The Role of Market Insights in Shaping Sustainable Mobility in Fast Developing Countries: The Case of Vietnam

Individual mobility represents one of the main contributors of air quality degradation in urban areas, with detrimental social and environmental impacts as well as economic loss. Mobility policies hence represent a key public instrument to curb congestion, pollution and health-related problems. In order to be effective, they need to rely on an adequate knowledge of demand, in terms of commuters’ attitudes, habit strength and perceived priorities. While most studies on the determinants of modal choice are rooted in Western countries or in developed economies little evidence is available for fast-developing countries, whose urban areas suffer from severe congestion and bad air quality. We test a comprehensive model to predict mobility behaviors in Vietnam, by means of an empirical investigation, with data from 898 participants (N = 898) collected via an online self-administered questionnaire. We discuss the implications for policy of the research findings, which provide an informational background representing a necessary prerequisite for the implementation of sound policies for the shift to more sustainable paradigms

Candidate composite biomarker to inform drug treatments for diabetic kidney disease

Introduction: Current guidelines recommend renin angiotensin system inhibitors (RASi) as key components of treatment of diabetic kidney disease (DKD). Additional options include sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide 1 receptor agonists (GLP1a), and mineralocorticoid receptor antagonists (MCRa). The identification of the optimum drug combination for an individual is difficult because of the inter-, and longitudinal intra-individual heterogeneity of response to therapy. Results: Using data from a large observational study (PROVALID), we identified a set of parameters that can be combined into a meaningful composite biomarker that appears to be able to identify which of the various treatment options is clinically beneficial for an individual. It uses machine-earning techniques to estimate under what conditions a treatment of RASi plus an additional treatment is different from the treatment with RASi alone. The measure of difference is the annual percent change (ΔeGFR) in the estimated glomerular filtration rate (ΔeGFR). The 1eGFR is estimated for both the RASi-alone treatment and the add-on treatment. Discussion: Higher estimated increase of eGFR for add-on patients compared with RASi-alone patients indicates that prognosis may be improved with the add-on treatment. The personalized biomarker value thus identifies which patients may benefit from the additional treatment

Rising damp in historical buildings: A Venetian perspective

Considering several real case studies, moisture distribution due to rising damp in Venetian brick masonries is discussed and empirical models are developed. Moisture content and soluble salt data of 25 historical buildings in Venice are analysed. Data are scrutinized using statistical methods, obtaining contour plots and estimating the validity of linear and non-linear models. The models confirm that masonries are usually soaked with water till 120–150 cm over sea level, while the evaporation zone ranges in height from 200 cm to 350 cm. In the perpendicular section, moisture distribution depends on several contingent factors such as, among them, the proximity and the exposition of the external façades to the water action

Candidate composite biomarker to inform drug treatments for diabetic kidney disease

IntroductionCurrent guidelines recommend renin angiotensin system inhibitors (RASi) as key components of treatment of diabetic kidney disease (DKD). Additional options include sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide 1 receptor agonists (GLP1a), and mineralocorticoid receptor antagonists (MCRa). The identification of the optimum drug combination for an individual is difficult because of the inter-, and longitudinal intra-individual heterogeneity of response to therapy.ResultsUsing data from a large observational study (PROVALID), we identified a set of parameters that can be combined into a meaningful composite biomarker that appears to be able to identify which of the various treatment options is clinically beneficial for an individual. It uses machine-earning techniques to estimate under what conditions a treatment of RASi plus an additional treatment is different from the treatment with RASi alone. The measure of difference is the annual percent change (ΔeGFR) in the estimated glomerular filtration rate (ΔeGFR). The 1eGFR is estimated for both the RASi-alone treatment and the add-on treatment.DiscussionHigher estimated increase of eGFR for add-on patients compared with RASi-alone patients indicates that prognosis may be improved with the add-on treatment. The personalized biomarker value thus identifies which patients may benefit from the additional treatment

Massive non-natural proteins structure prediction using grid technologies

Background The number of natural proteins represents a small fraction of all the possible protein sequences and there is an enormous number of pr oteins never sampled by nature, the so called "never born proteins" (NBPs). A fundamental question in this regard is if the ensemble of natural proteins possesses peculiar chemical and physical properties or if it is just the product of contingency coupled to functional selection. A key feature of natural proteins is thei r ability to form a well defined three-dimensional structure. T hus, the structural study of NBPs can help to understand if natural protein sequences were selecte d for their peculiar properties or if they are just one of the possible stable and functional ensembles. Methods The structural characterization of a huge number of random proteins cannot be approached experimentally, thus the problem has been tackled using a computational approach. A large random protein sequences library (2 × 10 ^4 sequences) was generated, discarding amino acid sequences with significant simi larity to natural proteins, and the corresponding structures were predicted using Rosetta. Given th e highly computational demanding problem, Rosetta was ported in grid and a user friendly job submission environment was developed within the GENIUS Grid Portal. Protein structures generated were analysed in terms of net charge, secondary structure content, surface/volume ratio, hydrophobic core composition, etc. Results The vast majority of NBPs, according to the Rosetta mode l, are characterized by a compact three-dimensional structure with a high secondary structure content. Structure compactness and surface polarity are comparable to those of natural proteins, suggesting similar stability and solubility. Deviations are observed in α helix- β strands relative content and inydrophobic core composition, as NBPs appear to be richer in helical structure and aromatic amino acids with respect to natural proteins. Conclusion The results obtained suggest that the abil ity to form a compact, ordered and water-soluble structure is an intrinsic property of polypeptides. The tendency of random sequences to adopt α helical folds indicate that all-α proteins may have emerged ea rly in pre-biotic evolution. Further, the lower percentage of aromatic residu es observed in natural proteins has important evolutionary implications as far as tolerance to mutati ons is concerned

Artificial Life and Evolutionary Computation.

