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Reptiles

Reptiles are a diverse group of cold-blooded vertebrates that include snakes, lizards, turtles, crocodiles, and alligators.
These fascinating creatures have adapted to a wide range of habitats, from deserts to rainforests, and play crucial roles in their ecosystems.
Reptile research is essential for understanding their biology, behavior, and conservation needs.
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Most cited protocols related to «Reptiles»

Our initial squamate classification is based on the June 2009 version of the Reptile Database [1 ] (http://www.reptile-database.org/), accessed in September of 2009 when this research was begun. Minor modifications to this scheme were made, primarily to update changes in colubroid snake taxonomy [41 (link)-44 (link),205 ]. This initial taxonomic database consists of 8650 species (169 amphisbaenians, 5270 lizards, 3209 snakes, and 2 tuataras), against which the classification of species in the molecular sequence database was fixed. While modifications and updates (i.e. new species, revisions) have been made to squamate taxonomy subsequently, these are minor and should have no impact on our phylogenetic results. This database represents ~92% of the current estimated diversity of squamates (~9400 species as of December 2012).
Throughout the paper, we refer to the updated version of squamate taxonomy from the December 2012 update of the Reptile Database [1 ], incorporating major, well-accepted changes from recent studies (summarized in [1 ]). However, for large, taxonomic groups that have recently been broken up for reasons other than resolving paraphyly or matters of priority (e.g. in dactyloid and scincid lizards; see Results), we generally retain the older, more inclusive name in the interest of clarity, while providing references to the recent revision. We attempt to alter existing classifications as little as possible (see also [113 (link)]). Therefore, we generally only make changes when there is strong support for non-monophyly of currently recognized taxa and our proposed changes yield strongly supported monophyletic groups. Similarly, we only erect new taxa if they are strongly supported. Finally, although numerous genera are identified as being non-monophyletic in our tree, we refrain from changing genus-level taxonomy, given that our taxon sampling within many genera is limited.
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Publication 2013
Inclusion Bodies Lizards Reptiles Snakes Trees
First, we created a reference database representative of the mitochondrial genomes of all vertebrates, by retrieving from Genbank all the complete mitochondrial genomes of Vertebrates available (accession: September 2007). Subsequently, we randomly selected one sequence per species, to reduce the overrepresentation of a few species (e.g., humans, mouse, zebrafish etc.). We obtained a set of 814 mitochondrial genomes representative of the five major monophyletic clades of vertebrates [Chondrichthyes: 8 species; Actinopterigii: 385 species; Amphibia: 79 species; Sauropsida (= birds + "reptiles"): 133 species; Mammalia: 202 species; other taxa: 7 species]. Most of species were the unique representative of their genus and the database corresponded to 633 genera.
To analyze the performance of each primer pair studied, we first performed an in silico PCR on the reference database and we evaluated the taxonomic coverage of each primer pair as the proportion of amplified taxa. Then, we performed an in silico PCR on the whole GenBank, to evaluate the resolution of the amplified fragments that represents the proportion of unambiguously identified taxa. These properties were evaluated for the whole Vertebrates and for each of the five clades which compose it.
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Publication 2010
Amphibians Aves Genome, Mitochondrial Homo Mammals Mice, House Oligonucleotide Primers Reptiles Vertebrates Zebrafish
Reconstructing time series of fisheries catch for all countries of the world from 1950 (the first year that FAO published its ‘Yearbook' of global fisheries statistics) to 2010 was undertaken by fisheries ‘sectors'. However, because a standardized global definition of fishing sectors based on vessel size does not exist (for example, a vessel considered large-scale (industrial) in a developing country may be considered small-scale (artisanal) in developed countries), reconstructions utilize each country's individual definitions for sectors, or a regional equivalent. These are described in each country reconstruction publication underlying this work. We consider four sectors:

Industrial: large-scale fisheries (using trawlers, purse-seiners, longliners) with high capital input into vessel construction, maintenance and operation, and which may move fishing gear across the seafloor or through the water column using engine power (for example, demersal and pelagic trawlers), irrespective of vessel size. This corresponds to the ‘commercial' sectors of countries such as the USA;

Artisanal: small-scale fisheries whose catch is predominantly sold (hence they are also ‘commercial fisheries'), and which often use a large variety of generally static or stationary (passive) gears. Our definition of artisanal fisheries relies also on adjacency: they are assumed to operate only in domestic waters (that is, in their country's EEZ). Within their EEZ, they are further limited to a coastal area to a maximum of 50 km from the coast or to 200 m depth, whichever comes first. This area is defined as the Inshore Fishing Area (IFA)44 . Note that the definition of an IFA assumes the existence of a small-scale fishery, and thus unpopulated islands, although they may have fisheries in their EEZ (which by our definition are industrial, whatever the gear used), have no IFA;

Subsistence: small-scale non-commercial fisheries whose catch is predominantly consumed by the persons fishing it, and their families (this may also include the ‘take-home' fraction of the catch of commercial fishers, which usually by-passes reporting systems); and

Recreational: small-scale non-commercial fisheries whose major purpose is enjoyment.

