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FIG. 5. — A in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil

FIG. 5. — A, mean richness; B, density of bryophytes in the sampled mangroves per light tolerance guilds; C, interaction plot between sampled zones and light tolerance guilds on mean richness of bryophytes; D, interaction plot between sampled zones and light tolerance guilds on mean density of bryophytes.

FIG. 4 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil

FIG. 4. — Violin plot with included boxplot: A, species richness; B, species density. Alpha-diversity indices: C, Shannon Index (H'); D, Pielou's Evenness (J').

FIG. 3 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil

FIG. 3. — Accumulation curves based on the abundance of individuals in the fringe and inland zones of the mangroves of Salvaterra, Pará, Brazil: A, species richness (q = 0); B, Shannon diversity (q = 1). The fringe zone is shown in red color and the inland zone in blue color. Continuous line represents interpolation and dotted line represents extrapolation.

FIG. 2 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil

FIG. 2. — Mangroves on the east coast of the municipality of Salvaterra, Marajó Island, Pará: A, B, mangrove in inland zone; C, D, fringe zone.

FIG. 1 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil

FIG. 1. — Location map of collection points in Marajó Island, Pará, Brazilian Amazon:A, localization of Marajó Island in Pará, Brazil, South America (red rectangle); B, localization of Salvaterra in Marajó Island; C, localization of sampling points on the east coast of the Salvaterra, with 1 km between the fringe zone and the inland zone in each area (map prepared by P.W.P. Gomes).

opencc-by-4.0Feb 2024View details →

Data for: Caspase-Based Fusion Protein Technology: Substrate Cleavability Described by Computational Modeling and Simulation

This dataset contains all files necessary to set up the simulations conducted in this work. It further contains the scripts that were used to do the stitching and combining of the CASPON-tag and the N-termini of the POIs. The manuscript was just submitted and accepted: <a href="https://doi.org/10.1021/acs.jcim.4c00316">10.1021/acs.jcim.4c00316</a>

opencc-by-4.0Dec 2023View details →

Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates

The data of paper "Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates" 1. The BAs file include lammps input file of EMD, BAs model file and NNP model. One can use follw command to perform simulation.                                                             lmp -in in.thermal    Besides, the  ShengBTE results from 3-phonon and 4-phono are included in ShengBTE file. 2. The GaN file include lammps input file of EMD, GaN model file and NNP model. One can use follw command to perform simulation.                                                             lmp -in in.thermal    Besides, the  ShengBTE results from 4-phonon are included in ShengBTE file. 3. The BAs-GaN file include lammps input file of NEMD, heterostructure model file and NNP model. One can use follw command to perform simulation.                                                             lmp -in in.thermal 4. The active learning framework that includes the initial file, running file, machine configuration file and other input files.It is worht to note that the  machine      configuration file may not normally work, duo to different cluster environment.  5. The FEM file contains python scripts for building finite element models.

opencc-zeroApr 2024View details →

Good vibrations: Remote-tactile foraging success of wading birds is positively affected by the water content of substrates they forage in

Some taxa of wading birds can locate buried prey by detecting vibratory cues in their foraging substrates while probe-foraging, using a sensory modality called "remote-touch". As more saturated substrates transmit vibrations better, we predict that these birds can detect prey in wetter substrates more easily. We used sensory assays to test whether substrate water content affects the remote-touch foraging success rate of Hadeda Ibises, Bostrychia hagedash. The birds were more successful at locating prey using vibratory cues than when relying on random direct contact with the beak alone. Their remote-touch foraging success rate was positively affected by increasing water contents of the soil, but water content had no effect on their direct contact foraging success (indicating this is not an artefact of ease of probing). This may partially explain the link between the range expansion of this species in southern Africa and increased soil irrigation, as it is easier for the birds to detect prey in wetter substrates. Thus, it is likely that the distribution of other remote-touch foraging birds is affected by substrate water content, and as many of these species are endangered and rely on sensitive wetland habitats, it is vital to understand their sensory requirements for foraging.

