scholarly journals Quantitative comparison of data-driven gating and external hardware gating for 18F-FDG PET-MRI in patients with esophageal tumors

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Sofia Kvernby ◽  
Nafsika Korsavidou Hult ◽  
Elin Lindström ◽  
Jonathan Sigfridsson ◽  
Gustav Linder ◽  
...  
Keyword(s):  
Noise Level ◽  
Pearson Correlation ◽  
Respiratory Gating ◽  
Principal Component ◽  
Data Driven ◽  
Quality Data ◽  
Lesion Volume ◽  
External Device ◽  
Routine Clinical Setting ◽  
Static Images

Abstract Background Respiratory motion during PET imaging reduces image quality. Data-driven gating (DDG) based on principal component analysis (PCA) can be used to identify respiratory signals. The use of DDG, without need for external devices, would greatly increase the feasibility of using respiratory gating in a routine clinical setting. The objective of this study was to evaluate data-driven gating in relation to external hardware gating and regular static image acquisition on PET-MRI data with respect to SUVmax and lesion volumes. Methods Sixteen patients with esophageal or gastroesophageal cancer (Siewert I and II) underwent a 6-min PET scan on a Signa PET-MRI system (GE Healthcare) 1.5–2 h after injection of 4 MBq/kg 18F-FDG. External hardware gating was done using a respiratory bellow device, and DDG was performed using MotionFree (GE Healthcare). The DDG raw data files and the external hardware-gating raw files were created on a Matlab-based toolbox from the whole 6-min scan LIST-file. For comparison, two 3-min static raw files were created for each patient. Images were reconstructed using TF-OSEM with resolution recovery with 2 iterations, 28 subsets, and 3-mm post filter. SUVmax and lesion volume were measured in all visible lesions, and noise level was measured in the liver. Paired t-test, linear regression, Pearson correlation, and Bland-Altman analysis were used to investigate difference, correlation, and agreement between the methods. Results A total number of 30 lesions were included in the study. No significant differences between DDG and external hardware-gating SUVmax or lesion volumes were found, but the noise level was significantly reduced in the DDG images. Both DDG and external hardware gating demonstrated significantly higher SUVmax (9.4% for DDG, 10.3% for external hardware gating) and smaller lesion volume (− 5.4% for DDG, − 6.6% for external gating) in comparison with non-gated static images. Conclusions Data-driven gating with MotionFree for PET-MRI performed similar to external device gating for esophageal lesions with respect to SUVmax and lesion volume. Both gating methods significantly increased the SUVmax and reduced the lesion volume in comparison with non-gated static acquisition. DDG resulted in reduced image noise compared to external device gating and static images.

Iterative Supervised Principal Component Analysis-Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

2020 ◽  
Author(s):  
Xin Yi See ◽  
Benjamin Reiner ◽  
Xuelan Wen ◽  
T. Alexander Wheeler ◽  
Channing Klein ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
De Novo ◽  
Principal Component ◽  
Component Analysis ◽  
Catalyst Design ◽  
Data Driven ◽  
Initial Reaction ◽  
Training Set ◽  
Reaction Conditions ◽  
Component Loadings

<div> <div> <div> <p>Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H- pyrrole (C) via Ti- catalyzed formal [2+2+1] cycloaddition of phenyl propyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space along with k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and only optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (> 90% C) by incorporating 2,6-dimethyl- 4-(pyrrolidin-1-yl)pyridine as a ligand. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development. </p> </div> </div> </div>

scholarly journals Bioprospecting of Sechium spp. varieties for the selection of characters with pharmacological activity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
María Isabel Iñiguez-Luna ◽  
Jorge Cadena-Iñiguez ◽  
Ramón Marcos Soto-Hernández ◽  
Francisco Javier Morales-Flores ◽  
Moisés Cortes-Cruz ◽  
...  
Keyword(s):  
Cell Lines ◽  
Proliferative Activity ◽  
Meta Analysis ◽  
Pearson Correlation ◽  
Statistical Significance ◽  
Economic Value ◽  
Principal Component ◽  
Malignant Cell ◽  
Length Polymorphisms ◽  
Relevant Variables

