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Table 4 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 4</b> Biting location of <i>An. gambiae</i> (<i>s.l</i>.) in IRS and control areas</p><table><tbody><tr><th>Period</th><th>Area</th><th>Location</th><th>No. collected</th><th>Person/night</th><th>HBR (b/p/n)</th><th><i>P</i> -value</th></tr></tbody><tbody><tr><th>Before IRS (June–October 2016)</th><td>Future IRS</td><td>Indoor</td><td>1156</td><td>128</td><td>9.03</td><td>0.00098</td></tr><tr><th></th><td></td><td>Outdoor</td><td>1002</td><td>128</td><td>7.83</td><td></td></tr><tr><th>After 1st IRS round (June–September 2017)</th><td>IRS</td><td>Indoor</td><td>544</td><td>112</td><td>4.86</td><td><0.0001</td></tr><tr><th></th><td></td><td>Outdoor</td><td>1150</td><td>112</td><td>10.27</td><td></td></tr><tr><th></th><td>Control</td><td>Indoor</td><td>678</td><td>52</td><td>13.04</td><td><0.0001</td></tr><tr><th></th><td></td><td>Outdoor</td><td>446</td><td>52</td><td>8.58</td><td></td></tr><tr><th>After 2nd IRS round (June–August 2018)</th><td>IRS</td><td>Indoor</td><td>599</td><td>80</td><td>7.49</td><td>0.233</td></tr><tr><th></th><td></td><td>Outdoor</td><td>642</td><td>80</td><td>8.03</td><td></td></tr><tr><th></th><td>Control</td><td>Indoor</td><td>568</td><td>40</td><td>14.2</td><td><0.0001</td></tr><tr><th></th><td></td><td>Outdoor</td><td>313</td><td>40</td><td>7.83</td><td></td></tr></tbody></table><p><i>AbbreviatioNS</i>: HBR,human biting rate;b/p/n,bite/person/night</p>
Table 1 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 1</b> <i>Anopheles</i> species composition in surveyed areas before and after IRS</p><table><tbody><tr><th>Species</th><th>Before IRS (May 2016–April 2017)</th><th>After 1st round of IRS (June 2017–March 2018)</th><th>Control (Bembereke, Kouande)</th><th>After 2nd round of IRS (June 2018–November 2018)</th><th>Control (Bembereke– Kouande)</th><th>Total</th></tr></tbody><tbody><tr><th><i>An.gambiae</i> (<i>s.l</i>.)</th><td>2465</td><td>2379</td><td>1546</td><td>1286</td><td>929</td><td>8605</td></tr><tr><th><i>An.funestus</i></th><td>54</td><td>54</td><td>16</td><td>9</td><td>7</td><td>140</td></tr><tr><th><i>An.coustani</i></th><td>9</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9</td></tr><tr><th><i>An.pharoensis</i></th><td>3</td><td>2</td><td>5</td><td>2</td><td>3</td><td>15</td></tr><tr><th><i>An.paludis</i></th><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><th><i>An.nili</i></th><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td></tr><tr><th><i>An.ziemanni</i></th><td>2</td><td>2</td><td>0</td><td>1</td><td>0</td><td>5</td></tr><tr><th>Total</th><td>2534</td><td>2437</td><td>1567</td><td>1299</td><td>939</td><td>8776</td></tr></tbody></table>
Table 3 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 3</b> Parity rates of <i>Anopheles gambiae</i> (<i>s.l</i>.) collected before and after the first round IRS</p><table><tbody><tr><th>IRS area</th><th>Variable</th><th>Before IRS (June–October 2016)</th><th>After 1st round of IRS (June– October 2017)</th><th><i>Χ</i> 2-value</th><th><i>P</i> -value</th></tr></tbody><tbody><tr><th>Alibori-Donga</th><td>No. dissected</td><td>1422</td><td>1214</td><td>–</td><td>–</td></tr><tr><th></th><td>No. parous</td><td>996</td><td>457</td><td>–</td><td>–</td></tr><tr><th></th><td>Parous (%)</td><td>70.04</td><td>37.64</td><td>276.57</td><td><0.0001</td></tr></tbody></table>