opencc-zeroApr 2024View details →

Data from: Tissue-specific O-GlcNAcylation profiling identifies substrates in translational machinery in the Drosophila mushroom body contributing to olfactory learning

O-GlcNAcylation is a dynamic post-translational modification that diversifies the proteome. Its dysregulation is associated with neurological disorders that impair cognitive function, and yet identification of phenotype-relevant candidate substrates in a brain-region-specific manner remains unfeasible. By combining an O-GlcNAc binding activity derived from Clostridium perfringens OGA (CpOGA) with TurboID proximity labeling in Drosophila, we developed an O-GlcNAcylation profiling tool that translates O-GlcNAc modification into biotin conjugation for tissue-specific candidate substrates enrichment. We mapped the O-GlcNAc interactome in major brain regions of Drosophila and found that components of the translational machinery, particularly ribosomal subunits, were abundantly O-GlcNAcylated in the mushroom body of Drosophila brain. Hypo-O-GlcNAcylation induced by ectopic expression of active CpOGA in the mushroom body decreased local translational activity, leading to olfactory learning deficits that could be rescued by dMyc overexpression-induced increase of protein synthesis. Our study provides a useful tool for future dissection of tissue-specific functions of O-GlcNAcylation in Drosophila and suggests a possibility that O-GlcNAcylation impacts cognitive function via regulating regional translational activity in the brain.

opencc-by-4.0Nov 2024View details →

AdaptFerm: Bioprocess Monitoring Using FTIR spectroscopy: Insights into Substrate Effects and Domain Adaptation

