Table 2_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.docx by Francesco Chiani (2661328)

“…To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Table 3_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.xlsx by Francesco Chiani (2661328)

“…To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Table 4_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.docx by Francesco Chiani (2661328)

“…To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO: a case study on liquid handler automation by Ryo Tamura (1957942)

“…We developed NIMO (formerly NIMS-OS, NIMS Orchestration System), an OS explicitly designed to integrate multiple artificial intelligence (AI) algorithms with diverse exploratory objectives. NIMO provides a framework for integrating AI into robotic experimental systems that are controlled by other OS platforms based on both Python and non-Python languages. …”

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…The analysis was conducted in a Jupyter Notebook environment, using Python and libraries such as Scikit-learn and Pandas. …”

Least Bridges Graphs by Richard Frost (14064363)

“…This toolbox was originally created for analysis of values arising in genetic distance measures. The contained algorithms correct errors in commonly used nearest-neighbor and cluster software that are not designed with distances in mind (e.g. nearest-neighbor topology functions currently found in Python, SPSS, etc).…”

Table1_Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning.pdf by Lijuan Liang (4277053)

“…We used the Covarep open-source algorithm to extract a total of 1200 high-level statistical functions for each sample. …”

Sudoku Dataset by David Towers (12857447)

“…</p> <p>NumPy (.npy) files can be opened through the NumPy Python library, using the `numpy.load()` function by inputting the path to the file into the function as a parameter. …”

The software structure. by Moritz Hoffmann (6411821)

“…<p><b>(a)</b> Python user interface: Provides a Python binding to the “C++ user interface” with some additional convenience functionality. …”

DataSheet1_Development of a Multilayer Deep Neural Network Model for Predicting Hourly River Water Temperature From Meteorological Data.docx by Reza Abdi (3636907)

“…We trained the LR and DNN algorithms on Google’s TensorFlow model using Keras artificial neural network library on Python. …”

Datasheet1_A Workflow for Rapid Unbiased Quantification of Fibrillar Feature Alignment in Biological Images.zip by Stefania Marcotti (5896853)

“…<p>Measuring the organization of the cellular cytoskeleton and the surrounding extracellular matrix (ECM) is currently of wide interest as changes in both local and global alignment can highlight alterations in cellular functions and material properties of the extracellular environment. …”

Known compounds and new lessons: structural and electronic basis of flavonoid-based bioactivities by Rohan J. Meshram (6563189)

