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Fig. 1 in Population dynamics of pests and natural enemies on sugar cane grown in a subtropical region of Brazil
Fig. 1. Population dynamics of sugar cane pests from Feb 2013 to Jan 2015 in the municipality of Salto do Jacuí, Rio Grande do Sul State, Brazil: (A) Means of internodes, attacked internodes, and number of Diatraea saccharalis larvae per culm; (B) Insects of Mahanarva fimbriolata per square m, and infested culms (%) by Saccharicoccus sacchari, and Melanaphis sacchari. Arrows indicate the harvesting and budding periods.
Figure 2 in Sexual size dimorphism in Rana (Pelophylax) ridibunda ridibunda Pallas, 1771 from a population in Darre-Shahr Township, Ilam Province, western Iran
Figure 2. The presence of vocal pouches (a) and digital pads (b) in male Rana (Pelophylax) ridibunda ridibunda distinguishes them from females.
Figure 3 in Sexual size dimorphism in Rana (Pelophylax) ridibunda ridibunda Pallas, 1771 from a population in Darre-Shahr Township, Ilam Province, western Iran
Figure 3. Ordination of the individual males and females of Rana (Pelophylax) ridibunda ridibunda on the first two principal components. Note the relative degree of isolation between males and females, which is mainly attributed to SVL, LHL, LFL, HL, and HW in the PC1 and EEL and ELW in the PC2.
Figure 1. A in Field surveys in Western Panama indicate populations of Atelopus varius frogs are persisting in regions where Batrachochytrium dendrobatidis is now enzootic
Figure 1. A female Harlequin frog, Atelopus varius. This species, classified as Critically Endangered by IUCN, has been found in small numbers in the mountains of Western Panama.
Figure 1 in Seasonal mite population distribution on Caryocar brasiliense trees in the Cerrado domain
Figure 1. Number of Acaridae, Agistemus sp., Eutetranychus sp., Histiostoma sp., Proctolaelaps sp., and Tetranychus sp.1 and 2 on leaf (per cm 2), and number of Proctolaelaps sp. and Histiostoma sp. per fruit on Caryocar brasiliense. Montes Claros, Minas Gerais State, Brazil.
Figure 2 in Seasonal mite population distribution on Caryocar brasiliense trees in the Cerrado domain
Figure 2. Temperature (°C), rainfall (mm), relative humidity of air (%), sunlight (h), and velocity of wind (m/sec). Montes Claros, Minas Gerais State, Brazil.
Table 2 in Predation and oviposition potential of Brazilian populations of the predatory mite Amblyseius tamatavensis (Acari: Phytoseiidae) on eggs of Bemisia tabaci (Insecta: Hemiptera)
<p><b>Table 2</b> Mean lengths (in micrometers ± standard error of the mean) of structures of populations of <i>Amblyseius tamatavensis</i> collected in different Brazilian municipalities (May 2015 to May 2016), and mean lengths given in the original description of the species (Blommers 1974).</p><table><tbody><tr><th>Parameter 1</th><th>Olho d’Água das Flores</th><th>Jataí</th><th>Nova- Crixás</th><th>Bom Repouso</th><th>Ituiutaba</th><th>Senador Amaral</th><th>Campinas</th><th>Cananéia</th><th>Mogi- Guaçu</th><th>Piracicaba (Areão)</th><th>Piracicaba (ESALQ)</th><th>Saltinho</th><th>Santa Maria da Serra</th><th>Lab.</th><th>Original descrip.