Structured Review

Intel bm3d algorithm
Dual wavelength DH. ( a , c , e , g ) color SLDH. ( b , d , f , h ) color <t>MLDH-BM3D.</t> Details of the image segments indicated by the white boxes are shown on the right side of the panel. The black box in h indicates the signal region where the noise measurements reported in column I of Table 1 were performed.
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Images

1) Product Images from "Quasi noise-free digital holography"

Article Title: Quasi noise-free digital holography

Journal: Light, Science & Applications

doi: 10.1038/lsa.2016.142

Dual wavelength DH. ( a , c , e , g ) color SLDH. ( b , d , f , h ) color MLDH-BM3D. Details of the image segments indicated by the white boxes are shown on the right side of the panel. The black box in h indicates the signal region where the noise measurements reported in column I of Table 1 were performed.
Figure Legend Snippet: Dual wavelength DH. ( a , c , e , g ) color SLDH. ( b , d , f , h ) color MLDH-BM3D. Details of the image segments indicated by the white boxes are shown on the right side of the panel. The black box in h indicates the signal region where the noise measurements reported in column I of Table 1 were performed.

Techniques Used:

Quantitative evaluation of the algorithm performance. ( a ) N C (%) vs. the number of looks, L , in the MLDH image (blue line), showing a ML improvement saturation. The theoretical ML improvement bound is plotted with a red line. The N C values corresponding to L =100 for MLDH, MLDH-NLM and MLDH-BM3D are indicated with a blue circle, an orange plus sign and a green cross, respectively. The plot shows the noise reduction percentage with respect to the noise level of the SLDH image (indicated by a red square). ( b ) N C (%) measured over the image segments A1-A5 of the SLDH (blue circles), MLDH (yellow triangles) and MLDH-BM3D (green crosses). The cascade of EG and SEF allows the theoretical ML improvement bound to be overcome and quasi noise-free DH reconstructions to be obtained.
Figure Legend Snippet: Quantitative evaluation of the algorithm performance. ( a ) N C (%) vs. the number of looks, L , in the MLDH image (blue line), showing a ML improvement saturation. The theoretical ML improvement bound is plotted with a red line. The N C values corresponding to L =100 for MLDH, MLDH-NLM and MLDH-BM3D are indicated with a blue circle, an orange plus sign and a green cross, respectively. The plot shows the noise reduction percentage with respect to the noise level of the SLDH image (indicated by a red square). ( b ) N C (%) measured over the image segments A1-A5 of the SLDH (blue circles), MLDH (yellow triangles) and MLDH-BM3D (green crosses). The cascade of EG and SEF allows the theoretical ML improvement bound to be overcome and quasi noise-free DH reconstructions to be obtained.

Techniques Used:

( Supplementary Movie 3 ) Contrast analysis. ( a ) SLDH. ( b ) MLDH-BM3D. ( c ) Image contrast plotted along the lines indicated in the insets of a and b . Red: SLDH. Green: MLDH-BM3D. A close up of the flag stripes is also shown, demonstrating the capability of MLDH-BM3D to resolve all seven stripes on the US flag.
Figure Legend Snippet: ( Supplementary Movie 3 ) Contrast analysis. ( a ) SLDH. ( b ) MLDH-BM3D. ( c ) Image contrast plotted along the lines indicated in the insets of a and b . Red: SLDH. Green: MLDH-BM3D. A close up of the flag stripes is also shown, demonstrating the capability of MLDH-BM3D to resolve all seven stripes on the US flag.

Techniques Used:

Numerical MLDH-BM3D applied to color holograms (green and red) of a matryoshka doll. ( a , c ) SLDH. ( b , d ) MLDH-BM3D. ( e ) Relative deviation (R D ) visualization of selected details on the object highlighted by green and red boxes in a and c , respectively. ( f ) The same regions extracted from MLDH-BM3D reconstructions in b and d for a direct comparison. Yellow and blue boxes in b and d identify background regions and signal regions, respectively, over which the percentage image contrast is evaluated.
Figure Legend Snippet: Numerical MLDH-BM3D applied to color holograms (green and red) of a matryoshka doll. ( a , c ) SLDH. ( b , d ) MLDH-BM3D. ( e ) Relative deviation (R D ) visualization of selected details on the object highlighted by green and red boxes in a and c , respectively. ( f ) The same regions extracted from MLDH-BM3D reconstructions in b and d for a direct comparison. Yellow and blue boxes in b and d identify background regions and signal regions, respectively, over which the percentage image contrast is evaluated.

