Università degli studi di Modena e Reggio Emilia

Alternating least squares, within the multivariate curve resolution framework has seen a lot of practical applications and shows its distinction with its relatively simple and flexible implementation. However, the limitations of least squares should be considered carefully when deviating from the standard assumed data structure. Within this work we highlight the effects of noise in the presence of minor components, and we propose a novel weighting scheme within the weighted multivariate curve-resolution-alternating least squares framework, to resolve it. Two simulated and one Raman imaging case is investigated, by comparing the novel methodology against standard multivariate curve resolution-alternating least squares and essential spectral pixel selection. A trade-off is observed between current methods, while the novel weighting scheme demonstrates a balance, where the benefits of the previous two methods are retained.

The emergence of new spectral imaging applications in many science fields and in industry has not come to be a surprise, considering the immense potential this technique has to map spectral information. In the case of near-infrared spectral imaging, a rapid evolution of the technology has made it more and more appealing in non-destructive analysis of food and materials as well as in process monitoring applications. However, despite its great diffusion, some challenges remain open from the data analysis point of view, with the aim to fully uncover patterns and unveil the interplay between both the spatial and spectral domains. Here we propose a new approach, called Image Decomposition, Encoding and Localization (IDEL), where a spatial perspective is taken for the analysis of spectral images, while maintaining the significant information within the spectral domain. The methodology benefits from wavelet transform to exploit spatial features, encoding the outcoming images into a set of descriptors and utilizing multivariate analysis to isolate and extract the significant spatial-spectral information. A forensic case study of near-infrared images of biological stains on cotton fabrics is used as a benchmark. The stain and fabric have hardly distinguishable spectral signatures due to strong scattering effects that originate from the rough surface of the fabric and the high spectral absorbance of cotton in the near-infrared range. There is no selective information that can isolate signals related to these two components in the spectral images under study, and the complex spatial structure is highly interconnected to the spectral signatures. IDEL was capable of isolating the stains, (spatial) scattering effects, and a possible drying effect from the stains. It was possible to recover, at the same time, specific spectral regions that mostly highlight these isolated spatial structures, which was previously unobtainable.

Histological studies focusing on the early cephalic tentacle regeneration in P. canaliculata, have demonstrated that wound closure and blastema formation took place within 24 h post amputation (hpa). A Matlab® plugin allowed the semi-automated identification and quantification of a phagocytic hemocyte sub-population in the blastema. Flow cytometry analysis showed that the injection of the phagocyte-specific drug Clophosome® (45 µg/g snail) could transiently remove circulating hemocytes, that recovered the pre-treatment level within 24 h. Consistently, histological experiment demonstrated that rare hemocytes were present in the early regenerating tentacles of Clophosome®-injected snails

In humans, injuries and diseases can result in irreversible tissue or organ loss. This wellknown fact has prompted several basic studies on organisms capable of adult regeneration, such as amphibians, bony fish, and invertebrates. These studies have provided important biological information and helped to develop regenerative medicine therapies, but important gaps concerning the regulation of tissue and organ regeneration remain to be elucidated. To this aim, new models for studying regenerative biology could prove helpful. Here, the description of the cephalic tentacle regeneration in the adult of the freshwater snail Pomacea canaliculata is presented. In this invasive mollusk, the whole tentacle is reconstructed within 3 months. Regenerating epithelial, connective, muscular and neural components are already recognizable 72 h post-amputation (hpa). Only in the early phases of regeneration, several hemocytes are retrieved in the forming blastema. In view of quantifying the hemocytes retrieved in regenerating organs, granular hemocytes present in the tentacle blastema at 12 hpa were counted, with a new and specific computer-assisted image analysis protocol. Since it can be applied in absence of specific cell markers and after a common hematoxylin-eosin staining, this protocol could prove helpful to evidence and count the hemocytes interspersed among regenerating tissues, helping to unveil the role of immune-related cells in sensory organ regeneration.

We propose a methodological framework to extract spatial features in hyperspectral imaging data and establish a link between these features and the spectral regions, capturing the observed structural patterns. The proposed approach consists of five main steps: i) two dimensional Stationary Wavelet Transform (2D-SWT) is applied to a hyperspectral data cube, decomposing each single-channel image with a selected wavelet filter up to the maximum decomposition level; ii) a grey-level co-occurrence matrix is calculated for every 2D-SWT image resulting from stage i); iii) distinctive spatial features are determined by computing morphological descriptors from each grey-level co-occurrence matrix; iv) the morphological descriptors are rearranged in a two dimensional data array; v) this data matrix is subjected to Principal Component Analysis (PCA) for exploring the variability of the aforementioned descriptors across spectral channels. As a result, groups of spectral wavelengths associated to specific spatial features can be pointed out yielding a better understanding and interpretation of the data. In principle, this information can also be further exploited, e.g. to improve the separation of pure spectral profiles in a multivariate curve resolution context.