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There is growing evidence that inflammatory, immunologic, and metabolic status is associated with cancer patients survival. Here, we built a simple algorithm to predict lung cancer outcome. Perioperative routine blood tests (RBT) of a cohort of patients with resectable primary lung cancer (LC) were analysed. Inflammatory, immunologic, and metabolic profiles were used to create a single algorithm (RBT index) predicting LC survival. A concurrent cohort of patients with resectable lung metastases (LM) was used to validate the RBT index. Charts of 2088 consecutive LC and 1129 LM patients undergoing lung resection were evaluated. Among RBT parameters, C-reactive protein (CRP), lymphocytes, neutrophils, hemoglobin, albumin and glycemia independently correlated with survival, and were used to build the RBT index. Patients with a high RBT index had a higher 5-year mortality than low RBT patients (adjusted HR 1.93, 95% CI 1.62–2.31). High RBT patients also showed a fourfold higher risk of 30-day postoperative mortality (2.3% vs. 0.5%, p 0.0019). The LM analysis validated the results of the LC cohort. We developed a simple and easily available multifunctional tool predicting short-term and long-term survival of curatively resected LC and LM. Prospective external validation of RBT index is warranted.

The interest in multitask and deep learning strategies has been increasing in the last few years, in application to large and complex dataset for quantitative structure-activity relationship (QSAR) analysis. Multitask approaches allow the simultaneous prediction of molecular properties that are related, through information sharing, whereas deep learning strategies increase the potential of capturing nonlinear relationships. In this work, we compare the binary classification capability of multitask deep and shallow neural networks to single-task strategies used as benchmark (i.e., as k-nearest neighbours, N-nearest neighbours, random forest and Naïve Bayes), as well as multitask supervised self-organizing maps. Comparison was carried out with an extended QSAR dataset containing annotations of molecular binding, agonism and antagonism activity on 11 nuclear receptors, for a total of 14,963 molecules, divided into training and test sets and labelled for their bioactivity on at least one of 30 binary tasks. Additional 304 chemicals were used as external evaluation set to further validate models. Although no approach systematically overperformed the others, task-specific differences were found, suggesting the benefit of multitask learning for tasks that are less represented. On average, some of the single-task approaches and multitask deep learning strategies had similar performances. However, the latter can have advantages, such as a simpler management of predictions and applicability domain assessment for future samples. On the other hand, the parameter tuning required by neural networks are generally time expensive suggesting that the modelling strategy should be evaluated case by case.

Hyphenated chromatography is among the most popular analytical techniques in omics related research. While great advancements have been achieved on the experimental side, the same is not true for the extraction of the relevant information from chromatographic data. Extensive signal preprocessing is required to remove the signal of the baseline, resolve the time shifts of peaks from sample to sample and to properly estimate the spectra and concentrations of co-eluting compounds. Among several available strategies, curve resolution approaches, such as PARAFAC2, ease the deconvolution and the quantification of chemicals. However, not all resolved profiles are relevant. For example, some take into account the baseline, others the chemical compounds. Thus, it is necessary to distinguish the profiles describing relevant chemistry. With the aim to assist researchers in this selection phase, we have tried three different classification algorithms (convolutional and recurrent neural networks, k-nearest neighbours) for the automatic identification of GC-MS elution profiles resolved by PARAFAC2. To this end, we have manually labelled more than 170,000 elution profiles in the following four classes: ‘Peak’, ‘Cutoff peak’,’ Baseline’ and ‘Others’ in order to train, validate and test the classification models. The results highlight two main points: i) neural networks seem to be the best solution for this specific classification task confirmed by the overall quality of the classification, ii) the quality of the input data is crucial to maximize the modelling performances.

Multitask learning allows to model multiple tasks simultaneously through information sharing. In the context of quantitative structure activity relationships and computational toxicology, multitask learning is gaining more and more interest, owed to its potential to improve the predictive performance of underrepresented tasks and to predict the multi-property profile of molecules. In this chapter, after introducing the multitask problem formulation, we present a hands-on tutorial on multitask neural networks.