Techniques Used:

Comparison between noisy and denoised DH reconstructions. ( a ) Noisy SLDH reconstruction. ( b ) MLDH. ( c ) SLDH-NLM cascade. ( d ) MLDH-NLM cascade. ( e ) SLDH-BM3D. ( f ) Quasi noise-free MLDH-BM3D reconstruction. ( g ) Details corresponding to SLDH (top row), MLDH-NLM (middle row) and MLDH-BM3D (bottom row). The enlarged areas correspond to the color boxes in a . In the bottom panels, the calculations of the percentage noise contrast improvements of MLDH-BM3D with respect to MLDH-NLM are reported.
Figure Legend Snippet: Comparison between noisy and denoised DH reconstructions. ( a ) Noisy SLDH reconstruction. ( b ) MLDH. ( c ) SLDH-NLM cascade. ( d ) MLDH-NLM cascade. ( e ) SLDH-BM3D. ( f ) Quasi noise-free MLDH-BM3D reconstruction. ( g ) Details corresponding to SLDH (top row), MLDH-NLM (middle row) and MLDH-BM3D (bottom row). The enlarged areas correspond to the color boxes in a . In the bottom panels, the calculations of the percentage noise contrast improvements of MLDH-BM3D with respect to MLDH-NLM are reported.

Techniques Used:

( Supplementary Movies 1 and 2 ). Numerical MLDH-BM3D is applied to objects rotated by means of a stage, as sketched in ( a ) along with the corresponding photos. ( b , d ) SLDH. ( c , e ) MLDH-BM3D reconstructions. Details of the image segments indicated by yellow boxes are shown on the right side of the panel. As a result of denoising, MLDH-BM3D reconstructions show finer details, are better resolved and have improved sharpness on the edges and flatness over the homogeneous segments. Images in b , c are frames extracted from Supplementary Movie 2 . Images in d , e are extracted from Supplementary Movie 1 .
Figure Legend Snippet: ( Supplementary Movies 1 and 2 ). Numerical MLDH-BM3D is applied to objects rotated by means of a stage, as sketched in ( a ) along with the corresponding photos. ( b , d ) SLDH. ( c , e ) MLDH-BM3D reconstructions. Details of the image segments indicated by yellow boxes are shown on the right side of the panel. As a result of denoising, MLDH-BM3D reconstructions show finer details, are better resolved and have improved sharpness on the edges and flatness over the homogeneous segments. Images in b , c are frames extracted from Supplementary Movie 2 . Images in d , e are extracted from Supplementary Movie 1 .

Techniques Used:

Block scheme of the MLDH-BM3D algorithm. EG and SEF are sequential and complementary steps returning the MLDH-BM3D reconstruction.
Figure Legend Snippet: Block scheme of the MLDH-BM3D algorithm. EG and SEF are sequential and complementary steps returning the MLDH-BM3D reconstruction.

Techniques Used: Blocking Assay

2) Product Images from "Enhancing sparse-view photoacoustic tomography with combined virtually parallel projecting and spatially adaptive filtering"

Article Title: Enhancing sparse-view photoacoustic tomography with combined virtually parallel projecting and spatially adaptive filtering

Journal: Biomedical Optics Express

doi: 10.1364/BOE.9.004569

Reconstructed results of the tumor-mimicking tissue sample: (a)-(c) UBP, TV-GD and IRT-BM3D results for #60-view case; (d)-(f) UBP, TV-GD and IRT-BM3D results for #30-view case.
Figure Legend Snippet: Reconstructed results of the tumor-mimicking tissue sample: (a)-(c) UBP, TV-GD and IRT-BM3D results for #60-view case; (d)-(f) UBP, TV-GD and IRT-BM3D results for #30-view case.

Techniques Used:

Flowchart of the implementation of IRT-BM3D.
Figure Legend Snippet: Flowchart of the implementation of IRT-BM3D.

Techniques Used:

Reconstructed results of the vessel phantom with tumor targets: (a)-(c) UBP, TV-GD and IRT-BM3D results for #60-view case; (d)-(f) UBP, TV-GD and IRT-BM3D results for #30-view case.
Figure Legend Snippet: Reconstructed results of the vessel phantom with tumor targets: (a)-(c) UBP, TV-GD and IRT-BM3D results for #60-view case; (d)-(f) UBP, TV-GD and IRT-BM3D results for #30-view case.

Techniques Used:

Reconstructed results of the vessel phantom with tumor targets in #60-view case with different SNR values: (a)-(d) UBP results in the cases of SNR = 5 dB, 10 dB, 15 dB and 20 dB; (e)-(h) IRT-BM3D results in the cases of SNR = 5 dB, 10 dB, 15 dB and 20 dB.
Figure Legend Snippet: Reconstructed results of the vessel phantom with tumor targets in #60-view case with different SNR values: (a)-(d) UBP results in the cases of SNR = 5 dB, 10 dB, 15 dB and 20 dB; (e)-(h) IRT-BM3D results in the cases of SNR = 5 dB, 10 dB, 15 dB and 20 dB.

