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BackgroundAfter mating, female mosquitoes need animal blood to develop their eggs. In the process of acquiring blood, they may acquire pathogens, which may cause different diseases to humans such as malaria, zika, dengue, and chikungunya. Therefore, knowing the parity status of mosquitoes is useful in control and evaluation of infectious diseases transmitted by mosquitoes, where parous mosquitoes are assumed to be potentially infectious. Ovary dissections, which currently are used to determine the parity status of mosquitoes, are very tedious and limited to very few experts. An alternative to ovary dissections is near-infrared spectroscopy (NIRS), which can estimate the age in days and the infectious state of laboratory and semi-field reared mosquitoes with accuracies between 80 and 99%. No study has tested the accuracy of NIRS for estimating the parity status of wild mosquitoes.Methods and resultsIn this study, we train artificial neural network (ANN) models on NIR spectra to estimate the parity status of wild mosquitoes. We use four different datasets: An. arabiensis collected from Minepa, Tanzania (Minepa-ARA); An. gambiae collected from Muleba, Tanzania (Muleba-GA); An. gambiae collected from Burkina Faso (Burkina-GA); and An.gambiae from Muleba and Burkina Faso combined (Muleba-Burkina-GA). We train ANN models on datasets with spectra preprocessed according to previous protocols. We then use autoencoders to reduce the spectra feature dimensions from 1851 to 10 and re-train ANN models. Before the autoencoder was applied, ANN models estimated parity status of mosquitoes in Minepa-ARA, Muleba-GA, Burkina-GA and Muleba-Burkina-GA with out-of-sample accuracies of 81.9 ± 2.8% (N=927), 68.7 ± 4.8% (N=140), 80.3 ± 2.0% (N=158), and 75.7 ± 2.5% (N=298), respectively. With the autoencoder, ANN models tested on out-of-sample data achieved 97.1 ± 2.2%, (N=927), 89.8 ± 1.7% (N=140), 93.3 ± 1.2% (N=158), and 92.7 ± 1.8% (N=298) accuracies for Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA, respectively.ConclusionThese results show that a combination of an autoencoder and an ANN trained on NIR spectra to estimate parity status of wild mosquitoes yields models that can be used as an alternative tool to estimate parity status of wild mosquitoes, especially since NIRS is a high-throughput, reagent-free, and simple-to-use technique compared to ovary dissections.

Deployment of Wolbachia to mitigate dengue (DENV), Zika (ZIKV) and chikungunya (CHIKV) transmission is ongoing in 12 countries. One way to assess the efficacy of Wolbachia releases is to determine invasion rates within the wild population of Aedes aegypti following their release. Herein we evaluated the accuracy, sensitivity and specificity of the Near Infrared Spectroscopy (NIRS) in estimating the time post death, ZIKV-, CHIKV-, and Wolbachia-infection in trapped dead female Ae. aegypti mosquitoes over a period of 7 days. Regardless of the infection type, time post-death of mosquitoes was accurately predicted into four categories (fresh, 1 day old, 2–4 days old and 5–7 days old). Overall accuracies of 93.2, 97 and 90.3% were observed when NIRS was used to detect ZIKV, CHIKV and Wolbachia in dead Ae. aegypti female mosquitoes indicating NIRS could be potentially applied as a rapid and cost-effective arbovirus surveillance tool. However, field data is required to demonstrate the full capacity of NIRS for detecting these infections under field conditions., Santos et al. demonstrate that the Near Infrared Spectroscopy (NIRS) can accurately estimate the death time of trapped female Aedes aegypti and vector infection with Zika virus, Chikungunya virus, or Wolbachia in a 7-day trapping period. This study suggests that NIRS may provide an accurate and inexpensive tool that improves arbovirus surveillance systems.

Background: Screening for Plasmodium spp. sporozoite infection in mosquitoes is routinely done using ELISA (enzyme-linked immunosorbent assay). Near infrared spectroscopy (NIRS), a fast and non-destructive method, has recently been shown to distinguish, with 95% accuracy, between uninfected and sporozoite-infected mosquitoes using laboratory strains of Plasmodium falciparum (PfN54). The aim of this present study was to further investigate the reproducibility of NIRS to identify sporozoite infection in mosquitoes infected using field isolates of P. falciparum gametocytes from asymptomatic carriers. Methods: Healthy individuals (aged 5 years and above) were screened for gametocytaemia by thick-smear microscopy in an area of moderate transmission along the Coast of Kenya between May and September 2018. Asymptomatic gametocyte carriers were recruited for mosquito feeding assays, direct membrane feeding (DMFA) and direct skin feeding (DFA), using insectary-reared Anopheles gambiae s.s (Kilifi strain). Mosquitoes were kept for 14-days post feeding after which they were scanned using NIRS and subsequently analysed for sporozoite infection using circumsporozoite-ELISA. Predictive models were explored using partial least square regressions (PLS).Results: Two hundred and ninety-nine (299) individuals were screened for malaria parasites, 74 (24.8%) were found with circulating asexual parasites, and 16 (5.4%) with P. falciparum gametocyte stages.Fourteen (14) asymptomatic gametocyte carriers were recruited to the study for mosquito feeding assays. A total of 134 (7%, 134/1881) sporozoite-infected mosquitoes were obtained from 9 successful experiments. Three different training datasets composed of infected and uninfected mosquitoes were analysed. The PLS models were unable to distinguish between sporozoite-infected and uninfected mosquitoes. A predictive model could not be generated.Conclusions: The results of this study were not consistent with previous published research on NIRS for detection of sporozoite infection in the same mosquito species and may reflect differences between laboratory and field conditions. The current findings indicate that methods for sporozoite detection should be tested on field isolates at an early stage in their development and are informative for future research seeking novel high-throughput methods for parasite detection in mosquitoes.

