cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/jk926nfcdr.3
title: Early life seizures project

description: Early-life status epilepticus produces a high-functioning autism-like phenotype: deficit in social interaction and vocalization, enhanced anxiety, no cognitive impairment and altered functional connectivity within the hippocampus (CA3-CA1) and between the hippocampus and prefrontal cortex. In this study, we used a social memory paradigm to evaluate social memory and social motivation, high demand cognitive tasks (Barnes and Octagonal mazes) to evaluate working memory, reference memory and cognitive flexibility followed by gene and protein expression of molecules related to social behavior, reward system and synapse function. At postnatal day 9, male Wistar rats were subjected to pilocarpine-induced early-life status epilepticus (380mg/kg, i.p.) and the control group received 0.9% saline (0.1 ml/10 g). In adulthood, animals were probed to social recognition memory and cognitive function followed by gene and protein expressions in hypothalamus, hippocampus, amygdala and striatum. In social recognition paradigm, experimental animals spent lower time interacting with the same conspecific, with no enhancement as the familiar animal was replaced by a novel one. In training phase of Barnes maze, the latency to get into the escape cage did not differ between groups. In test phase, as the escape cage has been removed, the time to reach the escape hole did not differ between groups, but the experimental group distributed more evenly the time exploring the peripheral zones the maze, rather than the control group that stayed next to the target hole. In training (TRP) and test phases (TP) of the octagonal maze task, both groups spent similar time to complete the task, but the experimental group exhibited fewer working memory errors during TP with no difference in reference memory errors. The gene expression for oxytocin, oxytocin receptors, dopamine receptors (DR1 and DR2), synapsin and NT3 did not change in experimental group that exhibited higher level of oxytocin in hypothalamus and lower expression of its receptor in hippocampus.
DOI: 10.17632/2hskypf8ch.2
title: Data on compressibility of NBR samples with various cross-linking degree and zinc oxide content immersed in gasoline and oil

description: DSC analysis of NBR samples K1, K2 and K3 (Operator: Nikolay Tikhonov)
DOI: 10.17632/cyr8wxnjp6.2
title: disclosing personal name better predicts learning outcomes
DOI: 10.18716/nmrshiftdb2/76105
title: NMR data for InChI=1S/C8H13NO2/c1-9-5-2-6(10)4-7(9)8(11)3-5/h5,7-8,11H,2-4H2,1H3/t5-,7+,8-/m1/s1
DOI: 10.17632/xwfsz3kprg.1
title: The relationship between Cognitive dissonance and Switching to another brand: The Mediating role of Purchase regret. The dataset from student’s online shopping behavior in North Cyprus

description: This dataset empirically investigated the comprehensive model related to post-purchase behavior regarding online shopping in North Cyprus. The dataset has collected via a self-administered questionnaire at Girne American University. Then, the dataset evaluated by using 354 participants. In which several techniques used for hypothesis testing, and structural equation modeling (SEM) was applied using IBM (SPSS and AMOS).
DOI: 10.17632/mwvz5dytjg.1
title: Mendeley Supplemental Figure 1. Geographic distribution of Dermatology Foundation awards between 2010-2019.

description: The distribution of all Dermatology Foundation awards by state from 2010 to 2019.
DOI: 10.18716/nmrshiftdb2/76108
title: NMR data for 1,5,6,7-tetrahydroindol-4-one
DOI: 10.17632/jmrw9x3vfv.1
title: Mendeley Supplemental Table 1. Recommendations for representation in Dermatology Foundation awards

description: Six recommendations for improving representation among Dermatology Foundation award recipients.
DOI: 10.17632/37xhvyrd3k.1
title: Structure Analysis of Open Ground Storey Buildings: A Survey Approach

description: In high rise building or multi story building for open ground story frame with soft story construction is a typical feature because of urbanization and the space occupancy considerations. These provisions reduce the stiffness of the lateral load resisting system and a progressive collapse becomes unavoidable in a severe earthquake for such buildings due to soft story. In the current study the focus is on the investigation of the effect of an open ground building story on the behavior of a structure.
DOI: 10.17632/94p8m47y58.1
title: The Influence of Intolerance of Uncertainty on Anxiety and Depression Symptoms in Chinese-speaking Samples: Structure and Validity of The Chinese Translation of The Intolerance of Uncertainty Scale

