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81 results for “Rio Grande do Norte”
FIGURE 3 in Gerromorpha (Insecta: Hemiptera: Heteroptera) from the state of Rio Grande do Norte, northeastern Brazil, including a new synonymy
FIGURE 3. (A–B) Distribution records for species of Gerromorpha in state of Rio Grande do Norte, northeastern Brazil.
FIGURE 18 in Gerromorpha (Insecta: Hemiptera: Heteroptera) from the state of Rio Grande do Norte, northeastern Brazil, including a new synonymy
FIGURE 18. (A–B) Distribution records for species of Gerromorpha in state of Rio Grande do Norte, northeastern Brazil.
FIGURE 13 in Gerromorpha (Insecta: Hemiptera: Heteroptera) from the state of Rio Grande do Norte, northeastern Brazil, including a new synonymy
FIGURE 13. Gerromorpha from state of Rio Grande do Norte. (A–F) Hydrometra argentina, (A) dorsal view of head, (B) anterior portion of head in dorsal view, (C) dorsal view of pronotum, (D) lateral view of thorax, (E) lateral and (F) ventral views of male abdomen apex, white arrows indicate pair of basal spinose projections of sternum VII. (G–H) Lipogomphus lacuniferus, (G) dorsal and (H) ventral views of macropterous female. cly = clypeus. Scale bars = 1.00 mm.
FIGURE 17 in Gerromorpha (Insecta: Hemiptera: Heteroptera) from the state of Rio Grande do Norte, northeastern Brazil, including a new synonymy
FIGURE 17. Species of Veliidae from state of Rio Grande do Norte. (A–B) Platyvelia brachialis, (A) dorsal and (B) ventral views of apterous male. (C–E) Stridulivelia tersa, (C) dorsal and (D) ventral views of micropterous male, (E) lateral view of part of abdomen, white arrows indicate transverse lateral sulci. Scale bars = 1.00 mm.
Distribution. NE Brazil, from Ceara and Rio Grande do Norte states, S to C Bahia State. in Echimyidae
Distribution. NE Brazil, from Ceara and Rio Grande do Norte states, S to C Bahia State.
Figure 1 from: Versieux LM, Dávila N, Delgado GC, de Sousa VF, de Moura EO, Filgueiras T, Alves MV, Carvalho E, Piotto D, Forzza RC, Calvente A, Jardim JG (2017) Integrative research identifies 71 new plant species records in the state of Rio Grande do Norte (Brazil) and enhances a small herbarium collection during a funding shortage. PhytoKeys 86: 43-74. https://doi.org/10.3897/phytokeys.86.13775
Figure 1 - Map of Rio Grande do Norte state and municipalities, phytogeographical domains (Atlantic Rainforest and Caatinga), conglomerates points and new botanical records.
Figure 2 from: Costa NKR, Paiva REC, Silva MJ, Ramos TPA, Lima SMQ (2017) Ichthyofauna of Ceará-Mirim River basin, Rio Grande do Norte State, northeastern Brazil. ZooKeys 715: 39-51. https://doi.org/10.3897/zookeys.715.13865
Figure 2 - Subset of the ichthyofauna of the Ceará-Mirim River basin, Rio Grande do Norte State, Brazil. A Leporinus piau B Serrapinnus piaba C Trachelyopterus galeatus D Sciades herzbergii E Hypostomus pusarum F Kryptolebias hermaphroditus G Cichlasoma orientale H Guavina guavina I Gobionellus oceanicus.
Figure 1 from: Costa NKR, Paiva REC, Silva MJ, Ramos TPA, Lima SMQ (2017) Ichthyofauna of Ceará-Mirim River basin, Rio Grande do Norte State, northeastern Brazil. ZooKeys 715: 39-51. https://doi.org/10.3897/zookeys.715.13865
Figure 1 - Map of the Ceará-Mirim River basin in Rio Grande do Norte State, northeastern Brazil, showing the sampling sites. Natal, the largest urban center of the state and location of the Potengi River estuary, is indicated by a black dot. Numbers are in accordance to Table 1.
FIGURE 5 in Previously unknown diversity: the marine sponge (Porifera) fauna from Rio Grande do Norte State, NE Brazil
FIGURE 5. Frequency of sponges at Ponta do Mel beach (Areia Branca, Rio Grande do Norte, Brazil).
FIGURE 4 in Previously unknown diversity: the marine sponge (Porifera) fauna from Rio Grande do Norte State, NE Brazil
FIGURE 4. Frequency of sponges at Baixa Grande beach (Areia Branca, Rio Grande do Norte, Brazil).
FIGURE 1 in New records, conservation assessments and distribution of Lamiaceae in Rio Grande do Norte, northeastern, Brazil
FIGURE 1. Rio Grande do Norte state and its Conservation Units.
