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14 results for “CLC”
CLC
<p>The CORINE Land Cover (CLC) is an inventory of European land cover. It provides a standardized, harmonized, and seamless coverage of land cover information for the entire European continent using 44 classes, which include artificial surfaces, forests, wetlands, water bodies, agricultural areas, and natural and semi-natural vegetation. This v2 includes metadata</p>
CLC
<p>The CORINE Land Cover (CLC) is an inventory of European land cover. It provides a standardized, harmonized, and seamless coverage of land cover information for the entire European continent using 44 classes, which include artificial surfaces, forests, wetlands, water bodies, agricultural areas, and natural and semi-natural vegetation.</p>
Fig. 24. Brembodus ridens Tintori, 1980. Sample CLC J-420. Small scales forming a in Osteology and relationships of Acrorhinichthys poyatoi gen. et sp. nov. (Pycnodontiformes) from the marine Late Cretaceous of Lebanon
Fig. 24. Brembodus ridens Tintori, 1980. Sample CLC J-420. Small scales forming a mosaic surrounding the cloaca.
Fig. 23. Brembodus ridens Tintori, 1980. Sample CLC J-420. A in Osteology and relationships of Acrorhinichthys poyatoi gen. et sp. nov. (Pycnodontiformes) from the marine Late Cretaceous of Lebanon
Fig. 23. Brembodus ridens Tintori, 1980. Sample CLC J-420. A. Scale of the dorsal region. B. Scale of the ventral region.
UrbanOccupationsOETR_temettuat_cultivation_CLC_6_region_rural_geosample
<p>With the UrbanOccupationsOETR, a European Research Councill, Starting Grant funded (Grant Number 679097, Industrialisation and Urban Growth from the mid-nineteenth century Ottoman Empire to Contemporary Turkey in a Comparative Perspective, 1850-2000, <a href="https://urbanoccupations.ku.edu.tr/">UrbanOccupationsOETR</a>) project hosted at Koç University 2016-2022, we wanted to highlight the importance of rural economic dynamics to explain differences in long-term regional economic development in the late Ottoman Empire. We provide an Excel dataset on the crop-specific agricultural mix and land area of an Ottoman region, Bursa, in the 1840s. This dataset is the result of a new geosampling methodology we devised, representing a novel development in the agricultural and overall economic history of Southeast Europe and the Middle East.</p> <p>The 1840s serve as a good period to choose for base years mainly due to three main factors to sample economic data on a regional scale. First, due to <em>Tanzimat</em> reforms (planned and only partially accomplished transformation of the Ottoman central administration in the mid-nineteenth century), the 1840s marked a watershed of bureaucratical information gathering. Especially, the <em>temettuat</em> registers were created as a by-product to realize a drastic change in tax collection. With at least in its first iteration, the unsuccessful abolishment of tax-farming by the Tanzimat decree in 1839, the Ottoman central administration aimed to transform the existing indirect and communal taxation with direct and individual modalities. To accomplish this goal, the administration had to survey the tax base, which was in disguise due to centuries-long tax farming practices. The <em>temettuat </em>registers were conducted in the core regions of the empire with the main exception of the imperial capital, Istanbul. Second, the 1840s correspond to the last period before the beginning of drastic territorial losses, primarily in Southeast Europe, which triggered in size and frequency unprecedented waves of emigration and immigration between the core territories of the empire both in Southeast Europe as well as in Anatolia, which continued until the official demise or the implosion of the empire. Third and lastly, the 1840s serves as a very suitable point to assess the dynamics of pre-industrial and <em>ancienne</em> regime agricultural dynamics due to the lack of modern means of mechanization, irrigation, and fertilization combined with extremely rudimentary transport facilities.</p> <p>The temettuat surveys are invaluable resources for they provide agricultural asset- / crop-type specific agricultural mix information with cultivation area per household. However, extracting their detailed information requires a team and years. To overcome this, we developed a sampling strategy that selected five locations per subdistrict using the Analytical Hierarchy Process (AHP), considering factors of agricultural suitability (85% weight), connectivity to historical roads (within a 500-meter to the closest road or to the Danube, 15% weight, justified by its impact on suitability), and subdistrict population size (chosen villages must represent at least 5% of the subdistrict's total population).