In addition to the reconstructions by sector, we also assign catches to either ‘landings' (that is, retained and landed catch) or ‘discards' (that is, discarded catch), and label all catches as either ‘reported' or ‘unreported' with regards to national and FAO data. Thus, reconstructions present ‘catch' as the sum of ‘landings' plus ‘discards'.
Discarded fish and invertebrates are generally assumed to be dead, except for the US fisheries where the fraction of fish and invertebrates reported to survive is generally available on a per species basis45 . Due to a distinct lack of global coverage of information, we do not account for so-called under-water discards, or net-mortality of fishing gears46 . We also do not address mortality caused by ghost-fishing of abandoned or lost fishing gear47 .
For commercially caught jellyfishes (particularly Rhizostomeae, but also other taxa), it has been shown that over 2.5 time more are caught than reported to FAO (mostly as ‘Rhizostoma spp.')48 . This factor is used to estimate missing catches of unidentified jellyfish. However, this additional catch is, pending further study, not allocated to any specific country or FAO area, and is thus counted only in the world's total catch.
We exclude from consideration all catches of marine mammals, reptiles, corals, sponges and marine plants (the bulk of the plant material is not primarily used for human consumption, but for cosmetic or pharmaceutical use). In addition, we do not estimate catches made for the aquarium trade, which can be substantial in some areas in terms of number of individuals, but relatively small in overall tonnage, as most aquarium fish are small or juvenile specimens49 (link).
Most catch reconstructions consist of six steps15 :
(1) Identification, sourcing and comparison of baseline catch times series, that is, (a) FAO reported landings data by FAO statistical areas, taxon and year; and (b) national or regional data series by area, taxon and year. Implicit in this first step is that the spatial entity be identified and named that is to be reported on (for example, EEZ of Germany in the Baltic Sea), something that is not always obvious, and which poses problems to some of our external collaborators, notably those in countries with a claimed EEZ overlapping with that of their neighbour.
For most countries, the baseline data are the statistics reported by member countries to FAO. We treat all countries recognized in 2010 (or acting like independent countries with regards to fisheries) by the international community as having existed from 1950 to 2010. This is necessary, given our emphasis on ‘places', that is, on time-series of catches taken from specific ecosystems. This also applies to islands and other territories, many of which were colonies, and which have changed status and borders since 1950.
For several countries, the baseline data are provided by international bodies. In the case of EU countries, the baseline data originate from the International Council for the Exploration of the Sea (ICES), which maintains fisheries statistics by smaller statistical areas, as required given the Common Fisheries Policy of the EU. A similar area is the Antarctic waters and surrounding islands, whose fisheries are managed by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR), where catch data are available by relatively small statistical areas50 .
When FAO data are used, care is taken to maintain their assignment to different FAO statistical areas for each country (Supplementary Fig. 1), as they often distinguish between strongly different ecosystems. For example, the Caribbean Sea versus the coast of the Eastern Central Pacific in the case of Panama, Costa Rica, Nicaragua, Honduras and Guatemala. For each maritime country, the area covered extends from the coastline to the edge of the EEZ, including any major coastal lagoons connected to the sea, and the mouths of rivers, that is, estuaries. However, freshwaters are excluded.
(2) Identification of sectors (for example, subsistence, recreational), time periods, species, gears and so on, not covered by (1), that is, missing data components. This is conducted via literature searches and consultations with local experts. This step is one where the contribution of local co-authors and experts is crucial. Potentially, all four sectors defined by us can occur in the marine fisheries of a given coastal country, with the distinction between large-scale and small-scale being the most important25 (link). For any entity, we check whether catches originating from the four sectors were included in the reported baseline of catch data, notably by examining their taxonomic composition, and any metadata, which were particularly detailed in the early decades of the FAO ‘Yearbooks'51 .
The absence of a taxon known to be caught in a country or territory from the baseline data (for example, cockles gleaned by women on the shore of an estuary)26 can also be used to identify a fishery that has been overlooked in the official data collection scheme, as can the absence of reef fishes in the coastal data of a Pacific Island state10 . To avoid double counting, tuna and other large pelagic fishes, unless known to be caught by a local small-scale fishery (and thus in the past not likely reported to a Regional Fisheries Management Organization or RFMO), are not included in this reconstruction step (see below under ‘High Seas and other catches of large pelagic fishes').
Finally, if gears are identified in national data, but a gear known to exist in a given country is not included, then it can be assumed that its catch has been missed, as documented for weirs (hadrah) in the Persian Gulf52 .
