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Experimental data for "Unusual magnetotransport in twisted bilayer graphene from strain-induced open Fermi surfaces" along with analysis notebook.

The models used to simulate Ignan Earths. There are two MATLAB models, one for the mantle evolution simulation and another for the climate simulation. Each one has a primary code (the executable one ending in EXE), which calls a function. The function is the other file.

The KISS Controller is used to create time-lapse videos or to take scheduled video recordings from a single position using a Smartphone Camera. The KISS Controller is operated by a web based user interface running on a Smartphone or Laptop to direct the sequence of the video recording or photographs to create a time lapse video.

Background Heart flush solutions (HFS) are pivotal in the heart procurement for transplant. The three most popular are University of Wisconsin (UW), Celsior, and histidine-tryptophan-ketoglutarate (HTK). Previous studies suggested UW might be the best choice, but some still disagreed. We would like to analyze the national UNOS (United Network of Organ Sharing) database, to test the hypothesis if UW could provide the best outcomes. Methods In the UNOS-STAR (Standard Transplant Analysis and Research) database, we selected the adult heart donors-recipients from 2015 to October 2021, excluding pediatric cases, machine perfusion, or multi-organ transplants. Firstly, intracellular (IC) solution (UW), and extracellular (EX) solution (Celsior or HTK) had a crude comparison for their outcomes: length of stay (LOS), graft failure, patient death, and 1-year rejection. Secondly, adjusted for ischemic time, they were compared by multiple regressions models. Thirdly, crude comparisons were made in three sub-groups (ischemia over 3 hours, ischemia over 4 hours, and donors over 50 years of age), and in the pre-defined high-risk sub-group (ischemia over 4 hours or donor over 50 years of age). SAS/SQL and Stata codes were shared online. Results There were 15,338 eligible cases: 9,104 (59.4%) intracellular (UW) and 6,234 (40.6%) extracellular (HTK or Celsior). Their ratios fluctuated around 60%: 40% from 2015 to 2021. UW had less favorable donor baseline such as longer ischemia time. Compared to HTK or Celsior, UW had shorter LOS (median 16 vs 17 days, p=0.0001 ) and less 1-year rejection (17.3% vs 22.2%, p=0.0001). When adjusted for ischemic time, UW had less 1-year rejection (17.3% vs 22.2% , p=0.0001). When ischemia was over 3 hours, UW had shorter LOS (median 16 vs 17 days , p=0.0001), less Kaplan-Meier graft failure (log-rank test p=0.0274; 16.7% vs 18.0% at 3 years), and less Kaplan-Meier patient death (log-rank test p=0.0523; 16.5% vs 17.2% at 3 years). With ischemia over 4 hours, donors age over 50 years, or in the high-risk sub-group (ischemia over 4 hours, or donors age over 50 years) , UW had shorter LOS (median 17 vs 18 days, p=0.0013). Conclusion For the heart procurement for transplant, UW showed superiority to HTK or Celsior in the outcomes of LOS, graft failure, patient death, and 1-year rejection, when ischemia was over 3 hours. UW still had shorter LOS, when ischemia was over 4 hours, or donors age was over 50 years. The UNOS-STAR data supported UW to be the flush solution of choice for heart procurement.

Code here was used to analyzed data presented in "Deconstructing the role of iron and heme in the structure of healthy human gut microbial communities". The response of the human gut microbiome to disruptions is often difficult to discern without model systems that remove the complexity of the host environment. Fluctuations in iron availability provide a case in point: the responses of pathogenic bacteria to iron in vitro are much better understood than those of the indigenous human gut commensal microbiota. In a non-clinical study of iron supplementation in healthy humans, we identified gradual, participant-specific microbiota shifts correlated with bacterial iron internalization. To identify direct effects due to taxon-specific iron sensitivity, we used stool samples from these participants as inocula to derive in vitro communities. Iron supplementation of these communities caused small shifts in structure, similar to in vivo, whereas iron deprivation dramatically inhibited growth with irreversible and cumulative reduction in diversity and replacement of some dominant species. Sensitivity of individual species to iron deprivation during growth in axenic culture generally predicted iron dependency in a community context. Finally, exogenous heme acted as a source of inorganic iron to prevent depletion of some community members. Our results highlight the complementarity of in vivo and in vitro studies in deconstructing how environmental factors affect gut microbiome structure.

This repository contains the codes and synthesized data for our paper "Learning from past respiratory infections to predict COVID-19 Outcomes: A retrospective study". In this paper we proposed a framework that used COVID-like cohorts (you can find the description in our paper) to train machine learning models and validated them on the COVID-19 population.If you just want to test the model on synthetic COVID dataset, you could run the predict_COVID.py directly, or if you want to train the model by your own data, you could run the "training_models.py" file.The file descriptions are:directory: contains the test data. is the data template we used.directory: contains the trained models by COVID-like patients.is used to predict intubation within 48 hours.is used to load test data from test data file.is used to train the modelsProcess:1) Input the command "pip install -r requirements.txt" to install the environment2) Using python command to train the model: python training_models.py3) Using python command to run the model: python predict_COVID.pyThe output is a score file in the "result" directory.

File manifest inventory for entire contents of one external drive provided by Dexter Morrill will full listing of documents and recordings originally created from 1958 to 2016.

A script to compare the protein sequences in the CRISPRi essential gene depletion library from B. subtilis strain 168 and B. subtilis strain 3610

Multiple linear regression (MLR) models were built to assess the environmental variables that influence the depth and magnitude of the PNM feature in the ETNP. The first set 45 of MLR models (“full” models) used semi-continuous measurements (temperature, density, oxygen, chlorophyll fluorescence, PAR, nitrate, nitrite and ammonium) from CTD/PPS casts collected at 16 stations on the 2016 cruise to predict nitrite concentration.