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Key delimitation trapping survey performance factorsTrap attractivenessThe performance of the current Medfly design was unexpectedly inferior to that of the leek moth even with a more vagile target insect, 2.8 times greater trap density in the core, and a grid size over three times larger. Despite all those factors, p(capture) for the leek moth grid with 1/λ = 20 m was 15 percentage points greater than that for Medfly at 30 days duration. Thus, trap attractiveness was the key determinant for delimiting survey performance, as it was for detection13.One straightforward way to improve p(capture) and the accuracy of boundary setting, while also cutting costs, would be to develop more attractive traps. Poorly attractive traps include food-based attractants48 and traps based solely on visual stimuli36. But developing better traps is difficult. Pheromone-based attractants generally perform best49, but these are unavailable for many insects. For instance, scientists have searched for decades for effective pheromones for Anastrepha suspensa (Loew) and A. ludens (Loew) without success50. Common issues include the complexity of components, costs of synthesis, and chemical stability.Trap densitiesAll else being equal, increasing the trap density will generally improve p(capture) for any survey grid, and intuitively this can help compensate for using less attractive traps. However, the impact of increasing density is limited when attractiveness is low13,47, and large surveys or grids with many traps can become prohibitively expensive51. The Medfly grid designers likely understood that the available trap and lure was not highly attractive, and used higher densities in inner bands to try to reach some desired (non-quantitative) survey performance level. By contrast, the designers of the leek moth grid used a (constant) density three times smaller, likely because the trap and lure were known to be relatively strong. Here, for both species, marginal ROI decreased as densities increased (Tables 2, 3). Hence, increasing densities has limited benefit, but may be useful when better lures are unavailable13.In that context, the use of variable densities in the Medfly grid is understandable. At its standard size, the survey grid would require 8,100 traps if the core trap density were constant (Table 1). The designers likely intuited that lower densities could be used in outer bands because captures there were less likely. However, doing so reduces the likelihood of detection in outer bands and could increase the possibility of undetected egress, especially with longer survey durations. As far as we know, natural egress has not been raised as a concern following the numerous Medfly quarantines that have used this survey grid over the years, in Southern California in particular52.Generally, however, we think the variable Medfly grid densities run counter to delimitation goals. Greater core and Band 2 densities have proportionally more impact on p(capture), but only a few detections in the core are necessary to confirm the presence of the population (Goal 1), and inner area detections probably contribute little to boundary setting (see below). Therefore, lower or intermediate densities (at most) may be optimal for the core when considering ROI. For the outer bands, increasing densities might improve boundary setting (Goal 2) and help mitigate potential egress, but the sizes of those bands already limit cost efficiency (Table 2), making greater densities less advisable. Our simulation results can help elucidate how to balance these interests to achieve delimitation goals while minimizing costs47.Grid size considerationsThe simulation results indicated that the standard survey sizes for these two pests were excessive. We have verified that empirically for Medfly using trapping detections data53. A 14.5-km grid has been widely used for many other insects in the CDFA (2013) guidelines10, such as Mexfly and OFF, and the same analysis indicated that those are also oversized for use in short-term delimitation surveys53. From the same analysis, the predicted survey radius for leek moth, with D = 500 m2 per day, would be 2,382 m, or a diameter of nearly 4.8 km, which matches the results here. Similarly, Dominiak and Fanson45 analyzed trapping data for Qfly and found that the recommended quarantine area distance of 15 km could be reduced to 3 to 4 km.Grids with radii larger than 4.8-km only seem necessary for highly vagile insects, those with D ≥ 50,000 m2 per day47. This should not be surprising. Small insect populations are unlikely to move very far31,54, especially if hosts are available20,39,55. The (proposed) short duration of a delimitation survey would also limit dispersal potential (see below). Many delimiting survey plans may be oversized, because they were developed before much dispersal research had been done37, thus uncertainty was high. Our dispersal distance analysis included species with a wide range of dispersal abilities, so it can be used generally to choose smaller survey grid radii53.Reducing grid sizes down to about 4.8-km diameters may have little impact on p(capture), since detections in bands outside that distance contributed little to overall performance. The cores of both the leek moth and Medfly grids accounted for 86 percent or more