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Fig. 4 in The Mad2 Spindle Checkpoint Protein Undergoes Similar Major Conformational Changes upon Binding to Either Mad1 or Cdc20
Fig. 4. Putative homologies of molar features among toothed monotremes, occlusal views; mesial is to the left in all the drawings. A. Monotrematum, the geologically oldest upper molar known for monotremes (left upper molar shown). Outline restoration for M2 (A1); the same in stipple drawing (A2). B. Steropodon, the geologically oldest lower molars known for monotremes (left lower molars shown). Diagrammatic drawing (B1); the same in stippled drawing (B2). C. Obdurodon dicksoni. Left upper molars (C1); left lower ultimate premolar and first molar (C2). D. Hypothetical occlusal relationship of the upper and lower molars for basal monotremes; hypothetical models of upper molar (reversed crown view of right M2 of Monotrematum) and lower molars (crown view of Steropodon). Hypothetical contacting relations between the upper and the lower structures at the beginning of occlusion (D1); contacting relations near the mid−point of occlusion (analogous to the centric occlusion in the boreosphenidan mammals with the "pestle−to−mortar" occlusion (D2); contacting relations near of the end of the occlusal cycle (D3). The matching of the upper and the lower molar models is based on the similarity in the lowers between Steropodon and Obdurodon dicksoni and the similarity in the uppers of Monotrematum and O. dicksoni. E. Three stages (E1–E3) showing the sequence of upper−to−lower occlusion, in correspondence with D1–D3, as the lower molars moved across the transversely wider upper molar. Arrows denote direction of movement of the lower molars. Relative positions of the overlapping upper and lower molars of E1, E2, and E3 correspond to the contact points of the upper and lower structures labelled in D1–D3. See text for explanation. All original drawings, based on: A, photos of Pascual et al. (1992a, b) reversed; B, a cast of the holotype, reversed; C, SEM photos of Archer et al. (1993), with premolar reversed from the right side to be consistent with the left m1; D and E originals.
Fig. 1 in The Mad2 Spindle Checkpoint Protein Undergoes Similar Major Conformational Changes upon Binding to Either Mad1 or Cdc20
Fig. 1. Phylogenetic relationships of all major Mesozoic mammal lineages (strict parsimony from unconstrained searches). Each of the 42 equally parsimonious trees has: TreeLength = 935; CI = 0.499; RI = 0.762. Multi−state characters unordered; PAUP4.0b5 heuristic search (stepwise addition) 1000 runs. Numbers in circles (1 and 2) denote the nodes of two unnamed clades, described on p. 20 and 21 respectively, (3) crown−group Mammalia. Shadowed areas denote: Australosphenida (upper shading) and Boreosphenida (lower shading).
Рис. 2. ΔенΑрограмма схоΑства фаун Αонных беспозвоночных воΑотоков заповеΑника «КомсомоΛьский» (UPGMA, коэффициент Сёренсена) Fig. 2. Dendrogram of the fauna similarity of benthic invertebrates of the Komsomolsky Nature Reserve watercourses (UPGMA, Sorensen coefficient) in Taxonomic composition of benthic invertebrates of the Komsomolsky Nature Reserve watercourses (Khabarovsky Region)
Рис. 2. ΔенΑрограмма схоΑства фаун Αонных беспозвоночных воΑотоков заповеΑника «КомсомоΛьский» (UPGMA, коэффициент Сёренсена) Fig. 2. Dendrogram of the fauna similarity of benthic invertebrates of the Komsomolsky Nature Reserve watercourses (UPGMA, Sorensen coefficient)
Рис. 2.Соотношение виΑов рыб на ΛитораΛи Тауйской губы: А — по их зоогеографической принаΑΛежности; Б — по принаΑΛежности к ихтиоцену. Обозначения см. в табΛице 1 Fig. 2. Ratio of fish species in the littoral zone of Tauysk Bay: А — according to their zoogeographic affiliation; Б — by belonging to the ichthyocene. Designations are similar to those in Table 1. in Species diversity and dominant species of the littoral area fishes of Tauysk bay, the Sea of Okhotsk
Рис. 2.Соотношение виΑов рыб на ΛитораΛи Тауйской губы: А — по их зоогеографической принаΑΛежности; Б — по принаΑΛежности к ихтиоцену. Обозначения см. в табΛице 1 Fig. 2. Ratio of fish species in the littoral zone of Tauysk Bay: А — according to their zoogeographic affiliation; Б — by belonging to the ichthyocene. Designations are similar to those in Table 1.
