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ES: Mapa georreferenciado de las zonas básicas de salud de ASTURIAS. Año 2017. España. || EN: Georeferenced map of primary care area of reference in ASTURIAS. Year 2017. Spain.
<p> ES::Español<br> Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria.</p> <p>Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN:English<br> Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area.</p> <p>Maps are projected in the geographic coordinates reference system.</p>
ES: Mapa georreferenciado de las zonas básicas de salud de GALICIA. Año 2017. España. || EN: Georeferenced map of primary care area of reference in GALICIA . Year 2017. Spain.
<p>ES: Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria. Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN: Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area. Maps are projected in the geographic coordinates reference system.</p>
ES: Mapa georreferenciado de las zonas básicas de salud de ANDALUCIA. Año 2017. España || EN: Georeferenced map of primary care area of reference in ANDALUCIA. Year 2017. Spain.
<p>ES:Español<br> Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria.</p> <p>Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN:English<br> Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area.</p> <p>Maps are projected in the geographic coordinates reference system.</p>
ES: Mapa georreferenciado de las zonas básicas de salud de PAIS VASCO. Año 2017. España. || EN: Georeferenced map of primary care area of reference in PAIS VASCO. Year 2017. Spain.
<p>ES: Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria. Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN: Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area. Maps are projected in the geographic coordinates reference system.</p>
ES: Mapa georreferenciado de las zonas básicas de salud de EXTREMADURA. Año 2017. España. || EN: Georeferenced map of primary care area of reference in EXTREMADURA . Year 2017. Spain.
<p>ES: Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria. Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN: Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area. Maps are projected in the geographic coordinates reference system.</p>
ES: Mapa georreferenciado de las zonas básicas de salud de NAVARRA. Año 2017. España. || EN: Georeferenced map of primary care area of reference in NAVARRA. Year 2017. Spain.
<p>ES: Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria. Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN: Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area. Maps are projected in the geographic coordinates reference system.</p>
ES: Mapa georreferenciado de las zonas básicas de salud de MURCIA. Año 2017. España. || EN: Georeferenced map of primary care area of reference in MURCIA . Year 2017. Spain.
<p>ES: Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria. Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN: Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area. Maps are projected in the geographic coordinates reference system.</p>
ES: Mapa georreferenciado de las zonas básicas de salud de LA RIOJA. Año 2017. España. || EN: Georeferenced map of primary care area of reference in LA RIOJA. Year 2017. Spain.
<p>ES: Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria. Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN: Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area. Maps are projected in the geographic coordinates reference system.</p> <p> </p>
ES: Mapa georreferenciado de las zonas básicas de salud de VALENCIA . Año 2017. España. || EN: Georeferenced map of primary care area of reference in VALENCIA . Year 2017. Spain.
<p>ES: Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma [CCAA], España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria. Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p>|| EN: Geographical information and maps of the primary care settings of the Autonomous Community [CCAA], Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area. Maps are projected in the geographic coordinates reference system.</p> <p> </p>
Annex B to the technical report on the raw primary commodity (RPC) model - Summary statistics of the output data
