Fusarium spp. are important pathogens on cereals, capable of causing considerable yield losses and significantly reducing the quality of harvested grains due to contamination with mycotoxins. The European Union intends to reduce the use of chemical-synthetic plant protection products (csPPP) by up to 50% by the year 2030. To realize this endeavor without significant economic losses for farmers, it is crucial to have both precise early detection of pathogens and effective alternatives for csPPP. To investigate both the early detection of Fusarium head blight (FHB) and the efficacy of selected biological control agents (BCAs), a pot experiment with spring wheat (cv. ‘Servus’) was conducted under semi-field conditions. Spikes were sprayed with different BCAs prior to inoculation with a mixture of F. graminearum and F. culmorum conidia. While early detection of FHB was investigated by hyperspectral imaging (HSI), the efficiency of the fungal  and bacterial (Bacillus subtilis HG77 and Pseudomonas fluorescens G308) BCAs was assessed by visual monitoring. Evaluation of the hyperspectral images using linear discriminant analysis (LDA) resulted in a pathogen detection nine days post inoculation (dpi) with the pathogen, and thus four days before the first symptoms could be visually detected. Furthermore, support vector machines (SVM) and a combination of LDA and distance classifier (DC) were also able to detect FHB symptoms earlier than manual rating. Scoring the spikes at 13 and 17 dpi with the pathogen showed no significant differences in the FHB incidence among the treatments. Nevertheless, there is a trend suggesting that all BCAs exhibit a diminishing effect against FHB, with fungal isolates demonstrating greater efficacy compared to bacterial ones. Fusarium. Especially, F. graminearum, F. culmorum and F. avenaceum cause foot rot and spike (head) blight on small-grain cereals [Fusarium spp. are transmitted by wind and water and infect the host through the flowers [Wheat is one of the most important crops with the largest cultivated area of all crops worldwide . In 2020 cereals ,6. Fusar cereals . Ascospo flowers . Thus, c flowers . Infecte flowers ,6,9. Hig flowers .Fungal plant pathogens can affect the quantity and quality of a wide variety of crops. As the European Union intends to reduce pesticide use by 50% by 2030, more sustainable crop protection alternatives have to be established in order to maintain the yield and quality of the crops . This inEarly detection of FHB infection must be followed by appropriate plant protection measures. In conventional farming, chemical-synthetic fungicides with active ingredients such as azoles are used in a large scale to control FHB, with a relatively high efficiency. One more sustainable control measure is the use of biological control agents (BCAs), which are coming into focus as an eco-friendly alternative for chemical-synthetic plant protection products ,25,26. BThe objective of this study was to evaluate how early and reliably FHB can be detected in wheat under laboratory conditions using HSI. HSI results were compared with the results of conventional visual monitoring. Furthermore, selected microorganisms were tested for their efficiency as BCAs applied against FHB in wheat within semi-field conditions.For a successful early detection of FHB in wheat by using HSI, it was first necessary to identify wavelength windows with clear differences in reflectance between non-infected, healthy and infected symptomatic spike areas. Additionally, differences from symptomatic areas to maturity as well as to flowers should be identified.Fusarium”, “T10 + Fusarium”, T16 + Fusarium” and “T23 + Fusarium”.Based on the 15-day measurement series from 6 to 20 days post inoculation (dpi) with FHB, early detection of the infection was tested using these hyperspectral images. Different supervised and unsupervised classification methods were used, so that the day of their first detection was compared with the day when FHB symptoms could be detected by visual assessment for the first time. This was performed for the treatments “Control + Fusarium” were contrasted with the RGB images for the days 9, 10, 11 and 13 dpi (In contrast to support vector machines (SVM), distance classifier (DC) was preceded by a linear discriminant analysis (LDA). In the following text, when the term DC is used, it refers to this combination. The classifications of SVM and DC of “Control + d 13 dpi . A visuaFusarium” is shown in Fusarium-infected areas are colored light green and became recognizable at 9 dpi. From 10 dpi on, these areas were clearly distinguishable from healthy tissue. As infection progressed, the color of the infected areas changed to turquoise and blue. In contrast to SVM and DC, flowers were not classified as symptoms by LDA.In addition to the combination of LDA and subsequent DC, pathogen detection was also tested for the LDA without subsequent DC. A comparison of the pseudo-RGB images and LDA for the variant “Control + K-Means clustering, an unsupervised method, was also tested . With K-Fusarium”. However, only the results of this variant are shown because pathogen identification occurred earliest here for all classification methods.Early pathogen detection of the different classification methods was not only tested for the treatment “Control + Using PCR as a molecular detection method, sampled spikelets were tested for the presence of FHB. This was necessary to check whether areas detected as symptomatic/infected by the classification methods were actually infected by FHB.Fusarium spp. in the sample was characterized by a band at approximately 400–500 base pairs (bp). The sample “Control 1-4” had a weak but clearly visible band at this level. This could either have been caused by contamination, or the corresponding spikelet was actually infected with FHB by natural infection. Except for “Control + Fusarium 9-4”, all samples from the variants inoculated with a Fusarium spore suspension showed a band in the range of 400–500 bp.The result of the gel electrophoresis, which was carried out with the PCR products, is shown for some samples in The supervised machine learning methods, SVM and DC, were tested for their reliability and precision. For this purpose, their classification of the pixels into the four classes was compared with a reference. For the reference, the pixel classification of the pseudo-RGB into the classes was performed manually by the experts M.L. and M.R. The comparisons of the classifications are listed as the number of pixels per class and the proportion of total pixels. K-Means clustering could not be evaluated this way because as an unsupervised classification method, it does not classify the pixels into the specified classes. Thus, a quantitative comparison with the reference is not possible.Fusarium” is shown in Fusarium” had a total of 55,110 pixels. With 50,438 pixels, the reference allocated 91.52% to the “Healthy” class. With 49,584 pixels (89.97%) for SVM and 47,921 pixels (86.96%) for DC, the proportion was lower for the supervised methods. In the manual classification, 1007 pixels (1.86%) were declared as flowers. Again, the values of SVM  and DC  were lower. With regards to maturity, this tendency was again evident. With 181 pixels (0.33%), the reference had a larger number of pixels assigned than SVM  or DC . The classification of the areas infected with FHB did not show this trend. There, with 3484 allocated pixels (6.32%), the reference had a smaller share than SVM  or DC .As example, the comparison for the variant “Control + Fusarium”. The three symptomatic areas visible in sub-image (a) were also identified by SVM and DC, where similarly sized areas were also classified as infected with FHB. However, both algorithms also classified pixels as symptoms, which were identified as flowers in the reference.In addition, the manual classification of spike areas and classifications by SVM and DC were also compared with each other graphically. Statistical analysis of the monitoring data based on the evaluation of pots 1–3 showed no significant differences between the treatments at both 13 and 17 dpi. Therefore, the results are only described, and tendencies are provided. For FHB incidence as well as FHB severity, the mean values of the treatments are shown in Fusarium spp. . “T16”, “T23”, “CRP1104” and “HG77” all had the same FHB incidence of 6.67%, which might be due to a naturally occurring FHB infection of some spikes of these treatments. Among the treatments inoculated