Techniques Used:

Reconstructed images of the tumor-mimicking tissue sample: (a)-(e) UBP results of #360-, #120-, #90- #60- and #30-view cases; (f)-(j) IRT-BM3D results of #360-, #120-, #90-, #60- and #30-view cases; (k)-(l) Photographs of the biological tissue sample; (m) Reconstructed details in yellow dotted boxes in (e) and (j).
Figure Legend Snippet: Reconstructed images of the tumor-mimicking tissue sample: (a)-(e) UBP results of #360-, #120-, #90- #60- and #30-view cases; (f)-(j) IRT-BM3D results of #360-, #120-, #90-, #60- and #30-view cases; (k)-(l) Photographs of the biological tissue sample; (m) Reconstructed details in yellow dotted boxes in (e) and (j).

Techniques Used:

Reconstructed results of the vessel phantom with tumor targets: (a)-(e) UBP results of #360-, #120-, #90- #60- and #30-view cases; (f)-(j) IRT-BM3D results of #360-, #120-, #90-, #60- and #30-view cases; (k) Exact image of the phantom; (l)-(p) Profiles (along line A-A’) of the recovered images in #360-, #120-, #90-, #60- and #30-view cases.
Figure Legend Snippet: Reconstructed results of the vessel phantom with tumor targets: (a)-(e) UBP results of #360-, #120-, #90- #60- and #30-view cases; (f)-(j) IRT-BM3D results of #360-, #120-, #90-, #60- and #30-view cases; (k) Exact image of the phantom; (l)-(p) Profiles (along line A-A’) of the recovered images in #360-, #120-, #90-, #60- and #30-view cases.

Techniques Used:

Reconstructed images of the ex vivo mouse intestinal tissue: (a) Photograph of the tissue sample; (b) #90-view IRT-BM3D result; (c) #90-view UBP result. The reconstructed details are shown in white dotted boxes.
Figure Legend Snippet: Reconstructed images of the ex vivo mouse intestinal tissue: (a) Photograph of the tissue sample; (b) #90-view IRT-BM3D result; (c) #90-view UBP result. The reconstructed details are shown in white dotted boxes.

Techniques Used: Ex Vivo

3) Product Images from "CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU"

Article Title: CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU

Journal: BMC Bioinformatics

doi: 10.1186/s12859-016-0946-4

Evaluations of CUDAMPF with Intel Xeon, i5 and i7 on SSVFilter
Figure Legend Snippet: Evaluations of CUDAMPF with Intel Xeon, i5 and i7 on SSVFilter

Techniques Used:

4) Product Images from "Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region"

Article Title: Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region

Journal: PLoS ONE

doi: 10.1371/journal.pone.0133908

1. Shortest-Path Based Graph Search for Boundary Detection C n 1 C n 2 C n 8 . Note that boundaries are delineated with red, yellow, magenta, white, cyan, green black and blue solid lines, respectively and the notations are summarized in Table 1 . A parafoveal scan is chosen in order to show all the layers that are segmented by OCTRIMA 3D.
Figure Legend Snippet: 1. Shortest-Path Based Graph Search for Boundary Detection C n 1 C n 2 C n 8 . Note that boundaries are delineated with red, yellow, magenta, white, cyan, green black and blue solid lines, respectively and the notations are summarized in Table 1 . A parafoveal scan is chosen in order to show all the layers that are segmented by OCTRIMA 3D.

Techniques Used:

OCTRIMA 3D framework to detect each intraretinal layer boundary using the shortest-path based graph search approach.
Figure Legend Snippet: OCTRIMA 3D framework to detect each intraretinal layer boundary using the shortest-path based graph search approach.

Techniques Used:

The comparison between OCTRIMA 3D (red solid line) and the algorithm by Chiu et al. (blue solid line) using manual labeling as the ground truth (green solid line).
Figure Legend Snippet: The comparison between OCTRIMA 3D (red solid line) and the algorithm by Chiu et al. (blue solid line) using manual labeling as the ground truth (green solid line).

Techniques Used: Labeling

Algorithms performance in the B-scan obtained from the patient with diabetic macular edema. (a) The raw OCT B-scan. (b) The boundaries delineated by the built-in Spectralis SD-OCT software for the ILM and RPE-CH. The yellow arrows are indicating the boundary detection errors by the built-in software of the Spectralis device. (c) The boundaries delineated by OCTRIMA 3D for the ILM and the RPE-CH.
Figure Legend Snippet: Algorithms performance in the B-scan obtained from the patient with diabetic macular edema. (a) The raw OCT B-scan. (b) The boundaries delineated by the built-in Spectralis SD-OCT software for the ILM and RPE-CH. The yellow arrows are indicating the boundary detection errors by the built-in software of the Spectralis device. (c) The boundaries delineated by OCTRIMA 3D for the ILM and the RPE-CH.