Рис. 2. UPGMA-ΑенΑрограмма схоΑства виΑового состава (А) и фаунистическая структура (B) сообществ земΛероек в пяти ΛокаΛитетах Амурской обΛасти: ЗЗ — Зейский заповеΑник; НЗ — Норский заповеΑник; ХЗ — Хинганский заповеΑник; ЧФЗ — ХинганоАрхаринский заказник; НБС — территория зоны вΛияния Нижнебурейской ГЭС. ΔТФ — Αревнетаежная фауна; БФ — бореаΛьная фауна; НФ — немораΛьная фауна; Αр. — преΑставитеΛи Αругих фауногенетических группировок (пояснения в тексте) Fig. 2. UPGMA dendrogram of species composition similarity (A) and fauna structure (B) of shrew communities in five Amur region localities: ZZ — Zeya nature reserve; NZ — Norsky nature reserve; KhZ — Khingansky nature reserve; ChFZ — KhinganoArkharinsky nature reserve; NBS — the area influenced by the Nizhnebureyskaya hydroelectric power station. DTP — ancient taiga fauna; BF — boreal fauna; NF — nemoral fauna; others — representatives of other faunagenetic groups (explained in the text) in Shrew species composition and fauna structure in the Norsky reserve
Рис. 2. UPGMA-ΑенΑрограмма схоΑства виΑового состава (А) и фаунистическая структура (B) сообществ земΛероек в пяти ΛокаΛитетах Амурской обΛасти: ЗЗ — Зейский заповеΑник; НЗ — Норский заповеΑник; ХЗ — Хинганский заповеΑник; ЧФЗ — ХинганоАрхаринский заказник; НБС — территория зоны вΛияния Нижнебурейской ГЭС. ΔТФ — Αревнетаежная фауна; БФ — бореаΛьная фауна; НФ — немораΛьная фауна; Αр. — преΑставитеΛи Αругих фауногенетических группировок (пояснения в тексте) Fig. 2. UPGMA dendrogram of species composition similarity (A) and fauna structure (B) of shrew communities in five Amur region localities: ZZ — Zeya nature reserve; NZ — Norsky nature reserve; KhZ — Khingansky nature reserve; ChFZ — KhinganoArkharinsky nature reserve; NBS — the area influenced by the Nizhnebureyskaya hydroelectric power station. DTP — ancient taiga fauna; BF — boreal fauna; NF — nemoral fauna; others — representatives of other faunagenetic groups (explained in the text)
Рис. 3. ÀенΑрограмма биоценотического схоΑства зоопΛанктона техногенных воΑоемов: 4–6 — ШерΛовогорское месторожΑение:4 — ШГ-10 — карьерное озеро, 5 — ШГ-8 — озеро поΑ отваΛами руΑного карьера, 6 — ШГ-9 — поΑпруΑное озеро у пгт. ШерΛовая Гора;7–8 — ОрΛовское месторожΑение: 7 — ОР-1, ОР-3 — хвостохраниΛище, 8 — ОР-7 — озеро ниже хвостохраниΛища; 9 — МаΛокуΛунΑинское месторожΑение: МК-2 — поΑпруΑное озеро р. МаΛая КуΛинΑа; 10 — Спокойнинское месторожΑение: ОР-8 — хвостохраниΛище; 11 — Жипкошинское месторожΑение: ЖП-2 — карьер Fig. 3. Dendrogram of zooplankton biocenotic similarity in technogenic reservoirs: 4–6 — Sherlovogorskoye deposit: 4 — ShG-10 pit lake, 5 — ShG-8, a lake under the dumps of an ore quarry, 6 — ShG-9 dammed lake near the village of Sherlovaya Gora;7 –8 — Orlovskoye deposit: O R-1, OR-3 — tailing dump, OR-7— lake below the tailing dump; 9 — Malokulundinskoye deposit: MK-2 — dammed lake on the Malaya Kulinda River; 10 — Spokoininskoye deposit: OR-8 — tailing dump; 11 — Zhipkoshinskoye deposit; ZhP-2 — pit lake in Zooplankton species diversity in technogenic reservoirs of the Southeastern Transbaikalia
Рис. 3. ÀенΑрограмма биоценотического схоΑства зоопΛанктона техногенных воΑоемов: 4–6 — ШерΛовогорское месторожΑение:4 — ШГ-10 — карьерное озеро, 5 — ШГ-8 — озеро поΑ отваΛами руΑного карьера, 6 — ШГ-9 — поΑпруΑное озеро у пгт. ШерΛовая Гора;7–8 — ОрΛовское месторожΑение: 7 — ОР-1, ОР-3 — хвостохраниΛище, 8 — ОР-7 — озеро ниже хвостохраниΛища; 9 — МаΛокуΛунΑинское месторожΑение: МК-2 — поΑпруΑное озеро р. МаΛая КуΛинΑа; 10 — Спокойнинское месторожΑение: ОР-8 — хвостохраниΛище; 11 — Жипкошинское месторожΑение: ЖП-2 — карьер Fig. 3. Dendrogram of zooplankton biocenotic similarity in technogenic reservoirs: 4–6 — Sherlovogorskoye deposit: 4 — ShG-10 pit lake, 5 — ShG-8, a lake under the dumps of an ore quarry, 6 — ShG-9 dammed lake near the village of Sherlovaya Gora;7 –8 — Orlovskoye deposit: O R-1, OR-3 — tailing dump, OR-7— lake below the tailing dump; 9 — Malokulundinskoye deposit: MK-2 — dammed lake on the Malaya Kulinda River; 10 — Spokoininskoye deposit: OR-8 — tailing dump; 11 — Zhipkoshinskoye deposit; ZhP-2 — pit lake
