Image_1_Novel Survivin Peptides Screened With Computer Algorithm Induce Cytotoxic T Lymphocytes With Higher Cytotoxic Efficiency to Cancer Cells.tiff by Qiuqiang Chen (9332735)

“…More importantly, SV_95–specific CTLs with higher cytotoxicity were successfully induced with both SV_95–6 and SV_95–7 peptides, which significantly eliminated target cells (not only SV_95–1 peptide pulsed T2 cells, but also both HLA-A2 and SV positive cancer cells) when compared to those generated with natural SV_95–1 peptide and TIL2080 cells. …”

Reward function related parameters. by Honglei Pang (22693724)

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Principal component analysis applied to both datasets. by Anny K. G. Rodrigues (11689849)

Supplementary file 1_Earthworm optimization algorithm for extracting parameters for solar cells and photovoltaic modules.docx by Fatima Wardi (22052360)

“…The extracted parameters for each case study are used to reconstruct the I–V and power–voltage (P–V) characteristic curves for the respective solar cell and photovoltaic module technologies. To validate the performance and efficiency of the algorithm, various statistical criteria are computed, including individual absolute error (IAE), relative error (RE), root mean square error (RMSE), mean absolute error (MAE), standard deviation (SD), tracking signal (TS), normalized forecast measure (NFM), and the autocorrelation function (ACF). …”

Skeletonisation algorithm. by Jordan Hembrow (16908051)

Functional enrichment of protein subnetworks in HIV-1 producer cells after co-culture with uninfected cells. by James W. Bruce (10204626)

DataSheet_1_Superiorization of projection algorithms for linearly constrained inverse radiotherapy treatment planning.pdf by Florian Barkmann (15152004)

“…Superiorization combines a feasibility-seeking projection algorithm with objective function reduction: The underlying projection algorithm is perturbed with gradient descent steps to steer the algorithm towards a solution with a lower objective function value compared to one obtained solely through feasibility-seeking.…”

DataSheet_1_Superiorization of projection algorithms for linearly constrained inverse radiotherapy treatment planning.pdf by Florian Barkmann (15152004)

“…Superiorization combines a feasibility-seeking projection algorithm with objective function reduction: The underlying projection algorithm is perturbed with gradient descent steps to steer the algorithm towards a solution with a lower objective function value compared to one obtained solely through feasibility-seeking.…”

DataSheet_1_Superiorization of projection algorithms for linearly constrained inverse radiotherapy treatment planning.pdf by Florian Barkmann (15152004)

“…Superiorization combines a feasibility-seeking projection algorithm with objective function reduction: The underlying projection algorithm is perturbed with gradient descent steps to steer the algorithm towards a solution with a lower objective function value compared to one obtained solely through feasibility-seeking.…”

Demonstration of the limitations of existing spatial analysis methods, namely nearest neighbor distance (NND), G-cross function and Morisita-Horn index. by Mai Chan Lau (3135303)

Protection of data-inferred tasks reduces the influence of extraction methods on model content. by Anne Richelle (6589178)

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Choice of extraction method is mainly responsible for the variability in the reaction content across models. by Anne Richelle (6589178)

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Data_Sheet_1_A multi-class logistic regression algorithm to reliably infer network connectivity from cell membrane potentials.PDF by Thierry Nieus (165355)

Example of the CNF function for off-axis DHM holograms recorded in a non-telecentric configuration. by Raul Castañeda (13932971)

Predictive performance evaluation on real heart data with selected cell types. by Nuray Sogunmez Erdogan (21533872)

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The proposed feature extraction algorithm. by Ali Ahmed (2567584)

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3D modeling qualities of the AS single-template models built by including DOPE in the objective function of MODELLER. by Giacomo Janson (8138517)

DataSheet1_Bayesian hidden mark interaction model for detecting spatially variable genes in imaging-based spatially resolved transcriptomics data.PDF by Jie Yang (121778)

“…Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. …”

Overcoming limitations in sparse cell-type predictions for a biologically reliable task. by Nuray Sogunmez Erdogan (21533872)

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Spatial characterization of cancer-associated cell types in human breast cancer malignancies. by Nuray Sogunmez Erdogan (21533872)

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