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Bidirectional Regulation of Motor Circuits Using Magnetogenetic Gene Therapy

Here we report a novel suite of magnetogenetic tools, based on a single anti-ferritin nanobody-TRPV1 receptor fusion protein, which regulated neuronal activity when exposed to magnetic fields. AAV-mediated delivery of a floxed nanobody-TRPV1 into the striatum of adenosine 2a receptor-cre driver mice resulted in motor freezing when placed in an MRI or adjacent to a transcranial magnetic stimulation (TMS) device. Functional imaging and fiber photometry both confirmed activation of the target region in response to the magnetic fields.  Expression of the same construct in the striatum of wild-type mice along with a second injection of an AAVretro expressing cre into the globus pallidus led to similar circuit specificity and motor responses. Finally, a mutation was generated to gate chloride and inhibit neuronal activity. Expression of this variant in subthalamic nucleus in PitX2-cre parkinsonian mice resulted in reduced local c-fos expression and motor rotational behavior. These data demonstrate that magnetogenetic constructs can bidirectionally regulate activity of specific neuronal circuits non-invasively in-vivo using clinically available devices.

opencc-by-4.0Mar 2017View details →

Fig. 1 in Actual and potential distribution of five regulated avocado pests across Mexico, using the maximum entropy algorithm

Fig. 1. Potential distribution of 5 insect pests of quarantine importance in Mexican avocados, based on ecological niche modeling. A) Conotrachelus aguacatae; B) Conotrachelus perseae; C) Copturus aguacatae; D) Heilipus lauri; and E) Stenoma catenifer. Current and potential distribution in Mexico was projected according to biogeographic provinces (Morrone 2005, 2014a), 1 = Baja California, 2 = California, 3 = Sonora, 4 = Sierra Madre Occidental, 5 = Mexican Plateau, 6 = Tamaulipeca, 7 = Mexican Pacific Coast, 8 = Trans-Mexican Volcanic Belt, 9 = Sierra Madre Oriental, 10 = Veracruzana, 11 = Balsas Basin, 12 = Sierra Madre del Sur, 13 = Chiapas, 14 = Yucatan. Scale values: 0 = absence, 1 = presence.

opencc-by-4.0Mar 2017View details →

Fig. 2 in Actual and potential distribution of five regulated avocado pests across Mexico, using the maximum entropy algorithm

Fig. 2. Geographic areas in Mexico where both the insect pest and avocados are found. Shading indicates hectares affected. A) Conotrachelus aguacatae; B) Conotrachelus perseae; C) Copturus aguacatae; D) Heilipus lauri; and E) Stenoma catenifer.

opencc-by-4.0Sep 2016View details →

Fig. 1 in Effect of plant growth regulators on Blissus insularis (Hemiptera: Blissidae)

Fig. 1. Mean Blissus insularis densities (± SE) in St. Augustinegrass, Stenotaphrum secundatum, treated with mefluidide, trinexapac-ethyl, or untreated control. Means with the same letter within a sampling date did not differ statis- tically (ANOVA, P> 0.05); there were 5 replicates per treatment.

Data used in "Storms regulate Southern Ocean summer warming"

