Cyclopiazonic acid production by aflatoxigenic and non-aflatoxigenic strains of aspergillus flavus

Twenty-eight of 54 isolates of Aspergillus flavus grown on autoclaved agricultural commodities such as wheat, rice and corn were found to produce the mycotoxin cyclopiazonic acid. Eighteen of the A. flavus isolates produced aflatoxin, and fourteen isolates produced both cyclopiazonic acid and aflatoxin. A preliminary screening of some aflatoxin-contaminated corn samples revealed for the first time the natural occurrence of cyclopiazonic acid in agricultural commodities.     

Three critical factors affecting automated image species recognition performance for camera traps

Ecological camera traps are increasingly used by wildlife biologists to unobtrusively monitor an ecosystems animal population. However, manual inspection of the images produced is expensive, laborious, and time‐consuming. The success of deep learning systems using camera trap images has been previously explored in preliminary stages. These studies, however, are lacking in their practicality. They are primarily focused on extremely large datasets, often millions of images, and there is little to no focus on performance when tasked with species identification in new locations not seen during training. Our goal was to test the capabilities of deep learning systems trained on camera trap images using modestly sized training data, compare performance when considering unseen background locations, and quantify the gradient of lower bound performance to provide a guideline of data requirements in correspondence to performance expectations. We use a dataset provided by Parks Canada containing 47,279 images collected from 36 unique geographic locations across multiple environments. Images represent 55 animal species and human activity with high‐class imbalance. We trained, tested, and compared the capabilities of six deep learning computer vision networks using transfer learning and image augmentation: DenseNet201, Inception‐ResNet‐V3, InceptionV3, NASNetMobile, MobileNetV2, and Xception. We compare overall performance on “trained” locations where DenseNet201 performed best with 95.6% top‐1 accuracy showing promise for deep learning methods for smaller scale research efforts. Using trained locations, classifications with <500 images had low and highly variable recall of 0.750 ± 0.329, while classifications with over 1,000 images had a high and stable recall of 0.971 ± 0.0137. Models tasked with classifying species from untrained locations were less accurate, with DenseNet201 performing best with 68.7% top‐1 accuracy. Finally, we provide an open repository where ecologists can insert their image data to train and test custom species detection models for their desired ecological domain.     

The effect of dietary aflatoxin on wild turkey poults

Aflatoxins, toxic metabolites of Aspergillus flavus or Aspergillus parasiticus, cause poor feed utilization, decreased weight gains, depressed immune function, liver dysfunction, coagulation abnormalities, and death in a wide variety of species including humans. Conservationists have become concerned that increasingly popular wildlife feeding or baiting practices could expose wildlife to toxic amounts of aflatoxin-contaminated grains. In particular, the effects of aflatoxins on the wild turkey (Meleagris gallopova silvestris) are of concern because the conspecific domestic turkey is highly susceptible to aflatoxins. To evaluate the effect of dietary aflatoxin on wild turkeys, four groups of 4-mo-old wild turkeys were fed diets containing either 0, 100, 200, or 400 micrograms aflatoxin/kg feed for 2 wk in September and October 1996. Aflatoxin-fed poults had decreased feed consumption and weight gains as compared with control poults. Decreased liver-to-body weight ratios, liver enzyme alterations, slightly altered blood coagulation patterns, and mild histologic changes indicated low-level liver damage. Compromise of cell-mediated immunity was indicated by decreased lymphoblast transformation. The effects were apparent in all treatment groups to variable levels, but significant differences most often were found at 400 micrograms aflatoxin/kg feed. This study shows that short-term aflatoxin ingestion by wild turkeys can induce undesirable physiologic changes; therefore, exposure of wild turkeys to feeds containing aflatoxin levels of 100 micrograms aflatoxin/kg feed or more should be avoided.     

A novel deep learning method for maize disease identification based on small sample-size and complex background datasets

Maize diseases are a major source of yield loss, but due to the lack of human experience and limitations of traditional image-recognition technology, obtaining satisfactory large-scale identification results of maize diseases are difficult. Fortunately, the advancement of deep learning-based technology makes it possible to automatically identify diseases. However, it still faces issues caused by small sample sizes and complex field background, which affect the accuracy of disease identification. To address these issues, a deep learning-based method was proposed for maize disease identification in this paper. DenseNet121 was used as the main extraction network and a multi-dilated-CBAM-DenseNet (MDCDenseNet) model was built by combining the multi-dilated module and convolutional block attention module (CBAM) attention mechanism. Five models of MDCDenseNet, DenseNet121, ResNet50, MobileNetV2, and NASNetMobile were compared and tested using three kinds of maize leave images from the PlantVillage dataset and field-collected at Northeast Agricultural University in China. Furthermore, auxiliary classifier generative adversarial network (ACGAN) and transfer learning were used to expand the dataset and pre-train for optimal identification results. When tested on field-collected datasets with a complex background, the MDCDenseNet model outperformed compared to these models with an accuracy of 98.84%. Therefore, it can provide a viable reference for the identification of maize leaf diseases collected from the farmland with a small sample size and complex background.     