AbstractBioprospecting identifies new sources of compounds with actual or potential economic value that come from biodiversity. An analysis was performed regarding bioprospecting purposes in ten genotypes of Sechium spp., through a meta-analysis of 20 information sources considering different variables: five morphological, 19 biochemical, anti-proliferative activity of extracts on five malignant cell lines, and 188 polymorphic bands of amplified fragment length polymorphisms, were used in order to identify the most relevant variables for the design of genetic interbreeding. Significant relationships between morphological and biochemical characters and anti-proliferative activity in cell lines were obtained, with five principal components for principal component analysis (SAS/ETS); variables were identified with a statistical significance (< 0.7 and Pearson values ≥ 0.7), with 80.81% of the accumulation of genetic variation and 110 genetic bands. Thirty-nine (39) variables were recovered using NTSYSpc software where 30 showed a Pearson correlation (> 0.5) and nine variables (< 0.05), Finally, using a cladistics analysis approach highlighted 65 genetic bands, in addition to color of the fruit, presence of thorns, bitter flavor, piriform and oblong shape, and also content of chlorophylls a and b, presence of cucurbitacins, and the IC50 effect of chayote extracts on the four cell lines.

scholarly journals Exploring the predictive value of lesion topology on motor function outcomes in a porcine ischemic stroke model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kelly M. Scheulin ◽  
Brian J. Jurgielewicz ◽  
Samantha E. Spellicy ◽  
Elizabeth S. Waters ◽  
Emily W. Baker ◽  
...  
Keyword(s):  
Motor Function ◽  
Pearson Correlation ◽  
Topological Analysis ◽  
Brain Atlas ◽  
Analysis Approach ◽  
Lesion Volume ◽  
Stroke Outcomes ◽  
Correlation Analyses ◽  
Diagnostic Potential ◽  
Functional Gait

AbstractHarnessing the maximum diagnostic potential of magnetic resonance imaging (MRI) by including stroke lesion location in relation to specific structures that are associated with particular functions will likely increase the potential to predict functional deficit type, severity, and recovery in stroke patients. This exploratory study aims to identify key structures lesioned by a middle cerebral artery occlusion (MCAO) that impact stroke recovery and to strengthen the predictive capacity of neuroimaging techniques that characterize stroke outcomes in a translational porcine model. Clinically relevant MRI measures showed significant lesion volumes, midline shifts, and decreased white matter integrity post-MCAO. Using a pig brain atlas, damaged brain structures included the insular cortex, somatosensory cortices, temporal gyri, claustrum, and visual cortices, among others. MCAO resulted in severely impaired spatiotemporal gait parameters, decreased voluntary movement in open field testing, and higher modified Rankin Scale scores at acute timepoints. Pearson correlation analyses at acute timepoints between standard MRI metrics (e.g., lesion volume) and functional outcomes displayed moderate R values to functional gait outcomes. Moreover, Pearson correlation analyses showed higher R values between functional gait deficits and increased lesioning of structures associated with motor function, such as the putamen, globus pallidus, and primary somatosensory cortex. This correlation analysis approach helped identify neuroanatomical structures predictive of stroke outcomes and may lead to the translation of this topological analysis approach from preclinical stroke assessment to a clinical biomarker.

scholarly journals Towards Understanding Variability in Droughts in Response to Extreme Climate Conditions over the Different Agro-Ecological Zones of Pakistan

2021 ◽  
Vol 13 (12) ◽  
pp. 6910
Author(s):  
Adil Dilawar ◽  
Baozhang Chen ◽  
Arfan Arshad ◽  
Lifeng Guo ◽  
Muhammad Irfan Ehsan ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Principal Component ◽  
Mitigation Strategies ◽  
Precipitation Extremes ◽  
Climate Mitigation ◽  
Mean Annual Temperature ◽  
Climate Extreme ◽  
Ecological Zones ◽  
The Mean ◽  
Agro Ecological Zones