Techniques Used: Software

The overview of OCTRIMA 3D framework. The boundaries labeled using blue and red fonts have the dark-to-bright and bright-to-dark transitions, respectively.
Figure Legend Snippet: The overview of OCTRIMA 3D framework. The boundaries labeled using blue and red fonts have the dark-to-bright and bright-to-dark transitions, respectively.

Techniques Used: Labeling

The comparison between Dufour’s Software (magenta solid line), IOWA reference algorithm (blue solid line) and OCTRIMA 3D (red solid line) using manual labeling as the ground truth (green solid line).
Figure Legend Snippet: The comparison between Dufour’s Software (magenta solid line), IOWA reference algorithm (blue solid line) and OCTRIMA 3D (red solid line) using manual labeling as the ground truth (green solid line).

Techniques Used: Software, Labeling

The segmentation results obtained for the B-scan in the eye with dry age-related macular degeneration using Dufour’s software and the OCTRIMA 3D algorithm. The legend of the boundaries is the same as Fig 1 . (a) THe raw OCT B-scan. (b) The segmentation result of Dufour’s software. The IS-OS delineation failed at the left most and center area of the B-scan. (c) The initial segmentation results of OCTRIMA 3D detected retinal boundaries reliably except for the IS-OS in the drusen area (green doted line). By adjusting the flattening step, the IS-OS is delineated correctly (green solid line).
Figure Legend Snippet: The segmentation results obtained for the B-scan in the eye with dry age-related macular degeneration using Dufour’s software and the OCTRIMA 3D algorithm. The legend of the boundaries is the same as Fig 1 . (a) THe raw OCT B-scan. (b) The segmentation result of Dufour’s software. The IS-OS delineation failed at the left most and center area of the B-scan. (c) The initial segmentation results of OCTRIMA 3D detected retinal boundaries reliably except for the IS-OS in the drusen area (green doted line). By adjusting the flattening step, the IS-OS is delineated correctly (green solid line).

Techniques Used: Software

Comparison of unsigned segmentation errors on six surfaces between Dufour’s algorithm (left column), the IOWA reference algorithm (middle column) and OCTRIMA 3D (right column) in the ETDRS regions. The graph bar scale indicates the error magnitude in microns. The mean unsigned segmentation errors are reported in Table 2 .
Figure Legend Snippet: Comparison of unsigned segmentation errors on six surfaces between Dufour’s algorithm (left column), the IOWA reference algorithm (middle column) and OCTRIMA 3D (right column) in the ETDRS regions. The graph bar scale indicates the error magnitude in microns. The mean unsigned segmentation errors are reported in Table 2 .

Techniques Used:

5) Product Images from "Constrained distance transforms for spatial atlas registration"

Article Title: Constrained distance transforms for spatial atlas registration

Journal: BMC Bioinformatics

doi: 10.1186/s12859-015-0504-5

3D warp with tail flip. Clockwise from bottom left: Target volume, source volume, source warped to target using conventional RBF (truncated and rescaled), source warped to target using CDT, cut section through source warped using CDT and cut section through target. Landmarks are shown as spheres on the source and target volumes.
Figure Legend Snippet: 3D warp with tail flip. Clockwise from bottom left: Target volume, source volume, source warped to target using conventional RBF (truncated and rescaled), source warped to target using CDT, cut section through source warped using CDT and cut section through target. Landmarks are shown as spheres on the source and target volumes.

Techniques Used:

6) Product Images from "Local error estimates for adaptive simulation of the Reaction–Diffusion Master Equation via operator splitting"

Article Title: Local error estimates for adaptive simulation of the Reaction–Diffusion Master Equation via operator splitting

Journal: Journal of computational physics

doi: 10.1016/j.jcp.2014.02.004

The figure shows the time steps selected by the adaptive DFSP method, along with the oscillation pattern of the membrane bound MinD protein for a representative trajectory. Note that the timestep adapts to the dynamics of the MinD oscillation. Due to
Figure Legend Snippet: The figure shows the time steps selected by the adaptive DFSP method, along with the oscillation pattern of the membrane bound MinD protein for a representative trajectory. Note that the timestep adapts to the dynamics of the MinD oscillation. Due to

Techniques Used:

7) Product Images from "GPU-based Green’s function simulations of shear waves generated by an applied acoustic radiation force in elastic and viscoelastic models"

Article Title: GPU-based Green’s function simulations of shear waves generated by an applied acoustic radiation force in elastic and viscoelastic models