Effect of high pressure ammoniation procedure on the detoxification of aflatoxins

Ammoniation represents the best technique to detoxify aflatoxin-contaminated grain and it is considered as economically practicable for commercial applications. In the present studyAspergillus parasiticus was used to contaminate yellow corn to produce the final concentration reached 4000 μg/kg corn total aflatoxin. Two procedures of ammoniation (in aqueous ammonia concentrations, 0.25, 0.5, 1, 1.5 and 2% ) were adopted for aflatoxin destruction. The first procedure was under atmospheric pressure at ambient temperature (AP/AT) for 24 hrs, and the second procedure was under high pressure (2 bar) at high temperature (121°C) (HP/HT ) for 15 min. Aflatoxin concentrations were determined by HPLC using fluorescence detection. The effect of HP/HT procedure was compared with the ammoniation procedure under AP/AT. The detoxification pattern of the two ammoniation procedures as well as the detoxification pattern of the different types of aflatoxins under the two procedures was studied.     

Distribution of sterigmatocystin in filamentous fungi

During the last 50y, the carcinogenic mycotoxin sterigmatocystin (ST) has been reported in several phylogenetically and phenotypically different genera: Aschersonia, Aspergillus, Bipolaris, Botryotrichum, Chaetomium, Emericella, Eurotium, Farrowia, Fusarium, Humicola, Moelleriella, Monocillium and Podospora. We have reexamined all available strains of the original producers, in addition to ex type and further strains of each species reported to produce ST and the biosynthetically derived aflatoxins. We also screened strains of all available species in Penicillium and Aspergillus for ST and aflatoxin. Six new ST producing fungi were discovered: Aspergillus asperescens, Aspergillus aureolatus, Aspergillus eburneocremeus, Aspergillus protuberus, Aspergillus tardus, and Penicillium inflatum and one new aflatoxin producer: Aspergillus togoensis (=Stilbothamnium togoense). ST was confirmed in 23 Emericella, four Aspergillus, five Chaetomium, one Botryotrichum and one Humicola species grown on a selection of secondary metabolite inducing media, and using multiple detection methods: HPLC-UV/Vis DAD, - HRMS and - MS/MS. The immediate precursor for aflatoxin, O-methylsterigmatocystin was found in Chaetomium cellulolyticum, Chaetomium longicolleum, Chaetomium malaysiense and Chaetomium virescens, but aflatoxin was not detected from any Chaetomium species. In all 55 species, representing more than 11 clades throughout the Pezizomycotina, can be reliably claimed to be ST producers and 13 of these can also produce aflatoxins. It is not known yet whether the ST/aflatoxin pathway has been developed independently 11 times, or is the result of partial horizontal gene transfer.     

Computer vision system for superpixel classification and segmentation of sheep

This paper presents an experiment with four different convolutional neural networks architectures that aim to classify segments of a sheep using a dataset of superpixels. The proposal used an image dataset with 512 images of 32 sheep. In this dataset of images, we applied the Simple Linear Iterative Clustering technique with a K number parameter of 600 to generate the dataset of superpixels that was later processed in the deep neural networks. We selected four architectures for training the models: VGG16, ResNet152V2, InceptionV3, and DenseNet201. The experiment was conducted using cross-validation with five-folds separating the dataset into training (60%), validation (20%), and testing (20%). The best result presented was from the approach with the DenseNet201 technique with an F-score of 0.928. When applying ANOVA, the P-value was 0.0000000000329 (3.29e-11***) between the tested architectures, which shows that the results are statistically significant. Therefore, DenseNet201 presented itself as a relevant architecture for this problem that aims to classify the superpixels referring to a sheep and the image's background, and the average IoU with post-processing for image segmentation with DenseNet201 obtained 0.8332. Thus, we can highlight the contributions of this research as a methodology to segment images of mixed-breed sheep of the Texel and Santa Inês breeds using convolutional neural networks and provide a non-invasive method that can support other implementations such as animal tracking and weight prediction.     

