A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage

A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin

Stigmergy-based modeling to discover urban activity patterns from positioning data

Positioning data offer a remarkable source of information to analyze crowds urban dynamics. However, discovering urban activity patterns from the emergent behavior of crowds involves complex system modeling. An alternative approach is to adopt computational techniques belonging to the emergent paradigm, which enables self-organization of data and allows adaptive analysis. Specifically, our approach is based on stigmergy. By using stigmergy each sample position is associated with a digital pheromone deposit, which progressively evaporates and aggregates with other deposits according to their spatiotemporal proximity. Based on this principle, we exploit positioning data to identify high density areas (hotspots) and characterize their activity over time. This characterization allows the comparison of dynamics occurring in different days, providing a similarity measure exploitable by clustering techniques. Thus, we cluster days according to their activity behavior, discovering unexpected urban activity patterns. As a case study, we analyze taxi traces in New York City during 2015

UV-Resonance Raman Spectra of Systems in Complex Environments: A Multiscale Modeling Applied to Doxorubicin Intercalated into DNA

UV-Resonance Raman (RR) spectroscopy is a valuable tool to study the binding of drugs to biomolecular receptors. The extraction of information at the molecular level from experimental RR spectra is made much easier and more complete thanks to the use of computational approaches, specifically tuned to deal with the complexity of the supramolecular system. In this paper, we propose a protocol to simulate RR spectra of complex systems at different levels of sophistication, by exploiting a quantum mechanics/molecular mechanics (QM/MM) approach. The approach is challenged to investigate RR spectra of a widely used chemotherapy drug, doxorubicin (DOX) intercalated into a DNA double strand. The computed results show good agreement with experimental data, thus confirming the reliability of the computational protocol

Amide Spectral Fingerprints are Hydrogen Bonding-Mediated

The origin of the peculiar amide spectral features of proteins in aqueous solution is investigated, by exploiting a combined theoretical and experimental approach to UVRR spectra are recorded by tuning Synchrotron Radiation at several excitation wavelengths and modeled by using a recently developed multiscale protocol based on a polarizable QM/ MM approach. Thanks to the unparalleled agreement between theory and experiment, we demonstrate that specific hydrogen bond interactions, which dominate hydration dynamics around these solutes, play a crucial role in the selective enhancement of amide signals. These results further argue the capability of vibrational spectroscopy methods as valuable tools for refined structural analysis of peptides and proteins in aqueous solution

Binding of SARS-CoV-2 to cell receptors: a tale of molecular evolution

The magnified infectious power of the SARS-CoV-2 virus compared to its precursor SARS-CoV is intimately linked to an enhanced ability in the mutated virus to find available hydrogen bond sites in the host cells. This characteristic is acquired during virus evolution because of the selective pressure exerted at the molecular level. We pinpoint the specific residue (in the virus) to residue (in the cell) contacts during the initial recognition and binding and show that the virus\ub7 \ub7 \ub7 cell interaction is mainly due to an extensive network of hydrogen bonds and to a large surface of non-covalent interactions. In addition to the formal quantum characterization of bonding interactions, computation of absorption spectra for the specific virus\ub7 \ub7 \ub7 cell interacting residues yields significant shifts of 06\u3bb max = 47 and 66 nm in the wavelength for maximum absorption in the complex with respect to the isolated host and virus, respectively

Six-Month Synbio® Administration Affects Nutritional and Inflammatory Parameters of Older Adults Included in the PROBIOSENIOR Project

The physiological changes associated with ageing contribute to the incidence of diseases, morbidity, and mortality. For modern society, it is essential to find solutions to improve elderly people’s health and quality of life. Among promising strategies, the PROBIOSENIOR project proposed a daily six-month supplementation with new probiotic functional foods and nutraceuticals. The aim of this work was to evaluate the modulating effects of the probiotic diet on inflammatory markers and nutritional status. Ninety-seven elderly volunteers were randomly assigned to either a placebo-diet group or a probiotic-diet group (SYNBIO®). Faeces, urine, and blood samples were collected before and after the supplementation to determine serum cytokines, biogenic amines, and inflammation markers. Comparing the results obtained before and after the intervention, probiotic supplementations significantly decreased the TNF- circulating levels and significantly increased those of IGF-1. Biogenic-amine levels showed high variability, with significant variation only for histamine that decreased after the probiotic supplementation. The supplementation influenced the serum concentration of some crucial cytokines (IL-6, IL-8, and MIP-1 ) that significantly decreased in the probiotic group. In addition, the Mini Nutritional Assessment questionnaire revealed that the probiotic-supplemented group had a significant improvement in nutritional status. In conclusion, the PROBIOSENIOR project demonstrated how SYNBIO® supplementation may positively influence some nutritional and inflammatory parameters in the elderly

