Summary:

A public repository of data processed for Mallm, Iskar, Ishaque et. al. 2018 Linking aberrant chromatin features in chronic lymphocytic leukemia to deregulated transcription factor networks

Website: http://www.cancerepisys.org/cancerepisys/index.html

Github: https://github.com/CancerEpiSys/Mallm-et-al-processing-scripts

Research mission

CancerEpiSys is a BMBF funded research project with the CancerSys program that dissects the epigenetic networks associated with chronic lymphocytic leukemia (CLL) to develop novel diagnostic and therapeutic approaches for the disease. It has been initiated based on the emerging view that signals encoded in the DNA sequence, epigenetic modifications (e.g. DNA methylation, post-translational histone modifications), non-coding RNAs and nucleosome positioning are not independently regulated properties. Rather, these chromatin features are governed by an interconnected network of molecular processes that determine the cellular gene expression program. Any errors that occur in the interplay of these factors can lead to aberrant gene regulation associated with cancer. To rationalize the mode of action of novel ‘epigenetic’ drugs in cancer therapy that change properties of this network, we will dissect experimentally and mathematically the interdependence of these processes, focusing on CLL.

The main objectives of CancerEpiSys are: (i) deciphering the relation of DNA sequence, epigenetic modifications, nucleosome positioning and aberrant gene expression in CLL, (ii) the identification of epigenetic network morphologies that describe the response to the epigenetic drugs panobinostat and 3-deazaneplanocin (DZNep) that inhibit histone deacetylases and methyltransferases, (iii) the characterization of epigenetic markers and chromatin features of CLL patient subgroups, and (iv) the integration of results into an analysis scheme for the epigenetic aberrations most relevant for prognostication, prediction of treatment relapse and stratification of patients with respect to therapeutic options. Moreover, our results will inform novel therapeutic approaches that target gene-expression programs at the epigenetic level to sensitize cancer cells towards apoptosis and anti-growth signals.

Summary: Using data from a randomized experiment, we find that poor rural Mexican households invested part of their cash transfers from the Oportunidades program in productive assets, increasing agricultural income by almost 10 percent after 18 months of benefits. We estimate that for each peso transferred, households consume 74 cents and invest the rest, permanently increasing long-term consumption by about 1.6 cents. Results suggest that cash transfers can achieve long-term increases in consumption through investment in productive activities, thereby permitting beneficiary households to attain higher living standards that are sustained even after transitioning off the program.

Summary:

The novel coronavirus (SARS-CoV-2) that has emerged from Wuhan, China in December 2019 has spread to almost all countries in the world causing a dramatic number of deaths. The current absence of antiviral treatment against the SARS-CoV-2 urges the scientific community to accelerate the drug discovery research process.

One way to identify potential treatments and to be able to administer it swiftly is to focus on drug repurposing studies, i.e. to investigate the SARS-CoV-2 antiviral potential of drugs that have already been approved for human use.

Proteins that are crucial for the survival and replication of the virus are the most attractive targets for such studies. Here we have focused on the SARS-CoV-2 RNA dependent RNA polymerase (RdRp) that plays an essential role in the virus replication process by screening ~2000 approved drugs (and 6 experimental ones) against this particular protein.

This is one part of a multi-target screen emphasising the main protease (Mrpo), the RNA-dependent-RNA-polymerase (RdRp) and human ACE2. The other datasets can found at the following locations:

• Mpro: Shape-based assay
• Mpro: Pharmacophore-based assay
• ACE2

More information about this screen along with interactive visualisations of the top compounds can be found on our website bonvinlab.org.

Summary:

The novel coronavirus (SARS-CoV-2) that has emerged from Wuhan, China in December 2019 has spread to almost all countries in the world causing a dramatic number of deaths. The current absence of antiviral treatment against the SARS-CoV-2 urges the scientific community to accelerate the drug discovery research process.

One way to identify potential treatments and to be able to administer it swiftly is to focus on drug repurposing studies, i.e. to investigate the SARS-CoV-2 antiviral potential of drugs that have already been approved for human use.