with Fusarium, “Skyway Xpro + Fusarium” (20.00%) and “CRP1104 + Fusarium” (20.00%) showed the lowest FHB incidence. Application of the bacterial antagonist G308 led to an FHB incidence of 46.67%, which was the least effective of all BCA treatments.For the treatments “Control”, “Skyway Xpro”, “T10” and “G308”, no infection with FHB could be visually observed, as in these treatments no artificial inoculation with the pathogen was performed and obviously, there was no natural infection of the evaluated spikes by air-transmitted spores of ium spp. . The treFusarium” had the highest FHB severity of 15.64% and a very high standard deviation. “Control + Fusarium” had 10.92% FHB severity. The lowest FHB severity among the Fusarium-inoculated treatments could be observed for “Skyway Xpro + Fusarium” (6.50%).FHB severity for “Control”, “Skyway Xpro”, “T10” and “G308” was 0% since no symptoms were detected on the spikes of these treatments at 13 dpi . “T16”, Fusarium” had the most symptoms and, therefore, with 5.78%, the highest FHB index of all treatments. In comparison, “Control + Fusarium” (5.63%) and “G308 + Fusarium” (5.59%) showed only slightly lower values. For “Skyway Xpro + Fusarium” (1.30%), the lowest FHB index among the treatments inoculated with FHB could be observed.Fusarium” (20.00%), there was no increase of symptomatic spikes. For all the other variants that were inoculated with the pathogen, FHB incidence increased compared to that at 13 dpi. Among the fungal BCAs, the spikes treated with CRP1104 before inoculation with FHB had the lowest FHB incidence of 26.67%. The highest FHB incidences were observed in the two treatments testing the bacterial BCAs, “HG77 + Fusarium” (53.33%) and “G308 + Fusarium” (60.00%). Only “Control + Fusarium” (73.33%) still showed more FHB infections.At 17 dpi, FHB incidence for the four treatments, “Control”, “Skyway Xpro”, “T10” and “G308”, was still the same (0%) . “T16”, Fusarium” with 19.77%, while “T16 + Fusarium” (19.51%) was slightly lower. Thus, the FHB severities of both treatments were higher than that of “Control + Fusarium”. There, an average of 16.32% of the spikelets were infected with FHB. The value for “Skyway Xpro + Fusarium” (10.43%) was the lowest of all Fusarium-inoculated treatments. The two bacterial variants, “HG77 + Fusarium” (12.41%) and “G308 + Fusarium” (13.67%), could reduce the FHB severity to a greater extent than the fungal ones.The FHB severity of all treatments not inoculated with FHB did not change from the first to the second monitoring date . The valFusarium” (11.79%). Among the treatments containing an inoculation with Fusarium spp., for “Skyway Xpro + Fusarium” (2.09%), the lowest FHB index was found. Comparing the treatments with BCAs and Fusarium, “T16 + Fusarium” (9.66%) had the highest value, while “CRP1104 + Fusarium” was the lowest, with 4.74%. Thus, the FHB index remained the same for the treatments not inoculated with Fusarium spp., while it increased for the other treatments with Fusarium inoculation.The FHB index for 17 dpi monitoring is shown in For identification of the spectral signatures by the fluxTrainer, the mean value of the pixels assigned to the classes as training data was used. Therefore, it was necessary to allocate a sufficient spike area to the classes to obtain representative values. This was a challenge for the class “Maturity”. Although spike areas from the last day of the measurement series (20 dpi) were used, the transition from healthy to senescent tissue was smooth. Thus, only clearly matured areas were selected.The reflectance values of the classes “Healthy”, “Flower”, “Maturity” and “Symptom” were located between 8% and 45% , and thuNumerous pathogens can already be detected and identified by HSI . Of partEarly detection of FHB infection was tested for all treatments inoculated with FHB. The first visible FHB symptoms appeared during the hyperspectral measurement series. On the day a pathogen infection is initially detected by a classification algorithm, usually only a few pixels of the infected area are assigned to the class “Symptom”. Thus, SVM and DC wrongly assigned a part of the flowers to the class “Symptom” due to misclassification . While aK-Means clustering, as an unsupervised method, showed the latest initial FHB detection of all tested classification methods . The infFusarium spp.-infected wheat grains. Compared to visual classification, a precision of over 95% was achieved. Huang et al. [Compared to the visual evaluation, the LDA without subsequent DC enabled the earliest detection of the FHB infection . Spike ag et al.  used a FTo conclude, the infection with FHB was detected at an earlier stage than by visual rating. This information can be used to initiate countermeasures at an early stage of infection.The spectral signature of senescent plant tissue is very similar to that of FHB-infected spike areas, as the plant tissue there also dies as the infection progresses . NeverthFor some sampled spikelets showing or resembling FHB symptoms, molecular detection was carried out by PCR for testing whether they were infected with FHB. Samples were taken at 21 dpi, one day after finishing the hyperspectral measurement series. At this time, advanced maturity was evident on the spikelets, which partially made visual differentiation of senescent spikelets to FHB symptoms difficult. This could explain why FHB was not found in one of the samples from the controls inoculated with FHB. However, since molecular detection was not performed for the entire spikes, it cannot be excluded that they were not infected by FHB at all. However, when entire spikes are tested in PCR, too many inhibitors are present that affect the reaction. Di Pinto et al.  report tFusarium 9-4”, a band representing DNA of Fusarium spp. was present in all examined spikelets of the treatments inoculated with Fusarium  as the fungicide in reducing the number of infected spikes. Similar results were achieved by Xue et al. [C. rosea strain ACM941 against FHB in wheat compared to a chemical-synthetic fungicide containing tebuconazole. Under greenhouse conditions, the foliar application of ACM941 two days before pathogen inoculation significantly reduced the number of infected spikelets by 64% compared to the untreated control. At 13 dpi, the bacterial BCA G308 was less effective and showed an FHB incidence almost the same as the BCA-untreated “Control + Fusarium”. This trend was also visible for both bacterial antagonists for FHB incidence at 17 dpi, where the values were highest compared with the other variants treated with BCAs and inoculated with FHB. In contrast, at 17 dpi, HG77 and G308 were able to reduce FHB severity better than the other BCAs. Accordingly, Taheri et al. [B. subtilis strain CB2, which reduced spore germination of the fungus growing on CB2 pre-treated wheat seeds, as well as infection with FHB and the Fusarium crown rot severity of wheat in the greenhouse. Similarly, strains of P. fluorescens performed well in the greenhouse and field trials, showing a good biocontrol efficacy against FHB and Fusarium root rot [The treatment “Skyway Xpro + e et al. , who evai et al.  found anroot rot ,49.Fusarium spp. reduces FHB infection, but only to a lesser extent than the chemical fungicide Skyway Xpro. Furthermore, the fungal BCAs seemed to be more effective than the bacterial BCAs, at least in terms of FHB incidence. A combined application of fungal and bacterial BCAs could improve their efficacy, as reported by Karuppiah et al. [T. asperellum GDFS1009 and B. amyloliquefaciens 1841 to the potting soil of wheat plants prior to the inoculation with F. graminearum resulted in a higher reduction of root rot compared to the control and single treatments. In our study, only Tween 20 and Break-Thru S 301, as wetting and adhesive agents, were added to the applied BCA suspensions. Thus, the spores and bacterial cells were sprayed without any other additives that might shield them from UV radiation or desiccation. Even under such circumstances, the more resistant spores of the fungal BCAs likely had a better chance of establishing themselves on and within the spikes following application, compared to the less robust bacterial cells. This resulted in a lower efficiency of the bacteria regarding the reduction of pathogen spread between different spikes of one treatment (FHB incidence). After successful establishment, however, they were also able to effectively reduce pathogen spread within the spikes (FHB severity).The results suggest