Table 2 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 2</b> Frequency of sibling species of the <i>An. gambiae</i> (<i>s.l.</i>) complex in IRS and control areas</p><table><tbody><tr><th>Period</th><th>No. of</th><th>Alibori (IRS Area 1)</th><th></th><th>Donga (IRS Area 2)</th><th></th><th>Total (IRS Areas)</th><th></th><th>Bembereke (Control area)</th></tr></tbody><tbody><tr><th></th><td>analysed samples in IRS areas</td><td><i>An. gambiae</i></td><td><i>An.coluzzii</i></td><td><i>An. arabiensis</i></td><td><i>An. gambiae</i></td><td><i>An. coluzzii</i></td><td><i>An. arabiensis</i></td><td><i>An. gambiae</i></td><td><i>An. coluzzii</i></td><td><i>An. arabiensis</i></td><td><i>An. gambiae</i></td><td><i>An. coluzzii</i></td><td><i>An.arabiensis</i></td></tr><tr><th>Before IRS (May 2016–October 2016)</th><td>980</td><td>170</td><td>271</td><td>0</td><td>387</td><td>152</td><td>0</td><td>557</td><td>423</td><td>0</td><td>–</td><td>–</td><td>–</td></tr><tr><th>After IRS (June 2017–November 2018)</th><td>1794</td><td>466</td><td>176</td><td>17</td><td>520</td><td>147</td><td>5</td><td>986</td><td>323</td><td>22</td><td>277</td><td>181</td><td>5</td></tr><tr><th>Proportion (%)</th><td>–</td><td>70.71</td><td>26.70</td><td>2.57</td><td>77.38</td><td>21.87</td><td>0.74</td><td>74.07</td><td>24.26</td><td>1.65</td><td>59.82</td><td>39.09</td><td>1.07</td></tr><tr><th>Total (Before+after IRS)</th><td>2774</td><td>636</td><td>447</td><td>17</td><td>907</td><td>299</td><td>5</td><td>1543</td><td>746</td><td>22</td><td>277</td><td>181</td><td>5</td></tr><tr><th>Proportion (%)</th><td>–</td><td>57.81</td><td>40.63</td><td>1.54</td><td>74.89</td><td>24.69</td><td>0.41</td><td>66.76</td><td>32.28</td><td>0.95</td><td>59.82</td><td>39.09 1.07</td></tr></tbody></table>
Table 8 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 8</b> Blood-feeding rates in <i>An. gambiae</i> (<i>s.l.</i>) in treated and control areas</p><table><tbody><tr><th>Period</th><th>Area</th><th>No. collected</th><th>Unfed</th><th>Fed</th><th>Gravid</th><th>Half-gravid</th><th>Bloodfeeding rate %</th><th><i>P</i> -value</th></tr></tbody><tbody><tr><th>Before IRS:June–October 2016</th><td>IRS areas</td><td>1001</td><td>26</td><td>922</td><td>12</td><td>41</td><td>96.20</td><td><0.0001</td></tr><tr><th>After 1st round of IRS:June–October 2017</th><td></td><td>273</td><td>99</td><td>171</td><td>1</td><td>2</td><td>63.37</td><td></td></tr><tr><th>After 1st round of IRS:June–September 2017</th><td>IRS areas</td><td>208</td><td>78</td><td>129</td><td>0</td><td>1</td><td>62.5</td><td><0.0001</td></tr><tr><th></th><td>Control areas</td><td>244</td><td>16</td><td>187</td><td>31</td><td>10</td><td>80.74</td><td></td></tr></tbody></table>
Table 6 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 6</b> Human-biting rate, sporozoite index and entomological inoculation rate in IRS and control areas</p><table><tbody><tr><th>Variable</th><th>After 1st IRS campaign (June–September 2017)</th><th>After 2nd IRS campaign (June–August 2018)</th></tr></tbody><tbody><tr><th></th><td>IRS areas</td><td>Control areas</td><td>Reduction (%)</td><td>IRS areas</td><td>Control areas</td><td>Reduction (%)</td></tr><tr><th></th><td>(Alibori–Donga)</td><td>(Bembereke–Kouande)</td><td>(Alibori–Donga)</td><td>(Bembereke–Kouande)</td></tr><tr><th>HBR/night</th><td>7.56</td><td>10.81</td><td>–</td><td>7.76</td><td>11.01</td><td>–</td></tr><tr><th>SI (%)</th><td>0.71</td><td>3.7</td><td>80.81</td><td>0.6</td><td>3</td><td>80</td></tr><tr><th>EIR (ib/person/night)</th><td>0.053</td><td>0.403</td><td>–</td><td>0.05</td><td>0.325</td><td>–</td></tr><tr><th>EIR (ib/person/month)</th><td>1.6</td><td>12.11</td><td>86.78</td><td>1.5</td><td>9.75</td><td>84.61</td></tr></tbody></table><p><i>AbbreviatioNS</i>: SI,sporozoite index;HBR,human-biting rate;EIR,entomological inoculation rate;ib,infective bite</p>
Table 5 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