of overall p(capture). While core area detections will confirm the presence of the population, they are less useful for defining spatial extent. The furthest detections from the presumed source are usually used to delimit the incursion46,56 (although in our experience formal boundary setting exercises seem rare). Delimiting surveys may often yield few captures anyway, because adventive populations can be very small and subject to high mortality31. Because size reductions eliminate traps in proportionally larger outer areas, the impact on survey costs is substantial. Removing just the outermost bands of each grid would directly reduce costs by $11,200 for leek moth (400 traps) and by $7,488 for Medfly (288 traps; Table 1).Another reason for the large size of the standard Medfly grid may be that it was designed for monitoring and management in addition to delimitation57. Medfly quarantines end after at least three generations without a detection, so the surveys may last for months. The grid size was reportedly originally determined by multiplying the estimated dispersal distance by three (PPQ, personal communication), to account for uncertainty. This implies that the estimated distance was about 2,400 m per 30 days. Thus, the design may not have been built for the 30-d duration used here, but our recommended design is valid if a shorter delimitation activity without further monitoring is appropriate.Although it seemed too large for leek moth, an 8-km grid for delimitation could be appropriate for some other moths. For example, the delimiting survey plans for Spodoptera littoralis (Boisduval) and S. exempta Walker use this size9. S. littoralis is described as dispersing “many miles”, and S. exempta can travel hundreds of miles9, which clearly exceeds the described dispersal ability of leek moth. On the other hand, the survey plan for summer fruit tortrix moth (Adoxophyes orana Fischer von Röeslerstamm) also specifies an 8-km grid for delimitation but contains little information on dispersal, suggesting only that most movement is local8. Like leek moth, a 4.8-km grid for that species seems likely to be more appropriate.Limiting egress potential is probably the main consideration when setting survey size, but uncertainty about the source population location may also be a factor. Survey grids placed over the earliest insect detection may sometimes be off center from the location of the source population54. However, so far as we know for our agency, most adventive populations have been localized, based on post-discovery detections (PPQ, personal communication). Likewise, we have found53 and other researchers have found that dispersal distances for different species in outbreaks and mark-recapture studies are often less than 1 km58,59,60. That may often be the case for detection networks of traps (e.g., for high risk fruit flies), which increase the likelihood of capture before the population has had much time to grow and disperse. Here, we focused explicitly on localized populations, but allowed for uncertainty in the simulations by varying outbreak locations over one mile in the central part of the grid. If the outbreak population is very large and has extensively spread out (e.g., spotted lanternfly, Lycorma delicatula (White) in 201461), delimitation will not be localized, but “area-wide”2. The results here do not apply to area-wide outbreaks, and we are currently studying how to effectively delimit them.Optimizing delimitation surveysMany trapping survey designs in use were based not on “hard” science but on local experience62. Scientists have recognized the need for more cost-effective surveillance strategies63,64. Quantitatively assessing p(capture) in different designs for the same target pest allows us to determine grid sizes and densities that lower costs while maintaining performance. Results here demonstrated that the sizes and densities of these two survey grids could be optimized to save up to $20,244 per survey for the leek moth and $38,168 per survey for the Medfly. In practical terms, that means more than five leek moth surveys could be run for the cost of one standard design survey. Additionally, over seven Medfly delimitation surveys could be funded by the budget of one standard plan. The magnitudes of reduction seen here may be typical, since about 90 percent of the costs in trapping surveys are for transportation and maintenance related to traps65.Quantifying survey performance was not possible until very recently, so it has been little discussed in the literature5,66, and no standard thresholds exist. We think 0.5 may be a reasonable minimum threshold for the choice of p(capture), to try to ensure that population detection is “more likely than not”. Designs that aim to maximize p(capture) could be realistic with high attractiveness traps, but those designs seem very likely to have lower ROIs (e.g., Table 2). Even for the most serious insect pests, we think targeting near-perfect population detection during delimitation is likely not justified. Designs achieving p(capture) from 0.6 to 0.75 could be highly effective in terms of both costs and performance.Another potential area of improvement is grid shape. Circular grids perform as well as square grids but use fewer traps and less service area to achieve equivalent p(capture)47. Moreover, detections in the corners