Introduction: Gastroesophageal reflux disease (GERD) as well as its treatment with acid-suppressive medications have been considered possible risk factors for the development of asthma, but few studies have disentangled the role of GERD with that of its treatment. The present study aimed at estimating the association of treated and untreated GERD in the first year of life with the risk of asthma. Methods: Retrospective cohort study including all children born between 2004 and 2015 registered in Pedianet, an Italian primary care database. We analyzed the association of children exposed to GERD (both treated and untreated) in the first year of life with the risk of developing clinically assessed asthma (clinical asthma) after 3 years. Secondary outcomes included asthma identified by anti-asthmatic medications (treated asthma) and wheezing after 3 years. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated comparing children with and without GERD, stratifying by treatment with acid-suppressive medications. Results: Out of 86,381 children, 1652 (1.9%) were affected by GERD in the first year of life, of which 871 (53%) were treated with acid-suppressive medications. Compared with controls, children with GERD were at increased risk of clinical asthma (HR: 1.40, 95% CI 1.15-1.70). Risks were similar between treated and untreated GERD (p = 0.41). Comparable results were found for treated asthma, but no risk increase was seen for wheezing. Discussion: Early-life GERD was associated with subsequent childhood asthma. Similar risks among children with treated and untreated GERD suggest that acid-suppressive medications are unlikely to play a major role in the development asthma.

Antineoplastic drugs are a wide and heterogeneous group of substances that, as universally known, can cause highly severe toxic effects to whoever is exposed. From an occupational safety point of view, surface contaminations inside preparation and administration units are a growing issue and therefore require the development and implementation of sensible and fast monitoring methods. The unlikelihood of a unique all-embracing chromatography, able to correctly retain and separate each analyte led to the need to create an orthogonal normal phase analysis, which might be able to fill the gaps in the more common reversed-phase ones. An existing hydrophilic interaction method has thus been expanded to 6 other drugs and applied to real samples after an evaluation of its performances. The experimental data were then used to evaluate the possibility of estimating reliable relationships between the chromatographic retention and the chemical-structural features of the drugs under analysis.

Mass spectrometry (MS) is widely used for the identification of chemical compounds by matching the experimentally acquired mass spectrum against a database of reference spectra. However, this approach suffers from a limited coverage of the existing databases causing a failure in the identification of a compound not present in the database. Among the computational approaches for mining metabolite structures based on MS data, one option is to predict molecular fingerprints from the mass spectra by means of chemometric strategies and then use them to screen compound libraries. This can be carried out by calibrating multi-task artificial neural networks from large datasets of mass spectra, used as inputs, and molecular fingerprints as outputs. In this study, we prepared a large LC-MS/MS dataset from an on-line open repository. These data were used to train and evaluate deep-learning-based approaches to predict molecular fingerprints and retrieve the structure of unknown compounds from their LC-MS/MS spectra. Effects of data sparseness and the impact of different strategies of data curing and dimensionality reduction on the output accuracy have been evaluated. Moreover, extensive diagnostics have been carried out to evaluate modelling advantages and drawbacks as a function of the explored chemical space.