Journal: Physics in medicine and biology

doi: 10.1088/1361-6560/aabe36

Computation times for serial and parallel implementations of time domain shear wave calculations with Green’s functions. The solid line with circle markers indicates the computation times for parallel simulations of shear waves with the elastic model, the dashed-dotted line with triangle markers indicates the computation times for parallel simulations of shear waves with the viscoelastic model, the dashed line with diamond markers indicates the computation times for serial simulations of shear waves with the elastic model, and the dotted line with square markers indicates the computation times for serial simulations of shear waves with the viscoelastic model.
Figure Legend Snippet: Computation times for serial and parallel implementations of time domain shear wave calculations with Green’s functions. The solid line with circle markers indicates the computation times for parallel simulations of shear waves with the elastic model, the dashed-dotted line with triangle markers indicates the computation times for parallel simulations of shear waves with the viscoelastic model, the dashed line with diamond markers indicates the computation times for serial simulations of shear waves with the elastic model, and the dotted line with square markers indicates the computation times for serial simulations of shear waves with the viscoelastic model.

Techniques Used:

8) Product Images from "MSAIndelFR: a scheme for multiple protein sequence alignment using information on indel flanking regions"

Article Title: MSAIndelFR: a scheme for multiple protein sequence alignment using information on indel flanking regions

Journal: BMC Bioinformatics

doi: 10.1186/s12859-015-0826-3

Boxplots for the distributions of the TC values of MSAIndelFR and the other MSA algorithms using the BAliBASE 3.0 benchmark, where the top and bottom of a box and the line in between represent the third quartile, first quartile and median, respectively
Figure Legend Snippet: Boxplots for the distributions of the TC values of MSAIndelFR and the other MSA algorithms using the BAliBASE 3.0 benchmark, where the top and bottom of a box and the line in between represent the third quartile, first quartile and median, respectively

Techniques Used:

Boxplots for the distributions of the SP values of MSAIndelFR and the other MSA algorithms using the BAliBASE 3.0 benchmark, where the top and bottom of a box and the line in between represent the third quartile, first quartile and median, respectively
Figure Legend Snippet: Boxplots for the distributions of the SP values of MSAIndelFR and the other MSA algorithms using the BAliBASE 3.0 benchmark, where the top and bottom of a box and the line in between represent the third quartile, first quartile and median, respectively

Techniques Used:

9) Product Images from "CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU"

Article Title: CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU

Journal: BMC Bioinformatics

doi: 10.1186/s12859-016-0946-4

Illustrations of proposed reordering and maximum functions for CUDAMPF. Assuming x 4 > x 3 > x 2 > x 1 after intra-warp reductions in ( b )
Figure Legend Snippet: Illustrations of proposed reordering and maximum functions for CUDAMPF. Assuming x 4 > x 3 > x 2 > x 1 after intra-warp reductions in ( b )

Techniques Used:

Evaluations of CUDAMPF with Intel Xeon, i5 and i7 on ViterbiFilter
Figure Legend Snippet: Evaluations of CUDAMPF with Intel Xeon, i5 and i7 on ViterbiFilter

Techniques Used:

CUDAMPF program with NVRTC. After obtaining query model size and device properties, program dynamically makes decisions on unrolling innermost loop and selects the proper kernel file with compiler options
Figure Legend Snippet: CUDAMPF program with NVRTC. After obtaining query model size and device properties, program dynamically makes decisions on unrolling innermost loop and selects the proper kernel file with compiler options

Techniques Used:

Evaluations of CUDAMPF with Intel Xeon, i5 and i7 on SSVFilter
Figure Legend Snippet: Evaluations of CUDAMPF with Intel Xeon, i5 and i7 on SSVFilter

Techniques Used:

Evaluations of CUDAMPF with Intel Xeon, i5 and i7 on MSVFilter
Figure Legend Snippet: Evaluations of CUDAMPF with Intel Xeon, i5 and i7 on MSVFilter

Techniques Used:

CUDAMPF: Multi-tiered Parallel Framework on CUDA-enabled GPU. ( a ) A single GPU consists of n SMXs with m concurrently mounted blocks on each; ( b ) within each block, q resident warps are scheduled by x warp scheduler for processing assigned sequences; ( c ) a warp of threads score alignment of all residues and model states in parallel (warp size is fixed to 32 currently); ( d ) based on 32-bit register and score ranges of different algorithms, each thread processes multiple model states in a single step. The virtual boundary, block , is only regarded as the container of warps rather than a separate tier
Figure Legend Snippet: CUDAMPF: Multi-tiered Parallel Framework on CUDA-enabled GPU. ( a ) A single GPU consists of n SMXs with m concurrently mounted blocks on each; ( b ) within each block, q resident warps are scheduled by x warp scheduler for processing assigned sequences; ( c ) a warp of threads score alignment of all residues and model states in parallel (warp size is fixed to 32 currently); ( d ) based on 32-bit register and score ranges of different algorithms, each thread processes multiple model states in a single step. The virtual boundary, block , is only regarded as the container of warps rather than a separate tier