The data included in this repository was used to generate the figures in the submitted manuscript "Storms regulate Southern Ocean summer warming" by du Plessis and co-authors. Abstract: "Sea surface temperature (SST) in the Southern Ocean (SO) is the fingerprint of ocean heat uptake and critical for air-sea interactions. However, SO SST is biased warm in climate models, reflecting our limited understanding of the mechanisms that set its magnitude and variability. An important factor driving SST variability is synoptic-scale weather systems, such as storms, yet their impacts are difficult to directly observe. Using in-situ observations from underwater and surface robotic vehicles in the subpolar SO, we show evidence that storms regulate the summer evolution of SST through altering the mixed layer effective heat capacity and entraining colder water from below. Through these mechanisms, we determine that interannual variations in SO SST reflect changes in storm intensity and prevalence, which, in turn, are driven by the Southern Annular Mode. Our results demonstrate a causal link between storm forcing and lower frequency SST variability, which has implications for addressing SST biases in climate models." <h3>Datasets</h3> The observations in this study were made as a part of the SOSCEx-STORM experiment, which fits into the larger observational programme the Southern Ocean Seasonal Cycle Experiment (Swart et al. 2012). SOSCEx-STORM undertook a twinned deployment of a Wave Glider and a profiling Slocum glider which were piloted in conjunction with each other. The platforms were deployed and retrieved from the R/V Agulhas II at 54°S, 0°E, south of the Polar Front, and sampled together between 20 December 2018 and 8 March 2019.  Slocum glider data The glider was equipped with a continuously pumped Seabird Slocum Glider CTD, which was processed with the GEOMAR MATLAB toolbox and vertically gridded to 1 m depth intervals.  Relevant data name: <code>slocum_grid_processed.nc</code> Slocum glider Microstructure data: The Webb Teledyne G2 Slocum glider was equipped with a Rockland Scientific Microstructure Profiler (MicroRider). The MicroRider was equipped with two piezo-electric accelerometers and two air-foil shear probes oriented orthogonally. Microstructure data was only collected during the glider climbs to prolong battery life and obtain dissipation estimates as close to the surface as possible. See Nicholson et al. (2022) for details of the MicroRider processing. The mixing layer depth (XLD) was estimated as in Brainnerd and Gregg et al. (1995). Disspitation data name: <code>slocum_eps.nc</code> Mixing layer depth data name: <code>slocum_xld.nc</code> Slocum glider SST data: Initial data processing removed temperature data from the upper 2 m during the glider climb phase, and so to obtain an SST value from the Slocum glider temperature profiles, we calculated the median value between 0.5 m and 10 m depth for each dive.   Slocum SST data name: <code>slocum_sst_median_10m.nc</code> Wave Glider data The Liquid Robotics SV3 Wave Glider was fitted with an Airmar WX-200 Ultrasonic Weather Station mounted on a mast at 0.7 m above sea level, providing wind speed measurements at a rate of 1 Hz, averaged into 1-hour bins. The wind measurements were corrected to a height of 10 m above sea level. Note that the Airmar WX-200 weather station of the Wave Glider was faulty and the wind speed, wind direction and wind stress data was replaced by hourly ERA5 data.  Wave Glider data name: <code>WG_era5_1h_processed_28Aug2022.nc</code> NOAA OI SST and sea ice Monthly SST data was obtained from the NOAA optimum interpolation (OI) SST V2 product, which uses both in-situ and satellite data from November 1981 to January 202329. Data is provided by the National Centers for Environmental Prediction and made available on a 1◦ grid. All SST data where co-located sea ice concentration was above 0 has been removed from this analysis. NOAA OI SST and sea ice were obtained from<a href="https://psl.noaa.gov/data/gridded/data.noaa"> https://psl.noaa.gov/data/gridded/data.noaa</a>. Datasets: <code>sst.mnmean.nc</code>, <code>icec.mnmean.nc</code>, <code>lsmask.nc</code> Storm tracking dataset To track storm trajectories, we used storm tracks contained in monthly files for the Southern Ocean identified and used in the JGR-Oceans publication: Lodise, J., Merrifield, S. T., Collins, C., Rogowski, P., Behrens, & J., Terrill,E, (In Review). Global Climatology of Extratropical Cyclones From a New Tracking Approach and Associated Wave Heights from Satellite Radar Altimeter. Journal of Geophysical Research: Oceans. <a href="https://doi.org/10.1029/2022JC018925" rel="nofollow">https://doi.org/10.1029/2022JC018925</a> Data can be accessed at <a href="https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker">https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker</a>  All Southern Ocean storm locations can be found at: <code>ec_centers_1981_2020.nc</code> Storm radius datasets ERA5 data of air-sea heat flux, 10 m wind speed for all hourly instances where a storm center was within 1000 km of the gliders (Figs. 2 and 