Analysis of Few-Shot Techniques for Fungal Plant Disease Classification and Evaluation of Clustering Capabilities Over Real Datasets.

Plant fungal diseases are one of the most important causes of crop yield losses. Therefore, plant disease identification algorithms have been seen as a useful tool to detect them at early stages to mitigate their effects. Although deep-learning based algorithms can achieve high detection accuracies, they require large and manually annotated image datasets that is not always accessible, specially for rare and new diseases. This study focuses on the development of a plant disease detection algorithm and strategy requiring few plant images (Few-shot learning algorithm). We extend previous work by using a novel challenging dataset containing more than 100,000 images. This dataset includes images of leaves, panicles and stems of five different crops (barley, corn, rape seed, rice, and wheat) for a total of 17 different diseases, where each disease is shown at different disease stages. In this study, we propose a deep metric learning based method to extract latent space representations from plant diseases with just few images by means of a Siamese network and triplet loss function. This enhances previous methods that require a support dataset containing a high number of annotated images to perform metric learning and few-shot classification. The proposed method was compared over a traditional network that was trained with the cross-entropy loss function. Exhaustive experiments have been performed for validating and measuring the benefits of metric learning techniques over classical methods. Results show that the features extracted by the metric learning based approach present better discriminative and clustering properties. Davis-Bouldin index and Silhouette score values have shown that triplet loss network improves the clustering properties with respect to the categorical-cross entropy loss. Overall, triplet loss approach improves the DB index value by 22.7% and Silhouette score value by 166.7% compared to the categorical cross-entropy loss model. Moreover, the F-score parameter obtained from the Siamese network with the triplet loss performs better than classical approaches when there are few images for training, obtaining a 6% improvement in the F-score mean value. Siamese networks with triplet loss have improved the ability to learn different plant diseases using few images of each class. These networks based on metric learning techniques improve clustering and classification results over traditional categorical cross-entropy loss networks for plant disease identification.

MRI Deep Learning-Based Solution for Alzheimer's Disease Prediction.

BACKGROUND: Alzheimer's is a degenerative dementing disorder that starts with a mild memory impairment and progresses to a total loss of mental and physical faculties. The sooner the diagnosis is made, the better for the patient, as preventive actions and treatment can be started. Although tests such as the Mini-Mental State Tests Examination are usually used for early identification, diagnosis relies on magnetic resonance imaging (MRI) brain analysis. METHODS: Public initiatives such as the OASIS (Open Access Series of Imaging Studies) collection provide neuroimaging datasets openly available for research purposes. In this work, a new method based on deep learning and image processing techniques for MRI-based Alzheimer's diagnosis is proposed and compared with previous literature works. RESULTS: Our method achieves a balance accuracy (BAC) up to 0.93 for image-based automated diagnosis of the disease, and a BAC of 0.88 for the establishment of the disease stage (healthy tissue, very mild and severe stage). CONCLUSIONS: Results obtained surpassed the state-of-the-art proposals using the OASIS collection. This demonstrates that deep learning-based strategies are an effective tool for building a robust solution for Alzheimer's-assisted diagnosis based on MRI data.

Histopathologic differences between cutaneous vasculitis associated with severe bacterial infection and cutaneous vasculitis secondary to other causes: study of 52 patients.

OBJECTIVES: To determine if cutaneous vasculitis (CV) associated with severe infection has some histopathologic findings that may help us to differentiate patients with this condition from other patients with CV. METHODS: We reviewed the skin biopsy specimens of patients with leukocytoclastic CV associated with a severe bacterial infection. Histopathologic findings of these patients were compared with those observed in leukocytoclastic CV secondary to other causes. Biopsy-proven leukocytoclastic CV were stratified as follows: group a): CV associated with severe underlying bacterial infection; group b): CV without severe bacterial infection but with systemic involvement; group c): CV without systemic involvement. Slides were reviewed by expert pathologists that were blind to the clinical information. The severity of vascular lesions was measured according to a semiquantitative scale (Hodge index). A comparative study between group a) and the other groups was conducted. RESULTS: group a) included 12 patients (2 women/10 men), mean age± SD 56±15 years; group b) 21 patients (10 women/11 men), 52±18 years; and group c) 19 patients (12 women/7 men), 59±24 years. Presence of neutrophilia was significantly increased in biopsies from group a) when compared with the other two groups. Also, a trend to higher frequency of pustular dermatosis was found in patients from group a). Hodge index, degree of inflammatory infiltrate and deep arterioles involvement were similar in all groups. CONCLUSIONS: Neutrophilia is common in skin biopsies of patients with CV associated with severe bacterial infection. No other histopathological findings help us to establish the presence of a severe underlying infection.

Serotype g12 rotaviruses, Lilongwe, Malawi.

To assess diversity of rotavirus strains in Lilongwe, Malawi, we conducted a cross-sectional study of children with acute gastroenteritis, July 2005-June 2007. Serotype G12 was identified in 30 (5%) of 546 rotavirus-positive fecal specimens. The G12 strain possessed multiple electropherotypes and P-types, but their viral protein 7 sequences were closely related, indicating that reassortment has occurred.

Chromosomal fragility in a behavioral disorder.

Numerous studies have shown there is consistent evidence implicating genetic factors in the etiology of autism. In some cases chromosomal abnormalities have been identified. One type of these abnormalities is gaps and breaks nonrandomly located in chromosomes, denominated fragile sites (FS). We cytogenetically analyzed a group of autistic individuals and a normal population, and we examined the FS found in both samples with the aim of (1) comparing their FS expression, (2) ascertaining whether any FS could be associated with our autistic sample, and (3) examining if there are differences between individual and pooled-data analyses. Different statistical methods were used to analyse the FS of pooled and individual data. Our results show that there are statistically significant differences in the spontaneous expression of breakages between patients and controls, with a minimal sex difference. Using the method for pooled data, eight autosomal FS have preferential expression in patients and five patients were found to be positive at FS Xq27.3. With the method per-individual analysis, four FS emerged as specific in our autistic sample. Inferences of FS from pooled data were different from those of individual data. The findings suggest that although analysis of pooled data is necessitated by the problem of sparse data, analysis of single individuals is essential to know the significance of FS in autism.