of a square grid are evidence that insects could have traveled beyond the square along the axes, resulting in uncertain boundary setting. Most published survey grids are square10,46, but many field managers tend to use approximately circular trapping grids in the field (PPQ, personal communication). The conversion to a circular grid with a radius of half the square side length reduces the area and number of traps by around 21 percent47. Our findings were consistent with that value.This new quantification ability also indicates that some delimiting survey designs in the U.S.A. may not be performing as well as expected47. For instance, the delimiting survey design for Mexfly uses approximately 31 traps per km2 in the core of a 14.5 km square grid11, but the traps are only weakly attractive (1/λ ≈ 5 m). In this scenario, p(capture) was only around 0.23 with a 30-d survey duration47. A much greater density ( > 80 traps per km2) could be used in the core to achieve p(capture) ≥ 0.5, but this may not be feasible depending on the survey budget.Technical and modeling considerationsExamining diffusion-based movement for these two insects in TrapGrid can give insight into why simulations indicated that smaller grids may be adequate47. The value of σ for Medfly after 30 days is only about 1,550 m. In a normal distribution, σ = 1,550 m gives a 95th percentile distance of 2,550 m, which is similar to the estimated distance above of 2,400 m. Over 90 days, σ = 2,700 m for Medfly, which gives a 95th percentile distance of 4,441 m, still much shorter than the grid radius of 7,250 m. A 95th percentile of 7,250 m requires σ ≈ 4,408 m, which equals t = 253 days. In addition, the maximum total distance (up to 39 days after detection) we observed in trapping detections data for Medfly in Florida was about 4,800 m53.The same calculations for leek moth give σ ≈ 490 m for 30 days, with a 95th percentile distance of only 806 m. That is half the length of the recommended shortened radius above of 2.4 km, and nearly five times shorter than the radius of the standard 8-km grid. A 95th percentile of 4,000 m requires σ = 2,432 m, which implies t = 740 days, which is about two years. Therefore, the leek moth grid is arguably even more oversized than the Medfly grid.The default capture probability calculation in the current version (Ver. 2019-12-11) of TrapGrid is not sensitive to population size32 and does not consider the effects of ambient factors (e.g., wind speed and direction, rainfall, temperature). Many other factors can also impact trapping survey outcomes, such as topography of the environment, availability of host plants, seasonality of pest, and population dynamics. These factors are not considered in the current version of TrapGrid. More

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Dynamic in-flight flock organizationIt is commonly assumed that during flocking, flock members follow three basic interaction rules: Attraction, Repulsion and Alignment, to coordinate spatial positions between each other18. To study the spatial organization of our zebra finch flock during flight, the spatial positions of all birds in the flight section were tracked in every fifth frame (sample rate: 24 Hz (that is, frames per second)) of the synchronized footage recorded by two high-speed digital video cameras (Camera 1: centred upwind view, Fig. 1a,b; Camera 2: upturned vertical view, Fig. 1a,c) for the entire duration (51.7, 58.3, 69.2 and 127 s) of four (session 2, 5, 8 and 13) out of 13 flight sessions. Flight paths were reconstructed from the tracking data for each bird in the flock, with horizontal and vertical coordinates delivered by Camera 1 and coordinates in wind direction delivered by Camera 2. The data show that each bird mainly occupied a particular area in the flight section, and that this spatial preference was stable over different flight sessions. Bird Green, for example, was preferentially flying very low above the flight section’s floor, and bird Lilac preferred to fly at upwind positions in front of the flock (Fig. 1d, Extended Data Figs. 1 and 3 and Supplementary Information).Despite their preference in flight area, all birds constantly changed their spatial positions fast and rhythmically along the horizontal dimension of the flight section (Fig. 1e–g, Extended Data Figs. 2 and 4, Supplementary Video 1 and Supplementary Information). This behaviour is reminiscent of the flight behaviour of wild zebra finches: when being surprised in flight by a predator, zebra finches fly in a rapid zig-zag course low above the ground, heading for nearby vegetation16. Whether the sideways oscillating flight manoeuvres, which are performed by both wild birds in open space and domesticated birds in the wind tunnel’s flight section, are caused by the close proximity to the ground or are part of an escape reaction is yet unknown.From the tracking data, we further calculated the spatial distances in all three dimensions between all pairwise combinations of birds throughout the four flight sessions (sample rate: 24 Hz). When normalized to the maximum distance detected for each bird pairing, each dimension and each flight session, mean distances of bird pairings in all dimensions were narrowly distributed within a range of 27.7–38.0% of maximum distance (Fig. 1h and Supplementary Table 1). This may indicate that