“…Abbreviations2′HFN-2′</p><p>hydroxy flavonone</p>2D<p>2 dimension</p>3D<p>3 dimension</p>3H7MF<p>3-hydroxy-7-methoxy flavone</p>4′HFN-4′<p>hydroxy flavonone</p>4′MF- 4′<p>methoxy flavone</p>7HFN<p>7-hydroxy flavonone</p>CHARMM<p>Chemistry at Harvard Macromolecular Mechanics</p>COX<p>cyclooxygenase</p>COX-1<p>cyclooxygenase-1</p>COX-2<p>cyclooxygenase-2</p>DM<p>dipole moment</p>DPPH- 2, 2<p>diphenyl-1-picryl hydrazine</p>EA<p>electron affinities</p>EGFR<p>epidermal growth factor receptor</p>E-HOMO<p>Highest occupied molecular orbital energy</p>E-LUMO<p>Lowest unoccupied molecular orbital energy</p>EPA<p>eicosapentaenoic acid</p>FROG2<p>FRee Online druG conformation generation</p>GA<p>Genetic Algorithm</p>GROMACS<p>GROningen MAchine for Chemical Simulations</p>HOMO<p>Highest occupied molecular orbital</p>IP<p>Ionization potential</p>LOMO<p>Lowest unoccupied molecular orbital</p>MD<p>Molecular dynamics</p>MO<p>Molecular orbital</p>NAMD<p>Nanoscale Molecular Dynamics</p>NSAIDs<p>Non-Steroidal Anti Inflammatory Drugs</p>Ns<p>nanoseconds</p>NVE<p>Ensemble-constant-energy, constant-volume, Constant particle ensemble</p>PDB-ID<p>Protein Data Bank Identifier</p>PME<p>Particle Mesh Ewald</p>PyRX<p>Python Prescription</p>RMSD<p>Root-Mean-Square Deviation</p>RMSF<p>Root-Mean-Square Fluctuation</p>RLS<p>reactive lipid species</p>ROS<p>Reactive Oxygen Species</p>SASA<p>solvent accessible surface area</p>SMILES<p>simplified molecular-input line-entry system</p>SOR<p>superoxide anion radical</p>UFF<p>Universal force field</p>VEGF<p>vascular endothelial growth factor</p>VEGFR<p>vascular endothelial growth factor receptor</p>VMD<p>Visual molecular dynamics</p><p></p> <p>hydroxy flavonone</p> <p>2 dimension</p> <p>3 dimension</p> <p>3-hydroxy-7-methoxy flavone</p> <p>hydroxy flavonone</p> <p>methoxy flavone</p> <p>7-hydroxy flavonone</p> <p>Chemistry at Harvard Macromolecular Mechanics</p> <p>cyclooxygenase</p> <p>cyclooxygenase-1</p> <p>cyclooxygenase-2</p> <p>dipole moment</p> <p>diphenyl-1-picryl hydrazine</p> <p>electron affinities</p> <p>epidermal growth factor receptor</p> <p>Highest occupied molecular orbital energy</p> <p>Lowest unoccupied molecular orbital energy</p> <p>eicosapentaenoic acid</p> <p>FRee Online druG conformation generation</p> <p>Genetic Algorithm</p> <p>GROningen MAchine for Chemical Simulations</p> <p>Highest occupied molecular orbital</p> <p>Ionization potential</p> <p>Lowest unoccupied molecular orbital</p> <p>Molecular dynamics</p> <p>Molecular orbital</p> <p>Nanoscale Molecular Dynamics</p> <p>Non-Steroidal Anti Inflammatory Drugs</p> <p>nanoseconds</p> <p>Ensemble-constant-energy, constant-volume, Constant particle ensemble</p> <p>Protein Data Bank Identifier</p> <p>Particle Mesh Ewald</p> <p>Python Prescription</p> <p>Root-Mean-Square Deviation</p> <p>Root-Mean-Square Fluctuation</p> <p>reactive lipid species</p> <p>Reactive Oxygen Species</p> <p>solvent accessible surface area</p> <p>simplified molecular-input line-entry system</p> <p>superoxide anion radical</p> <p>Universal force field</p> <p>vascular endothelial growth factor</p> <p>vascular endothelial growth factor receptor</p> <p>Visual molecular dynamics</p> <p>Communicated by Ramaswamy H. …”

PySilsub—a toolbox for silent substitution by Joel Martin (11864048)

“…Device settings that will produce lights to selectively stimulate the photoreceptor(s) of interest can be found using a variety of analytic and algorithmic approaches. Here we present <em>PySilSub</em>, a novel Python package for silent substitution featuring object-oriented support for individual colorimetric observer models, multi-primary stimulation devices, and solving silent substitution problems with linear algebra and constrained numerical optimisation. …”

Table1_Natural and artificial selection of multiple alleles revealed through genomic analyses.xlsx by Jana Biová (11287971)

“…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …”

Table8_Natural and artificial selection of multiple alleles revealed through genomic analyses.xlsx by Jana Biová (11287971)

“…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …”

Table4_Natural and artificial selection of multiple alleles revealed through genomic analyses.xlsx by Jana Biová (11287971)

“…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …”

Table3_Natural and artificial selection of multiple alleles revealed through genomic analyses.xlsx by Jana Biová (11287971)

“…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …”

Table2_Natural and artificial selection of multiple alleles revealed through genomic analyses.xlsx by Jana Biová (11287971)

“…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …”

Table7_Natural and artificial selection of multiple alleles revealed through genomic analyses.docx by Jana Biová (11287971)

“…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …”

Table5_Natural and artificial selection of multiple alleles revealed through genomic analyses.xlsx by Jana Biová (11287971)

“…We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. …”