<p><b>Table 5</b> Human-biting rate, sporozoite index and entomological inoculation rate in Alibori and Donga regions (IRS areas) before and after the first round of IRS 2017</p><table><tbody><tr><th>Region</th><th>Variable</th><th>Before IRS (June–October 2016)</th><th>After 1st round of IRS (June– October 2017)</th><th>Reduction (%)</th></tr></tbody><tbody><tr><th>IRS areas (Alibori, Donga)</th><td>HBR/night</td><td>8.43</td><td>7.37</td><td>–</td></tr><tr><th></th><td>SI (%)</td><td>8.4</td><td>1.2</td><td>85.71</td></tr><tr><th></th><td>EIR (ib/person/night)</td><td>0.707</td><td>0.09</td><td>–</td></tr><tr><th></th><td>EIR (ib/person/month)</td><td>21.21</td><td>2.7</td><td>87.27</td></tr></tbody></table><p><i>AbbreviatioNS</i>: SI,sporozoite index;HBR,human-biting rate;EIR,entomological inoculating rate;ib,infected bite</p>
Dataset for "Switching Residues: A Platform for the Synthesis of Fidaxomicin Antibiotics"
<p>ZIP File:</p> <p>Characterisation data (such as e.g. NMR, IR, MS spectra)</p> <p>NMR raw data, .mnova files</p> <p> </p>
Residual stress in steel benchmark determined by synchrotron X-ray and neutron diffraction
<p>The data presented is the residual stress, determined by synchrotron X-ray diffraction (SXRD) at DESY operated by Hereo (P07 and P61A) and neutron diffraction (ND) at ILL(SALSA). The data is for two benchmark samples having the same geometry (U shape) that can be measured by diffraction techniques and adapted to other material systems to validate RS measurements. One of them was elastically loaded while the other had stresses generated by plastic deformation, while having the same geometry. </p> <p>The U-flexure sample flexure-compression(FC), flexure-tension(FT_ and flexure-neutral(FN) were electrical discharge machine from a rolled plate. The U-bend(B) sample was obtained by three-point bending a cuboidal blank. The thicknes of the machine part is close to 10 mm and runs parallel to the z-direction. The measurement line runs from the centre of the top surface near the bend (defined at the origin) down into the bend (positive z-direction). The FN samples acted as stress-free reference for the U-flexures and a pin extracted from the bend was used for the U-bend. For the U-flexures, a finite element analysis (FEA) model was developed to account for the slight variations in loading conditions observed in the samples used for ND and SXRD. The results of the FEA model is also presented for comparison.</p> <p>The gauge volume sizes and shapes were dependent on the technique and research facility. The gauge volumes were: a) P07 – 0.2 x 0.2 x ~2.0 mm<sup>3 </sup>(b) P61A- 0.15 x 0.15 x ~3.4 µm<sup>3 </sup>(c) SALSA- 0.6 x 0.6 x 2 mm<sup>3</sup>. </p>
Associated coordinate and mtz files for "Selective Oxidation of Secondary Coordination Sphere Tyrosine Residues in Engineered Cu Proteins"
<p>Coordinate and mtz files for the associated protein structures reported in "Selective Oxidation of Secondary Coordination Sphere Tyrosine Residues in Engineered Cu Proteins"</p> <p>9CST: Streptavidin-E101Q-K121A bound to Cu(II)-biotin-ethyl-dipicolylamine cofactor (Cu-2xm-Sav)</p> <p>9CSU: Streptavidin-E101Q-S112Y-K121A bound to Cu(II)-biotin-ethyl-dipicolylamine cofactor (Cu-2xm-S112Y-Sav)</p> <p>9CSV: Streptavidin-E101Q-S112Y-K121A bound to Cu(II)-biotin-ethyl-dipicolylamine cofactor oxidized by hydrogen peroxide (Cu-2xm-S112Y-Sav + hydrogen peroxide)</p> <p>9CSW: Streptavidin-E101Q-S112A-K121Y bound to Cu(II)-biotin-ethyl-dipicolylamine cofactor (Cu-2xm-S112A-K121Y-Sav)</p> <p>9E6Z: Streptavidin-E101Q-S112F-K121A bound to Cu(II)-biotin-ethyl-dipicolylamine cofactor (Cu-2xm-S112F-Sav)</p>
Landslide age, elevation and residual vegetation determine tropical montane forest canopy recovery and biomass accumulation after landslide disturbances in the Peruvian Andes