(3) Sourcing of alternative information sources on missing sectors identified in (2), via literature searches (peer-reviewed and grey) and consultations with local experts. Information sources include social science studies (anthropology, economics and so on), reports, data sets and expert knowledge. The major initial source of information for catch reconstructions is governments' websites and publications (specifically their Department of Fisheries or equivalent agency), both online and in hard copies. Contrary to what could be expected, it is often not the agency responsible for fisheries research and initial data collection that supplies the catch statistics to FAO, but other agencies, for example, statistical office or agency. As a result, much of the granularity of the original data (that is, catch by sector, by species or by gear) may be lost even before data are prepared for submission to FAO. Furthermore, the data request form sent by FAO each year to each country does not encourage improvements or changes in taxonomic composition, as the form that requests the most recent year's data contains the country's previous years' data in the same composition as submitted in earlier years. This encourages the pooling of detailed data at the national level into the taxonomic categories inherited through earlier (often decades old) FAO reporting schemes, as was discovered, for example, for Bermuda in the early 2000s (ref. 53 ). Thus, by getting back to the original data, much of the original granularity can be regained during reconstructions.
Additional sources of information on national catches are international organizations such as FAO, ICES or SPC (Secretariat of the Pacific Community), or a Regional Fisheries Management Organization (RFMO) such as NAFO (Northwest Atlantic Fisheries Organization), or CCAMLR54 , or current or past regional fisheries development and/or management projects (many of them launched and supported by FAO), such as the Bay of Bengal Large Marine Ecosystem project (BOBLME). All these organizations and projects issue reports and publications describing—sometimes in considerable details—the fisheries of their member countries. Another source of information is the academic literature, now widely accessible through Google Scholar.
A good source of information for the earlier decades (especially the 1950s and 1960s) for countries that were part of former colonial empires (especially British or French) are the colonial archives in London (British Colonial Office) and the ‘Archives Nationales d'Outre-Mer', in Aix-en-Provence, and the publications of ORSTOM (Office de la recherche scientifique et technique d'outre-mer), for former French colonies. A further source of information and data are non-fisheries sources, including household and/or nutritional surveys, which are occasionally used for estimating unreported subsistence catches. Our global network of local collaborators is also crucial in this respect, as they have access to key data sets, publications and local knowledge not available elsewhere, often in languages other than English.
Supplementary Figure 2 shows a plot of the publications used for slightly over 110 reconstructions against their date of publication. Although, recent publications predominate, older publications firmly anchor the 1950s catch estimates of many reconstructions. On average, around 35 unique publications were used per reconstruction (not counting online sources and personal communications).
Potential language bias is taken seriously in the Sea Around Us, to ensure that data are collated in languages other than English. Besides team members who read Chinese, others speak Arabic, Danish, Filipino/Tagalog, French, German, Hindi, Japanese, Portuguese, Russian, Spanish, Swedish and Turkish. To deal with other languages, research assistants are hired who speak, for example, Korean or Malay/Indonesian. We also rely on our multilingual network of colleagues and friends throughout the world, for example, for Greek or Thai. While it is true that English has now become the undisputed language of science55 , other languages are used by billions of people, and assembling knowledge about the fisheries of the world is not possible without the capacity to explore the literature in languages other than English.
(4) Development of data ‘anchor points' in time for each missing data item, and expansion of anchor point data to country-wide catch estimates. ‘Anchor' points are catch estimates usually pertaining to a single year and sector, and often to an area not exactly matching the limits of the EEZ or IFA in question. Thus, an anchor point pertaining to a fraction of the coastline of a given country may need to be expanded to the country as a whole. For expansion, we use fisher or population density, or relative IFA or shelf area as raising factor, as appropriate given the local condition. In all cases, we consider that case studies underlying or providing the anchor point data may had a case-selection bias (for example, representing an exceptionally good area or community for study, compared with other areas in the same country), and thus use raising factors very conservatively.
(5) Interpolation for time periods between data anchor points, either linearly or assumption based for commercial fisheries, and generally via per capita (or per fisher) catch rates for non-commercial sectors. Fisheries are often difficult to govern, as they are social activities involving multiple actors. In particular, fishing effort is often difficult to reduce, at least in the short term. Thus, if anchor points are available for years separated by multi-year intervals, it usually will be more reasonable to assume that the underlying fishing activity continues in the intervening years with no data. We tread this ‘continuity' assumption as a default proposition. Exceptions to such continuity assumptions are major environmental impacts such a hurricanes or tsunamis56 , or major socio-political disturbances, such as military conflicts or civil wars57 , which we explicitly consider with regards to the use of raising factors and the structure of time series estimates. In such cases, our reconstructions mark the event through a temporary change (for example, decline) in the catch time series, which is documented in the text of each catch reconstruction. At the very least, this provides pointers for future research on the relationship between fishery catches and natural catastrophes or conflicts. We note that the absence of such signals (such as a reduction in catch for a year or two) in the officially reported catch statistics for countries having experienced a major natural or socio-political disturbance can be a sign that their official catch data may not accurately reflect what occurs on the ground. This contributes to the emergence of ‘poor numbers'40 . Overall, our reconstructions assume—when no information to the contrary is available—that commercial catches (that is, industrial and artisanal) can be linearly interpolated between anchor points, while non-commercial catches (that is, subsistence and recreational) can generally be interpolated between anchor points using non-linear trends in human population numbers or number of fishers over time (via per capita rates).