description: The current study used confirmatory factor analysis to compare fit of the two-factor and bifactor models of the original and two short versions of the Intolerance of Uncertainty Scale (IUS) among a large sample of Chinese-speaking adults. The current sample consisted of 696 college or university students and 706 adults who were not currently in school. In order to examine the IUS' psychometric properties and validity, all participants completed the full-length IUS (n = 1402) and Penn State Worry Questionnaire (PSWQ; n = 1402), while some completed the neuroticism subscale of the Revised Eysenck Personality Questionnaire Short Scale for Chinese (EPQ-N; n = 1026), Beck Anxiety Inventory (BAI; n = 1026), Generalized Anxiety Disorder Scale-7 (GAD-7; n = 1026), and Beck Depression Inventory (BDI; n = 838). All measures were in Chinese and were delivered via online survey methods.
DOI: 10.17632/ptpg7r7wz4.1
title: Meteorological and hydrological observations at the daily scale

description: This dataset includes the daily meterological series over China, which is primarly downloaded from the China Meteorological Data Service Center and then be corrected and temporally interpolated by our group to fill the missing data. The hydrological observations include daily streamflow series over several stations, and have been quality control and error observations are detected and flaged.
DOI: 10.17632/jbs44b2hrk.1
title: Data for: Identifying the spatiotemporal changes of annual harvesting areas for three staple crops in China by integrating multi-data sources
DOI: 10.18716/nmrshiftdb2/76107
title: NMR data for furan-2-ylmethanethiol
DOI: 10.17632/sbkbzdkkz4.1
title: Supplemental Methods and Results

description: Supplemental methods and results with attached flowchart detailing patient inclusion or exclusion during retrospective chart review.
DOI: 10.5523/bris.1dnhjcw6w4m1n29u2yuxjywde1
title: The impact of selection on non-alcoholic vs alcoholic drink availability: an online experiment

description: This online study assessed the proportion of participants (n = 808 UK adults) selecting a non-alcoholic drink (vs. an alcoholic drink) in four different alcohol and non-alcoholic availability conditions (reference 1: two non-alcoholic drinks and two beers, reference 2: four non-alcoholic drinks and four beers, increased non-alcoholic: six non-alcoholic drinks and two beers, increased alcohol: two non-alcoholic drinks and six beers).
DOI: 10.3886/e113993v1-22941
title: Replication data for: Machine Learning: An Applied Econometric Approach

description: Machines are increasingly doing "intelligent" things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the presence y of a face from pixels x. This similarity to econometrics raises questions: How do these new empirical tools fit with what we know? As empirical economists, how can we use them? We present a way of thinking about machine learning that gives it its own place in the econometric toolbox. Machine learning not only provides new tools, it solves a different problem. Specifically, machine learning revolves around the problem of prediction, while many economic applications revolve around parameter estimation. So applying machine learning to economics requires finding relevant tasks. Machine learning algorithms are now technically easy to use: you can download convenient packages in R or Python. This also raises the risk that the algorithms are applied naively or their output is misinterpreted. We hope to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble—and thus where they can be most usefully applied.
DOI: 10.5519/qd.kxc1lput
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.3706667
title: Upscaling capillary pressure curves for numerical modeling of gravity-capillary driven flow

description: This work investigates upscaling of capillary pressure curves for modeling gravity segregation under the influence of capillary heterogeneity. We consider two flowing phases driven by gravity and capillary forces and seek the saturation spatial and temporal variation until equilibrium is reached. Existing upscaling methods, found in the literature, are applied to a number of cases. Resulting saturation solutions are compared to fine-scale simulations and the different methods are evaluated, showing in general that the popular capillary limit method produces the best results. However, a large number of cases are found to have significant errors. This leads to the conclusion that existing methods are often inadequate. We therefore propose a new optimization-based upscaling method. Using this approach it is shown that capillary pressure can be upscaled to Brooks-Corey type functions and produce accurate upscaled simulations matching the fine-scale solutions. Optimization upscaling is computationally demanding and requires the fine-scale simulations for minimizing an objective function. However, it is shown that the upscaled curves are robust, i.e., they can be applied to different permeability realizations to calculate ensemble average saturation solutions.<br>
DOI: 10.5281/zenodo.3706666
title: Upscaling capillary pressure curves for numerical modeling of gravity-capillary driven flow