BackgroundScreening for Plasmodium spp. sporozoite infection in mosquitoes is routinely done using ELISA (enzyme-linked immunosorbent assay). Near infrared spectroscopy (NIRS), a fast and non-destructive method, has recently been shown to distinguish, with 95% accuracy, between uninfected and sporozoite-infected mosquitoes using laboratory strains of Plasmodium falciparum (PfN54). The aim of this present study was to further investigate the reproducibility of NIRS to identify sporozoite infection in mosquitoes infected using field isolates of P. falciparum gametocytes from asymptomatic carriers.MethodsHealthy individuals (aged 5 years and above) were screened for gametocytaemia by thick-smear microscopy in an area of moderate transmission along the Coast of Kenya between May and September 2018. Asymptomatic gametocyte carriers were recruited for mosquito feeding assays, direct membrane feeding (DMFA) and direct skin feeding (DFA), using insectary-reared Anopheles gambiae s.s (Kilifi strain). Mosquitoes were kept for 14-days post feeding after which they were scanned using NIRS and subsequently analysed for sporozoite infection using circumsporozoite-ELISA. Predictive models were explored using partial least square regressions (PLS).ResultsTwo hundred and ninety-nine (299) individuals were screened for malaria parasites, 74 (24.8%) were found with circulating asexual parasites, and 16 (5.4%) with P. falciparum gametocyte stages. Fourteen (14) asymptomatic gametocyte carriers were recruited to the study for mosquito feeding assays. A total of 134 (7%, 134/1881) sporozoite-infected mosquitoes were obtained from 9 successful experiments. Three different training datasets composed of infected and uninfected mosquitoes were analysed. The PLS models were unable to distinguish between sporozoite-infected and uninfected mosquitoes. A predictive model could not be generated.ConclusionsThe results of this study were not consistent with previous published research on NIRS for detection of sporozoite infection in the same mosquito species and may reflect differences between laboratory and field conditions. The current findings indicate that methods for sporozoite detection should be tested on field isolates at an early stage in their development and are informative for future research seeking novel high-throughput methods for parasite detection in mosquitoes.

To date, no methodology has been described for predicting the age of Aedes albopictus Skuse mosquitoes, commonly known as Asian tiger mosquitoes. In this study, we report the potential of near-infrared spectroscopy (NIRS) technique for characterizing the age of female laboratory reared Ae. albopictus. Using leave-one-out cross-validation analysis on a training set, laboratory reared mosquitoes preserved in RNAlater for up to a month were assessed at 1, 3, 7, 9, 13, 16, 20 and 25 days post emergence. Mosquitoes (N = 322) were differentiated into two age classes (13 days old) with 76% accuracy, and on a continuous age scale to within ±3 days of their actual average age. Similarly, models predicted mosquitoes (N = 146) excluded from the training model with 94% and 71% accuracy to the two and the three age groups, respectively. We show for the first time that NIRS, with an improved spectrometer and fibre configuration, can be used to predict the age of laboratory reared female Ae. albopictus. Characterization of the age of Ae. albopictus populations is crucial for determining the efficacy of vector control interventions that target their survival.

Near-infrared spectroscopy (NIRS) is a rapid detection technique that has been used to characterize biomass. The objective of this study was to develop suitable NIRS models to predict the acetic acid, furfural, and 5-hydroxymethylfurfural (HMF) contents in biomass hydrolysates. Using a uniform distribution of pretreatment conditions, 60 representative biomass hydrolysates were prepared. Partial least-squares regression was used to develop models capable of predicting acetic acid, furfural, and HMF contents. Optimal models were built using the wavenumber range of 9000–8000 and 7000–5000 cm–1 with high R2 for calibration and validation models, small root-mean-square error of calibration (2.7. The NIRS method largely reduced the analytical time from ∼55 to

Deoxynivalenol (DON) levels in harvested grain samples are used to evaluate the Fusarium head blight (FHB) resistance of wheat cultivars and breeding lines. Fourier transform near-infrared (FT-NIR) calibrations were developed to estimate the DON level and moisture content (MC) of bulk wheat grain samples harvested from FHB screening trials. Grains in a rotating glass petri dish were scanned in the 10,000–4,000 cm−1 (1,000–2,500 nm) spectral range using a Perkin Elmer Spectrum 400 FT-IR/FT-NIR spectrometer. The DON calibration predicted the DON levels in test samples with R2 = 0.62 and root mean square error of prediction (RMSEP) = 8.01 ppm. When 5–25 ppm of DON was used as the cut-off to classify samples into low- and high-DON groups, 60.8–82.3% of the low-DON samples were correctly classified, whereas the classification accuracy of the high-DON group was 82.3–94.0%. The MC calibration predicted the MC in grain samples with R2 = 0.98 and RMSEP = 0.19%. Therefore, these FT-NIR calibrations can be used to r...

