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opencc-by-4.0Aug 2016View details →

BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 4. Flowchart and results of ICP algorithm

The key concept of the standard ICP algorithm can be summarized in two steps: - Compute correspondences between the two scans. - Compute a transformation which minimizes the distance between corresponding points. It is forced to add a maximum matching threshold dmax. In most implementations of ICP, the choice of dmax represents a tradeoff between convergence and accuracy. A low-value result in bad convergence, a large value causes incorrect correspondences to pull the final alignment away from the correct value. Figure 4 describes the steps of the algorithm which determines the point features closest to object boundary. The result of the algorithm is described by images cut from the program (Нгуен, 2016)

opencc-by-4.0Jun 2016View details →

BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 3. The unified hardware unit based on NodeMcu Lua ESP8266 WiFi development board, ACS712T ELC-30A current sensor, and relay SRD-05VDC-SL-C

The software consists of two parts, low-level Arduino sketches and high-level C# Windows form appplication. They are connected using the open-source message MQTT broker Mosquitto.11 Every hardware unit has the unique identifier and commands to control the relay. The MQTT topic “/VPP/Relays” is used by subscribers and publishers. The number “50” sent from C# Windows form (it equals number “2” sent from the standard Mosquitto publisher) is a command to switch on the second relay, “51” (“3”) – to switch off, respectively. The prototype was developed with one root controller and two descendant relays. The commands are as follows: “52” (“4”) / “53” (“5”) – to switch on / off the first relay, “54” (“6”) / “55” (“7”) – to switch on / off the third relay, respectively. This solution is similar to the one presented in [22], but ACS712T ELC-30A current sensor and ESP8266WiFi.h library are applied here. In addition, other commands, e.g. “56” (“8”) to get the value of the current in the 3rd segment, are in use as well.

BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 5. Weekly session stats for 2016

On following figures, statistic usage is given as Monthly and Weekly statistic for individual users and sessions. The number of the individual users and of the sessions is far better for the 2015 period, especially for the extent of time until the end of June. This is reasonable because it was during the project and the site is frequently put forward in promotional conferences, in press, in direct contacts with schools, and companies. After that period the site was not promoted additionally, and the only pointer to the site is a number of links found on our institutional websites as one of the services we are offering to the students. Considering all that, the figures for 2015 and even 2016, seems to be quite satisfactory. (Figure 1, Figure 4, and Figure 5).

BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 3. Monthly user/session stats for 2016

It is also interesting to observe that in Figure 3, where the usage for 2016 is presented, that in the non-promotional period, the site is mostly used in January (before January exam term), in April, May, and June (before the June exam session and during colloquial exams) or in July and August (before the September exam session).

BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 2. Monthly user/session stats for 2015

BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 1. Components of EduWebCast System

The aim of this partnership would be to implement an infrastructure for live and on-demand video streaming of learning material for the targeted groups and, to this purpose, to establish a long and fruitful cooperation between teachers, pupils, and students on both sides of the border. The joint creation and administration of the webcast project is the ground stone of the partnership between the two universities and will result in more common projects based on the materials obtained through the project, contests between pupils and students, possible periodic educational exchanges.

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

Table 3. Gestation and brain size. The predicted gestation period was derived by applying the Sacher and Staffeldt formula and using our brain mass data. Sheep (O. aries), cows (B. taurus), giraffes (G. camelopardalis), and hippopotamuses (H. amphibius) were included in the table to compare cetaceans to other members of the Cetartiodactyla taxonomic order. Humans (H. sapiens) 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.<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>C. commersonii</th><td>370</td><td>783</td><td>47.3</td><td>334</td><td>324</td></tr><tr><th>D. delphis</th><td>430</td><td>715</td><td>60.2</td><td>363</td><td>359</td></tr><tr><th>G. griseus</th><td>796</td><td>2,132</td><td>37.3</td><td>410</td><td>386</td></tr><tr><th>L. acutus</th><td>733</td><td>1,285</td><td>57</td><td>365</td><td>401</td></tr><tr><th>L. obliquidens</th><td>523</td><td>1,198</td><td>43.6</td><td>356</td><td>352</td></tr><tr><th>O. orca S. attenuata S. longirostris</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>S. bredanensis</th><td>706</td><td>1,454</td><td>48.6</td><td>378</td><td>388</td></tr><tr><th>T. truncatus</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>D. leucas</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>P. phocoena</th><td>242</td><td>506</td><td>47.7</td><td>316</td><td>266</td></tr><tr><th>P. dalli</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>P. macrocephalus</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>P. blainvillei</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>M. europaeus</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>B. physalus</th><td>2,640</td><td>6,718</td><td>39.3</td><td>342</td><td>537</td></tr><tr><th>Bovidae B. taurus O. aries</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 G. camelopardalis</th><td>428f</td><td>537f</td><td>79.7</td><td>459c</td><td>363</td></tr><tr><th>Hippopotamidae H. amphibius</th><td>195b</td><td>590b</td><td>33.1</td><td>240e</td><td>258</td></tr><tr><th>Hominidae H. sapiens</th><td>380g</td><td>1,400b</td><td>27</td><td>280e</td><td>324</td></tr></tbody></table>a Perrin et al. (1977).b Sacher and Staffeldt (1974).c Kiltie (1982).d Minervini et al. (2016).e Hayssen et al. (1993).f Gra&Dot;&imath;c et al. (2017).g Blinkov and Glezer (1968).