Techniques Used: Blocking Assay

10) Product Images from "CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU"

Article Title: CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU

Journal: BMC Bioinformatics

doi: 10.1186/s12859-016-0946-4

CUDAMPF: Multi-tiered Parallel Framework on CUDA-enabled GPU. ( a ) A single GPU consists of n SMXs with m concurrently mounted blocks on each; ( b ) within each block, q resident warps are scheduled by x warp scheduler for processing assigned sequences; ( c ) a warp of threads score alignment of all residues and model states in parallel (warp size is fixed to 32 currently); ( d ) based on 32-bit register and score ranges of different algorithms, each thread processes multiple model states in a single step. The virtual boundary, block , is only regarded as the container of warps rather than a separate tier
Figure Legend Snippet: CUDAMPF: Multi-tiered Parallel Framework on CUDA-enabled GPU. ( a ) A single GPU consists of n SMXs with m concurrently mounted blocks on each; ( b ) within each block, q resident warps are scheduled by x warp scheduler for processing assigned sequences; ( c ) a warp of threads score alignment of all residues and model states in parallel (warp size is fixed to 32 currently); ( d ) based on 32-bit register and score ranges of different algorithms, each thread processes multiple model states in a single step. The virtual boundary, block , is only regarded as the container of warps rather than a separate tier

Techniques Used: Blocking Assay

11) Product Images from "Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie, and Ballgown"

Article Title: Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie, and Ballgown

Journal: Nature protocols

doi: 10.1038/nprot.2016.095

Transcript assembly and quantification with StringTie
Figure Legend Snippet: Transcript assembly and quantification with StringTie

Techniques Used:

12) Product Images from "Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie, and Ballgown"

Article Title: Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie, and Ballgown

Journal: Nature protocols

doi: 10.1038/nprot.2016.095

Box 2: Creating a HISAT2 index
Figure Legend Snippet: Box 2: Creating a HISAT2 index

Techniques Used:

13) Product Images from "Quasi noise-free digital holography"

Article Title: Quasi noise-free digital holography

Journal: Light, Science & Applications

doi: 10.1038/lsa.2016.142

Dual wavelength DH. ( a , c , e , g ) color SLDH. ( b , d , f , h ) color MLDH-BM3D. Details of the image segments indicated by the white boxes are shown on the right side of the panel. The black box in h indicates the signal region where the noise measurements reported in column I of Table 1 were performed.
Figure Legend Snippet: Dual wavelength DH. ( a , c , e , g ) color SLDH. ( b , d , f , h ) color MLDH-BM3D. Details of the image segments indicated by the white boxes are shown on the right side of the panel. The black box in h indicates the signal region where the noise measurements reported in column I of Table 1 were performed.

Techniques Used:

Quantitative evaluation of the algorithm performance. ( a ) N C (%) vs. the number of looks, L , in the MLDH image (blue line), showing a ML improvement saturation. The theoretical ML improvement bound is plotted with a red line. The N C values corresponding to L =100 for MLDH, MLDH-NLM and MLDH-BM3D are indicated with a blue circle, an orange plus sign and a green cross, respectively. The plot shows the noise reduction percentage with respect to the noise level of the SLDH image (indicated by a red square). ( b ) N C (%) measured over the image segments A1-A5 of the SLDH (blue circles), MLDH (yellow triangles) and MLDH-BM3D (green crosses). The cascade of EG and SEF allows the theoretical ML improvement bound to be overcome and quasi noise-free DH reconstructions to be obtained.
Figure Legend Snippet: Quantitative evaluation of the algorithm performance. ( a ) N C (%) vs. the number of looks, L , in the MLDH image (blue line), showing a ML improvement saturation. The theoretical ML improvement bound is plotted with a red line. The N C values corresponding to L =100 for MLDH, MLDH-NLM and MLDH-BM3D are indicated with a blue circle, an orange plus sign and a green cross, respectively. The plot shows the noise reduction percentage with respect to the noise level of the SLDH image (indicated by a red square). ( b ) N C (%) measured over the image segments A1-A5 of the SLDH (blue circles), MLDH (yellow triangles) and MLDH-BM3D (green crosses). The cascade of EG and SEF allows the theoretical ML improvement bound to be overcome and quasi noise-free DH reconstructions to be obtained.

Techniques Used:

( Supplementary Movie 3 ) Contrast analysis. ( a ) SLDH. ( b ) MLDH-BM3D. ( c ) Image contrast plotted along the lines indicated in the insets of a and b . Red: SLDH. Green: MLDH-BM3D. A close up of the flag stripes is also shown, demonstrating the capability of MLDH-BM3D to resolve all seven stripes on the US flag.
Figure Legend Snippet: ( Supplementary Movie 3 ) Contrast analysis. ( a ) SLDH. ( b ) MLDH-BM3D. ( c ) Image contrast plotted along the lines indicated in the insets of a and b . Red: SLDH. Green: MLDH-BM3D. A close up of the flag stripes is also shown, demonstrating the capability of MLDH-BM3D to resolve all seven stripes on the US flag.