description: This work investigates upscaling of capillary pressure curves for modeling gravity segregation under the influence of capillary heterogeneity. We consider two flowing phases driven by gravity and capillary forces and seek the saturation spatial and temporal variation until equilibrium is reached. Existing upscaling methods, found in the literature, are applied to a number of cases. Resulting saturation solutions are compared to fine-scale simulations and the different methods are evaluated, showing in general that the popular capillary limit method produces the best results. However, a large number of cases are found to have significant errors. This leads to the conclusion that existing methods are often inadequate. We therefore propose a new optimization-based upscaling method. Using this approach it is shown that capillary pressure can be upscaled to Brooks-Corey type functions and produce accurate upscaled simulations matching the fine-scale solutions. Optimization upscaling is computationally demanding and requires the fine-scale simulations for minimizing an objective function. However, it is shown that the upscaled curves are robust, i.e., they can be applied to different permeability realizations to calculate ensemble average saturation solutions.<br>
DOI: 10.7910/dvn/eberm1/iyamct
title: KCH_Pediatric_Admission_1989_2018_deidentified.zip

description: Zipped file contains: KCH_Pediatric_Admission_1989_2018_deidentified.dta (full dataset) KCH_Pediatric_Admission_1989_2000_deidentified.csv KCH_Pediatric_Admission_2001_2010_deidentified.csv KCH_Pediatric_Admission_2011_2018_deidentified.csv
DOI: 10.5281/zenodo.3706690
title: Machine-learning-long-term-wind-power-time-series

description: In this research work we assess how time-series generated by machine learning models (MLM) compare to Renewables.ninja in terms of their ability to replicate the characteristics of observed nationally aggregated wind power generation for Germany. With this archive, we want to make three machine learning derived time-series available in the Feather format for everyone interested.
DOI: 10.5281/zenodo.3706691
title: Machine-learning-long-term-wind-power-time-series

description: In this research work we assess how time-series generated by machine learning models (MLM) compare to Renewables.ninja in terms of their ability to replicate the characteristics of observed nationally aggregated wind power generation for Germany. With this archive, we want to make three machine learning derived time-series available in the Feather format for everyone interested.
DOI: 10.17632/f7ntbw4rt6.1
title: Plastic pollution on eight beaches of Tenerife (Canary Islands, Spain): An annual study: Wind and Wave parameters

description: Additonal information to the article "Plastic pollution on eight beaches of Tenerife (Canary Islands, Spain): An annual study" by Reinold et al. Excel shows data about wind and wave parameters around the hour of high tide on the sampling dates in relation to the amount of washed ashore plastic particles. Data were obtained from the SIMAR data set, which consists of time series from numerical modeling.
DOI: 10.5255/ukda-sn-854208
title: Under the radar: the effects of monitoring firms on tax compliance 2010-2015
DOI: 10.25387/g3.11968362
title: Supplemental Material for Dimitriadou et al., 2020

description: Figure S1, Tables S1 - S13.<br>
DOI: 10.25387/g3.11968362.v1
title: Supplemental Material for Dimitriadou et al., 2020

description: Figure S1, Tables S1 - S13.<br>
DOI: 10.7910/dvn/oxtlfn/v58yme
title: Policies cube 2018.pdf

description: :unav
DOI: 10.5519/qd.8p9f7f0f
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.25387/g3.11919495
title: Supplemental Material for Deng et al., 2020

description: Figure S1 shows additional data supporting that SKN-1 activation suppresses stress resistance and DAF-16. Figure S2 shows that heat exposure suppresses SKN-1. Figure S3 shows additional data supporting that inhibition of SKN-1 activates DAF-16. Figure S4 shows that <i>vit</i> mutations activate the DAF-16 reporters. Table S1 shows survival data. Table S2 shows the Ct values of qPCR.
DOI: 10.25387/g3.11919495.v1
title: Supplemental Material for Deng et al., 2020

description: Figure S1 shows additional data supporting that SKN-1 activation suppresses stress resistance and DAF-16. Figure S2 shows that heat exposure suppresses SKN-1. Figure S3 shows additional data supporting that inhibition of SKN-1 activates DAF-16. Figure S4 shows that <i>vit</i> mutations activate the DAF-16 reporters. Table S1 shows survival data. Table S2 shows the Ct values of qPCR.
DOI: 10.7910/dvn/1vemis/wbkksn
title: T13_KO_CD31_2.15.tif

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DOI: 10.7910/dvn/1vemis/vhyuma
title: T21_WT_SMA_1.15.tif

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DOI: 10.7910/dvn/1vemis/igrots
title: T21_WT_SMA_1.22.tif