</th></tr></tbody><tbody><tr><th>DSL</th><td>341±6 a</td><td>336±3 b</td><td>328±4 c</td><td>337±4 b</td><td>336±3 b</td><td>335 ± 7 b</td><td>333±3 b</td><td>338±5 b</td><td>337±5 b</td><td>333±2 c</td><td>336±4 b</td><td>324±6 c</td><td>325±2 c</td><td>342±5 a</td><td>340</td></tr><tr><th>DSW</th><td>231±4 a</td><td>226±4 b</td><td>223±3 bc</td><td>226±2 b</td><td>227±4 b</td><td>225±3 b</td><td>219±2 c</td><td>220±1 c</td><td>224±1 b</td><td>226±3 b</td><td>226±3 b</td><td>224±3 b</td><td>226 ± b</td><td>233±2 a</td><td>250</td></tr><tr><th><i>j1</i></th><td>32±0 a</td><td>31±0 a</td><td>32±0 a</td><td>32±0 a</td><td>32±0 a</td><td>32±1 a</td><td>30±3 a</td><td>31±1 a</td><td>33±0 a</td><td>32±1 a</td><td>31±2 a</td><td>31±1 a</td><td>31 ± 0 a</td><td>32±0 a</td><td>32</td></tr><tr><th><i>j3</i></th><td>51±1 b</td><td>54±2 a</td><td>50±4 b</td><td>52±3 b</td><td>54±1 a</td><td>55±2 a</td><td>52±1 b</td><td>51±5 b</td><td>55±2 a</td><td>52±1 b</td><td>55±1 a</td><td>52±1 b</td><td>51 ± 1 b</td><td>55±1 a</td><td>54</td></tr><tr><th><i>j4</i></th><td>5±0 a</td><td>5±0 a</td><td>5±0 a</td><td>5±0 a</td><td>5±0 a</td><td>6±0 a</td><td>5±0 a</td><td>5±1 a</td><td>5±0 a</td><td>5±0 a</td><td>5±0 a</td><td>5±0 a</td><td>5 ± 0 a</td><td>5±0 a</td><td>4</td></tr><tr><th><i>j5</i></th><td>4±0 a</td><td>3±0 a</td><td>4±0 a</td><td>4±0 a</td><td>4±0 a</td><td>4±0 a</td><td>3±0 a</td><td>3±0 a</td><td>4±0 a</td><td>4±0 a</td><td>4±0 a</td><td>4±0 a</td><td>4±0 a</td><td>4±0 a</td><td>3</td></tr><tr><th><i>J2</i></th><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>5±0 a</td><td>5±0 a</td><td>6±0 a</td><td>5±1 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6 ± 0 a</td><td>6±0 a</td><td>5</td></tr><tr><th><i>J5</i></th><td>8±0 a</td><td>7±1 a</td><td>7±1 a</td><td>7±0 a</td><td>7±1 a</td><td>7±0 a</td><td>7±1 a</td><td>8±0 a</td><td>7±1 a</td><td>8±0 a</td><td>7±1 a</td><td>7±0 a</td><td>7 ± 1 a</td><td>7±1 a</td><td>6</td></tr><tr><th><i>z2</i></th><td>6±0 a</td><td>6±1 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±1 a</td><td>6±0 a</td><td>6±0 a</td><td>7±0 a</td><td>7±0 a</td><td>6±1 a</td><td>7 ± 0 a</td><td>6±0 a</td><td>6</td></tr><tr><th><i>z4</i></th><td>7±0 a</td><td>7±0 a</td><td>6±0 a</td><td>7±0 a</td><td>6±1 a</td><td>7±0 a</td><td>6±1 a</td><td>6±0 a</td><td>7±0 a</td><td>7±0 a</td><td>7±0 a</td><td>6±0 a</td><td>6 ± 0 a</td><td>7±0 a</td><td>6</td></tr><tr><th><i>z5</i></th><td>4±0 a</td><td>4±1 a</td><td>4±0 a</td><td>4±0 a</td><td>4±0 a</td><td>4±0 a</td><td>4±0 a</td><td>3±0 a</td><td>4±0 a</td><td>4±0 a</td><td>4±0 a</td><td>4±0 a</td><td>4 ± 0 a</td><td>4±0 a</td><td>3</td></tr><tr><th><i>Z1</i></th><td>6±0 a</td><td>6±1 a</td><td>7±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±1 a</td><td>6±0 a</td><td>6±0 a</td><td>7±0 a</td><td>6±0 a</td><td>7±0 a</td><td>6 ± 0 a</td><td>7±0 a</td><td>5</td></tr><tr><th><i>Z4</i></th><td>105±2 a</td><td>107±3 a</td><td>105±2 a</td><td>105±2 a</td><td>105±2 a</td><td>107±3 a</td><td>104±1 a</td><td>106±2 a</td><td>105±3 a</td><td>105±3 a</td><td>104±2 a</td><td>104±2 a</td><td>106 ±1 a</td><td>106±1 a</td><td>115</td></tr><tr><th><i>Z5</i></th><td>229±2 a</td><td>226±5 a</td><td>225±2 a</td><td>227±2 a</td><td>228±3 a</td><td>227±4 a</td><td>225±3 a</td><td>228±4 a</td><td>228±3 a</td><td>228±1 a</td><td>228±2 a</td><td>229±2 a</td><td>227±1a</td><td>229±2 a</td><td>250</td></tr><tr><th><i>s4</i></th><td>86±3 a</td><td>88±2 a</td><td>87±3 a</td><td>87±3 a</td><td>88±2 a</td><td>89±2 a</td><td>86±2 a</td><td>86±2 