that a foliar application of all tested fungal and bacterial BCA strains five and two days before an artificial inoculation of the spikes with h et al.  in a greThe trial was conducted in the vegetation hall of the Institute of Phytomedicine at the University of Hohenheim, which consists of a glass-enclosed indoor space with an adjacent outdoor area.Fusarium spp.: Control, fungicide Skyway Xpro , Trichoderma sp. T10, T. harzianum T16, T. asperellum T23, C. rosea CRP1104, B. subtilis HG77 and P. fluorescens G308. The BCAs were obtained from the isolate collection from the Department of Phytomedicine of the University of Hohenheim . Each treatment comprised five pots, which also represented the replicates. The treatments that were artificially inoculated with Fusarium spp. are labeled with “+ Fusarium”.The following variants were investigated, each with and without inoculation of 4NO3). For each pot, 0.47 g of NH4NO3 was dissolved in 100 mL of water and evenly distributed on the substrate (0.165 g of nitrogen per pot). In order to protect the plants from infestation of aphids, cereal leaf beetles and other insect pests, they were treated with the insecticide Biscaya  with 1 mL/L of water on 8 July 2022. To prevent bird predation on the ripening spikes, wooden frames were built on the trollies and bird protection nets were mounted on top before the inoculations of the plants started.Spring wheat cv. ‘Servus’  was sown 3–4 cm deep in pots (20 cm height × 15 cm width × 15 cm length) filled with a mixture of equal parts of Classic Profisubstrat CP Topf  and damped garden compost. For each pot, 20 seeds were used. This wheat cultivar was chosen because of its high susceptibility to FHB, with a score of 6, while being less susceptible to other leaf pathogens according to the official classification of the Federal Plant Variety Office in Germany . The pla2PO4, 0.5 g of MgSO4 × 7 H2O, 0.05 g of CaCl2 and 0.01 g of FeCl3 × 6 H2O were dissolved in 900 mL of deionized water, pH was adjusted to 5.5 and then autoclaved at 121 °C and 1 bar for 20 min. Due to heat sensitivity, 3 g of L-asparagine, 0.001 g of thiamine and 20 g of D (+)-glucose monohydrate were dissolved separately in 100 mL of deionized water and sterilized with a vacuum filter before being added to the 900 mL autoclaved medium after cooling to approximately 60 °C. Potato dextrose agar (PDA) plates, well covered with the fungal sporulating BCA cultures, were used to prepare the inoculum for the GM7 medium. Here, 10 mL of 0.01% Tween 20  was pipetted per plate before the biomass was carefully scraped off with a spatula and transferred to a Falcon tube. From this suspension, 0.5 mL was spread on the surface of a GM7 plate and sealed with air-permeable adhesive tape. Incubation of the plates took place at 25 ± 2 °C and in natural light/dark conditions, avoiding direct sunlight for approximately 14 days.The fungal BCAs were cultivated on solid glucose medium 7 (GM7), which was prepared according to the recipe of Karlovsky , with mi7 spores/mL with 0.01% Tween 20, and then 200 μL/L of Break-Thru S 301  was added. The bacterial BCAs were multiplied and provided by Bio-Protect GmbH . The culture medium for HG77 contained, per liter, 25 g of sucrose, 2.5 g of Hakaphos green , 2.7 g of KH2PO4 and 5.3 g of K2HPO4. G308 was cultured in Luria-Bertani (LB) medium . After propagation, the bacterial liquid cultures were stored at 8 °C in the refrigerator. Before application, they were diluted with the aqueous solution of 0.01% Tween 20 to the final concentration of 1 × 108 cfu/mL, and 200 μL/L of Break-Thru S 301 was added. All BCA suspensions were stored at 4 °C until the evening application.To prepare the BCA inoculum for the wheat plants, 2 to 5 plates from 14-day-old cultures of the respective BCAs were used, depending on the amount of sporulation. Here, 2 × 25 mL of an aqueous solution consisting of 0.01% Tween 20 was added to each plate, mycelium and spores were scraped off and the suspension was filtered through four layers of cotton gauze . The spore concentration was determined using a Fuchs–Rosenthal counting chamber, adjusted to 1 × 10Fusarium spp., plants of both fungicide treatments were sprayed with 20 mL/pot of Skyway Xpro (2.5 mL/L). Skyway Xpro  was used according to the instruction manual, with only one application. Control treatments were sprayed with 0.01% Tween 20 + 0.02% Break-Thru S 301 on both application dates, as well as the fungicide treatments 2 days before pathogen inoculation. Subsequently, the spikes were equally moistened with the same volume of water per pot three times on each of the following two days by a hand-held sprayer.The Inoculation of the wheat plants with the BCAs was performed 7 and 2 days before pathogen inoculation at a spray rate of 20 mL/pot, using a spray chamber and a chromatography atomizer. Seven days before inoculation with F. graminearum and F. culmorum was used in this trial. Spore production of Fusarium isolates was performed in carboxymethyl cellulose (CMC) liquid medium, containing, per liter, 15 g of carboxymethylcellulose low viscosity, 1 g of yeast extract, 1 g of NH4NO3, 1 g of KH2PO4 and 0.5 g of MgSO4 × 7 H2O. CMC liquid medium was autoclaved in Erlenmeyer flasks at 121 °C and 1 bar for 20 min.To simulate a natural FHB infection, a mixture of isolates of Fusarium cultures was cut into small plugs, and the plugs from one complete petri plate were added to 1 L of CMC medium in an Erlenmeyer flask. After inoculation, the flasks were sealed with paper plugs and loosely placed aluminum foil to allow air exchange. Liquid cultures were then incubated on a shaker at 150 rpm and 25 ± 2 °C with natural light/darkness conditions for 18 days until sufficient spores were found.As an inoculum, carrot agar (CA) medium  in petri plates (Ø 9 cm) containing the respective Conidia were harvested by first filtering the liquid cultures through a kitchen sieve to separate agar plugs and mycelium from liquid medium and spores. The solid residues in the kitchen sieve were then washed in deionized water, stirred with a spatula and filtered again through the sieve to transfer the remaining spores into the filtrate as well. Afterwards, the spore suspension was filtered through two layers of cotton gauze to remove smaller mycelium pieces and then centrifuged at 8000 rpm and 25 °C for 20 min. The supernatant was discarded, and the spore pellets were resuspended in the remaining CMC medium and stored at −20 °C until inoculation.Fusarium, 350 mL of spore suspension was thawed from each of the four isolates. After thawing, the suspensions were cloudier than before freezing because solid structures had formed again. Therefore, the spore suspensions were filtered again with two layers of cotton gauze and the filtrate was centrifuged at 8000 rpm and 25 °C for 20 min. The supernatant was discarded, and spore pellets were resuspended in 0.01% Tween 20. Spore concentration was determined for every isolate separately using a Fuchs-Rosenthal counting chamber and brought to the concentration of application of 1 × 106 spores/mL by dilution with 0.01% Tween 20. Before application, a mixture containing equal parts of all four Fusarium isolates was prepared and 200 μL/L of Break-Thru S 301 was added. Inoculation with FHB was carried out at full flowering (BBCH 65) in the early evening to ensure successful inoculation. Each pot was sprayed from all sides with 20 mL of spore suspension using a chromatography atomizer. The treatments without Fusarium were sprayed only with 0.01% Tween 20 + 0.02% Break-Thru S 301. For four days after inoculation, wheat spikes were sprayed equally with the same volume of water per pot three times a day to increase the chance of successful infection using a hand-held sprayer.Prior to the inoculation of the wheat with After the first visible symptoms appeared, visual ratings were carried out to assess the FHB infection. Both FHB incidence and severity were evaluated, and the FHB index was determined. While FHB incidence describes the proportion of spikes examined that showed symptoms of FHB infection, FHB severity represents the proportion of spikelets of a spike that were infected by the pathogen. The FHB index is based on both values and was calculated with the formula of Góral et al. :FHB indeMonitoring was carried out for all pots and treatments at 13 and 17 dpi with FHB. FHB incidence was determined on five spikes per pot and FHB severity on three spikes per pot. In both cases, spikes were randomly chosen. For the HSI, before