<p><strong>The raw primary commodity model</strong>:</p> <p>Dietary exposure is typically calculated by combining food consumption data with occurrence data. EFSA’s food consumption data are stored in the Comprehensive European Food Consumption Database (Comprehensive Database). Some of these data, however, cannot be used in exposure assessments when the occurrence data are reported for the raw primary commodities (RPCs). The RPC model aims to bridge this gap by transforming the Comprehensive Database into RPC consumption data. Using the RPC model, EFSA successfully developed a new RPC Consumption Database, which contains 51 dietary surveys from 23 different countries. These surveys cover a total of 94,532 subjects and 26,573,088 RPC consumption records. The consumption data generated by the RPC model were manually checked and validated by means of case studies. These case studies demonstrated that the RPC consumption data are suitable for assessing dietary exposure to chemicals where the occurrence data are predominantly available for RPCs.</p> <p><strong>Annex B to the technical report on the raw primary commodity model:</strong></p> <p>Annex B is an excel file which presents summary statistics of the output data generated by the RPC model. The following tables are included in Annex B:</p> <p>Table B.1 :Summary statistics of chronic RPC consumption expressed in g/kg bw per day (total population)</p> <p>Table B.2 :Summary statistics of chronic RPC consumption expressed in g/day (total population)</p> <p>Table B.3 :Summary statistics of acute RPC consumption expressed in g/kg bw (consumers only)</p> <p>Table B.4 :Summary statistics of acute RPC consumption expressed in g (consumers only)</p> <p>Table B.5 :Comparison of the RPC consumption data with RPC consumption data used in EFSA's Pesticides Residues Intake Model (PRIMo)</p> <p>Table B.6 :Contribution of processed products to the average chronic RPC consumption</p>
Annex A to the technical report on the raw primary commodity (RPC) model - Input data
<p><strong>The raw primary commodity model</strong>:</p> <p>Dietary exposure is typically calculated by combining food consumption data with occurrence data. EFSA’s food consumption data are stored in the Comprehensive European Food Consumption Database (Comprehensive Database). Some of these data, however, cannot be used in exposure assessments when the occurrence data are reported for the raw primary commodities (RPCs). The RPC model aims to bridge this gap by transforming the Comprehensive Database into RPC consumption data. Using the RPC model, EFSA successfully developed a new RPC Consumption Database, which contains 51 dietary surveys from 23 different countries. These surveys cover a total of 94,532 subjects and 26,573,088 RPC consumption records. The consumption data generated by the RPC model were manually checked and validated by means of case studies. These case studies demonstrated that the RPC consumption data are suitable for assessing dietary exposure to chemicals where the occurrence data are predominantly available for RPCs.</p> <p><strong>Annex A to the technical report on the raw primary commodity model:</strong></p> <p>Annex A is an excel file which presents input data tables used by the RPC model. The annex contains the following tables:</p> <p>Table A.1 (Survey table) - An overview of the food consumption surveys incorporated in the RPC model</p> <p>Table A.2 (FoodEx table) - An outline of the food classification system used in the RPC model (EFSA's FoodEx classification system with additional codes)</p> <p>Table A.3 (Probability table) - Manages foods coded at food group level (example, breakfast cereals)</p> <p>Table A.4 (Disaggregation table) - Disassembles composite foods into their single components (RPC derivatives and/or RPCs)</p> <p>Table A.5 (Conversion table) - Converts amounts of RPC derivatives into corresponding amounts of RPC</p> <p>Table A.6 (Component table) - Overview of the search strings used for the probability analysis of components</p>
Bibliography of Primary Studies
<p>A list of primary papers found in our systematic literature review into research on user feedback classification.techniques and categories. Used as reference for two papers published at the NLP4RE 2019 workshop: "A Taxonomy for User Feedback Classifications" and "An Overview of User Feedback Classification Approaches". Available in the CEUR Workshop Proceedings for the REFSQ'19 Collocated Events.</p>
Data and code: Evaluation of the General Practice Pharmacist (GPP) intervention to optimise prescribing in Irish primary care: a non‐randomised pilot study