<p>Landslides are common natural disturbances in tropical montane forests. While the geomorphic drivers of landslides in the Andes have been studied, factors controlling post-landslide forest recovery across the steep climatic and topographic gradients characteristic of tropical mountains are poorly understood.</p> <p>Here we use a LiDAR-derived canopy height map coupled with a 25-year landslide time series map to examine how landslide, topographic, and biophysical factors, along with residual vegetation, affect canopy height and heterogeneity in regenerating landslides. We also calculate aboveground biomass accumulation rates and estimate the time for landslides to recover to mature forest biomass levels.</p> <p>We find that age and elevation are the biggest determinants of forest recovery, and that the jump-start in regeneration that residual vegetation provides lasts for at least 18 years. Our estimates of time to biomass recovery (31.6-37.1 years) are surprisingly rapid, and as a result we recommend that future research pair LiDAR with hyperspectral imagery to estimate forest aboveground biomass in frequently disturbed landscapes.</p> <p>Synthesis: Using a high-resolution LiDAR dataset and a time-series inventory of 608 landslides distributed across a wide elevational gradient in Andean montane forest, we show that age and elevation are the most influential predictors of forest canopy height and canopy variability. Other features of landslides, in particular the presence of residual vegetation, shape post-landslide regeneration trajectories. LiDAR allows for a detailed analysis of forest structural recovery across large landscapes and numbers of disturbances, and provides a reasonable upper bound on aboveground biomass accumulation rates. However, because this method does not capture the effect of compositional change through succession on aboveground biomass, wherein high-wood density species gradually replace light-wooded pioneer species, it overestimates aboveground biomass. Given previously estimated stem turnover rates along this elevational gradient, we posit that aboveground biomass recovery takes at least three times as long as our recovery time estimates based on LiDAR-derived structure alone.</p>
Supplementary datasets for the paper of "Multi-resBind: a residual network-based multi-label classifier for in vivo RNA binding prediction and preference visualization"
<p>There are two eCLIP datasets (cell lines of K562 and HepG2). The eCLIP datasets were then divided into five categories for each cell line: low, medium 1, medium 2, high 1 and high 2 with peaks of >1,000 but <2,000, >2,000 but <4,000, >4,000 but <7,000, >7,000 but <10,000 and >10,000, respectively. </p>
Supplementary Material for "TITANIA Model-Free Interpretation of Residual Dipolar Couplings in the Context of Organic Compounds"
<p>Simulation input (RDC data, input geometries, keywords) and output files (simulation / geometry trajectories, alignment data, SECONDA analysis) for isopinocampheol, tubocurarine and strychnine runs with the TITANIA software.</p>
Cuisine in transition? Organic residue analysis of domestic containers from 9th-14th century Sicily
From the 9th to 14th centuries AD, Sicily experienced a series of rapid and quite radical changes in political regime, but the impact of these regime changes on the lives of the people that experienced them remains largely elusive within the historical narrative. We use a multi-faceted lipid residue approach to give direct chemical evidence of the use of 248 everyday domestic ceramic containers from Islamic and post-Islamic contexts in western Sicily to aid our understanding of daily habits throughout this period of political change. A range of commodities was successfully identified, including animal