FIGURE 6 in New records, conservation assessments and distribution of Lamiaceae in Rio Grande do Norte, northeastern, Brazil
FIGURE 6. Lamiaceae species richness in Rio Grande do Norte State, Brazil.
FIGURE 1 in Flora of Rio Grande do Norte, Brazil: Boraginales
FIGURE 1. Location of the State of Rio Grande do Norte, Brazil.
Urban forest fragments as a living laboratory for teaching botany: an example from Federal University of Rio Grande do Norte, Brazil
Open the record for dataset details and reuse information.
Figure 2 in Host effect on morphology of the fruit fly Anastrepha zenildae (Diptera: Tephritidae) from the Semi-Arid region of Rio Grande do Norte
Figure 2. Female of Anastrepha zenildae showing body measurements used to compare flies between hosts and sexes. HW = Head width; FW = Face width; TH = Thorax length; WL = Wing length; WW = Wing width; OVP = Ovipositor length.
Distribution. NE & E Brazil, from Rio Grande do Norte S to N Minas Gerais, apparently mostly restricted to Caatinga Tropical Dry Forest ecoregion. in Phyllostomidae
Distribution. NE & E Brazil, from Rio Grande do Norte S to N Minas Gerais, apparently mostly restricted to Caatinga Tropical Dry Forest ecoregion.
FIGURE 1 in Hohenbergia densa (Bromeliaceae), a new species from Rio Grande do Norte, Brazil
FIGURE 1. Geographic distribution of Hohenbergia densa (red circles) and the type of vegetations that the new species inhabits.
FIGURE 5 in Gerromorpha (Insecta: Hemiptera: Heteroptera) from the state of Rio Grande do Norte, northeastern Brazil, including a new synonymy
FIGURE 5. Species of Neogerris from state of Rio Grande do Norte. (A–C) Neogerris lubricus, (A) dorsal and (B) ventral views of apterous male, inset showing abdomen apex, (C) dorsal view of micropterous female. (D–L) Neogerris lotus, (D) dorsal, (E) ventral and (F) lateral views of apterous male, (G) dorsal view of macropterous male, (H) dorsal view of apterous female, (I) apex of female abdomen in latera view, (J–L) apex of male abdomen in dorsal view showing variation of tergum VIII. Scale bars = 1.00 mm.
FIGURE 2 in Gerromorpha (Insecta: Hemiptera: Heteroptera) from the state of Rio Grande do Norte, northeastern Brazil, including a new synonymy
FIGURE 2. Species of Limnogonus from state of Rio Grande do Norte. (A–C) Limnogonus aduncus, (A) dorsal, (B) ventral and (C) lateral views of apterous male, white arrow indicates single curved spine-like protuberance on abdominal sternum VIII. (D–F) Limnogonus ignotus, (D) dorsal, (E) ventral and (F) of macropterous male. (G–I) Limnogonus recurvus, (G) dorsal, (H) ventral and (I) lateral view of apterous male. Scale bars = 1.00 mm.
Effectiveness of COVID-19 vaccination on reduction of hospitalizations and deaths in elderly patients in Rio Grande do Norte, Brazil
<p><strong>Data Repository </strong></p> <p><strong>Dataset name:</strong> covid19_rn-br.csv </p> <p><strong>Version: </strong>1.0 </p> <p><strong>Data collection period:</strong> 04/2020 - 08/2021 </p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances:</strong> 12,635</p> <p><strong>Number of Attributes: </strong>16</p> <p><strong>Missing Values:</strong> Yes </p> <p><strong>Area(s): </strong>Health</p> <p><strong>Sources:</strong> </p> <p>- <em>Primary</em>: </p> <ul> <li>RegulaRN (<a href="https://regulacao.saude.rn.gov.br/sala-situacao/sala_publica/">https://regulacao.saude.rn.gov.br/sala-situacao/sala_publica/</a>).</li> </ul> <p>- <em>Secondary</em>: </p> <ul> <li>RN Mais Vacina (<a href="https://rnmaisvacina.lais.ufrn.br/cidadao/">https://rnmaisvacina.lais.ufrn.br/cidadao/</a>).</li> </ul> <p><strong>Description:</strong> The covid19_rn-br.csv dataset is composed of data from individuals who were hospitalized due to the Sars-CoV-2 virus. The data comes from the ecosystem of services that includes the regulatory system for clinical and critical beds related to Covid-19 (RegulaRN) and the vaccination system against Covid-19 that records the data of the general population (RN Mais Vacina) from Rio Grande do Norte state, Brazil. This dataset provides elementary data to analyze the impact of vaccination on patients hospitalized in the state. Table 1 presents the dictionary used during the data analysis.