during flocking flight, zebra finches actively balance Attraction and Repulsion to maintain a stable 3D distance towards all other members of the flock. Owing to the spatial limitations in the wind tunnel’s flight section, we did not expect the zebra finches to perform large-scale flight manoeuvres with movements aligned between all flock members (Extended Data Fig. 5 and Supplementary Information), as can be observed, for example, in freely flying flocks of homing pigeons (Columba livia domestica)19 and white storks (Ciconia Ciconia)20.Visually guided horizontal repositioningWhen observing the dynamic spatial organization of our zebra finch flock, a question immediately arises: how do the birds prevent collisions during their frequent horizontal position changes? When considering the spatial limitation experienced by the flock of six birds during flight in the flight section and their highly dynamic flight style, collision rates seemed to be astonishingly low (median: 0.02 Hz; interquartile range (IQR): 0–0.03 Hz; n = 13 sessions) during flocking flight (in total 16 collisions in 13 min of analysed flight time). In birds, the visual system represents the main input channel for environmental information. To tackle the above question, we therefore first investigated the role of vision during flocking flight, and tested whether a bird’s viewing direction was correlated with the direction of horizontal position change. As gaze changes are governed by head movements in birds21, we used a bird’s head direction as an indicator for the orientation of its visual axis. We tracked (sample rate: 120 Hz) the position of a bird’s beak tip and neck in each frame of the footage during ten horizontal position changes (Fig. 2a and Supplementary Video 2) per bird, and found a strong interaction between a bird’s head angle relative to the wind direction and its direction of horizontal position change. During horizontal position changes, the birds always turned their heads in the direction of the position change (Fig. 2b). While the population’s median absolute angle of position change was 84.0° (IQR: 78.6–87.2°; n = 60) relative to 0° in wind direction, the population’s median absolute head turning angle was 36.0° (IQR: 26.4–42.5°; n = 60; see Supplementary Information for results on head movements during solo flight). The eyes of zebra finches are positioned laterally on their heads22 and each retina features a small region of highest ganglion cell density (fovea, that is, region of highest visual spatial resolution) at an area that receives visual input from horizontal positions at 60° relative to the midsagittal plane23. By turning their heads by about 36° during horizontal position changes, the zebra finches roughly align the foveal area in the retina of one eye with their direction of position change, and in the retina of the other eye with the wind direction (Fig. 2c,d). Thus, head turns in the direction of position change may indicate that the birds use visual cues while repositioning themselves within the flock. This hypothesis is supported by a study on zebra finch head movements performed during an obstacle avoidance task. In this study, instead of fixating on the obstacle, zebra finches turned their head in the direction of movement while navigating around the obstacle24.Fig. 2: Horizontal position changes are accompanied by head turns.a, Head and body orientation of bird Orange (ventral view) during one example of position changes to the right, tracked (sample rate: 120 Hz) in the footage of Camera 2. Circles: beak tip positions; plus signs: neck positions; upward pointing triangles: tail base positions. 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Sample collection from sea turtlesIn total, 45 serum samples from 33 juvenile green turtles (C. mydas), including 6 sea turtles with tumors, 5 juvenile hawksbill turtles (Eretmochelys imbricate), and 7 olive ridley turtles (Lepidochelys olivacea) (juvenile = 5; sub-adult = 2). All turtles were sourced from: eastern Taiwan (n = 24), southern Taiwan (n = 14), central Taiwan (n = 6), and northern Taiwan (n = 1). Among the 45 sea turtle samples, 6 green turtles developed FP (n = 1 with tumor score 1; n = 1 with tumor score 2; n = 4 with tumor score 3)32, while 39 did not have FP. FP tumor tissues were collected from 6 green turtles (from shoulder/flippers/inguinal regions) with FP during surgical procedures. Regarding the collection of normal skin tissues, one normal skin tissue (from shoulder) was collected from one necropsied dead green turtles (stranding and discovered from southern Taiwan) confirmed without FP. All tissue samples were fixed in 10% neutral buffered formalin prior to further analysis. In this study, all sea turtles were discovered and rescued through the official reporting system of the Marine Animal Rescue Network (established by the Ocean Conservation Administration) and admitted to the National Museum of Marine Biology and Aquarium (NMMBA), between 2017 and 2020.Detection of ChHV5 DNA by polymerase chain reaction (PCR)Total DNA was extracted from blood of 45 sea turtles by DNeasy blood & tissue kit (Cat. No. 69504, Qiagen, Valencia, CA, USA) following manufacturer’s instructions. Subsequently, the ChHV5 infection status all 45 sea turtles was determined by PCR using primers targeting on UL18 (capsid protein