3, Extended Data Figs. 2 and 4)  Datasets of <code>combined_storms_{variable}_no_ice.nc</code> are data of air-sea heat flux and 10 m wind speed for all instances for 1000 km x 1000 km box around each storm center during summer months from 1981-2020 (> 570,000) used in Figs. 4 and 5. These data are considerably large (combined total >40 GB). Please contact me at marcel.du.plessis@gu.se to find a suitable way to share the data. The data was processed as follows: 1. Download storm centers from <a href="https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker/tree/main">https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker/tree/main</a> 2. Run process-lodise-storm-centers.ipynb to save all the cyclone center data as '<code>ec_centers_1981_2020.nc</code>' 3. Run filter-cyclone-centers.ipynb:     - cut all cyclone centers south of 40S     - only choose cyclone centers in DJF     - calculates minimum distance to land for each storm     - saves a dataset called '<code>ec_centers_1981_2020_with_min_dist_to_land.nc</code>'           - contains the variable distance to land for all filtered cyclones         - we do this step because it takes about an hour to run     - remove cyclones within 500 km from land     - remove cyclones less than 24 hours     - we are left with 11005 storms     - data saved as 'ec_centers_1981_2020_500km_from_land_filtered_24hours' 4. Run storm_processing_cutouts.ipynb (this took several days)     - loads <code>ec_centers_1981_2020_500km_from_land_filtered_24hours.nc</code>     - runs through each summer, processes storm_localization.py      - saves data as <code>storms_{variable}_{year}.nc</code>         - e.g. <code>storms_winds_1981.nc</code> - winds for all 1000 km radius cyclones in DJF 1981/82 5. combine_storm_years.ipynb      - creates datasets for each variable that has storms for all year called <code>combined_storms_{variable}.nc</code> 6. remove_sea_ice_from_storms.ipnyb      - makes data nan where sea ice is present in each cyclone     - creates datasets called <code>combined_storms_{variable}_no_ice.nc</code> 7. seasonal-means-storms.ipynb     - calculates the mean for all storms for each year      - saves them one dataset: <code>combined_storms_{variable}_seasonal_means.nc'</code> EN4 mixed layer depths We use the EN4 database of quality controlled temperature and salinity profiles from 2004 to 2022 to produce our MLD for the interannual analysis (Good et al. 2013). We use the profiles that contain the Cheng et al. (2014) XBT corrections and Gouretski and Cheng (2020) MBT corrections. We limit the data intake to 2004 as this marks the beginning of the Argo period. All under-ice profiles are removed. We calculate the MLD for each individual profile using the density threshold of de Boyer Montegut et al. (2004) where the density value first exceeds the 10 m reference value by 0.03 kg m-3. We then determine the median MLD value for each month within 3 x 3 degree grid cells, then obtain a mean value for each DJF season per 3 x 3 degree grid cell.  Relevant data name: <code>en4_monthly_mixed_layer_depth_median.nc</code> Southern Ocean Fronts Position of the Subantarctic Front and Polar Front are from:  <div>Sokolov, S. and Rintoul, S.R., 2009. Circumpolar structure and distribution of the Antarctic Circumpolar Current fronts: 1. Mean circumpolar paths. Journal of Geophysical Research: Oceans, 114(C11).</div> <div> </div> <div>Relevant data name: <code>ACCfronts.csv</code> </div> <div> </div> <div>ERA5 The ERA5 data provided was by ECMWF available at <a href="https://doi.org/10.24381/cds.bd0915c6">https://doi.org/10.24381/cds.bd0915c6</a>.</div> <div> </div> <div>The various datasets used in this study are described below: Wind speed, air temperture, dew point temperature for the observational period: <code>ds_era5_vars.nc</code> Fluxes for the observational period: <code>ds_era5_flux.nc</code> Wind speed, air temperture, dew point temperature, fluxes for the case study day in Figure 3: <code>era5_case_study.nc</code> Mean winds and fluxes for each DJF period between 1981 and 2022.: <code>mean_summer_winds_fluxes_1981_2023.nc</code></div> <div>Monthly-mean 10 m wind speed and mean sea level pressure during SOSCEx-Storm: <code>201812_month_avg_wind_mslp.nc</code> and <code>20190102_month_avg_wind_mslp.nc</code></div> <div> </div> <div>Cloud Top Pressure The MODIS Level-2 Cloud product was obtained from <a href="http://dx.doi.org/10.5067/MODIS/MYD06_L2.061">http://dx.doi.org/10.5067/MODIS/MYD06_L2.061</a> (Fig. 3). </div> <div> Processed dataset: <code>modis_ctt_ctp.nc</code> Southern Annular Mode The SAM is the principal mode of variability in the atmospheric circulation of the Southern Hemisphere mid-and-high latitudes. We use the Marshall SAM Index from station-based observations of the zonal pressure difference between the latitudes of 40◦S and 65◦S. SAM Index was retrieved from <a href="https://climatedataguide.ucar.edu/climate-data/marshall-southern-annular-mode-sam-index-station-based">https://climatedataguide.ucar.edu/climate-data/marshall-southern-annular-mode-sam-index-station-based</a>. SAM dataset: <code>ds_sam.nc</code>   </div>