Preliminary determination of mycotoxin binding to soil when leaching through soil with water

Mycotoxin-contaminated crops that are left in the field are potential contaminants of groundwater. Aflatoxin B₁ (AFB₁) and fumonisin B₁ (FB₁) distribution in soil-water systems and the comparative response of aflatoxin-contaminated corn and pure aflatoxin when leached through soil were investigated using columns. Each experiment was repeated once. Eluates and soil extracts were analyzed for AFB₁ and its metabolites and FB₁ along with different amounts of pure FB₁ and water mixtures. AFB₁ was detected in water samples from columns containing 10% and 20% silty clay loam soil and aflatoxin-contaminated corn mixtures and in the upper (top) 2.5 cm of soil from the 10 cm soil column. Aflatoxin B₂ (AFB₂) was detected in eluates from the column containing 10% soil and aflatoxin-contaminated corn mixture and from the column containing aflatoxin-contaminated corn alone. No AFB₂ was detected in eluates from the column containing 20% soil and aflatoxin-contaminated corn mixture. No detectable amount of aflatoxin was observed in eluates from the containing 50% silty clay loam soil and aflatoxin contaminated corn. No detectable amount of FB₁ was observed in eluates or soil extracts, but FB₁ was detected in the mixtures of pure FB₁ and water.     

Aflatoxins: Detection,toxicity, and biosynthesis

Aflatoxins are toxic and carcinogenic secondary metabolites produced mainly byAspergillus flavus andAspergillus parasiticus. The aflatoxins present in food and feed are hazardous to both human and animal health. A number of studies have been conducted on the detection, toxicity, biosynthesis, and regulation of aflatoxins due to the discovery of serious aflatoxicosis in farm animals, and the presence of aflatoxins in many food products. There are many reviews that focus on the biosynthesis of aflatoxin, yet there are few examinations of the overall aspects of aflatoxins, including detection, toxicity, and the regulation on biosynthesis. Thus, the goal of this article is to give an overview of the overall aspects of aflatoxins. This review consists of four parts; i) detection methods for aflatoxins, ii) the toxicity mechanism of aflatoxin B1, iii) gene cluster for aflatoxin biosynthesis, and iv) the regulation of aflatoxin biosynthesis.     

Ochratoxin production by the Aspergillus ochraceus group and Aspergillus alliaceus

Ochratoxin A is a toxic and carcinogenic fungal secondary metabolite; its presence in foods is increasingly regulated. Various fungi are known to produce ochratoxins, but it is not known which species produce ochratoxins consistently and which species cause ochratoxin contamination of various crops. We isolated fungi in the Aspergillus ochraceus group (section Circumdati) and Aspergillus alliaceus from tree nut orchards, nuts, and figs in California. A total of 72 isolates were grown in potato dextrose broth and yeast extract-sucrose broth for 10 days at 30 degrees C and tested for production of ochratoxin A in vitro by high-pressure liquid chromatography. Among isolates from California figs, tree nuts, and orchards, A. ochraceus and Aspergillus melleus were the most common species. No field isolates of A. ochraceus or A. melleus produced ochratoxin A above the level of detection (0.01 microg/ml). All A. alliaceus isolates produced ochratoxin A, up to 30 microg/ml. We examined 50,000 figs for fungal infections and measured ochratoxin content in figs with visible fungal colonies. Pooled figs infected with A. alliaceus contained ochratoxin A, figs infected with the A. ochraceus group had little or none, and figs infected with Penicillium had none. These results suggest that the little-known species A. alliaceus is an important ochratoxin-producing fungus in California and that it may be responsible for the ochratoxin contamination occasionally observed in figs.     

Improved method of screening for aflatoxin with a coconut agar medium.

Nine isolates of Aspergillus flavus and Aspergillus parasiticus were screened for aflatoxin production on a coconut extract agar medium. Aflatoxin-producing colonies were detected under long-wave UV light (365 nm) by blue fluorescence on the reverse side after 2 to 5 days of growth. Aflatoxin production was verified by chemical analysis. Several types of shredded coconut available in the United States were tested and found to be satisfactory. No additives were required. Various parameters affecting the test were investigated.     

Improved method of screening for aflatoxin with a coconut agar medium

Nine isolates of Aspergillus flavus and Aspergillus parasiticus were screened for aflatoxin production on a coconut extract agar medium. Aflatoxin-producing colonies were detected under long-wave UV light (365 nm) by blue fluorescence on the reverse side after 2 to 5 days of growth. Aflatoxin production was verified by chemical analysis. Several types of shredded coconut available in the United States were tested and found to be satisfactory. No additives were required. Various parameters affecting the test were investigated.     