Here, we provided a comprehensive analysis of long-term drought and climate extreme patterns in the agro ecological zones (AEZs) of Pakistan during 1980–2019. Drought trends were investigated using the standardized precipitation evapotranspiration index (SPEI) at various timescales (SPEI-1, SPEI-3, SPEI-6, and SPEI-12). The results showed that droughts (seasonal and annual) were more persistent and severe in the southern, southwestern, southeastern, and central parts of the region. Drought exacerbated with slopes of −0.02, −0.07, −0.08, −0.01, and −0.02 per year. Drought prevailed in all AEZs in the spring season. The majority of AEZs in Pakistan’s southern, middle, and southwestern regions had experienced substantial warming. The mean annual temperature minimum (Tmin) increased faster than the mean annual temperature maximum (Tmax) in all zones. Precipitation decreased in the southern, northern, central, and southwestern parts of the region. Principal component analysis (PCA) revealed a robust increase in temperature extremes with a variance of 76% and a decrease in precipitation extremes with a variance of 91% in the region. Temperature and precipitation extremes indices had a strong Pearson correlation with drought events. Higher temperatures resulted in extreme drought (dry conditions), while higher precipitation levels resulted in wetting conditions (no drought) in different AEZs. In most AEZs, drought occurrences were more responsive to precipitation. The current findings are helpful for climate mitigation strategies and specific zonal efforts are needed to alleviate the environmental and societal impacts of drought.

scholarly journals Key soil parameters affecting the survival of Panax notoginseng under continuous cropping

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wumei Xu ◽  
Fengyun Wu ◽  
Haoji Wang ◽  
Linyan Zhao ◽  
Xue Liu ◽  
...  
Keyword(s):  
Soil Ph ◽  
Pearson Correlation ◽  
Principal Component ◽  
Panax Notoginseng ◽  
Soil Parameters ◽  
Continuous Cropping ◽  
Poor Growth ◽  
Available N ◽  
The Poor ◽  
Replant Problem

AbstractNegative plant-soil feedbacks lead to the poor growth of Panax notoginseng (Sanqi), a well-known herb in Asia and has been used worldwide, under continuous cropping. However, the key soil parameters causing the replant problem are still unclear. Here we conducted a field experiment after 5-year continuous cropping. Sanqi seedlings were cultivated in 7 plots (1.5 m × 2 m), which were randomly assigned along a survival gradient. In total, 13 important soil parameters were measured to understand their relationship with Sanqi’s survival. Pearson correlation analysis showed that 6 soil parameters, including phosphatase, urease, cellulase, bacteria/fungi ratio, available N, and pH, were all correlated with Sanqi’s survival rate (P < 0.05). Principal component analysis (PCA) indicated that they explained 61% of the variances based on the first component, with soil pH being closely correlated with other parameters affecting Sanqi’s survival. The optimum pH for Sanqi growth is about 6.5, but the mean soil pH in the study area is 5.27 (4.86–5.68), therefore it is possible to ameliorate the poor growth of Sanqi by increasing soil pH. This study may also help to reduce the replant problem of other crops under continuous cropping since it is widespread in agricultural production.

PSXIII-26 Supplementation with a polyphenolic pine bark extract reduces ammonia nitrogen production in a RUSITEC system as determined by principal component analysis

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 435-436
Author(s):  
Nelson Vera ◽  
Constanza Gutierrez ◽  
Pamela Williams ◽  
Cecilia Fuentealba ◽  
Rodrigo Allende ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Repeated Measures ◽  
Pearson Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
Ammonia Nitrogen ◽  
Pine Bark ◽  
Bark Extract ◽  
Ruminal Fermentation ◽  
Fermentation Parameters

Abstract The aim of the study was to correlate the effects of supplementation with a polyphenolic pine bark extract (PBE) in diets with different forage to concentrate (F:C) ratio on methane (CH4), ammonia nitrogen (NH3–N) production and ruminal fermentation parameters using the Rumen Simulation Technique (RUSITEC). The experimental diets were F:C 70:30 (HF) or F:C 30:70 (HC) with or without 2% PBE on a DM basis. The four diets were isoproteic (15% CP), with similar OM (HF 94% and HC 96%), but different NDF (HF 40% and HC 25%). The treatments, in duplicate, were assigned in an 8 fermenter RUSITEC apparatus. Incubations were run twice, with 5 days of sampling after 10 days adaptation. The experimental design was a 2x2 factorial arrangement in a randomized complete block with repeated measures. Pearson correlation and principal component analysis (PCA) were conducted to elucidate relationships among PBE total polyphenols (TP) and the variables evaluated. The TP was highly correlated with NH3–N (r = –0.98; P &lt; 0.001) and butyrate (r = –0.85; P &lt; 0.001), and had a high correlation with propionate (r = 0.75; P &lt; 0.001) and acetate (r = 0.68; P = 0.001). Correlation with total VFA was moderate (r = –0.59; P = 0.006), and CH4 yield and IVDMD there were not correlated (r ≤ –0.07; P ≥ 0.188). The PCA (KMO = 0.655; BTS &lt; 0.001) shows that 75.2% of the total variation is explained by the first two principal components (PC1 = 46.5% and PC2 = 28.7%). In the score plot, PC1 discriminated between diets with and without PBE, while the PC2 separated based on NDF. The loading plot showed that TP and propionate were clustered, and had inverse directions to NH3–N. In conclusion, the PBE supplementation reduces NH3–N production in a RUSITEC system without decreasing CH4 yield or negatively affecting ruminal fermentation parameters.

