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Fig. 4 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations
Fig. 4 Phylogenetic medianjoining network based on 256 mitochondrial control region sequences. Sizes of circles correspond to the number of birds sharing this haplotype; branch lengths are proportional to the number of substitutions and those over 2 are shown at the branches. Haplogroups 1–6 are indicated by numbers
Fig. 2 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations
Fig. 2 Map of sampling localities for mitochondrial DNA analysis in the zone of contact between Pica pica leucoptera and Pica pica jankowskii. Distribution of haplotypes is indicated by colours: Pica
Figure 3. Distribution of Q3 values-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm
<p>The estimated accuracy for the α- helices (QH), β- strands (QE), C-coil states (QC), and three<br> state together (Q3) for the system is shown in Figure 3.</p>
Figure 2. PAM250 matrix for the encoded sequence-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm
<p>The PAM matrix (Dayhoff et al., 1978) describes the probability that original amino acid<br> will be replaced by another amino acid over a defined evolutionary interval. The unit of<br> evolutionary divergence is defined as the interval in which 1% of the amino acids have been<br> changed between two sequences. The work uses PAM250, which assumes the occurrence of 250-<br> point mutations per 100 amino acids.<br> So, for the given the protein sequence GIVEQCCASVCSLYQLENYCN, A will be replaced<br> by 1 -3 0 1 -3 -1 0 5 -2 -3 -4 -2 -3 -5 0 1 0 -7 -5 -1 as shown in Figure 2.</p>
Figure 1: Snapshot of the CB396 dataset-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning AlgorithmSecondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm
<p>The dataset used for this work is CB396. This dataset contains 396 non-redundant sequences<br> derived from the 3Dee database created by Cuff and Barton (Cuff & Barton, 1999). It contains 396<br> proteins with their respective secondary structure as shown in Figure 1.</p>
Secondary ice production parameterization output - COSMO model
<p>Secondary ice production via processes like rime splintering, frozen droplet shattering, and breakup upon ice hydrometeor collision have been proposed to explain discrepancies between in-cloud ice crystal and ice-nucleating particle numbers. To understand the impact of this kind of additional ice number generation on surface precipitation, we present one of the first studies to implement frozen droplet shattering and ice-ice collisional breakup parameterizations in a larger-scale model. We simulate a cold frontal rainband from the Aerosol Properties, PRocesses, And InfluenceS on the Earth's Climate campaign and investigate the impact of the new parameterizations on the simulated ice crystal number concentrations (ICNC) and precipitation. Near the convective regions of the rainband, contributions to ICNC can be as large from secondary production as from primary nucleation, but ICNCs greater than 50 L-1 remain underestimated by the model. Addition of the secondary production parameterizations also clearly intensifies the differences in both accumulated precipitation and precipitation rate between the convective towers and non-convective gap regions. We suggest, then, that secondary ice production parameterizations be included in large-scale models on the basis of large hydrometeor concentration and convective activity criteria.</p>
Secondary metabolites from nectar and pollen: a resource for ecological and evolutionary studies
<p>Floral chemistry mediates plant interactions with herbivores, pathogens, and pollinators. The chemistry of floral nectar and pollen—the primary food rewards for pollinators—can affect both plant reproduction and pollinator health. Although the existence and functional significance of nectar and pollen secondary metabolites has long been known, comprehensive quantitative characterizations of secondary chemistry exist for only a few species. Moreover, little is known about intraspecific variation in nectar and pollen chemical profiles. Because the ecological effects of secondary chemicals are dose-dependent, heterogeneity across genotypes and populations could influence floral trait evolution and pollinator foraging ecology. To better understand within- and across-species heterogeneity in nectar and pollen secondary chemistry, we undertook exhaustive LC-MS and LC-UV-based chemical characterizations of nectar and pollen methanol extracts from 31 cultivated and wild plant species. </p> <p>Nectar and pollen were collected from farms and natural areas in Massachusetts, Vermont, and California, USA, in 2013 and 2014. For wild species, we aimed to collect 10 samples from each of 3 sites. For agricultural and horticultural species, we aimed for 10 samples from each of 3 cultivars. Our dataset (1535 samples, 102 identified compounds) identifies and quantifies each compound recorded in methanolic extracts, and includes chemical metadata that describe the molecular mass, retention time, and chemical classification of each compound. A reference phylogeny is included for comparative analyses.</p> <p>We found that each species possessed a distinct chemical profile; moreover, within species, few compounds were found in both nectar and pollen. The most common secondary chemical classes were flavonoids, terpenoids, alkaloids and amines, and chlorogenic acids. The most common compounds were quercetin and kaempferol glycosides. Pollens contained high concentrations of hydroxycinnamoyl-spermidine conjugates, mainly triscoumaroyl and trisferuloyl spermidine, found in 71% of species. When present, pollen alkaloids and spermidines had median nonzero concentrations of 23,000 µM (median 52% of recorded micromolar composition). Although secondary chemistry was qualitatively consistent within each species and sample type, we found significant quantitative heterogeneity across cultivars and sites. These data provide a standard reference for future ecological and evolutionary research on nectar and pollen secondary chemistry, including its role in pollinator health and plant reproduction.</p>
Fig. 3 in Effects of the Secondary Metabolite Producing Pseudomonas fluorescens CHA0 on Soil Protozoa and Bacteria
Fig. 3. Colony forming curves of culturable bacteria in soil microcosms harvested after 1, 7, and 14 days on non-selective agar media. For each harvest event the same plates were counted repeatedly. Statistical significant differences between the treatments at the last counting event of each harvest are indicated by different letters.