Radical and rapid effort reductions as a result of an intentional policy decision and implementation do not occur widely. One example we are aware of is the trawl ban of 1980 in Western Indonesia58 . The ban had little or no impact on official Indonesian fisheries statistics for Western Indonesia, another indication that these statistics may have little to do with the realities on the ground. FAO hints at this being widespread in the Western Central Pacific and the Eastern Indian Ocean (the only FAO areas where reported catches appear to be increasing) when they note that ‘while some countries (i.e., the Russian Federation, India and Malaysia) have reported decreases in some years, marine catches submitted to FAO by Myanmar, Vietnam, Indonesia and China show continuous growth, i.e., in some cases resulting in an astonishing decadal increase (e.g., Myanmar up 121 percent, and Vietnam up 47 percent)'.42
(6) Estimation of total catch times series. A reconstruction is completed when the estimated catch time series derived through steps 2–5 are combined and harmonized with the reported catch of step 1. Generally, this results in an increase of the overall catch, but several cases exist where the reconstructed total catch is lower than the reported catch. The best documented case of this is that of mainland China14 (link), whose over-reported catches for local waters in the Northwest Pacific are compensated for by under-reported catches taken by Chinese distant water fleets fishing elsewhere. In the 2000s, Chinese distant water fleets operated in the EEZs of over 90 countries, that is, in most parts of the world's oceans5 . Harmonizing reconstructed catches with the reported baselines goes hand-in-hand with documenting the entire reconstruction procedure. Thus, every reconstruction is documented and published, either in the peer-reviewed scientific literature, or as detailed technical reports in the publicly accessible and indexed Fisheries Centre Research Reports series or the Fisheries Centre Working Paper series, or other regional organization reports (Supplementary Table 5).
Several reconstructions were conducted in the mid- to late 2000s, when official reported data (that is, FAO statistics or national data) were not available to 2010 (refs 15 , 59 ). All these cases are updated to 2010, in line with each country's individual reconstruction approach to estimating missing catch data. Thus, all reconstructions are brought to 2010 to ensure identical time coverage (Supplementary Table 5).
Since these six points were originally proposed, a seventh point has come to the fore that cannot be ignored10 :
(7) Quantifying the uncertainty associated with each reconstruction. In fisheries research, catch data are rarely associated with a measure of uncertainty, at least not in the form resembling confidence intervals. This may reflect the fact that the issue with catch data is not a lack of precision (that is, whether we could expect to produce similar results upon re-estimation), but about accuracy, that is, attempting to eliminate a systematic bias, a type of error which statistical theory does not really address.
We deal with this issue through a procedure related to ‘pedigrees'60 and the approach used by the Intergovernmental Panel on Climate Change to quantify the uncertainty in its assessments61 . The authors of the reconstructions are asked to attribute a ‘score' expressing their evaluation of the quality of the time series data to each fisheries sector (industrial, artisanal and so on) for each of the three time periods (1950–1969, 1970–1989 and 1990–2010). These ‘scores' are (1) ‘very low', (2) ‘low', (3) ‘high' and (4) ‘very high' (Table 1). There is a deliberate absence of an uninformative ‘medium' score, to avoid the effective ‘non-choice' that this option would represent. Each of these scores is assigned a percentage uncertainty range (Table 1). Thereafter, the overall mean weighted percentage uncertainty (over all countries and sectors) was computed (Fig. 1).
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Publication 2016
BaseLine dental cement Blood Vessel Cardiidae Caribbean People Chinese Climate Change Coral Cytoplasmic Granules Dietary Fiber Ecosystem Estuaries Fishes Friend Head Hispanic or Latino Homo sapiens Households Human Body Hurricanes Impacts, Environmental Invertebrates Japanese Koreans M-200 Mammals Marines Military Personnel Oral Cavity Pharmaceutical Preparations Plants Pleasure Porifera Reconstructive Surgical Procedures Red Cell Ghost Reptiles Rivers SELL protein, human Thai Tuna Woman
In order to test the hypotheses outlined in the introduction, we amassed an extensive dataset of limb bone measurements of 200 mammal and 47 non-avian reptile species from individuals that were weighed on a scale either prior to death or skeletonization; no extant body masses were estimated. For the most part, the dataset was built with newly measured specimens; however, it was augmented with published measurements from Christiansen and Harris [109 (link)] and Anderson et al. [73 ] [See Additional file 1, Dataset]. Measurements were taken from stylopodial elements, including maximum lengths and minimum circumference. Length measurements less than 150 mm were taken with digital callipers, longer dial callipers were used for measurements between 150 to 300 mm, and fiberglass measuring tape for those greater than 300 mm. Following the Anderson method, we use minimum circumference (thinnest region along the diaphysis) as a proxy for limb robusticity. In addition to reproducing the analysis presented by Anderson et al. [73 ], minimum circumference should provide a proxy of the minimum cross-sectional area of the bone and therefore be related to the overall compressive strength of the limb. Cross-sectional area was not used due to the cost