the pls toolbox 2.0 Search Results


96
MathWorks Inc pls toolbox
Pls Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/the+pls+toolbox+2%2E0/Optimization+Toolbox/10__3390_slash_rs11111331-130-8-12
Average 96 stars, based on 1 article reviews
pls toolbox - by Bioz Stars, 2026-09
96/100 stars
  Buy from Supplier

86
Eigenvector Research Inc pls toolbox
Pls Toolbox, supplied by Eigenvector Research Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/the+pls+toolbox+2%2E0/pls+toolbox/pmc13150815-60-22-24
Average 86 stars, based on 1 article reviews
pls toolbox - by Bioz Stars, 2026-09
86/100 stars
  Buy from Supplier

96
MathWorks Inc pls toolbox in matlab
Figure 6. (A) Absorbance–concentration standard curve of MB (inset photos present standard solutions of MB with concentrations ranging from 0 to 54 µM); and <t>(B)</t> <t>RGB-based</t> <t>PLS</t> model for rapid monitoring of MB concentrations.
Pls Toolbox In Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/the+pls+toolbox+2%2E0/Database+Toolbox/pm40431987-64-11-14
Average 96 stars, based on 1 article reviews
pls toolbox in matlab - by Bioz Stars, 2026-09
96/100 stars
  Buy from Supplier

90
Baycrest Technology Pty Ltd pls toolbox
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
Pls Toolbox, supplied by Baycrest Technology Pty Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/the+pls+toolbox+2%2E0/pls+toolbox/pmc10570707-84-25-28
Average 90 stars, based on 1 article reviews
pls toolbox - by Bioz Stars, 2026-09
90/100 stars
  Buy from Supplier

90
Baycrest Technology Pty Ltd rotman-baycrest pls toolbox
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
Rotman Baycrest Pls Toolbox, supplied by Baycrest Technology Pty Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/the+pls+toolbox+2%2E0/rotman+baycrest+pls+toolbox/pmc08481307-278-9-9
Average 90 stars, based on 1 article reviews
rotman-baycrest pls toolbox - by Bioz Stars, 2026-09
90/100 stars
  Buy from Supplier

90
The Matworks Company LLC pls_toolbox version 8.2.1
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
Pls Toolbox Version 8.2.1, supplied by The Matworks Company LLC, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/the+pls+toolbox+2%2E0/pls+toolbox+version+8+2+1/pmc08750308-109-9-12
Average 90 stars, based on 1 article reviews
pls_toolbox version 8.2.1 - by Bioz Stars, 2026-09
90/100 stars
  Buy from Supplier

90
PLS design fm-pls design
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
Fm Pls Design, supplied by PLS design, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/the+pls+toolbox+2%2E0/fm+pls+design/pmc09861249-49-35-11
Average 90 stars, based on 1 article reviews
fm-pls design - by Bioz Stars, 2026-09
90/100 stars
  Buy from Supplier

86
Sarstedt pls
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
Pls, supplied by Sarstedt, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/the+pls+toolbox+2%2E0/pls/10__5267_slash_j__ijdns__2023__7__004-229-0-18
Average 86 stars, based on 1 article reviews
pls - by Bioz Stars, 2026-09
86/100 stars
  Buy from Supplier

90
SmartPLS GmbH finite mixture pls (fimix-pls)
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
Finite Mixture Pls (Fimix Pls), supplied by SmartPLS GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/the+pls+toolbox+2%2E0/finite+mixture+pls++fimix+pls++module+of+smartpls+3+software/pmc08710570-324-17-21
Average 90 stars, based on 1 article reviews
finite mixture pls (fimix-pls) - by Bioz Stars, 2026-09
90/100 stars
  Buy from Supplier

Image Search Results


Figure 6. (A) Absorbance–concentration standard curve of MB (inset photos present standard solutions of MB with concentrations ranging from 0 to 54 µM); and (B) RGB-based PLS model for rapid monitoring of MB concentrations.

Journal: Sensors (Basel, Switzerland)

Article Title: Rapid Quantitative Detection of Dye Concentration in Pt/TiO 2 Photocatalytic System Based on RGB Sensing.

doi: 10.3390/s25103195

Figure Lengend Snippet: Figure 6. (A) Absorbance–concentration standard curve of MB (inset photos present standard solutions of MB with concentrations ranging from 0 to 54 µM); and (B) RGB-based PLS model for rapid monitoring of MB concentrations.

Article Snippet: Extracted RGB data and corresponding MB concentrations were imported into the PLS toolbox in MATLAB (R2024a) to establish a quantitative calibration model correlating RGB values with MB concentrations.

Techniques: Concentration Assay

Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), and PLSC to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, partial least squares correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.

Journal: Neurobiology of Stress

Article Title: Pre-COVID brain network topology prospectively predicts social anxiety alterations during the COVID-19 pandemic

doi: 10.1016/j.ynstr.2023.100578

Figure Lengend Snippet: Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), and PLSC to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, partial least squares correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.

Article Snippet: To evaluate multivariate patterns of correlation between the nodal-level topological property (degree centrality) and SA alterations across subjects, we used PLSC via the publicly available PLS toolbox ( https://www.rotman-baycrest.on.ca/index.php?section=84 ) in MATLAB R2018b (MathWorks, Natick, USA).

Techniques: Functional Assay, Magnetic Resonance Imaging