Figure 3. Robotic faces, similar to smiley emoticons-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>The smiley Gestalt is the result from a dynamic (evolved in time) cognitive process, it is<br> immediately given in cognition, memorable, emotionally rich and socially relevant and reflects the<br> special kind of Gestalt complexity as defined by Edwin Rausch (1988). Conventional representations<br> of holistic entities like smileys or novel robotic faces come to life because they capture essential<br> Gestalt qualities of the perceived image. For example, in figure 3 the robotic faces resemble the<br> smileys in terms of the evoked internal/emotional reactions.</p>
Figure 4. Simulation (Monte-Carlo, n=200) results elderly patients taking a 10mg oral dose resulting in similar Cmax, maximum plasma concentration, to the young patients taking a 40mg oral dose. The dotted lines illustrate the 10th and 90th percentiles of plasma levels of the elderly population with a 10mg oral administration of propranolol.-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>Thus, the package insert (see 1) recommends clinicians start at the lower end of the dosing<br> range, without further details.<br> Similarly, Pfizer manufactures Inderal® LA (Propranolol HCI), which is the long-acting<br> form of propranolol and their package insert (see 2) states, “There is no information available for<br> elderly patients.” Though the kinetics for the long-acting formdiffers from the standard form,<br> manufactured by Wyeth, we would suspect a 10mg dose for the elderly would achieve a similar<br> maximum plasma concentration (Cmax) to that of the younger patient cohort.This 10mg, which is<br> 25% of the original 40mg, dosing schedule is based on our simulations at 10mg in the geriatric<br> population.</p>
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 1. Being Brain and Chess Game Strategy - similarities
<p>Finally, the following similar reactions between a chess player and a human being must be mentioned and considered. The power of reason for every being, human brain, or chess game player lies in similarities and has three main directions (see Figure 1)</p>
Questionnaire, R Scripts and Response Data Set of the Survey on Functionally Similar Code Clones
<p>In 2017, we conducted an open online survey regarding functionally similar code clones with practitioners. We make the used questionnaire, the data from the response to the questionnaire and our used R script for the analysis openly available.</p>
Data_MathyChekafCowan_JOC2018_Simple and Complex Working Memory Tasks Allow Similar Benefits of Information Compression
<p>Original data files for the article Mathy, Fabien, Chekaf, Mustapha, & Cowan Nelson (2018). Simple and Complex Working Memory Tasks Allow Similar Benefits of Information Compression. Journal of Cognition.