</p> <p> </p> <p><strong>Table 1: </strong>Description of Dataset Features. </p> <table> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>usp</strong></p> </td> <td> <p>Unified Score for Prioritization scale, which combines the parameters described in the quick Sequential Organ Failure Assessment (qSOFA), the Charlson Comorbidity Index (CCI), the Clinical Frailty Scale (CFS) and The Karnofsky Performance Status scores</p> </td> <td> <p>Numerical</p> </td> <td> <p>2.0. 3.0, 4.0, 5.0, 6.0+</p> </td> </tr> <tr> <td> <p><strong>age</strong></p> </td> <td> <p>Informs the patient's age</p> </td> <td> <p>Numerical.</p> </td> <td> <p>integer value for age</p> </td> </tr> <tr> <td> <p><strong>outcome</strong></p> </td> <td> <p>Informs the outcome of the hospitalized patient after leaving the hospital</p> </td> <td> <p>Categorical</p> </td> <td> <p>“Discharge” or “Death"</p> </td> </tr> <tr> <td> <p><strong>comorbidities</strong></p> </td> <td> <p>Informs if the patient has comorbidities</p> </td> <td> <p>Categorical.</p> </td> <td> <p>“Yes” or “No”</p> </td> </tr> <tr> <td> <p><strong>vaccine</strong></p> </td> <td> <p>Informs which type of vaccine was applied to the patient</p> </td> <td> <p>Categorical</p> </td> <td> <p>“Vaccine #1”, “Vaccine #2” or NaN</p> </td> </tr> <tr> <td> <p><strong>bed_date_admission</strong> </p> </td> <td> <p>Informs the date the patient was hospitalized</p> </td> <td> <p>Date</p> </td> <td> <p>Date</p> </td> </tr> <tr> <td> <p><strong>bed_date_outcome</strong></p> </td> <td> <p>Informs the date that the patient left the hospital bed</p> </td> <td> <p>Date</p> </td> <td> <p>Date</p> </td> </tr> <tr> <td> <p><strong>length_hospitalization</strong></p> </td> <td> <p>Informs the number of days that the patient was hospitalized</p> </td> <td> <p>Numerical</p> </td> <td> <p>An integer value for days</p> </td> </tr> <tr> <td> <p><strong>interval_d1_hospitalization</strong></p> </td> <td> <p>Informs the interval (in days) that the patient had between the first dose and admission</p> </td> <td> <p>Numerical</p> </td> <td> <p>An integer value for days or NaN</p> </td> </tr> <tr> <td> <p><strong>interval_d2_hospitalization</strong></p> </td> <td> <p>Informs the interval (in days) that the patient had between the second dose and admission</p> </td> <td> <p>Numerical</p> </td> <td> <p>An integer value for days or NaN</p> </td> </tr> <tr> <td> <p><strong>dt_d1</strong></p> </td> <td> <p>Informs the date of application of the patient's first dose</p> </td> <td> <p>Date</p> </td> <td> <p>Date or NaN</p> </td> </tr> <tr> <td> <p><strong>dt_d2</strong></p> </td> <td> <p>Informs the patient's second dose application date</p> </td> <td> <p>Date</p> </td> <td> <p>Date or NaN</p> </td> </tr> <tr> <td> <p><strong>comorbidities_txt</strong></p> </td> <td> <p>Informs patients' comorbidities</p> </td> <td> <p>Categorical</p> </td> <td> <p>Free text or NaN</p> </td> </tr> <tr> <td> <p><strong>immunization</strong></p> </td> <td> <p>It informs the patient's immunization level according to the number of doses received and the interval (in days) of application of these doses</p> </td> <td> <p>Categorical</p> </td> <td> <p>“Partially”, “Fully” or “Not vaccinated”</p> </td> </tr> <tr> <td> <p><strong>health_professionals</strong></p> </td> <td> <p>Informs if the patient is a health professional</p> </td> <td> <p>Boolean</p> </td> <td> <p>0 or 1</p> </td> </tr> <tr> <td> <p><strong>age_group</strong></p> </td> <td> <p>Informs the age group of the hospitalized patient according to their age</p> </td> <td> <p>Categorical</p> </td> <td> <p>0-19, 20-49, 50-59, 60-69, 70-79, 80-89, 90+</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Article</strong>: Effectiveness of COVID-19 vaccination on reduction of hospitalizations and deaths in elderly patients in Rio Grande do Norte, Brazil</p> <p><br> <strong>Authors</strong>: Ana Isabela Lopes Sales-Moioli, Leonardo J. Galvão-Lima, Talita K. B. Pinto, Pablo H. Cardoso, Rodrigo D. Silva; Felipe Fernandes, Ingridy M. P. Barbalho, Fernando L. O. Farias, Nicolas V. R. Veras, Daniele M. S. Barros; Agnaldo S. Cruz; Ion G. M. Andrade; Lúcio Gama and Ricardo A. M. Valentim</p>
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