Aspergillus species identification in the clinical setting

Multiple recent studies have demonstrated the limited utility of morphological methods used singly for species identification of clinically relevant aspergilli. It is being increasingly recognised that comparative sequence based methods used in conjunction with traditional phenotype based methods can offer better resolution of species within this genus. Recognising the growing role of molecular methods in species recognition, the recently convened international working group meeting entitled “Aspergillus Systematics in the Genomic Era” has proposed several recommendations that will be useful in such endeavors. Specific recommendations of this working group include the use of the ITS regions for inter section level identification and the β-tubulin locus for identification of individual species within the various Aspergillus sections

Computer-Aided Detection of Skin Cancer Detection from Lesion Images via Deep-Learning Techniques

More and more genetic and metabolic abnormalities are now known to cause cancer, which is typically fatal. Any particular body part may become infected by cancerous cells, which can be fatal. One of the most prevalent types of cancer is skin cancer, which is spreading worldwide.The primary subtypes of skin cancer are squamous and basal cell carcinomas, as well as melanoma, which is clinically aggressive and accounts for the majority of fatalities. Screening for skin cancer is so crucial.Deep Learning is one of the best options to quickly and precisely diagnose skin cancer (DL).This study used the Convolution Neural Network (CNN) deep learning technique to distinguish between the two primary types of cancers, malignant and benign, using the ISIC2018 dataset. The 3533 skin lesions in this dataset range from benign to malignant, and nonmelanocytic to melanocytic malignancies. The images were initially enhanced and edited using ESRGAN. The preprocessing stage involved resizing, normalising, and augmenting the images. By combining the results of numerous repetitions, the CNN approach might be used to categorise images of skin lesions. Several transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were then used for fine-tuning. The uniqueness and contribution of this study are the preprocessing stages using ESRGAN and the testing of various models (including the intended CNN, Resnet50, InceptionV3, and Inception Resnet). Results from the model we developed matched those from the pretrained model exactly. The efficiency of the suggested strategy was proved by simulations using the ISIC 2018 skin lesion dataset. In terms of accuracy, the CNN model performed better than the Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%) models

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

In comparison to all other malignancies, breast cancer is the most common form of cancer, among women. Breast cancer prediction has been studied by several researchers and is considered a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR) is used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting (XGBoost).  For performance comparison, we employed the k-fold cross-validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

In comparison to all other malignancies, breast cancer is the most common form of cancer, among women. Breast cancer prediction has been studied by several researchers and is considered a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR) is used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting (XGBoost).  For performance comparison, we employed the k-fold cross-validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms

Security Enhancement in Surveillance Cloud Using Machine Learning Techniques

Most industries are now switching from traditional modes to cloud environments and cloud-based services. It is essential to create a secure environment for the cloud space in order to provide consumers with a safe and protected environment for cloud-based transactions. Here, we discuss the suggested approaches for creating a reliable and safe environment for a surveillance cloud. When assessing the security of vital locations, surveillance data is crucial. We are implementing machine learning methods to improve cloud security to more precisely classify image pixels, we make use of Support Vector Machines (SVM) and Fuzzy C-means Clustering (FCM). We also extend the conventional two-tiered design by adding a third level, the CloudSec module, to lower the risk of potential disclosure of surveillance data.In our work we  evaluates how well our proposed model (FCM-SVM) performed against contemporary models like ANN, KNN, SVD, and Naive Bayes. Comparing our model to other cutting-edge models, we found that it performed better, with an average accuracy of 94.4%

Polyphasic taxonomy of Aspergillus section Fumigati and its teleomorph Neosartorya

The taxonomy of Aspergillus section Fumigati with its teleomorph genus Neosartorya is revised. The species concept is based on phenotypic (morphology and extrolite profiles) and molecular (β-tubulin and calmodulin gene sequences) characters in a polyphasic approach. Four new taxa are proposed: N. australensis N. ferenczii, N. papuaensis and N. warcupii. All newly described and accepted species are illustrated. The section consists of 33 taxa: 10 strictly anamorphic Aspergillus species and 23 Neosartorya species. Four other Neosartorya species described previously were not available for this monograph, and consequently are relegated to the category of doubtful species

Emergence of Azole Resistance in Aspergillus fumigatus and Spread of a Single Resistance Mechanism

Paul Verweij and colleagues show that azole resistance has emerged inAspergillus fumigatus in The Netherlands and that a dominant resistance mechanism is present in clinical isolates

Emericella quadrilineata as Cause of Invasive Aspergillosis

This opportunistic fungus is frequently misidentified because of its morphologic similarity to E. nidulans