a</td><td>89±3 a</td><td>88±2 a</td><td>88±3 a</td><td>86±2 a</td><td>88±1 a</td><td>87±3 a</td><td>88</td></tr><tr><th><i>S2</i></th><td>7 ±0 a</td><td>6±1 a</td><td>7±0 a</td><td>6±0 a</td><td>7±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>7±0 a</td><td>7±0 a</td><td>6 ± 0 a</td><td>6±0 a</td><td>5</td></tr><tr><th><i>S4</i></th><td>6 ±1 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>7±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6 ± 0 a</td><td>6±0 a</td><td>6</td></tr><tr><th><i>S5</i></th><td>6 ±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6±0 a</td><td>6 ± 0 a</td><td>6±0 a</td><td>6</td></tr><tr><th><i>r3</i></th><td>14±1 a</td><td>15±1 a</td><td>13±1 a</td><td>14±1 a</td><td>14±1 a</td><td>14±1 a</td><td>13±2 a</td><td>14±1 a</td><td>15±1 a</td><td>15±1 a</td><td>15±1 a</td><td>14±1 a</td><td>14 ± 0 a</td><td>15±1 a</td><td>15</td></tr><tr><th><i>R1</i></th><td>7±0 a</td><td>7±1 a</td><td>7±0 a</td><td>7±0 a</td><td>7±0 a</td><td>7±0 a</td><td>7±0 a</td><td>7±1 a</td><td>7±1 a</td><td>7±1 a</td><td>7±0 a</td><td>7±0 a</td><td>7 ± 0 a</td><td>7±1 a</td><td>5</td></tr><tr><th>VASL</th><td>117±2 a</td><td>110±4 b</td><td>110±5 b</td><td>114±1 ab</td><td>110±2 b</td><td>108±4 b</td><td>111±3 b</td><td>112±3 b</td><td>113±2 b</td><td>113±2 b</td><td>108±3 b</td><td>109±3 b</td><td>111 ± 1 b</td><td>119±3 a</td><td>120</td></tr><tr><th>VASWant</th><td>99±3 a</td><td>97±4 a</td><td>94±3 b</td><td>95±3 b</td><td>95±2 b</td><td>93±2 b</td><td>91±4 b</td><td>98±4 a</td><td>97±3 a</td><td>95±2 b</td><td>97±2 a</td><td>91±3 b</td><td>92 ± 1 b</td><td>100±4 a</td><td>100</td></tr><tr><th>VASWpost</th><td>62±3 a</td><td>60±2 a</td><td>64±3 a</td><td>63±2 a</td><td>62±2 a</td><td>63±2 a</td><td>62±1 a</td><td>63±2 a</td><td>64±3 a</td><td>63±2 a</td><td>64±2 a</td><td>62±3 a</td><td>62± 2 a</td><td>62±4 a</td><td>-</td></tr><tr><th><i>St1-St2</i></th><td>32±0 a</td><td>32±0 a</td><td>32±1 a</td><td>33±1 a</td><td>33±0 a</td><td>32±0 a</td><td>32±1 a</td><td>32±0 a</td><td>34±0 a</td><td>32±1 a</td><td>32±1 a</td><td>32±1 a</td><td>33 ± 0 a</td><td>33±0 a</td><td>-</td></tr><tr><th><i>St2-St2</i></th><td>68±2 a</td><td>69±0 a</td><td>68±2 a</td><td>68±1 a</td><td>68±0 a</td><td>68±1 a</td><td>67±1 a</td><td>69±1 a</td><td>69±1 a</td><td>67±1 a</td><td>68±0 a</td><td>68±0 a</td><td>68 ± 1 a</td><td>70±0 a</td><td>-</td></tr><tr><th><i>St3-St3</i></th><td>76±2 a</td><td>77±2 a</td><td>76±2 a</td><td>78±3 a</td><td>76±2 a</td><td>78±1 a</td><td>75±1 a</td><td>77±2 a</td><td>77±2 a</td><td>76±2 a</td><td>76±2 a</td><td>76±1 a</td><td>77 ± 1 a</td><td>78±2 a</td><td>-</td></tr><tr><th><i>St1-St3</i></th><td>59±1 a</td><td>60±2 a</td><td>61±1 a</td><td>60±1 a</td><td>60±1 a</td><td>60±1 a</td><td>59±1 a</td><td>60±1 a</td><td>61±1 a</td><td>60±1 a</td><td>59±1 a</td><td>59±1 a</td><td>61 ± 0 a</td><td>62±1 a</td><td>-</td></tr><tr><th><i>St2-St3</i></th><td>27±1 a</td><td>27±1 a</td><td>28±1 a</td><td>27±1 a</td><td>28±1 a</td><td>28±0 a</td><td>27±1 a</td><td>28±1 a</td><td>28±1 a</td><td>28±1 a</td><td>28±1 a</td><td>27±0 a</td><td>28 ± 0 a</td><td>29±1 a</td><td>-</td></tr><tr><th><i>St4-St4</i></th><td>94±3 a</td><td>86±2 b</td><td>81±3 c</td><td>86±2 b</td><td>84±1 b</td><td>88±8 ab</td><td>81±3 c</td><td>84±2 b</td><td>84±3 b</td><td>86±3 b</td><td>86±2 b</td><td>81±3 