Forest biodiversity, ecosystem functioning and the provision of ecosystem services

Forests are critical habitats for biodiversity and they are also essential for the provision of a wide range of ecosystem services that are important to human well-being. There is increasing evidence that biodiversity contributes to forest ecosystem functioning and the provision of ecosystem services. Here we provide a review of forest ecosystem services including biomass production, habitat provisioning services, pollination, seed dispersal, resistance to wind storms, fire regulation and mitigation, pest regulation of native and invading insects, carbon sequestration, and cultural ecosystem services, in relation to forest type, structure and diversity. We also consider relationships between forest biodiversity and multifunctionality, and trade-offs among ecosystem services. We compare the concepts of ecosystem processes, functions and services to clarify their definitions. Our review of published studies indicates a lack of empirical studies that establish quantitative and causal relationships between forest biodiversity and many important ecosystem services. The literature is highly skewed; studies on provisioning of nutrition and energy, and on cultural services, delivered by mixed-species forests are under-represented. Planted forests offer ample opportunity for optimising their composition and diversity because replanting after harvesting is a recurring process. Planting mixed-species forests should be given more consideration as they are likely to provide a wider range of ecosystem services within the forest and for adjacent land uses. This review also serves as the introduction to this special issue of Biodiversity and Conservation on various aspects of forest biodiversity and ecosystem services

Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given

Evaluation of Big Data based CNN Models in Classification of Skin Lesions with Melanoma

Copyright © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. This is the accepted manuscript version of Naronglerdrit P., Mporas I. (2021) Evaluation of Big Data Based CNN Models in Classification of Skin Lesions with Melanoma. In: Kose U., Alzubi J. (eds) Deep Learning for Cancer Diagnosis. Studies in Computational Intelligence, vol 908. Springer, Singapore. https://doi.org/10.1007/978-981-15-6321-8_5This chapter presents a methodology for diagnosis of pigmented skin lesions using convolutional neural networks. The architecture is based on convolutional neural networks and it is evaluated using new CNN models as well as retrained modification of pre-existing CNN models were used. The experimental results showed that CNN models pre-trained on big datasets for general purpose image classification when re-trained in order to identify skin lesion types offer more accurate results when compared to convolutional neural network models trained explicitly from the dermatoscopic images. The best performance was achieved by retraining a modified version of ResNet-50 convolutional neural network with accuracy equal to 93.89%. Analysis on skin lesion pathology type was also performed with classification accuracy for melanoma and basal cell carcinoma being equal to 79.13% and 82.88%, respectively.Peer reviewe

Mass Spectrometry Screening Reveals Peptides Modulated Differentially in the Medial Prefrontal Cortex of Rats with Disparate Initial Sensitivity to Cocaine

To better understand why certain individuals are more vulnerable to cocaine abuse and addiction, we identify peptide markers associated with individual variation in sensitivity to the behavioral effects of cocaine. Previous studies in rats show that low, compared to high, cocaine responders are more sensitive to cocaine-induced behavioral plasticity (sensitization), exhibit enhanced conditioning to cocaine’s rewarding effects, and are more motivated to self administer cocaine. In the current study, we combine matrix-assisted laser desorption/ionization mass spectrometry with multivariate statistical methods to analyze tissue extracts from rat dorsal striatum, nucleus accumbens, and medial prefrontal cortex (mPFC) to examine trends in peptide changes that coincide with behavioral phenotype. Peptide profiles of these three regions from individual animals were characterized via mass spectrometry. Resulting mass peaks that were statistically different between these groups were identified using principal component analysis. The mass peaks were then identified in pooled samples via multistage liquid chromatography mass spectrometry. A total of 74 peptides from 28 proteins were sequenced from defined brain regions. Statistically significant changes in peak intensities for seven peptides were found in the mPFC of rats given a single injection of 10 mg/kg cocaine, with low cocaine responders showing ∼2-fold increase in peak intensities for the acetylated N terminus peptides of stathmin and Hint 1, as well as truncated ATP synthase. These results suggest that distinct peptide profiles in the mPFC are associated with individuals that exhibit reduced sensitivity to the behavioral effects of cocaine