International challenges without borders: a descriptive study of family physicians' educational needs in the field of diabetes

<p>Abstract</p> <p>Background</p> <p>The optimal care of persons with diabetes by general practitioners and family physicians (GP/FP) is complex and requires multiple competencies. This is a fairly unrecognized key challenge in the healthcare systems. In some cases, local and national Continuous Professional Development (CPD) initiatives target these challenges; however there have been few international initiatives, possibly because challenges emerging from different studies have not been linked across national boundaries. In this context, the authors have compiled data about gaps and/or barriers inherent to GP/FP care of persons with type 2 diabetes from Austria, Canada, Germany and the United Kingdom.</p> <p>Methods</p> <p>Secondary analyzes of pre-existing studies were conducted to identify challenges in the care of patients with type 2 diabetes as faced by GPs/FPs. Two sources of data were reviewed: unpublished research data from collaborating organizations and articles from a literature search (in English and German). Articles retrieved were scanned by the research team for relevance to the study objectives and to extract existing gaps and barriers. The identified challenges were then categorized along three major axes: (1) phase of the continuum of care {from screening to management}; (2) learning domain {knowledge, skills, attitudes, behavior, context}; and (3) by country/region. Compilation and categorization were performed by qualitative researchers and discrepancies were resolved through discussion until concordance was achieved.</p> <p>Results and discussion</p> <p>Thirteen challenges faced by GPs/FPs in the care for patients with type 2 diabetes were common in at least 3 of the 4 targeted countries/regions. These issues were found across the entire continuum of care and included: pathophysiology of diabetes, diagnostic criteria, treatment targets assessment, drugs' modes of action, decision-making in therapies, treatment guidelines, insulin therapy, adherence, management of complications, lifestyle changes, team integration, bureaucracy and third-party payers. The issues reported were not restricted to the physicians' knowledge, but also related to their skills, attitudes, behaviours and context.</p> <p>Conclusions</p> <p>This study revealed challenges faced by GPs/FPs when caring for patients with diabetes, which were similar across international and health system borders. Common issues might be addressed more efficiently through international educational designs, adapted to each country's healthcare system, helping develop and maintain physicians' competencies.</p

A STIGMERGY-BASED FRAMEWORK FOR MINING INFREQUENT ACTIVITY PATTERNS IN URBAN HOTSPOTS USING TAXI GPS DATA

The aim of this thesis is to identify and characterise both frequent and infrequent patterns in crowd dynamics of a metropolis like New York. To this purpose, a stigmergy based framework was developed. The elements composing the framework mimic biological mechanisms present in nature, thus allowing collective behaviours to emerge. The emergent paradigm on which the developed framework relies allows to avoid the explicit modelling of the system. Our approach exploits taxis' positional data to identify high-density areas (hotspots) within a city, hence allowing us to analyse how the hotspots' activity levels evolve over time. The hotspots found in the first phase of the work are then used to sample the number of people being picked up or dropped off at any given time. This set of samples composes a signal that is then used to recognise reference activity patterns arising during the course of a day. This is carried out by means of a second framework that enhances the activity level signals, enabling the frequent patterns to emerge. By clustering those frequent patterns, we are able to identify exceptional patterns which do not fall in any of the clusters defined. On the basis of the results of this research, it can be concluded that there is indeed a cyclic behaviour that can be observed in passenger activity in a big metropolis as New York. Although this metropolis is diverse and many events take place daily, the stigmergy-based approach has proven to be an outstanding method to enhance frequent patterns, allowing them to emerge spontaneously. Moreover, exploiting the frequent patterns detected, it is possible to infer whether a new input day falls outside the known patterns, being it either a novelty or an anomaly