performing the monitoring, two pots of each treatment (pots 4 and 5) were randomly selected and fixed in a measuring frame, where they were exposed to different conditions than pots 1–3. Therefore, the spikes of pots 4 and 5 were also assessed in the evaluation of FHB infection but were not considered and not equated with the other three replicates (pots 1–3).For the pots 4 and 5 of all treatments, the number of spikes was reduced to five spikes per pot by cutting off the surplus stalks close to the ground, and the remaining spikes were each fixed in a measuring frame by a metal bar padded with foam. The laboratory measurements were carried out on a measuring stand with the Corning microHSI 410 hyperspectral sensor , which measures the reflection in a spectrum of 402.76–994.54 nm at a distance of 2 nm and calculates the reflectance values. The sensor and four halogen lamps  were attached to a linear motor and guide rail, which moved them horizontally .The halogen lamps, which were switched on 30 min before starting the measurement to avoid fluctuations in the spectrum of their emitted light, were the only light source in the measurement laboratory during the recordings. The recordings were produced by use of the fluxTrainer Pro 4.6 software  as control and recording software for the affiliated hardware. To ensure comparability of the measurements, calibration was performed with the dark and white references  and the mean value from 100 frames was used in each case.The measurements of the spikes were then carried out. For the measurements, the frame was rotated by 90° and the spikes were placed horizontally under the hyperspectral sensor with the same distance from the sensor. Due to the horizontal position of the spikes, a larger spike area could be observed. In order to avoid differences by the time of day between the recording days, the different treatments were always measured at the same time. Starting at 6 dpi, hyperspectral images of the treatments were taken daily. The measurement series was finished at 20 dpi, as the spikes showed progressive maturity.Fusarium” at 20 dpi were added to the class “Symptom” if they were visually infected by FHB. The training data of the class “Maturity” originated from the hyperspectral image “Control” at 20 dpi. During classification, the pixels were converted by the machine learning algorithms into the color of the assigned classes: “Symptom” red, “Healthy” green, “Flower” yellow and “Maturity” blue.The software fluxTrainer Pro 4.14.1.5  was used to evaluate the hyperspectral images. It acts as a basic tool to apply various data analysis methods, such as LDA, SVM and DC, for the analysis of the pre-processed masked hyperspectral images without background. For all evaluated hyperspectral images, stalks and leaves were manually marked and cut out, as otherwise these would interfere with the classification of the spikes. Spike areas were manually assigned to the created classes “Healthy”, “Flower”, “Symptom” and “Maturity” as training data for pathogen detection with the supervised algorithms. To ensure comparability, the training data were identical in all projects. Non-symptomatic spike areas of the hypercube “Control” at 9 dpi were assigned to the class “Healthy”. The flowers were excluded and assigned to the class “Flower” instead. Only spike areas from “Control + Fusarium”, “T10 + Fusarium”, “T16 + Fusarium” and “T23 + Fusarium”, the hyperspectral images of all measurement days were evaluated with the fluxTrainer. For each treatment, three representative and adjacent spikes, which showed clear FHB symptoms at the end of the measurement series, were selected for evaluation.For the treatments “Control + For objective evaluation of the classification methods SVM and DC, their classification of pixels was compared with the manual classification of the pixels, the reference. The evaluation was carried out with the image processing software GIMP Version 2.10.12 (The GIMP Development Team) by loading the hyperspectral images of the respective treatments recorded at 13 dpi as pseudo-RGB images and classifying each pixel into one of the four classes, “Healthy”, “Flower”, “Maturity” and “Symptom”, by means of manual coloring by the experts M.L. and M.R.. The pseudo-RGB images were generated from the respective hyperspectral images. To ensure comparability, the same processing chain was used as for the classification methods and the same spikes were evaluated. The total pixels assigned to the respective classes and the pixel proportions by the different classification methods were compared with each other.In addition to the comparison of the pixel proportions assigned to the four classes, the images of the reference and those of the classifications by SVM and DC were also compared. This was performed to check not only whether the pixel proportions were similar, but also whether the spike areas were assigned to the same classes for all the classification methods.Fusarium inoculation. In this case, necrotic or conspicuous spikelets were sampled. The samples were ground using liquid nitrogen. DNA extraction was performed according to the protocol of Liu et al. [2Opure instead of TRIS-EDTA. The concentration and quality of the isolated DNA were checked, and PCR was performed with the C1000 Touch Thermal Cycler  using Taq polymerase. The reaction mix is shown in Several spikelets per treatment were taken as samples and stored at −20 °C until molecular analysis. While symptomatic spikelets were selected for the treatments inoculated with FHB, this was not possible for most of the treatments without u et al. , wherebyFusarium spp. binding to the gene TEF-1α. Furthermore, it was proven that DNA of the BCAs was not amplified by the primers. In addition to the DNA of the spikelet samples, a no-template (NT) control and a positive control were included. For the positive control, DNA was extracted from mycelium of the four Fusarium isolates used, mixed and added to the PCR reaction.PCR was performed using the primer pair “TEF-Fu3 f” and “TEF-Fu3 r”  with theAfter completion of 40 cycles, the PCR products were determined by gel electrophoresis. Therefore, 6 μL of PCR product was mixed with 1 μL of 6x Loading Dye and pipetted into the pockets of a 2% agarose gel. Then, 6 μL of GeneRuler 100 bp  was used as a DNA ladder. The gel was then run at 100 volts for 45 min, followed by staining in an ethidium bromide bath for 10 min and then, after 20 min in a water bath, analyzed.Evaluation of the monitoring data for FHB incidence and severity of replicates 1–3 (pots 1–3) was planned to be performed by analysis of variance for the data of the two monitoring dates, separately. R Version 4.2.2  was used as statistical software. The prerequisites of the analysis of variance are the homogeneity of the variances and the normal distribution of the data. The homoscedasticity could be proven with the help of the Levene test from the “car” package. It was also confirmed visually with the help of residual plots. However, the Shapiro–Wilk test and a QQ plot proved that there was no normal distribution. Therefore, non-parametric tests had to be used. Various non-parametric tests for independent samples, for example the Kruskal–Wallis test or the Wilcoxon test, could not be applied due to ties within the data. Instead, a one-factorial general linear regression model was generated with the “Logistic Model” based on the existing data. This was carried out with the function “glm”. “Least Square Means” was then used to test for significant differences between the treatments by the function “lsmeans” from the package “emmeans”. The alpha error accumulation was adjusted according to Bonferroni. Graphs were created in Excel .Healthy spike tissue was characterized by very low reflectance values in the range of 500–680 nm, as these wavelengths are used for photosynthesis. Flowers, mature and FHB-infected spike areas were limited in their photosynthetic activity and showed higher reflectance for 500–680 nm than healthy spike tissue. The differences in the spectral signatures for differentiating between flowers, senescent tissue and FHB infection were small. While flowers consistently had the highest reflectance values in the range of 450–740 nm, senescence and FHB symptoms could be distinguished primarily on the basis of the 420–600 nm spectra, as well as 900 nm and above.FHB infection was first detected by the LDA at 9 dpi, or reliably at 10 dpi, which means four days before visual identification (13 dpi). With