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A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context-Figure 2. Identified risk factors hierarchy
<p>AED - antiepileptic drug, CP - cerebral palsy, GDD - global developmental delay, GA - gestational age, BW- birth weight, RS - repeated/recurrent seizure, MD - type/mode of delivery, AS1 - Apgar score at 1 minute, AS5 - Apgar score at 5 minute, AS10 - Apgar score at 10 minute, SO - seizure onset, ST_EPI - status epilepticus, UBS - ultrasound brain scan, MSU- maternal substance used, MIS – maternal inflammatory state, PRM- prolonged rupture of membranes, PNN – postnatal neuroimaging, PNS – postnatal seizure. The most frequently identified risk factors were the EEG findings (abnormal / severe electroencephalogram results), seizure characteristics (type, onset, duration, semiology), etiology, birth weight, Apgar score, cerebral ultrasound scan findings (abnormal) (Figure 2).</p>
A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context-A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context
<p>R retrospective, P prospective, CC case – control study, MC multicenter controlled trail, PB populational based, HB hospital based, C clinical, CT computed tomographic scan, MRI cerebral magnetic resonance imaging, CUS cranial ultrasonography / cerebral ultrasound, USG ultrasonography, EEG electroencephalogram (standard), CpH cord Ph, BpH blood Ph, HT therapeutic hypothermia It can be noticed that seizure diagnosis was based on clinical grounds and functional explorations naming neuroimaging and/or EEG procedures (conventional EEG, aEEG, vEEG, CUS, MRI). The minimum number of newborns considered in these studies was 55, while the maximum was 403 with a mean of 148 (SD=86.75, median=112, IQR: (98,175)) and a total of 2226 evaluated cases.</p>
A magnetic resonance multi-atlas for the neonatal rabbit brain - Dataset
<p>Dataset related to the Neuroimage paper https://doi.org/10.1016/j.neuroimage.2018.06.029. Download links, documentation and code for the manipulation are available from the software repository https://github.com/gift-surg/SPOT-A-NeonatalRabbit</p>
Fig. 1 in Larval pheromone disrupts pre-excavation aggregation of Cactoblastis cactorum (Lepidoptera: Pyralidae) neonates precipitating colony collapse
Fig. 1. Percent survival of caterpillars in cohorts of Cactoblastis cactorum on plants sprayed with caterpillar extract (gray bar), solvent-only (white bar), or unsprayed (black bar) for 4 separate experiments. Experiment 1 = laboratory study; experiment 2 = greenhouse study; experiment 3 = field study 1; experiment 4 = field study 2.
Neonate germ free and specific pathogen free plethysmography recordings and metadata files
<p>This repository contains recordings of neonate mice during our autoresuscitation assay. Also included is a metadata sheet with additional information about the mice and settings to be used with out accompanying software for analysis.</p>
Figure 4 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls
Figure 4. Encephalization quotient (EQ) and body lengths. Body lengths are used as a general indicator for maturity of these animals. Three delphinids (Orcinus orca, Tursiops truncatus, and Stenella coeruleoalba) are compared with EQ and body length against two members of Physeteroidea (Kogia breviceps and Physeter macrocephalus) and one member of Phocoenidae (Phocoenoides dalli). In each case, EQ declines as the animal grows toward a mature body length and perhaps beyond. EQ was measured directly from brain masses, except for a few of the larger O. orca for which brain mass was calculated from endocranial volume. Body mass varies considerably in mature animals. As a result, EQ in mature T. truncatus varies from around 3 to 5 and in O. orca from about 1.5 to 3. One outlier EQ value of 2 from a male T. truncatus was from an overweight animal.