Proteins that are crucial for the survival and replication of the virus are the most attractive targets for such studies. Here we have focused on the Angiotensin Converting Enzyme 2 receptor (ACE2) that acts as one of the main gateways for viral entry in the host cell. We have screened ~2000 compounds against the inhibitor-bound closed form of the receptor.

This is one part of a multi-target screen emphasising the main protease (Mrpo), the RNA-dependent-RNA-polymerase (RdRp) and human ACE2. The other datasets can found at the following locations:

• Mpro: Shape-based assay
• Mpro: Pharmacophore-based assay
• RdRp

More information about this screen along with interactive visualisations of the top compounds can be found on our website bonvinlab.org.

Summary:

The novel coronavirus (SARS-CoV-2) that has emerged from Wuhan, China in December 2019 has spread to almost all countries in the world causing a dramatic number of deaths. The current absence of antiviral treatment against the SARS-CoV-2 urges the scientific community to accelerate the drug discovery research process.

One way to identify potential treatments and to be able to administer it swiftly is to focus on drug repurposing studies, i.e. to investigate the SARS-CoV-2 antiviral potential of drugs that have already been approved for human use.

Proteins that are crucial for the survival and replication of the virus are the most attractive targets for such studies. Here we have focused on the SARS-CoV-2 main protease (3CLpro) that plays an essential role in the virus replication process by screening ~2000 approved drugs (and 6 experimental ones) against this particular protein using a shape-based assay.

This is one part of a multi-target screen emphasising the main protease (Mrpo), the RNA-dependent-RNA-polymerase (RdRp) and human ACE2. The other datasets can found at the following locations:

• Mpro: Pharmacophore-based assay
• ACE2
• RdRp

More information about this screen along with interactive visualisations of the top compounds can be found on our website bonvinlab.org.

Summary:

The DataUp source code package includes three components: (1) the Excel add-in; (2) the public web client; and (3) the private web service that mediates between the add-in and client and the ONEShare repository.  All code is written in C# and relies on the .NET framework.

Summary: We study employers' perceptions of the value of postsecondary degrees using a field experiment. We randomly assign the sector and selectivity of institutions to fictitious resumes and apply to real vacancy postings for business and health jobs on a large online job board. We find that a business bachelor's degree from a for-profit online institution is 22 percent less likely to receive a callback than one from a nonselective public institution. In applications to health jobs, we find that for-profit credentials receive fewer callbacks unless the job requires an external quality indicator such as an occupational license.

Summary:

A semi-empirical model is presented that describes the distribution of Active Galactic Nuclei (AGN) on the cosmic web. It populates dark-matter halos in N-body simulations (MultiDark) with galaxy stellar masses using empirical relations based on abundance matching techniques, and then paints accretion events on these galaxies using state-of-the-art measurements of the AGN occupation of galaxies. The explicit assumption is that the large-scale distribution of AGN is independent of the physics of black-hole fueling. The model is shown to be consistent with current measurements of the two-point correlation function of AGN samples. It is then used to make inferences on the halo occupation of the AGN population. Mock AGN are found in halos with a broad distribution of masses with a mode of $\approx 10^{12}\,h^{-1} \, M_{\odot}$ and a tail extending to cluster-size halos. The clustering properties of the model AGN depend only weakly on accretion luminosity and redshift. The fraction of satellite AGN in the model increases steeply toward more massive halos, in contrast with some recent observational results. This discrepancy, if confirmed, could point to  a dependence of the halo occupation of AGN on the physics of black-hole fueling.

Summary:

Data corresponding to the figures of the publication "Nuclear spin coherence properties of 151Eu3+ and 153Eu3+ in a Y2O3 transparent ceramic" by J. Karlsson et al., (https://doi.org/10.1088/1361-648X/aa529a). A text file describes data in each compressed folder, please refer to the caption in the publication for more details. 

Summary:

Description for Zenodo Data Set DOI:10.5281/zenodo.220992

This dataset accompanies the article, to appear in Physics of Plasmas, titled "Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus," by Garnier, Mauel, Roberts, Kesner, and Woskov. 