c</td><td>85 ± 1 b</td><td>92±3 a</td><td>-</td></tr><tr><th><i>St5-St5</i></th><td>74±2 a</td><td>74±1 a</td><td>74±2 a</td><td>75±1 a</td><td>74±2 a</td><td>74±2 a</td><td>73±1 a</td><td>75±1 a</td><td>76±2 a</td><td>76±2 a</td><td>74±1 a</td><td>74±2 a</td><td>75 ± 2 a</td><td>76±2 a</td><td>-</td></tr><tr><th><i>Jv1</i></th><td>15±1 a</td><td>16±0 a</td><td>16±1 a</td><td>17±1 a</td><td>16±1 a</td><td>17±1 a</td><td>16±1 a</td><td>15±1 a</td><td>17±1 a</td><td>16±1 a</td><td>17±0 a</td><td>16±1 a</td><td>17 ± 0 a</td><td>17±1 a</td><td>-</td></tr><tr><th><i>Jv2</i></th><td>15±1 a</td><td>16±1 a</td><td>15±1 a</td><td>15±1 a</td><td>15±0 a</td><td>15±1 a</td><td>16±0 a</td><td>15±1 a</td><td>15±2 a</td><td>15±1 a</td><td>15±0 a</td><td>15±0 a</td><td>15 ± 0 a</td><td>16±1 a</td><td>-</td></tr><tr><th><i>Jv4</i></th><td>8±0 a</td><td>7±0 a</td><td>7±1 a</td><td>8±0 a</td><td>8±0 a</td><td>8±0 a</td><td>8±0 a</td><td>8±1 a</td><td>8±1 a</td><td>8±0 a</td><td>8±0 a</td><td>8±0 a</td><td>8 ± 0 a</td><td>7±0 a</td><td>-</td></tr><tr><th><i>Jv5</i></th><td>78±3</td><td>73±2</td><td>77±4</td><td>78±3</td><td>79±3</td><td>81±4</td><td>74±1</td><td>80±3</td><td>83±4</td><td>81±3</td><td>82±2</td><td>80±3</td><td>81 ± 2 a</td><td>82±3</td><td>84</td></tr><tr><th><i>Zv1</i></th><td>14±1 a</td><td>16±0 a</td><td>14±1 a</td><td>14±1 a</td><td>15±0 a</td><td>15±0 a</td><td>14±1 a</td><td>14±1 a</td><td>14±1 a</td><td>14±1 a</td><td>15±0 a</td><td>15±1 a</td><td>15 ± 0 a</td><td>14±0 a</td><td>-</td></tr><tr><th><i>Zv2</i></th><td>12±1 a</td><td>12±0 a</td><td>12±1 a</td><td>12±1 a</td><td>12±1 a</td><td>12±1 a</td><td>11±1 a</td><td>12±1 a</td><td>12±0 a</td><td>12±0 a</td><td>13±0 a</td><td>12±1 a</td><td>12 ± 0 a</td><td>13±1 a</td><td>-</td></tr><tr><th><i>Zv3</i></th><td>9±0 a</td><td>9±1 a</td><td>9±0 a</td><td>9±0 a</td><td>9±0 a</td><td>9±0 a</td><td>9±0 a</td><td>9±1 a</td><td>9±1 a</td><td>9±0 a</td><td>10±1 a</td><td>9±0 a</td><td>9 ± 0 a</td><td>10±0 a</td><td>-</td></tr><tr><th><i>SgeIV</i></th><td>98±2 a</td><td>97±3 a</td><td>97±3 a</td><td>98±3 a</td><td>97±2 a</td><td>99±2 a</td><td>98±2 a</td><td>100±2 a</td><td>100±4 a</td><td>100±1 a</td><td>98±2 a</td><td>98±3 a</td><td>99 ± 2 a</td><td>100±3 a</td><td>120</td></tr><tr><th><i>StiIV</i></th><td>66±4 a</td><td>65±3 a</td><td>66±2 a</td><td>66±3 a</td><td>67±4 a</td><td>64±2 a</td><td>65±1 a</td><td>66±4 a</td><td>65±4 a</td><td>64±3 a</td><td>66±3 a</td><td>64±3 a</td><td>65 ± 2 a</td><td>64±4 a</td><td>75</td></tr><tr><th><i>StIV</i></th><td>61±4 a</td><td>63±2 a</td><td>62±3 a</td><td>63±1 a</td><td>64±2 a</td><td>63±2 a</td><td>62±3 a</td><td>62±2 a</td><td>64±1 a</td><td>64±3 a</td><td>64±2 a</td><td>62±2 a</td><td>63 ± 0 a</td><td>65±2 a</td><td>73</td></tr><tr><th>Calyx</th><td>20±1 a</td><td>19±1 a</td><td>19±1 a</td><td>20±1 a</td><td>19±1 a</td><td>18±2 a</td><td>19±1 a</td><td>20±1 a</td><td>19±1 a</td><td>19±1 a</td><td>19±0 a</td><td>20±1 a</td><td>19±1 a</td><td>21±1 a</td><td>16</td></tr></tbody></table><p><sup>1</sup> DSL: dorsal shield length along the midline; DSW: dorsal shield width at the widest level of the podonotal region; VASL: length of the ventrianal shield along the midline; VSWant and VSWpost: anterior (at level of <i>ZV2</i>) and posterior (at level of anal opening) width of the ‘ventrianal shield; <i>SgeIV, StiIV</i> and <i>StIV</i>: lengths of macrosetae of genu, tibia and tarsus of leg IV; calyx: length of calyx of spermatheca. In a same line, means followed by the same letters are not statistically different (Tukey´s test; <i>P</i>> 0.05); (-) Not given.</p>