Fig. 4 in Effects of the Secondary Metabolite Producing Pseudomonas fluorescens CHA0 on Soil Protozoa and Bacteria
Fig. 4. Abundance of culturable protozoa in the four different soil microcosms. The protozoa were counted by MPN as fast-growing protozoa after 1 week of incubation and as total protozoa after 3 weeks of incubation by inspecting the same plates twice. Significant differences of treatments within each sampling time and incubation time are shown as different small letters above the bars. After one day protozoa was only counted in the control microcosm. Significant differences between the abundance of protozoa in the control microcosm are shown as capital letters. bd: below detection limit of 157 protozoa g–1 dw. nd: not determined.
Fig. 2 in Effects of the Secondary Metabolite Producing Pseudomonas fluorescens CHA0 on Soil Protozoa and Bacteria
Fig. 2. Fate of inoculated P. fluorescence CHA0/gfp1 and P. fluorescens CHA0/pME3424 during incubation in soil microcosms determined as CFU on selective agar media (see Materials and Methods for selective agents). The individual data points for each replicate are shown along with the linear regression line for each strain.
Fig. 1 in Effects of the Secondary Metabolite Producing Pseudomonas fluorescens CHA0 on Soil Protozoa and Bacteria
Fig. 1. Soil respiration measured as accumulated CO 2 during the incubation of microcosms consisting of soil, shredded barley straw and either of three bacterial inoculants: E. aerogenes, P. fluorescens CHA0/gfp1, P. fluorescens CHA0/pME3424. Control treatment did not receive any bacteria.
Text-fig. Fig. 9. Cucurbitaceae 1–4. Cucurbitaciphyllum lobatum (KNOWLTON) comb. nov. Specimens from Shirley Canal, Montana, USGS loc. 8519. 1. Trilobate leaf, with rounded lobal sinues, and entire to serrated margin. USNM 313167. 2. Leaf with pronounced secondary lobes and cordate base, USNM 313179. 3. Detail of central lobe from fig. 1. 4. Higher magnification, showing abundant trichome impressions. Scales = 3 cm in 1–3; 3 mm in 4. in Revisions To Roland Brown'S North American Paleocene Flora
Text-fig. Fig. 9. Cucurbitaceae 1–4. Cucurbitaciphyllum lobatum (KNOWLTON) comb. nov. Specimens from Shirley Canal, Montana, USGS loc. 8519. 1. Trilobate leaf, with rounded lobal sinues, and entire to serrated margin. USNM 313167. 2. Leaf with pronounced secondary lobes and cordate base, USNM 313179. 3. Detail of central lobe from fig. 1. 4. Higher magnification, showing abundant trichome impressions. Scales = 3 cm in 1–3; 3 mm in 4.
