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Fig. 4 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 4 Building blocks of the residual learning module. CBM, Convolutional, Batch normalisation and Mish (modules)

Fig. 5 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 5 Visual representation of the removal of residual blocks from C3 and C4 Res-block body. YOLO,You Only Look Once (model)

Fig. 1 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 1 Comparison of malaria diagnosis using deep learning CNN models and deep learning object detectors. CNN, Convolutional neural network

Fig. 2 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 2 Cropping of infected cells using the coordinates of predictions by the object detectors. RBC, Red blood cell; YOLO,You Only Look Once (model)

opencc-by-4.0Dec 2023View details →

Fig. 1 in Pre-treatment of canine plasma with heat, rather than acid, efficiently enhances Dirofilaria immitiS antigen detection

Fig. 1 Detection of Dirofilaria immitis antigen by ELISA for twofold-diluted plasma samples with and without treatment. A spectrophotometric OD reading at a wavelength of 415 nm was obtained for each sample using the microplate reader SpectraMax® i Series-Spectramax Id3. Filled black circle, untreated sera. Open blue circle, ICD-1 protocol. Filled blue circle, ICD-2 protocol. Open red circle, ICD-3 protocol. Filled red circle, ICD-4 protocol. The error was represented as mean ± 95% confidence interval

CMAPLE: efficient phylogenetic inference in the pandemic era

This supplementary data contains testing scripts and input/output data for benchmarking, validating, and assessing the code quality of CMAPLE.

opencc-by-4.0Dec 2022View details →

Dataset for "High photosynthesis rates in Brassiceae species are mediated by leaf anatomy enabling high biochemical capacity, rapid CO2 diffusion and efficient light use"

