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FIG. 4 in Description of a new Myzocallis (Hemiptera Aphididae) living on Valonia oak in Southern Italy with DNA barcoding accounts on allied species-group
FIG. 4. Neighbour-Joining tree reporting COI barcode sequence distances for Myzocallis spp.
FIG. 1 in New Lipaphis species (Hemiptera, Aphididae, Macrosiphini) from central Türkiye living on Sisymbrium sp. (Brassicaceae)
FIG. 1
FIG.3 in New Lipaphis species (Hemiptera, Aphididae, Macrosiphini) from central Türkiye living on Sisymbrium sp. (Brassicaceae)
FIG.3
FIG. 2 in New Lipaphis species (Hemiptera, Aphididae, Macrosiphini) from central Türkiye living on Sisymbrium sp. (Brassicaceae)
FIG. 2
Optical signature dataset for living macrophages and monocytes
<p>Pro-inflammatory (M1) and anti-inflammatory (M2) macrophage phenotypes play a fundamental role in the immune response. The interplay and consequently the classification between these two functional subtypes is significant for many therapeutic applications. Albeit, a fast classification of macrophage phenotypes is challenging. For instance, image-based classification systems need cell staining and coloration, which is usually time and cost-consuming, such as multiple cell surface markers, transcription factors and cytokine profiles are needed. A simple alternative would be to identify such cell types by using a single-cell, label-free and high-throughput light scattering pattern analyses combined with a straightforward machine-learning-based classification. Here, we compared different machine learning algorithms to classify distinct macrophage phenotypes based on their optical signature obtained from an ad-hoc developed wide angle static light scattering apparatus. As the main result, we were able to identify unpolarized macrophages from M1- and M2-polarized phenotypes and distinguished them from naive monocytes with an average accuracy above 85%. Therefore, we suggest that optical single-cell signatures within a lab-on-a-chip approach along with machine learning could be used as a fast, affordable, non-invasive macrophage phenotyping tool to supersede resource-intensive cell labelling.</p>
"A Lot Takes Place Digitally Now, so It Can Be Good to Train on It'': A Large-Scale Repeated Cross-Sectional Study on Recording Live-Streamed Educational Activities among Health, Social, and Education Students
<p>Research article</p>
Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods
<p>The physiology of living organisms, such as living plants, is complex and particularly difficult to understand on a macroscopic, organism-holistic level. Among the many options for studying plant physiology, electrical potential and tissue impedance are arguably simple measurement techniques that can be used to gather plant-level information. Despite the many possible uses, our research is exclusively driven by the idea of phytosensing, that is, interpreting living plants’ signals to gather information about surrounding environmental conditions. As ready-to-use plant-level physiological models are not available, we consider the plant as a blackbox and apply statistics and machine learning to automatically interpret measured signals. In simple plant experiments, we expose <em>Zamioculcas zamiifolia</em> and <em>Solanum lycopersicum</em> (tomato) to four different stimuli: wind, heat, red light and blue light. We measure electrical potential and tissue impedance signals. Given these signals, we evaluate a large variety of methods from statistical discriminant analysis and from deep learning, for the classification problem of determining the stimulus to which the plant was exposed. We identify a set of methods that successfully classify stimuli with good accuracy, without a clear winner. The statistical approach is competitive, partially depending on data availability for the machine learning approach. Our extensive results show the feasibility of the blackbox approach and can be used in future research to select appropriate classifier techniques for a given use case. In our own future research, we will exploit these methods to derive a phytosensing approach to monitoring air pollution in urban areas.</p> <p>Data repository for our paper " <em>Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods</em> ", submitted to the journal Bioinspiration & Biomimetics . Please refer to the paper for more information.</p> <p> </p> <p><strong>Contents of this repository</strong></p> <ul> <li><em>mu_interface:</em> Code for our data collection plant experiments, based on Raspberry Pis and the <a href="http://cybertronica.co/?q=products/phytosensor">Cybertronica phytosensing and phytoactuating system</a>.</li> <li><em>SupplementaryCode</em>: Includes the discriminant analysis classifier, raw datasets, calculated features, test-train split and the corresponding code.</li> <li><em>dl-4-tsc:</em> Deep learning framework developed by <a href="https://doi.org/10.1007/s10618-019-00619-1">Fawaz et. al (Deep learning for time series classification: a review)</a> and adapted to our use case. </li> <li><em>DeepClassifier: </em>Trained deep learning time series classifier.</li> <li><em>classification_results.xlsx: </em>Overview of the results from the deep learning framework (accuracy, precision, recall, training time, confusion matrix) and the achieved accuracies using discriminant analysis with sequential forward section (further evaluation metrics of the discriminant analysis can be found in SupplementaryCode.</li> </ul>
FIGURE 4 in Description of a new ant- and stingless-bee-loving species of Cryptostigma Ferris (Hemiptera: Coccomorpha: Coccidae) from Ecuador living inside internodes of Cecropia (Urticaceae), with an updated key to the adult females and first-instar nymphs of the genus
FIGURE 4. Cryptostigma cecropiaphilum Kondo & Roubik, sp. nov., first-instar nymph.
