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Preliminary Evaluation of Hai-Nan Lightning Detection Network (HNLDN)

Lightning location data for "Preliminary Evaluation of Hai-Nan Lightning Detection Network (HNLDN)"

opencc-zeroJul 2021View details →

African elephants can detect water from natural and artificial sources via olfactory cues

Water is vital for mammals. Yet, as ephemeral sources can be difficult to find, it raises the question, how do mammals locate water? Elephants (Loxodonta africana) are water-dependent herbivores that possess exceptional olfactory capabilities, and it has been suggested that they may locate water via smell. However, there is no evidence to support this claim. To explore this, we performed two olfactory choice experiments with semi-tame elephants. In the first, we tested whether elephants could locate water using olfactory cues alone. For this, we used water from two natural dams and a drinking trough utilised by the elephants. Distilled water acted as a control. In the second, we explored whether elephants could detect three key volatile organic compounds (VOCs) commonly associated with water (geosmin, 2-methylisoborneol, and dimethyl sulphide). We found that the elephants could locate water olfactorily, but not the distilled water. Moreover, they were also able to detect the three VOCs associated with water. However, these VOCs were not in the odour profiles of the water sources in our experiments. This suggests that the elephants were either able to detect the unique odour profiles of the different water sources or used other VOCs that they associate with water. Ultimately, our findings indicate that elephants can locate water olfactorily at small spatial scales, but the extent to which they, and other mammals, can detect water over larger scales (e.g. km) remains unclear.

HeteroFuzz: Fuzz Testing to Detect Platform Dependent Divergence for Heterogeneous Applications

HeteroFuzz supplementary material.

Image dataset from Automatic analysis of Trypanosoma cruzi: A machine learning approach for the detection of blood trypomastigotes in low resolution images

Image files of mice blood smear containing T. cruzi trypomastigotes. The images were used to train and test the classification model of the objects (nuclei and/or kinetoplasts) present in the images.

Figure 6 from: Li SN, Xu F, Jiang M, Liu F, Wu F, Zhang P, Fan YG, Chen ZH (2021) Two new toxic yellow Inocybe species from China: morphological characteristics, phylogenetic analyses and toxin detection. MycoKeys 81: 185-204. https://doi.org/10.3897/mycokeys.81.68485

Figure 6 SEM images showing basidiospores of aInocybe squarrosoluteabInocybe squarrosofulva. Scale bars: 5 µm.

Figure 2 from: Li SN, Xu F, Jiang M, Liu F, Wu F, Zhang P, Fan YG, Chen ZH (2021) Two new toxic yellow Inocybe species from China: morphological characteristics, phylogenetic analyses and toxin detection. MycoKeys 81: 185-204. https://doi.org/10.3897/mycokeys.81.68485

Figure 2 Basidiomata of Inocybe squarrosoluteaa, b MHHNU8536 c MHHNU31006, and d MHHNU31427. Scale bars: 10 mm.

Figure 5 from: Li SN, Xu F, Jiang M, Liu F, Wu F, Zhang P, Fan YG, Chen ZH (2021) Two new toxic yellow Inocybe species from China: morphological characteristics, phylogenetic analyses and toxin detection. MycoKeys 81: 185-204. https://doi.org/10.3897/mycokeys.81.68485

Figure 5 Microscopic features of Inocybe squarrosofulva (MHHNU31548, holotype) a, b basidiospores, c basidia with probasidium d gill edge e pleurocystidia f cheilocystidia and paracystidia g caulocystidia and cauloparacystidia h oleiferous hyphae i hymenial hyphae, and j, k pileipellis. Scale bars: 10 µm.

Figure 1 from: Li SN, Xu F, Jiang M, Liu F, Wu F, Zhang P, Fan YG, Chen ZH (2021) Two new toxic yellow Inocybe species from China: morphological characteristics, phylogenetic analyses and toxin detection. MycoKeys 81: 185-204. https://doi.org/10.3897/mycokeys.81.68485

Figure 1 Phylogenetic relationship and placement of Inocybe squarrosofulva and I. squarrosolutea inferred from the combined dataset (ITS, nrLSU, and rpb2) using ML. Bootstrap values ≥80% and Bayesian posterior probabilities ≥0.95 are reported on the branches. Sequences generated in this study are shown in bold. The new species is indicated in red. The red branch indicates the confirmed presence of muscarine, the gray branch indicates ambiguous for muscarine, and the black branch indicates a lack of muscarine.