of collecting this data. Moreover, circumference can be more easily measured on both extant and fossil samples, providing a larger extant dataset and a more inclusive framework for future predictive studies. Circumference measurements were taken with thin paper measuring tapes of different widths, depending on the size of the specimen being measured. All measurements were taken from both sides of the specimen, where possible, and averaged. Specimens measured are of adult body size. For most of the mammalian sample, the ontogenetic status of the specimen was determined based on the level of epiphyseal fusion. For the non-avian reptile sample, as well as some of the largest mammals, maturity was established by verifying that the body mass of the measured specimen is similar to published reports of average body masses for that species (for example, [84 (link),110 -112 (link)]). In general, only a single specimen of each species could be obtained; however, in instances where more than one adult individual was available, the largest individual was used in this study. In these cases, none of the exemplars used seem unusually large compared to the reported adult body mass in that species. Finally, this study compares taxa with different growth strategies (mammals have determinate growth whereas growth in reptiles is generally considered indeterminate, but asymptotic [113 ]) that may result in differences in size structuring within and between populations of taxa with these different strategies. If, and/or how, these differences affect limb to body mass scaling analyses is unknown at this time. However, the masses of the reptiles used here fall within the range of what is considered typical for an adult of each species, and, given our large sample and the nature of our results (see below), we expect that these effects will be minimal, yet may warrant future consideration.
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Publication 2012
Adult allobarbital Aves Body Size Bones Diaphyses Epiphyses Fingers Human Body Inclusion Bodies Mammals Population Group Reptiles
All datasets are global in scale, in raster (grid) format, and projected into Albers Equal Area projection at a one km2 resolution. We used ArcGIS 9.1 to harmonize projections, cell size, and extent and used Python 2.4 in order to remove all marine areas and to create individual text files for each variable for each country. All further analyses were done in R 2.7.1 [40] .
The analytical framework we used was a general linear model (package “glm” in R 2.7.1) with a probit link because the regressions we run here involve binary outcomes. In the first set of regressions (for Table 1a), we are explaining whether or not a location is in the nation's protected area network. In the second set of regressions (for Table 1b), we are examining only the locations that are in the network and explaining whether or not a location is in a protected area that is accorded higher status. One important point is that the coefficients for a given variable not only need not be the same in those regressions and could even be different in sign (for instance, protection may be biased towards steep slopes but, within the protected network, higher status could be biased towards flat areas). Thus Table 1a and 1B do not have similar results by construction. These probabilities were generated using the “predict” command, along with the coefficients from the original regression models, in the “stats” package in R 2.7.1.
Information on PA location came from the 2007 World Database on Protected Areas (WDPA) [41] . Only PAs classified by the International Union for Conservation of Nature (IUCN) in categories 1 through 6, and only countries with PA networks of 100 km2 or more, were included. When two PAs overlapped, we assigned that area the highest IUCN classification of the two. Due to high potential error rates [3] , PAs without polygon boundaries (i.e., point representation only) were not included.
For comparisons across different management categories, we included only countries with 100 km2 or more of categories I – II and 100 km2 or more of categories III – VI. To analyze protection over time (Figure 1), we used information included in the WDPA on date of PA creation. When analyzing over time, PAs with no date were included in each temporal step, distributing the error uniformly over the analysis.
We obtained elevation data from the Shuttle Radar Topography Mission (SRTM) [18] . The source for this data set was the Global Land Cover Facility (www.landcover.org). The SRTM gathered elevation data on a near-global scale, generating a very complete high-resolution elevation database. We calculated slope values from the SRTM elevation dataset. All slope values are degrees from horizontal. Distance to roads was calculated from a vector road network extracted from the VMAP Level0 dataset [19] . While the quality of this data is variable it is the only freely available global road dataset to characterize the global road network. Distance to urban areas was calculated using the Gridded Rural Urban Population dataset (GRUMP) [20] , which provides a gridded and global extent of urban population. Agricultural suitability was taken from a dataset provided by the International Institute for Applied Systems Analysis [21] . The dataset (plate 28) incorporates climate, soil type, land cover, and slope of terrain to measure agricultural suitability, ranking each grid cell from 0 (no constraints) to 9 (severe constraints). We used the World Wide Fund for Nature (WWF) Ecoregions product to determine ecoregion type [23] . The Ecoregions product delineates 8 biogeographic realms, 14 biomes, and 867 ecoregions. Included in the WWF Ecoregions project are data on terrestrial vertebrate species richness. These data encompass more than 26,000 terrestrial vertebrates (amphibians, reptiles, birds, and mammals) and were gathered from literature, expert opinion, and online datasets. Richness is at the resolution of ecoregion.
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Publication 2009
Amphibians Aves Biome Climate Cloning Vectors cytokine receptor, GLM-R Grid Cells Mammals Marines Python Reptiles Rural Population STEEP1 protein, human Urban Population Vertebrates