---------------------------------------------

Data is presented as HDF5 datafiles 
(see https://www.hdfgroup.org/HDF5/) 
as HDF4 datafiles
(see https://www.hdfgroup.org/release4/doc/index.html)
and as CSV datafiles 
(see http://www.digitalpreservation.gov/formats/fdd/fdd000323.shtml).

Data files are associated with FIGURES 2, 3, 4, 5

Data plotted in other figures are derived from the dataset as described
in the paper.

---------------------------------------------
start of figure list
---------------------------------------------
FIGURE 1:   No data set

---------------------------------------------
FIGURE 2:   (HDF4 Files)

Four-channel Microwave (60 GHz) Interferometer 
S140529016_DensityData.hdf          
time range:     5.00 sec - 7.00 sec
time sample:    8 micro-sec
samples:        250,000 x 4 channels + 250,000 (total)
Unit:           radian "Interferometer"
Unit:           1.0E18 particles "Total-Particles"

16-channel Photodiode Array 1   
S140529016_PDAData.hdf  
time range:     5.00 sec - 7.00 sec
time sample:    20 micro-sec
samples:        100,000 x 16 channels
Unit:           A.U. "PDA-1"

TOTAL ECRH Injected Heating Power
S140529016_ECRHData.hdf     
time range:     5.00 sec - 7.00 sec
time sample:    20 micro-sec
samples:        100,000
Unit:           kW "Microwave-Power"

S140529016_LoopVoltage.hdf  
time range:     5.00 sec - 7.00 sec
time sample:    80 micro-sec
samples:        25,000 
Unit:           milli-Volt x sec "Loop-Voltage"

---------------------------------------------
FIGURE 3(a):    (HDF4 Files)

Four-channel Microwave (60 GHz) Interferometer 
S140529016_DensityData.hdf          
time range:     5.00 sec - 7.00 sec
time sample:    8 micro-sec
samples:        250,000 x 4 channels + 250,000 (total)
Unit:           radian "Interferometer"
Unit:           1.0E18 particles "Total-Particles"

16-channel Photodiode Array 1   
S140529016_PDAData.hdf  
time range:     5.00 sec - 7.00 sec
time sample:    20 micro-sec
samples:        100,000 x 16 channels
Unit:           A.U. "PDA-1"

---------------------------------------------
FIGURE 4 (a), (b), (c), (d), (e), (f):   (CSV Files)

time period:            5.0 - 6.0 sec
Ensemble Window:        8 msec
sample period:          8 micro-sec
Nyquist Freq:           62.475 kHz

(a) Fig4a-Line-Density-Coherence.csv
Frequency (Hz)          Hz
d(nl-1)^2               dimensionless
d(nl-2)^2               dimensionless   
d(nl-3)^2               dimensionless
d(nl-4)^2               dimensionless
Lambda 1-2              dimensionless
Lambda 1-3              dimensionless
Lambda 1-4              dimensionless

(b) Fig4b-Isat-Coherence.csv                       
Frequency (Hz)          Hz
d(I)^2                  dimensionless
Lambda 8deg             dimensionless
Lambda 16deg            dimensionless
Lambda 24deg            dimensionless

(c) Fig4c-Float-Potential-Coherence.csv
Frequency (Hz)          Hz
d(Pot)^2/Te^            dimensionless
Lambda 8deg             dimensionless
Lambda 16deg            dimensionless
Lambda 24deg            dimensionless

(d) Fig4d-Line-Density-Coherence.csv
Frequency (Hz)          Hz
d(nl-1)^2               dimensionless
d(nl-2)^2               dimensionless   
d(nl-3)^2               dimensionless
d(nl-4)^2               dimensionless
Lambda 1-2              dimensionless
Lambda 1-3              dimensionless
Lambda 1-4              dimensionless

(e) Fig4e-Isat-Coherence.csv                       
Frequency (Hz)          Hz
d(I)^2                  dimensionless
Lambda 8deg             dimensionless
Lambda 16deg            dimensionless
Lambda 24deg            dimensionless

(f) Fig4f-Float-Potential-Coherence.csv
Frequency (Hz)          Hz
d(Pot)^2/Te^            dimensionless
Lambda 8deg             dimensionless
Lambda 16deg            dimensionless
Lambda 24deg            dimensionless