Background Large-scale surveillance of mosquito populations is crucial to assess the intensity of vector-borne disease transmission and the impact of control interventions. However, there is a lack of accurate, cost-effective and high-throughput tools for mass-screening of vectors. Methods A total of 750 Anopheles gambiae (Keele strain) mosquitoes were fed Plasmodium falciparum NF54 gametocytes through standard membrane feeding assay (SMFA) and afterwards maintained in insectary conditions to allow for oocyst (8 days) and sporozoite development (14 days). Thereupon, each mosquito was scanned using near infra-red spectroscopy (NIRS) and processed by quantitative polymerase chain reaction (qPCR) to determine the presence of infection and infection load. The spectra collected were randomly assigned to either a training dataset, used to develop calibrations for predicting oocyst- or sporozoite-infection through partial least square regressions (PLS); or to a test dataset, used for validating the calibration’s prediction accuracy. Results NIRS detected oocyst- and sporozoite-stage P. falciparum infections with 88% and 95% accuracy, respectively. This study demonstrates proof-of-concept that NIRS is capable of rapidly identifying laboratory strains of human malaria infection in African mosquito vectors. Conclusions Accurate, low-cost, reagent-free screening of mosquito populations enabled by NIRS could revolutionize surveillance and elimination strategies for the most important human malaria parasite in its primary African vector species. Further research is needed to evaluate how the method performs in the field following adjustments in the training datasets to include data from wild-caught infected and uninfected mosquitoes. Electronic supplementary material The online version of this article (10.1186/s12936-019-2719-9) contains supplementary material, which is available to authorized users.

To detect popcorn kernels infested by the internal feeding stored-product insect pest Sitophilus zeamais , maize weevil, a laboratory roller mill was modified so that the electrical conductivity of the grain is measured while the kernels are milled between the rolls. When a kernel with a S. zeamais larvae inside is milled, the moisture from the crushed insect abruptly changes the conductivity of the test circuit. The objective of this study was to determine the potential of the modified conductance mill to detect popcorn infested with different developmental stages. Two laboratory milling units were tested that had differing sharpness, which affected the feed-rate through the rolls. One mill averaged 135 s to feed 1 kg of popcorn while the second mill with sharper teeth averaged 100 s to feed 1 kg of popcorn. Four popcorn varieties were evaluated, with their average kernel weight ranging from 12.5 g to 18.5 g per 100 kernels. Known numbers of infested popcorn kernels were added to 1 kg samples of popcorn. The slower feeding mill detected 81% of the pupae, 91% of the medium larvae, and 47% of the small larvae. The faster feeding mill detected 75% of the pupae, 80% of the medium larvae, and 43% of the small larvae. Our results indicate that the conductance mill is a good method for quickly evaluating popcorn samples for kernels infested with late stage larvae and pupae.

We investigated the use of insecticide-treated material and modified atmosphere storage for reducing insect damage in stored maize. Results showed that insecticide-treated netting and insecticide-treated seed bags protected grain from insect damage, with the treated seed bags protecting grain for up to nine months if the grain was free from insects before storing. Covering the opening of grain storage containers with treated netting provided good control of stored-grain insects and may offer additional protection for grain silos being promoted in developing countries. Neither treated bags nor netting provided control if the grain contained insects before storage, thus emphasising the need to provide some means of controlling initial infestations at the time of storage when using bags or netting. We also showed that good control of insects can be achieved if the grain is stored in air-tight rigid containers that cannot be penetrated by insects or rodents. Hermetic plastic bags were readily penetrated by in...

Fusarium head blight (FHB) is a serious disease in wheat that affects grain quality owing to the accumulation of mycotoxins such as deoxynivalenol (DON) in grains. Near-infrared (NIR) spectroscopy has been used to develop techniques to estimate DON levels in single wheat kernels to facilitate rapid, nondestructive screening of FHB resistance in wheat breeding lines. The effect of moisture content (MC) variation on the accuracy of single-kernel DON prediction by NIR spectroscopy was investigated. Sample MC considerably affected accuracy of the current NIR DON calibration by underestimating or overestimating DON at higher or lower moisture levels, respectively. DON in single kernels was most accurately estimated at 13–14% MC. Major NIR absorptions related to Fusarium damage were found around 1,198–1,200, 1,418–1,430, 1,698, and 1,896–1,914 nm. Major moisture related absorptions were observed around 1,162, 1,337, 1,405–1,408, 1,892–1,924, and 2,202 nm. Fusarium damage and moisture related absorptions overlap...

Fusarium head blight (FHB) reduces grain yield and accumulates mycotoxins in wheat kernels and flour and thus compromise the end-use quality. In this study, quantitative trait loci (QTL) associated with components of FHB resistance were mapped in the Kansas wheat variety Everest. A doubled haploid (DH) population with 176 lines was developed from a cross between Everest and WB-Cedar, which are moderately resistant and moderately susceptible to FHB, respectively. Two-year field evaluations were conducted using a randomized complete block design with three replications. Deoxynivalenol (DON) accumulation and Fusarium-damaged kernels (FDK) were estimated using a single-kernel near-infrared (SKNIR) system. Four QTL from Everest were identified on 1BS, 3DS, 4BL, and 5AS, indicating that this variety is a source of native FHB resistance conditioned by multiple loci. A large-effect QTL (Qksu-fhbE-5AS) from Everest explained up to 13% of the DON variation in the 2-year experiment average. A single QTL from WB-Cedar, Qksu.fhbC-1AS, was identified contributing FHB resistance. QTL grouping analysis showed that individuals containing multiple QTL were significantly more resistant, indicating that QTL pyramiding enhances FHB resistance. A 36% reduction in DON content was observed in DH lines carrying all mapped QTL for DON accumulation. Further validation of the QTL mapped here will allow breeding programs to increase FHB resistance in other genetic backgrounds. This is the first study that identified QTL associated with low DON accumulation in multiple years of field experiment using the SKNIR technology, supporting the use of this methodology in future genetics and breeding studies.