Revised Selected Papers of the 12th Italian Workshop, WIVACE 2017, Venice, Italy, September 19-21, 2017

Candidate composite biomarker to inform drug treatments for diabetic kidney disease

IntroductionCurrent guidelines recommend renin angiotensin system inhibitors (RASi) as key components of treatment of diabetic kidney disease (DKD). Additional options include sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide 1 receptor agonists (GLP1a), and mineralocorticoid receptor antagonists (MCRa). The identification of the optimum drug combination for an individual is difficult because of the inter-, and longitudinal intra-individual heterogeneity of response to therapy.ResultsUsing data from a large observational study (PROVALID), we identified a set of parameters that can be combined into a meaningful composite biomarker that appears to be able to identify which of the various treatment options is clinically beneficial for an individual. It uses machine-earning techniques to estimate under what conditions a treatment of RASi plus an additional treatment is different from the treatment with RASi alone. The measure of difference is the annual percent change (ΔeGFR) in the estimated glomerular filtration rate (ΔeGFR). The 1eGFR is estimated for both the RASi-alone treatment and the add-on treatment.DiscussionHigher estimated increase of eGFR for add-on patients compared with RASi-alone patients indicates that prognosis may be improved with the add-on treatment. The personalized biomarker value thus identifies which patients may benefit from the additional treatment

A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices

In this paper we address the problem of designing new energy-efficient static daylight devices that will surround the external windows of a residential building in Madrid. Shading devices can in fact largely influence solar gains in a building and improve thermal and lighting comforts by selectively intercepting the solar radiation and by reducing the undesirable glare. A proper shading device can therefore significantly increase the thermal performance of a building by reducing its energy demand in different climate conditions. In order to identify the set of optimal shading devices that allow a low energy consumption of the dwelling while maintaining high levels of thermal and lighting comfort for the inhabitants we derive a multi-objective optimization methodology based on Harmony Search and Pareto front approaches. The results show that the multi-objective approach here proposed is an effective procedure in designing energy efficient shading devices when a large set of conflicting objectives characterizes the performance of the proposed solutions. (C) 2016 Elsevier Ltd. All rights reserved

Do Natural Proteins Differ from Random Sequences Polypeptides? Natural vs. Random Proteins Classification Using an Evolutionary Neural Network

Are extant proteins the exquisite result of natural selection or are they random sequences slightly edited by evolution? This question has puzzled biochemists for long time and several groups have addressed this issue comparing natural protein sequences to completely random ones coming to contradicting conclusions. Previous works in literature focused on the analysis of primary structure in an attempt to identify possible signature of evolutionary editing. Conversely, in this work we compare a set of 762 natural proteins with an average length of 70 amino acids and an equal number of completely random ones of comparable length on the basis of their structural features. We use an ad hoc Evolutionary Neural Network Algorithm (ENNA) in order to assess whether and to what extent natural proteins are edited from random polypeptides employing 11 different structure-related variables (i.e. net charge, volume, surface area, coil, alpha helix, beta sheet, percentage of coil, percentage of alpha helix, percentage of beta sheet, percentage of secondary structure and surface hydrophobicity). The ENNA algorithm is capable to correctly distinguish natural proteins from random ones with an accuracy of 94.36%. Furthermore, we study the structural features of 32 random polypeptides misclassified as natural ones to unveil any structural similarity to natural proteins. Results show that random proteins misclassified by the ENNA algorithm exhibit a significant fold similarity to portions or subdomains of extant proteins at atomic resolution. Altogether, our results suggest that natural proteins are significantly edited from random polypeptides and evolutionary editing can be readily detected analyzing structural features. Furthermore, we also show that the ENNA, employing simple structural descriptors, can predict whether a protein chain is natural or random

Detection of volatile metabolites of moulds isolated from a contaminated library

The principal fungal species isolated from a contaminated library environment were tested for their microbial volatile organic compound (MVOC) production ability. Aspergillus creber, A. penicillioides, Cladosporium cladosporioides, Eurotium chevalieri, E. halophilicum, Penicillium brevicompactum and P. chrysogenum were cultivated on suitable culture media inside sample bottles specifically designed and created for direct MVOC injection to a GC-MS instrument. The fungal emissions were monitored over several weeks to detect changes with the aging of the colonies, monitored also by respirometric tests. A total of 55 different MVOCs were detected and isopropyl alcohol, 3-methyl-1-butanol and 2-butanone were the principal compounds in common between the selected fungal species. Moreover, 2,4-dimethylheptane, 1,4-pentadiene, styrene, ethanol, 2-methyl-1-butanol, acetone, furan and 2-methylfuran were the most detected compounds. For the first time, the MVOC production for particular fungal species was detected. The species A. creber, which belongs to the recently revised group Aspergillus section Versicolores, was characterized by the production of ethanol, furan and 1,4-pentadiene. For the xerophilic fungus E. halophilicum, specific production of acetone, 2-butanone and 1,4-pentadiene was detected, supported also by respirometric data. The results demonstrated the potential use of this method for the detection of fungal contamination phenomena inside Cultural Heritage's preservation environments. (C) 2016 Elsevier B.V. All rights reserved