Text-fig. 3. Geological map and schematic geological section of the discovery site of the Late Upper Palaeolithic skull from Moča (southern Slovakia). I. – Primary position (?), II. – The discovery site (secondary position), A – B – The schematic geological section of the discovery site 1. H – Fluvial clayey to sandy loams (subordinately humolites) – Holocene; secondary discovery site layer, 2. lm-pH – Loam – peat – Holocene, 3. e Wl – Eolian sands – Late Würm (Late glacial of Würm), 4. lm,sWl – Fluvial clayey (to humic) loams or fine sands – Late Würm (Late glas cial of Würm); original discovery site layer, now eroded, 4a. fe Wl – Fluvial – aeolian silty sands (calcareous) – Late Würm (Late glacial s-lm of Würm), 5. lmW3 – Fluvial loams, sandy loams – final Würm (W3), 5a. W3 – Fluvial sands – final (?) Würm (?W3), 6. gW2+3 – Fluvial gravs els, sandy gravels, sands with gravel – Pleniglacial of Würm (W2+3), 7. lW – Aeolian loess and loess loams – Würm (undivided) in A Late Upper Palaeolithic Skull From Moča (The Slovak Republic) In The Context Of Central Europe
Text-fig. 3. Geological map and schematic geological section of the discovery site of the Late Upper Palaeolithic skull from Moča (southern Slovakia). I. – Primary position (?), II. – The discovery site (secondary position), A – B – The schematic geological section of the discovery site 1. H – Fluvial clayey to sandy loams (subordinately humolites) – Holocene; secondary discovery site layer, 2. lm-pH – Loam – peat – Holocene, 3. e Wl – Eolian sands – Late Würm (Late glacial of Würm), 4. lm,sWl – Fluvial clayey (to humic) loams or fine sands – Late Würm (Late glas cial of Würm); original discovery site layer, now eroded, 4a. fe Wl – Fluvial – aeolian silty sands (calcareous) – Late Würm (Late glacial s-lm of Würm), 5. lmW3 – Fluvial loams, sandy loams – final Würm (W3), 5a. W3 – Fluvial sands – final (?) Würm (?W3), 6. gW2+3 – Fluvial gravs els, sandy gravels, sands with gravel – Pleniglacial of Würm (W2+3), 7. lW – Aeolian loess and loess loams – Würm (undivided)
Figure 3 in Ant (Hymenoptera: Formicidae) species diversity in secondary forest and three agricultural land uses of the Colombian Pacific Coast
Figure 3. Number of shared ant species and total number of specimens caught (pitfall and Winkler sack) between four areas of different land use. Two oil palm plots of three and seven years of age were pooled. El Mira Research Center, Tumaco, Pacific Coast of Colombia. / Número de especies de hormigas compartidas y número total de individuos capturados (Pitfall y sacos Winkler) entre cuatro áreas con diferente uso de tierra. Las dos parcelas de palma de aceite de tres y siete años fueron agrupadas. Centro de Investigación El Mira, Tumaco, Nariño, costa pacÍfica de Colombia.
Figure 1 in Ant (Hymenoptera: Formicidae) species diversity in secondary forest and three agricultural land uses of the Colombian Pacific Coast
Figure 1. Map of El Mira Research Center of the Corporación Colombiana de Investigación Agropecuaria, Tumaco, Nariño, Pacific Coast of Colombia, with the location (arrows) of the pitfall trap transects. Yellow hybrid oil palm 7 years old; red hybrid oil palm 3 years old; black peach palm; white secondary forest. / Mapa del Centro de Investigación El Mira de la Corporación Colombiana de Investigación Agropecuaria, Tumaco, Nariño, costa pacÍfica de Colombia con la ubicación (flechas) de las trampas pitfall en los transectos. Amarillo palma de aceite hÍbrido 7 años; rojo palma de aceite hÍbrido 3 años; negro palma de chontaduro; blanco bosque secundario.
Figure 2 in Ant (Hymenoptera: Formicidae) species diversity in secondary forest and three agricultural land uses of the Colombian Pacific Coast
Figure 2. Variation in 0D diversity (species number) of Formicidae between four areas of different land use: El Mira Research Center, Tumaco, Pacific Coast of Colombia. SF: secondary forest, PP: Peach palm, OP7: Oil palm 7 years old, OP3: Oil palm 3 years old. / Variación en la diversidad 0D (número de especies) de Formicidae entre cuatro áreas con diferente uso de tierra. Centro de Investigación El Mira de la Corporación Colombiana de Investigación Agropecuaria, Tumaco, Nariño, costa pacÍfica de Colombia.
Fig. 2 in Camera Trapping The Indochinese Tiger, Panthera Tigris Corbetti, In A Secondary Forest In Peninsular Malaysia
Fig. 2. Cumulative number of individual tiger captured per month around FELDA Jerangau Barat, Terengganu between April 2000 to September 2000.
Fig. 3 in Camera Trapping The Indochinese Tiger, Panthera Tigris Corbetti, In A Secondary Forest In Peninsular Malaysia
Fig. 3. Identification of tiger individuals from infra-red sensor camera traps. Example of individual identification of tiger cubs (a, b) and adults (c, d) based on stripe patterns.
Figure 1 in Discovery of a Nearctic vicariant bumblebee (Hymenoptera: Apidae) in Eurasia uncovers secondary trans-Beringian exchanges of insect faunas
Figure 1. Distribution map of Bombus kirbiellus Curtis 1835. The red circles indicate new samples from Asia (this study; N = 3); the blue circles indicate published records from North America (GBIF Secretariat 2023; GBIF Occurrence Download: https://doi.org/10.15468/dl.ck3xk3; N = 185). Map: Mikhail Y. Gofarov.
Figure 2 in Discovery of a Nearctic vicariant bumblebee (Hymenoptera: Apidae) in Eurasia uncovers secondary trans-Beringian exchanges of insect faunas
Figure 2. Morphology of Bombus kirbiellus Curtis 1835 from north-eastern Asia (Chukotka Peninsula). (A) Lateral view of a female. (B) Hind view of a female. (C) Lateral view of a male. (D) Hind view of a male. (E) Male genitalia. Scale bars = 5 mm (A, B, C, D) and 2 mm (E). (Photos: Grigory S. Potapov).
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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)
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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.