I recettori nucleari sono fattori di trascrizione coinvolti in processi critici per la salute umana e sono un obiettivo rilevante per la valutazione del rischio tossicologico e il processo di scoperta dei farmaci. I modelli computazionali possono essere uno strumento utile (i) per dare priorità alla sperimentazione di sostanze chimiche che possono imitare gli ormoni naturali e quindi essere interferenti endocrini e (ii) per identificare nuovi possibili candidati farmaci. Pertanto, l'obiettivo principale di questo progetto è quello di studiare le potenziali interazioni tra sostanze chimiche e recettori nucleari, con il duplice scopo di sviluppare strumenti in silico per la ricerca di nuovi modulatori e di identificare possibili sostanze chimiche che alterano il sistema endocrino. Dopo aver creato una collezione esaustiva di modulatori di recettori nucleari, abbiamo applicato metodi di apprendimento automatico per colmare il vuoto di dati e predire nuovi possibili modulatori tramite modelli predittivi. In particolare, le strategie di modellazione hanno incluso algoritmi di apprendimento automatico multi-tasking per indagare le complesse relazioni tra sostanze chimiche e diversi recettori nucleari., Nuclear receptors are transcription factors involved in processes critical to human health and are a relevant target for toxicological risk assessment and the drug discovery process. Computational models can be a useful tool (i) to prioritize chemicals that can mimic natural hormones and thus be endocrine disruptors and (ii) to identify new possible lead for drug discovery. Therefore, the main goal of this project is to study potential interactions between chemicals and nuclear receptors, with the dual purpose of developing in silico tools to search for new modulators and to identify possible endocrine disrupting chemicals. After creating an exhaustive collection of nuclear receptor modulators, we applied machine learning methods to fill the data gap and prioritize modulators by building predictive models. In particular, modeling strategies included multi-tasking machine learning algorithms to investigate the complex relationships between chemicals and multiple nuclear receptors.

Sixty-six dairy farms (as average, 167.0 ± 106.5 milking cows and milk yield of 31.4 ± 5.1) were visited to identify different nutritional approaches adopted in dairy cows diet formulation. Forage, TMR, faecal, and milk samples from lactating herds were collected and diet composition, in vitro methane production, in vivo nutrient digestibility, faecal fermentation profile and milk yield and milk quality were characterised. A hierarchical cluster analysis was applied and six nutritional approaches in diet formulation corresponding with six different clusters (CL) were identified. The CL discriminated the farm feeding choices in the following nutritional strategies, based on high use of: (i) high moisture corn (HMC) and legume silage, (ii) compound feed, (iii) corn and soy meals, (iv) HMC and soy meal, (v) corn meal and protein compound feeds, or (vi) HMC and protein compound feed strategies, respectively for CL1 to CL6. The milk yield tended (p =.061) to differ among CL. The greatest (>1.60) feed efficiency was calculated for CL1 and 5, whereas the lowest (<1.45) for CL 2. The CL2 and 5 had the highest feed costs (i.e. 0.24 or 0.22 €/kg of milk, respectively). Interestingly, the high use of HMC or steam flaked corn resulted associated to higher farm performances. Particularly, HMC and legume silage (CL1), HCM and soy meal (CL4) and HCM and protein compound feed (CL6) nutritional strategies resulted more efficient and profitable, having the greatest feed efficiency, protein and starch degradability as well as income over feed costs. Differentiate carbohydrates in dairy cow diets seems highly convenient.HIGHLIGHTS Six different nutritional strategies were identified based on use of single ingredients and a great variability in both economic and feed efficiency was observed for dairy farms clustering into different groups The use of high moisture ear corn in substitution of corn meal increased dry matter intake, feed efficiency, milk yield and lactose co

Minimum Spanning Tree (MST) is a well-known clustering algorithm that provides a graphical tree representation of the objects in a data set by exploiting local information to link each pair of similar objects. The a-posteriori analysis of this tree in terms of nodes and edges provides the basis to derive simple classifiers, namely semi-supervised classification approaches based on the minimum spanning tree approach. In this work, we propose different metrics to evaluate the MST ability to group objects of the same a-priori known classes. The classification capability of the proposed approach, using 13 different distance measures, was compared with that of classical supervised classification approaches such as N-Nearest Neighbour (N3), Binned Nearest Neighbour (BNN), Partial Least Squares-Discriminant Analysis (PLS-DA), K-Nearest Neighbour (KNN), exponentially weighted K-Nearest Neighbour (wKNN) and Support Vector Machine with radial functions (SVM-RBF) on 31 data sets. The proposed approach resulted to be competitive and comparable with the considered classical supervised classification methods. Finally, we analysed the role of the 13 different measures in terms of performance and percentage of not-assigned objects.