Figure 3 from: Li SN, Xu F, Jiang M, Liu F, Wu F, Zhang P, Fan YG, Chen ZH (2021) Two new toxic yellow Inocybe species from China: morphological characteristics, phylogenetic analyses and toxin detection. MycoKeys 81: 185-204. https://doi.org/10.3897/mycokeys.81.68485

Figure 3 Microscopic features of Inocybe squarrosolutea (MHHNU31427) a basidiospores b basidia with probasidium c gill edge d cheilocystidia and paracystidia e pleurocystidia f caulocystidia and cauloparacystidia g, h pileipellis i oleiferous hyphae, and j hymenial hyphae. Scale bars: 10 µm.

opencc-zeroAug 2021View details →

Specialized metabolites in floral resources: effects and detection in buff-tailed bumblebees

The selection of appropriate food resources by bees is a critical aspect for the maintenance of their populations, especially in the current context of global change and pollinator decline. Wild bees have a sophisticated ability to forage selectively on specific resources, and can assess the quality of pollen using contact chemosensory perception (taste). While numerous studies have investigated the detection of pollen macronutrients in bees and their impact on bee health and reproductive success, only a few studies have described the gustatory responses of bees towards specialized metabolites. In addition, these studies mostly focused on the response to nectar and neglected pollen, which is the main food resource for both bee imagines and larvae. Whether bees have the ability to detect specialized toxic metabolites in pollen and then rapidly adapt their foraging behavior to avoid them is very little studied. In this study, we tested whether pollen specialized metabolites affect bumblebees at both the micro-colony and individual levels (i.e. bioassays using supplemented pollen), and whether foragers detect these specialized metabolites and potentially display an avoidance behavior (i.e. preference tests using supplemented syrup). Bumblebees were fed with either amygdalin-, scopolamine- or sinigrin-supplemented pollen diets in ratios that mimic 50%, 100% and 200% of naturally occurring concentrations. We found no effect of these specialized metabolites on resource collection, reproductive success and stress response at the micro-colony level. At the individual level, bumblebees fed on 50%-amygdalin or 50%-scopolamine diets displayed the highest scores for damage to their digestive systems. Interestingly, during the preference tests, the solution with 50%-scopolamine displayed a phagostimulatory activity, whereas solution with 50%-amygdalin had a deterrent effect and could trigger an active avoidance behavior in bumblebees, with a faster proboscis retraction. Our results suggest that regulation of toxin intake is not as well-established and effective as the regulation of nutrient intake in bees. Bees are therefore not equally adapted to all specialized pollen metabolites that they can come into contact with.

openAug 2021View details →

Sentinel-2 parallax-based cloud detection - sample dataset

This repository archives the Sentinel-2 sample data used in: D. Frantz, E. Haß, A. Uhl, J. Stoffels, and J. Hill (2018): Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects. Remote Sensing of Environment 215, 471-481. <a href="https://doi.org/10.1016/j.rse.2018.04.046">https://doi.org/10.1016/j.rse.2018.04.046</a>

opennotspecifiedMar 2020View details →

Fig 2 in Ernobius mollis (Linnaeus) (Coleoptera: Ptinidae: Ernobiinae), an Adventive Wood-Boring Beetle Detected in Western Canada

Fig 2. Collection method, using trap logs.

Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning

This is an open dataset.

opencc-zeroSep 2021View details →

Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning

In this study, specifically for the detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared from captured images by a camera mounted on a mobile robot. One is a machine learning approach, known as 'Cascaded Object Detector' (COD) and the other is a composition of traditional customised methods, individually known as 'Colour Transformation': 'Colour Segmentation' and 'Circular Hough Transformation'. The (Viola-Jones) COD generates 'histogram of oriented gradient' (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, colour thresholding is applied to detect tomatoes either from natural or solid background and RGB colour is adjusted to identify ripened tomatoes. This algorithm is shown to be optimally feasible for any micro-controller based miniature electronic devices in terms of its run time complexity of O(n3) for a traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB.

opencc-zeroSep 2021View details →

Primary detection records for aquatic nonindigenous species in global estuarine and marine ecosystems and the Great Lakes