Wegulo, S. N., Bockus, W. W., Hernandez Nopsa, J., De Wolf, E. D., Eskridge, K. M., Peiris, K. H. S., and Dowell, F. E. 2011. Effects of integrating cultivar resistance and fungicide application on Fusarium head blight and deoxynivalenol in winter wheat. Plant Dis. 95:554-560. Fusarium head blight (FHB) or scab, incited by Fusarium graminearum, can cause significant economic losses in small grain production. Five field experiments were conducted from 2007 to 2009 to determine the effects on FHB and the associated mycotoxin deoxynivalenol (DON) of integrating winter wheat cultivar resistance and fungicide application. Other variables measured were yield and the percentage of Fusarium-damaged kernels (FDK). The fungicides prothioconazole + tebuconazole (formulated as Prosaro 421 SC) were applied at the rate of 0.475 liters/ha, or not applied, to three cultivars (experiments 1 to 3) or six cultivars (experiments 4 and 5) differing in their levels of resistance to FHB and DON accumulation. The effect of cultivar on FHB index was highly significant (P < 0.0001) in all five experiments. Under the highest FHB intensity and no fungicide application, the moderately resistant cultivars Harry, Heyne, Roane, and Truman had less severe FHB than the susceptible cultivars 2137, Jagalene, Overley, and Tomahawk (indices of 30 to 46% and 78 to 99%, respectively). Percent fungicide efficacy in reducing index and DON was greater in moderately resistant than in susceptible cultivars. Yield was negatively correlated with index, with FDK, and with DON, whereas index was positively correlated with FDK and with DON, and FDK and DON were positively correlated. Correlation between index and DON, index and FDK, and FDK and DON was stronger in susceptible than in moderately resistant cultivars, whereas the negative correlation between yield and FDK and yield and DON was stronger in moderately resistant than in susceptible cultivars. Overall, the strongest correlation was between index and DON (0.74 ≤ R ≤ 0.88, P ≤ 0.05). The results from this study indicate that fungicide efficacy in reducing FHB and DON was greater in moderately resistant cultivars than in susceptible ones. This shows that integrating cultivar resistance with fungicide application can be an effective strategy for management of FHB and DON in winter wheat.

Large-scale surveillance of mosquito populations is crucial to assess the intensity of vector-borne disease transmission and the impact of control interventions. However, there is a lack of accurate, cost-effective and high-throughput tools for mass-screening of vectors. This study demonstrates proof-of-concept that near-infrared spectroscopy (NIRS) is capable of rapidly identifying laboratory strains of human malaria infection in African mosquito vectors. By using partial least square regression models based on malaria-infected and uninfected Anopheles gambiae mosquitoes, we showed that NIRS can detect oocyst- and sporozoite-stage Plasmodium falciparum infections with 88% and 95% accuracy, respectively. Accurate, low-cost, reagent-free screening of mosquito populations enabled by NIRS could revolutionize surveillance and elimination strategies for the most important human malaria parasite in its primary African vector species. Further research is needed to evaluate how the method performs in the field following adjustments in the training datasets to include data from wild-caught infected and uninfected mosquitoes.

Stalk rot diseases are among the most ubiquitous and damaging fungal diseases of sorghum (Sorghum bicolor (L.) Moench) worldwide. Although reports of quantitative yield losses to stalk rots are available, the impact of stalk rot on grain quality attributes is unknown. This study was conducted to test whether stalk rot diseases could affect grain mineral (N, P, K; Ca, Mg, Cu, Fe, Mn, and Zn) and macronutrient (protein, fat, and starch) content, ash content, and physical traits (unit grain weight, hardness, and diameter). A field experiment was conducted in 2013 and 2014 with four sorghum genotypes (two hybrids and two lines). Plants from each genotype were inoculated with four stalk rot pathogens (Fusarium andiyazi, F. proliferatum, F. thapsinum, and Macrophomina phaseolina) and mock-inoculated with phosphate-buffered saline (control). Grains collected from infected and control plants were analyzed for macronutrient and ash content using near-infrared reflectance spectroscopy, grain hardness and diameter using the single-kernel characterization system, and mineral content using the Rapid Flow Analyzer (Model RFA-300 for N) and inductively coupled plasma spectrometer (for P, K, Ca, Mg, Cu, Fe, Mn, and Zn). Although stalk rot pathogens significantly reduced unit grain weight, they did not significantly affect grain hardness and diameter and, therefore, may not affect milling quality. Pathogens significantly reduced all macronutrient and most mineral contents across genotypes and environments on a per-unit-grain basis, except N and Mg, which were affected in a genotype- and environment-specific manner, and Fe, which was not significantly affected. Most minerals tested were significantly and negatively correlated with disease severity (lesion length) and total grain weight per panicle. The hybrid tested (Pioneer 84G62) exhibited reduced mineral and macronutritional changes after stalk rot infection, providing insights into the possibility of producing high-yielding, nutritionally stable hybrids under stalk rot disease pressure through dedicated breeding efforts.