opencc-by-sa-4.0Sep 2018View details →

Architecture of Pol II(G) and molecular mechanism of transcription regulation by Gdown1

This repository contains the modeling files and the analysis related to the article <a href="https://www.ncbi.nlm.nih.gov/pubmed/30190596">"Architecture of Pol II(G) and molecular mechanism of transcription regulation by Gdown1"</a> by Jishage et al. in Nat Struct Mol Biol 2018. For more information about how to reproduce this modeling, see the <a href="https://salilab.org/pol_ii_g/">Sali lab website</a> or the README file.

opencc-by-4.0Oct 2018View details →

Inputs for the publication "A new solution to mitigate hydropeaking? Batteries versus re-regulation reservoirs"

This file contains the main inputs for the publication "A new solution to mitigate hydropeaking? Batteries versus re-regulation reservoirs".

opencc-by-4.0Sep 2019View details →

Dataset related to article "Pentraxin 3 regulates synaptic function by inducing AMPA receptor clustering via ECM remodeling and β1-integrin"

This record contains raw data related to article "Pentraxin 3 regulates synaptic function by inducing AMPA receptor clustering via ECM remodeling and β1-integrin" Abstract Control of synapse number and function in the developing central nervous system is critical to the formation of neural circuits. Astrocytes play a key role in this process by releasing factors that promote the formation of excitatory synapses. Astrocyte-secreted thrombospondins (TSPs) induce the formation of structural synapses, which however remain post-synaptically silent, suggesting that completion of early synaptogenesis may require a two-step mechanism. Here, we show that the humoral innate immune molecule Pentraxin 3 (PTX3) is expressed in the developing rodent brain. PTX3 plays a key role in promoting functionally-active CNS synapses, by increasing the surface levels and synaptic clustering of AMPA glutamate receptors. This process involves tumor necrosis factor-induced protein 6 (TSG6), remodeling of the perineuronal network, and a β1-integrin/ERK pathway. Furthermore, PTX3 activity is regulated by TSP1, which directly interacts with the N-terminal region of PTX3. These data unveil a fundamental role of PTX3 in promoting the first wave of synaptogenesis, and show that interplay of TSP1 and PTX3 sets the proper balance between synaptic growth and synapse function in the developing brain.

opencc-by-4.0Sep 2019View details →

Tributary bay oscillations generated by diurnal discharge regulation in Three Gorges Reservoir

The dataset include absolute water level (in m.a.s.l.) from six gauging stations along Three Gorges Reservoir and discharge data for the period January to November 2018. And the relative water level and vertical profiles of flow velocity were measured in the middle (Xiakou) and upper reach (Pingyikou) of Xiangxi bay by two RDI ADCP from September 16 - October 12, 2018. For water level, water depth and flow velocity, the respective time series were high-pass filtered with cut-off frequencies corresponding to periods of 36 h and 4 h (see excel files), respectively (half-power frequencies of a zero-phase, 20-pole Butterworth filter). Power spectra were calculated using Welch’s method. Horizontal current velocities were measured in earth coordinates and rotated into longitudinal (along the river channel) and transversal velocity components by rotation into the respective mean (depth and temporarily averaged) flow direction at the sampling sites (see 'long_trans_vel.mat').

Utrecht Codebook on Fiscal Fraud to Empower Regulators

The Tax and Money Laundering Database was made as part of the Horizon 2020 project COFFERS, it took almost two years to make, from data collection planning and methodology to actual data collection and then verification of data and sources. The legal database gathers legislation of all European Union Member States regarding tax evasion and money laundering, as well as other relevant legal variables such as legal origins of each jurisdictions’ legislation

opencc-by-4.0Jun 2016View details →

Fig. 2 in Biotic factors are more important than abiotic factors in regulating the abundance of Plutella xylostella L., in Southern Brazil

Fig. 2. Abundance of Plutella xylostella on broccoli (A) and cauliflower crops (B) in the county of Colombo, Paraná State, Southern Brazil.

opencc-by-4.0Jun 2018View details →

Figure 1. Prostaglandin E 2 in The role of a novel Wolbachia (Rickettsiales: Anaplasmataceae) synthetic peptide, WolFar, in regulating prostaglandin levels in the hemolymph of Acheta domesticus (Orthoptera: Gryllidae)

Figure 1. Prostaglandin E 2 activity of Acheta domesticus at different postinjection times relative to the concentrations of WolFar applied. Each point represents the mean ± SE.

opencc-by-4.0Jun 2018View details →

Figure 2 in The role of a novel Wolbachia (Rickettsiales: Anaplasmataceae) synthetic peptide, WolFar, in regulating prostaglandin levels in the hemolymph of Acheta domesticus (Orthoptera: Gryllidae)

Figure 2. Formation of nodules in the internal system and fat body of Acheta domesticus following injections of 100% concentration of WolFar. The red triangles indicate the positions of the nodules. A: Negative control; B: at 24 h; C: at 48 h; D: at 72 h.