gene), UL22 (glycoprotein H gene), and UL27 (glycoprotein B gene) regions4. The sequence of primer sets are: UL18F: 5′-CACCACGAGGGGGAAAATGA, UL18R:5′-TCAAATCCCCCGTTCACTCG; UL22F: 5′-ACGGCGTTGGCTAGTGAATC, UL22R: 5′-GCAGTTCGGTACACACCTCT; UL27F: 5′-TAACAAGAAAGAACCGCGCG; UL27R: 5′-ATTTTCCCGGTCAGTGCCAA. PCR amplifications were performed in a total volume of 50 μl. The reaction included 1 μl of the template DNA, 1 μl of each primer (10 μM), 22 μl of distilled water (DDW), and 25 μl of the AmpliTaq Gold® 360 Master Mix (Cat. No. 4398876, Life Technologies, Valencia, CA, USA). The thermocycle for amplification was: Initial denaturing at 95 °C for 10 min, followed by 40 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 60 s, and then a final extension at 72 °C for 7 min. Results were visualized by gel electrophoresis (2% agarose) with SYBR Safe DNA Gel Stain (Cat. No. S33102, Invitrogen, Carlsbad, CA, USA).Sequence optimization of the UL27 gene for expression of the ChHV5 glycoprotein protein using E. coli
To express large quantities of ChHV5 gB, we adopted the prokaryotic Escherichia coli (E. coli) expression system. The construct (namely UL27/pUC57) containing sequences of the full length UL27 fused with FLAG tag sequence (GenBank accession no. AF035003.3) was synthesized by Allbio Science Co., Ltd, Taiwan. The sequence information of the glycoprotein (gB) datasets used and analyzed for protein expression during the current study was obtained and available from the GenBank repository [https://www.ncbi.nlm.nih.gov/nuccore/AF035003.3]. Considering the difference in tRNA-codon usages between prokaryotes and eukaryotes would possibly affect subsequent protein expression, the optimized UL27 gene sequence, without altering the translated amino acid sequences, to fit the E. coli expression system was synthesized. The codon optimized UL27 gene was further used as the template for amplification of different gene fragments by Polymerase Chain Reaction (PCR).Construction of plasmids expressing partial fragments of ChHV5 gB proteinTo determine the relative antigenicity and also to increase the expression yield, plasmids expressing various regions of gB protein were constructed. Briefly, the five regions covering different fragments of the UL27 gene were amplified from plasmid UL27/pUC57 by PCR using specific primer sets with built-in restriction enzyme sequences shown as underlined in Table 1. The thermal cycling conditions were: 98 °C (5 min) followed by 35 cycles of denaturation (98 °C, 30 s), annealing (58 °C, 1 min), and extension (72 °C, 2 min), and finished with a final extension (72 °C, 10 min). PCR amplicons with expected sizes were isolated from gel and trimmed with the restriction enzymes followed by ligation with vectors either pET24a or pET32b (Novagen, Germany) linearized with the same restriction enzymes. The resulting plasmids with expected insert sizes as confirmed by restriction enzymes were sent for automated DNA sequencing (Mission Biotech, Taipei, Taiwan).Table 1 Information on the constructs expressing the UL27 fragments. The bold characters indicate sequences recognized by restriction enzymes for the ease of further cloning procedure.Full size tableExpression of recombinant gB fragments in E. coli
In the current study, the recombinant gB protein is a key reagent that served as antigen for seroprevalence of ChHV5 as well as for the generation of ChHV5 gB antibody (conducted by Yao-Hong Biotech Inc., Taiwan). The plasmids expressing individual gB fragment were transformed into E. coli host cells, strain BL21 (DE3), Rosetta. Expression of all the recombinant gB fragments was induced by 0.8 mM of isopropyl β-d-1-thiogalactopyranoside (IPTG) at 28 °C for 16 h. As all the gB fragments cloned into the pET series vectors were expressed as a fusion protein with a 6-histidine tag at C-terminus end, they could be further purified by Ni–NTA column chromatography using the chelating Sepharose Fast Flow (GE Healthcare) following the method described in one previous study33. The yield and purity of recombinant gB proteins were confirmed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Subsequently, 6 M urea and 0.4 M imidazole contained in the purified protein were depleted by step-wise dialysis against 1 × PBS buffer (0.02 M phosphate, 0.15 M NaCl) with gradually decreased concentrations of urea at 4 °C. The concentration of recombinant proteins were then estimated by National Institutes of Health ImageJ software (https://imagej.nih.gov/ij/, 1997–2018.) using the standard curve established by bovine serum albumin (BSA) with known concentrations42.Western blot analysisRecombinant gB fragments were separated by 12.5% or 15% SDS-PAGE and electrotransferred to PVDF membrane by using Mini Proten III apparatus (Cat. No. 165-3301, BioRad). The filters were blocked in PBS-T buffer (0.02 M phosphate, 0.15 M NaCl, 0.05% Tween-20) containing 5% skimmed milk and reacted with mouse anti-his tag antibody (1:5,000, Cat. No. GTX40628, GeneTex) at 4 °C for overnight. After six-time wash with PBS-T buffer, the PVDF filter was then incubated with the secondary antibody, 1:5000 diluted goat anti-mouse IgG conjugated with horseradish peroxidase (HRP), or 1:500 diluted Protein A/G-HRP (Cat. No. 32400, Thermo fisher scientific™, United States) for sea turtle antibody detection, at room temperature for 1 h