SVM, the first detection was at 11 dpi, while DC with preceding LDA showed the later symptomatic areas at 10 dpi. Pathogen detection by K-Means clustering did not occur earlier than visual detection with the pseudo-RGB images. Based on these results, a monitoring system enabling a rapid and precise identification and quantification of FHB infections at the field level can be developed and used in practical agriculture. This allows conducting appropriate countermeasures quickly and in a targeted manner.Molecular PCR analysis was used to verify the selection of training data and the classifications of the machine learning algorithms. The results showed that a small proportion of the manually selected training data was misclassified. Although this probably did not have a significant impact on the spectral signatures or the classification algorithms, it highlights the high relevance of automatic FHB detection to avoid subjectivity and errors in visual rating.Concerning accuracy, both supervised methods, SVM and DC, performed better than the unsupervised K-Means clustering, as K-means clustering was not able to distinguish between flowers, maturity and symptomatic areas. SVM and DC showed high agreement compared to the manually evaluated reference as well as to the results of PCR analysis. However, differentiation between FHB-infected areas and flowers, as well as between FHB-infected areas and senescent plant tissue, still needs further optimization.The tested BCAs had no significant differences between the treatments concerning their efficiency in reducing FHB infection, but all of them tendentially seemed to have a reducing effect, with a greater effect of the fungal than the bacterial BCAs. However, without a supporting formulation of the fungal spores and bacterial cells, their efficiency is still lower than that of a chemical-synthetic fungicide.


Autism spectrum disorder (ASD) is a neurodevelopmental condition with symptoms that affect the whole personality and all aspects of life. Although there is a high degree of heterogeneity in both its etiology and its characteristic behavioral patterns, the disorder is well-captured along the autistic triad. Currently, ASD status can be confirmed following an assessment of behavioral features, but there is a growing emphasis on conceptualizing autism as a spectrum, which allows for establishing a diagnosis based on the level of support need, free of discrete categories. Since ASD has a high genetic predominance, the number of genetic variations identified in the background of the condition is increasing exponentially as genetic testing methods are rapidly evolving. However, due to the huge amount of data to be analyzed, grouping the different DNA variations is still challenging. Therefore, in the present review, a multidimensional classification scheme was developed to accommodate most of the currently known genetic variants associated with autism. Genetic variations have been grouped according to six criteria , which are themselves not discrete categories, but form a coherent continuum in line with the autism spectrum approach. In the early 2000s, the Human Genome Project (HGP) established the first complete genome sequence as part of a broad international collaboration. The knowledge of the three billion base pairs of the human genome, together with the methodological and technical skills accumulated during the project, has clearly set the direction of research for professionals in the field since the turn of the millennium . As in mIn terms of methodology, psychogenetics has typically investigated the genetic variations of genes selected on the basis of psychological theories and physiological functions as well as their molecular biological effects. However, it soon became clear that a single or even a few genetic variations alone could not explain the vast majority of ASD cases, and the complex, polygenic nature of the developmental disorder, involving hundreds of genes in complex networks, became apparent. If, however, it is indeed the combined effect of many variants of many genes that result in the development of the autistic phenotype, it is legitimate to raise the idea in the psychology, psychiatry, and special education communities that ASD is nothing more than a different organization of the brain from what is currently considered typical. Due to the diversity of the genetic background of autism, research is nowadays shifting toward high-throughput genetic screening tests to understand the mechanisms of ASD at the molecular level, which are highly complex and involve many physiological and biochemical processes.In addition to reviewing the milestones underlying the genetic determination of ASD, given the complexity of genetic variations in autism and their inconsistent clustering principles in the literature and varying dimensions from publication to publication, the present review aimed to develop a possible classification of DNA-level variations, illustrated with specific examples.Autism spectrum disorder is a pervasive neurodevelopmental condition with symptoms that affect the whole personality and everyday behavior patterns and persist throughout life. ASD can be considered as a developmental disorder, a specific developmental pathway, but the difficulties encountered are highly dependent on the environment, in addition to the severity of the autism . There iThe study of the epidemiological aspects of autism spectrum disorder is extremely important for several reasons, as the prevalence of ASD and its alteration influence the planning of the range and availability of care and services for those affected at the societal level. In addition, the prevalence characteristics of a condition may be related to its underlying causes. Accordingly, the temporal and spatially uniform distribution of autism implies a genetic background, as opposed to other conditions that are temporally and geographically uneven and mainly dependent on environmental factors.Since the 2000s, data on the prevalence of autism worldwide have shown an increasing trend . While bThus, over the last ten years, the prevalence of autism has increased from 1.47% to 2.76%, according to the U.S. public health data. The rate of increase is clear for both sexes A, but thAutism is more common in males, but while published data in the 2000s suggested that 4–5 times more boys than girls were affected, recent studies suggest that the male-to-female ratio is probably closer to 3:1 . It is aThe prevalence of autism among siblings of individuals diagnosed with ASD is much higher than in the general population . On averThe hereditary nature of autism spectrum disorder is now a clear scientific fact, but there is still some uncertainty regarding its extent. A meta-analysis in 2016 reviewed 6413 sets of monozygotic and dizygotic twins with at least one child diagnosed with ASD. Their results suggest an estimated heritability of 0.64–0.91, which has since been confirmed by others , suggestResearch into the development of autism spectrum disorder is no longer concerned with specific root causes, but rather with predisposing and protective factors . While tiv) low socioeconomic status. It is important to emphasize, however, that none of these factors are autism-specific; they increase the susceptibility of many other mental or physical disorders in addition to ASD. Furthermore, some of the environmental factors cannot be sharply separated from genetic effects, as, for example, higher paternal age increases the prevalence of de novo mutations during spermiogenesis . Su. Su58]. If a large segment of one chromosome, usually an arm, is lost, the cell is functionally monosomic with respect to the genetic information in that region. In autism, such defects of chromosome 11 are frequently reported in the literature, for example, it is known that loss of a long arm of chromosome 11 (Jacobsen syndrome) is associated with the development of severe autistic symptoms . NeverthThe question may arise as to how long a chromosomal abnormality is considered a partial monosomy and when it is considered a deletion, which can be classified more as a structural change. There is no clear consensus on this point. In this work, partial monosomy was considered when one arm of the chromosome was missing in its entirety, while it was classified as a structural aberration when this absence was partial.Although the structural variations no longer affect the whole chromosome or one of its arms, they are still very extensive, affecting