</p> <p>Our geosampling methodology of the 1840s tax registers (<em>temettuat</em>) is based on contemporary Ottoman population registers. With this geosampling method, we aim to estimate the regional (district (<em>sancak</em>) and subdistrict (<em>kaza</em>)) level total area of cultivation and shares of the agricultural mix for key products. We are using two mid-nineteenth-century datasets: Ottoman tax (TMT) (<em>temettuat</em>) surveys for crop type and cultivation area and the population (<em>nüfus</em>) (NFS) registers for population-based sampling. Connectivity is based on a detailed and provenly accurate 1940s German military map of Turkey, <em>Deutsche Heereskarte </em>(DHK). The agricultural suitability raster is an amalgamation of the Land Capability Classification (LCC) encapsulating the variables of soil quality and quantity and the Digital Elevation Model (DEM) based on Shuttle Radar Topography Mission with 30-meter-resolution and comprising elevation, slope, and ruggedness data.</p> <p>In the end, a geosampling initiative was undertaken across six regions in Southeast Europe and Anatolia, namely Ankara, Bursa, Plovdiv, Ruse, Manisa, and Edirne, covering a total of 277 locations with 17,675 households. Our project team entered the economic data from those records into a Microsoft Access database. We employed a specially crafted data entry template to organize the tax survey data into multiple categories systematically.</p> <p>After geosampling locations, our objective extended to deriving estimates for the total cultivated area within each subdistrict and region. To achieve this goal, it was imperative that the data undergoes coding the cultivation areas into a standardized and comparable land-use scheme. We adopted the Corine Land Cover (CLC) nomenclature from the European Union's Earth Observation Programme (Copernicus), established in 1985 and regularly updated. Our study followed the revised guidelines issued by the European Environment Agency on 10.05.2019. Despite its primary design for contemporary land cover analysis, CLC nomenclature proved well-suited for accurately representing the agricultural tax data and the historical context of the 1845 Ottoman tax surveys.</p> <p>In our analysis, we coded micro-level cultivated land entries associated with individual households by using CLC's highest detail level. Successfully, every cultivated land entry was coded into the third level of detail in CLC, encompassing sub-categories such as 2.1 – “Arable land”, 2.2 – “Permanent crops”, 2.3 – “Pastures”, and 2.4 – “Heterogeneous agricultural areas”—all falling under the overarching category of 2 - Agricultural areas. Additionally, we coded entries related to 3.1 - “Forest” and 3.2 – “Shrub and/or herbaceous vegetation associations”, falling under the primary category of 3 – “Forest and seminatural areas.”</p> <p>Finally, cultivation areas expressed in Ottoman measurement units like <em>dönüm</em> (1/9,2 of a hectare) were converted into hectares to ensure consistency and ease of spatiotemporal comparison.</p> <p>This Zenodo dataset offers agricultural data for the entire geosampling area per household. It includes 39,002 agricultural entries coded according to CLC, detailing both the quantity and the cultivated area, corresponding to 14,997 individuals across 13,564 households. Please note that this is a rural geosample. Although the tax surveys of the primary (urban) and secondary (subdistrict centers) locations of all regions were read and entered, they are not included in this dataset.</p> <p>The categories and descriptions of the variables of the geosample dataset are as follows:</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p> </p> <p>“GeoCode”</p> </td> <td> <p> </p> <p>UniqueID belonging to a specific geosampled location</p> </td> </tr> <tr> <td> <p> “Longitude” & “Latitude”</p> </td> <td> <p>Geographical coordinates used to specify the precise location of a geosampled location on the Earth's surface</p> </td> </tr> <tr> <td> <p>“Region” & “SubDistrict” & “Location”</p> </td> <td> <p>Geographic unit of entry, including region (district/<em>sancak</em>); subdistrict (<em>kaza</em>); and geosampled location as they appear in the population registers</p> </td> </tr> <tr> <td> <p>“RegisterNo”</p> </td> <td> <p>Archival code of the population register whose data is being