<h2> </h2> <h2>1. Introduction</h2> The AdaptFerm dataset is designed to support the development of a monitoring framework for lactic acid production fermentation using Fourier Transform Infrared (FTIR) spectroscopy. Its primary goal is to facilitate the control strategies for continuous fermentation processes to maximize the lactic acid production. The AdaptFerm encompasses data from two distinct batch fermentation environments: one employing simple sugar (glucose) as the substrate and the other utilizing complex sugars derived from bio-waste. The study focuses on developing accurate predictive models for glucose and lactic acid concentrations, with an emphasis on applying classical machine learning techniques and enhancing domain generalization capabilities. <h2>2. Prediction Model for Different Substrate Environments</h2> The chemical composition of substrates are presented in <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">Table 1 [1]</a>. The dataset is utilized to train and test models within the same substrate domain. For instance, data from a single fermentation environment (e.g., glucose substrate) is used for both training and testing phases. The applied machine learning models showed accurate prediction within the same domain <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">[1]</a>. For more details on the methods applied, please refer to the following link: <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">https://doi.org/10.1016/j.heliyon.2024.e38791</a>. In this study, the MIR results correspond to the AdaptFerm dataset. The spectra of the glucose and biowaste hydrolysate fermentation process are presented in <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">Figure 3</a> and <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">Figure 4</a>. <h2>3. Domain Adaptation</h2> The dataset was also used to address the challenge posed by shifts in FTIR data when substrates change. Transitioning from simple sugar (glucose) to complex sugar (bio-waste) causes significant variations in the FTIR spectra, making it difficult for models trained on glucose fermentation data to maintain prediction accuracy in the complex sugar fermentation environment. This results in reduced robustness and performance when applied to out-of-distribution data. To address these challenges, we explore methods that improve the generalization ability and robustness of models in such scenarios without using labels from complex sugar fermentation <a title="Domain-Invariant Monitoring for Lactic Acid Production: Transfer Learning from Glucose to Bio-Waste Using Machine Learning Interpretation" href="https://dx.doi.org/10.2139/ssrn.5012080" target="_blank" rel="noopener">[2]</a>. It shows the application of machine learning interpretation to find domain invariant features for glucose and lactic acid. For more details on the methods applied, please refer to the following link: <a title="Domain-Invariant Monitoring for Lactic Acid Production: Transfer Learning from Glucose to Bio-Waste Using Machine Learning Interpretation" href="https://dx.doi.org/10.2139/ssrn.5012080" target="_blank" rel="noopener">https://dx.doi.org/10.2139/ssrn.5012080</a>. The code is available at <a title="ShapFS" href="https://github.com/shl-shawn/ShapFS" target="_blank" rel="noopener">https://github.com/shl-shawn/ShapFS</a>. <h2>4. Real-World Use Cases</h2> <h3>4.1. Regression Task</h3> AdaptFerm serves as a benchmark for machine learning model applications in fermentation processes, specifically for predicting glucose and lactic acid concentrations, measured in g/L (grams per liter), while considering issues of out-of-distribution generalization. <h3>4.2. Domain Adaptation Regression Task</h3> The dataset is also suitable for evaluating different  domain adaptation methods. In particular, the glucose substrate fermentation data can be used as the source domain, while the complex sugar fermentation data from bio-waste serves as the target domain. For semi-supervised domain adaptation approaches, it is recommended to use the initial data points (i.e., those collected at the beginning of the fermentation process) from the target domain, as the dataset is organized chronologically by collection day. These approaches aim to improve the robustness of models by transferring knowledge across domains and mitigating the effects of out-of-distribution data. <h3>4.3. Anomaly Detection</h3> The dataset can be used to train anomaly detection models to identify outliers or deviations from normal fermentation behavior. This could be valuable in industrial bioprocessing, where early detection of issues like contamination or process failure is crucial. Techniques like Isolation Forests, One-Class SVM, or Autoencoders could be applied to identify unusual patterns in FTIR spectra. <h3>4.4. Classification Task</h3> Although the main task is regression, the dataset could also be used in classification tasks by discretizing the concentrations of glucose and lactic acid into categories (e.g., low, medium, high). This would allow for the application of classification algorithms like Support Vector Machines (SVM), Random Forests, or Neural Networks for predicting the fermentation phase or identifying specific operational conditions. <h3>4.5. Transfer Learning</h3> Given the nature of the domain adaptation approach in this dataset, transfer learning models can be explored. Models pre-trained on glucose fermentation data can be fine-tuned on complex sugar fermentation data, enabling quicker model convergence and improved performance in data-scarce environments. <h3>4.6. Multi-Task Learning</h3> In a multi-task learning scenario, models could simultaneously predict both glucose and lactic acid concentrations from the same FTIR data. This could help in improving model accuracy by leveraging shared representations across the two tasks. <h3>4.7. Feature Selection</h3> The FTIR spectral data contains a large number of features (wavelengths), and feature selection techniques such as Recursive Feature Elimination (RFE), Lasso regression, or mutual information could be applied to identify the most relevant wavelengths for predicting glucose and lactic acid concentrations, improving model performance and interpretability. <h2>5. Dataset Structure and Meta Information</h2> The dataset is organized into four Excel files, corresponding to two main fermentation domains (different substrates) and two key process variables: a) Simple Sugar Substrate This domain contains data for the fermentation process using glucose as the substrate to produce lactic acid. It includes two files—one for glucose concentrations and one for lactic acid concentrations. Both are measured in g/L. b) Complex Sugar Substrate This doman contains data for the fermentation process using bio-waste as the substrate to produce lactic acid. Similar to the previous domain, it includes two files—one for glucose concentrations and one for lactic acid concentrations. Both are measured in g/L. Each file is structured as follows: <ul> <li>The first column contains the sample ID, which serves as the timeline of measurements (Sample ID 1 represents the first measurement in the fermentation process).</li> <li>From the second column onwards, the FTIR data is provided, covering the spectral range from 549.6 cm-1 to 3999.6 cm-1 comprising 3,579 features.</li> <li>The final column contains the ground truth data, the chemical measurements of fermentation variables such as glucose and lactic acid concentrations, both measured in g/L.</li> </ul> <h2>6. Conclusion</h2> The AdaptFerm features FTIR spectra data from two distinct fermentation environments: simple sugar (glucose) and complex sugar (bio-waste). The dataset is designed to be used in regression tasks, including domain adaptation, and can be applied in machine learning model development for fermentation process monitoring, with a focus on enhancing model robustness and handling out-of-distribution data. This dataset provides a valuable resource for exploring domain shift and improving the robustness of machine learning models in bioengineering and fermentation processes. It enables further research into domain generalization techniques and offers a wide range of possibilities for machine learning applications. <h2>References</h2>  [1] Arman Arefi, Barbara Sturm, Majharulislam Babor, Michael Horf, Thomas Hoffmann, Marina Höhne, Kathleen Friedrich, Linda Schroedter, Joachim Venus, Agata Olszewska-Widdrat, Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates, Heliyon, Volume 10, Issue 19, 2024, e38791, ISSN 2405-8440, DOI: 10.1016/j.heliyon.2024.e38791, <a href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">https://doi.org/10.1016/j.heliyon.2024.e38791</a>. [2]  Majharulislam Babor, Shanghua Liu, Arman Arefi, Agata Olszewska-Widdrat,  Barbara Sturm, Joachim Venus, and Marina M.-C. Höhne, Domain-Invariant Monitoring for Lactic Acid Production: Transfer Learning from Glucose to Bio-Waste Using Machine Learning Interpretation. Available at <a href="https://dx.doi.org/10.2139/ssrn.5012080" target="_blank" rel="noopener">http://dx.doi.org/10.2139/ssrn.5012080.</a>