Table 2 in Delphinid brain development from neonate to adulthood with comparisons to other cetaceans and artiodactyls

Table 2. 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.<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>D. dorcas phillipsi S. scrofa</th><td>Blesbok antelope Wild boar</td><td>60a 149b</td><td>155a 133b</td><td>— —</td><td>— —</td><td>D. delphis L. acutus</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>T. strepsiceros G. camelopardalis C. bactrianus</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>T. truncatus G. griseus G. macrorhynchus</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>B. taurus H. amphibius</th><td>Cow Hippopotamus</td><td>598e 1,351f</td><td>492e 720f</td><td>25g 40g</td><td>199g 195g</td><td>D. leucas G. melas</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>L. africana</th><td>African elephant</td><td>5,000a</td><td>4,619a</td><td>—</td><td>1,724h</td><td>O. orca</td><td>whale Killer whale</td><td>3,723</td><td>6,642</td><td>171</td><td>3,006</td></tr></tbody></table>a Herculano-Houzel (2015).b Minervini et al. (2016).c Gra&Dot;&imath;c et al. (2017).d Xie et al. (2011).e Ballarin et al. (2016).f Silva and Downing (1995).g Sacher and Staffeldt (1974).h Shoshani et al. (2006).

opencc-by-4.0Oct 2024View details →

Urban Air Pollution and Children's Brain Development: A Systematic Bibliometric Review

The dataset for this research was compiled through an advanced PubMed search targeting publications from a one-year period. Keywords focused on air pollution, neurodevelopment, and associated disorders. From an initial pool of 450 publications, filtering based on co-occurrence of relevant keywords reduced this to approximately 50 papers. VOSviewer was employed to analyze co-occurrences and generate a visual map of relationships between air pollution and child neurodevelopment. The thesaurus was applied to standardize terminology, refining the final network for detailed analysis of keyword clusters and their interactions.

opencc-by-4.0Sep 2024View details →

Impact of prenatal THC exposure on mouse brain development; a lifespan approach with MRI

Prenatal cannabis exposure has been demonstrated to impact neurodevelopment in offspring at different ages. To date, to our knowledge, no study has longitudinally examined the effects from embryos to adulthood. Here we collected and analyzed data to explore how prenatal exposure to delta-9-tetrahydrocannabinol (5 mg/kg subcutaneous injections, gestational dat [GD] 3-10) in mice impacts trajectories of brain development with structural magnetic resonance imaging. We supplement these findings with behavioural analyses and electron microscopy as described below. In the first cohort (embryos) embryos were extracted on GD 17 and scanned with MRI postnatally, as described in the methods of the accompanying paper. Electron microscopy was used to investigate dark neural and glial cells, apoptotic cells, and dividing cells in the hippocampus. In the second cohort (neonates) pups were born and scanned postnatally with manganese enhanced MRI on postnatal day (PND) 3, 5, 7, and 10. Separation-induced ultrasonic vocalizations were acquired on PND 12 and pups were perfused on PND 13. EM analyses were repeated in the neonatal hippocampi. In the third cohort (adults) pups were scanned on PND 25, 35, 60, and 90. Behavioral assessments for anxiety-like behavior with open-field test and sensorimotor gating with prepulse inhibition were performed on PND 35 and 37 respectively.  Findings showed altered prenatal body volumes and weight-trajectories, altered brain volumes (especially sustained in females until adulthood), and indications of changes to behavior, including anxiety-like phenotypes in neonates and adolescents. Evidence from electron microscopy suggests increased cell division in the embryo hippocampus. Together these data suggest a profound and sustained impact of early gestation prenatal THC exposure on brain development. For further details on the methods, approach, and results, please see the forthcoming publication. In this dataset you will find the following data: Pregnancy/dam-level outcomes can be found in maternal_outcomes.zip <ul> <li><a href="../api/records/13820978/draft/files/zenodo_pregnancy_README.txt/content" target="_blank" rel="noopener noreferrer">zenodo_pregnancy_README.txt</a>: includes description of the data and fields available in each csv.</li> <li>dam_weights.csv: A spreadsheet including the information related to each dam pooled across the studies</li> <li>nest_quality.csv: A spreadsheet including the manually-rated nest quality from a pilot and the full experiment</li> <li>master_maternal_observations_old_thc.csv: A spreadsheet including data for time spent on and off nest extracted automatically and manually from Ethovision</li> </ul> Embryo outcomes: <ul> <li>zenodo_embryos_README.txt: includes description of the data and fields available in each csv.</li> <li>demographics_for_analysis.csv: A spreadsheet with relevant information for each embryo sample.</li> <li>raw_embryo_mincs.zip: includes 84 embryo scans, full body</li> <li>embryo_heads.zip: includes 57 embryo scans that all passed qc, head only niftis </li> <li>squish_qc.csv: QC of whether the embryos were squished or not</li> <li>em_embryo_hc_mm2csv.csv: cells per mm^2 from electron microscopy</li> </ul> Neonate outcomes: <ul> <li>zenodo_neonates_README.txt: includes description of data and fields available in each csv</li> <li>demographics.csv: A spreadsheet with the demographic information for each pup and timepoint in the study</li> <li>raw_neonate_niftis.zip: 172 scans from neonates in nifti format</li> <li>agreement_qc.csv: quality control file with assessments of raw images</li> <li>milestones_999_as_NA.csv: Record of which milestones were tested and whether they were obtained</li> <li>master_usv.csv: Spreadsheet including data for ultrasonic vocalizations from all tested pups</li> <li>neo_cell_counts_mm2.csv: cells per mm^2 from electron microscopy for the neonates</li> </ul> Adult outcomes:  <ul> <li>zenodo_adult_README.txt: includes description of the data and fields available in each csv.</li> <li>demographics.csv: A spreadsheet with the demographic information for each mouse and timepoint in the study</li> <li>adult_raw_niftis.zip: The raw data (before preprocessing) in nifti format</li> <li>master_qc.csv: Quality control assessment of the raw images</li> <li>master_oft.csv: Values for open field test extracted from Ethovision</li> <li>avg_trials_ppi.csv: Data from prepulse inhibition trials, average startle of 100 ms following pulse</li> <li>max_trials_ppi.csv Dat afrom prepulse inhibition trials, maximum startle of 100 ms following pulse</li> </ul>