</p> <p>Abstract : Complex working memory span tasks were designed to engage multiple aspects of working memory and impose interleaved processing demands that limit the use of mnemonic strategies, such as chunking. Consequently, the average span is usually lower (4 ± 1 items) than in simple span tasks (7 ± 2 items). One possible reason for the higher span of simple span tasks is that participants can take advantage of the spare time to chunk multiple items together to form fewer independent units, approximating 4 ± 1 chunks. It follows that the respective spans of these two types of tasks could be equal (at around 4 ± 1) if stimulus lists exclusively used nonchunkable stimulus items. To manipulate the chunkability of the stimulus lists, our method involved a measure of their compressibility, i.e., the extent to which a pattern exists that can be detected and used as a basis of chunk formation. We predicted an interaction between the types of tasks and chunkability/compressibility, supporting a single higher span for the condition in which a simple span task was combined with chunkable items. The three other conditions were predicted to prevent chunking processes, either because the interleaved processing task did not allow any chunking process to occur or because the noncompressible material inherently limited the chunkability of information. The prediction that chunking is important solely in simple spans was not confirmed: Effects of information compression contributed to performance levels to a similar extent in both tasks according to a theoretically-based metric. This result suggests that i) complex span tasks might overestimate storage capacity in general, and ii) the difference between simple and complex span performance levels must rest in some mechanism other than prevention of a chunking strategy by the interleaved processing task in complex span tasks.</p>
Dog gut gene catalog. Supplemental data for "Similarity of the dog and human gut microbiomes in gene content and response to diet"
<p>Gene catalogue for the dog gut microbiome including</p> <ol> <li>FASTA file of nucleotide sequences (including padding, see coords file for exact coordinates)</li> <li>FASTA file of amino-acid sequences</li> <li>coords file (gene coordinates)</li> <li>Taxonomic predictions</li> <li>Functional predictions</li> </ol> <p>See the paper "<em>Similarity of the dog and human gut microbiomes in gene content and response to diet</em>" by Coelho et al. in Microbiome for details. We ask that you cite that publication when using this dataset in published literature</p>
Supplement data for the article "The impact of OTU sequence similarity threshold on diatom-based bioassessment: A case study of the rivers of Mayotte (France, Indian Ocean)", in preparation
<p>These are supplement data for the article "The impact of OTU sequence similarity threshold on diatom-based bioassessment: A case study of the rivers of Mayotte (France, Indian Ocean)", in preparation</p> <p>The folowing files are available:</p> <ul> <li>Supplement 1. Map of Mayotte with the sampling sites and the rivers.</li> <li>Supplement 2. <em>rbcL</em> primers, reaction mixture, and conditions used for the PCR of the 312-bp <em>rbcL</em> fragment. The information provided is for a single reaction with a final volume of 25µL.</li> <li>Supplement 3. The 20 fastq files containing the demultiplexed DNA reads.</li> <li>Supplement 4. Number of sequence reads for each sample before and after the trimming procedure.</li> <li>Supplement 5. The 20 OTU lists, corresponding to the 20 SSTs, including the number of DNA reads within the 90 samples and their assigned taxonomy.</li> <li>Supplement 6. Sampling site description with sample codes, names of rivers, year, number of raw DNA reads and GPS coordinates.</li> <li>Supplement 7. Values and summary statistics for the environmental variables.</li> <li>Supplement 8. The script used in Mothur for the bioinformatic analysis from trimming to the used OTU lists.</li> </ul> <p> </p>
netDx: Interpretable patient classification using integrated patient similarity networks