In the recent years, ecotoxicological hazard potential of biocidal products has been receiving increasing attention in the industries and regulatory agencies. Biocides/pesticides are currently one of the most studied groups of compounds, and their registration cannot be done without the empirical toxicity information. In view of limited experimental data available for these compounds, we have developed Quantitative Structure-Activity Relationship (QSAR) models for the toxicity of biocides to fish and Daphnia magna following principles of QSAR modeling recommended by the OECD (Organization for Economic Cooperation and Development). The models were developed using simple and interpretable 2D descriptors and validated using stringent tests. Both models showed encouraging statistical quality in terms of determination coefficient R 2 (0.800 and 0.648), cross-validated leave-one-out Q 2 (0.760 and 0.602) and predictive R 2pred or Q 2ext (0.875 and 0.817) for fish (n Training = 66, n Test = 22) and Daphnia magna (n Training = 100, n Test = 33) toxicity datasets, respectively. These models should be applicable for data gap filling in case of new or untested biocidal compounds falling within the applicability domain of the models. In general, the models indicate that the toxicity increases with lipophilicity and decreases with polarity, branching and unsaturation. We have also developed interspecies toxicity models for biocides using the daphnia and fish toxicity data and used the models for data gap filling.

Extracorporeal carbon dioxide removal (ECCO2R) is a promising strategy to manage acute respiratory failure. We hypothesized that ECCO2R could be enhanced by ventilating the membrane lung with a sodium hydroxide (NaOH) solution with high CO2 absorbing capacity. A computed mathematical model was implemented to assess NaOH-CO2 interactions. Subsequently, we compared NaOH infusion, named "alkaline liquid ventilation", to conventional oxygen sweeping flows. We built an extracorporeal circuit with two polypropylene membrane lungs, one to remove CO2 and the other to maintain a constant PCO2 (60 ± 2 mmHg). The circuit was primed with swine blood. Blood flow was 500 mL × min-1. After testing the safety and feasibility of increasing concentrations of aqueous NaOH (up to 100 mmol × L-1), the CO2 removal capacity of sweeping oxygen was compared to that of 100 mmol × L-1 NaOH. We performed six experiments to randomly test four sweep flows (100, 250, 500, 1000 mL × min-1) for each fluid plus 10 L × min-1 oxygen. Alkaline liquid ventilation proved to be feasible and safe. No damages or hemolysis were detected. NaOH showed higher CO2 removal capacity compared to oxygen for flows up to 1 L × min-1. However, the highest CO2 extraction power exerted by NaOH was comparable to that of 10 L × min-1 oxygen. Further studies with dedicated devices are required to exploit potential clinical applications of alkaline liquid ventilation.

Many of the metrics used to evaluate farm performance are only partial indicators of farm operations, which are assumed to be best predictors of the whole farm efficiency. The main objective of this work was to identify aggregated multiple indexes of profitability using common partial indicators that are routinely available from individual farms to better support the short-term decision-making processes of the cattle-feeding process. Data were collected from face-to-face interviews with farmers from 90 dairy farms in Italy and used to calculate 16 partial indicators that covered almost all indicators currently used to target feeding and economic efficiency in dairy farms. These partial indicators described feed efficiency, energy utilization, feed costs, milk-to-feed price ratio, income over feed costs, income equal feed cost, money-corrected milk, and bargaining power for feed costs. Calculations of feeding costs were based on lactating cows or the whole herd, and income from milk deliveries was determined with or without considering the milk quality payment. Multivariate factor analysis was then applied to the 16 partial indicators to determine simplified and latent structures. The results indicated that 5 factors explained 70% of the variability. Each of the original partial indicator was associated with all factors in different proportions, as indicated by loading scores from the multivariate factor analysis. Based on the loading scores, we labeled these 5 factors as “economic efficiency,” “energy utilization,” “break-even point,” “milk-to-feed price,” and “bargaining power of the farm,” in decreasing order of explained communality. The first 3 factors shared 83% of the total communality. Feed efficiency was similarly associated with factor 1 (53% loading) and factor 2 (66% loading). Only factor 4 was significantly affected by farm location. Milk production and herd size had significant effects on factor 1 and factor 2. Our multivariate approach eliminated the p

