Search

The taxonomy of calcareous red algae continues to undergo revisions, especially since the spread of molecular techniques (Pezzolesi et al., 2019; Caragnano et al., 2021). Genetic analyses challenged the morphological approach to species identification, that relies on thallus organization, morphology of cells and reproductive structures observed with high magnification scanning electron microscopy (SEM), or in thin sections under optical microscope. Recently, a growing attention is also given to the calcified structures and to the shape of the crystallites composing the cell walls, which are diagnostic at the level of family (Auer and Piller, 2020). Looking for new identification tools therefore represents a current challenge, which could have crucial implications in paleontology. Convolutional neural networks (CNN) have successfully been applied for the classification of fish, bivalves, and foraminifera (de Lima et al., 2020). To the best of our knowledge, there is no literature about CNN for species identification using SEM images. We tested the potential of CNN to classify SEM images taken from different species of coralline algae commonly found in Mediterranean waters: Lithothamnion corallioides (P. Crouan & H. Crouan) P. Crouan & H. Crouan 1867, Mesophyllum philippii (Foslie) Adey 1970 and Lithophyllum racemus (Lamarck) Foslie 1901. The model provided promising results in terms of accuracy, considering the small set of images used (~40 per species). SEM acquisition, indeed, has significant costs that limit the number of images available for cross-validation. Further efforts should focus on enhancing the image dataset to improve the CNN classification capability and on the interpretation of the features determining the classes.

Liquid chromatography tandem mass spectrometry (LC-MS/MS) is routinely used in many clinical applications, including toxicology, drug monitoring, endocrinology, microbiology, and proteomics, thanks to its versatility and effectiveness in the determination of small molecules. The standard approach for analyzing spectral data is the spectral matching that is based on creating a library of annotated spectra against which individual spectrum can be searched for. Building an own spectral library is time-consuming and dependent on the LC-MS/MS instrumentation used, whereas freely available libraries only cover a limited number of molecules. In many cases and especially with newly synthetized substances, the spectrum could not be found in any spectral databases. In-silico fingerprints are binary vectors that, through a hashing algorithm, encode features of molecules. Due to their easy computation and instrument-independence, in-silico fingerprint databases are bigger and more updated than spectral ones. Prediction of molecular fingerprints starting from the LC-MS/MS spectra would consequently assist the match of any target compound under investigation, which would benefit from the increased dimension of fingerprint databases. In this study, we developed a multi-task neural network able to predict molecular fingerprints starting from the LC-MS/MS spectra. We initially collected and pruned around 70’000 MS spectra from available sources (MassBank of North America). For each compound, fingerprints were calculated (MACCS167 keys and Dragon ECFP of 512 bits) and then multi-task feedforward neural networks were trained to predict the binary bits of molecular fingerprints. Models were validated through specific validation protocols and demonstrated to have suitable performances in terms of predictive accuracy (>85% of bits correctly predicted). Thus, the proposed networks can represent a potential approach for the development of a reliable method to enhance matching of MS spectral data to larger molecular databases. Moreover, with respect to recent studies, the proposed model is simple and works with spectra obtained in different experimental conditions.

Quantifying the diversity content of an incidence matrix is challenging in several scientific fields. The existing indices capture diverse facets of diversity and thus comparing their behaviour is not a straightforward task. For example, an application of diversity measures involves ensembles of classifiers which usually in real applications contain missing values. Therefore, we analysed 14 statistics and, after making them comparable and able to deal with missing values, we applied them on more than one hundred incidence matrices in order to examine the relationships among the measures themselves. In particular, we highlighted the importance of the inter-row agreement of factors, the general agreement of incident factors, as well as the influence on the indices of the proportion of missing values and matrix dimensions, the sensitivity to missing values, the uniform distribution of entries and the invariance to matrix transposition.

In the last ten years, the concept of activity cliff gained a lot of interest by medicinal chemists and QSAR modellers. The reason of this interest is its capacity to highlight problematic as well as interesting situations in analysing the activity/property of pairs of compounds, thus focusing the research on the reasons why two similar compounds show different activities. In the literature, activity cliffs as well as their dual aspect, i.e. structural cliffs, are calculated from activities represented by a continuous response. In this paper, the proposal is to estimate the activity cliffs of categorical responses, i.e. for compounds partitioned into different classes.

Intestinal microbiota is composed by symbiotic innocuous bacteria and potential pathogens also called pathobionts. Even if the mechanism of action of intestinal bacteria remain still unknown, specific microbial species seem to have important role in the maintenance of immunological equilibrium in the gut through the direct interaction with immune cells. Some studies have found a dysregulated interaction between the intestinal bacteria, the gut barrier, and the intestinal associated immune system in Inflammatory Bowel Disease (IBD) patients and in the pathogenesis of these pathologies. In IBD patients some Butyrate producing bacteria, as Faecalibacterium Prausnitzii, are under represented and this could be related with their chronic inflammatory state.