Figure 3 in mir-331 negatively regulates thyroglobulin secretion via ERp29

Figure 3. Regulation of Tg expression and secretion by small interfering RNAs. (A) Regulation of ERp29 and ThrB expression using siERp29 and miRNA-331, respectively. (B) Regulation of Tg gene expression by siERp29 and miRNA-331. (C) Regulation of Tg secretion by siERp29 and miRNA-331. All experimental conditions in Figure 1 are described in the text. Data represent means ± standard deviation (SD) of at least 3 independent experiments. Statistical significance between multiple groups: one-way analysis of variance (ANOVA) test. GraphPad Prism 6 software (GraphPad Software Inc., San Diego, CA, USA). *P <0.05, **P <0.005, ***P <0.001, ****P <0.0001.

Figure 1 in mir-331 negatively regulates thyroglobulin secretion via ERp29

Figure 1. Gene expression of endoplasmic reticulum (ER) chaperones and Tg secretion by ERp29 overexpression. (A) In PCCL3 and ERp29over PCCL3 cells, gene expression was estimated for both ER chaperones and ER stress sensors using reverse-transcription polymerase chain reaction (RT-PCR). (B) ER chaperone protein expression was measured using western blotting. (C) Tg secretion by ER29 was measured using western blotting. (D) Tg secretion was measured using tunicamycin treatment followed by western blotting. All experimental conditions are described in detail in the text. Data represent means of at least 3 independent experiments.

Figure 2 in mir-331 negatively regulates thyroglobulin secretion via ERp29

Figure 2. Regulation of ER stress sensors by ERp29 overexpression. (A) Results of western blotting in PCCL3 and ERp29over PCCL3 cells, showing full-length ATF6 (*), partial-length ATF6 (**), IRE1 (←), and phosphorylated eIF2 alpha (←). (B) RT-PCR analysis showed the expression of 2 isoforms: spliced (XBP1S) and unspliced (XBP1U) xbp1 transcripts. All experimental conditions are described in detail in the text. Data represent means of at least 3 independent experiments.

Source data for the kinetic assays in publication "Deciphering the allosteric regulation of mycobacterial inosine-5′-monophosphate dehydrogenase"

Datasets of enzyme kinetics related to the publication "Deciphering the allosteric regulation of mycobacterial inosine-5′-monophosphate dehydrogenase", published in Nature Communications with DOI: https://doi.org/10.1038/s41467-024-50933-6  Individual files contain raw kinetic reaction data of the mycobacterial IMPDHs (wild type and mutant forms Mycobacterium smegmatis or wild type Mycobacterium tuberculosis) as a function of IMP, NAD+, GTP, ATP, ppGpp and Mg2+ concentration. Individual data sets are presented as time data points of the absorbance at 340 nm in an Excel file with a linked Graphpad graphical link. Detailed experimental conditions are available in the related publication.

Source data for the HDX-MS experiments in publication "Deciphering the allosteric regulation of mycobacterial inosine-5′-monophosphate dehydrogenase"

Dataset of HDX-MS experiments related to the publication "Deciphering the allosteric regulation of mycobacterial inosine-5′-monophosphate dehydrogenase", published in Nature Communications with DOI: https://doi.org/10.1038/s41467-024-50933-6  The differential HDX-MS experiments compare the apo and ligand-bound states of IMPDH from Mycobacterium smegmatis. A description of the dataset is provided in the attached README file: IMPDH_HDX-MS_README.txt. Detailed experimental conditions are available in the related publication.

opencc-by-4.0Aug 2024View details →

Foxg1 regulation of translation: RNASeq data

Osvaldo Artimagnella & Antonello Mallamaci.  Foxg1 regulation of translation: RNASeq data It includes raw: (1) Total (totRNA) (2) Translating Ribosome Affinity Purification (trapRNA) (3) RNA ImmunoPrecipitation (ripRNA) sequence data, referred to by: Osvaldo Artimagnella, Elena Sabina Maftei, Mauro Esposito, Remo Sanges, Antonello Mallamaci. "Foxg1 regulates translation of neocortical neuronal genes, including the main NMDA receptor subunit gene, Grin1". (submitted).