Extended 1.0 Dataset of "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary"
<p><strong>Introduction</strong></p> <p>We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file <em>Short_Description_of_Data_Analysis.pdf </em>and <em>Statistical_formulas.pdf </em></p> <p>The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed.</p> <p><em>The dataset was expanded on <strong>23rd September 2024</strong> to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software.</em></p> <p><strong>Short Description of Data Analysis and Attached Files (datasets):</strong></p> <p>Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in.</p> <p>Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: <a href="https://www.ksh.hu/stadat_files/nep/hu/nep0035.html">https://www.ksh.hu/stadat_files/nep/hu/nep0035.html</a>, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the <em>Data_of_demography.xlsx</em> file.</p> <p>In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission.</p> <p>The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): <a href="https://www.nnk.gov.hu/index.php/efi">https://www.nnk.gov.hu/index.php/efi</a> (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: <a href="https://semmelweis.hu/egeszsegfejlesztes/en/">https://semmelweis.hu/egeszsegfejlesztes/en/</a> (n=1). (accessed 05 Dec. 2023.)</p> <p>Geocoordinates were determined using Google Maps (N=108): <a href="https://www.google.com/maps">https://www.google.com/maps</a>. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: <em>Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv</em> file.</p> <p>The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from:</p> <p><a href="https://qgis.org/en/site/forusers/download.html">https://qgis.org/en/site/forusers/download.html</a>. (accessed 04 Jan. 2024.)</p> <p>The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the</p> <p><em>Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv</em> file,</p> <p>imported via .csv file.</p> <p>The OpenStreetMap tileset is directly accessible from <a href="http://www.openstreetmap.org">www.openstreetmap.org</a> in QGIS. (accessed 04 Jan. 2024.)</p> <p>The Hungarian county administrative boundaries were downloaded from the following website: <a href="https://data2.openstreetmap.hu/hatarok/index.php?admin=6" target="_new">https://data2.openstreetmap.hu/hatarok/index.php?admin=6</a> (accessed 04 Jan. 2024.)</p> <p>HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km.</p> <p>Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation).</p> <p>A brief description of the statistical formulas applied is included in the <em>Statistical_formulas.pdf.</em></p> <p>Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format.</p> <ul> <li>Aggregated number of HDOs by county: <em>Number_of_HDOs.xlsx</em></li> <li>Standardised data (Number of HDOs per 100,000 residents): <em>Standardized_data.xlsx</em></li> <li>Calculation of the Lorenz curve: <em>Lorenz_curve.xlsx</em></li> <li>Calculation of the Gini index: <em>Gini_Index.xlsx</em></li> <li>Calculation of the LQ index: <em>LQ_Index.xlsx</em></li> <li>Calculation of the Herfindahl-Hirschman Index: <em>Herfindahl_Hirschman_Index.xlsx</em></li> <li>Calculation of the Entropy index: <em>Entropy_Index.xlsx</em></li> <li>Regression and correlation analysis calculation: <em>Regression_correlation.xlsx</em></li> </ul> <p>Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav:</p> <ul> <li>Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.