Sorghum and millets find extensive use in traditional food products and for animal feed. Today, they are increasingly used in modern food products, especially gluten-free and healthy food products, lager beers, and bioethanol. These wide uses dictate desired quality parameters for the grains and their products, which are measured by a variety of methods. The parameters mostly concern food safety, grain physical characteristics, and chemical composition. Quality management systems exist for sorghum and millets from various specific statutory bodies. Their major purpose is to facilitate trade in the grains. Traceability systems for the sorghum and millets value chain also need to be introduced. Furthermore, a more multidisciplinary approach to strengthening quality management systems for the sorghum and millets value chain is needed. The goal of this approach would be to create synergies between different types of expertise relevant for the sorghum and millets value chain and, importantly, must include the consumer.

BackgroundNear infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into < or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier.Methods and findingsWe explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. The ANN regression model scored root mean squared error (RMSE) of 1.6 ± 0.2 for An. gambiae and 2.8 ± 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 ± 0.2 for An. gambiae, and 4.5 ± 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the accuracy of the ANN regression model was 93.7 ± 1.0% for An. gambiae, and 90.2 ± 1.7% for An. arabiensis; while PLS regression model scored the accuracy of 83.9 ± 2.3% for An. gambiae, and 80.3 ± 2.1% for An. arabiensis. We also find that a directly trained binary classifier yields higher age estimation accuracy than a regression model interpreted as a binary classifier. A directly trained ANN binary classifier scored an accuracy of 99.4 ± 1.0 for An. gambiae and 99.0 ± 0.6% for An. arabiensis; while a directly trained PLS binary classifier scored 93.6 ± 1.2% for An. gambiae and 88.7 ± 1.1% for An. arabiensis. We further tested the reproducibility of these results on different independent mosquito datasets. ANNs scored higher estimation accuracies than when the same age models are trained using PLS. Regardless of the model architecture, directly trained binary classifiers scored higher accuracies on classifying age of mosquitoes than regression models translated as binary classifiers.ConclusionWe recommend training models to estimate age of An. arabiensis and An. gambiae using ANN model architectures (especially for datasets with at least 70 mosquitoes per age group) and direct training of binary classifier instead of training a regression model and interpreting it as a binary classifier.

We report the first use of near-infrared spectroscopy for rapid, low-cost, noninvasive detection of Zika virus in Aedes aegypti., The accelerating global spread of arboviruses, such as Zika virus (ZIKV), highlights the need for more proactive mosquito surveillance. However, a major challenge during arbovirus outbreaks has been the lack of rapid and affordable tests for pathogen detection in mosquitoes. We show for the first time that near-infrared spectroscopy (NIRS) is a rapid, reagent-free, and cost-effective tool that can be used to noninvasively detect ZIKV in heads and thoraces of intact Aedes aegypti mosquitoes with prediction accuracies of 94.2 to 99.3% relative to quantitative reverse transcription polymerase chain reaction (RT-qPCR). NIRS involves simply shining a beam of light on a mosquito to collect a diagnostic spectrum. We estimated in this study that NIRS is 18 times faster and 110 times cheaper than RT-qPCR. We anticipate that NIRS will be expanded upon for identifying potential arbovirus hotspots to guide the spatial prioritization of vector control.

Fusarium head blight (FHB) symptoms, single-kernel deoxynivalenol (DON) levels, and distribution of DON levels among kernels in response to artificial FHB inoculation were investigated in three selected wheat cultivars that had different reported levels of FHB resistance. DON levels were estimated with near-infrared spectroscopy. The percentages of DON-containing spikelets per spike of 15.2, 49.7, and 89.1% were significantly different among point-inoculated spikes of Everest, Karl 92, and Overley, respectively. The percentage of visually Fusarium-damaged kernels in point-inoculated Karl 92 and Overley spikes was significantly higher than for Everest. However, the DON-containing spikelets per spike and visually Fusarium-damaged kernels values for spray-inoculated spikes were not significantly different among the three cultivars. In spray-inoculated spikes, DON levels in kernels ranged from 0 to 291.3 ppm, whereas the variation of DON levels in spikelet positions was random. In contrast, DON levels in spik...

Mosquito control with bednets, residual sprays or fumigation remains the most effective tool for preventing vector-borne diseases such as malaria, dengue and Zika, though there are no widely used entomological methods for directly assessing its efficacy. Mosquito age is the most informative metric for evaluating interventions that kill adult mosquitoes but there is no simple or reliable way of measuring it in the field. Near-Infrared Spectroscopy (NIRS) has been shown to be a promising, high-throughput method that can estimate the age of mosquitoes. Currently the ability of NIRS to measure mosquito age is biased, and has relatively high individual mosquito measurement error, though its capacity to rigorously monitor mosquito populations in the field has never been assessed. In this study, we use machine learning methods from the chemometric literature to generate more accurate, unbiased estimates of individual mosquito age. These unbiased estimates produce precise population-level measurements, which are relatively insensitive to further increases in NIRS accuracy when feasible numbers of mosquitoes are sampled. The utility of NIRS to directly measure the impact of pyrethroid resistance on mosquito control is illustrated, showing how the technology has potential as a highly valuable tool for directly assessing the efficacy of mosquito control interventions.