scholarly journals Space time evolution of the trophic state of a subtropical lagoon: Lagoa da Conceição, Florianópolis Island of Santa Catarina, Brazil

RBRH ◽  
2017 ◽  
Vol 22 (0) ◽  
Author(s):  
Victor Eduardo Cury Silva ◽  
Davide Franco ◽  
Alessandra Larissa Fonseca ◽  
Maria Luiza Fontes ◽  
Alejandro Rodolfo Donnangelo
Keyword(s):  
Water Column ◽  
Trophic Level ◽  
Algal Blooms ◽  
Trophic State ◽  
Coastal Lagoons ◽  
Principal Component ◽  
Quality Data ◽  
Santa Catarina ◽  
Water Quality Data ◽  
Subtropical Lagoon

ABSTRACT High levels of eutrophication in coastal lagoons due to human activity have been documented worldwide. Among the main impacts observed are anoxia, hypoxia, toxic algal blooms, fish kills, loss of biodiversity and loss of bathing. This study aimed to evaluate the evolution of the trophic state of Lagoa da Conceição, a subtropical lagoon located in an urbanized watershed on the island of Santa Catarina - Brazil. Spatio temporal patterns of stratification and eutrophication were investigated to understand the main biochemical changes over time. The water quality data were obtained from field campaigns supplemented with literature of the last 15 years. The vertical structure of the water column and the trophic state were evaluated by the stratification index and the TRIX index, respectively. Analyses of variance were performed in order to identify possible temporal variations in vertical stratification and trophic level. Eutrophication effects on biogeochemical cycles were verified through a multi-dimensional cluster analysis (MDS) and correlations between variables related to physical, chemical and biological processes were verified by principal component analysis (PCA). The results showed that the water column is homogeneous in all regions except in the central region of the lagoon, and the highest ammonia concentrations and lowest dissolved oxygen concentrations with periods of anoxia are observed in bottom waters. The study looked at the high trophic level of the lagoon and its inability to process the biogeochemical changes imposed by urban development.

scholarly journals Development and Field Evaluation of Data-driven Whole Building Fault Detection and Diagnosis Strategy

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Yimin Chen ◽  
Jin Wen
Keyword(s):  
Fault Detection ◽  
Principal Component ◽  
Field Evaluation ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Component Level ◽  
Automation Systems ◽  
Tightly Coupled ◽  
Detection And Diagnosis

Faults, i.e., malfunctioned sensors, components, control, and systems, in a building have significantly adverse impacts on the building’s energy consumption and indoor environment. To date, extensive research has been conducted on the development of component level fault detection and diagnosis (FDD) for building systems, especially the Heating, Ventilating, and Air Conditioning (HVAC) system. However, for faults that have multi-system impacts, component level FDD tools may encounter high false alarm rate due to the fact that HVAC subsystems are often tightly coupled together. Hence, the detection and diagnosis of whole building faults is the focus of this study. Here, a whole building fault refers to a fault that occurs in one subsystem but triggers abnormalities in other subsystems and have significant adverse whole building energy impact. The wide adoption of building automation systems (BAS) and the development of machine learning techniques make it possible and cost-efficient to detect and diagnose whole building faults using data-driven methods. In this study, a whole building FDD strategy which adopts weather and schedule information based pattern matching (WPM) method and feature based Principal Component Analysis (FPCA) for fault detection, as well as Bayesian Networks (BNs) based method for fault diagnosis is developed. Fault tests are implemented in a real campus building. The collected data are used to evaluate the performance of the proposed whole building FDD strategies.

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