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DOI: 10.7910/dvn/1vemis/kfkyz7
title: T16_WT_CD31_2.18.tif

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DOI: 10.7910/dvn/1vemis/u2d331
title: T21_WT_CD31_1.25.tif

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DOI: 10.7910/dvn/1vemis/xyvdxl
title: T1_WT_SMA_3.4.tif

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DOI: 10.7910/dvn/1vemis/gvhmfz
title: T16_WT_SMA_2.5.tif

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DOI: 10.7910/dvn/1vemis/qefej6
title: T12_WT_SMA_2.17.tif

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DOI: 10.7910/dvn/1vemis/dahvk2
title: T6_WT_SMA_2.5.tif

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DOI: 10.7910/dvn/1vemis/homsd8
title: T18_WT_SMA_1.16.tif

description: :unav
DOI: 10.7910/dvn/1vemis/rattnr
title: T1_WT_SMA_1.7.tif

description: :unav
DOI: 10.7910/dvn/1vemis/beccff
title: T16_WT_CD31_1.12.tif

description: :unav
DOI: 10.7910/dvn/1vemis/b9dyut
title: T14_KO_CD31_1.34.tif

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DOI: 10.7910/dvn/1vemis/0lfp4q
title: T20_WT_SMA_2.11.tif

description: :unav
DOI: 10.7910/dvn/1vemis/a7fb39
title: T6_WT_CD31_1.2.tif

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DOI: 10.7910/dvn/1vemis/cqad2g
title: T1_WT_SMA_2.5.tif

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DOI: 10.7910/dvn/1vemis/3drfvk
title: T6_WT_SMA_2.14.tif

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DOI: 10.7910/dvn/1vemis/twxd44
title: T13_KO_CD31_1.14.tif

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DOI: 10.7910/dvn/1vemis/biyvmt
title: T20_WT_CD31_2.11.tif

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DOI: 10.7910/dvn/1vemis/nbnhtm
title: T14_KO_CD31_2.14.tif

description: :unav
DOI: 10.25345/c55h6h
title: MassIVE MSV000085080 - Arsenic binding siderophores from Rhodococcus erythropolis S43_positive mode
title: Rhodococcus erythropolis S43 produces arsenic-binding siderophores. Siderophores like heterobactin were extracted and investigated by mass spectrometry.
DOI: 10.7910/dvn/1vemis/ccseoz
title: T20_WT_SMA_2.5.tif

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DOI: 10.7910/dvn/1vemis/dytqhn
title: T1_WT_CD31_1.2.tif

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DOI: 10.7910/dvn/1vemis/wyhc3v
title: T14_KO_SMA_2.37.tif

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DOI: 10.7910/dvn/1vemis/yc4hud
title: T13_KO_SMA_1.10.tif

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DOI: 10.7910/dvn/1vemis/m1kr8x
title: T18_WT_SMA_1.10.tif

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DOI: 10.7910/dvn/1vemis/ybpjhk
title: T16_WT_SMA_1.17.tif

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DOI: 10.7910/dvn/1vemis/geqys6
title: T14_KO_SMA_1.31.tif

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DOI: 10.7910/dvn/1vemis/h4c0y2
title: T13_KO_SMA_2.21.tif

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DOI: 10.7910/dvn/1vemis/eaamsm
title: T12_WT_CD31_1.4.tif

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DOI: 10.7910/dvn/1vemis/m9ivwb
title: T12_WT_SMA_1.7.tif

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DOI: 10.7910/dvn/1vemis/mftgal
title: T21_WT_SMA_2.28.tif

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DOI: 10.7910/dvn/1vemis/pqyzl3
title: T13_KO_CD31_2.19.tif

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DOI: 10.7910/dvn/1vemis/srr1ag
title: T6_WT_SMA_2.18.tif

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DOI: 10.7910/dvn/1vemis/9tva77
title: T21_WT_CD31_2.9.tif

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DOI: 10.7910/dvn/1vemis/i8b2cq
title: T20_WT_SMA_1.4.tif

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DOI: 10.7910/dvn/1vemis/khswfl
title: T12_WT_SMA_1.1.tif

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DOI: 10.7910/dvn/1vemis/nlzcwm
title: T1_WT_CD31_2.5.tif

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DOI: 10.7910/dvn/1vemis/v4jpen
title: T18_WT_SMA_2.19.tif

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DOI: 10.7910/dvn/1vemis/zhrep2
title: T20_WT_CD31_1.4.tif