Figure 3 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls
Figure 3. Brain mass relative to maturity (assessed by body length) in six different species. The horizontal line in each species plot represents the length at maturity. Female killer whales (a) (O. orca) reach sexual maturity at about 460 cm body length and as young as 8 yr of age (Dahlheim and Heyning 1999), while male killer whales (b) reach sexual maturity at about 520 cm length when they are around 15 yr of age (Dahlheim and Heyning 1999). Female Common bottlenose dolphins (c) (T. truncatus) reach sexual maturity at a length of 235 cm and at an average age of 8–9 yr (Wells and Scott 1999), and males (d) reach sexual maturity at a length of about 245 cm and an approximate age of 10 yr (Wells and Scott 1999). Female striped dolphins (e) (S. coeruleoalba) reach sexual maturity at 180 cm and about 7 yr of age (Perrin et al. 1994); males (f) reach sexual maturity at about 185 cm and about 11 yr of age (Perrin et al. 1994). Female pygmy sperm whales (g) (K. breviceps) reach sexual maturity at about 266 cm body length (Caldwell and Caldwell 1989), and males (h) reach sexual maturity at about 270 cm length (Caldwell and Caldwell 1989). Female spinner dolphins (i) (S. longirostris) reach sexual maturity at a length of 165 cm and at an average age of 4–7 yr (Perrin and Gilpatrick 1994) while males of this species (j) attain sexual maturity at a length of about 160 cm and an approximate age of 7–10 yr (Perrin and Gilpatrick 1994). Lastly, female Dall's porpoises (k) (P. dalli) reach sexual maturity at 174 cm and about 5 yr of age (Houck and Jefferson 1999), and males (l) reach sexual maturity at about 175 cm and about 5 yr of age (Houck and Jefferson 1999).
Table 3 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls
<p><i>Table 3.</i> Gestation and brain size. The predicted gestation period was derived by applying the Sacher and Staffeldt formula and using our brain mass data. Sheep (<i>O. aries</i>), cows (<i>B. taurus</i>), giraffes (<i>G. camelopardalis</i>), and hippopotamuses (<i>H. amphibius</i>) were included in the table to compare cetaceans to other members of the Cetartiodactyla taxonomic order. Humans (<i>H. sapiens</i>) were also included for comparison. Cetaceans appear to have similar neonatal/adult brain mass ratios compared to other animals of the Cetartiodactlya order. Sources for the published gestation durations and cetacean brain masses can be found in Table S1.</p><table><thead><tr><th></th><th></th><th></th><th></th><th>Published</th><th>Predicted</th></tr></thead><tbody><tr><th>Taxonomic family</th><td>Neonatal</td><td>Adult brain</td><td>Neonate/</td><td>gestation</td><td>gestation</td></tr><tr><th>Genus species</th><td>brain mass (g)</td><td>mass (g)</td><td>adult (%)</td><td>(days)</td><td>(days)</td></tr><tr><th colspan="6">Delphinidae</th></tr><tr><th><i>C. commersonii</i></th><td>370</td><td>783</td><td>47.3</td><td>334</td><td>324</td></tr><tr><th><i>D. delphis</i></th><td>430</td><td>715</td><td>60.2</td><td>363</td><td>359</td></tr><tr><th><i>G. griseus</i></th><td>796</td><td>2,132</td><td>37.3</td><td>410</td><td>386</td></tr><tr><th><i>L. acutus</i></th><td>733</td><td>1,285</td><td>57</td><td>365</td><td>401</td></tr><tr><th><i>L. obliquidens</i></th><td>523</td><td>1,198</td><td>43.6</td><td>356</td><td>352</td></tr><tr><th><i>O. orca S. attenuata S. longirostris</i></th><td>3,006 353 247</td><td>6,642 711 541</td><td>45.3 49.6 45.6</td><td>553 — —</td><td>566 304a 286a</td></tr><tr><th><i>S. bredanensis</i></th><td>706</td><td>1,454</td><td>48.6</td><td>378</td><td>388</td></tr><tr><th><i>T. truncatus</i></th><td>685</td><td>1,550</td><td>44.2</td><td>376</td><td>377</td></tr><tr><th colspan="6">Monodontidae</th></tr><tr><th><i>D. leucas</i></th><td>938</td><td>2,087</td><td>44.9</td><td>456</td><td>414</td></tr><tr><th colspan="6">Phocoenidae</th></tr><tr><th><i>P. phocoena</i></th><td>242</td><td>506</td><td>47.7</td><td>316</td><td>266</td></tr><tr><th><i>P. dalli</i></th><td>270</td><td>803</td><td>33.6</td><td>334</td><td>282</td></tr><tr><th colspan="6">Physeteridae</th></tr><tr><th><i>P. macrocephalus</i></th><td>3,308</td><td>7,693</td><td>43</td><td>547</td><td>582</td></tr><tr><th colspan="6">Pontoporiidae</th></tr><tr><th><i>P. blainvillei</i></th><td>154.9</td><td>223.9</td><td>69.2</td><td>319</td><td>271</td></tr><tr><th