<p>This is a dataset and Stata analytical code relating to prescribing issues identified in the GPP pilot feasibility study. The paper reporting this study has been published as follows: </p> <p>Cardwell K, Smith SM, Clyne B on behalf of the General Practice Pharmacist (GPP) Study Group, et al. Evaluation of the General Practice Pharmacist (GPP) intervention to optimise prescribing in Irish primary care: a non-randomised pilot study. BMJ Open 2020;10:e035087. doi: 10.1136/bmjopen-2019-035087</p> <p>The abstract of the study is included below:</p> <p><strong>Objective:</strong> Limited evidence suggests integration of pharmacists into the general practice team could improve medicines management for patients, particularly those with multimorbidity and polypharmacy. This study aimed to develop and assess the feasibility of an intervention involving pharmacists, working within general practices, to optimise prescribing in Ireland.</p> <p><strong>Design:</strong> Non-randomised pilot study</p> <p><strong>Setting:</strong> Primary care in Ireland</p> <p><strong>Participants:</strong> Four general practices, purposively sampled and recruited to reflect a range of practice sizes and demographic profiles.</p> <p><strong>Intervention:</strong> A pharmacist joined the practice team for six months (10 hours/week) and undertook medication reviews (face-to-face or chart-based) for adult patients, provided prescribing advice, supported clinical audits, and facilitated practice-based education.</p> <p><strong>Outcome measures:</strong> Anonymised practice-level medication (e.g. medication changes) and cost data were collected. Patient-Reported Outcome Measure (PROM) data were collected on a subset of older adults (aged ≥65 years) with polypharmacy using patient questionnaires, before and six weeks after medication review by the pharmacist.</p> <p><strong>Results:</strong> Across four practices, 787 patients were identified as having 1,521 prescribing issues by the pharmacists. Issues relating to potentially inappropriate or high-risk prescribing were addressed most often by the prescriber (51.8%), compared to cost-related issues (7.5%). Medication changes made during the study equated to approximately €57,000 in cost savings assuming they persisted for 12 months. Ninety-six patients aged ≥65 years with polypharmacy were recruited from the four practices for PROM data collection and 64 (66.7%) were followed up. There were no changes in patients’ treatment burden or attitudes to deprescribing following medication review, and there were conflicting changes in patients' self-reported quality of life.</p> <p><strong>Conclusions:</strong> This non-randomised pilot study demonstrated that an intervention involving pharmacists, working within general practices is feasible to implement and has potential to improve prescribing quality. This study provides rationale to conduct a randomised controlled trial to evaluate the clinical and cost-effectiveness of this intervention.</p>
Figure 2 in Carbon primary sources and estuarine habitat use by two congeneric ariid catfishes in a subtropical coastal lagoon
Figure 2. Carbon isotope ratios (d13C) and total length (TL, mm) of individuals of Genidens genidens (closed circles) and Genidens barbus (open circles) collected in the interface between the estuarine and freshwater zones of Patos Lagoon in present study. DISCUSSION According to the model of the life cycle suggested by ARAúJO (1988), G. barbus move between freshwater to the estuary during their first year of life. After reaching sexual maturity, adults migrate to the ocean, returning to freshwater to spawn. Our work with stable isotopes corroborates the general movement pattern proposed in this model by providing evidence that the primary producers at the estuary are an important source of carbon for juveniles of G. barbus during the initial phase of their development. There is no current model describing the life cycle of G. genidens at the Patos Lagoon. ARAúJO (1988) mentioned that this species remains in the upper limit of the estuarine zone or in the limnetic portion of the lagoon and that its juveniles are occasionally found in the estuary. Based on fish sampling restricted to the mixohaline zone of the Patos Lagoon, some authors classified this species as estuarine resident (CHAO et al. 1985, ARAúJO 1988). However, VIEIRA et al. (2010) demonstrated that G. genidens occurs from the estuary to the uppermost northern portion of the lagoon, which is located ~180 km from the lagoon's connection with the sea, and can remain year round at freshwater. Our work provides new evidence that this catfish species derives energy from the estuarine and freshwater zones of
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)
Text-fig. 7. Projection of the declinations and inclinations of primary component of the DRM vectors and a mean direction based on Fisher statistics A – samples with normal polarity (down - projection on the lower hemisphere), B – samples with reversed polarity (up - projection on the upper hemisphere). in New Updated Results Of Paleomagnetic Dating Of Cave Deposits Exposed In Za Hájovnou Cave, Javoříčko Karst
Text-fig. 7. Projection of the declinations and inclinations of primary component of the DRM vectors and a mean direction based on Fisher statistics A – samples with normal polarity (down - projection on the lower hemisphere), B – samples with reversed polarity (up - projection on the upper hemisphere).