Hair type,intake, and detection method influence Rhodamine B detectability

Rhodamine B (RB) is a popular bait-marking tool in wildlife research, although its use has required many assumptions about marking reliability. These assumptions have limited dosage ranges and detection methods in research applications. Identification of alternate detection methods, hair types, and dosage rates could create new opportunities for RB use and potentially reduce adverse effects on target animals. We examined the relationship between hair types, dosage rate, and detection method for RB applications using black rats (Rattus rattus) in laboratory settings. We gave study animals varying doses of RB, and examined vibrissae and guard hairs for the detection of RB bands using ambient light, ultraviolet (UV) light, and fluorescence microscopy techniques. Fluorescence microscopy detected more RB marked hairs than UV light or ambient light. Fluorescence microscopy was more sensitive to RB dose rate detection; with receiver operating characteristic curves suggesting that the minimum dose rates at which RB intake can be correctly detected are 3.9 and 33 mg/kg for vibrissae and guard hairs, respectively. Guard hair RB detection was less reliable under ambient light and UV lamps, but equally detectable as vibrissae under fluorescence microscopy. Our results confirmed the interrelationship between dosage and detection methods in the efficacy of bait markers. We demonstrated that guard hairs can be a reliable hair type for the detection of RB. This application is less invasive than sampling animal tissues to detect bait uptake. © 2012 The Wildlife Society.     

Characterization of aflatoxin-producing fungi outside of Aspergillus section Flavi

Most aspergilli that produce aflatoxin are members of Aspergillus section Flavi, however isolates of several Aspergillus species not closely related to section Flavi also have been found to produce aflatoxin. Two of the species, Aspergillus ochraceoroseus and an undescribed Aspergillus species SRRC 1468, are morphologically similar to members of Aspergillus section Circumdati. The other species have Emericella teleomorphs (Em. astellata and an undescribed Emericella species SRRC 2520) and are morphologically distinctive in having ascospores with large flanges. All these aflatoxin-producing isolates were from tropical zones near oceans, and none of them grew on artificial media at 37 C. Aflatoxins and sterigmatocystin production were quantified by high-pressure liquid chromatography (HPLC) and confirmed by HPLC-mass spectrometry (LC-MS) detection. Phylogenetic analyses were conducted on these four species using A. parasiticus and Em. nidulans, (which produce aflatoxin and the aflatoxin precursor sterigmatocystin, respectively) for comparison. Two aflatoxin/sterigmatocystin biosynthesis genes and the beta tubulin gene were used in the analyses. Results showed that of the new aflatoxin-producers, Aspergillus SRRC 1468 forms a strongly supported clade with A. ochraceoroseus as does Emericella SRRC 2520 with Em. astellata SRRC 503 and 512.     

Simple fluorescence method for rapid estimation of aflatoxin levels in a solid culture medium

Aflatoxin concentrations in agar media were estimated with a direct technique that quantifies the fluorescence of agar containing aflatoxins. Tubes containing 5 ml of an agar medium inoculated with spores of aflatoxin-producing Aspergillus isolates were incubated for 3 days at 30 degrees C and set in a carriage specifically designed to carry culture tubes in a scanning densitometer. Fluorescence (450 nm and above) was elicited in the agar by UV light (365 nm) and photometrically measured. Agar fluorescence directly correlated (r2 = 0.89 +/- 0.05, P less than 0.001) with the concentration of aflatoxin within the range 0 to 18.7 micrograms/g. The lowest aflatoxin concentration detected was 50 ng/g. The technique successfully differentiated the aflatoxigenic potentials of Aspergillus isolates.     

Simple fluorescence method for rapid estimation of aflatoxin levels in a solid culture medium.

Aflatoxin concentrations in agar media were estimated with a direct technique that quantifies the fluorescence of agar containing aflatoxins. Tubes containing 5 ml of an agar medium inoculated with spores of aflatoxin-producing Aspergillus isolates were incubated for 3 days at 30 degrees C and set in a carriage specifically designed to carry culture tubes in a scanning densitometer. Fluorescence (450 nm and above) was elicited in the agar by UV light (365 nm) and photometrically measured. Agar fluorescence directly correlated (r2 = 0.89 +/- 0.05, P less than 0.001) with the concentration of aflatoxin within the range 0 to 18.7 micrograms/g. The lowest aflatoxin concentration detected was 50 ng/g. The technique successfully differentiated the aflatoxigenic potentials of Aspergillus isolates.     