Dataset used in the paper Retta MA, Van Doorselaer L, Driever SM, Yin X, de Ruijter NCA, Verboven P, Nicolaï BM, Struik PC. High photosynthesis rates in Brassiceae species are mediated by leaf anatomy enabling high biochemical capacity, rapid CO2 diffusion and efficient light use. New Phytol. 2024 Sep 18. doi: 10.1111/nph.20136. PMID: 39294895. Please cite the paper presenting this datase. <h1>Plant Species and Inbred Lines:</h1> <ul> <li>Hirschfeldia incana L. (7th generation inbred line 190003 HIN-NIJ-07-B) </li> <li>Brassica nigra L. (3rd generation inbred line 210093 BNI-DG1-03-B)</li> <li>Brassica rapa L. (inbred line ‘R-o-18’)</li> <li>Arabidopsis thaliana (accession Columbia)</li> </ul> <h1>Growth Conditions:</h1> <ul> <li>Media: Rock-wool blocks (Grodan Plantop, Roermond, Netherlands, 10×10×7.5 cm)</li> <li>Fertigation: Nitrogen-rich nutrient solution via automated dripping system.</li> <li>Light Conditions: 12 h day/12 h night, light intensity of 200 µmol m-2 s-1 and 1800 µmol m-2 s-1</li> <li>Temperature: Day/Night temperatures of 23 °C and 20 °C, respectively.</li> <li>Relative Humidity: 70%</li> </ul> <h1>Codes</h1> Species: <ul> <li>Hirschfeldia incana L. - H. incana</li> <li>Brassica nigra L. - B. nigra</li> <li>Brassica rapa L. - B. rapa</li> <li>Arabidopsis thaliana - A. thaliana</li> </ul> Light conditions: <ul> <li>High light - HL</li> <li>Low light - LL</li> </ul> Replicates: <ul> <li>Biological replicates were labeled with numbers, e.g. replicate one from high light grown Hirschfeldia incana is referred to as HiHL1</li> </ul> <h1>Measurements</h1> <h2>Leaf Gas Exchange and Chlorophyll Fluorescence Measurements (GasExchangeData.zip):</h2> <ul> <li>Four leaves per species per treatment.</li> <li>Conducted using a LI-6800 (LI-COR, Lincoln, NE, USA) on the mid-position of the youngest fully expanded leaf.</li> <li>The resoponse of photosynthesis to irradiance and external CO2 concentrations augumneted with multi-phase flash fluorescence were made</li> </ul> <h2>Optical Properties Measurement (Absorbance & chlorophyll.zip):</h2> <ul> <li>Leaves: Four leaves per species per treatment.</li> <li>Leaf transmittance and reflectance measured using a dual channel spectrophotometer (absorptance_reflac_data_355_750.xlsx)</li> <li>Chlorophyll content measured using a spectrophotometer (Chlorophyll.xlsx).</li> </ul> <h2>Stomatal Density and Size Analysis (Stomata.zip):</h2> Sampling: <ul> <li>Leaves: Four leaves per species per treatment.</li> <li>Plants: Samples taken from three different plants.</li> <li>Leaf-side: abaxial and adxial leaf side.</li> </ul> Microscopy Setup: <ul> <li>Stomatal imprints made using clear nail polish, imaged using a light microscope at 20x.</li> </ul> Data Output: <ul> <li>Imaging Results: .jpg files organised under folders for species e.g. AtHL\R1 T+B.zip contains images ofimprints of top (T) and bottom (B) leaf sides from replicate plant 1 (R1) of A. thaliana grown under high light (AtHL). The images are named as for example, AT_HL_BOTTOM_R1_A_stacked_minimum.jpg, The leters A to E label various imges made from one imprint.</li> </ul> <h2>Light and Electron Microscopy of Leaf Sections (CellwallChloroplast.zip):</h2> Sampling: <ul> <li>Leaves: Four leaves per species per treatment.</li> <li>Plants: Samples taken from four different plants.</li> </ul> Sample preparation <ul> <li>Leaf samples fixed, dehydrated, embedded in Araldite, and sectioned for imaging.</li> <li>1 µm think sections were made for light microscopy</li> <li>Sections  of 70 nm were double stained for TEM</li> </ul> Microscopy Setup: <ul> <li>Mesophyll cells imaged at 400x and 700x to measure chloroplast coverage.</li> <li>Electron microscopy performed with Zeiss EM900 electron microscope.</li> </ul> <h2>Mesophyll Chlorophyll (ConfocalData.zip):</h2> Sampling: <ul> <li>Leaves: Three leaves per species per treatment.</li> <li>Plants: Samples taken from three different plants.</li> <li>Thickness: 200 ± 10 µm sections prepared using a sliding microtome</li> </ul> Microscopy Setup: <ul> <li>Microscope: Leica DM8 inverted scope equipped with a Stellaris 5 confocal microscope (Leica Microsystems, Wetzlar, Germany).</li> <li>Excitation: 490 nm excitation laser line</li> <li>Fluorescence Recording: Chlorophyll autofluorescence recorded in a spectral range of 660−700 nm.</li> <li>Objective: Leica objective ×10/0.4 NA.</li> <li>Z-Stacks:  85–112 µm depth, two random positions per sample</li> </ul> Data Output: <ul> <li>Imaging Results: Z-stacks of chlorophyll autofluorescence in mesophyll cells.</li> <li>Spectral Information: Chlorophyll autofluorescence recorded in the 660−700 nm range.</li> </ul> Analysis: <ul> <li>Software: The confocal files are in .lif format and can be viewed using Leica application suite (LASx), ImageJ</li> </ul>

opencc-by-4.0Dec 2015View details →

Fig. 1 in Neem oil increases the efficiency of the entomopathogenic fungus Metarhizium anisopliae for the control of Aedes aegypti (Diptera: Culicidae) larvae

Fig. 1 Dacls survcval curves of Aedes aegypti larvae exposed to dcfferent concentratcons of neem ocl. Note: Results are the means (± SE) of three expercments for each treatment wcth 30 cnsects used per treatment for each expercment

opencc-by-4.0Dec 2015View details →

Fig. 2 in Neem oil increases the efficiency of the entomopathogenic fungus Metarhizium anisopliae for the control of Aedes aegypti (Diptera: Culicidae) larvae

Fig. 2 Dacls survcval curves of Aedes aegypti larvae exposed to dcfferent concentratcons of Metarhizium anisopliae concdca. Note: Results are the means (± SE) of three expercments for each treatment wcth 30 cnsects used per treatment for each expercment

opencc-by-4.0May 2024View details →

Replication Data and Analyses for: J. Monsimet, S. Sjögersten, N.J. Sanders, M. Jonsson, J. Olofsson & M. Siewert, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, Remote Sensing in Ecology and Conservation.