BackgroundThe proportion of mosquitoes infected with malaria is an important entomological metric used to assess the intensity of transmission and the impact of vector control interventions. Currently the prevalence of mosquitoes with salivary gland sporozoites is estimated by dissecting mosquitoes under a microscope or using molecular methods. These techniques are laborious, subjective, and require either expensive equipment or training. This study evaluates the potential of near infra-red spectroscopy (NIRS) to identify laboratory reared mosquitoes infected with rodent malaria.MethodsAnopheles stephensi mosquitoes were reared in the laboratory and fed on Plasmodium berghei infected blood. After 12 and 21 days post-feeding mosquitoes were killed, scanned and analysed using NIRS and immediately dissected by microscopy to determine the number of oocysts on the midgut wall or sporozoites in the salivary glands. A predictive classification model was used to determine parasite prevalence and intensity status from spectra.ResultsThe predictive model correctly classifies infectious and uninfectious mosquitoes with an overall accuracy of 72%. The false negative and false positive rates are, respectively, 30% and 26%. While NIRS was able to differentiate between uninfectious and highly infectious mosquitoes, differentiating between mid-range infectious groups was less accurate. Multiple scans of the same specimen, with repositioning the mosquito between scans, is shown to improve accuracy. On a smaller dataset NIRS was unable to predict whether mosquitoes harboured oocysts.ConclusionsWe provide the first evidence that NIRS can differentiate between infectious and uninfectious mosquitoes. Currently the method has moderate accuracy and distinguishing between different intensities of infection is challenging. The classification model provides a flexible framework and allows for different error rates to be optimised, enabling the sensitivity and specificity of the technique to be varied according to requirements.

Background Understanding the age-structure of mosquito populations, especially malaria vectors such as Anopheles gambiae, is important for assessing the risk of infectious mosquitoes, and how vector control interventions may impact this risk. The use of near-infrared spectroscopy (NIRS) for age-grading has been demonstrated previously on laboratory and semi-field mosquitoes, but to date has not been utilized on wild-caught mosquitoes whose age is externally validated via parity status or parasite infection stage. In this study, we developed regression and classification models using NIRS on datasets of wild An. gambiae (s.l.) reared from larvae collected from the field in Burkina Faso, and two laboratory strains. We compared the accuracy of these models for predicting the ages of wild-caught mosquitoes that had been scored for their parity status as well as for positivity for Plasmodium sporozoites. Results Regression models utilizing variable selection increased predictive accuracy over the more common full-spectrum partial least squares (PLS) approach for cross-validation of the datasets, validation, and independent test sets. Models produced from datasets that included the greatest range of mosquito samples (i.e. different sampling locations and times) had the highest predictive accuracy on independent testing sets, though overall accuracy on these samples was low. For classification, we found that intramodel accuracy ranged between 73.5–97.0% for grouping of mosquitoes into “early” and “late” age classes, with the highest prediction accuracy found in laboratory colonized mosquitoes. However, this accuracy was decreased on test sets, with the highest classification of an independent set of wild-caught larvae reared to set ages being 69.6%. Conclusions Variation in NIRS data, likely from dietary, genetic, and other factors limits the accuracy of this technique with wild-caught mosquitoes. Alternative algorithms may help improve prediction accuracy, but care should be taken to either maximize variety in models or minimize confounders. Electronic supplementary material The online version of this article (10.1186/s13071-017-2501-1) contains supplementary material, which is available to authorized users.

Fusarium head blight (FHB) is a devastating disease that threatens wheat (Triticum aestivum) production in many areas worldwide. FHB infection results in Fusarium-damaged kernels (FDK) and deoxynivalenol (DON) that dramatically reduce grain yield and quality. More effective and accurate disease evaluation methods are imperative for successful identification of FHB-resistant sources and selection of resistant cultivars. To determine the relationships among different types of resistance, 363 (74 soft and 289 hard) U.S. winter wheat accessions were repeatedly evaluated for FDK and DON concentration in greenhouse and field experiments. Single-kernel near-infrared (SKNIR)-estimated FDK and DON were compared with visually estimated FDK and gas chromatography-mass spectroscopy-estimated DON. Significant correlations were detected between percentage of symptomatic spikelets and visual FDK in the greenhouse and field, although correlations were slightly lower in the field. High correlation coefficients also were observed between visually scored FDK and SKNIR-estimated FDK (0.72, P < 0.001) and SKNIR-estimated DON (0.68, P < 0.001); therefore, both visual scoring and SKNIR methods are useful for estimating FDK and DON in breeding programs.

This report describes a method to estimate the bulk deoxynivalenol (DON) content of wheat grain samples with the single-kernel DON levels estimated by a single-kernel near-infrared (SKNIR) system combined with single-kernel weights. The described method estimated the bulk DON levels in 90% of 160 grain samples to within 6.7 ppm of DON when compared with the DON content determined with the gas chromatography–mass spectrometry method. The single-kernel DON analysis showed that the DON content among DON-containing kernels (DCKs) varied considerably. The analysis of the distribution of DON levels among all kernels and among the DCKs of grain samples is helpful for the in-depth evaluation of the effect of varieties or fungicides on Fusarium head blight (FHB) reactions. The SKNIR DON analysis and estimation of the single-kernel DON distribution patterns demonstrated in this study may be helpful for wheat breeders to evaluate the FHB resistance of varieties in relation to their resistance to the spread ...

‘Mattern’ (Reg. No. CV-1092, PI 665947) hard winter waxy wheat (L.) was developed cooperatively by the USDA–ARS and the Nebraska Agricultural Experiment Station and released in 2012. Mattern has red grain color and waxy (amylose-free) endosperm starch. It was released primarily for its unique end-use quality attributes and for grain yield competitive with currently grown Nebraska-adapted cultivars. The waxy starch is conditioned by the presence of three naturally occurring mutations that eliminate production of the enzyme granule-bound starch synthase. Granule-bound starch synthase synthesizes amylose in typical wheats and other cereal crops. Mattern was tested experimentally as NX04Y2107 and was selected from the cross NW98S061/99Y1442.