opencc-by-4.0Nov 2024View details →

Dataset of the publication: Hybrid Heterostructures of a Spin Crossover Coordination Polymer on MoS2: Elucidating the Role of the 2D Substrate. Small 2023, 19, e2304954.

Dataset of the publication: Hybrid Heterostructures of a Spin Crossover Coordination Polymer on MoS2: Elucidating the Role of the 2D Substrate. A. Núñez-López, R. Torres-Cavanillas, M. Morant-Giner, N. Vassilyeva, R. Mattana, S. Tatay, P. Ohresser, E. Otero, E. Fonda, M. Paulus, V. Rubio-Giménez, A. Forment-Aliaga, E. Coronado, Small 2023, 19, e2304954. doi: 10.1002/smll.202304954 10.1002/smll.20230495410.1002/smll.20230495410.1002/smll.202304954

opencc-by-4.0Nov 2024View details →

NIR/SWIR Spectral Library of Plastic-Substrate Mixtures

NIR/SWIR spectra of substrate-plastic mixtures at varying concentrations Plastics include polyethylene (PE), polyethylene terephthalate (PET), polylactic acid (PLA), polypropylene (PP), polyvinyl chloride (PVC), and styrene-butadiene rubber (SBR) Substrates included 3 soils (Bu5, W6, TG), crushed cement (C), oak leaf powder (V), and water (DIW) Concentrations of 0% (pure substrate), 0.15%, 1.5%, 5%, 15%, 50%, and 100% (pure plastic) Datasets are in .csv format for reflectance spectra, absorbance spectra, absorbance 1st derivative, and absorbance 2nd derivative. Reflectance spectra are also included as .hdr and .sli for ease of importing into ENVI or other hyperspectral image processing software. Additionally, raw ASD spectra and the python script for processing are included for custom spectral processing or analyses.

opencc-by-4.0Oct 2021View details →

HDAC6 screening dataset using tau-based substrate in an enzymatic assay yields selective inhibitors and activators

