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FIGURE 5 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies

FIGURE 5. Effect of varying transition duration. Each panel represents a set of simulations generated at transition interval lengths of 0.001 times the event duration for A, 0.5 times the event duration for B, 1.0 times the event duration for C, and 5.0 times the event duration for D. Background DCA-1 value is -0.5 and completeness is 100%. Excursion magnitude (y-axis) indicates the difference between the simulated background DCA-1 value and the simulated event DCA-1 value. Resolution potential (x-axis) is the event duration divided by the time represented by the sample interval.Color shading indicates the median excursion magnitude of all samples that intersect an event. Contour lines show the parameter space where 50% (dashed line), 75% (dotted line) and 95% (solid line) of events are accurately detected.

FIGURE 9. DCA-1 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies

FIGURE 9. DCA-1 values observed when assemblages simulated at different event values are mixed with different proportions of the background assemblage. For A, the maximum DCA-1 value observed is shown; for B, the median DCA-1 value observed; and for C, the minimum DCA-1 value observed. In all cases, background assemblages are simulated at a DCA-1 value of -0.5 and are mixed with an event assemblage with a DCA-1 value as given on the x-axis.

FIGURE 3 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies

FIGURE 3. Multivariate comparison of empirical and simulated foraminiferal assemblages. A. Detrended correspondence analysis showing empirical (red filled) and simulated (black open) assemblages in the same ordination space. B. DCA1 scores for empirical samples paired with the DCA1 score of the corresponding simulated sample. Dotted line is the 1:1 line and is largely obscured by the points. C. All pairwise dissimilarities among empirical samples plotted against the average pairwise dissimilarity of 300 corresponding samples simulated at the same DCA1 values. Color scale depicts the density of dissimilarities with higher concentrations of dissimilarities in brighter colors. Black line is the 1:1 line.

FIGURE 7 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies

FIGURE 7. The interaction of bioturbation, completeness and transition duration. All simulations figured include bioturbation and use a background DCA-1 value of -0.5. Simulation results for 100% sampling completeness and transition durations 0.001 times the event duration for A; 100% sampling completeness and transition durations five times the event duration for B; 25% sampling completeness and transition durations 0.001 times the event duration for C; 25% sampling completeness and transition durations five times the event duration for D. Excursion magnitude (yaxis) indicates the difference between the simulated background DCA-1 value and the simulated event DCA-1 value. Resolution potential (x-axis) is the event duration divided by the time represented by the sample interval. Color shading indicates the median excursion magnitude of all samples that intersect an event. Contour lines show the parameter space where 50% (dashed line), 75% (dotted line) and 95% (solid line) of events are accurately detected.

FIGURE 14 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies

FIGURE 14. Estimated ability of the record to detect 100 year-long events with 3 cm samples, given a known sedimentation rate at three excursion magnitudes (0.4, 1.2 and 2.8), based on 'worst-case scenario' simulations with 25% sampling, bioturbation, rapid transitions between events and the background condition (-0.5 DCA-1 value). A and B are simulated with an excursion magnitude of 0.4; C and D are simulated with an excursion magnitude of 1.2; and E and F are simulated with an excursion magnitude of 2.8. A, C and E show the percentage of events that are detectable above a background value of -0.5. An event is detected if a sample has an observed DCA-1 value exceeding the 95% quantile of the DCA-1 value of assemblages simulated at the background value. B, D and F show the median DCA-1 values recovered from samples intersecting simulated events. Values that fall above the blue dashed line are detected; that is, they exceed the 95% quantile of the DCA-1 value of assemblages simulated at the background value. Values that fall above the red dotted line are accurately detected; that is, they exceed the 2.75% quantile on DCA-1 values recovered from simulations at the event value.

opencc-by-4.0Jun 2024View details →

Data to How to best detect threatened deadwood fungi – comparing metabarcoding and fruit body surveys

