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Particle trajectories generated by the eDNA fate and transport model for the Atlantic bottlenose dolphin (Tursiops truncatus) -- Part II

<ul> <li>"release_no_wind" includes particle trajectories generated by the eDNA fate and transport model, which was driven by the hydrodynamics simulated without wind forcing. The purpose is to diagnose the influence of wind on particle distributions.</li> <li>The python codes used to read the particle trajectories and get the particle counts in each model grid cell can be found in https://github.com/Jilian0717/eDNA_fate_transport_model/tree/main/particle_density</li> </ul>

opencc-by-4.0Aug 2024View details →

Blobs form during the single-file transport of proteins across nanopores

Electrophysiology data corresponding to the main text figures and supporting information figures. One representative set was chosen for each triplicate and included in this data set. Data was categorised based on the nanopore that was used.

Cilia structure and intraflagellar transport differentially regulate sensory response dynamics within and between C. elegans chemosensory neurons

List of data with corresponding files Figure 1 A)    File name: Figure 1A- ASH_IFT_mutants_length_copy.pzfx B)    File names: (Folder) ASH_1Mglycerol_IFT_mutants and (Folder) ASH_10-2IAA_IFT_mutants C)    File names: (Folder) ASH_1Mglycerol_IFT_mutants > Peak_responses and (Folder) ASH_10-2IAA_IFT_mutants > Peak_responses - first pulse D)   File names: Figure 1D – ASH Cher and Marianas Lengths Truncation osm-3(ts) 27 and 30 degree graph and Figure 1D – ASH Cher and Marianas Lengths Truncation osm-3(ts) 27 degrees E)    File name: Figure 1E – ASH IFT Quantification.xlsx F)    File name: (Folder) ASH_1Mglycerol_osm3ts G)   File names: Figure 1 – Glycerol_Assays_Data.pzfx and Figure 1 – Glycerol_Assays_Data.xlsx H)   File name: Figure 1H – ALL ASH Marianas and Cher Lengths che-3(nx159ts) I)     File names: Figure 1- ASH IFT PY12007 v AP457 and Figure 1- ASH IFT PY12007 v AP457.xlsx J)    File name: (Folder) ASH_1Mglycerol_che3ts K)   File names: Figure 1 – Glycerol_Assays_Data.pzfx and Figure 1 – Glycerol_Assays_Data.xlsx Figure 2 B)  File name: (Folder) 10-7Diacetyl_Calcium_IFT_mutants C)  File names: (Folder) 10-6Pyrazine_Calcium_IFT_mutants and (Folder) 10-7Pyrazine_Calcium_IFT_mutants D)  File names: (Folder) Microfluidics > (Folder) 10-6pyrazine and (Folder) 10-7diacetyl E)  File names: (Folder) Microfluidics > Diacetyl_Chemotaxis_index_osm6_osm3kap1.pzfx and Pyrazine_Chemotaxis_index_pyrazine_osm3kap1_osm6.pzfx G)  File name: (Folder) 10-6Pyrazine_Calcium_misexpression Figure 3 A)   File name: Figure 3A- AWA IFT AP303 v AP379.xlsx C)  File names: (Folder) AWA_Calcium_Diacetyl and (Folder) AWA_Calcium_Pyrazine D)  File names: (Folder) AWA_Calcium_Diacetyl and (Folder) AWA_Calcium_Pyrazine E)  File name: Figure 3E- SRX64 and ODR10 within dendritic branches.pzfx Figure 4 A)   File name: (Folder) AWA_Calcium_Diacetyl_grk2 B)   File name: (Folder) AWA_Calcium_Pyrazine_grk2 C)   File name: (Folder) Microfluidics_diacetyl D)   File name: (Folder) Microfluidics_pyrazine E)    File name: Figure 4E- GRK2tRFP_kap1osm3ts.pzfx Figure 5 A)   File names: (Folder) AWA_Diacetyl_Adaptation_osm3ts and (Folder) AWA_Pyrazine_Adaptation_osm3ts B)    File name: Figure 5B and S7B- FRAP.pzfx C)    File names: Figure 5C- SRX64_bbs7jhu590.pzfx and Figure 5C- SRX64_bbs7ok1351.pzfx and Figure 5C- oy158_bbs7jhu590.pzfx and Figure 5C- oy158_bbs7ok1351.pzfx D)   File names: (Folder) AWA_Diacetyl_Adaptation_bbs7 and (Folder) AWA_Pyrazine_Adaptation_bbs7 E)    File name: Figure 5E- Marianas_EV_ODR10_SRX64_osm3tskap1.pzfx Figure S1 A)   File names: (Folder) ASH_IFTmutants_0.5and2Mglycerol > (Folder) 0.5Mglycerol and (Folder) 2Mglycerol B)    File names: (Folder) ASH_IFTmutants_quinine > (Folder) 5mM_quinine and (Folder) 10mM_quinine C)    File name: ASH_IAA_t1/2 D)   File names: (Folder) ASH_IFTmutants_heptanol > (Folder) 10-3_heptanol and (Folder) 10-4_heptanol E)    File names: (Folder) ASH_IFTmutants_2Mglycerol_GABA and (Folder) ASH_IFTmutants_2Mglycerol_bicuculline Figure S2 B)    File name: Figure S2B- Dye-filling_data C)    File name: Figure S2C- ASH_IFT_mutants_length.pzfx D)   File names: Figure S2D- ASH IFT PY6345 v PY12007 and Figure S2D- ASH IFT Quantification Figure S3 A)   File name: (Folder) AWA_10-7Diacetyl_IFTmutants B)    File names: (Folder) AWA_Calcium_heptanone > (Folder) 10-4_heptanone and (Folder) 10-5_heptanone C)    File name: (Folder) AWA_10-5Pyrazine_IFTmutants D)   File names: (Folder) AWA_Calcium_methylpyrazine > (Folder) 10-8_methylpyrazine and (Folder) 10-9_methylpyrazine E)    File name: Figure S3- Chemotaxis IFT_mutants.pzfx Figure S4 A)  File name: Figure S4A – AP419_pyrazine_Chemotaxis.pzfx C)  File name: Figure S4C – srx64SL2gfp_osm5.pzfx Figure S6 A)   File names: Diacetyl_prism_decay_grk2 and Pyrazine_prism_decay_grk2 Figure S7  A)  File name: (Folder) AWA_Calcium_Adaptation_diacetyl_IFT_mutant  B)  File name: Figure 5B and S7B- FRAP  C)  File name: Figure S7C- oy158_grk2_rescue.pzfx  F)  File names: (Folder) AWA_Calcium_Adaptation_diacetyl_grk2 > (Folder) grk2 and (Folder) grk2bbs7double Additional materials             All code is listed under Code from Github > MF.matR-master and Additional Methods