colspan="6">Ziphiidae</th></tr><tr><th><i>M. europaeus</i></th><td>971</td><td>1,680</td><td>57.8</td><td>—</td><td>—</td></tr><tr><th colspan="6">Balaenopteridae</th></tr><tr><th><i>B. physalus</i></th><td>2,640</td><td>6,718</td><td>39.3</td><td>342</td><td>537</td></tr><tr><th>Bovidae <i>B. taurus O. aries</i></th><td>199b 69</td><td>456b 130d</td><td>43.6 53</td><td>278c 150e</td><td>270 208</td></tr><tr><th>Giraffidae <i>G. camelopardalis</i></th><td>428f</td><td>537f</td><td>79.7</td><td>459c</td><td>363</td></tr><tr><th>Hippopotamidae <i>H. amphibius</i></th><td>195b</td><td>590b</td><td>33.1</td><td>240e</td><td>258</td></tr><tr><th>Hominidae <i>H. sapiens</i></th><td>380g</td><td>1,400b</td><td>27</td><td>280e</td><td>324</td></tr></tbody></table><p><sup>a</sup> Perrin <i>et al.</i> (1977).</p><p><sup>b</sup> Sacher and Staffeldt (1974).</p><p><sup>c</sup> Kiltie (1982).</p><p><sup>d</sup> Minervini <i>et al.</i> (2016).</p><p><sup>e</sup> Hayssen <i>et al.</i> (1993).</p><p><sup>f</sup> <i>Gra¨ıc et al.</i> (2017).</p><p><sup>g</sup> Blinkov and Glezer (1968).</p>
Table 2 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls
<p><i>Table 2.</i> Comparison of seven terrestrial cetartiodactyls (and the African elephant) with eight aquatic cetartiodactyls on brain and body mass for neonates and adults. ABoM = adult body mass; ABrM = adult brain mass; NBoM = neonatal body mass; NBrM = neonatal brain mass. All brain and body mass data for the aquatic species come from Table S1.</p><table><thead><tr><th></th><th></th><th>ABoM</th><th>ABrM</th><th>NboM</th><th>NBrM</th><th>Aquatic</th><th></th><th>AboM</th><th>ABrM</th><th>NboM</th><th>NBrM</th></tr></thead><tbody><tr><th>Terrestrial species</th><td>Common name</td><td>(kg)</td><td>(g)</td><td>(kg)</td><td>(g)</td><td>species</td><td>Common name</td><td>(kg)</td><td>(g)</td><td>(kg)</td><td>(g)</td></tr><tr><th><i>D. dorcas phillipsi S. scrofa</i></th><td>Blesbok antelope Wild boar</td><td>60a 149b</td><td>155a 133b</td><td>— —</td><td>— —</td><td><i>D. delphis L. acutus</i></td><td>Common dolphin Atlantic white-sided</td><td>68 156</td><td>715 1,285</td><td>11 28</td><td>430 733</td></tr><tr><th><i>T. strepsiceros G. camelopardalis C. bactrianus</i></th><td>Greater kudu Giraffe Bactrian camel</td><td>218a 470c 594d</td><td>307a 537c 518d</td><td>— 150c —</td><td>— 428c —</td><td><i>T. truncatus G. griseus G. macrorhynchus</i></td><td>dolphin Bottlenose dolphin Risso’s dolphin Short-finned pilot</td><td>190 301 654</td><td>1,550 2,132 2,679</td><td>18 85 —</td><td>685 796 —</td></tr><tr><th><i>B. taurus H. amphibius</i></th><td>Cow Hippopotamus</td><td>598e 1,351f</td><td>492e 720f</td><td>25g 40g</td><td>199g 195g</td><td><i>D. leucas G. melas</i></td><td>whale Beluga Long-finned pilot</td><td>560 1,369</td><td>2,087 3,499</td><td>50 —</td><td>938 —</td></tr><tr><th><i>L. africana</i></th><td>African elephant</td><td>5,000a</td><td>4,619a</td><td>—</td><td>1,724h</td><td><i>O. orca</i></td><td>whale Killer whale</td><td>3,723</td><td>6,642</td><td>171</td><td>3,006</td></tr></tbody></table><p><sup>a</sup> Herculano-Houzel (2015).</p><p><sup>b</sup> Minervini <i>et al</i>. (2016).</p><p><sup>c</sup> <i>Gra¨ıc et al</i>. (2017).</p><p><sup>d</sup> Xie <i>et al.</i> (2011).</p><p><sup>e</sup> Ballarin <i>et al</i>. (2016).</p><p><sup>f</sup> Silva and Downing (1995).</p><p><sup>g</sup> Sacher and Staffeldt (1974).</p><p><sup>h</sup> Shoshani <i>et al.</i> (2006).</p>
Clusters of cause specific neonatal mortality and its association with per capita gross domestic product: a structured spatial analytical approach
<p>Database used for the analysis of the manuscript entitled: Clusters of cause specific neonatal mortality and its association with per capita gross domestic product: a structured spatial analytical approach. The aim of the study was to investigate the cluster areas of asphyxia-associated neonatal mortality and to explore the per capita gross domestic product (GDP) as an associated risk factor in São Paulo State (SP), Brazil.</p>
Towards a more informative representation of the fetal-neonatal brain connectome using Variational Autoencoder