</li> <li>Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv</li> <li>Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv</li> <li>Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv</li> <li>Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv</li> </ul> <p>For easier readability, the files have been provided in both SPV and PDF formats.</p> <p>The translation of these supplementary files into English was completed on 23rd Sept. 2024.</p> <p> </p> <p><em>If you have any further questions regarding the dataset, please contact the corresponding author: <a target="_new">domjan.peter@phd.semmelweis.hu</a></em></p> <p> </p>
Table 1 in Indotyphlops braminus (Daudin, 1803): distribution and oldest record of collection dates in Oceania, with report of a newly established population in French Polynesia (Tahiti Island, Society Archipelago)
<p><b>Table 1</b>. Earliest known arrival dates of the snake <i>Indotyphlops braminus</i> on the Oceanian islands and archipelagos according to publications or collection vouchers.</p><table><thead><tr><th><b>Country</b></th><th><b>Earliest date</b></th><th><b>Reference</b></th></tr></thead><tbody><tr><th>Mariana Islands</th><td>1819</td><td>A.M.C. Duméril & Bibron 1844</td></tr><tr><th>Mariana Islands, Guam</th><td>1819</td><td>A.M.C. Duméril & Bibron 1844</td></tr><tr><th>New Guinea</th><td>1912</td><td>Barbour 1912</td></tr><tr><th>Hawai'i</th><td>1930</td><td>Slevin 1930</td></tr><tr><th>Carolines (Pohnpei)</th><td>1936</td><td>Buden 2000</td></tr><tr><th>Mariana Islands, Tinian</th><td>1945</td><td>Cagle 1946</td></tr><tr><th>Mariana Islands, Saipan</th><td>1945</td><td>Cagle 1946</td></tr><tr><th>Marshall Islands</th><td>1954</td><td>Knight 1984</td></tr><tr><th>Palau (Belau)</th><td>1955</td><td>Crombie & Pregill 1999</td></tr><tr><th>Solomon Isl.</th><td>1961</td><td>McDowell 1974</td></tr><tr><th>Vanuatu</th><td>1971</td><td>Medway & Marshall 1975</td></tr><tr><th>Bonin Island</th><td>1971</td><td>Shigei 1971</td></tr><tr><th>New Caledonia</th><td>1974</td><td>Ineich & Bauer 1992</td></tr><tr><th>Fiji</th><td>1983</td><td>Clunie 1983</td></tr><tr><th>Mariana Islands, Rota</th><td>1987</td><td>Wiles et al. 1990</td></tr><tr><th>Loyalty Islands</th><td>1991</td><td>Shea & Wallach 2000</td></tr><tr><th>American Samoa</th><td>1992</td><td>CAS 195919</td></tr><tr><th>Midway Atoll</th><td>1998</td><td>Wallach 2008</td></tr><tr><th>Nauru</th><td>2007</td><td>Buden 2008</td></tr><tr><th>Kiribati (Gilbert Isl.)</th><td>2007</td><td>Craven & Shea 2010</td></tr><tr><th>Western Samoa</th><td>2009</td><td>Bonin & Shea 2009</td></tr><tr><th>Tahiti Island, French Polynesia</th><td>2014</td><td>This paper</td></tr></tbody></table>
Figure 3 in A population of Greater Ground Robin Amalocichla sclateriana (Petroicidae) from central New Guinea
Figure 3. Activity pattern of Greater Ground Robin Amalocichla sclateriana on Hides Ridge. X-axis shows the start time for each 30-minute sampling period.