Direct visual detection of aflatoxin synthesis by minicolonies of Aspergillus species

Single-spore colonies of Aspergillus flavus and Aspergillus parasiticus, grown for 4 to 5 days at 25 degrees C on a coconut extract agar containing sodium desoxycholate as a growth inhibitor, produced aflatoxin, readily detectable as blue fluorescent zones under long-wave (365 nm) UV light. Over 100 colonies per standard petri dish were scored for aflatoxin production by this procedure. Progeny from some strains remained consistently stable for toxin production after repeated subculture, whereas instability for toxin synthesis was revealed among progeny from other strains. Spore color markers were used to rule out cross-contamination in monitoring strains. A yellow-spored and nontoxigenic strain of A. flavus, reported previously to produce aflatoxin in response to cycloheximide treatment, proved to be toxin negative even after repeated exposure to cycloheximide. Extended series of progeny from another strain of A. flavus and from a strain of A. parasiticus were each compared by this plating procedure and by fluorometric analysis for aflatoxin when grown in a coconut extract broth. Both of these strains showed variation for toxin synthesis among their respective progeny, and specific progeny showed a good correlation for aflatoxin synthesis when examined by the two procedures.     

Direct visual detection of aflatoxin synthesis by minicolonies of Aspergillus species.

Single-spore colonies of Aspergillus flavus and Aspergillus parasiticus, grown for 4 to 5 days at 25 degrees C on a coconut extract agar containing sodium desoxycholate as a growth inhibitor, produced aflatoxin, readily detectable as blue fluorescent zones under long-wave (365 nm) UV light. Over 100 colonies per standard petri dish were scored for aflatoxin production by this procedure. Progeny from some strains remained consistently stable for toxin production after repeated subculture, whereas instability for toxin synthesis was revealed among progeny from other strains. Spore color markers were used to rule out cross-contamination in monitoring strains. A yellow-spored and nontoxigenic strain of A. flavus, reported previously to produce aflatoxin in response to cycloheximide treatment, proved to be toxin negative even after repeated exposure to cycloheximide. Extended series of progeny from another strain of A. flavus and from a strain of A. parasiticus were each compared by this plating procedure and by fluorometric analysis for aflatoxin when grown in a coconut extract broth. Both of these strains showed variation for toxin synthesis among their respective progeny, and specific progeny showed a good correlation for aflatoxin synthesis when examined by the two procedures.     

A simple test tube screening method for identifying aflatoxin-producing strains of Aspergillus sp. using coconut milk agar medium

Most agricultural commodities are susceptible to Aspergillus sp. infestation and aflatoxin elaboration. A simple test-tube screening procedure using fresh coconut milk agar medium (CMAM), for identifying toxigenic strains of Aspergillus sp., based on u.v. fluorescence (365 nm) and visual detection has been proposed and evaluated.     

WilDect-YOLO: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection

Objective. With climatic instability, various ecological disturbances, and human actions threaten the existence of various endangered wildlife species. Therefore, an up-to-date accurate and detailed detection process plays an important role in protecting biodiversity losses, conservation, and ecosystem management. Current state-of-the-art wildlife detection models, however, often lack superior feature extraction capability in complex environments, limiting the development of accurate and reliable detection models. Method. To this end, we present WilDect-YOLO, a deep learning (DL)-based automated high-performance detection model for real-time endangered wildlife detection. In the model, we introduce a residual block in the CSPDarknet53 backbone for strong and discriminating deep spatial features extraction and integrate DenseNet blocks to improve in preserving critical feature information. To enhance receptive field representation, preserve fine-grain localized information, and improve feature fusion, a Spatial Pyramid Pooling (SPP) and modified Path Aggregation Network (PANet) have been implemented that results in superior detection under various challenging environments. Results. Evaluating the model performance in a custom endangered wildlife dataset considering high variability and complex backgrounds, WilDect-YOLO obtains a mean average precision (mAP) value of 96.89%, F1-score of 97.87%, and precision value of 97.18% at a detection rate of 59.20 FPS outperforming current state-of-the-art models. Significance. The present research provides an effective and efficient detection framework addressing the shortcoming of existing DL-based wildlife detection models by providing highly accurate species-level localized bounding box prediction. Current work constitutes a step toward a non-invasive, fully automated animal observation system in real-time in-field applications.