Most recents protocols related to «Reptiles»

Natural antibody-mediated haemagglutination and complement-mediated haemolysis (NAbs, Lysis) ability are reported to be the first line of defense against pathogens in vertebrates (reviewed in [94 (link)]), and these measures of innate immune function have been studied in many reptile species (e.g., [4 (link), 5 (link), 32 , 80 , 82 (link), 95 (link)]). We completed the assays for NAbs and lysis ability according to the haemolysis–haemagglutination assay adapted from [96 (link)] for use in painted turtles [5 (link), 97 (link)]) using rabbit red blood cells. We used two bottles of rabbit red blood cells (HemoStat HemoStat Laboratories, Dixon, CA, USA) to complete all assays. We ran all plates with positive and negative controls and samples in duplicate. Higher titres for haemagglutination indicate greater abundance of natural antibodies in the plasma sample, as high titres are an indication that natural antibodies are at high concentrations even in increasingly diluted plasma [96 (link)]. Similarly, higher titres for haemolysis indicate the plasma is able to lyse RRBCs even at more dilute concentrations [96 (link)]. Thus, increased natural antibody levels and lysis ability are associated with increased immune function. We assessed bactericidal competence (BC) of plasma by quantifying its ability to inhibit growth of Escherichia coli using our published protocol for painted turtles [5 (link), 97 (link)], adapted from [98 ]. Five lyophilized pellets of E. coli (Microbiologics, ATCC#8739) were used in the present experiment, with each new pellet used to generate a new control solution as we progressed through samples. Increased bactericidal competence corresponds to increased immune function. All immune assays were conducted in spring of 2019 on samples collected in spring of 2018.
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Publication 2023
Antibodies Biological Assay Cardiac Arrest Erythrocytes Escherichia coli Hemolysis Hemostatics Immune System Processes Immunoglobulins pathogenesis Pellets, Drug Plasma Rabbits Reptiles Test, Hemagglutination Turtle Vertebrates
The phylogeny was built based on two sets of UCEs: 5,472 baits for 5,060 UCEs in tetrapods57 (link) and 2,628 baits for 1,314 UCEs in acanthomorphs69 (link). We used the Phyluce software70 (link) to locate the probes in the reference genomes of our 68 species with 6 additional species contained in our original dataset. We extracted a flanking region of ±1,000 bp for each probe and aligned them with Mafft aligner version 7.470 (ref. 71 (link)). We then created a 75% completion matrix, that is, each alignment contains at least 75% of the taxa (55 species), resulting in 63 alignments from the acanthomorph set and 2,742 probes from the tetrapod set (all alignments are available on Figshare). A phylogenetic tree was built using IQ-TREE version 2.0.3 (ref. 72 (link)), with the appropriate substitution model inferred for each of the 2,805 alignments, a maximum likelihood tree search and 1,000 bootstrap replicates. To validate our tree, we also estimated a second tree based on a MultiZ alignment to the human genome and obtained similar results (Extended Data Fig. 9). The phylogenetic tree was calibrated to absolute time using the chronos function of the ‘ape’ package in R, with a smoothing parameter lambda of 0 and a ‘relaxed’ model73 (link),74 (link). Fourteen nodes were calibrated following previously published calibrations. The robustness of the tree was assessed by removing each node independently (see Extended Data Fig. 3).