Figure 2 in A population of Greater Ground Robin Amalocichla sclateriana (Petroicidae) from central New Guinea
Figure 2. Camera trap images of Greater Ground Robin Amalocichla sclateriana, Hides Ridge, Hela Province, Papua New Guinea.
Figure 1 in A population of Greater Ground Robin Amalocichla sclateriana (Petroicidae) from central New Guinea
Figure 1. Confirmed localities of Greater Ground Robin Amalocichla sclateriana (Coates 1990, Beehler & Pratt 2016; www.vertnet.org). Land above 2,600 m is shaded darker grey.
Resources for "BMF CP 95: Temperature and population density as predictors of embeddedness and hierarchy cultural values "
<p><span>The current study is conducted to examine the following research questions:</span></p> <ul> <li><span>How are the temperature and population density associated with the countries’ cultural values of embeddedness and hierarchy cultural values?</span></li> </ul>
Spatially resolved single-cell atlas unveils a distinct cellular signature of fatal lung COVID-19 in a Malawian population
<p>This record contains all the fully processed single cell RNA sequencing objects included in the paper Nyirenda et al (2024) '<em>Spatially resolved single-cell atlas unveils a distinct cellular signature of fatal lung COVID-19 in a Malawian population</em>'. </p> <h3>Abstract</h3> <p><em>Postmortem single-cell studies have transformed understanding of lower respiratory tract diseases (LRTD) including Covid19, but there is minimal data from African settings where HIV, malaria and other environmental exposures may affect disease pathobiology and treatment targets. We used histology and high-dimensional imaging to characterise fatal lung disease in Malawian adults with (n=9) and without (n=7) Covid19, and generated single-cell transcriptomics data from lung, blood and nasal cells. Data integration with other cohorts showed a conserved Covid19 histopathological signature, driven by contrasting immune and inflammatory mechanisms: in USA, European and Asian cohorts by type I/III interferon responses, particularly in blood-derived monocytes, in the Malawi cohort, by response to interferon-gamma (IFN-γ) in lung-resident macrophages. HIV status had minimal impact on histology or immunopathology. Our study provides a data resource and highlights the importance of studying the cellular mechanisms of disease in underrepresented populations, indicating shared and distinct targets for treatment. </em></p> <h3>Figure 3</h3> <p>COSMIC_Lung_Atlas.h5ad</p> <p>COSMIC_Lung_Stromal_Atlas.h5ad</p> <p>COSMIC_Lung_Immune_Atlas.h5ad</p> <h3>Figure 4</h3> <p>COSMIC_HCLA_Integration.rds</p> <h3>Figure 5</h3> <p>COSMIC_Nasal_Atlas.h5ad</p> <p>COSMIC_Blood_Atlas.h5ad</p> <h3>Total Post-mortem COVID-19 Lung Autopsy Papers Integration</h3> <p>This data object was generated whereby all publicly available fatal COVID-19 lung single cell cohorts were integrated together. We include this analysis to show that the observed interferon responses detailed in the paper are preserved in not only the HLCA, but also existing post-mortem lung cohorts. The codebase for this additional analysis can be found here: <strong><a href="https://github.com/olympiahardy/Malawi_Integration">https://github.com/olympiahardy/Malawi_Integration</a> </strong></p> <p>COSMIC_Delorey_Melms_Integration.rds</p> <p> </p> <p> </p> <h3>Imaging Mass Cytometry Analysis</h3> <p>This Zenodo record can be accessed using the DOI here: <strong><a href="10.5281/zenodo.13899297">10.5281/zenodo.13899297</a> </strong>. The GitHub repository can be accessed here:<strong> <a href="https://github.com/joaolsf/Spatial_Single_Cell_Lung_Atlas_Malawi_COVID">https://github.com/joaolsf/Spatial_Single_Cell_Lung_Atlas_Malawi_COVID</a></strong></p> <p> </p>
Linked collectors and determiners for: Revision of Neacomys spinosus (Thomas, 1882) (Rodentia: Cricetidae) with emphasis on Peruvian populations and the description of a new species.