several to hundreds of genes at a time. Their development requires a spontaneous or induced breakage of the chromosome(s) at one or more points. In an unbalanced translocation, the displacement of two chromosome fragments results in the loss of genetic information and phenotypic consequences. The imbalanced variant of the translocation between the short arms of chromosome 8 and chromosome 4 shows familial accumulation and is more common in the ASD population ,83. In aCopy number variations (CNVs) are very frequent, accounting for 5–10% of the human genome . The vasAs CNVs can result in the simultaneous loss or duplication of up to dozens of genes, the exact connection between autism and these genetic variations as well as their mechanism remains a major challenge. The association between phenotype and changes in the dosage of the affected gene (deletion or duplication) may provide guidance, although the connection is not always predictive. A good example is the microdeletion of the 7q11.23 region in Williams syndrome, which is associated with a high degree of sociability. At the same time, duplication of the same region is likely to result in autism and communication deficits ,98,99. TInterestingly, the deletion and duplication of another CNV affecting the 16p11.2 locus were both equally associated with autism, but while deletion was correlated with macroencephaly and obesity , duplicaSingle nucleotide variations (SNVs) compose about 0.1% of the human genome, the majority of which are common variants and, like CNVs, are a major source of genetic diversity. Due to their extent, SNVs can usually affect only one gene. On the one hand, this may facilitate the identification of their role in a given phenotype, in this case autism, but on the other hand, the complex nature of ASD means that the small effect of a single gene can easily be ‘lost’ in the interpretation of the different neurodevelopmental background of an intricate interplay of tens or even hundreds of genes. Today, a very large number of genes and their SNVs are associated with autism in the literature, and the list is expected to grow with the routine application of next-generation whole-genome sequencing. Processing this dynamically growing amount of data is largely facilitated by a number of searchable databases of SNVs and genes associated with ASD. The most recent online databases available without restrictions are listed in SHANK gene, which encode post-synaptic scaffolding proteins of glutaminergic synapses in the central nervous system. Many of their SNVs have been consistently shown to be associated with the development of autism [NLGN3 and NLGN4 genes, which encode neuronal cell surface proteins and are thought to be important in regulating intercellular communication, in the etiology of ASD is also clear, and their mutations have been widely identified and studied [However, the results of the various high-throughput studies and the growing number of summaries written on the topic ,104,105 f autism ,108. In  studied ,110.A de novo variation is the result of a mutation in the parental gametes or a genetic modification during embryogenesis that arises for the first time in the individual. Genetic alterations during embryogenesis or later in development result in mosaicism . Such genetic variation can occur at the level of chromosomes, CNVs, and SNVs. Although it is not yet entirely clear what proportion of ASD cases involve de novo events, the prevalence of somatic variations in autism has recently been estimated to be between 5 and 7.5% ,112. MosHereditary variations can be found in the parental genome. They can affect large gene regions or only a single nucleotide. In general, due to the complexity of the genetic background of autism spectrum disorder, recessively inherited see also . geneticGiven the complex genetic background of ASD, it is not surprising that an extremely large number of autism-related genetic variants affecting the coding region have been described, so this review can only highlight the main trends.The three billion base pairs of the human genome encode only 20–25 thousand genes, which are translated into protein via mRNA. Genetic variations affecting exons therefore have a high probability of being reflected in the structure of the protein, altering its function. In most cases, this change affects the protein negatively, causing a loss of function and disrupting a very complex and precisely engineered system. Less frequently, variations in the coding region can create a protein that works more efficiently or has a new function (gain-of-function variations). Larger, deletional CNVs tend to corrupt genes, which can often be partially or completely lost. Their duplications, however, can lead to a stronger gene expression, which of course is not necessarily beneficial for the organism. The effect of missense SNVs can range from undetectable to gain-of-function or to complete loss-of-function. Longer or shorter insertions or deletions may also show a variable phenotypic picture depending on the number and position of nucleotides being excised or incorporated. A large-scale study sequencing the whole genomes of more than 100,000 individuals concluded that rare loss-of-function variants in coding regions significantly increased the risk of autism, among several other neurological conditions .Although intron variations do not appear in the protein structure, specific regions of the introns are essential for mature mRNA function. During the splicing process, the regulatory role of certain intron sequences is essential, so variations in these sequences may already clearly contribute to the phenotype, as has been shown in the case of autism spectrum disorder . It has FOXP1, MAL, and C11orf30 [Regulatory regions ultimately affect the amount of protein produced from a gene in a given cell under given conditions. While the promoter regulates gene expression, the 3′ region affects the stability of the mRNA produced and the process of translation. However, in addition to studying genetic variation in regulatory regions , there iC11orf30 ,124,125.C11orf30 ,127.Only 1.5% of the human genome codes for proteins. Currently known regulatory regions account for a further 1.5%. A part of the remaining 97% encodes other RNAs , while the function of other parts is still unknown. It is understandable that at the beginning of genetic studies and even today, this large DNA pool has mostly been outside the focus of autism research. Nevertheless, data on the genetic variation of these regions are steadily accumulating with the results of high-throughput whole genome sequencing ,129. HowThe average difference of 0.1–0.5% between the genomes of two unrelated individuals is the sum of rare and common variants. The frequency of rare variants or mutations in the population is below 1%, correspondingly, the frequency of common variants or polymorphisms is above 1%. Other definitions set this threshold as 5%, so the frequency range between 1 and 5% is considered a grey zone.In general, mutations tend to, but not exclusively, affect the coding region or the entire gene and have a drastic effect on the expressed protein. As a result, a larger proportion of them is created through de novo events. Chromosomal abnormalities are understandably clearly rare variants, but they are relatively common in autism see .. HowevePolymorphisms are mainly found in regulatory or non-coding regions, with no or mild functional consequences. They largely make up the genetic complexity of polygenic traits, and hence of autism, involving many variations in many genes. Common variants may underlie around 40–60% of ASD cases ,134. TwoA trait can be considered monogenic if a single change at the DNA level or one or more genetic modifications in a single gene is sufficient for its development, which is obligate regardless of environmental influences, and is merely the consequence of a change in the genetic information. These conditions usually form discrete categories, with no transition between them. The development of polygenic traits, conditions, and diseases involves many variations of many genes in combination with environmental influences. As a consequence, they usually do not create discrete categories, but a diverse phenotypic continuum moving across a broader spectrum, depending on the number and quality of the genes involved and the environmental influences. Despite the fact that many types of genetic predisposition have been successfully identified and characterized, currently