This data set contains occurence data of threatened (red listed) fungi on deadwood objects in the National Park Bavarian Forest and the Žofínský prales National Nature Reserve to the the publication "How to best detect threatened deadwood fungi – comparing metabarcoding and fruit body surveys" by Rieker et al. (2024). This data is mainly uploaded to recreate analyses.  Site: Information on the sampling site ObjectID: ID of the sampled deadwood object Campaign: Sampling year Decaystage: Decaystage of the deadwood object at sampling time (ranging between 1 and 4) Kingdom: Kingdom the eadwood object tree species belongs to Method: sampling method Species: threatend species name  Presence: only contains the value "1" for present

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Data from: Ecological forensic testing: Using multiple primers for eDNA detection of marine vertebrates in an estuarine lagoon subject to anthropogenic influences

Many critical aquatic habitats are in close proximity to human activity (i.e., adjacent to residences, docks, marinas, etc.), and it is vital to monitor biodiversity in these and similar areas that are subject to ongoing urbanization, pollution, and other environmental disruptions. Environmental DNA (eDNA) metabarcoding is an accessible, non-invasive genetic technique used to detect and monitor species diversity and is a particularly useful approach in areas where traditional biodiversity monitoring methods (e.g., visual surveys or video surveillance) are challenging to conduct. In this study, we implemented an eDNA approach that used a combination of three distinct PCR primer sets to detect marine vertebrates within a canal system of Biscayne Bay, Florida, an ecosystem representative of challenging sampling conditions and a myriad of impacts from urbanization. We detected fish species from aquarium, commercial, and recreational fisheries, as well as invasive, cryptobenthic, and endangered vertebrate species, including charismatic marine mammals such as the protected West Indian manatee, Trichechus manatus. Our results support the potential for eDNA analyses to supplement traditional biodiversity monitoring methods and ultimately serve as an important tool for ecosystem management. This approach minimizes stress or disturbance to organisms and removes the intrinsic risk and logical limitations of SCUBA diving, snorkeling, or deploying sensitive equipment in areas that are subject to high vessel traffic and/or low visibility. Overall, this work sets the framework to understand how biodiversity may change over different spatial and temporal scales in an aquatic ecosystem heavily influenced by urbanization and validates the use of eDNA as a complementary approach to traditional ecological monitoring methods.

opencc-zeroJul 2024View details →

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Data from: Sequence-based detection of emerging antigenically novel influenza A viruses

The detection of evolutionary transitions in influenza A (H3N2) viruses' antigenicity is a major obstacle to effective vaccine design and development. In this study, we describe NIAViD, an unsupervised machine learning tool, adept at identifying these transitions, using HA1 sequence and associated physicochemical properties. NIAViD, performed with 88.9% (95% CI, 56.5%–98.0%) and 72.7% (95% CI,43.4%– 90.3%) sensitivity in training and validation respectively, outperforming the uncalibrated null model – 33.3% (95% CI,12.1%–64.6%) and does not require the need for potentially biased, time-consuming and costly laboratory assays. The pivotal role of Boman's index, indicative of the virus's cell surface binding potential, is underscored, enhancing the precision of detecting antigenic transitions. NIAViD's efficacy is not only in identifying influenza isolates that belong to novel antigenic clusters, but also in pinpointing potential sites driving significant antigenic changes, without the reliance on explicit modeling of hemagglutinin inhibition titers. Our approach holds immense promise to augment existing surveillance networks, offering timely insights for the development of updated, effective influenza vaccines. Consequently, NIAViD, in conjunction with other resources, could be used to support surveillance efforts and inform the development of updated influenza vaccines.

opencc-zeroJul 2024View details →

opencc-by-4.0Dec 2022View details →

Figure 3. Chromatogram from the sample fairs MAC 02 and ARA 0 in Detection of enteropathogens and research of pesticide residues in Lactuca sativa from traditional and agroecological fairs

Figure 3. Chromatogram from the sample fairs MAC 02 and ARA 0, with the peaks of Diphenoconazole compared with the pattern.

opencc-by-4.0Dec 2022View details →

Figure 2 in Detection of enteroparasites in foliar vegetables commercialized in street- and supermarkets in Aparecida de Goiânia, Goiás, Brazil

Figure 2. Protozoa detected in samples of lettuce and collard greens from street market and supermarkets in Aparecida de Goiânia, Goiás. (A) Cyst; (B) coccid oocyst with multiple sporocysts; (C) oocyst from Cystoisospora sp. resemble the C. belli; (D) oocyst of C. canis, (E) and (F) oocyst of Eimeriidae.

opencc-by-4.0Apr 2017View details →

Figs. 4-5 in Detection of the invasive western conifer seed bug Leptoglossus occidentalis Heidemann, 1910 (Heteroptera: Coreidae: Coreinae) in Chile.