followed by PBS-T wash to remove the unbound antibodies. Ultimately, the signal was detected by ECL reagents (Thermo Fisher Scientific, United States) and the image was acquired by ImageQuant LAS 4000 Mini (GE Healthcare).Immunohistochemical (IHC) analysisTo establish IHC protocol, normal skin tissue from PCR-negative sea turtles served as the negative control. In total, the FP on skin tissue from six individual sea turtles that were detected positive for ChHV5 DNA (positive tissue samples), and one normal tissue detected negative (the negative tissue) were included in the IHC analysis.IHC procedure was conducted as reported in our previous study34. In brief, sections of formalin-fixed and wax-embedded skin tissues of sea turtles were made using a rotary microtome (Leica RM2245, Leica Biosystems, Germany) and were further deparaffinized and rehydrated. Antigen retrieval was carried out by heat-induced epitope retrieval method: slides immersed into boiled sodium citrate buffer (0.01 M, pH 6.0), which was preheated up to 100 °C, for 20 min and cooled at room temperature for 20 min. Subsequently, the slides were incubated with peroxidase-blocking reagent (Cat. No. S200389, Dako, Denmark) for 30 min, and then treated with or without primary antibodies (the anti-gB serum prepared from this study). In each interval of the following procedures, sections were rinsed with a mixture of TBST buffer. Tissue sections were then reacted with secondary antibody (HRP anti-rabbit/mouse, DAKO, Denmark), followed by incubation of DAB and chromogen (dilution 1 μL in 100 μL) from a commercial ChemMate EnVision detection kit (Cat. No. K5007, Dako, Denmark). Ultimately, tissue sections were counterstained with Mayer’s hematoxylin reagents (Code S3309, Dako, Denmark) for 2 min followed by wash with DDW, and reacted with 37 mM ammonia water for 5 s and rinsed with DDW.Immunofluorescent assay (IFA)Human 293 T cells were transfected with plasmids expressing full-length ChHV5 gB protein fused with FLAG tag at its C-terminus. At 24 h post transfection, 293 T cells (CRL-3216, ATCC, USA) were fixed with 2% formaldehyde for 10 min, followed by permeabilization with 0.1% Triton X-100 for another 10 min. Subsequently, cells were incubated with anti-FLAG antibody (1:500) (F7425; Sigma-Aldrich), or antisera (F1, F2, F3, F2–3) at the dilution of 1:500 for 1 h at room temperature. After six times of washes with PBS containing1% bovine serum, goat anti-mouse IgG (1:2,000 fold diluted) (Cat. No. A28175, Alexa Fluor® 488, Invitrogen) was used as secondary antibody. After one-hour incubation, nuclei were stained with 4, 6-diamidino-2-phenylindole (DAPI, Cat. No. D9542, Sigma-Aldrich) for 10 min, followed by confocal microscopy (FV1000, Olympus, Tokyo, Japan) with Olympus FV10-ASW 1.3 viewer software.Statistical analysisTo evaluate the association between seropositivity and FP or viremia tested by PCR of UL27 gene, Fisher’s exact test was applied due to very limited number of sea turtles with FP. The statistical significance was determined by p  More

Prosopis juliflora leaves collection and processing for RibotypingProsopis juliflora species of the genus Prosopis, family of Fabaceae had its genetic variation in Doha evaluated. Seven samples of P. juliflora leaves were collected from six different locations in Doha, Qatar, during five field trips. Plant leaves were collected after proper permissions and all methods were carried out in accordance with relevant guidelines and regulations. Trees in all locations were naturally growing around urbanization areas in their normal arid habitat without artificial irrigation, samples were collected from fully mature trees. Table 1 shows the samples details. Figure 1 shows the location sites of where the samples were collected on the map of Qatar, Doha. Leaf samples were kept in sterile labeled bags until having reached the laboratory where few leaflets were washed with sterile distilled water and sterilized using 70% ethanol to be used for DNA extraction.Table 1 Location details of the collection sites of P. juliflora leaves.Full size tableFigure 1Location map of collection sites of P. juliflora leaf samples (ArcGIS software).Full size imageRibotyping analysisThe leaflets of each sample were transferred into a sterile mortar previously cooled at -20 ˚C and used for DNA extraction following the kit manufacturer instructions (DNeasy Plant Mini Kit-QIAGEN-USA).Extracted DNA of each sample were subject to PCR using ITS1 and ITS4 primers. PCR products obtained were purified using the Invitrogen Quick PCR Purification Kit (QIAGEN, Germany) as indicated by the manufacturer and sequenced using Sanger sequencer (3130/3130xl DNA Analyzers, Thermofisher Scientific, USA) as previously described22.Sanger sequencer raw data was read using BioEdit software. Basic Local Alignment Search Tool (BLAST) network services of the National Centre for Biotechnology Information (NCBI) database were used to compare the obtained sequences to the existing sequences. Sequences were submitted to NCBI for accession numbers. The various P. juliflora ribosomal sequences obtained were also uploaded on MEGA-X software and the phylogeny tree was generated using the neighbor-joining algorithm26.Minimum inhibitory concentrations of PJ-WS-LE extracts prepared using leaf samples collected from various locations against A. alternata and C. gloeosporioides