<p>Docker image containing installed netDx software in Ubuntu to reproduce examples from the published manuscript. The R implementation of netDx is hosted at: https://github.com/BaderLab/netDx</p> <p>---<br> Publication abstract: Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be easily interpretable to drive hypothesis-driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks. netDx meets the above criteria and particularly excels at data integration and model interpretability. We compared classification performance of this method against other machine-learning algorithms, using a cancer survival benchmark with four cancer types, each requiring integration of up to six genomic and clinical data types. In these tests, netDx has significantly higher average performance than most other machine-learning approaches across most cancer types. In comparison to traditional machine learning-based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway-level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in diverse data sets of breast cancer and asthma. Thus, netDx can serve both as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a freely available software implementation of netDx along with sample files and automation workflows in R.</p>
Similarity of salivary microbiome in parents and adult children.
<p>The raw NGS edata of our manuscript Similarity of salivary microbiome in parents and adult children.</p>
Data set on linguistic similarity of German dialects
<p>- The data provide information on pairwise dialect similarities for 439 NUTS 3 regions in Germany</p> <p><em>- </em>Data come from the maps and questionnaires of the Sprachatlas des Deutschen Reichs, digitized using ArcGIS software</p> <p>- The data source is a questionnaire with translations of standardized German sentences into local dialects between 1879 and 1888</p> <p>- The measure is defined as the number of co-occurrences for all pairs of sites (z-scaled)</p>
Do synaesthesia and mental imagery tap into similar crossmodal processes?
<p>Examples of video stimuli used in<strong> Experiment 1</strong> of paper - Do synaesthesia and mental imagery tap into similar crossmodal processes?</p> <p> </p> <p> </p> <p> </p>
Dataset for: "Big data suggest strong constraints of linguistic similarity on adult language learning"
<p>This dataset is adapted from raw data with fully anonymized results on the State Examination of Dutch as a Second Language. This exam is officially administred by the Board of Tests and Examinations (College voor Toetsen en Examens, or CvTE). See cvte.nl/about-cvte. The Board of Tests and Examinations is mandated by the Dutch government.</p> <p>The article accompanying the dataset:</p> <p>Schepens, Job, Roeland van Hout, and T. Florian Jaeger. “Big Data Suggest Strong Constraints of Linguistic Similarity on Adult Language Learning.” <em>Cognition</em> 194 (January 1, 2020): 104056. <a href="https://doi.org/10.1016/j.cognition.2019.104056">https://doi.org/10.1016/j.cognition.2019.104056</a>.</p> <p>Every row in the dataset represents the first official testing score of a unique learner.<br> The columns contain the following information as based on questionnaires filled in at the time of the exam:</p> <p>"L1" - The first language of the learner<br> "C" - The country of birth<br> "L1L2" - The combination of first and best additional language besides Dutch<br> "L2" - The best additional language besides Dutch<br> "AaA" - Age at Arrival in the Netherlands in years (starting date of residence)<br> "LoR" - Length of residence in the Netherlands in years<br> "Edu.day" - Duration of daily education (1 low, 2 middle, 3 high, 4 very high). From 1992 until 2006, learners' education has been measured by means of a side-by-side matrix question in a learner's questionnaire. Learners were asked to mark which type of education they have had (elementary, secondary, or tertiary schooling) by means of filling