Structure and information of the data file DATA SET; Contains the information to which data set this information belongs. There are four possibilities denoted 1 to 4. Data set1: Enzymatic assay of human HDAC6 with commercial peptide substrate. Data set2: Enzymatic assay of human HDAC6 with custom peptide substrate. Data set3: Hit confirmation of the active molecules of the enzymatic assay of human HDAC6 with custom peptide substrate. Data set4: Determination of IC50 values for inhibition of enzymatic assay of human HDAC6 with custom peptide substrate. INTERNAL NAME; An internal name which enables identification of the compound within data sets from Fraunhofer ITMP ScreeningPort. TYPE; Type of data. Either 'inhibition' for normalized inhibition values or 'IC50' for enzymatic IC50. RELATION; Relation between TYPE and VALUE, always '='. VALUE; Value of the normalized inhibition or the enzymatic IC50. UNITS; Unit of the value. Either '%' for the normalized inhibition or 'uM' for the enzymatic IC50. NAME; Trade name of the chemical compound. SMILES; The canonical Smile of the chemical compound.   Abstract Histone deacetylase 6 (HDAC6) and HDAC10 are unique among the other HDACs as they consist of two domains instead of one. Only in the case of HDAC6 both domains are active resulting in a number of unique deacetylase reactions. Interestingly, HDAC6 can regulate the microtubule network and plays a role in the degradation of misfolded and aggregated proteins. We therefore developed a substrate (Boc-Ile-Asp-(Dimethyl)Lys-(Ac)Lys-aminoluciferin) based on a critical acetylation site of misfolded human Tau, a hallmark of Alzheimer’s Disease. This substrate was used to screen a 5632 compound encompassing repurposing library at 10 µM in a coupled, luminescence based assay. The assay was miniaturised to 10 µL per enzymatic reaction. For comparison, a generic HDAC substrate (BOC-Gly-(Ac)Lys-aminoluciferin) was also used to screen the same library. Both substrates rely on a cascade of enzymatic reactions. First, HDAC6 deacetylates the substrate followed by cleavage of aminluciferin from the peptide by porcine Trypsin and conversion of the aminoluciferin using firefly Luciferase. Compounds with an activity of at least 75% inhibition against the custom human Tau based substrate were confirmed in triplicates at the screening concentration of 10 µM. Confirmed hits, activity of at least 75%, where analysed in 8 point or 15 point dose response curves, depending on their activity. The data presented here encompass both primary data sets including 5632 compounds as well as 249 values from hit confirmation screening against the hTau based substrate and 151 IC50 values from confirmed hits.   Methods of data generation Enzymatic assay of human HDAC6 with commercial peptide substrate. The assay using the commercial peptide substrate (BOC-Gly-(Ac)Lys-aminoluciferin) was obtained from Promega Inc.. In the beginning the assay buffer is thawed and the lyophilized substrate is dissolved according to the technical manual (Promega Inc.) to create the substrate reagent. HDAC6 (obtained from BPS Biosciences) is dissolved in assay buffer at 0.2 nM, which is twice the final assay concentration. Compounds and controls are added to the plates using acoustic dispensing to reach a final concentration of 10 µM in the assay followed by 5 µl enzyme solution per well. Plates are centrifuged shortly and incubated for 10 min at RT. Afterwards, 5 µL/well substrate solution are added to the wells, centrifuged shortly and incubated for 10 min prior detection of the luminescence signal on a multimode reader. Primary screening was done at one concentration (10 µM) in singlicates.   