---------------------------------------------
FIGURE 5 (a), (b):   (CSV Files)

Figure 5(a)
time period:            5.0 - 6.0 sec
Ensemble Window:        8 msec
sample period:          8 micro-sec
Nyquist Freq:           62.475 kHz

(a) Fig5a-Float-Isat-CrossPhase.csv                
Frequency (Hz)          Hz
alpha-Float             degree
alpha-Isat              degree

Figure 5(b)
time period:            6.02 - 6.05 sec
Ensemble Window:        1.6 msec
sample period:          8 micro-sec
Nyquist Freq:           62.475 kHz

(b) Fig5b-Float-Isat-DuringCrossPhase.csv 
Frequency (Hz)          Hz
alpha-Float             degree
alpha-Isat              degree
kappa-Float             dimensionless
kappa-Isat              dimensionless
---------------------------------------------
FIGURE 6:   No data set

---------------------------------------------
FIGURE 7:   No data set

---------------------------------------------
FIGURE 8:   No data set

---------------------------------------------
FIGURE 9:   No data set

---------------------------------------------
FIGURE 10:  No data set

---------------------------------------------
end of figure list
---------------------------------------------
---------------------------------------------
start of file list
---------------------------------------------
Filename                            Size    
----------------------------------------------
All-Probe-Data.h5                       9.8 MB
Average-Isat-Data.h5                    422 KB
Average-Probe-Data.h5                   1.1 MB
Fig4a-Line-Density-Coherence.csv        413 KB
Fig4b-Isat-Coherence.csv                251 KB
Fig4c-Float-Potential-Coherence.csv     250 KB
Fig4d-Line-Density-Coherence.csv        103 KB
Fig4e-Isat-Coherence.csv                62 KB
Fig4f-Float-Potential-Coherence.csv     62 KB
Fig5a-Float-Isat-CrossPhase.csv         138 KB
Fig5b-Float-Isat-DuringCrossPhase.csv   36 KB
Potential-Time-Angle-Data.h5            255 KB
S140529016_DensityData.hdf              6 MB
S140529016_ECRHData.hdf                 803 KB
S140529016_LoopVoltage.hdf              203 KB
S140529016_PDAData.hdf                  6.8 MB

LDX-Pellet-Supplementary.pdf            1.8 MB
S140529016_frPlots.mp4                  3.2 MB
---------------------------------------------
end of file list
---------------------------------------------

 

 

Summary:

Meow J(1):

• Release new version for debugging

Summary:

Dataset for the publication: Schomaker, J., Rau, E.M., Einhäuser, W., & Wittmann, B.C. (2017) Motivational Objects in Natural Scenes (MONS): A database of >800 objects. Front. Psychol. 8:1669. doi: 10.3389/fpsyg.2017.01669

Summary:

The dataset for the manuscript "Gradients of seasonality and mortality risk cause rapid shifts in body size of determinate growers". The dataset is packed in ZIP archive 'seasonality_size_clines.zip' and contain the results of the dynamic optimization model. Each file in the archive contains data (stored as Matlab workspaces).

Summary:

In a broad range of applied magnetic fields and material parameters isolated magnetic skyrmions
condense into skyrmion lattices. While the geometry of isolated skyrmions and their lattice
counterparts strongly depend on field and Dzyaloshinski–Moriya interaction, this issue has not been
adequately addressed in previous studies. Meanwhile, this information is extremely important for
applications, because the skyrmion size and the interskyrmion distance have to be tuned for skyrmion
based memory and logic devices. In this investigation we elucidate the size and density-dependent
phase diagram showing traditional phases in field versus material parameters space by means of
Monte-Carlo simulations on a discrete lattice. The obtained diagram permits us to establish that, in
contrast to the continuum limit, skyrmions on a discrete lattice cannot be smaller than some critical
size and have a very specific shape. These minimal skyrmions correspond to the micromagnetic
configuration at the energy barrier between the ferromagnetic and the skyrmionic states.
Furthermore, we use atomistic Landau–Lifshitz–Gilbert simulations to study dynamics of the
skyrmion annihilation. It is shown that this procees consists of two stages: the continuous skyrmion
contraction and its discontinuous annihilation. The detailed analysis of this dynamical process is
given.