opencc-by-4.0Jan 2024View details →

Snow particle number, aerosol concentration and 10 meter windspeed data from MOSAiC, N-ICE, Weddel Sea expeditions and chemistry transport model data (p-TOMCAT) .

The folder contains data for the MOSAiC, N-ICE and Weddell sea expedition for Snow particle counter measurements. Coarse aerosol measurements from the MOSAiC expedition are also included. Simulation data from a chemistry transport model (p-TOMCAT) is also available. This version includes both .mat and .nc files

SPARCS-WP3_Espoo_City_Total number of vehicles in local transportation

Number of vehicles registered in the Espoo area.

Data and software for "A numerical study of inhibiting effect and mechanism of thermal front on sediment transport in a stratified continental sea"

Data and software for "A numerical study of inhibiting effect and mechanism of thermal front on sediment transport in a stratified continental sea"

Dataset and codes for 'Enhancing glymphatic fluid transport by pan-adrenergic inhibition suppresses epileptogenesis in male mice'

This is the code and dataset repository for the publication entitled 'Enhancing glymphatic fluid transport by pan-adrenergic inhibition suppresses epileptogenesis in male mice' on Nature Communications, 2024. Author list:  Qian Sun&dagger;, Sisi Peng&dagger;,  Qiwu Xu, Pia Weikop, Rashad Hussain, Wei Song, Maiken Nedergaard§, Fengfei Ding§

Using Multi-Modal Path-Specific Transit Trips in Transportation Social Sustainability Analysis: Case Study in Atlanta, GA

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Sand transport processes change in response to changing atmospheric properties at higher elevations

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Relating North Atlantic Deep Water transport to ocean bottom pressure variations as a target for satellite gravimetry missions

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Interactive maps: Unveiling hidden risks in healthcare from flood-induced transportation disruption

We provide interactive maps in HTML format, viewable in standard web browsers, to illustrate the impact of flood events (indicated by the index in the title) on specific regions, focusing on hospitals at risk. This collection includes 321 maps, each representing a hospital where the change in service population exceeds 0.3. Hovering over the hospital symbols (marked with a plus) reveals meta information, including service population details before and after a flood event. Additionally, hovering over the polygons provides further insights into the hospital in focus, offering a detailed view of each facility’s status and impact zone.

