Objective: A Clinical Decision Support System (CDSS) that can amass Electronic Health Record (EHR) and other patient data holds promise to provide accurate classification and guide treatment choices. Our objective is to develop the Decision Support System for Making Personalized Assessments and Recommendations Concerning Breast Cancer Patients (DPAC), which is a CDSS learned from data that recommends the optimal treatment decisions based on a patient’s features.

Method: We developed a Bayesian network architecture called Causal Modeling with Internal Layers (CAMIL), and an algorithm called Treatment Feature Interactions (TFI), which learns from data the interactions needed in a CAMIL model. Using the TFI algorithm, we learned interactions for six treatments from the Lynn Sage Data Set (LSDS). We created a CAMIL model using these interactions, resulting in a DPAC which recommends treatments towards preventing 5-year breast cancer metastasis.

Results: In a 5-fold cross-validation analysis, we compared the probability of being metastasis free in 5 years for patients who made decisions recommended by DPAC to those who did not. These probabilities are (the probability for those making the decisions appears first): chemotherapy (.938, .872); breast/chest wall radiation (.939, .902); nodal field radiation (.940, .784); antihormone (.941, .906); HER2 inhibitors (.934, .880); neadjuvant therapy (.931, .837). In an application of DPAC to the independent METABRIC dataset, the probabilities for chemotherapy were (.845, .788).

Discussion: Patients who took the advice of DPAC had, as a group, notably better outcomes than those who did not. We conclude that DPAC is effective at amassing and analyzing data towards treatment recommendations. Some of the findings in DPAC are controversial. For example, DPAC says that chemotherapy increases the chances of metastasis for many node negative patients. This controversy shows the importance of developing a conclusive version of DPAC to ensure we provide patients with the best patient-specific treatment recommendations.

Samples with assignment probabilities < 95% are excluded from the analysis.Mutation rates from [23].

This archive contains E3SM Land Model simulation results associated with the Journal of Advances in Modeling Earth Systems (JAMES) article titled "More Realistic Intermediate Depth Dry Firn Densification in the Energy Exascale Earth System Model (E3SM)," by Adam M. Schneider, Charles S. Zender, and Stephen F. Price. Also included in the archive are python scripts used to analyze associated data and an offline, statistical firn model.

This submission contains several shapefiles used for a deterministic PFA, as well as a heat composite risk segment with union overlay, and training sites used for weights of evidence. More detailed metadata can be found in the specific file.

This submission includes maps of the spatial distribution of basaltic, and felsic rocks in the Oregon Cascades. It also includes a final Play Fairway Analysis (PFA) model, with the heat and permeability composite risk segments (CRS) supplied separately. Metadata for each raster dataset can be found within the zip files, in the TIF images

The protection afforded by inactivated influenza vaccines can theoretically be improved by inducing T-cell responses to conserved internal influenza A antigens. We hypothesized that in an influenza-controlled human infection challenge, susceptible individuals receiving a vaccine boosting T cell responses would exhibit lower viral load and decreased symptoms compared to placebo recipients. Healthy European volunteers with microneutralization titers < 20 to the H3N2 challenge strain were randomized double-blind using a permuted-block list with a 3:2 allocation ratio to receive IM MVA expressing H3N2 NP and M1 or placebo. Over six weeks later, participants were challenged intranasally. Nasal swabs were collected twice daily for viral PCR, and symptoms of influenza were recorded through day 11. T-cell responses were monitored both pre- and post-vaccination (0,8, and 28 days) and challenge (0 and 28 days) by ELISpot and multiparameter flow cytometry. There was no significant effect of NVA..., Standard eCRFs and then put into CDISC format and exported to Excel for the upload. Columns with duplicate names were deleted., , # Effect of T cell vaccine in an influenza human challenge model

https://doi.org/10.5061/dryad.rr4xgxdgt

Description of the data and file structure

Submitted datasets follow CDISC’s Analysis Data Model (ADaM) and include the following Excel files.

All empty cells have no associated data.

Files and Variables

File: `adqs1.xlsx`

Description: Influenza symptoms

Variables:

• Unique Subject Identifier
• Pooled Subject Group 1
• Sex
• Actual Treatment for Period 01
• Question Symptom Short Name
• Question Name
• Parameter
• Parameter Category 1 (local or systemic)
• Parameter Category 2 (anatomical location)
• Parameter Category 3 (Description)
• Category of Question (if solicited on scoring card)
• Visit Name
• Analysis Visit
• Planned Time Point Name (morning or evening)
• Phase
• Analysis Relative Day
• Time Point Reference
• Character Results/Findings in standard grading format
• Analysis Value
• Baseline Value
• Cha...

