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opencc-by-4.0Feb 2023View details →

Deep Representation Learning of Physical Activity and Sleep Patterns During Pregnancy Identifies post-hoc Inferences Associated with Prematurity

Running title: series2signal gestational age "clock" for pregnancy monitoring Summary:  Preterm birth (PTB) is the leading cause of infant mortality globally. While research has focused on the development of predictive models for PTB, cost-effective interventions have remained understudied. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations. However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy that decays over time. In this study, we use physical activity data collected using a wearable device comprising over 181, 944 hours of data across N = 1, 083 patients. Using a new state-of-the art deep learning time-series classification architecture, we first develop a ”clock” of healthy dynamics in physical activity patterns during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also developed a novel interpretability algorithm that integrates unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and static clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy based on measures of physical activity and sleep. Importantly, we found that deviations from this normal ”clock” of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e − 67) and when our model overestimates GA, there are 1.44 times (P = 2.82e − 39) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043), indicating that our model assigns a more advanced GA when an individual’s daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e − 21). Combining prediction with interpretability allows us to robustly signal when activity behaviors increase or decrease the likelihood of preterm birth and advocates for the future development of clinical decision support through passive monitoring and suggestions around exercise habits and sleep patterns, which are easily implemented in low- and middle-income countries (LMICs). Beyond this particular application, the presented pipeline can be used to analyze high-fidelity time-series data in other translational studies utilizing wearable devices.   Data description (brief): the raw wearables data is available as .mtn files with the GA encoded in the filename after the underscore. The processed data with sleep annotations can be loaded using the pickle module for serialized objects in python. See https://github.com/nealgravindra/wearables for examples.

Tissue microarray data and processing scripts for The molecular consequences of androgen activity in the human breast

This repository contains raw and processed data from the CODEX imaging dataset in this publication. The RAW data tables provide the resulting nuclei and membrane staining signals obtained from the nuclei segmentation described in the Methods. The processed data file provides the clustered and annotated version described in Methods. The repository also contains two scripts describing the processing of snRNA-seq and snATAC-seq data.

Aerodynamics of a Floating Wind Turbine Scale Model with Active Control

This dataset is about the aerodynamic response of a wind turbine scale model subjected to prescribed platform pitch motion, as it would occur during normal operation of floating wind turbines. The wind turbine has active control functionalities representative of those of utility-scale turbines. The turbine controller is the Reference Open Source Controller (ROSCO), which has been implemented in MATLAB Simulink for wind tunnel testing and co-simulation with OpenFAST. The dataset contains an OpenFAST model of the scaled turbine and its controller.

Kilometer-scale global warming simulations and active sensors reveal changes of tropical deep convection

This zip file contains data and codes to reproduce the figures of a manuscript on X-SHiELD. Contact mbolot@princeton.edu for questions.

Active Region Magnetograms for Solar Flare Prediction: Reduced Resolution Dataset Images

This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.jq2bvq898.  These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO).  These data are the reduced resolution (224x224 pixels) images in .png format.

Activity of a freshwater turtle varies across a latitudinal gradient: implications for the success of assisted colonisation

The value of assisted colonisation as a response to climate change can only be realised if focal species are well suited to their new habitats. For ectotherms, new habitats must offer microclimates that promote crucial behaviours such as thermoregulation and foraging. The Western Swamp Turtle (Pseudemydura umbrina), a Critically Endangered species from southwestern Australia, serves as a global case-study of assisted colonisation in action. Initial trials where juvenile P. umbrina were released into wetter and cooler climates found that individuals spent considerable time at body temperatures that apparently limited their growth. Using high-resolution biologging data (temperature and depth), here we tested if turtle activity is thermally constrained in cooler latitudes by releasing 48 juveniles into seasonal swamps at three sites. One site was core natural habitat, and the other sites were wetlands 380 km apart that offered either warmer or cooler microclimates. Generalised additive mixed models were used to evaluate behaviours and time spent at optimal temperatures for approximately one month following release, and growth rates were measured and analysed after release until the end of the hydroperiod 4-5 months later. We found that turtles released into the most poleward (southern) wetland spent significantly less time active and basking and grew significantly less compared to turtles released further north. When analysed together, behavioural and growth datasets showed that activity was positively correlated with growth rates. We conclude that poor growth of turtles in the southern wetland was likely a result of lower body temperatures, stemming from a reduced ability to thermoregulate in water. Consequently, for assisted colonisation of P. umbrina to be successful, recipient wetlands must offer aquatic microclimates that are sufficiently warm to promote foraging activity that leads to growth, and ultimately to maturation.

