Comparison between algorithm and expert cell counting. by Adam L. Tyson (10892822)

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Algorithm detection steps. by José M. Rivera-Arbeláez (12418512)

Algorithm performance compared to mean of expert manual counts with varying axial sampling. by Adam L. Tyson (10892822)

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Algorithm timings on a laptop computer with Intel i9–9900K CPU, 32GB RAM and an NVIDIA RTX2080 GPU. by Adam L. Tyson (10892822)

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Application of the algorithm to unseen data. Presynaptic neurons labelled by rabies viral injection into layer 2/3 primary visual cortex in Penk-Cre mice. by Adam L. Tyson (10892822)

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Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic by NELSON L. DIAS (8078003)

“…With these practical rules, simple summation algorithms provide sufficiently robust results for the calculation of the density function and its definite integrals. …”

Python-based Monte Carlo Simulation Tool by Yangfan Jiang (9372782)

Default cell detection parameters. by Adam L. Tyson (10892822)

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Assignment of signal to individual cell candidates in the third dimension. Saggital sections (i.e. orthogonal to acquisition) shown. by Adam L. Tyson (10892822)

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Total number of cells in each region, per brain, projecting to layer 2/3 neurons in primary visual cortex. by Adam L. Tyson (10892822)

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Architecture of the 3D ResNet. by Adam L. Tyson (10892822)

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Overview of the initial cell candidate detection steps, from raw data to a cuboid of data fed into the classification network. by Adam L. Tyson (10892822)

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Illustration of the cell detection process. by Adam L. Tyson (10892822)

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Architecture of the bottleneck 3D ResNet. by Adam L. Tyson (10892822)

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Simplified schematic diagram of the serial two-photon microscope and data acquisition process. by Adam L. Tyson (10892822)

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Cell classification. by Adam L. Tyson (10892822)

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Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”

<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…”

Functional validation of the metastasis-predictive genes inferred by PLUS from TCGA data. by Junyi Zhou (495644)