no single variant, gene, or polygenic score has a high predictive value in the diagnosis of ASD. Even CNVs with a high effect size for ASD present with variable psychiatric characteristic traits, and risk may be attributed to a combination of rare and common variants .For many of the genetic variations associated with ASD, the exact pattern of inheritance is still unknown, but where it is, virtually all known possibilities can occur. The present work does not aim to describe all possibilities, but merely to illustrate, through a few examples, another segment of the extremely complex genetic components of autism.TSC1 and TSC2. Thus, in addition to tumor lesions affecting virtually all organs, ASD symptoms are also very common [In tuberous sclerosis with autosomal dominant inheritance, the prevalence of autism is quite high, around 25–50% see . The devy common .DHCR7). Although the disorder itself is quite rare, the phenotypic picture of the syndrome is closely associated with autistic behavioral features [Autism-related Smith–Lemli–Opitz syndrome, an autosomal recessive inheritance disorder, is caused by gene defects in a key enzyme of cholesterol metabolism , are involved in the development of Rett syndrome. MECP2 is a transcriptional regulator expressed throughout the body that regulates the function of several other genes, and therefore the full mechanism of disease development is still unclear [Fragile X syndrome and Rett syndrome are characterized by dominant inheritance linked to the X chromosome. While the former is the most common cause of mental retardation in men, the latter mainly affects women. Both disorders have a relatively high prevalence of autism  or only a few dimensions are highlighted. The principles for grouping genetic variations also vary from publication to publication, making it very difficult to compare them and to form a coherent picture. This review has attempted to classify the genetic variations involved in autism into six categories based on their extent, time of onset, localization inside the gene or genome , frequency of variation, inheritance pattern, and the number of genes involved in a given individual, thus covering the full spectrum of variations in DNA along several dimensions.The picture is further complicated by the fact that a given genetic variation can often belong to more than one of the dimensions discussed in It is also noteworthy that each of the six dimensions summarized in Currently, the bottleneck in autism research is no longer the lack of eligible subjects, the complexity of genetic analysis, or the inaccuracy of diagnostic systems, but the evaluation of the accumulated data and the definition of interpretative frameworks. Recent research trends represent an interdisciplinary systems biology and network approach. Nowadays, there is a growing number of consortia studies in this field, where representatives from several disciplines are working together to develop a complex genetic model of autism ,142,143.It is undoubtable that the translation of rapidly growing genetic knowledge into clinical, pedagogical, and special education practice is a rather slow process. A question has arisen as to whether and how the person with autism and their family will benefit from the genetic knowledge detailed in this review. The current view is that, although it is now feasible, genetic variants associated with autism are not screened for during pregnancy, as the link between a particular genetic condition and autism is still unclear. There is no guidance on what to do when a variation closely associated with autism is detected in a fetal sample. Today, since it is still only possible to identify genetic constellations that predispose to autism, but not necessarily cause it, ethical questions have been rightly raised in connection with this type of testing. However, there is also a strong argument for pre- and perinatal genetic screening, since if parents are aware of the higher likelihood of autism in the newborn child, intervention as early as possible can give them the best possible outcome.Genetic testing following a diagnosis of autism can have a number of benefits, as it can provide families with a reassuring explanation of the cause of ASD, help them accept the condition, and avoid self-blame. It can also help people with similar genetic alterations and their families to join a support group. Advanced and detailed genetic testing can also help to identify the presence of other genetically determined conditions  that may require medical intervention and may go unnoticed in the shadow of autism. Finally, the long-term benefits must be taken into account, as the growing pool of autism-specific genetic information will have an impact on the well-being not only of children currently living with autism, but also of children born with the condition in the future. Overall, the genetic panels developed for screening ASD associated variations cannot yet provide definitive information on whether a person has autism . However, they offer at least partial answers to the cause of the condition . FinallyAs researchers gain a deeper understanding of the genetic background of autism, they may be able to assign developmental pathways specific to a genetic pattern, which may allow for the development of tailored, effective interventions .With the opportunities offered by technological advances, it is now possible to sequence whole genomes in a matter of days, allowing comparative analyses to be carried out. Larger studies often compare the genomes of thousands or even tens of thousands of autistic and typically developing individuals. This has, of course, not only brought technical innovations in molecular biology techniques, but has also led to dramatic advances in comparative analysis, statistics, and, above all, bioinformatics. Alongside genome sequencing, exome and RNA sequencing, which allow for qualitative and quantitative analysis of the ‘active’ part of the genetic material that is being transcribed, are becoming increasingly common. All this can now be carried out from a single cell and without invasive sampling procedures. It should not be overlooked, however, that currently ASD patients are diagnosed and categorized on the basis of behavioral characteristics, the criteria for which may vary between clinical trials and thus affect the interpretation of the results obtained, and it is therefore not surprising that heterogeneous genetic results are reported in a heterogeneous patient population.In recent years, a huge amount of genetic information has accumulated—and is still accumulating—in the field of autism research, which may legitimately raise the question of whether we are any closer to understanding the etiology of ASD. The answer is not obvious, despite the huge amount of data and work that has been conducted. It can be stated with certainty, thanks to ever-evolving diagnostic tools and newer and newer molecular biology and bioinformatics techniques, that considerably more is known about the molecular genetic background of this condition than was earlier the case, but it is still far from being fully understood. Similarly, with a few exceptions, the possibility of reliable genetic screening or even personalized treatment at the genetic level—if this is even necessary—is still a long way off. Only the details are known, and further network-based research would be needed to discover systems in the dataset that clearly identify the key gene clusters underlying ASD.According to the current state-of-the-art understanding of autism, ASD is a spectrum condition with no distinct categories. However, this approach is not always useful in clinical and genetic research, which often requires grouping subjects according to certain criteria  to achieve greater homogeneity. Ultimately, a clearer diagnostic categorization of ASD will lead to clearer genetic correlations, and clearer genetic definitions would be of great help in autism identification and diagnosis.