Summary:

Key microalgal species are currently being exploited as biomanufacturing platforms using mass cultivation systems. The opportunities to enhance productivity levels or produce non-native compounds are increasing as genetic manipulation and metabolic engineering tools are rapidly advancing. Regardless of the end product, there are both environmental and industrial risks associated to open pond cultivation of mutant microalgal strains. A mutant escape could be detrimental to local biodiversity and increase the risk of algal blooms. Similarly, if the cultivation pond is invaded by a wild-type microalgae or the mutant reverts to wild-type phenotypes, productivity could be impacted. To investigate these potential risks, a response surface methodology was applied to determine the competitive outcome of two Chlamydomonas reinhardtii strains, a wild-type (CC-124) and a high-lipid accumulating mutant (CC-4333), grown in mixotrophic conditions, with differing levels of nitrogen and initial wild-type to mutant ratios. Results of the growth experiments show that mutant cells have double the exponential growth rate of the wild-type in monoculture. However, due to a slower transition from lag phase to exponential phase, mutant cells are outcompeted by the wild-type in every co-culture treatment. This suggests that, under the conditions tested, outdoor cultivation of the C. reinhardtii cell wall-deficient mutant strains does not carry a significant environmental risk to its wild-type in an escape scenario. Furthermore, lipid results show the mutant strain accumulates over 200% more TAGs per cell, at 50 mg/L NH₄Cl, compared to the wild-type, therefore, the fragility of the mutant strain could impact on overall industrial productivity.

Summary:

The novel coronavirus (SARS-CoV-2) that has emerged from Wuhan, China in December 2019 has spread to almost all countries in the world causing a dramatic number of deaths. The current absence of antiviral treatment against the SARS-CoV-2 urges the scientific community to accelerate the drug discovery research process.

One way to identify potential treatments and to be able to administer it swiftly is to focus on drug repurposing studies, i.e. to investigate the SARS-CoV-2 antiviral potential of drugs that have already been approved for human use.

Proteins that are crucial for the survival and replication of the virus are the most attractive targets for such studies. Here we have focused on the SARS-CoV-2 main protease (3CLpro) that plays an essential role in the virus replication process by screening ~2000 approved drugs (and 6 experimental ones) against this particular protein using a pharmacophore-based assay.

This is one part of a multi-target screen emphasising the main protease (Mrpo), the RNA-dependent-RNA-polymerase (RdRp) and human ACE2. The other datasets can found at the following locations:

• Mpro: Shape-based assay
• ACE2
• RdRp

More information about this screen along with interactive visualisations of the top compounds can be found on our website bonvinlab.org.

Summary:

Data used in the paper “Investigation of different sparsity transforms for the PICCS algorithm in small-animal respiratory gated CT”, J F P J Abascal, M Abella, A Sisniega, J J Vaquero, and M Desco. Plos One (accepted 2015) http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0120140

Summary:

This is a www.zenodo.org data and R script upload for publication:

 

Schmid, M.S. et al., The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean. Methods in Oceanography (2016), http://dx.doi.org/10.1016/j.mio.2016.03.003

 

Downloadable script: Script_Schmid_Mio_2016_data_upload.R

Downloadable data: Schmid_2016_MIO_CGlac.csv

Summary:

The influence of in-plane and canted magnetic fields on spin spirals and skyrmions in atomic bilayer
islands of palladium and iron on an Ir(111) substrate is investigated by scanning tunneling microscopy
at low temperatures. It is shown that the spin spiral propagation direction is determined by the island’s
border which can be explained by equilibrium state calculations on a triangular lattice.Wefind a
different response of spin spirals to in-plane magnetic fields for a propagation direction parallel to the
applied field as compared to perpendicular, which originates from their cycloidal nature. As a result,
the spin spiral propagation direction may be reorientated by in-plane fields. Furthermore, it is
demonstrated that also skyrmions are distorted in canted fields which allows to determine the sense of
magnetization rotation as enforced by the interfacial Dzyaloshinskii–Moriya interaction.

Summary: Adaptive high-order numerical simulations of hyperbolic PDEs in Julia