This dataset corresponds to the article: "Jérémy Monsimet*¹, Sofie Sjögersten², Nathan J. Sanders³, Micael Jonsson¹, Johan Olofsson¹, Matthias Siewert¹, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, Remote Sensing in Ecology and Conservation" DOI: <a href="https://doi.org/10.1002/rse2.400" target="_blank" rel="nofollow noreferrer noopener">10.1002/rse2.400</a> 1 Department of Ecology and Environmental Science, Umeå University, Sweden 2 School of Biosciences, University of Nottingham, Loughborough, UK 3 Department of Ecology and Evolutionary Biology, University of Michigan, US The gitlab repository of this dataset is available at: <a href="https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/">https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/</a> In this repository, you will find the analyses and results presented in the paper. In each folder, there is a html file that can be read after downloading locally the whole folder. You can either run the .qmd file used to produce the html file or walk through the html files (see the readme.md for more information). Paper abstract: High‐resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in ecosystem functioning, yet their distribution and effects on entire landscapes remain poorly understood, in part because they and their mounds are too small for satellite remote sensing. This research maps the distribution and impact of ant mounds in a 20 ha treeline ecotone. We evaluate the detectability from UAV imagery using a deep learning model for object detection and different combinations of RGB, thermal and multispectral sensor data. We were able to detect ant mounds in all imagery using manual detection and deep learning. However, the highest precision rates were achieved by deep learning using RGB data which has the highest spatial resolution (1.9 cm) at comparable UAV flight height. While multispectral data were outperformed for detection, it allows for novel insights into the ecology of ants and their spatial impact on vegetation productivity using the normalized difference vegetation index. Scaling up, this suggests that ant mounds quantifiably impact vegetation productivity for up to 4% of our study area and up to 8% of the Betula nana vegetation communities, the vegetation type with the highest abundance of ant mounds. Therefore, they could have an overlooked role in nutrient‐limited tundra vegetation, and on the shrubification of this habitat. Further, we show the powerful combination UAV multi‐sensor data and deep learning for efficient ecological tracking and monitoring of mound‐building ants and their spatial impact.

opencc-by-4.0Feb 2024View details →

Supplementary material for the paper: "One-shot procedures for efficient minimum compliance topology optimization"

MATLAB codes and results used in the article: "One-shot procedures for efficient minimum compliance topology optimization", published in Structural and Multidisciplinary Optimization, 2024.

pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection (Supp data)

Supplemental tables for article: pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection

Winter wheat yield analysis using the Light Use Efficiency model in LandKlif project

The crop yield of Winter wheat is calculated using the light use efficiency (LUE) model for the state of Bavaria (adopted from Dhillon et al 2020). The yield output is received by inputting the synthetic dataset of Sentinel-2-MODIS with 10-meter spatial and 8-day temporal resolution (generated by Dhillon et al 2022) plus climate elements. The model output is validated using the Landesamt regional crop yield data of Bavaria for 2019 with an R2 of 0.86 and RMSE of 5.03 dt/ha. The NDVI and the yield product was created by Carina Kübert-Flock, Thorsten Dahms, and Maninder Singh Dhillon from TP 7 in LandKlif project. LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html">Bavarian State Ministry of Science and the Arts</a> within the <a href="https://www.bayklif.de/">Bavarian Climate Research Network (bayklif)</a>.  Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.

opencc-zeroJun 2024View details →

dryad40/100

Data from: A framework for modeling the impacts of searcher behavior on the efficiency of abundance surveys