Techniques Used:

Numerical MLDH-BM3D applied to color holograms (green and red) of a matryoshka doll. ( a , c ) SLDH. ( b , d ) MLDH-BM3D. ( e ) Relative deviation (R D ) visualization of selected details on the object highlighted by green and red boxes in a and c , respectively. ( f ) The same regions extracted from MLDH-BM3D reconstructions in b and d for a direct comparison. Yellow and blue boxes in b and d identify background regions and signal regions, respectively, over which the percentage image contrast is evaluated.
Figure Legend Snippet: Numerical MLDH-BM3D applied to color holograms (green and red) of a matryoshka doll. ( a , c ) SLDH. ( b , d ) MLDH-BM3D. ( e ) Relative deviation (R D ) visualization of selected details on the object highlighted by green and red boxes in a and c , respectively. ( f ) The same regions extracted from MLDH-BM3D reconstructions in b and d for a direct comparison. Yellow and blue boxes in b and d identify background regions and signal regions, respectively, over which the percentage image contrast is evaluated.

Techniques Used:

Comparison between noisy and denoised DH reconstructions. ( a ) Noisy SLDH reconstruction. ( b ) MLDH. ( c ) SLDH-NLM cascade. ( d ) MLDH-NLM cascade. ( e ) SLDH-BM3D. ( f ) Quasi noise-free MLDH-BM3D reconstruction. ( g ) Details corresponding to SLDH (top row), MLDH-NLM (middle row) and MLDH-BM3D (bottom row). The enlarged areas correspond to the color boxes in a . In the bottom panels, the calculations of the percentage noise contrast improvements of MLDH-BM3D with respect to MLDH-NLM are reported.
Figure Legend Snippet: Comparison between noisy and denoised DH reconstructions. ( a ) Noisy SLDH reconstruction. ( b ) MLDH. ( c ) SLDH-NLM cascade. ( d ) MLDH-NLM cascade. ( e ) SLDH-BM3D. ( f ) Quasi noise-free MLDH-BM3D reconstruction. ( g ) Details corresponding to SLDH (top row), MLDH-NLM (middle row) and MLDH-BM3D (bottom row). The enlarged areas correspond to the color boxes in a . In the bottom panels, the calculations of the percentage noise contrast improvements of MLDH-BM3D with respect to MLDH-NLM are reported.

Techniques Used:

( Supplementary Movies 1 and 2 ). Numerical MLDH-BM3D is applied to objects rotated by means of a stage, as sketched in ( a ) along with the corresponding photos. ( b , d ) SLDH. ( c , e ) MLDH-BM3D reconstructions. Details of the image segments indicated by yellow boxes are shown on the right side of the panel. As a result of denoising, MLDH-BM3D reconstructions show finer details, are better resolved and have improved sharpness on the edges and flatness over the homogeneous segments. Images in b , c are frames extracted from Supplementary Movie 2 . Images in d , e are extracted from Supplementary Movie 1 .
Figure Legend Snippet: ( Supplementary Movies 1 and 2 ). Numerical MLDH-BM3D is applied to objects rotated by means of a stage, as sketched in ( a ) along with the corresponding photos. ( b , d ) SLDH. ( c , e ) MLDH-BM3D reconstructions. Details of the image segments indicated by yellow boxes are shown on the right side of the panel. As a result of denoising, MLDH-BM3D reconstructions show finer details, are better resolved and have improved sharpness on the edges and flatness over the homogeneous segments. Images in b , c are frames extracted from Supplementary Movie 2 . Images in d , e are extracted from Supplementary Movie 1 .

Techniques Used:

Block scheme of the MLDH-BM3D algorithm. EG and SEF are sequential and complementary steps returning the MLDH-BM3D reconstruction.
Figure Legend Snippet: Block scheme of the MLDH-BM3D algorithm. EG and SEF are sequential and complementary steps returning the MLDH-BM3D reconstruction.

Techniques Used: Blocking Assay

14) Product Images from "GLIMMPSE: Online Power Computation for Linear Models with and without a Baseline Covariate"

Article Title: GLIMMPSE: Online Power Computation for Linear Models with and without a Baseline Covariate

Journal: Journal of statistical software

doi:

GLIMMPSE results screen.
Figure Legend Snippet: GLIMMPSE results screen.

Techniques Used:

GLIMMPSE mode selection screen.
Figure Legend Snippet: GLIMMPSE mode selection screen.