Preparation of PJ-WS-LE extractFresh, young full leaves of P. juliflora were collected from various locations as indicated in Fig. 1. Samples were washed, dried and ground into powder to be used in the preparation of PJ-WS-LE extract as previously described22. Briefly, every 20 g of the leaf powder were incubated in 200 mL of 70% ethanol for 48 h. The supernatant has its solvent evaporated, the extract was then re-dissolved in sterile distilled water. Only water-soluble phytochemicals were tested by centrifuging the final preparation tubes and excluding the pellet. Stock solution of 100 g L−1 was stored at 4 °C to be used for later experiments. PJ-WS-LE extract concentration used in treatments was 8 g L−1 which is double the highest minimum inhibitory concentration of the extract against spoiling microorganisms as previously determined22.Determination of minimum inhibitory concentrationThe MIC test was conducted in a sterile 96-well plate, with each well containing 100 μl of potato dextrose broth (PDB) (HIMEDIA-India). Every four wells made one replication, nine different concentrations of the crude extracts were tested (1:1 dilutions) ranging from 42 to 0.16 g L−1. Wells were then inoculated with one of the two tested microorganisms’ spore suspensions (A. alternata and C. gloeosporioides). The last three rows are control rows: no spores and no extract control wells, negative control with spores but no extract wells, and positive control with spores and 10 µl of the fungicidal Clatrimazole (1%) wells.Fungal spore suspensions were adjusted to the range of 104 spores L−1 using a 10 day old fungal plate and sterile distilled water, the spore concentration was calculated using a heamatocytometer.Fungal growth in each well was monitored using Resazurin (HIMEDIA-India) dye. Upon cells division, Resazurin changes its color from blue to pink and fluorescent27. Results were taken within 48 h of incubation at 25 °C. MIC was recorded as the last extract concentration that shows no change in the color of Resazurin within the incubation period.Curative and preventive effects of PJ-WS-LE extract against A. alternata and C. gloeosporioides induced infection in mangoesPathogensThe two fungal strains used C. gloeosporioides and A. alternata were obtained from our laboratory collection, Department of Biological and Environmental Sciences, Qatar University, Qatar. Both fungal isolates were previously isolated from locally collected fruit samples. Isolates were molecularly identified by sequencing the Internal Transcribed Spacer (ITS) regions of fungal ribosomal DNA (rDNA) that was amplified by PCR. Identified fungal isolates were given the strains code of AaltQU17 for A. alternata and CgloQU17 for C. gloeosporioides22. Preserved cultures were sub-cultured on potato dextrose agar (PDA) plates and incubated at 25 °C for 10 days. Plates were then flooded with 10 mL of sterile distilled water each, to prepare the needed spores suspension solutions. The concentrations of spores suspensions were adjusted to 106 spores L−1 using a heamatocytometer18.FruitThe mango (Mangifera indica) type known as Neelam imported from India was used in the experiments. Fruit were bought from the whole sale market upon their arrival to the country. Only undamaged mature fruit were used in the experiment. Fruits chosen were ripen but not yet soft with firmness average of 20 ± 5.1 N, weight average of 177.61 ± 0.2 g and TSS average of 70 ± 5.3%. Fruit were first washed with sink water and sterilized twice with 70% ethanol to be then washed with sterile distilled water and left to air dry.Preventive and curative effects of PJ-WS-LE extractWounded mango fruit were used during the experiment, the wounds were made through three needle pricks (2 mm deep) in three different places for each plant using a sterile syringe. A completely randomized design was used and each treatment was made of a triplicate of 10 fruit each. The experiment was repeated twice.PJ-WS-LE extract of leaves collected from Qatar university field was first tested for its efficacy in preventing fungal contamination in wounded mango fruit (preventive effect). Therefore, the wounded zone of each fruit was sprayed with 8 g L−1 PJ-WS-LE extract and then left to air-dry. Once dry the fruit were sprayed again with the extract at the same concentration and left to dry. Control fruit were only treated with sterile distilled water without the plants extract. After two hours all wounds were inoculated with 20 μL of conidia aqueous solution (106 spores mL−1) of one of the tested fungi. The extract was then tested for its ability to cure fungal contamination in wounded fruit. Therefore, wounds were inoculated first with 20 μL of conidia aqueous solution (106 spores mL−1) and left to dry. Wounds were then sprayed twice with 8 g L−1 PJ-WS-LE extract.All mangoes were stored in sterilized plastic trays inside an incubator at 25 °C and 75% humidity. Fruit were observed every 24 h for 5 days for C. gloeosporioides inoculated fruit and for 10 days for A. alternata inoculated fruit. Three parameters were recorded at the end of the experiment: disease incidence (DI), disease severity (DS), and percent plant extract efficacy (%EE). To calculate disease severity, the diameter of the infected area of each fruit was measured in two perpendicular directions and mean diameter mycelial growth was calculated28,29.