Study 4: Getting inside the expert’s head: An analysis of physician cognitive processes during trauma resuscitations. Crisis resource management skills are integral to leading the resuscitation of a critically ill patient. Despite their importance, crisis resource management skills (and their associated cognitive processes) have traditionally been difficult to study in the real world. The objective of this study was to derive key cognitive processes underpinning expert performance in resuscitation medicine, using a new eye-tracking–based video capture method during clinical cases. During an 18-month period, a sample of 10 trauma resuscitations led by 4 expert trauma team leaders was analyzed. The physician team leaders were outfitted with mobile eye-tracking glasses for each case. After each resuscitation, participants were debriefed with a modified cognitive task analysis, based on a cued-recall protocol, augmented by viewing their own first-person perspective eye-tracking video from the clinical encounter.

Non-traditional data signals from social media and employment platforms for ADAM stock analysis

Tabulated data collected with analysis (e.g., Image analysis + statistical analysis)

This repository includes the SAM simulation output analyzed in Sokol et al. (2024).

File descriptions:

1. SAM_analysis.ipyn -- Jupyter notebook with code to conduct the analysis and recreate the figures in the paper
2. funcs.py and spm.py -- additional code used in the analysis notebook
3. data_files.zip -- contains 11 data files used in the analysis notebook.

Contact: Adam Sokol (adam.sokol@princeton.edu)

Citation and publication

To cite this work or access the publication, please use the citation information listed here: https://github.com/flavell-lab/AtanasKim-Cell2023/tree/main#citation

Initially published as preprint in:

Brain-wide representations of behavior spanning multiple timescales and states in C. elegans

Adam A. Atanas*, Jungsoo Kim*, Ziyu Wang, Eric Bueno, McCoy Becker, Di Kang, Jungyeon Park, Cassi Estrem, Talya S. Kramer, Saba Baskoylu, Vikash K. Mansingkha, Steven W. Flavell bioRxiv 2022.11.11.516186; doi: https://doi.org/10.1101/2022.11.11.516186

• Equal Contribution

Contents

1. deepnet-weights.tar.bz2

contains the trained weights of the neural networks used in this project.

3dunet_540nm_voxels: 3D U-Net for segmenting neurons

head_detector_unet: finding worm head landmark used in ANTSUN registration

head_detector_unet_0622: an alternative version of the above, optimal for NeuroPAL datasets

microscope_tracker: detecting keypoints for online tracking on the microscope

behavior_nir: segmentation of the recorded NIR behavior images for behavior quantification

1. data files

ANTSUN processed datasets and CePNEM processed model fits and analysis data. Check the project packages and notebooks in the project github repository (https://github.com/flavell-lab/AtanasKim-Cell2023/) on using these datasets.

This submission includes a Na/K geothermometer probability greater than 200 deg C map, as well as two play fairway analysis (PFA) models. The probability map acts as a composite risk segment for the PFA models. The PFA models differ in their application of magnetotelluric conductors as composite risk segments. These PFA models map out the geothermal potential in the region of SE Great Basin, Utah.

summary data from simulation modelThese data are the average percent of real loci and the average number of spurious loci detected given the various parameter combinations tested by the model.Flanagan_and_Jones_molecol_data.docx.xlsxmultiple populations comparisonThis data contains Fst values for loci in replicate populations with the same QTLs, which were: chrom 0: 702, 978; chrom 1: 516, 341; chrom 2: 878, 76; chrom 3: 46, 153.12May_same-qtls_Fsts.txtpopulationsHeader file for C++ simulation programlife_cycleC++ program file for simulation modelchi_squareHeader file containing functions to calculate chi-square p-values for C++ simulation model.rand_numsC++ header file containing functions to calculate and use random numbers for simulation model program. File is available in GitHub at https://github.com/spflanagan/gwsca_simulation_model/blob/master/simulation_model/simulation_model/rand_nums.hSimulation_DataAll summary data from the simulation model (the average number of real and spur...

