Leveraging multi-view deep learning for next activity prediction

Predicting the next activity in a running trace is a fundamental problem in business process monitoring since such predictive information may allow analysts to intervene proactively and prevent undesired behaviors. This paper describes a predictive process approach that couples multi-view learning and deep learning, in order to gain accuracy by accounting for the variety of information possibly recorded in event logs. Experiments with benchmark event logs show the accuracy of the proposed approach compared to several recent state-of-the-art methods

DARWIN: An online deep learning approach to handle concept drifts in predictive process monitoring

Predictive process monitoring (PPM) is a specific task under the umbrella of Process Mining that aims to predict several factors of a business process (e.g., next activity prediction) based on the knowledge learned from historical event logs. Despite recent PPM algorithms have gained predictive accuracy using deep learning, they commonly perform an offline analysis of event data assuming that logged processes remain in a steady state over time. However, this is often not the real-world case due to concept drifts. The main goal of this work is to solve the next-activity prediction problem under dynamic conditions of business data streams. To this aim, we propose DARWIN as a novel PPM method that detects concept drifts and adapts a deep neural model to concept drifts. A deep empirical analysis of different factors that may influence the performance of DARWIN in streaming scenarios is provided. Experiments with various benchmark event streams show the effectiveness of the proposed approach

Computational methods for the identification of molecular targets of toxic food additives. Butylated hydroxytoluene as a case study

Butylated hydroxytoluene (BHT) is one of the most commonly used synthetic antioxidants in food, cosmetic, pharmaceutical and petrochemical products. BHT is considered safe for human health; however, its widespread use together with the potential toxicological effects have increased consumers concern about the use of this synthetic food additive. In addition, the estimated daily intake of BHT has been demonstrated to exceed the recommended acceptable threshold. In the present work, using BHT as a case study, the usefulness of computational techniques, such as reverse screening and molecular docking, in identifying protein–ligand interactions of food additives at the bases of their toxicological effects has been probed. The computational methods here employed have been useful for the identification of several potential unknown targets of BHT, suggesting a possible explanation for its toxic effects. In silico analyses can be employed to identify new macromolecular targets of synthetic food additives and to explore their functional mechanisms or side effects. Noteworthy, this could be important for the cases in which there is an evident lack of experimental studies, as is the case for BHT

Evaluating end-user perception towards a cardiac self-care monitoring process

This study examined the perception of end-users regarding the monitoring process offered by an innovative cardiac self-care system. The main goal was to assess the efficacy of the process implemented by a smart device designed to support people for real-time monitoring of cardio-vascular parameters in everyday life, thereby encouraging patients to be more proactive in heath management. Most participants showed positive response about the potential benefits of the proposed self-care solution and were willing to adopt the system despite some concerns related to trust and privacy

FOX: a neuro-Fuzzy model for process Outcome prediction and eXplanation

Predictive process monitoring (PPM) techniques have become a key element in both public and private organizations by enabling crucial operational support of their business processes. Thanks to the availability of large amounts of data, different solutions based on machine and deep learning have been proposed in the literature for the monitoring of process instances. These state-of-the-art approaches leverage accuracy as main objective of the predictive modeling, while they often neglect the interpretability of the model. Recent studies have addressed the problem of interpretability of predictive models leading to the emerging area of Explainable AI (XAI). In an attempt to bring XAI in PPM, in this paper we propose a fully interpretable model for outcome prediction. The proposed method is based on a set of fuzzy rules acquired from event data via the training of a neuro-fuzzy network. This solution provides a good trade-off between accuracy and interpretability of the predictive model. Experimental results on different benchmark event logs are encouraging and motivate the importance to develop explainable models for predictive process analytics

Uncovering the structure and function of Pseudomonas aeruginosa periplasmic proteins by an in silico approach

Pseudomonas aeruginosa is an opportunistic human pathogen highly relevant from a biomedical viewpoint. It is one of the main causes of infection in hospitalized patients and a major cause of mortality of cystic fibrosis patients. This is also due to its ability to develop resistance to antibiotics by various mechanisms. Therefore, it is urgent and desirable to identify novel targets for the development of new antibacterial drugs against Pseudomonas aeruginosa. In this work this problem was tackled by an in silico approach aimed at providing a reliable structural model and functional annotation for the Pseudomonas aeruginosa periplasmic proteins for which these data are not available yet. A total of 83 protein sequences were analyzed, and the corresponding structural models were built, leading to the identification of 32 periplasmic ‘substrate-binding proteins’, 14 enzymes and 4 proteins with different functions, including lipids and metals binding. The most interesting cases were found within the ‘enzymes’ group with the identification of a lipase, which can be regarded as a virulence factor, a protease involved in the assembly of β-barrel membrane proteins and a l,d-transpeptidase, which could contribute to confer resistance to β-lactam antibiotics to the bacterium. Communicated by Ramaswamy H. Sarma

Neonicotinoid trapping by the FA1 site of human serum albumin

Neonicotinoids are a widely used class of insecticides that target the acetylcholine recognition site of the nicotinic acetylcholine receptors in the central nervous system of insects. Although neonicotinoids display a high specificity for insects, their use has been recently debated since several studies led to the hypothesis that they may have adverse ecological effects and potential risks to mammals and even humans. Due to their hydrophobic nature, neonicotinoids need specific carriers to allow their distribution in body fluids. Human serum albumin (HSA), the most abundant plasma protein, is a key carrier of endogenous and exogenous compounds. The in silico docking and ligand binding properties of acetamiprid, clothianidin, dinotefuran, imidacloprid, nitenpyram, thiacloprid, and thiamethoxam to HSA are here reported. Neonicotinoids bind to multiple fatty acid (FA) binding sites, preferentially to the FA1 pocket, with high affinity. Values of the dissociation equilibrium constant for neonicotinoid binding FA1 of HSA (i.e., calcKn) derived from in silico docking simulations (ranging between 3.9 × 10−5 and 6.3 × 10−4 M) agree with those determined experimentally from competitive inhibition of heme-Fe(III) binding (i.e., expKn; ranging between 2.1 × 10−5 and 6.9 × 10−5 M). Accounting for the HSA concentration in vivo (~7.5 10−4 M), values of Kn here determined suggest that the formation of the HSA:neonicotinoid complexes may occur in vivo. Therefore, HSA appears to be an important determinant for neonicotinoid transport and distribution to tissues and organs, particularly to the liver where they are metabolized

Dynamical behavior of the human ferroportin homologue from bdellovibrio bacteriovorus: Insight into the ligand recognition mechanism

Members of the major facilitator superfamily of transporters (MFS) play an essential role in many physiological processes such as development, neurotransmission, and signaling. Aberrant functions of MFS proteins are associated with several diseases, including cancer, schizophrenia, epilepsy, amyotrophic lateral sclerosis and Alzheimer's disease. MFS transporters are also involved in multidrug resistance in bacteria and fungi. The structures of most MFS members, especially those of members with significant physiological relevance, are yet to be solved. The lack of structural and functional information impedes our detailed understanding, and thus the pharmacological targeting, of these transporters. To improve our knowledge on the mechanistic principles governing the function of MSF members, molecular dynamics (MD) simulations were performed on the inward-facing and outward-facing crystal structures of the human ferroportin homologue from the Gram-negative bacteriumBdellovibrio bacteriovorus(BdFpn). Several simulations with an excess of iron ions were also performed to explore the relationship between the protein's dynamics and the ligand recognition mechanism. The results reinforce the existence of the alternating-access mechanism already described for other MFS members. In addition, the reorganization of salt bridges, some of which are conserved in several MFS members, appears to be a key molecular event facilitating the conformational change of the transporter