Current wet chemical methods for biomass composition analysis using two-step sulfuric acid hydrolysis are time-consuming, labor-intensive, and unable to provide structural information about biomass. Infrared techniques provide fast, low-cost analysis, are non-destructive, and have shown promising results. Chemometric analysis has allowed researchers to perform qualitative and quantitative study of biomass with both near-infrared and mid-infrared spectroscopy. This review summarizes the progress and applications of infrared techniques in biomass study, and compares the infrared and the wet chemical methods for composition analysis. In addition to reviewing recent studies of biomass structure and composition, we also discuss the progress and prospects for the applications of infrared techniques.

The release of Wolbachia infected mosquitoes is likely to form a key component of disease control strategies in the near future. We investigated the potential of using near-infrared spectroscopy (NIRS) to simultaneously detect and identify two strains of Wolbachia pipientis (wMelPop and wMel) in male and female laboratory-reared Aedes aegypti mosquitoes. Our aim is to find faster, cheaper alternatives for monitoring those releases than the molecular diagnostic techniques that are currently in use. Our findings indicate that NIRS can differentiate females and males infected with wMelPop from uninfected wild type samples with an accuracy of 96% (N = 299) and 87.5% (N = 377), respectively. Similarly, females and males infected with wMel were differentiated from uninfected wild type samples with accuracies of 92% (N = 352) and 89% (N = 444). NIRS could differentiate wMelPop and wMel transinfected females with an accuracy of 96.6% (N = 442) and males with an accuracy of 84.5% (N = 443). This non-destructive technique is faster than the standard polymerase chain reaction diagnostic techniques. After the purchase of a NIRS spectrometer, the technique requires little sample processing and does not consume any reagents., Author Summary Near infrared spectroscopy (NIRS) is a technique that measures specific frequencies of light absorbed by C-H, O-H, S-H and N-H functional groups. Mosquito samples are grouped based upon absorption differences between their chemical properties. In this study, we used NIRS to differentiate 1) Aedes aegypti infected with either of the two strains of intracellular bacterium Wolbachia (wMel and wMelPop) from wild type Ae. aegypti and 2) Aedes aegypti infected with wMel from those infected with wMelPoP. NIRS facilitated the differentiation of wMel and wMelPop from wild type samples and samples infected with either of the Wolbachia infected strains with high prediction accuracies over their lifespan. Predictive models were derived from initial calibration data sets and validated against independent cohorts. Prediction accuracies were high (82–98%) regardless of the cohort mosquitoes were sampled from. The results show that NIRS may have real potential as an alternative method for monitoring Wolbachia incidence in mosquitoes. A rapid, simple and cost-effective surveillance tool suitable for resource-poor areas and large urban release programs would be of great utility for evaluating Wolbachia-based interventions. The models developed during this study require further validation using field collections.

The aim of the study was to determine the accuracy of near-infrared spectroscopy (NIRS) in determining species, gender, age, and the presence of the common endosymbiont Wolbachia in laboratory-reared Drosophila. NIRS measures the absorption of light by organic molecules. Initially, a calibration model was developed for each study. An independent set with flies not involved in initial cross-validation was then used to validate the accuracy of each calibration model. Flies from the independent sets were correctly classified into Drosophila melanogaster and Drosophila simulans with 94% and 82% accuracy, respectively, whereas flies were successfully classified by gender with accuracy greater than 90%. In the age grading test, correlation plots of the actual and predicted age for males and females of D. melanogaster and D. simulans were shown to be overlapping between the adjacent age groups. It is, however, possible to predict the age of flies as less than 9 days of age with 62–88% accuracy and flies that are equal to or older than 9 days of age with 91–98% accuracy. Finally, we used NIRS to detect the presence of Wolbachia in flies. Flies from the independent sets were successfully identified as infected or not infected with Wolbachia with approximately 90% accuracy. These results suggest that NIRS has the potential to quantify the species, gender, and presence of Wolbachia in fly populations. However, additional optimization of the protocol may be necessary before the technique can reliably estimate fly age.

Mid-infrared attenuated total reflection (Mid-IR-ATR) spectra (4000-380 cm−1) of pericarp, germ, and endosperm sections from sound and Fusarium-damaged wheat kernels of cultivars Everest and Tomahawk were collected using a Fourier transform infrared (FT-IR) spectrometer. The differences in infrared absorption bands between sound and Fusarium-damaged kernels were examined. Absorption bands in which differences were identified were compared with the mid-IR-ATR absorption bands of deoxynivalenol (DON) and Fusarium graminearum hyphae. Marked differences in absorption patterns were observed between sound and Fusarium-damaged pericarp and germ spectra, whereas those differences were negligible in the endosperm spectra. Fusarium-damaged pericarp and germ spectra exhibited a shift in the peak position of the band near 1035 cm−1 along with increased absorptions at 1160, 1203, 1313, and 1375 cm−1, likely due to the influence of DON and fungi in the Fusarium-damaged kernel tissue matrix. These results suggest that infrared spectroscopy can detect DON in the surface tissues of Fusarium-damaged wheat kernels.