Figs. 4-5.- Metatibial dilations of Leptoglossus from Chile. 4.- Leptoglossus occidentalis. 5.- Leptoglossus chilensis.

Figure 2 in A powerful new light source for ultraviolet detection of scorpions in the field

Figure 2: Photographs of a prototype LED array lamp. A. Frontal view showing diode array. B. Side view showing lamp housing. Ventilation holes were cut out of the top and bottom walls of the housing, and a small cooling fan was mounted on the bottom surface. Battery power is supplied by the BNC connection on the back. C. Side-by-side comparison of LED and BLB tube lamp housings, showing the substantially reduced profile of the solid-state lamp. Ruler scale: 12 inches.

Figure 4 in A powerful new light source for ultraviolet detection of scorpions in the field

Figure 4: Excitation spectrum of a blacklight tube and fluorescence spectrum of the scorpion cuticle. The data were recorded with a scanning spectrometer during blacklight illumination of Hadrurus obscurus (Iuridae). The broad peak below 400 nm and the discrete Hg lines correspond to the tube emission (F6T5/BLB, Sylvania), and the broad peak in the visible region (450-550 nm) corresponds to the blue-green fluorescence of the scorpion. This visible peak disappeared when the scorpion was removed and only light from the BLB tube was scanned. Identically shaped 450- 550 nm fluorescence spectra were obtained from Paruroctonus silvestrii and Paruroctonus becki (Vaejovidae), which suggests that the chemistry of fluorescence is conserved across different scorpion families. We therefore expect that UV detection results presented here will be applicable to a wide variety of scorpion taxa. We did not record the UV LED spectrum. Typical published spectra for these devices peak sharply at the rated wavelength of 395 nm, with 50% peak widths of only about 20 nm.

Figure 3 in A powerful new light source for ultraviolet detection of scorpions in the field

Figure 3: Comparison of scorpion fluorescence excited by different light sources. The pair of CCD camera images show a Vaejovis confusus illuminated by (A) a fluorescent lamp equipped with two 15 W BLB tubes (2.3 A current), and (B) a 168-diode UV LED array (1.8 A current). The scorpion fluorescence in (B) was attenuated 10.5-fold by a neutral density filter to prevent CCD saturation, so it appears equally bright in this picture. Scale bar: 15 mm.

Figure 1 in A powerful new light source for ultraviolet detection of scorpions in the field

Figure 1: Circuit diagram of the prototype LED array. The array has a scalable architecture, here with N = 56 parallel branches. Each branch includes three LEDs in series with a single current-limiting resistor (Rs = 39 Ω). Typical forward voltage drop measured across one diode was 3.4 V, giving a total diode drop of 10.2 V in each branch. During operation, voltage across the 12 V battery terminals was Vb = 11.5 V, so the current in each branch was (11.5-10.2)/39 A ~ 33 mA. This kept the current near the maximum rating of 30 mA specified by the manufacturer for 100,000 h lifetime. Each resistor dissipates ~ 1.3 V × 33 mA = 43 mW; the 3 diodes in a branch dissipate 3 × 3.4 × 33 mA = 337 mW. By using 3 diodes to maximizing total diode voltage drop, resistor power loss is kept down to about 13% of the total power. We have implemented the simplest possible circuit to drive the LEDs, one that can be easily built by biologists who have not trained in electronic engineering. More sophisticated driver circuits might be devised to regulate or balance current flow to different branches of the array, but they probably offer little additional benefit for the purposes of scorpion detection.

opencc-by-4.0Jul 2024View details →

Dataset for What are key factors for detection of peptides using mass spec-trometry on boron-doped diamond surfaces?