Actinopterygii/Sarcopterygii: divergence time 416 million years ago (Ma), upper bound 425.4 Ma75 (link)

The first node in the Actinopterygii group: divergence time 378.2 Ma76 (link)

Sauropsida (birds and reptiles)/Synapsida (mammals): divergence time 313.4 Ma77 (link)

Archosauria (birds)/Testudines: divergence time 260 Ma78 (link)

The basal nodes of the Lepidosauria: divergence time 222.8 Ma79 (link)

First mammalian node, Eutheria/Metatheria: divergence time 160.7 Ma75 (link)

Galloanserae/Neoaves: divergence time 66 Ma77 (link)

Glire/Primates: divergence time 61.7 Ma77 (link)

Basal gekkotan node: divergence time 54 Ma80 (link)

Passeriformes/Psittaciformes: divergence time 51.81 Ma81 (link)

Cynoglossidae/Paralichthyidae: divergence time 50 Ma76 (link)

Sus scrofa/other Cetartiodactyla: divergence time 48.5 Ma77 (link)

Canidae/Arctoidea: divergence time 37.1 Ma75 (link)

Hominoidea/Cercopithecoidea: divergence time 23.5 Ma77 (link)

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Publication 2023
Artiodactyla Aves Canidae Eutheria Genome Genome, Human Mammals Marsupialia Passeriformes Primates Psittacines Reptiles Sus scrofa Trees
We used pairwise sequentially Markovian coalescent (PSMC) models to estimate the effective population size of each species84 (link). Fastq sequences were obtained using bam format aligned sequences of one randomly selected father per species and were converted into fastq format using samtools mpileup command and vcf2fq. As recommended, the minimum depth was set to one-third of the average for the sample and twice the average for the maximum. For mammals, fish and reptiles, the parameters of the PSMC were set to –N25 for the maximum number of iterations of the algorithm, –t15 as the upper limit for the time to the most recent common ancestor, –r5 for the initial θ/ρ value, and finally the atomic intervals –p of ‘4 + 25 × 2 + 4 + 6’. These parameters were used previously for PSMC analysis of various species, including primates84 (link),85 (link), cetaceans86 (link), Felidae87 (link), fishes88 (link),89 (link) and turtles90 (link). For birds, we used different parameters according to the literature with –N30 –t5 –r5 (ref. 91 (link)). Finally, to simulate the history inferred by PSMC, we parameterized the generation time and the mutation rate inferred from the UCE alignment. We then explored the effect of the harmonic mean Ne over windows of 30,000 years to 1,000,000 years. We also compared Ne estimated obtained with this method with those estimated based on Ne = π/4μ. Nucleotide diversity (π) was calculated using ANGSD92 (link). This approach was implemented in three consecutive steps. From the alignment files, a global estimate of the site frequency spectrum was inferred using a maximum likelihood method, then the empirical π value was estimated per site, and finally, a sliding window approach was used to estimate π for each species. We used a window size of 50 kb and a step size of 10 kb together with an average pairwise estimation of the π to obtain global estimates of π. This analysis was restricted to unrelated individuals from each species, which corresponded to the 2 unrelated parents for 55 species, between 3 and 7 individuals for 10 species, and 3 species were excluded from this analysis as the parents were first-degree relatives.
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Publication 2023
Aves Fishes Mammals Nucleotides Parent Reptiles
To analyse the spectrum of mutation, we grouped the trios into higher taxonomic levels, that is, mammals, birds, fishes and reptiles. Thus, the percentages reported are based on the total candidate mutations from each group of species. We explored the genomic context of the mutations from a C or a G base to determine whether they were located in CpG sites (respectively followed by a G or preceded by a C) (see Supplementary Table 4). We phased the DNMs to their parental origin using the read-backed phasing method described previously (GitHub: https://github.com/besenbacher/POOHA)82 (link). This method uses the read-pairs containing a DNM and another heterozygous variant to determine the parental origin of the mutation when the heterozygous variant is present in both the offspring