When planning abundance surveys, the impact of search effort on the quality of the density estimates is rarely considered. We constructed a time-budget modeling framework for abundance surveys using principles from optimal foraging theory. We link search effort to the number of sample units surveyed, searcher detection probability, the number of detections made, and the precision of the estimated resource density. This framework allowed us to determine how a surveyor should behave to produce optimal density estimates. Using data collected from quadrat and removal surveys of zebra mussels (Dreissena polymorpha) in central Minnesota, we applied this framework to evaluate potential improvements. By tuning searcher behavior, we find that density estimates from removal surveys of zebra mussels could be improved by up to 60% in some cases, without changing the overall survey effort. Our framework also predicts a critical population density where the best survey method switches from removal surveys at low densities to quadrat surveys at high densities, consistent with past empirical work. Our results provide insights into how to improve the performance of many survey methods in high-density environments by either tuning searcher behavior or decoupling the estimation of resource density and detection probability.

Oil Seed Rape yield analysis using the Light Use Efficiency model in LandKlif project

The crop yield of Oil Seed Rape (OSR) is calculated using the light use efficiency (LUE) model for the state of Bavaria (adopted from Dhillon et al 2020). The yield output is received by inputting the synthetic dataset of Sentinel-2-MODIS with 10-meter spatial and 8-day temporal resolution (generated by Dhillon et al 2022) plus climate elements. The model output is validated using the Landesamt regional crop yield data of Bavaria for 2019 with an R2 of 0.82 and RMSE of 2.14 dt/ha. The NDVI and the yield product was created by Carina Kübert-Flock, Thorsten Dahms, and Maninder Singh Dhillon from TP 7 in LandKlif project. LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html">Bavarian State Ministry of Science and the Arts</a> within the <a href="https://www.bayklif.de/">Bavarian Climate Research Network (bayklif)</a>.  Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.

Biomass estimation of oilseed rape using the light use efficiency model in Bavaria (Atlas) in LandKlif project

This dataset shows the predicted biomass (g/m2) of oilseed rape (OSR) using the light use efficiency (LUE) model for Bavaria in 2019. The LUE model uses satellite data (Landsat-8, MODIS) and climate data (temperature and solar radiation) to calculate the plant biomass. The crop yield is predicted and validated at district level using LfStat data. The validation results show an R2 of 0.79 with an RMSE of 2.21 dt/ha.  This dataset is conducted under LandKlif project. LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html">Bavarian State Ministry of Science and the Arts</a> within the <a href="https://www.bayklif.de/">Bavarian Climate Research Network (bayklif)</a>.  Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.

Biomass estimation of winter wheat using the Light Use Efficiency model in Bavaria (Atlas) in LandKlif project

This dataset shows the predicted biomass (g/m2) of winter wheat (WW) using the Light Use Efficiency (LUE) model for Bavaria in 2019. The LUE model uses satellite data (Landsat-8, MODIS) and climate data (temperature and solar radiation) to calculate the plant biomass. The crop yield is predicted and validated at district level using LfStat data. The validation results show an R2 of 0.82 with an RMSE of 5.46 dt/ha.  This dataset is conducted under LandKlif project. LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html">Bavarian State Ministry of Science and the Arts</a> within the <a href="https://www.bayklif.de/">Bavarian Climate Research Network (bayklif)</a>.  Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.

opencc-by-4.0Jan 2023View details →

Figure 1 in Amazonian soil fungi are efficient degraders of glyphosate herbicide; novel isolates of Penicillium, Aspergillus, and Trichoderma

Figure 1. Mass spectrum resulting from the HPLC-MS of the isolated Penicillium 4A21 filtered. The filtrate presents possible peaks of glyphosate (170.07), AMPA (112.13) and sarcosine (89).

opencc-by-4.0Mar 2022View details →

Fig. 4 in Carnassiform notches improve the functional efficiency of bat molar shearing crests

Fig. 4. Furipterid bat Furipterus horrens (Cuvier, 1828) (USNM 549505) from Brazil, Recent. Right lower molars in occlusal view (stereopair), showing angular entocristids and carnassiform notches (arrows) with accessory troughs in the cristids obliqua and postcristids in m1–m3.