fats, vegetable products, fruit products, (potentially including wine), and plant resins. The study highlights the complexity of residues in Early Medieval Mediterranean society, as in many cases mixtures of commodities were observed, reflecting sequential cooking events and/or the complex mixtures reflective of medieval recipes. However, overall there were no clear changes in the composition of the residues following the imposition of Norman control over the island and through subsequent periods, despite some differences between urban centres and rural sites. Thus, lending to the idea that post-Islamic populations largely flourished and benefited from the agricultural systems, resources, and recipes left by their predecessors.
Effect of Single-Residue Mutations on CTCF Binding to DNA: Insights from Molecular Dynamics Simulations
<p>Supplementary dataset for manuscript "Effect of Single-Residue Mutations on CTCF Binding to DNA: Insights from Molecular Dynamics Simulations". </p>
SlumberNet: Deep learning classification of sleep stages using residual neural networks
<p>Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture, designed to classify sleep states in mice using electroencephalogram (EEG) and electromyogram (EMG) signals. Our model was trained and tested on data from mice undergoing baseline sleep, sleep deprivation, and recovery sleep, enabling it to handle a wide range of sleep conditions. Employing k-fold cross-validation and data augmentation techniques, SlumberNet achieved high levels of accuracy (~98%) in predicting sleep stages and showed robust performance even with a small and diverse training dataset. Comparison of SlumberNet's performance to manual sleep stage classification revealed a significant reduction in analysis time (~50x faster), without sacrificing accuracy. Our study showcases the potential of deep learning to facilitate sleep research by providing a more efficient, accurate, and scalable method for sleep stage classification. Our work with SlumberNet demonstrates the power of deep learning in sleep research, and looking forward, SlumberNet could be adapted to human EEG analysis and sleep stage classification. Thus, SlumberNet could be a valuable tool in understanding both sleep physiology and disorders in mammals.</p>
Response mechanism of residual strength to meso-structure of shear surface in gravelly slip zone of ancient landslide
<p>Response mechanism of residual strength to meso-structure of shear surface in gravelly slip zone of ancient landslide-Original data</p>
Figure 1 in General Protocol to Obtain D-Glucosamine from Biomass Residues: Shrimp Shells, Cicada Sloughs and Cockroaches
Figure 1. Chitin hydrolysis to obtain D-glucosamine hydrochloride.
Pairwise residue-residue Euclidean distances for a Histatin-5 ensemble
<p>Pairwise residue-residue Euclidean distances for a pair of independent replicas of the protein ensemble Histatin-5.</p><p>Each file corresponds to a n x p matrix, where n is the number of conformers and p is the number of pairwise inter-residue distances, that is, p = L(L-1)/2 where L is the number of sequence residues. Each row corresponds to a p-vector of distances featuring the corresponding conformation. Distances are computed between CB atoms (CA for glycines) and are given in Angstroms (Å).</p><p>Conformations were generated using Flexible-Meccano (Ozenne et al. 2012, Bernado et al. 2005) and refined using previously reported small-angle X-ray scattering (SAXS) data (Sagar et al. 2021).</p>
Acute and Residual Effects of Caffeinated Beer
ClinicalTrials.gov study NCT00515294. IPD Sharing: NO. Countries: 1. Publications: 1.
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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.