and one of the parents. The phasing allowed us to identify parental biases in the contribution of the DNMs by grouping multiple species to increase the number of phased mutations and obtain a minimum of 30 phased mutations per taxon. From this analysis, we omitted the Egyptian roussette (Rousettus aegyptiacus), Chinese tree shrew (Tupaia belangeri), griffon vulture (Gyps fulvus), blue-throated macaw (Ara glaucogularis), snowy owl (Bubo scandiacus) and Darwin’s rhea (Rhea pennata), as these could not be grouped with another monophyletic clade. To quantify the effect of parental age, a linear regression between the per-generation mutation rate and the average parental age at the time of reproduction was implemented using the lm function in R. Multiple linear regression was also used to identify whether paternal or maternal age was the strongest predictor of the empirical mutation rate.
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Publication 2023
Aves Chinese Fishes Heterozygote Mammals Mutation Parent Reproduction Reptiles Rhea Rousettus Snow TRIO protein, human Tupaia Tupaiidae
We used datasets encompassing open chromatin (ATAC-seq) and active enhancers (H3K27ac ChIP-seq) experimental datasets of H9-hESC (Day 0), MGE-like progenitors (Day 26), and inhibitory-like interneurons (Day 39). We used Model-based Analysis of ChIP-Seq (MACS) (Feng et al., 2012 (link)) for peak calling to identify open chromatin and H3K27ac-enriched genomic regions based on raw sequencing files (GEO; accession number GSE218668). Then, we identified active and non-active regions using REPTILE which locates enhancers based on genome-wide DNA methylation and histone modification profiling (He et al., 2017 (link)). As methylation data was not available in our study, we only used the H3K27ac epigenetic mark, which is associated with active enhancers. We trained REPTILE on ChIP-seq experiments conducted in mouse embryonic stem cells, which were provided as example files with the REPTILE software package. This training data included a H3K27ac ChIP-seq dataset in bigwig format, and a ground truth file with annotations of active and non-active enhancers. We trained REPTILE to identify active enhancers in open chromatin regions based on the H3K27ac mark alone (Figure 2A). The output of REPTILE is a set of predicted active enhancers among the input open chromatin regions. From the regions defined by REPTILE, we further extracted the putative active enhancers that overlap an H3K27ac ChIP-seq peak, and putative non-active (poised/repressed) enhancers that do not overlap any H3K27ac ChIP-seq peak (Figure 2A). To extract the sequences corresponding to the genomic coordinates, we used BEDTools (Quinlan and Hall, 2010 (link)), an efficient tool to analyze and process large genomic datasets. Since deep neural networks require fixed-size samples, we set all sequence lengths to be the length of the shortest sample size in the set. For length N, we selected N/2 nucleotides upstream and downstream of the center of each peak (Figure 2A). We set the sample size of the dataset to the shortest sample size, which was 500 nt for Day 26 and Day 39 and 101 nt for Day 0.
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Publication 2023
ATAC-Seq Chromatin Chromatin Immunoprecipitation Sequencing DNA Methylation Genome Histones Human Embryonic Stem Cells Interneurons Methylation Mouse Embryonic Stem Cells Nucleotides Psychological Inhibition Reptiles

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More about "Reptiles"

Reptiles are a diverse group of cold-blooded vertebrates that include snakes, lizards, turtles, crocodiles, and alligators.
These fascinating creatures, also known as sauropsids, have adapted to a wide range of habitats, from arid deserts to lush rainforests, and play crucial roles in their ecosystems.
Reptile research is essential for understanding their intricate biology, fascinating behavior, and pressing conservation needs.
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Whether you're a seasoned herpetologist or a budding researcher, exploring the captivating realm of reptiles can be a truly rewarding and enlightening experience.
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