network data and scripts

Expression and purification of DNMT1 for biochemical work Full length murine DNMT1 (UniProtKB P13864) was overexpressed and purified as described (Adam, et al. 2020) using the Bac-to-Bac baculovirus expression system (Invitrogen). The expression construct of the DNMT1 with mutated CXXC domain was taken from Bashtrykov, et al. (2012). Synthesis long DNA substrate and methylation reactions with them The sequence of the 349 bp substrate with 44 CpG sites was taken from Adam et al. 2020. It was used in unmethylated and hemimethylated form. Generation of the substrates and the methylation reaction were conducted as described (Adam, et al. 2020). In brief, for the generation of hemimethylated substrates, the unmethylated DNA was methylated in vitro by M.SssI (purified as described in Adam, et al. 2020) to introduce methylation at all CpG sites, or by M.HhaI (NEB) together with M.MspI (NEB) to introduce methylation at GCGC and CCGG sites. For the synthesis of hemimethylated substrates, the upper strand of the methylated substrate was digested with lambda exonuclease, the ss-DNA purified and finally ds hemimethylated DNA was generated by by primer extension using Phusion® HF DNA Polymerase (Thermo). Methylation reaction were conducted using mixtures of UM, fully hemimethylated and patterned substrate (total DNA concentration 200 ng in 20 µL) in methylation buffer (100 mM HEPES, 1 mM EDTA, 0.5 mM DTT, 0.1 mg mL-1 BSA, pH 7.2 with KOH) containing 1 mM AdoMet. DNMT1 concentrations and incubation times are indicated in the text. Methylation was followed by bisulfite conversion using the EZ DNA Methylation-LightningTM Kit (ZYMO RESEARCH) followed by library generation and Illumina paired-end sequencing (Novogene). Flanking sequence preference analysis with randomized single-site substrates Methylation reactions of the randomized substrate with DNMT1 were performed similarly as described (Adam, et al. 2020; Gao, et al. 2020). Briefly, single-stranded oligonucleotides containing a methylated, hydroxymethylated or unmethylated CpG site embedded in a 10 nucleotide random context were obtained from IDT and used for generation of 67 bps long double-stranded DNA substrates by primer extension. Pools of these randomized substrates were then mixed in different combination, methylated by DNMT1 in methylation buffer (100 mM HEPES, 1 mM EDTA, 0.5 mM DTT, 0.1 mg mL-1 BSA, pH 7.2 with KOH) containing 1 mM AdoMet. DNMT1 concentrations and incubation times are indicated in the text. Methylation was followed by bisulfite conversion using the EZ DNA Methylation-LightningTM Kit (ZYMO RESEARCH) followed by library generation and Illumina paired-end sequencing (Novogene). Bioinformatics analysis NGS data sets were bioinformatically analyzed using a local instance of the Galaxy server as described (Adam, et al. 2020; Dukatz, et al. 2020; Dukatz, et al. 2022). In brief, for the long substrate, reads were trimmed, filtered by quality, mapped against the reference sequence and demultiplexed using substrate type and experiment specific barcodes. Afterwards, methylation information was assigned and retrieved by home-made skripts. For the randomized substrate, reads were trimmed and filtered according to the expected DNA size. The original DNA sequence was then reconstituted based on the bisulfite converted upper and lower strands to investigate the average methylation state of both CpG sites and the NNCGNN flanks using home-made skripts. Methylation rates of 256 NNCGNN sequence contexts in the competitive methylation experiments with the mixed single-site substrates were determined by fitting to monoexponential reaction progress curves with variable time points with MatLab skripts as described (Adam, et al. 2022). Pearson correlation factors were calculated with Excel using the correl function. Structure of the deposited data Methylation data of long substrates are placed in the “long DNA substrates” folder. Methylation data of short single-site substrates with randomized flanks are placed in the “single sites substrates” folder. In both folder an explanatory pdf file gives further information. Subfolders are arranged by enzyme (CXXC mutant or DNMT1 WT). Then, for each enzyme, the different substrates or substrate mixtures are provided in separate subfolders. References Adam S, Bräcker J, Klingel V, Osteresch B, Radde NE, Brockmeyer J, Bashtrykov P, Jeltsch A. Flanking sequences influence the activity of TET1 and TET2 methylcytosine dioxygenases and affect genomic 5hmC patterns. Communications Biology 5, 92 (2022) Adam S, Anteneh H, Hornisch M, Wagner V, Lu J, Radde NE, Bashtrykov P, Song J, Jeltsch A. DNA sequence-dependent activity and base flipping mechanisms of DNMT1 regulate genome-wide DNA methylation. Nature Commun 11, 3723 (2020) Bashtrykov P, et al. Specificity of Dnmt1 for methylation of hemimethylated CpG sites resides in its catalytic domain. Chem Biol 19, 572-578 (2012) Dukatz M, Dittrich M, Stahl E, Adam S, de Mendoza A,...

Clinical characteristics of the datasets included in the analysis.

No description is available. Visit https://dataone.org/datasets/doi%3A10.18739%2FA22F7JQ7B for complete metadata about this dataset.

This dataset contains 344 different digitized and tagged Tafel slope datasets from the CO2 reduction literature. We re-analyze this data with a Bayesian data analysis procedure that estimates a Tafel slope and yields distributional uncertainty information about its value. We are releasing this dataset along with our study to facilitate re-analyzing and refitting our data using different models and approaches.

2015 USGS publication titled "Spatially distributed groundwater recharge for 2010 land cover estimated using a water-budget model for the island of O'ahu, Hawaii" which includes groundwater recharge data for Oahu.

The data was collected to track the usage and downloads of the Global Food Security-support Analysis Data at 30 meters (GFSAD30) Cropland Extent Product. This data supports an Open File Report titled Global Food-Security-Support-Analysis Data at 30-m Resolution (GFSAD30) Cropland-Extent Products—Download Analysis. The GFSAD30 data is available for download on the National Aeronautics and Spate Administration (NASA) Land Processes Distributed Active Archive Center (LPDAAC). LPDAAC requires users who download to sign in and provided that user data and the statistics on the frequency the products were downloaded so we could create this report.