Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Automated and accurate segmentation of leaf venation networks via deep learning

Leaf vein network geometry can predict levels of resource transport, defence, and mechanical support that operate at different spatial scales. However, it is challenging to quantify network architecture across scales, due to the difficulties both in segmenting networks from images, and in extracting multi‐scale statistics from subsequent network graph representations. Here we develop deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein networks. Thirty‐eight CNNs were trained on subsets of manually‐defined ground‐truth regions from >700 leaves representing 50 southeast Asian plant families. Ensembles of 6 independently trained CNNs were used to segment networks from larger leaf regions (~100 mm2). Segmented networks were analysed using hierarchical loop decomposition to extract a range of statistics describing scale transitions in vein and areole geometry. The CNN approach gave a precision‐recall harmonic mean of 94.5% ± 6%, outperforming other current network extraction methods, and accurately described the widths, angles, and connectivity of veins. Multi‐scale statistics then enabled identification of previously undescribed variation in network architecture across species. We provide a LeafVeinCNN software package to enable multi‐scale quantification of leaf vein networks, facilitating comparison across species and exploration of the functional significance of different leaf vein architectures

Additional data for: Linking functional traits to multiscale statistics of leaf venation networks

Data are described in Blonder et al., Linking functional traits to multiscale statistics of leaf venation networks. New Phytologist (2020)

Autologous peripheral blood progenitor cell transplantation with <2 x 10(6) CD34(+)/kg: an analysis of variables concerning mobilisation and engraftment

INTRODUCTION: This study analyses the factors affecting mobilisation and engraftment in autologous peripheral blood progenitor cell transplantation according to the number of CD34(+) re-infused. MATERIALS AND METHODS: A total of 190 patients underwent mobilisation with G-CSF alone (n=113) or in combination with chemotherapy (n=77). A total of 116 patients (61%) were autografted with 2 x 10(6) CD34(+) cells/kg. Rates of granulocyte and platelet recovery were estimated using the product-limit method of Kaplan-Meier and compared using a log-rank test. The Cox regression model was used for the multivariate analysis of factors influencing engraftment. Differences between cohorts were evaluated by one-way ANOVA or Mann-Whitney tests, and multivariate analysis was performed using a stepwise lineal regression. RESULTS: Neutrophil and platelet engraftment was significantly longer with 2 x 10(6)/CD34(+)/kg, the Cox model did not identify prognostic factors for haematopoietic recovery. CONCLUSION: Although mobilisation schedules and disease status influenced not only the yield of progenitor cells, but also the engraftment kinetics, the number of CD34(+) re-infused was the main predictor of haematopoietic recovery. While engraftment succeeded in most of the cases, the re-infusion of >2 x 10(6)/CD34(+)/kg resulted in significantly shorter recovery times