FIGURE 3 in An Updated Checklist of Free-living Marine Nematodes from Coastal India
FIGURE 3. Distribution of Free-living Marine Nematodes across Coasts and Islands of India
FIGURE 2 in An Updated Checklist of Free-living Marine Nematodes from Coastal India
FIGURE 2. Familywise species richness of free-living marine nematodes in Indian Waters
Figure 1 from: Pyšková K, Storch D, Horáček I, Kauzál O, Pyšek P (2016) Golden jackal (Canis aureus) in the Czech Republic: the first record of a live animal and its long-term persistence in the colonized habitat. ZooKeys 641: 151-163. https://doi.org/10.3897/zookeys.641.10946
Figure 1 - Location of the study area in central Bohemia, western part of the Czech Republic (black rectangle). Previous records relating to dead animals (solid circles), and the first unconfirmed observation (empty circle) are also shown. The records are given by the year of observation, see details in Table 1.
Figure 2 from: Pyšková K, Storch D, Horáček I, Kauzál O, Pyšek P (2016) Golden jackal (Canis aureus) in the Czech Republic: the first record of a live animal and its long-term persistence in the colonized habitat. ZooKeys 641: 151-163. https://doi.org/10.3897/zookeys.641.10946
Figure 2 - Photographs of a golden jackal (Canis aureus) individual in the summer (A) and winter (B).
Figure 4 from: Pyšková K, Storch D, Horáček I, Kauzál O, Pyšek P (2016) Golden jackal (Canis aureus) in the Czech Republic: the first record of a live animal and its long-term persistence in the colonized habitat. ZooKeys 641: 151-163. https://doi.org/10.3897/zookeys.641.10946
Figure 4 - Distribution of the golden jackal (Canis aureus) individual's circadian activity over the study period. Records from the whole monitoring period were pooled and related to the time of the day in which the photographs were taken.
Figure 3 from: Pyšková K, Storch D, Horáček I, Kauzál O, Pyšek P (2016) Golden jackal (Canis aureus) in the Czech Republic: the first record of a live animal and its long-term persistence in the colonized habitat. ZooKeys 641: 151-163. https://doi.org/10.3897/zookeys.641.10946
Figure 3 - Distribution of the records of golden jackal. The numbers of photographs per week are given.
FIGURE 2 in Checklist of the Free-Living Marine Nematodes of the Iberian Peninsula (north east Atlantic)
FIGURE 2. Histogram showing the distribution of cited species for each of the corresponding orders.
scan_liveness
<p>This data has been generated using the code here: https://github.com/sheharbano/scan_liveness</p>
THE ROLE OF FINANCIAL LITERACY IN IMPROVING THE STANDARD OF LIVING OF THE POPULATION AND ITS IMPACT ON ECONOMIC DEVELOPMENT
Open the record for dataset details and reuse information.
Supplementary Material for "For Us By Us": Intentionally Designing Technology for Lived Black Experiences
<div>Supplementary Material for "For Us By Us": Intentionally Designing Technology for Lived Black Experiences which allows others to reproduce and extend the results presented in the paper.</div> <p>While there has been an increase in understanding how people across racial groups create community across different platforms, there is still a lack of studies that explicitly center on how Black technologists design with and for their own communities.</p> <p>In this paper, we present findings from a series of semi-structured interviews with Black technologists that have used, created, or curated resources to support lived Black experiences</p> <p>This database contains supplemental materials used in our study.</p>
Figure 1 in Living together in dead coral rocks: macrosymbiotic communities associated with Bonellia echiuran worms (Annelida: Thalassematidae: Bonelliinae), involving new commensal bivalve and amphipod species
Figure 1. Geographic location of the study site (Kushimoto, Wakayama Prefecture, Japan).
Figure 8 in Monitoring the feeding and parental care behavior of a pair of free-living owls (Tyto furcata) in the nest during the reproductive period in Rio de Janeiro, Brazil
Figure 8. Activities schedule of a Tyto furcata family registered within a urban nest box in Campos dos Goytacazes. Each bar shows the total number of days of the activity.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.