Our results confirmed that homologous recombination occurred more often in maternal chromosomes, and the age effect was more significant in maternal homologous recombination. A total of 6,494 homologous recombination hotspots (32.3%) were identified in genes of Online Mendelian Inheritance in Man. A uniform association between homologous recombination and aneuploidy was not established. In addition, carriers with identified breakpoints of reciprocal translocations were analyzed, and locations of breakpoints were found partly overlapped with homologous recombination hotspots, implying a possible similar mechanism behind both events. This study highlights the significance of constructing a recombination map, which may improve the accuracy of haplotype analysis for preimplantation genetic testing for monogenic diseases. Overlapping locations of translocation and recombination are worthy of further investigation.Recombination is essential for physical attachments and genetic diversity. The Han Chinese population is the largest ethnic group worldwide, therefore, the construction of a genetic map regarding recombination for the population is essential. In this study, 164 and 240 couples who underwent preimplantation genetic testing for monogenic diseases or segmental rearrangement were included in the analysis. Blastocysts and probands from couples who underwent preimplantation genetic testing for monogenic diseases by single nucleotide polymorphism array were included for recombination analysis. The location of recombination was determined from haplotype phase transitions in parent-offspring pairs at loci where the parents were heterozygous. The genetic map for Chinese  During meiosis, recombination between paired homologous chromosomes contributes to genetic diversity by introducing new allele combinations . Most DSGiven its critical role in biological and evolutionary processes, the occurrence of recombination might be expected to be conservative across different populations. Yet, tremendous heterogeneity in the rates and patterns of recombination persists among individuals, sexes, and populations . To the One way to learn human meiotic recombination is to estimate the locations of crossovers from genomic segments shared among relatives (In preimplantation genetic testing for monogenic diseases (PGT-M), DNA from biopsied cells of preimplantation embryos is available, which may provide valuable genetic information for meiotic recombination . In addition, locations of breakpoints of reciprocal translocation were determined, and overlapping locations with those of HR hotspots were analyzed. Our study may provide further evidence to support the hypothesis that reciprocal translocations occur more often in HR hotspots.Institutional review board approval was obtained for this study by the institutional review board of the First Affiliated Hospital of Sun Yet-Sen University. All studies were conducted in accordance with the Helsinki Declaration. Informed consent was waived due to the retrospective analysis of anonymized data. All primary data were encoded such that informative single nucleotide polymorphisms (SNPs) were represented as A and B. Only secondary data with informative SNPs coded as A and B were used for data analysis.This retrospective study included couples for PGT-M by Karyomapping at the Reproductive Medicine Center of the First Affiliated Hospital of Sun Yet-Sen University from 2016 November 1 to 2019 December 31 to construct the genetic map. Patients with reciprocal translocation were also included for PGT for segmental rearrangement (PGT-SR) from 2017 August 1 to 2019 December 31. All karyotypes of couples and probands in PGT were normal, except for reciprocal translocation carriers. Only the Han Chinese population was included. Exclusion criteria were as follows: (1) no embryo available for biopsy, (2) no euploid embryo after PGT-M, (3) breakpoint of reciprocal translocation could not be identified, and (4) embryos with a call rate of <0.9 or allele drop out of >0.05, indicating poor whole-genome amplification (WGA).2 and 6% CO2). A trophectoderm biopsy was performed on expanded blastocysts on postretrieval day 5 or 6, depending on the developmental stage of individual embryos. All embryos underwent a single-blastocyst biopsy and were vitrified separately after the biopsy. As described previously, the cryotop method for vitrification was used, and warmed cycles were performed according to the routine protocol  according to the manufacturer's instructions. Embryonic WGA and parental DNA samples were used for SNP genotyping to detect chromosome status and monogenic disease  algorithm  using thThe parent-of-origin and mechanistic origin of aneuploidy were assessed according to haplotype and this study's algorithm. Theoretically, it was considered an error in meiotic prophase I with the heterogeneity in centromere and an error in mitosis with homozygosity in the whole chromosome. The rest with homozygosity in the centromere but not the whole chromosome would be considered an error in meiotic prophase II (n = 97), Duchenne muscular dystrophy (n = 11), hemophilia (n = 8), and other monogenic diseases. To investigate the relationship between translocation breakpoints and locations of HR, 240 reciprocal translocation carriers who performed PGT-SR were included in the analysis.A total of 194 cycles from 164 families for PGT-M by Karyomapping were included. The average maternal and paternal ages were 31.45 ± 3.95 and 33.85 ± 4.63 years per cycle, respectively. There were 142 living offspring, 10 terminated pregnant tissues with normal karyotypes, and 918 euploid embryos generated from PGT-M cycles analyzed for genetic maps . To inveMicroarray genotype data from 293,109 SNPs allowed identifying 31,005 crossovers in 1,070 paternal meiosis and 49,466 crossovers in 1,070 maternal meiosis . Each ofP = 3.6 × 10−8). An age-related increase in the amount of recombination  was observed in paternal chromosomes  was different between sex-specific maps. There were 9,709 hotspots for paternal autosomes and 10,418 hotspots for maternal chromosomes  had a higher incidence of recombination compared with euploid embryos but not in trisomy 16 only  or trisomy 21 only  overlapped with HR hotspots when the distance of the overlap was defined within 55 and 44 kb from translocation breakpoints in paternal and maternal genetic maps, respectively  hotspots found in genes of OMIM. Interestingly, part of the breakpoints of reciprocal translocations overlapped with HR hotspots.et al. analyzed 389 preimplantation blastocysts from 98 couples in the United States  hotspots were in genes of OMIM, which would pose a great challenge for PGT-M. Future clinical works should pay more attention to the impact of recombination, which may cause misdiagnosis of embryos. If recombination occurs in the mutation gene, the risk of misdiagnosis would be high. Direct mutation detection by Sanger sequence or PCR is essential in this situation.n = 8) or trisomy 21 only (n = 5) had a lower incidence of recombination compared to euploid embryos but not in meiotic trisomy 22 only (n = 7). However, our results were discordant with the previous study. In this study, embryos affected with trisomy 22 only  had a higher incidence of recombination compared with euploid embryos but not in trisomy 16 only  or trisomy 21 only  translocation  overlapped with HR hotspots in paternal and maternal genetic maps.This study has several advantages. First, 164 families including parents and 1,070 offspring were studied. Each pedigree constituted members ranging from 2 to 25, which was seldomly found in human populations, as only a few gametes per individual generate offspring. Therefore, more detailed information could be obtained from each pedigree. Second, HumanKaryomap-12, applied for PGT-M in this study, is particularly designed for monogenic diseases. It allowed the analysis of SNP data and recombination closely related to monogenic diseases, which may have clinical value for future applications. This study also provided the first genetic map for Han Chinese embryos. Lastly, our results supported the hypothesis that reciprocal translocation may occur more often in HR hotspots, as 25% of translocation breakpoints (However, several limitations were also presented. First, the sample size and generation were small, which may introduce selection bias. The result of trophectoderm biopsy would not excellently represent the karyotypes of inner cell mass (In conclusion, we found that HR occurred more often in maternal chromosomes, and the age effect was more significant in maternal HR. Part of the breakpoints of reciprocal translocations overlapped with HR hotspots, which implied a similar mechanism behind these events. Our study may provide a valuable basis for future genetic research, such as linkage studies, prediction of disease-causing structural rearrangements, and genome-wide association studies on the Han Chinese population.jkad031_Supplementary_DataClick here for additional data file.