exploring the efficient use of fertilizers. The various data mining techniques are used on data for environment. The data is related to humidity, PH. value, water, soil type and atmospheric pressure these are responsible for crop yield. This result is obtained by this algorithm are useful for farmers to take decisions about further implantation of crop yield. Crop selection method is widely used to build decision tree to overcome many problems in real time real world. One of the most important fields is decision tree. By analyzing the soil and atmosphere at particular region best crop in order to have more crop yield and the net crop yield can be predicted. This prediction will help the farmers to choose appropriate crops for their farm according to the soil type, temperature, humidity, water level, spacing depth, soil PH, season, fertilizer and months. This prediction can be carried out using Random Forest classification machine learning algorithm.

scholarly journals Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm

2021 ◽  
Vol 13 (23) ◽  
pp. 4762
Author(s):  
Panpan Wei ◽  
Weiwei Zhu ◽  
Yifan Zhao ◽  
Peng Fang ◽  
Xiwang Zhang ◽  
...  
Keyword(s):  
Random Forest ◽  
Learning Algorithm ◽  
Animal Husbandry ◽  
Feature Space ◽  
Google Earth ◽  
Random Forest Classification ◽  
Topographic Features ◽  
Forest Classification ◽  
Feature Optimization ◽  
Land Types

Africa has the largest grassland area among all grassland ecosystems in the world. As a typical agricultural and animal husbandry country in Africa, animal husbandry plays an important role in this region. The investigation of grassland resources and timely grasping the quantity and spatial distribution of grassland resources are of great significance to the stable development of local animal husbandry economy. Therefore, this paper uses Kenya as the study area to investigate the effective and fast approach for grassland mapping with 100-m resolution using the open resources in the Google Earth Engine cloud platform. The main conclusions are as follows. (1) In the feature combination optimization part of this paper, the machine learning algorithm is used to compare the scores and standard deviations of several common algorithms combined with RFE. It is concluded that the combination of RFE and random forest algorithm has the highest stability in modeling and the best feature optimization effect. (2) After feature optimization by the RFE-RF algorithm, the number of features is reduced from 12 to 8, which compressed the original feature space and reduced the redundancy of features. The optimal combination features are applied to random forest classification, and the overall accuracy and Kappa coefficient of classification are 0.87 and 0.85, respectively. The eight features are: elevation, NDVI, EVI, SWIR, RVI, BLUE, RED, and LSWI. (3) There are great differences in topographic features among the local land types in the study area, and the addition of topographic features is more conducive to the recognition and classification of various land types. There exists “salt-and-pepper phenomenon” in pixel-oriented classification. Later research focus will combine the RFE-RF algorithm and the segmentation algorithm to achieve object-oriented land cover classification.

scholarly journals Classification of emotions based on electrodermal activity and transfer learning - a pilot study

2021 ◽  
Vol 12 (1) ◽  
pp. 178-183
Author(s):  
Fredrik A. Jacobsen ◽  
Ellen W. Hafli ◽  
Christian Tronstad ◽  
Ørjan G. Martinsen
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Electrodermal Activity ◽  
Machine Learning Algorithm ◽  
Electrodermal Response ◽  
Random Forest Classification ◽  
Forest Classification

Abstract This paper describes the development, execution and results of an experiment assessing emotions with electrodermal response measurements and machine learning. With ten participants, the study was carried out by eliciting emotions through film clips. The data was gathered with the Sudologger 3 and processed with continuous wavelet transformation. A machine learning algorithm was used to classify the data with the use of transfer learning and random forest classification. The results showed that the experiment lays a foundation for further exploration in the field. The addition of augmented data strengthened the classification and proved that more data would benefit the machine learning algorithm. The pilot study brought to light several areas to help with the expansion of the study for larger scale assessment of emotions with electrodermal response measurements and machine learning for the benefit of fields like psychology.

scholarly journals Postoperative Complications Are Associated with Long-Term Changes in the Gut Microbiota Following Colorectal Cancer Surgery

