scholarly journals The Spread of the Soil-Borne Pathogen Fusarium solani in Stored Potato Can Be Controlled by Terrestrial Woodlice (Isopoda: Oniscidea)

Agriculture ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 45
Author(s):  
Anett Mészárosné Póss ◽  
Anikó Südiné Fehér ◽  
Franciska Tóthné Bogdányi ◽  
Ferenc Tóth
Keyword(s):  
Fusarium Solani ◽  
Fungal Pathogens ◽  
Food Source ◽  
Macrophomina Phaseolina ◽  
Storage Conditions ◽  
Potato Tubers ◽  
Feeding Trial ◽  
Storage Pests ◽  
Set Up

Fusarium solani, a soil-borne pathogen of stored potato may be disseminated, and thus, the damage caused by the pathogen may be aggravated by the grazing activities of arthropods. To investigate whether terrestrial woodlice contribute to the spread or, instead, to the control of F. solani, we launched a series of pilot experiments. First, a laboratory feeding trial was set up to find whether and to what extent woodlice consume the mycelia of fungal pathogens, namely, Aspergillus niger, F. solani, Macrophomina phaseolina, and Sclerotinia sclerotiorum. This was followed by a second set of experiments to simulate storage conditions where potato tubers, either healthy or infected with F. solani, were offered to woodlice. We found that: (1) F. solani was accepted by woodlice but was not their most preferred food source; (2) the presence of woodlice reduced the spread of F. solani among potato tubers. Our results suggest that the classification of terrestrial woodlice as “storage pests” needs re-evaluation, as isopods have the potential to disinoculate infective plant remnants and, thus, reduce the spread of storage pathogens.

scholarly journals Identity and prevalence of wheat damping-off fungal pathogens in different fields of Basrah and Maysan provinces

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Mohammed Hamza Abass ◽  
Qusai Hattab Madhi ◽  
Abdulnabi Abdul Ameer Matrood
Keyword(s):  
Fusarium Solani ◽  
Seed Treatment ◽  
Fungal Pathogens ◽  
Laboratory Experiments ◽  
Disease Incidence ◽  
Macrophomina Phaseolina ◽  
Cereal Crops ◽  
Damping Off ◽  
Fungal Isolation ◽  
The World

Abstract Background Wheat is the most consumed cereal crops in the world infected by several pathogens and pests causing significant losses. The most threatening pathogens are fungi which cause serious diseases on roots, leaves and heads as one of the most threatening pathogens in specific wheat-growing countries. This study aimed to identify and evaluate the prevalence of damping-off fungal pathogens in different wheat fields at Basra and Maysan provinces. Results Disease incidence determination and fungal isolation were carried out from two sites at Basra province (Al-Qurna and Al-Madinah) and three sites at Maysan province (Al-Amarah, Kumit, Ali Al Sharqi and Ali Al Gharbi). Al-Qurna fields had the highest disease incidence (32%), while Ali-Alsharqi fields had the lowest one (11%). Fourteen fungal genera were identified. Rhizoctonia solani had the highest appearance (21.6) and frequency (20.20%) percentages followed by Fusarium solani (16.11,14.01) percentages and Macrophomina phaseolina (12.2,11.1) percentages. Seed treatment with R. solani (Rs1 isolate) showed significant decrease in germination (56.6%) compared to F. solani and M. phaseolina treatments. Seed treatment with R. solani (Rs1 isolate) showed significant decrease in germination (56.6%) compared to F. solani and M. phaseolina treatments. Conclusions These results revealed the prevalence of wheat damping-off disease in all examined fields at both Basra and Maysan province; the highest disease incidence was seen in Basra wheat fields (Al-Qurna fields); the identification of fungal pathogens showed that the most isolated fungus was R. solani followed by F. solani and M. phaseolina. Laboratory experiments showed the pathogenicity of isolated fungi which varied according to the isolate type.

scholarly journals Estimation of The Age of Bloodstains on Soil Matrices By ATR-FTIR Spectroscopy and Chemometrics

2019 ◽  
Vol 9 (1) ◽  
pp. 4750-4755
Keyword(s):  
Ftir Spectroscopy ◽  
Mean Squared Error ◽  
Crime Scene ◽  
Storage Conditions ◽  
Attenuated Total Reflection ◽  
Least Squares Regression ◽  
Set Up ◽  
Non Destructive ◽  
Indoor And Outdoor