Natural history specimen data linked to collectors and determiners held within, "Revision of Neacomys spinosus (Thomas, 1882) (Rodentia: Cricetidae) with emphasis on Peruvian populations and the description of a new species". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/893e07da-8ed6-4e9d-ba47-44b13bb49b65">https://bionomia.net/dataset/893e07da-8ed6-4e9d-ba47-44b13bb49b65</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/893e07da-8ed6-4e9d-ba47-44b13bb49b65">https://gbif.org/dataset/893e07da-8ed6-4e9d-ba47-44b13bb49b65</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Morphological and molecular data reveal the cryptic diversity among populations of Aegla paulensis (Decapoda, Anomura, Aeglidae), with descriptions of four new species and comments on dispersal routes and conservation status.
Natural history specimen data linked to collectors and determiners held within, "Morphological and molecular data reveal the cryptic diversity among populations of Aegla paulensis (Decapoda, Anomura, Aeglidae), with descriptions of four new species and comments on dispersal routes and conservation status". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/53780a8e-66fa-4263-8542-ff9c540ab37d">https://bionomia.net/dataset/53780a8e-66fa-4263-8542-ff9c540ab37d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/53780a8e-66fa-4263-8542-ff9c540ab37d">https://gbif.org/dataset/53780a8e-66fa-4263-8542-ff9c540ab37d</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Quercus cerris populations from Central-Eastern Europe and Middle East (IBBR-QUERCUS-CERRIS).
Natural history specimen data linked to collectors and determiners held within, "Quercus cerris populations from Central-Eastern Europe and Middle East (IBBR-QUERCUS-CERRIS)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/3a80944e-06ce-4226-ac9f-052ff0dcbd97">https://bionomia.net/dataset/3a80944e-06ce-4226-ac9f-052ff0dcbd97</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3a80944e-06ce-4226-ac9f-052ff0dcbd97">https://gbif.org/dataset/3a80944e-06ce-4226-ac9f-052ff0dcbd97</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Polyzosteria cockroaches in Tasmania (Blattodea: Blattidae: Polyzosteriinae) represent a new, endemic species, with allopatric alpine and coastal sub-populations.
Natural history specimen data linked to collectors and determiners held within, "Polyzosteria cockroaches in Tasmania (Blattodea: Blattidae: Polyzosteriinae) represent a new, endemic species, with allopatric alpine and coastal sub-populations". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/72fbd015-d727-4e48-b8e1-54496db6c932">https://bionomia.net/dataset/72fbd015-d727-4e48-b8e1-54496db6c932</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/72fbd015-d727-4e48-b8e1-54496db6c932">https://gbif.org/dataset/72fbd015-d727-4e48-b8e1-54496db6c932</a>. Formatted as a Frictionless Data package.
Genome streamlining: effect of mutation rate and population size on genome size reduction: simulated data
<p>Lineages data of populations simulated with Aevol (<a href="https://gitlab.inria.fr/aevol/aevol">https://gitlab.inria.fr/aevol/aevol</a>), and the Wild-Types sequences used for that.</p> <p>Conditions: change of mutation rate, population size, or both.<br>Mutational bias: none, insertion bias or deletion bias</p>
Data from: Difference in reproductive mode rather than ploidy explains niche differentiation in sympatric sexual and apomictic populations of Potentilla puberula
Apomicts tend to have larger geographical distributional ranges and to occur in ecologically more extreme environments than their sexual progenitors. However, the expression of apomixis is typically linked to polyploidy. Thus, it is a priori not clear whether intrinsic effects related to the change in the reproductive mode or rather in the ploidy drive ecological differentiation. We used sympatric sexual and apomictic populations of Potentilla puberula to test for ecological differentiation. To distinguish the effects of reproductive mode and ploidy on the ecology of cytotypes, we compared the niches (i) of sexuals (tetraploids) and autopolyploid apomicts (penta-, hepta- and octoploids) and (ii) of the three apomictic cytotypes. We based comparisons on a ploidy screen of 238 populations along a latitudinal transect through the Eastern European Alps and associated bioclimatic, soil and topographic data. Sexual tetraploids preferred primary habitats at drier, steeper, more south-oriented slopes, while apomicts mostly occurred in human-made habitats with higher water availability. Contrariwise, we found no or only marginal ecological differentiation among the apomictic higher ploids. Based on the pronounced ecological differences found between sexuals and apomicts, in addition to the lack of niche differentiation among cytotypes of the same reproductive mode, we conclude that reproductive mode rather than ploidy is the main driver of the observed differences. Moreover, we compared our system with others from the literature, to stress the importance of identifying alternative confounding effects (such as hybrid origin). Finally, we underline the relevance of studying ecological parthenogenesis in sympatry, to minimise the effects of differential migration abilities
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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OpenNeuro
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