opencc-zeroMar 2023View details →

4D time-lapse images of brain and trunk development in the larvacean Oikopleura dioica

The larvacean, Oikopleura dioica is a planktonic chordate, which is an emerging model organism with a short life cycle of 5 days and belongs to tunicates (urochordates). Organ formation in the trunk proceeds in seven hours from hatching of tailbud larvae at three hours after fertilization (hpf) to completion of organ formation in fully functional juveniles that start feeding at 10 hpf and are just miniature of adult form. Development of O. dioica has been described (Nishida, H., 2008 Development of the appendicularian Oikopleura dioica: culture, genome, and cell lineages. Dev. Growth Differ. 50, S239–S256.). The dataset contains 4D (3D+time) time-lapse images that were acquired during larval development using differential interference contrast optics, and wide-field fluorescent microscope, which visualize cell membrane and nuclei of the entire trunk region. In some cases, animal or vegetal hemisphere blastomeres are labelled to trace the descendants. The dataset would be generally utilized as basic morphological data during the larval development and for tracing cell lineages at a single cell level. This data set is related to the manuscript "Formation of the brain by stem cell divisions of large neuroblasts in Oikopleura dioica, a simple chordate".

opencc-zeroMay 2023View details →

Data from: Extensive, transient, and long-lasting gene regulation in a song-controlling brain area during testosterone-induced song development in adult female canaries

Like other canary reproductive behaviors, song production occurs seasonally and can be triggered by gonadal hormones. Adult female canaries treated with testosterone sing first songs after four days and progressively develop towards typical canary song structure over several weeks, a behavior that females otherwise rarely or never show. We compared gene regulatory networks in the song-controlling brain area HVC after 1 hour (h), 3 h, 8 h, 3 days (d), 7d, and 14d testosterone treatment with placebo-treated control females, paralleling HVC and song development. Rapid onset (1 h or less) of extensive transcriptional changes (2,700 genes) preceded the onset of song production by four days. The highest level of differential gene expression occurred at 14 days when song structure was most elaborate, and song activity was highest. The transcriptomes changed massively several times during the two-week of song production. A total of 9,710 genes were differentially expressed, corresponding to about 60% of the known protein-coding genes of the canary genome. Most (99%) of the differentially expressed genes were regulated only at specific stages. The differentially expressed genes were associated with diverse biological functions, of which cellular level occurring early and nervous system level occurring primarily after prolonged testosterone treatment. Thus, the development of adult songs requires restructuring the entire HVC, including most HVC cell types, rather than altering only neuronal subpopulations or cellular components. Parallel regulation directly by androgen and estrogen receptors and by other hub genes such as the transcription factor SP8, which are under steroidogenic control, lead to massive transcriptomic and neural changes in the specific behavior-controlling brain areas and gradual seasonal occurrence of singing behavior.

publicAug 2024View details →

Endocranial development in non-avian dinosaurs reveals an ontogenetic brain trajectory distinct from extant archosaurs

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publicDec 2024View details →

Extensive, transient, and long-lasting gene regulation in a song-controlling brain area during testosterone-induced song development in adult female canaries

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publicMar 2023View details →

4D time-lapse images of brain and trunk development in the larvacean Oikopleura dioica

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