In the analysis of complex and multivariate systems, set priorities and rank all the available objects is not a trivial task. In this framework, several multi-criteria decision-making (MCDM) techniques have been proposed, with successful applications in different research fields. However, it would be desirable to customize the ranking by weighting differently the criteria and to quantitatively assess the influence of each criterion on the final ranking. In this study, we applied a recently proposed ranking approach, called Deep Ranking Analysis by Power Eigenvectors (DRAPE) to study the sustainability of 154 countries expressed by 21 human, environmental and economic wellbeing criteria. The application of two systems for weighting the criteria and a retrospective analysis of the contribution of each criterion to the final ranking allowed the great potential of the considered approach to be expressed.

The classification of coralline algae commonly relies on the morphology of cells and reproductive structures, along with thallus organization, observed through Scanning Electron Microscopy (SEM). Nevertheless, species identification based on morphology often leads to uncertainty, due to their general plasticity. Evolutionary and environmental studies featured coralline algae for their ecological significance in both recent and past Oceans and need to rely on robust taxonomy. Research efforts towards new putative diagnostic tools have recently been focused on cell wall ultrastructure. In this work, we explored a new classification tool for coralline algae, using fine-tuning pretrained Convolutional Neural Networks (CNNs) on SEM images paired to morphological categories, including cell wall ultrastructure. We considered four common Mediterranean species, classified at genus and at the species level (Lithothamnion corallioides, Mesophyllum philippii, Lithophyllum racemus, Lithophyllum pseudoracemus). Our model produced promising results in terms of image classification accuracy given the constraint of a limited dataset and was tested for the identification of two ambiguous samples referred to as L. cf. racemus. Overall, explanatory image analyses suggest a high diagnostic value of calcification patterns, which significantly contributed to class predictions. Thus, CNNs proved to be a valid support to the morphological approach to taxonomy in coralline algae.

Neural networks are rapidly gaining popularity in chemical modeling and Quantitative Structure–Activity Relationship (QSAR) thanks to their ability to handle multitask problems. However, outcomes of neural networks depend on the tuning of several hyperparameters, whose small variations can often strongly affect their performance. Hence, optimization is a fundamental step in training neural networks although, in many cases, it can be very expensive from a computational point of view. In this study, we compared four of the most widely used approaches for tuning hyperparameters, namely, grid search, random search, tree-structured Parzen estimator, and genetic algorithms on three multitask QSAR datasets. We mainly focused on parsimonious optimization and thus not only on the performance of neural networks, but also the computational time that was taken into account. Furthermore, since the optimization approaches do not directly provide information about the influence of hyperparameters, we applied experimental design strategies to determine their effects on the neural network performance. We found that genetic algorithms, tree-structured Parzen estimator, and random search require on average 0.08% of the hours required by grid search; in addition, tree-structured Parzen estimator and genetic algorithms provide better results than random search.

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 spec

Irritable bowel syndrome (IBS), a common functional gastrointestinal disorder, is classified according to bowel habits as IBS with constipation (IBS-C), with diarrhea (IBS-D), with alternating constipation and diarrhea (IBS-M), and unsubtyped (IBS-U). The mechanisms leading to the different IBS forms are mostly unknown. This study aims to evaluate whether specific fecal bacterial taxa and/or short-chain fatty acids (SCFAs) can be used to distinguish IBS subtypes and are relevant for explaining the clinical differences between IBS subcategories. We characterized five fecal samples collected at 4-weeks intervals from 40 IBS patients by 16S rRNA gene profiling and SCFA quantification. Finally, we investigated the potential correlations in IBS subtypes between the fecal microbial signatures and host physiological and clinical parameters. We found significant differences in the distribution of Clostridiales OTUs among IBS subtypes and reduced levels of SCFAs in IBS-C compared to IBS-U and IBS-D patients. Correlation analyses showed that the diverse representation of Clostridiales OTUs between IBS subtypes was associated with altered levels of SCFAs; furthermore, the same OTUs and SCFAs were associated with the fecal cytokine levels and stool consistency. Our results suggest that intestinal Clostridiales and SCFAs might serve as potential mechanistic biomarkers of IBS subtypes and represent therapeutic targets.