Estimating the accurate time of a crime occurred is one of the priceless information in forensics practice and for the investigation purposes. There are profuse of evidence can be found at the crime scene and each of the evidence will give an important information for the investigation purposes. In this study, the Attenuated Total Reflection (ATR)- Fourier Transform Infrared (FTIR) technique combined with advanced chemometrics method was deployed. For the purpose of determining the age of the bloodstain, two storage conditions; indoor and outdoor were set up to simulate real crime scene scenario and bloodstains on soil matrices were exposed and analyzed for selected time intervals for up to 63 days. Six partial least squares regression-discriminant analysis (PLSR-DA) models were constructed-indoor and outdoor models with 1-63 days-exhibited good performance with acceptable values of predictive root mean squared error (7.04-16.0) and r2 values (0.45-0.89), respectively. Using these models, correct classification of the aged bloodstains was calculated up to 70%. In conclusion, the multivariate analysis based on PLS-DA models indicates that ATR-FTIR spectroscopy, coupled with chemometrics provides acceptable discrimination for rapid and non-destructive determination the age of bloodstains on soil matrices in particularly for outdoor and very aged bloodstains.

scholarly journals Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Gomesh Nair ◽  
Pradeep Isawasan ◽  
Abdul Hafiz Ab Majid
Keyword(s):  
Deep Learning ◽  
Aedes Aegypti ◽  
Real Time ◽  
Aedes Albopictus ◽  
Automated Classification ◽  
Expert Performance ◽  
Deep Convolutional Neural Networks ◽  
Significant Difference ◽  
Set Up

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

scholarly journals Rapid Classification of Clostridioides difficile Strains Using MALDI-TOF MS Peak-Based Assay in Comparison with PCR-Ribotyping

2021 ◽  
Vol 9 (3) ◽  
pp. 661
Author(s):  
Adriana Calderaro ◽  
Mirko Buttrini ◽  
Monica Martinelli ◽  
Benedetta Farina ◽  
Tiziano Moro ◽  
...  
Keyword(s):  
Ionization Time ◽  
Maldi Tof Ms ◽  
Maldi Tof ◽  
Flight Mass Spectrometry ◽  
Clostridioides Difficile ◽  
Pcr Ribotyping ◽  
Typing Methods ◽  
Set Up ◽  
Tof Ms

Typing methods are needed for epidemiological tracking of new emerging and hypervirulent strains because of the growing incidence, severity and mortality of Clostridioides difficile infections (CDI). The aim of this study was the evaluation of a typing Matrix-Assisted Desorption/Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS (T-MALDI)) method for the rapid classification of the circulating C. difficile strains in comparison with polymerase chain reaction (PCR)-ribotyping results. Among 95 C. difficile strains, 10 ribotypes (PR1–PR10) were identified by PCR-ribotyping. In particular, 93.7% of the isolates (89/95) were grouped in five ribotypes (PR1–PR5). For T-MALDI, two classifying algorithm models (CAM) were tested: the first CAM involved all 10 ribotypes whereas the second one only the PR1–PR5 ribotypes. Better performance was obtained using the second CAM: recognition capability of 100%, cross-validation of 96.6% and agreement of 98.4% (60 correctly typed strains, limited to PR1–PR5 classification, out of 61 examined strains) with PCR-ribotyping results. T-MALDI seems to represent an alternative to PCR-ribotyping in terms of reproducibility, set up time and costs, as well as a useful tool in epidemiological investigation for the detection of C. difficile clusters (either among CAM included ribotypes or out-of-CAM ribotypes) involved in outbreaks.

Using transfer learning for diabetic retinopathy stage classification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Enas M.F. El Houby
Keyword(s):  
Diabetic Retinopathy ◽  
Transfer Learning ◽  
Grade Level ◽  
Design Methodology ◽  
Content Type ◽  
Stage Classification ◽  
Set Up ◽  
Retinal Fundus ◽  
Automated Methods

PurposeDiabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers.Design/methodology/approachIn this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection.FindingsBy conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively.Originality/valueIn this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.

scholarly journals Occurrence and epidemiological aspects of potato silver scurf in California