Fusarium head blight (FHB) or scab, caused by Fusarium graminearum Schwabe [sexual stage Gibberella zeae (Schwein.) Petch], is a destructive disease of small grain cereals. Fusarium graminearum produces the mycotoxin deoxynivalenol (DON), which accumulates in and lowers the value and quality of grain. Field experiments were conducted from 2007 to 2009 to determine if two winter wheat cultivars varying in FHB phenotype response differentially accumulated DON. Secondary objectives were to model the relationship between FHB severity and DON, determine if environment (= year) influenced DON accumulation in the two cultivars, and measure the percentage of Fusarium-damaged kernels (FDK) in the two cultivars. The cultivar ‘Harry’ with a moderately resistant FHB phenotype consistently accumulated more DON (P ≤ 0.0358) than ‘2137’ with a susceptible phenotype. The relationship between FHB severity and DON was linear and positive for both cultivars in all three years (0.32 ≤ R2 ≤ 0.60; 0.0053 ≤ P ≤ 0.1092)...

The vinegar flies Drosophila subobscura and D. obscura frequently serve as study organisms for evolutionary biology. Their high morphological similarity renders traditional species determination difficult, especially when living specimens for setting up laboratory populations need to be identified. Here we test the usefulness of cuticular chemical profiles collected via the non-invasive method near-infrared spectroscopy for discriminating live individuals of the two species. We find a classification success for wild-caught specimens of 85%. The species specificity of the chemical profiles persists in laboratory offspring (87-92% success). Thus, we conclude that the cuticular chemistry is genetically determined, despite changes in the cuticular fingerprints, which we interpret as due to laboratory adaptation, genetic drift and/or diet changes. However, because of these changes, laboratory-reared specimens should not be used to predict the species-membership of wild-caught individuals, and vice versa. Finally, we demonstrate that by applying an appropriate cut-off value for interpreting the prediction values, the classification success can be immensely improved (to up to 99%), albeit at the cost of excluding a considerable portion of specimens from identification.

Determining mosquito age is important to evaluate vector control programs because the ability to transmit diseases is age dependent. Current age-grading techniques require dissection or RNA extraction. Near infrared spectroscopy has been used to rapidly and nondestructively determine the age of fresh mosquitoes and specimens stored in RNAlater, but other preservation techniques have not been examined. Thus, in this study, we investigate whether age can be predicted from insects preserved by various common methods. Results from this study show that age can be predicted from mosquitoes preserved with desiccants, ethanol, Carnoy, RNAlater, or refrigeration with confidence intervals < 1.4 days. The best results were generally obtained from mosquitoes stored using desiccants, RNAlater, or refrigeration.

Single kernel moisture content (MC) is important in the measurement of other quality traits in single kernels because many traits are expressed on a dry weight basis. MC also affects viability, storage quality, and price. Also, if near-infrared (NIR) spectroscopy is used to measure grain traits, the influence of water must be accounted for because water is a strong absorber throughout the NIR region. The feasibility of measurement of MC, fresh weight, dry weight, and water mass of single wheat kernels with or without Fusarium damage was investigated using two wheat cultivars with three visually selected classes of kernels with Fusarium damage and a range of MC. Calibration models were developed either from all kernel classes or from only undamaged kernels of one cultivar that were then validated using all spectra of the other cultivar. A calibration model developed for MC when using all kernels from the wheat cultivar Jagalene had a coefficient of determination (R2) of 0.77 and standard error of ...

Molecular weight distribution (MWD) of proteins extracted from hard red spring wheat was analyzed by size-exclusion HPLC to investigate associations with wheat and breadmaking quality characteristics. Certain protein fractions were related to associations between wheat and breadmaking parameters, specifically when effect of quantitative variation of protein on those parameters was statistically eliminated by partial correlation analysis. SDS-unextractable high molecular weight polymeric proteins had positive partial correlations with percent vitreous kernel content and breadmaking parameters, including mix time and bread loaf volume. SDS-extractable protein fractions that were eluted before the primary gliadin peak had positive partial correlations with kernel hardness and water absorption parameters. The proportion of main gliadin fractions in total protein had a negative partial correlation with bread loaf volume and positive correlations with kernel hardness and water absorption parameters. In...

Cereal Chem. 87(6):511–517 Fusarium Head Blight (FHB), or scab, can result in significant crop yield losses and contaminated grain in wheat (Triticum aestivum L.). Growing less susceptible cultivars is one of the most effective methods for managing FHB and for reducing deoxynivalenol (DON) levels in grain, but breeding programs lack a rapid and objective method for identifying the fungi and toxins. It is important to estimate proportions of sound kernels and Fusarium-damaged kernels (FDK) in grain and to estimate DON levels of FDK to objectively assess the resistance of a cultivar. An automated single kernel near-infrared (SKNIR) spectroscopic method for identification of FDK and for estimating DON levels was evaluated. The SKNIR system classified visually sound and FDK with an accuracy of 98.8 and 99.9%, respectively. The sound fraction had no or very little accumulation of DON. The FDK fraction was sorted into fractions with high or low DON content. The kernels identified as FDK by the SKNIR system had better correlation with other FHB assessment indices such as FHB severity, FHB incidence and kernels/g than visual FDK%. This technique can be successfully employed to nondestructively sort kernels with Fusarium damage and to estimate DON levels of those kernels. Single kernels could be predicted as having low ( 60 ppm) DON with ≈96% accuracy. Single kernel DON levels of the high DON kernels could be estimated with R 2 = 0.87 and standard error of prediction (SEP) of 60.8 ppm. Because the method is nondestructive, seeds may be saved for generation advancement. The automated method is rapid (1 kernel/sec) and sorting grains into several fractions depending on DON levels will provide breeders with more information than techniques that deliver average DON levels from bulk seed samples.