The data set to paper:  What are key factors for detection of peptides using mass spec-trometry on boron-doped diamond surfaces? Juvissan Aguedo1, Marian Vojs2, Martin Vr&scaron;ka2, Marek Nemcovic3, Zuzana Pakanova3, Katerina Aubrechtova Dragounova4, Oleksandr Romanyuk4, Alexander Kromka4, Marian Varga5, Michal Hatala6, Marián Marton2 and Jan Tkac1,* 1 Institute of Chemistry, Slovak Academy of Sciences, Bratislava, Slovakia 2 Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Bratislava, Slovakia 3 Centre of Excellence for Glycomic, Slovak Academy of Sciences, Bratislava, Slovakia 4 Institute of Physics, Czech Academy of Sciences, Prague 6, Czech Republic 5 Institute of Electrical Engineering, Slovak Academy of Sciences, Bratislava, Slovakia 6 Department of Graphic Arts Technology and Applied Photochemistry, Faculty of Chemical and Food Technology, Slovak University of Technology, Bratislava, Slovakia * corresponding author: jan.tkac@savba.sk Data manager: Kristýna Dostálová: dostalovak@fzu.cz Date of data collection: 1. 5. 2023 - 31. 5. 2024 All the data shown in the pictures are provided with a described sample in X-Y or X-Y-Z format. The respective figure to which the data belong is always provided in high resolution. The data are in the following formats:  Scheme 1: pdf Figure 1: pdf Figure 2: pdf, csv Figure 3: pdf Figure 4: pdf, csv Figure 5: pdf Figure 6: pdf, csv Figure 7: pdf, csv Figure 8: pdf, csv Figure 9: pdf, csv The comma separated values file (csv) always contain the description of the columns in the first row. In case of composed image the name of the file corresponds to the corresponding figure. Data acquistion and processing is provided in the Experimental part in the publication: DOI: 10.3390/nano14151241

Fig. 3 in Rapid detection of insecticide resistance in Diaphorina citri (Hemiptera: Liviidae) populations, using a bottle bioassay

Fig. 3. Susceptibility of laboratory and field-collected populations of Diaphorina citri to imidacloprid tested at the diagnostic exposure time–concentration combination (A: Lake Alfred 1; B: Winter Garden; C: Lake Alfred 2; D: Frostproof; LB: laboratory strain, FL: Florida field population). Each bar represents mean ± SE. An asterisk (*) indicates significant difference between laboratory and field population at a time period based on a Bonferroni test (P ≤ 0.05).

Fig. 2 in Rapid detection of insecticide resistance in Diaphorina citri (Hemiptera: Liviidae) populations, using a bottle bioassay

Fig. 2. Susceptibility of laboratory and field-collected populations of Diaphornia citri to dimethoate tested at the diagnostic exposure time–concentration combination (A: Lake Alfred 1; B: Winter Garden; C: Lake Alfred 2; D: Frostproof; LB: laboratory strain, FL: Florida field population). Each bar represents mean ± SE. An asterisk (*) indicates significant difference between laboratory and field population based on a Bonferroni test (P ≤ 0.05).

Fig. 1 in Rapid detection of insecticide resistance in Diaphorina citri (Hemiptera: Liviidae) populations, using a bottle bioassay

Fig. 1. Susceptibility of laboratory and field-collected populations of Diaphorina citri of bifenthrin tested at the diagnostic exposure time–concentration combination (A: Lake Alfred 1; B: Winter Garden; C: Lake Alfred 2; D: Frostproof;LB:laboratory strain, FL: Florida field population). Each bar represents mean ± SE.An asterisk (*) indicates significant difference between laboratory and field population at a time period based on a Bonferroni test (P ≤ 0.05).

Fig. 4 in Rapid detection of insecticide resistance in Diaphorina citri (Hemiptera: Liviidae) populations, using a bottle bioassay

Fig. 4. Susceptibility of laboratory and field-collected populations of Diaphorina citri to fenpropathrin tested at the diagnostic exposure time–concentration combination (A: Lake Alfred 1; B: Winter Garden; C: Lake Alfred 2; Frostproof; LB: Laboratory strain, FL: Florida Field strain). Each bar represents mean ± SE. An asterisk (*) indicates significant difference between laboratory and field population at a time period based on a Bonferroni test (P ≤ 0.05).