in for how many years they have been enrolled, in which country, and whether or not they have graduated. Based on this information we were able to estimate how many years learners have had education on a daily basis from six years of age onwards. Since 2006, the question about learners' education has been altered and it is asked directly how many years learners have had formal education on a daily basis from six years of age onwards. Possible answering categories are: 1) 0 thru 5 years; 2) 6 thru 10 years; 3) 11 thru 15 years; 4) 16 years or more. The answers have been merged into the categorical answer.<br> "Sex" - Gender<br> "Family" - Language Family<br> "ISO639.3" - Language ID code according to Ethnologue<br> "Enroll" - Proportion of school-aged youth enrolled in secondary education according to the World Bank. The World Bank reports on education data in a wide number of countries around the world on a regular basis. We took the gross enrollment rate in secondary schooling per country in the year the learner has arrived in the Netherlands as an indicator for a country's educational accessibility at the time learners have left their country of origin.<br> "STEX_speaking_score" - The STEX test score for speaking proficiency.<br> "Dissimilarity_morphological" - Morphological similarity<br> "Dissimilarity_lexical" - Lexical similarity<br> "Dissimilarity_phonological_new_features" - Phonological similarity (in terms of new features)<br> "Dissimilarity_phonological_new_categories" - Phonological similarity (in terms of new sounds)</p> <p><br> A few rows of the data:</p> <p>"L1","C","L1L2","L2","AaA","LoR","Edu.day","Sex","Family","ISO639.3","Enroll","STEX_speaking_score","Dissimilarity_morphological","Dissimilarity_lexical","Dissimilarity_phonological_new_features","Dissimilarity_phonological_new_categories"<br> "English","UnitedStates","EnglishMonolingual","Monolingual",34,0,4,"Female","Indo-European","eng ",94,541,0.0094,0.083191,11,19<br> "English","UnitedStates","EnglishGerman","German",25,16,3,"Female","Indo-European","eng ",94,603,0.0094,0.083191,11,19<br> "English","UnitedStates","EnglishFrench","French",32,3,4,"Male","Indo-European","eng ",94,562,0.0094,0.083191,11,19<br> "English","UnitedStates","EnglishSpanish","Spanish",27,8,4,"Male","Indo-European","eng ",94,537,0.0094,0.083191,11,19<br> "English","UnitedStates","EnglishMonolingual","Monolingual",47,5,3,"Male","Indo-European","eng ",94,505,0.0094,0.083191,11,19</p>
Network data for the paper: Intellectual and social similarity among scholarly journals.
<p>Network data used for the analysis contained in Baccini A, Barabesi L, Gingras Y, Kalfaoui M (2019) Intellectual and social similarity among scholarly journals. An exploratory comparison of the networks of editors, authors and co-citations.</p> <p>Data are in .net format for Pajek software</p> <p>CC indicates co-citation network.</p> <p>IA indicated Interlocking authorship network.</p> <p>IE indicates interlocking editorship network.</p> <p>Stat is for statistics; Econ is for economics; ILS is for information and library science.</p> <p> </p> <p> </p>
Text-fig. 4. A – Alasia sp., pollen ornamentation, compared with B – extant Quercus castaneifolia C.A. Mey (courtesy of Natalia Naryshkina, Institute of Biology and Soil Science, Vladivostok), with similar verrucate – scabrate elements. Scale bar 1 µm. in In Situ Pollen Of Alasia, A Supposed Staminate Inflorescence Of Trochodendroides Plant
Text-fig. 4. A – Alasia sp., pollen ornamentation, compared with B – extant Quercus castaneifolia C.A. Mey (courtesy of Natalia Naryshkina, Institute of Biology and Soil Science, Vladivostok), with similar verrucate – scabrate elements. Scale bar 1 µm.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.