Background: Evidence suggests a role of intestinal microbiota-host interactions in the pathophysiology and symptoms of irritable bowel syndrome (IBS). Objective: The objective of this article is to assess the effects of Lactobacillus paracasei CNCM I-1572 on clinical and gut microbiota-related factors in IBS. Methods: We conducted a multicenter, randomized, double-blind, cross-over, 18-week, placebo-controlled, pilot trial assessing the effect of Lactobacillus paracasei CNCM I-1572 on symptoms, gut microbiota composition, fecal short chain fatty acid (SCFA), immunoglobulin A, and cytokines in IBS. The intestinal microbial ecosystem was characterized by 16S rRNA gene profiling. Results: Forty IBS patients were enrolled from five Italian centers. Lactobacillus paracasei CNCM I-1572 did not significantly improve IBS symptoms, including primary efficacy variables worst abdominal pain/discomfort and IBS degree of relief. Interestingly, Lactobacillus paracasei CNCM I-1572 induced a significant reduction in genus Ruminococcus, dominated by taxa related to Ruminococcus bromii and Ruminococcus callidus, a significant increase in the SCFAs acetate and butyrate, and a significant reduction in the pro-inflammatory cytokine interleukin-15. Conclusions: This pilot study shows that Lactobacillus paracasei CNCM I-1572 is able to modulate gut microbiota structure/function and reduce immune activation in IBS. As no statistically significant effect on IBS-symptoms was found, further studies are necessary to determine the role of this probiotic in IBS. The study was registered at ClinicalTrials.gov registry under identifier NCT02371499.

Consensus strategies have been widely applied in many different scientific fields, based on the assumption that the fusion of several sources of information increases the outcome reliability. Despite the widespread application of consensus approaches, their advantages in quantitative structure-activity relationship (QSAR) modeling have not been thoroughly evaluated, mainly due to the lack of appropriate large-scale data sets. In this study, we evaluated the advantages and drawbacks of consensus approaches compared to single classification QSAR models. To this end, we used a data set of three properties (androgen receptor binding, agonism, and antagonism) for approximately 4000 molecules with predictions performed by more than 20 QSAR models, made available in a large-scale collaborative project. The individual QSAR models were compared with two consensus approaches, majority voting and the Bayes consensus with discrete probability distributions, in both protective and nonprotective forms. Consensus strategies proved to be more accurate and to better cover the analyzed chemical space than individual QSARs on average, thus motivating their widespread application for property prediction. Scripts and data to reproduce the results of this study are available for download.

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.

Nuclear receptors (NRs) are key regulators of human health and constitute a relevant target for medicinal chemistry applications as well as for toxicological risk assessment. Several open databases dedicated to small molecules that modulate NRs exist; however, depending on their final aim (i.e., adverse effect assessment or drug design), these databases contain a different amount and type of annotated molecules, along with a different distribution of experimental bioactivity values. Stemming from these considerations, in this work we aim to provide a unified dataset, NURA (NUclear Receptor Activity) dataset, collecting curated information on small molecules that modulate NRs, to be intended for both pharmacological and toxicological applications. NURA contains bioactivity annotations for 15,247 molecules and 11 selected NRs, and it was obtained by integrating and curating data from toxicological and pharmacological databases (i.e., Tox21, ChEMBL, NR-DBIND and BindingDB). Our results show that NURA dataset is a useful tool to bridge the gap between toxicology- and medicinal-chemistry-related databases, as it is enriched in terms of number of molecules, structural diversity and covered atomic scaffolds compared to the single sources. To the best of our knowledge, NURA dataset is the most exhaustive collection of small molecules annotated for their modulation of the chosen nuclear receptors. NURA dataset is intended to support decision-making in pharmacology and toxicology, as well as to contribute to data-driven applications, such as machine learning. The dataset and the data curation pipeline can be downloaded free of charge on Zenodo at the following DOI: https://doi.org/10.5281/zenodo.3991561.