Life ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 246
Author(s):  
Felix C.F. Schmitt ◽  
Martin Schneider ◽  
William Mathejczyk ◽  
Markus A. Weigand ◽  
Jane C. Figueiredo ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Postoperative Complications ◽  
Gut Microbiota ◽  
Gut Microbiome ◽  
Tumor Resection ◽  
Microbial Composition ◽  
Colorectal Cancer Surgery ◽  
Long Term Changes ◽  
Stool Samples

Changes in the gut microbiome have already been associated with postoperative complications in major abdominal surgery. However, it is still unclear whether these changes are transient or a long-lasting effect. Therefore, the aim of this prospective clinical pilot study was to examine long-term changes in the gut microbiota and to correlate these changes with the clinical course of the patient. Methods: In total, stool samples of 62 newly diagnosed colorectal cancer patients undergoing primary tumor resection were analyzed by 16S-rDNA next-generation sequencing. Stool samples were collected preoperatively in order to determine the gut microbiome at baseline as well as at 6, 12, and 24 months thereafter to observe longitudinal changes. Postoperatively, the study patients were separated into two groups—patients who suffered from postoperative complications (n = 30) and those without complication (n = 32). Patients with postoperative complications showed a significantly stronger reduction in the alpha diversity starting 6 months after operation, which does not resolve, even after 24 months. The structure of the microbiome was also significantly altered from baseline at six-month follow-up in patients with complications (p = 0.006). This was associated with a long-lasting decrease of a large number of species in the gut microbiota indicating an impact in the commensal microbiota and a long-lasting increase of Fusobacterium ulcerans. The microbial composition of the gut microbiome shows significant changes in patients with postoperative complications up to 24 months after surgery.

scholarly journals A synbiotic intervention modulates meta-omics signatures of gut redox potential and acidity in elective caesarean born infants

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Christophe Lay ◽  
Collins Wenhan Chu ◽  
Rikky Wenang Purbojati ◽  
Enzo Acerbi ◽  
Daniela I. Drautz-Moses ◽  
...  
Keyword(s):  
Risk Factor ◽  
Gut Microbiome ◽  
Reference Group ◽  
Mode Of Delivery ◽  
Controlled Study ◽  
Double Blind ◽  
Lipopolysaccharide Biosynthesis ◽  
Infant Gut Microbiome ◽  
Stool Samples ◽  
The Impact

Abstract Background The compromised gut microbiome that results from C-section birth has been hypothesized as a risk factor for the development of non-communicable diseases (NCD). In a double-blind randomized controlled study, 153 infants born by elective C-section received an infant formula supplemented with either synbiotic, prebiotics, or unsupplemented from birth until 4 months old. Vaginally born infants were included as a reference group. Stool samples were collected from day 3 till week 22. Multi-omics were deployed to investigate the impact of mode of delivery and nutrition on the development of the infant gut microbiome, and uncover putative biological mechanisms underlying the role of a compromised microbiome as a risk factor for NCD. Results As early as day 3, infants born vaginally presented a hypoxic and acidic gut environment characterized by an enrichment of strict anaerobes (Bifidobacteriaceae). Infants born by C-section presented the hallmark of a compromised microbiome driven by an enrichment of Enterobacteriaceae. This was associated with meta-omics signatures characteristic of a microbiome adapted to a more oxygen-rich gut environment, enriched with genes associated with reactive oxygen species metabolism and lipopolysaccharide biosynthesis, and depleted in genes involved in the metabolism of milk carbohydrates. The synbiotic formula modulated expression of microbial genes involved in (oligo)saccharide metabolism, which emulates the eco-physiological gut environment observed in vaginally born infants. The resulting hypoxic and acidic milieu prevented the establishment of a compromised microbiome. Conclusions This study deciphers the putative functional hallmarks of a compromised microbiome acquired during C-section birth, and the impact of nutrition that may counteract disturbed microbiome development. Trial registration The study was registered in the Dutch Trial Register (Number: 2838) on 4th April 2011.

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.

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