2004 ◽  
Vol 22 (4) ◽  
pp. 690-695 ◽  
Author(s):  
Marcos G. Cunha ◽  
David M. Rizzo
Keyword(s):  
Storage Period ◽  
Storage Conditions ◽  
Silver Scurf ◽  
Inoculum Potential ◽  
Helminthosporium Solani ◽  
Potato Tubers ◽  
Commercial Potato ◽  
Tuber Disease ◽  
Fusarium Sp ◽  
Over Time

A new potato tuber disease has been observed in the Tulelake region, California, USA, since 1995, with tuber symptoms suggestive of silver scurf disease (Helminthosporium solani). In this work we isolated, identified and demonstrated the nature of the causal agent of this potato disease in California. In addition, the distribution of H. solani in potato fields and the inoculum potential at harvest time were investigated. Disease progress and H. solani spore populations were also characterised under commercial storage conditions. The main fungal genera associated with potato tubers in storage were Helminthosporium solani, Colletotrichum sp., Fusarium sp., and Rhizoctonia sp. The results of Koch's postulates indicated that H. solani is responsible for the outbreak of silver scurf in the Tulelake region. In a disease survey in three commercial potato fields naturally infested, H. solani infections occurred in all fields. However, the extension of the infections differed significantly between the fields. During potato storage, silver scurf usually increased over time. The percentage of the tuber surface covered by silver scurf varied from 3.5% up to 35.5% during the storage period. The number of H. solani lesions per tuber also progressively increased from 6% up to 35%, six months after storage. H. solani spore populations also increased over time in all studied potato stores; nevertheless, they followed no consistent pattern, exhibiting multiple and variable peaks of increase and reduction during the period of storage.

scholarly journals Mycoflora associated with two species of Liliaceae in Bangladesh

2013 ◽  
Vol 22 (1) ◽  
pp. 39-46
Author(s):  
Shamim Shamsi ◽  
Najmun Naher
Keyword(s):  
Fusarium Solani ◽  
Alternaria Alternata ◽  
New Record ◽  
Macrophomina Phaseolina ◽  
Fungal Species ◽  
Colletotrichum Orbiculare ◽  
Lasiodiplodia Theobromae ◽  
Colletotrichum Capsici ◽  
Colletotrichum Dematium

A total of nine fungi were isolated from two ornamental angiosperms, namely Hemerocallis fulva L. and Pancratium verecundum Ant. belong to Liliaceae. Three species of fungi were found to be associated with the leaf of Hemerocallis fulva. The fungi were Colletotrichum capsici, (Syd.) Bull. & Bisby, Colletotrichum dematium (Pers. Ex Fr.) and Glomerella montana (Sacc.)v Arx & E. Muller. Six species of fungi, namely Alternaria alternata (Fries) Keissler, Colletotrichum orbiculare (Berk & Mont.) Arex., Curvularia clavata Jain, Fusarium solani (Mort.) Sacc., Lasiodiplodia theobromae (Pat.) Griffon and Maubol and Macrophomina phaseolina (Tassi) Goid were found to be associated with P. verecundum. Among the fungal species Glomerella montana is new record for Bangladesh. Dhaka Univ. J. Biol. Sci. 22(1): 39-46, 2013 (January)

scholarly journals Anti-fungal activities of constituents from Uvaria scheffleri and Artabotrys brachypetalus

2006 ◽  
Vol 51 (1) ◽  
pp. 79-86 ◽  
Author(s):  
A.C. Odebode ◽  
S.A. Jonker ◽  
C.C. Joseph ◽  
S.W. Wachira
Keyword(s):  
Fusarium Solani ◽  
Radial Growth ◽  
Fungal Pathogens ◽  
Dry Weight ◽  
Stem Bark ◽  
Inhibitory Effect ◽  
In Vitro Bioassay ◽  
Botryodiplodia Theobromae ◽  
Anti Fungal

The anti-fungal activity of schefflone, a mixture of dimmer, 3,5 dimethoxy carvacrol and annonaceous acetogenin, extracted from stem-bark and root of Uvaria scheffleri and Artabotrys bruchypetalus against Fusarium solani, Botryodiplodia theobromae, Asperillus niger and Aspergillus flavus was determined. An in-vitro bioassay showed that the minimum inhibitory effect of the compounds to the fungal pathogens occurred at 200 ppm in both radial growth and mycelia dry weight measurements. Acetogenin from A brachypetalus had a very strong anti-fungal effect on all the test fungi. The effects of the compounds were more pronounced on F solani than on the other. The bioassay methods also play a significant role in the sensitivity of the samples on the pathogens. .

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