entered</p> </td> </tr> <tr> <td> <p>“HaneNo”</p> </td> <td> <p>Number of the household (specified by the registers as <em>Hane</em>), as appears in the register</p> </td> </tr> <tr> <td> <p>"HouseID"</p> </td> <td> <p>Unique ID belonging to a specific household, automatically generated by Microsoft Access</p> </td> </tr> <tr> <td> <p>"IndividualID"</p> </td> <td> <p>Unique ID belonging to a specific individual, automatically generated by Microsoft Access</p> </td> </tr> <tr> <td> <p>"AgrID"</p> </td> <td> <p>UniqueID belonging to a specific agricultural asset / crop belonging to an individual</p> </td> </tr> <tr> <td> <p>"Cultivation"</p> </td> <td> <p>Type of the agricultural asset / crop</p> </td> </tr> <tr> <td> <p>"CLC_Cultivation_Code"</p> </td> <td> <p>CLC-code of the agricultural asset / crop</p> </td> </tr> <tr> <td> <p>"CtgUnit"</p> </td> <td> <p>Is applicable when the quantity of an agricultural asset or crop is specified using specific terms ("aded", "res", "eşcar", "sak" [usually for individual trees]), and when the area of an agricultural asset or crop is described in vague terms ("bab", "kıta" [usually for fields, gardens, and vineyards])</p> </td> </tr> <tr> <td> <p>"Unit"</p> </td> <td> <p>Quantity of the "CategoryUnit"</p> </td> </tr> <tr> <td> <p>"CtgArea"</p> </td> <td> <p>The land area type of the agricultural asset / crop in Ottoman measurement units, "Dönüm" and “Evlek” (1/4 of a “Dönüm”)</p> </td> </tr> <tr> <td> <p>"Area"</p> </td> <td> <p>Quantity of the "CategoryArea"</p> </td> </tr> <tr> <td> <p>“Area_Ottoman_dönüm”</p> </td> <td> <p>"Area" converted into Ottoman “Dönüm”</p> </td> </tr> <tr> <td> <p>“Area_hectare”</p> </td> <td> <p>"Area" converted into hectares</p> </td> </tr> </tbody> </table> <p> </p>
Roles of the ClC chloride channel CLH-1 in food-associated salt chemotaxis behavior of C. elegans
<p>The ability of animals to process dynamic sensory information facilitates foraging in an ever changing environment. However, molecular and neural mechanisms underlying such ability remain elusive. The ClC anion channels/transporters play a pivotal role in cellular ion homeostasis across all phyla. Here we find a ClC chloride channel is involved in salt concentration chemotaxis of <em>C. elegans</em>. Genetic screening identified two altered-function mutations of <em>clh-1</em> that disrupt experience-dependent salt chemotaxis. Using genetically encoded fluorescent sensors, we demonstrate that CLH-1 contributes to regulation of intracellular anion and calcium dynamics of salt-sensing neuron, ASER. The mutant CLH-1 reduced responsiveness of ASER to salt stimuli in terms of both temporal resolution and intensity, which disrupted navigation strategies for approaching preferred salt concentrations. Furthermore, other ClC genes appeared to act redundantly in salt chemotaxis. These findings provide insights into the regulatory mechanism of neuronal responsivity by ClCs that contribute to modulation of navigation behavior.</p>
Complete set of data and script for the Kallisto/Sleuth & CLC analysis comparison
<p>Complete set of files that underlies the final report for the Kallisto/Sleuth and CLC analysis comparison.</p>
Simulation Training of Closed-Loop Communication (CLC)
ClinicalTrials.gov study NCT03600298. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Roles of the ClC chloride channel CLH-1 in food-associated salt chemotaxis behavior of C. elegans
Open the record for dataset details and reuse information.
Fig. 20. Brembodus ridens Tintori, 1980. Sample CLC J-420 in Osteology and relationships of Acrorhinichthys poyatoi gen. et sp. nov. (Pycnodontiformes) from the marine Late Cretaceous of Lebanon
Fig. 20. Brembodus ridens Tintori, 1980. Sample CLC J-420 from Zogno, Zorzino Formation, Upper Norian (Late Triassic), Lombardy, Italy.
Randomized Controlled Trial of Combined Letrozole and Clomid (CLC II) Versus Letrozole Alone for Women With Anovulation
ClinicalTrials.gov study NCT05206448. IPD Sharing: Not stated. Countries: 1. Publications: 0.
β2-adrenergic agonists rescue lysosome acidification and function in PSEN1 deficiency by reversing defective ER to lysosome delivery of ClC-7
GEO Series GSE136894. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
CLC Parent-Child Emotional Preparation Program
ClinicalTrials.gov study NCT04626037. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
CLCNKA (Ka Renal Chloride Channel[ClC-Ka]) Polymorphism Effects on Hypertrophy Regression
ClinicalTrials.gov study NCT01275352. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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