Enzymatic assay of human HDAC6 with custom peptide substrate. The assay was designed based on a commercial HDAC6 assay available from Promega Inc. This luminescence assay works by an aminoluciferin coupled HDAC6 peptide substrate. Upon deacetylation of the peptidic substrate by HDAC6 (obtained from BPS Biosciences) Trypsin (obtained from Sigma-Aldrich) can cleave the aminoluciferine from the peptide which can be converted by Luciferase (obtained from AAT Bioquest) to the detected signal. First, a twofold concentrated enzyme solution was generated, consisting of 4 nM HDAC6 and 0.1% BSA in HEPES buffer (25 mM HEPES, 137 mM NaCl, 2.7 mM KCl and 1 mM MgCl2, pH 7.0). Second, a twofold peptide solution was generated containing 100 µM custom made peptide (Boc-Ile-Asp-(Dimethyl)Lys-(Ac)Lys-aminoluciferin) in HEPES buffer. Compounds and controls are added to the plates using acoustic dispensing to reach a final concentration of 10 µM in the assay followed by 5 µl enzyme solution per well. Plates are centrifuged shortly and 5 µL/well peptide solution are added to the wells, centrifuged shortly and incubated for 30 min at RT. Afterwards, 5 µL detection reagent (0.067 mg/mL Luciferase, 133.3 µM ATP, 0.133 mg/mL Trypsin in HEPES buffer) were added to each well. Plates were centrifuged shortly and measured on a multimode reader after 30 min incubation at RT in the dark. Primary screening was done at one concentration (10 µM) in singlicates.   Hit confirmation of the active molecules of the enzymatic assay of human HDAC6 with custom peptide substrate The assay was designed based on a commercial HDAC6 assay available from Promega Inc. This luminescence assay works by an aminoluciferin coupled HDAC6 peptide substrate. Upon deacetylation of the peptidic substrate by HDAC6 (obtained from BPS Biosciences) Trypsin (obtained from Sigma-Aldrich) can cleave the aminoluciferine from the peptide which can be converted by Luciferase (obtained from AAT Bioquest) to the detected signal. First, a twofold concentrated enzyme solution was generated, consisting of 4 nM HDAC6 and 0.1% BSA in HEPES buffer (25 mM HEPES, 137 mM NaCl, 2.7 mM KCl and 1 mM MgCl2, pH 7.0). Second, a twofold peptide solution was generated containing 100 µM custom made peptide (Boc-Ile-Asp-(Dimethyl)Lys-(Ac)Lys-aminoluciferin) in HEPES buffer. Compounds and controls are added to the plates using acoustic dispensing to reach a final concentration of 10 µM in the assay followed by 5 µl enzyme solution per well. Plates are centrifuged shortly and 5 µL/well peptide solution are added to the wells, centrifuged shortly and incubated for 30 min at RT. Afterwards, 5 µL detection reagent (0.067 mg/mL Luciferase, 133.3 µM ATP, 0.133 mg/mL Trypsin in HEPES buffer) were added to each well. Plates were centrifuged shortly and measured on a multimode reader after 30 min incubation at RT in the dark. Hit confirmation was done at one concentration (10 µM) in triplicates.   Determination of IC50 values for inhibition of enzymatic assay of human HDAC6 with custom peptide substrate The assay was designed based on a commercial HDAC6 assay available from Promega Inc. This luminescence assay works by an aminoluciferin coupled HDAC6 peptide substrate. Upon deacetylation of the peptidic substrate by HDAC6 (obtained from BPS Biosciences) Trypsin (obtained from Sigma-Aldrich) can cleave the aminoluciferine from the peptide which can be converted by Luciferase (obtained from AAT Bioquest) to the detected signal. First, a twofold concentrated enzyme solution was generated, consisting of 4 nM HDAC6 and 0.1% BSA in HEPES buffer (25 mM HEPES, 137 mM NaCl, 2.7 mM KCl and 1 mM MgCl2, pH 7.0). Second, a twofold peptide solution was generated containing 100 µM custom made peptide (Boc-Ile-Asp-(Dimethyl)Lys-(Ac)Lys-aminoluciferin) in HEPES buffer. Compounds and controls are added to the plates using acoustic dispensing to reach a final concentration of 10 µM in the assay followed by 5 µl enzyme solution per well. Plates are centrifuged shortly and 5 µL/well peptide solution are added to the wells, centrifuged shortly and incubated for 30 min at RT. Afterwards, 5 µL detection reagent (0.067 mg/mL Luciferase, 133.3 µM ATP, 0.133 mg/mL Trypsin in HEPES buffer) were added to each well. Plates were centrifuged shortly and measured on a multimode reader after 30 min incubation at RT in the dark. IC50 values were determined using 7 point dose response curves (DRCs) between 20 µM and 312 nM. In case inhibition values were not below 50% additional 7 point DRCs were measured, starting at 312 nm with a dilution factor of 2. All DRCs were recorded in triplicates.