Data related to the publication "structure and transport properties of LiTFSI-based deep eutectic electrolytes from machine-learned interatomic potential simulations"

Reference training and test datasets, trained ML potential models, and input scripts for the training (Allegro) and MD simulations (LAMMPS).

opencc-by-4.0Nov 2024View details →

Results Maritime Transportation - Deep-Sea and Short-Sea Shipping Instances

Each instance has a filename "scip_details_deep_n_i.txt"/"scip_details_short_n_i.txt", in which deep/short indicates that the trips are international/regional (deep sea shipping/short sea shipping), n is the number of ports (including the depot), and i is the index of the instance in the same group. The data in the file represent the console output of scip. It contains information on: <ul> <li>computation time</li> <li>gap</li> <li>primal- and dualbound</li> <li>best found integer solution</li> </ul>

opencc-by-4.0Aug 2024View details →

Theory-based integrated modelling of tungsten transport in ITER plasmas

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opencc-zeroDec 2018View details →

Data from: Pollen analogues are transported across greater distances in bee-pollinated than in hummingbird-pollinated species of Justicia (Acanthaceae)

Several hummingbird-pollinated plant lineages have been demonstrated to show increased rates of diversification compared to related insect-pollinated lineages. It has been argued that this pattern is produced by a higher degree of specialization on part of both hummingbirds and plants. We here test an alternative hypothesis: The often highly territorial hummingbirds may on average carry pollen over shorter distances than other pollinators and drive diversification by reducing gene flow distances. We present experimental data from pollen analogue tracking showing shorter dispersal distances in hummingbird- than in bee-pollinated species among ten neotropical species of Justicia (Acanthaceae).

opencc-zeroDec 2014View details →

Data from: Expressions of nitrate transporter genes reveal different nitrogen statuses of dominant diatom groups in the southern East China Sea

In this study, the mRNA levels of the Nrt2 nitrate transporter gene were used as a molecular indicator of nitrogen status in two dominant diatom groups, Skeletonema and Chaetoceros, which inhabit the southern East China Sea (ECS). To accurately interpret the abundance of Nrt2 transcripts in situ, maximum and minimum expression levels were determined under conditions of nitrogen deprivation and ammonium addition, respectively. In August 2010, Nrt2 transcript levels in Skeletonema at the inner shelf region exhibited a mean of 111 mmole/(mole EFL); at the mid-shelf region, the mean Nrt2 mRNA levels were 298 mmole/(mole EFL), which was very close to the maximum levels observed under nitrogen starvation. By contrast, the Nrt2 transcript levels in Chaetoceros were low at all of the shelf locations, except at one station in the mid-shelf region. The cross-shelf mean was 2.86 mmole/(mole EFL), which was similar to the expression levels observed in cultured Chaetoceros under conditions of sufficient ammonium. Similar expression patterns were observed in diatoms in the southern ECS in June 2011, but the Nrt2 transcript levels in Skeletonema at the inner shelf region were reduced to a mean of 28.6 mmole/(mole EFL). Regression analysis indicated that cell abundance and Nrt2 expression were closely related to the nutricline depth in the coastward half of the southern ECS for Skeletonema but not for Chaetoceros. These results indicate that the evaluated species differ in nitrogen status, which may reflect their evolutionary strategies to survive in a fluctuating marine environment.

opencc-zeroDec 2017View details →

Data from: Environmental DNA reveals quantitative patterns of fish biodiversity in large rivers despite its downstream transportation

Despite the ecological and societal importance of large rivers, fish sampling remains costly and limited to specific habitats (e.g., river banks). Using an eDNA metabarcoding approach, we regularly sampled 500 km of a large river (Rhône River). Comparisons with long-term electrofishing surveys demonstrated the ability of eDNA metabarcoding to qualitatively and quantitatively reveal fish assemblage structures (relative species abundance) but integrated a larger space than the classical sampling location. Combination of a literature review and field data showed that eDNA behaves in the water column like fine particulate organic matter. Its detection distance varied from a few km in a small stream to more than 100 km in a large river. To our knowledge, our results are the first demonstration of the capacity of eDNA metabarcoding to describe longitudinal fish assemblage patterns in a large river, and metabarcoding appears to be a reliable, cost-effective method for future monitoring.

opennotspecifiedJul 2006View details →

FIGURE 3 in A study of the SNARE protein syntaxin 16 and its role in the intracellular trafficking of glucose transporter GLUT4 in 3T3-L1 adipocytes

FIGURE 3. Scanning electron micrographic images of dorsal sides of (a) male and (b) female Aetacarus elanoides sp.n. Scales = 100 µm.

opennotspecifiedJul 2006View details →

FIGURE 2 in A study of the SNARE protein syntaxin 16 and its role in the intracellular trafficking of glucose transporter GLUT4 in 3T3-L1 adipocytes

FIGURE 2. Aetacarus elanoides sp. n., female. a – dorsal view, b – genital area; gl = gland opening, gp = genital papilla, ia = cupule.