<p>Recent advances in functional magnetic resonance imaging (fMRI) have helped elucidate previously inaccessible trajectories of early-life prenatal and neonatal brain development. To date, the interpretation of fetal-neonatal fMRI data has relied on linear analytic models, akin to adult neuroimaging data. However, unlike the adult brain, the fetal and newborn brain develops extraordinarily rapidly, far outpacing any other brain development period across the lifespan. Consequently, conventional linear computational models may not adequately capture these accelerated and complex neurodevelopmental trajectories during this critical period of brain development along the prenatal-neonatal continuum. To obtain a nuanced understanding of fetal-neonatal brain development, including non-linear growth, for the first time, we developed quantitative, systems-wide representations of brain activity in a large sample (>500) of fetuses, preterm, and full-term neonates using an unsupervised deep generative model called Variational Autoencoder (VAE), a model previously shown to be superior to linear models in representing complex resting state data in healthy adults. Here, we demonstrated that non-linear brain features, i.e., latent variables, derived with the VAE pretrained on rsfMRI of human adults, carried important individual neural signatures, leading to improved representation of prenatal-neonatal brain maturational patterns and more accurate and stable age prediction in the neonate cohort compared to linear models. Using the VAE decoder, we also revealed distinct functional brain networks spanning the sensory and default mode networks. Using the VAE, we are able to reliably capture and quantify complex, non-linear fetal-neonatal functional neural connectivity. This will lay the critical foundation for detailed mapping of healthy and aberrant functional brain signatures that have their origins in fetal life.</p>
Blinded Trial of Buprenorphine or Morphine in the Treatment of the Neonatal Abstinence Syndrome
ClinicalTrials.gov study NCT01452789. IPD Sharing: YES. Countries: 1. Publications: 4.
In Vivo Effects of Fibrinogen Concentrate (FC) Versus Cryoprecipitate on the Neonatal Fibrin Network Structure After Cardiopulmonary Bypass (CPB)
ClinicalTrials.gov study NCT03932240. IPD Sharing: YES. Countries: 1. Publications: 1.
Pharmacokinetics, Safety, Tolerability and Efficacy of a New Artemether-lumefantrine Dispersible Tablet in Infants and Neonates <5 kg Body Weight With Acute Uncomplicated Plasmodium Falciparum Malaria
ClinicalTrials.gov study NCT04300309. IPD Sharing: YES. Countries: 2. Publications: 1.
Data and code from: Cost-effectiveness Analysis of Alternative Infant and Neonatal Rotavirus Vaccination Schedules in Malawi
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Prenatal treatment with the antidepressant fluoxetine on maternal and neonatal behavior in sheep
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Towards a more informative representation of the fetal-neonatal brain connectome using Variational Autoencoder
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Arousal state transitions occlude sensory-evoked neurovascular coupling in neonatal mice
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Neonate personality affects early-life resource acquisition in a large social mammal
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Behavioral and ephys data of research paper: Microsecond interaural time difference discrimination restored by cochlear implants after neonatal deafness
<p>The uploaded raw behavioral and electrophysiological data form the basis for our research study on "Microsecond Interaural Time Difference Discrimination Restored by Cochlear Implants After Neonatal Deafness". Based on this data we were able to show that neonatally deafened (ND) rats provided with precisely synchronized cochlear implant stimulation in adulthood can be trained to lateralize interaural time differences (ITDs) with essentially normal behavioral thresholds near 50 μs. Furthermore, comparable ND rats show high physiological sensitivity to ITDs immediately after binaural implantation in adulthood.</p> <p>In addition to the raw data, we provided scripts to analyze the psychometric functions for the ITD sensitivity of our acoustically or electrically stimulated rats (see Fig. 1 of the manuscript). To reproduce the analysis of the electrophysiological data (see Figs. 3+4 of the manuscript), various analysis scripts were added in addition to the raw data. For a detailed description of the data analysis of these data, see section "Data analysis" of the Methods section of our manuscript.</p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.