opencc-by-4.0Nov 2021View details →

Data for "Microscopic origin of the effect of substrate metallicity on interfacial free energies"

Data related to the article "Microscopic origin of the effect of substrate metallicity on interfacial free energies" Laura Scalfi, Benjamin Rotenberg, arXiv:2105.06799 [physics.chem-ph]

opencc-zeroNov 2021View details →

Data from: Evidence for morph-specific substrate choice in a green-brown polymorphic grasshopper

Orthopteran insects are characterized by high variability in body coloration, in particular featuring a widespread green-brown color polymorphism. The mechanisms that contribute to the maintenance of this apparently balanced polymorphism are not yet understood. To investigate whether morph-dependent microhabitat choice might contribute to the continued coexistence of multiple morphs, we studied substrate choice in the meadow grasshopper Pseudochorthippus parallelus. The meadow grasshopper occurs in multiple discrete, genetically determined color morphs that range from uniform brown to uniform green. We tested whether three common morphs preferentially choose differently colored backgrounds in an experimental arena. We found that a preference for green backgrounds was most pronounced in uniform green morphs. If differential choices improve morph-specific performance in natural habitats via crypsis and/or thermoregulatory benefits, they could help to equalize fitness differences among color morphs and potentially produce frequency-dependent microhabitat competition, though difference appear too small to serve as the only explanation. We also measured the reflectance of the grasshoppers and backgrounds and used visual modelling to quantify the detectability of the different morphs to a range of potential predators. Multiple potential predators, including birds and spiders, are predicted to distinguish between morphs chromatically, while other species, possibly including grasshoppers themselves, will perceive only differences in brightness. Our study provides the first evidence that morph-specific microhabitat choice might be relevant to the maintenance of the green-brown polymorphisms in grasshoppers and shows that visual distinctness of color morphs varies between perceivers.

opencc-by-4.0Feb 2022View details →

Imaging ellipsometry on exfoliated GaS on sapphire substrate

Delta and Psi maps measured for wavelengths  200 nm to 900 nm with an Accurion EP4 ellipsometer. Ellipsometric enhanced contrast micrograph of the measured region - map.jpg Plotted Delta and Psi maps for 2, 2.5, 3, 3.2 and 3.5 eV are included.

opencc-by-4.0Oct 2017View details →

Fig. 3 in Harpacticoida (Crustacea, Copepoda) Of Mussel Beds And Macroalgae On The Rocky Substrates In The North-Western Black Sea

Fig. 3. Average number of harpactocoid copepods per m2 and their percentage in total meiobenthos community.

opencc-by-4.0Oct 2017View details →

Fig. 2 in Harpacticoida (Crustacea, Copepoda) Of Mussel Beds And Macroalgae On The Rocky Substrates In The North-Western Black Sea

Fig. 2. Percentage of harpactocoid copepods in total meiobenthos community on different species of macrophyts.

opencc-zeroMay 2022View details →

Data from: eDNA metabarcoding of log hollow sediments and soils highlights the importance of substrate type, frequency of sampling and animal size, for vertebrate species detection

Fauna monitoring often relies on visual monitoring techniques such as camera trappings, which have biases leading to underestimates of vertebrate species diversity. Environmental DNA (eDNA) has emerged as a new source of biodiversity data that may improve biomonitoring; however, eDNA based assessments of species richness remain relatively untested in terrestrial environments. We investigated the suitability of fallen log hollow sediment as a source of vertebrate eDNA, across two sites in south-western Australia - one with a Mediterranean climate and the other semi-arid. We compared two different approaches (camera trapping and eDNA metabarcoding) for monitoring of vertebrate species, and investigated the effect of other factors (frequency of species, timing of visits, frequency of sampling, body size) on vertebrate species detectability. Metabarcoding of hollow sediments resulted in the detection of higher species richness in comparison Hollow sediment detected higher species richness (29 taxa: six birds, three reptiles and 20 mammals) to metabarcoding of soil at the entrance of the hollow (13 taxa: three birds, two reptiles and eight mammals). We detected 31 taxa in total with eDNA metabarcoding and 47 with camera traps, with 14 taxa detected by both (12 mammals and two birds). By comparing camera trap data with eDNA read abundance, we were able to detect vertebrates through eDNA metabarcoding that had visited the area up to two months prior to sample collection. Larger animals were more likely to be detected, and so were vertebrates that were identified multiple times in the camera traps. These findings demonstrate the importance of substrate selection, frequency of sampling, and animal size, on eDNA based monitoring. Future eDNA experimental design should consider all these factors as they affect detection of target taxa.