An Alkali-Activated Concrete Dataset for Sustainable Building Materials

<ul> <li>Size: 1630 mixes of alkali-activated concretes </li> <li>Material type: Alkali-activated concretes</li> <li>Features: <ul> <li>Molecular composition of the binder powder</li> <li>Weights of precursor types</li> <li>Aggregate weights</li> <li>Alkali solution chemical composition</li> <li>Amount of additional water/superplasticizer</li> <li>Curing process</li> </ul> </li> <li>Properties: <ul> <li>Compressive strengths of each mix.</li> <li>Other mechanical and structural properties (not for each mix) </li> <li>Corresponding CO2 footprints, calculated using an approach that takes into account constituent CO2 footprint values of the mix's constituents obtained from literature, as well as a linear regression of the CO2 footprint due to oven curing</li> </ul> </li> <li>Use: Valuable for selecting alkali activated concrete mixes for various applications as both compressive strength and CO2 emissions are critical factors. Aimed at facilitating the development of sustainable and environmentally friendly construction materials.</li> </ul>

Active region magnetograms for solar flare prediction: Reduced resolution dataset

In this dataset, we provide a comprehensive collection of magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset incorporates data from three sources and provides SDO Helioseismic and Magnetic Imager (HMI) magnetograms of solar active regions as well as labels of corresponding flaring activity. This dataset will be useful for image analysis or solar physics research related to magnetic structure, its evolution over time, and its relation to solar flares. The dataset will be of interest to those researchers investigating automated solar flare prediction methods, including supervised and unsupervised machine learning (classical and deep), binary and multi-class classification, and regression. This dataset is a minimally processed, user configurable dataset of consistently sized images of solar active regions that can serve as a benchmark dataset for solar flare prediction research. This dataset consists of reduced resolution images (see usage notes below).

Active cell divisions generate fourfold orientationally ordered phase in living tissue

Morphogenesis, the process through which genes generate form, establishes tissue scale order as a template for constructing the complex shapes of the body plan. The extensive growth required to build these ordered substrates is fuelled by cell proliferation, which, naively, should destroy order. Understanding how active morphogenetic mechanisms couple cellular and mechanical processes to generate order — rather than annihilate it — remains an outstanding question in animal development. We show that cell divisions are the primary drivers of tissue flow leading to a fourfold orientationally ordered phase. Waves of anisotropic cell proliferation propagate across the embryo with precise patterning. Defects introduced into the nascent lattice by cell divisions are moved out of the tissue bulk towards the boundary by subsequent divisions. Specific cell proliferation rates and orientations enable cell divisions to organize rather than fluidize the tissue. We observe this using live imaging and tissue cartography to analyse the dynamics of fourfold tissue ordering in the trunk segmental ectoderm of the crustacean Parhyale hawaiensis beginning 72 hours after egg laying. The result is a robust, active mechanism for generating global orientational order in a non-equilibrium system that sets the stage for the subsequent development of shape and form.

Data from: Global change in brain state during spontaneous and forced walk in Drosophila is composed of combined activity patterns of different neuron classes

Movement-correlated brain activity has been found across species and brain regions. Here, we used fast whole-brain lightfield imaging in adult Drosophila to investigate the relationship between walk and brain-wide neuronal activity. We observed a global change in activity that tightly correlated with spontaneous bouts of walk. While imaging specific sets of excitatory, inhibitory, and neuromodulatory neurons highlighted their joint contribution, spatial heterogeneity in walk- and turning-induced activity allowed parsing unique responses from subregions and sometimes individual candidate neurons. For example, previously uncharacterized serotonergic neurons were inhibited during walk. While activity onset in some areas preceded walk onset exclusively in spontaneously walking animals, spontaneous and forced walk elicited similar activity in most brain regions. These data suggest a major contribution of walk and walk-related sensory or proprioceptive information to global activity of all major neuronal classes.

Dataset of paper Privacy Orientation during Online Teaching-Learning Activities: Practices Adopted and Lessons Learned

Dataset of the paper accepted for publication in the Journal on Interactive Systems (JIS). DA SILVA, M.; VITERBO, J.; SALGADO, L. C. C.; MOURÃO, E. Privacy Orientation during Online Teaching-Learning Activities: Practices Adopted and Lessons Learned. Journal on Interactive Systems, Porto Alegre, RS, 2023.

Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1307 through 1505

This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n.  These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO).  These data are the full sized images (600x600 pixels) for active regions (ARs) 1307 through 1505 in .fits format.

Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1064 through 1306

Stimulation of the catalytic activity of the tyrosine kinase Btk by the adaptor protein Grb2: Part 2

The Tec-family kinase Btk contains a lipid-binding Pleckstrin homology and Tec homology (PH-TH) module connected by a proline-rich linker to a "Src module", an SH3-SH2-kinase unit also found in Src-family kinases and Abl. We showed previously that Btk is activated by PH-TH dimerization, which is triggered on membranes by the phosphatidyl inositol phosphate PIP3, or in solution by inositol hexakisphosphate (IP6) (Wang et al. 2015, https://doi.org/10.7554/eLife.06074). We now report that the ubiquitous adaptor protein growth-factor-receptor-bound protein 2 (Grb2) binds to and substantially increases the activity of PIP3-bound Btk on membranes. Using reconstitution on supported-lipid bilayers, we find that Grb2 can be recruited to membrane-bound Btk through interaction with the proline-rich linker in Btk. This interaction requires intact Grb2, containing both SH3 domains and the SH2 domain, but does not require that the SH2 domain be able to bind phosphorylated tyrosine residues – thus Grb2 bound to Btk is free to interact with scaffold proteins via the SH2 domain. We show that the Grb2-Btk interaction recruits Btk to scaffold-mediated signaling clusters in reconstituted membranes. Our findings indicate that PIP3-mediated dimerization of Btk does not fully activate Btk, and that Btk adopts an autoinhibited state at the membrane that is released by Grb2.

Stimulation of the catalytic activity of the tyrosine kinase Btk by the adaptor protein Grb2: Part 3

The Tec-family kinase Btk contains a lipid-binding Pleckstrin homology and Tec homology (PH-TH) module connected by a proline-rich linker to a "Src module", an SH3-SH2-kinase unit also found in Src-family kinases and Abl. We showed previously that Btk is activated by PH-TH dimerization, which is triggered on membranes by the phosphatidyl inositol phosphate PIP3, or in solution by inositol hexakisphosphate (IP6) (Wang et al. 2015, https://doi.org/10.7554/eLife.06074). We now report that the ubiquitous adaptor protein growth-factor-receptor-bound protein 2 (Grb2) binds to and substantially increases the activity of PIP3-bound Btk on membranes. Using reconstitution on supported-lipid bilayers, we find that Grb2 can be recruited to membrane-bound Btk through interaction with the proline-rich linker in Btk. This interaction requires intact Grb2, containing both SH3 domains and the SH2 domain, but does not require that the SH2 domain be able to bind phosphorylated tyrosine residues – thus Grb2 bound to Btk is free to interact with scaffold proteins via the SH2 domain. We show that the Grb2-Btk interaction recruits Btk to scaffold-mediated signaling clusters in reconstituted membranes. Our findings indicate that PIP3-mediated dimerization of Btk does not fully activate Btk, and that Btk adopts an autoinhibited state at the membrane that is released by Grb2.

Stimulation of the catalytic activity of the tyrosine kinase Btk by the adaptor protein Grb2: Part 1

Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1506 through 1707

opencc-by-4.0Sep 2021View details →

Co-activation probability between neurons in the largest brain connectome of the fruit fly

This is a data set containing the co-activation probability between neurons in the largest brain connectome of the fruit fly released by the FlyEM project. The co-activation probability is measured based on neural dynamics computation, where a standard leaky integrate-and-fire (LIF) model is applied on the connectome to generate neural dynamics. Please read the paper "Yang Tian, Pei Sun; Percolation may explain efficiency, robustness, and economy of the brain. Network Neuroscience 2022; 6 (3): 765–790. doi: <a href="https://doi.org/10.1162/netn_a_00246">https://doi.org/10.1162/netn_a_00246</a>" for more details. This is the newest version of the data set. The following is a list of variable information: (1) SomaLocation is a 23008*3 matrix that contains the three-dimensional coordinates of neurons; (2) LambdaVector is the vector of a vector of synaptic excitation-inhibition (E/I) balance (see "Percolation may explain efficiency, robustness, and economy of the brain" for detailed explanations). (3) DirectedCoactivationPattern is a cell of co-activation probability matrices generated under each E/I balance condition, which is used in "Percolation may explain efficiency, robustness, and economy of the brain" for computational experiments. The (i,j)-th element in the matrix is the probability for neuron i to activate neuron j under the corresponding E/I balance condition. Note that the (i,j)-th element can be differnt from the (j,i)-th element.  (4) SymmetricCoactivationPattern is a cell of symmetric co-activation probability matrices generated under each E/I balance condition. This is a new data that has not been used in "Percolation may explain efficiency, robustness, and economy of the brain" yet. The (i,j)-th element in the matrix is the probability for neurons i and j to be co-activated under the corresponding E/I balance condition. If we define D as the directed co-activation probability matrix and denote S as the symmetric co-activation probability matrix, then we have S(i,j)=S(j,i)=0.5*(D(i,j)+D(j,i)).  The earlist version of this data can be seen in https://zenodo.org/record/5497516, which may lack detailed explanations. The second version of this data can be seen in https://zenodo.org/record/7869532, where a small mistake is found while calculating the SymmetricCoactivationPattern. This mistake is resolved in the newest version.

Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1708 through 1918

Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1919 through 2103