Multi-criteria decision making processes comprehend several ranking methods able to handle multiple and often conflicting criteria and sorting objects according to a definition of the optimality direction for each criterion. In the field of polypharmacology it can be useful for virtual screening or prioritization purposes to rank molecules distinguishing the most multi-target ones, i.e. molecules able to interact with different targets, from the selective or inactive ones. In this work, the Deep Ranking Analysis by Power Eigenvectors approach is applied to a small set of molecules characterized by their half maximal binding concentrations for seven different nuclear receptors. The existence of a correspondence between the DRAPE ranking and a manually grouping of molecules based on their multi-target behaviour was verified. Moreover, a comparison between DRAPE rankings and those obtained from five traditional methods was carried out in order to highlight the main similarities and the differences of the approaches.

Legislators have included bioaccumulation in the evaluation of chemicals in the framework of the European Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) regulation. REACH requires information on the bioconcentration factor (BCF), which is a parameter for assessing bioaccumulation and encourages the use of a weight-of-evidence approach, including predictions from quantitative structure-activity relationships (QSARs). This study presents a novel approach, based on structural alerts, to be used as a decision-support system for the identification of substances with bioaccumulation potential. In a regulatory framework, these alerts can be integrated with other sources of information, such as experimental and in silico data, to reduce the uncertainty of the assessment, thereby supporting a weight-of-evidence approach. Moreover, the identified alerts have a direct connection with relevant structural features, thus fostering the applicability and interpretability of the approach. The structural alerts were identified on 779 chemicals annotated for their fish BCF, and the approach was then validated on 278 external molecules. The developed decision-support system allowed identification of 77% of bioaccumulative chemicals and was competitive with more complex QSAR models used in regulatory assessments. The approach is implemented in an easy-to-use workflow, provided free of charge. Integr Environ Assess Manag 2018;00:000-000. © 2018 SETAC.

In Roman law a gift mortis causa was a transfer of property carried out in contemplation or anticipation of the donor’s death. A dispute aroused among Roman lawyers about the status and the effects of this kind of transaction. Due to its anomalous character, some of them deemed that a gift in contemplation of death was comparable with a last will, while some others thought that it was assimilable to contracts. Emperor Justinian eventually decided that a donatio mortis causa had to be considered as a legacy. Nevertheless, law scholars in the Middle Ages and in the Modern Era who studied the Justinian body of civil law were not satisfied with such a classification. Thus, new disputes arose regarding this topic. Some highlighted the affinity of gift mortis causa with legacy, others with donatio inter vivos. The idea that a gift in contemplation of death was amphibious, i.e. it had a mixed nature, also started to be defended: it was a peculiar type of gift among the categories of gifts inter vivos and legacies, and it therefore belonged to a category which appeared, in some respects, to be sui generis. This was not only a theoretical question since different consequences derived from the qualification of the gift in contemplation of death. In this way, the jurists of the ius commune deepened the discipline of donatio mortis causa, particularly focusing on its essential requirements, so that they could distinguish it from other similar transactions (above all from donatio inter vivos). This helped lawyers to find a solution to problems arising in everyday life. For this purpose, the provision of the revocability of the gift played a key role (and the elements of testamentary dispositions were assumed to be predominant). Thus, donatio mortis causa is a clear example of medieval law scholars’ approach to the Justinian Corpus iuris civilis. Indeed, it was their unavoidable starting point, from which they then moved on in order to build up new theories to face the changes of

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.