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DOI: 10.7910/dvn/1vemis/eizryz
title: T2_KO_SMA_2.7.tif

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DOI: 10.7910/dvn/1vemis/sm9plw
title: T16_WT_CD31_1.2.tif

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DOI: 10.7910/dvn/1vemis/xmfihf
title: T1_WT_CD31_2.3.tif

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DOI: 10.7910/dvn/1vemis/7uulqh
title: T21_WT_CD31_2.7.tif

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DOI: 10.7910/dvn/1vemis/8933iz
title: T12_WT_CD31_1.8.tif

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DOI: 10.7910/dvn/1vemis/etdutx
title: T14_KO_SMA_2.11.tif

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DOI: 10.7910/dvn/1vemis/f1h45h
title: T21_WT_SMA_1.7.tif

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DOI: 10.7910/dvn/1vemis/yikcvs
title: T2_KO_SMA_2.6.tif

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DOI: 10.7910/dvn/1vemis/qhaqmi
title: T14_KO_SMA_1.23.tif

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DOI: 10.7910/dvn/1vemis/snirin
title: T20_WT_CD31_2.1.tif

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DOI: 10.7910/dvn/1vemis/xwzwf2
title: T13_KO_CD31_1.18.tif

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DOI: 10.7910/dvn/1vemis/ylwoi2
title: T21_WT_CD31_1.3.tif

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DOI: 10.7910/dvn/1vemis/ytnbk8
title: T21_WT_CD31_2.28.tif

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DOI: 10.7910/dvn/1vemis/d9ls5k
title: T18_WT_SMA_2.8.tif

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DOI: 10.7910/dvn/1vemis/fr4py0
title: T2_KO_CD31_2.2.tif

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DOI: 10.7910/dvn/1vemis/ywizk9
title: T18_WT_CD31_2.10.tif

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DOI: 10.7910/dvn/1vemis/1kgd5m
title: T6_WT_CD31_1.3.tif

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DOI: 10.7910/dvn/1vemis/ruxev4
title: T16_WT_SMA_1.4.tif

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DOI: 10.7910/dvn/1vemis/vnvady
title: T21_WT_CD31_1.20.tif

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DOI: 10.7910/dvn/1vemis/wevu6v
title: T13_KO_SMA_2.15.tif

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DOI: 10.7910/dvn/1vemis/ax81l2
title: T13_KO_SMA_2.9.tif

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DOI: 10.7910/dvn/1vemis/f0eawg
title: T14_KO_SMA_2.4.tif

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DOI: 10.7910/dvn/1vemis/gvojvw
title: T18_WT_CD31_2.4.tif

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DOI: 10.7910/dvn/1vemis/tzb23c
title: T14_KO_SMA_1.18.tif

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DOI: 10.7910/dvn/1vemis/xmjtrd
title: T13_KO_CD31_1.23.tif

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DOI: 10.7910/dvn/1vemis/3fe9za
title: T14_KO_SMA_2.20.tif

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DOI: 10.7910/dvn/1vemis/8qegub
title: T16_WT_SMA_2.12.tif

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DOI: 10.7910/dvn/1vemis/bnpmbg
title: T13_KO_SMA_2.1.tif

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DOI: 10.7910/dvn/1vemis/bxkxbp
title: T2_KO_CD31_1.6.tif

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DOI: 10.7910/dvn/1vemis/wyawxa
title: T16_WT_SMA_1.23.tif

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DOI: 10.7910/dvn/1vemis/0jtyia
title: T6_WT_SMA_1.13.tif

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DOI: 10.7910/dvn/1vemis/1lhgzy
title: T6_WT_SMA_1.8.tif

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DOI: 10.7910/dvn/1vemis/iqgsih
title: T20_WT_SMA_3.7.tif

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DOI: 10.7910/dvn/1vemis/lbemug
title: T12_WT_SMA_1.8.tif

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DOI: 10.7910/dvn/1vemis/mvjcot
title: T2_KO_CD31_1.4.tif

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DOI: 10.7910/dvn/1vemis/ycrknf
title: T16_WT_SMA_2.15.tif

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DOI: 10.7910/dvn/1vemis/8jf4gl
title: T20_WT_CD31_3.1.tif

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DOI: 10.7910/dvn/1vemis/br2ygg
title: T21_WT_SMA_1.24.tif

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DOI: 10.7910/dvn/1vemis/v7tmgx
title: T13_KO_SMA_2.17.tif