Figure. Comparison of longest primary feather, tail length, and chest circumference in male and female common snipe (* = p <0.05; **= p <0.01). Table 3. Weight of gut variables in male and female common snipe. in Revision of common snipe, Gallinago gallinago in morphometric analysis and building the standard reference haematological values for further studies
Figure. Comparison of longest primary feather, tail length, and chest circumference in male and female common snipe (* = p <0.05; **= p <0.01). Table 3. Weight of gut variables in male and female common snipe.
FIG. 26. Melanoblossiinae Roewer, 1933 in Cheliceral Morphology in Solifugae (Arachnida): Primary Homology, Terminology, and Character Survey
FIG. 26. Melanoblossiinae Roewer, 1933, Melanoblossia sp., 3 (NMNW 13396), chelicera (A) and setiform flagellar complex (B, C), prolateral views. Abbreviations: FD, fixed finger, distal tooth; FM, fixed finger, medial tooth; FP, fixed finger, proximal tooth; FSM, fixed finger, submedial tooth; MM, movable finger, medial tooth; MP, movable finger, proximal tooth; mpd, movable finger prodorsal setae; mpm, movable finger promedial setae; mpv, movable finger proventral setae; MSM, movable finger, submedial tooth; pic, prolateral interdigital condyle; pvd, proventral distal setae; pvsd, proventral subdistal setae; RFM, retrofondal medial tooth; sfc, setiform flagellar complex; STF, subterminal flange.
FIG. 24. Solpugidae Leach, 1815 in Cheliceral Morphology in Solifugae (Arachnida): Primary Homology, Terminology, and Character Survey
FIG. 24. Solpugidae Leach, 1815, chelicerae and cheliceral fixed fingers, prolateral views (A–D), indicating stridulatory organs (A, D), bulbous base of flagellum, prolateral view (E), and apex of flagellar shaft, prolateral view (F). A, B. Solpugiba lineata (C.L. Koch, 1842), 3 (NMNW 13814), shaft distorted during processing for scanning electron microscopy. Inset: closeup of mucron organ (mo). C. Zeriassa cuneicornis (Purcell, 1899), 3 (NMNW 13883). D, E. Zeria sericea (Pocock, 1897), 3 (NMNW 13800). F. Metasolpuga picta (Kraepelin, 1899), 3, Namibia: Gobabeb. Arrows indicate suture in bulbous base (B, C, E) and shaft (F). Abbreviations: FD, fixed finger, distal tooth; FGP, flagellar groove process; FM, fixed finger, medial tooth; FP, fixed finger, proximal tooth; FSD, fixed finger, subdistal tooth; FSM, fixed finger, submedial teeth; MD, movable finger, distal tooth; MM, movable finger, medial tooth; mo, mucron organ; MP, movable finger, proximal tooth; MSM, movable finger, submedial tooth; pvd, proventral distal setae.
FIG. 13. Solpugidae Leach, 1815 in Cheliceral Morphology in Solifugae (Arachnida): Primary Homology, Terminology, and Character Survey
FIG. 13. Solpugidae Leach, 1815, Solpugiba lineata (C.L. Koch, 1842), 3 (NMNW 13814), cheliceral movable finger, prolateral view, illustrating dorsodistally directed prodorsal (mpd) setae, distally directed promedian (mpm) setae, and ventrodistally directed proventral (mpv) setae (A); closeup of distal setal area illustrating long apical mpd seta (arrow) and modified distal mpv setae (B); and closeup of socket of modified subapical mpv seta with distally directed insertion (C). Additional abbreviations: MD, movable finger, distal tooth; MP, movable finger, proximal tooth; MSD, movable finger, subdistal tooth.
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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)
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.