$$mathrm{DI}=frac{(mathrm{Number, of, rotten, fruit})times 100}{mathrm{Total, number, of, fruit}}$$$$mathrm{DS }=frac{(mathrm{Average, lesion, diameter, of, treated, plants})times 100}{mathrm{Average, Lesion, diameter, of, control, plants})}$$$$mathrm{%EE}=frac{(mathrm{Disease, incidence, in, Control, batch}-mathrm{Disease, incidence, in, treated, batch})times 100}{mathrm{Disease, incidence, in, Control, batch}}$$End of the trial samples firmnessAt the end of the trial, remaining mango fruit were tested for their flesh quality using a penetrometer (Agriculture Solutions, USA) to test the flesh firmness. Fruit were peeled, then the stainless steel probe of the instrument was inserted in three different points towards the equator of the fruit. Firmness in Newton was recorded and compared with standard fruit firmness to judge fruit quality18.Effectiveness of PJ-WS-LE extract as long-term coating material and the preservative value of its chitosan-embedded formCoating solutions preparationChitosan solution of 1% concentration was prepared by stirring chitosan powder (CAS 9012-76-4, Himedia, India) in 1% glacial acetic acid (IsoLab, Germany) overnight. The final chitosan solution pH was adjusted to 5.6 using 0.1 M NAOH (Sigma-Aldrich, Germany). To prepare PJ-WS-LE extract chitosan-embedded coating material, filter-sterilized PJ-WS-LE extract stock solution was added to 1% chitosan to achieve a final concentration of 8 g L−130.Samples preparationEighty-four mango samples chosen as described above, were divided into four groups of 18 samples each. Samples were divided into four treatment batches and treated as following:

Batch A: non-treated fruit.

Batch B: PJ-WS-LE extract at 8 g L–1 was used to spray the fruit.

Batch C: 1% chitosan was used to spray the fruit.

Batch D: 8 g L−1 PJ-WS-LE extract embedded in 1% chitosan was used to spray the fruit.

Every experimental replicate was made up of three mango samples that were stored together in one sterile bag at 4 °C. The number of replications per treatment was six. The experiment was repeated twice31.Evaluation of sensory qualityA five-points scale was used for the evaluation of the sensory quality of the samples for overall quality, smell, and color change. The attributes were evaluated weekly using the fruit of one experimental replicate. Scores were given using the following scale: 5 points indicate “extremely liked”, 4 points indicate “liked”, 3 points indicate “acceptable” 2 points indicate “disliked” and 1 point indicates “extremely disliked”. The weekly average score per batch was also calculated32.Estimation of weight lossUpon treatment at day zero, all mango samples were weighed and their weights were recorded as initial weights. Weights of all remaining samples were measured at the end of every week. The variation between the start weight and weekly weights is calculated as weekly weight loss. The average percent of weekly weight loss of each batch was calculated32.Determination of samples firmnessThe samples of each experimental replicate evaluated on a weekly basis had their firmness measured as previously described. The weekly average samples firmness (N) of every treatment batch was also calculated33.pH measurementMango fruit of each experimental replicate were blended weekly into juice, after filtration, a digital pH meter (Jenway, UK) was used to measure pH. The weekly average fruit pH of every treatment batch was also calculated. The pH meter was calibrated using a buffer solution of pH 734.Total soluble solids (TSS) measurementTotal soluble solids of the prepared mango juice samples were measured in percent brix using a refractometer (ANTAHI, New Zealand). The weekly average fruit TSS (%) for each treatment batch was also calculated. The refractometers was calibrated using distilled water35.DPPH radical scavenging assayA 1/10 mango juice dilution was prepared using sterile distilled water. 100 μL of each dilution was mixed with 1 mL of 2,2-diphenyl-1-picrylhydrazyl (DPPH) (100 mg L−1) to be incubated in the dark at 37 °C for 45 min. After incubation, samples were centrifuged and the pellet was discarded. The intensity of the change in color of the supernatant was measured by spectrophotometry at 517 nm using methanol as a blank. 100 μL of methanol in 1 mL DPPH was used as the control for the experiment. Percent radical scavenging activity was calculated as per the below formula:$$ % {text{ radical scavenging activity}}, = ,left( {{text{absorbance of the control solution}} – {text{ absorbance of the juice sample}}} right)*{1}00/{text{absorbance of the control solution}}. $$The weekly average % radical scavenging activity for each treatment batch was finally calculated31.Statistical analysisThe experimental design used was Completely Randomized Design (CRD). One-way ANOVA followed by Tukey Post-Hoc test was used to evaluate the significance of the weekly percent change in weight among treatment batches at P ≤ 0.05. The significances of pH and TSS variation within different treatment batches were evaluated using One-way ANOVA test at P ≤ 0.05. Data was presented as average ± standard error of the Means (SEM). SPSS (Ver. 27, SPSS Inc. Chicago, USA) was used to perform the statistical analysis tests. More

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