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DOI: 10.7910/dvn/1vemis/4zjwfe
title: T6_WT_CD31_1.8.tif

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DOI: 10.7910/dvn/1vemis/gzsjct
title: T12_WT_CD31_2.13.tif

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DOI: 10.7910/dvn/1vemis/jdnx0o
title: T21_WT_SMA_2.24.tif

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DOI: 10.7910/dvn/1vemis/sqlqzc
title: T21_WT_CD31_2.11.tif

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DOI: 10.7910/dvn/1vemis/uf3di1
title: T21_WT_SMA_1.11.tif

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DOI: 10.7910/dvn/1vemis/0xb2ln
title: T14_KO_SMA_2.30.tif

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DOI: 10.7910/dvn/1vemis/asbbgh
title: T13_KO_CD31_2.2.tif

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DOI: 10.7910/dvn/1vemis/bwd5oq
title: T18_WT_SMA_2.21.tif

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DOI: 10.7910/dvn/1vemis/lsqben
title: T1_WT_CD31_1.6.tif

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DOI: 10.7910/dvn/1vemis/yoiyy8
title: T21_WT_SMA_2.15.tif

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DOI: 10.7910/dvn/1vemis/5zezru
title: T18_WT_SMA_1.5.tif

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DOI: 10.7910/dvn/1vemis/m5j61e
title: T14_KO_CD31_2.20.tif

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DOI: 10.7910/dvn/1vemis/oewvd2
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DOI: 10.7910/dvn/1vemis/qg23np
title: T6_WT_SMA_2.17.tif

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DOI: 10.7910/dvn/1vemis/1rn4j7
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DOI: 10.7910/dvn/1vemis/3grcx8
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DOI: 10.7910/dvn/1vemis/o9t9vi
title: T6_WT_SMA_1.6.tif

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DOI: 10.7910/dvn/1vemis/rxwfyt
title: T13_KO_CD31_2.14.tif

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DOI: 10.7910/dvn/1vemis/sgh8gg
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DOI: 10.7910/dvn/1vemis/20vrnh
title: T20_WT_CD31_3.3.tif

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DOI: 10.7910/dvn/1vemis/2wkmhh
title: T16_WT_CD31_2.21.tif

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DOI: 10.7910/dvn/1vemis/9xj0aq
title: T14_KO_SMA_1.38.tif

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DOI: 10.7910/dvn/1vemis/aq7slz
title: T14_KO_SMA_2.21.tif

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DOI: 10.7910/dvn/1vemis/bsffqt
title: T14_KO_CD31_2.21.tif

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DOI: 10.7910/dvn/1vemis/itysyx
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DOI: 10.7910/dvn/1vemis/x8zc0x
title: T20_WT_CD31_1.7.tif

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DOI: 10.7910/dvn/1vemis/ccxh7e
title: T21_WT_CD31_1.15.tif

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DOI: 10.7910/dvn/1vemis/cmuxjf
title: T16_WT_SMA_1.36.tif

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DOI: 10.7910/dvn/1vemis/l5bukb
title: T18_WT_CD31_1.13.tif

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DOI: 10.7910/dvn/1vemis/lu2suf
title: T21_WT_SMA_2.12.tif

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DOI: 10.7910/dvn/1vemis/tc0oa7
title: T13_KO_SMA_1.9.tif

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DOI: 10.7910/dvn/1vemis/wbkqt0
title: T12_WT_SMA_2.3.tif

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DOI: 10.7910/dvn/1vemis/8uekkl
title: T18_WT_SMA_1.9.tif

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DOI: 10.7910/dvn/1vemis/hn6mla
title: T16_WT_SMA_1.28.tif

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DOI: 10.7910/dvn/1vemis/k1njqg
title: T16_WT_CD31_2.9.tif

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DOI: 10.7910/dvn/1vemis/rbeoti
title: T2_KO_CD31_1.12.tif

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DOI: 10.7910/dvn/1vemis/vakfsf
title: T16_WT_CD31_1.10.tif

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DOI: 10.7910/dvn/1vemis/wudkjy
title: T14_KO_SMA_1.27.tif

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DOI: 10.7910/dvn/1vemis/jmg6he
title: T13_KO_CD31_2.18.tif

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DOI: 10.7910/dvn/1vemis/n0rafc
title: T14_KO_CD31_2.29.tif

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DOI: 10.7910/dvn/1vemis/vbtqpd
title: T1_WT_CD31_1.8.tif

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