Techniques Used: Selection

3.2. How to use GLIMMPSE
Figure Legend Snippet: 3.2. How to use GLIMMPSE

Techniques Used:

Overview of the GLIMMPSE architecture.
Figure Legend Snippet: Overview of the GLIMMPSE architecture.

Techniques Used:

15) Product Images from "GPU Acceleration of Optical Mapping Algorithm for Cardiac Electrophysiology"

Article Title: GPU Acceleration of Optical Mapping Algorithm for Cardiac Electrophysiology

Journal: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

doi: 10.1109/EMBC.2012.6346240

The performance of the GPU implementation in comparison to the Matlab CPU implementation, the serial C++ implementation and the OpenMP C++ implementation.
Figure Legend Snippet: The performance of the GPU implementation in comparison to the Matlab CPU implementation, the serial C++ implementation and the OpenMP C++ implementation.

Techniques Used:

16) Product Images from "Enhancing sparse-view photoacoustic tomography with combined virtually parallel projecting and spatially adaptive filtering"

Article Title: Enhancing sparse-view photoacoustic tomography with combined virtually parallel projecting and spatially adaptive filtering

Journal: Biomedical Optics Express

doi: 10.1364/BOE.9.004569

Reconstructed results of the vessel phantom with tumor targets: (a)-(c) UBP, TV-GD and IRT-BM3D results for #60-view case; (d)-(f) UBP, TV-GD and IRT-BM3D results for #30-view case.
Figure Legend Snippet: Reconstructed results of the vessel phantom with tumor targets: (a)-(c) UBP, TV-GD and IRT-BM3D results for #60-view case; (d)-(f) UBP, TV-GD and IRT-BM3D results for #30-view case.

Techniques Used:

Reconstructed results of the vessel phantom with tumor targets in #60-view case with different SNR values: (a)-(d) UBP results in the cases of SNR = 5 dB, 10 dB, 15 dB and 20 dB; (e)-(h) IRT-BM3D results in the cases of SNR = 5 dB, 10 dB, 15 dB and 20 dB.
Figure Legend Snippet: Reconstructed results of the vessel phantom with tumor targets in #60-view case with different SNR values: (a)-(d) UBP results in the cases of SNR = 5 dB, 10 dB, 15 dB and 20 dB; (e)-(h) IRT-BM3D results in the cases of SNR = 5 dB, 10 dB, 15 dB and 20 dB.

Techniques Used:

Reconstructed results of the vessel phantom with tumor targets: (a)-(e) UBP results of #360-, #120-, #90- #60- and #30-view cases; (f)-(j) IRT-BM3D results of #360-, #120-, #90-, #60- and #30-view cases; (k) Exact image of the phantom; (l)-(p) Profiles (along line A-A’) of the recovered images in #360-, #120-, #90-, #60- and #30-view cases.
Figure Legend Snippet: Reconstructed results of the vessel phantom with tumor targets: (a)-(e) UBP results of #360-, #120-, #90- #60- and #30-view cases; (f)-(j) IRT-BM3D results of #360-, #120-, #90-, #60- and #30-view cases; (k) Exact image of the phantom; (l)-(p) Profiles (along line A-A’) of the recovered images in #360-, #120-, #90-, #60- and #30-view cases.

Techniques Used:

Reconstructed images of the ex vivo mouse intestinal tissue: (a) Photograph of the tissue sample; (b) #90-view IRT-BM3D result; (c) #90-view UBP result. The reconstructed details are shown in white dotted boxes.
Figure Legend Snippet: Reconstructed images of the ex vivo mouse intestinal tissue: (a) Photograph of the tissue sample; (b) #90-view IRT-BM3D result; (c) #90-view UBP result. The reconstructed details are shown in white dotted boxes.

Techniques Used: Ex Vivo

17) Product Images from "CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU"

Article Title: CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU

Journal: BMC Bioinformatics

doi: 10.1186/s12859-016-0946-4

Evaluations of CUDAMPF with Intel Xeon, i5 and i7 on ViterbiFilter
Figure Legend Snippet: Evaluations of CUDAMPF with Intel Xeon, i5 and i7 on ViterbiFilter

Techniques Used:

Related Articles

Software:

Article Title: Pluri-IQ: Quantification of Embryonic Stem Cell Pluripotency through an Image-Based Analysis Software
Article Snippet: .. Software Availability Pluri-IQ was implemented using MATLAB on a 64-bit Windows OS laptop with intel i7 processor with 8 GB of RAM memory. .. The software will be hosted at the CNC website ( http://www.cnbc.pt/equipment/microscopyUnit.asp#divImageAnalysis ) both as a compiled MATLAB standalone application (requires installation of 64 bit MATLAB runtime, available for free at www.mathworks.com/products/compiler/mcr.html ) and MATLAB.m files.

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