Aim The introduction of aquatic non-indigenous species (ANS) has become a major driver for global changes in species biogeography. We examined spatial patterns and temporal trends of ANS detections since 1965 to inform conservation policy and management. Location Global Methods We assembled an extensive dataset of first records of detection of ANS (1965-2015) across 49 aquatic ecosystems, including the i) year of first collection, ii) population status and iii) potential pathway(s) of introduction. Data were analysed at global and regional levels to assess patterns of detection rate, richness, and transport pathways. Results An annual mean of 43 (± 16 S.D.) primary detections of ANS occurred – one new detection every 8.4 days for 50 years. The global rate of detections was relatively stable during 1965-1995, but increased rapidly after this time, peaking at roughly 66 primary detections per year during 2005-2010, then declining marginally. Detection rates were variable within and across regions through time. Arthropods, molluscs and fishes were the most frequently reported ANS. Most ANS were likely introduced as stowaways in ships' ballast water or biofouling, although direct evidence is typically absent. Main conclusions This synthesis highlights the magnitude of recent ANS detections, yet almost certainly represents an underestimate as many ANS go unreported due to limited search effort and diminishing taxonomic expertise. Temporal rates of detection are also confounded by reporting lags, likely contributing to the lower detection rate observed in recent years. There is a critical need to implement standardized, repeated methods across regions and taxa to improve the quality of global-scale comparisons and sustain core measures over longer timescales. It will be fundamental to fill in knowledge gaps given that invasion data representing broad regions of the world's oceans are not yet readily available and to maintain knowledge pipelines for adaptive management.

opencc-zeroSep 2021View details →

Targeted and passive environmental DNA approaches outperform established methods for detection of quagga mussels, Dreissena rostriformis bugensis in flowing water

<ol> <li>The early detection of invasive non-native species (INNS) is important for informing management actions. Established monitoring methods require the collection or observation of specimens, which is unlikely at the beginning of an invasion when densities are likely to be low. Environmental DNA (eDNA) analysis is a highly promising technique for the detection of INNS – particularly during the early stages of an invasion. </li> <li>Here, we compared the use of traditional kick-net sampling with two eDNA approaches (targeted detection using both conventional and quantitative PCR, and passive detection via metabarcoding with conserved primers) for detection of quagga mussel, Dreissena rostriformis bugensis; a high priority INNS, along a density gradient on the River Wraysbury, UK. </li> <li>All three molecular tools outperformed traditional sampling in terms of detection. Conventional PCR and qPCR both had 100% detection rate in all samples, and outperformed metabarcoding when the target species was at low densities. Additionally, quagga mussel DNA copy number (qPCR) and relative read count (metabarcoding) were significantly influenced by both mussel density and distance from source population, with distance being the most significant predictor. </li> <li> Synthesis and application. All three molecular approaches were more sensitive than traditional kick-net sampling for the detection of the quagga mussel in flowing water, and both qPCR and metabarcoding enabled estimates of relative abundance. Targeted approaches were more sensitive than metabarcoding, but metabarcoding has the advantage of providing information on the wider community, and consequently impacts of INNS. </li> </ol>

Data for "Defect detection in atomic-resolution images via unsupervised learning with translational invariance"

This dataset accompanies the paper titled Defect detection in atomic-resolution images via unsupervised learning with translational invariance by Yueming Guo*, Sergei V. Kalinin, Hui Cai, Kai Xiao, Sergiy Krylyuk, Albert V Davydov, Qianying Guo, Andrew R. Lupini*

Rectangles detected with the discrete Central multi-scale Radon transform

A screen recording of a demonstration mobile phone application that uses the discrete Central Radon transfom to detect, in real-time, the most prominent rectangle seen by the device's camera.

Raw Data for Data Set of Publication on Detecting Violations of Access Control and Information Flow Policies in Data Flow Diagrams

This data set contains the build script, software and intermedia results that we used to create the result data set for the publication "<a href="https://doi.org/10.1016/j.jss.2021.111138">Detecting Violations of Access Control and Information Flow Policies in Data Flow Diagrams</a>".

Trained deep neural networks for MSI/dMMR detection in colorectal cancer histology

These are trained neural network models in PyTorch format to process tessellated images of colorectal cancer histology samples. The input is expected to be 224x224 px RGB image tiles normalized with the Macenko method. The output is a probability of the image tile for being MSI/dMMR or MSS/pMMR.  The models have been trained on eight cohorts but not on the validation cohort. The validation cohorts are: Ex_0  : DACHS Ex_1  :  DUSSEL Ex_2  :  MECC Ex_3  : QUASAR Ex_4  : RAINBOW Ex_5  : TCGA Ex_6  : UMM Ex_7  : YORKSHIRE Ex_8  : MUNICH The models can be loaded in Python with  >>> model = torch.load(path, map_location=torch.device('cpu')) Further details are given in the manuscript.