Forecasting Visitors’ behaviour in Crowded Museums

In this paper, we tackle the issue of measuring and understanding the visitors’ dynamics in a crowded museum in order to create and calibrate a predictive mathematical model. The model is then used as a tool to manage, control and optimize the fruition of the museum. Our contribution comes with one successful use case, the Galleria Borghese in Rome, Italy

Human subjective response to aluminum coating surfaces

The research described in this study establishes whether measured physical material parameters could be used as a predictor of the human subjective response to the tactile and visual stimuli characteristics of aluminum coating surfaces. Twenty surfaces were used consisting of four uncoated aluminum substrates and four different type of coatings applied on each of the four uncoated substrates. Forty volunteers (20 female and 20 males) were asked to rate the surfaces using semantic differential scales. The results suggest that coatings obtained by matte polyurethane which contains a fine dispersion of silica microparticles have the capability to veil the effect of the manufacturing process of the aluminum substrates on both the felt slipperiness and felt roughness. The dynamic coefficient of friction was found to be a good predictor of the felt slipperiness with a negative power law exponent of 0.86 (R-2=0.85), confirming that greater friction is associated with less felt slipperiness. The physical gloss was also found to be highly negatively correlated (R-2=0.87) with the felt slipperiness of the tactile stimuli, suggesting that glossier surfaces could be mostly perceived as sticky. These results provide useful suggestions relating to the sensory perception and experience of materials, helpful for the industrial and product design in numerous application fields such as the automotive and electronics industries

Human Centred Design of engineered surfaces and coatings

This study presents the measurement and evaluation of the expressive-sensorial properties of newly manufactured surfaces and coatings that find many applications in the automotive field. The study was conducted with the purpose of understanding how material properties communicate with users through research into product-user interaction with the materials under development. Users perceive product quality through many aspects, including tactile properties, such as roughness and slipperiness. Sensory properties of interaction are expected to be a key product success factor [1]. User trials were performed in order to identify the perceptual qualities that characterise the sensorial properties of the concepts under examination (roughness, slipperiness and brilliancy) and the objective material qualities that can be physically measured (e.g. physical roughness, coefficient of fiction and gloss)

Fat and not sugar as the determining factor for gut microbiota changes, obesity and related metabolic disorders in mice

Diet-induced obesity contributes to the development of type 2 diabetes, insulin resistance, metabolic inflammation, oxidative and endoplasmic reticulum (ER) stress. Overall, obesity is associated with deviations in the composition and functionality of the gut microbiota. There are many divergent findings regarding the link between the excessive intake of certain dietary components (i.e., fat and sugar) and obesity development. We therefore investigated the effect of specific diets, with a different content in sugar and fat, in promoting obesity and related comorbidities as well as their impact on microbial load and gut microbiota composition/diversity.C57BL/6J mice were fed either a low-sugar, low-fat control diet (CT), a high-sugar diet (HS), a high-fat, high-sugar diet (HF/HS), or a high-fat diet (HF) for 8 weeks. The impact of the different diets on obesity, glucose metabolism, inflammation, and oxidative and ER stress were determined. Diet-induced changes in the gut microbiota composition and density were also analyzed.HF diet-fed mice showed the highest body weight and fat mass gains and displayed the most impaired glucose and insulin profiles. HS, HF/HS, and HF diets differently affected hepatic cholesterol content and mRNA expression of several markers associated with immune cells, inflammation, oxidative and ER stress in several organs/tissues. Additionally, HF diet feeding resulted in a decreased microbial load at the end of the experiment. When analyzing the gut microbiota composition, we found that HS, HF/HS, and HF diets induced specific changes in the abundance of certain bacterial taxa. Taken together, our results highlight that dietary intake of different macronutrients distinctively impacts the development of an obese/diabetic state and the regulation of metabolic inflammation in specific organs. We propose that these differences are not only obesity-driven but that changes in the gut microbiota composition may play a key role in this context

Novel insights into the genetically obese (ob/ob) and diabetic (db/db) mice: two sides of the same coin

Background: Leptin-deficient ob/ob mice and leptin receptor-deficient db/db mice are commonly used mice models mimicking the conditions of obesity and type 2 diabetes development. However, although ob/ob and db/db mice are similarly gaining weight and developing massive obesity, db/db mice are more diabetic than ob/ob mice. It remains still unclear why targeting the same pathway-leptin signaling-leads to the development of two different phenotypes. Given that gut microbes dialogue with the host via different metabolites (e.g., short-chain fatty acids) but also contribute to the regulation of bile acids metabolism, we investigated whether inflammatory markers, bacterial components, bile acids, short-chain fatty acids, and gut microbes could contribute to explain the specific phenotype discriminating the onset of an obese and/or a diabetic state in ob/ob and db/db mice. Results: Six-week-old ob/ob and db/db mice were followed for 7 weeks; they had comparable body weight, fat mass, and lean mass gain, confirming their severely obese status. However, as expected, the glucose metabolism and the glucose-induced insulin secretion were significantly different between ob/ob and db/db mice. Strikingly, the fat distribution was different, with db/db mice having more subcutaneous and ob/ob mice having more epididymal fat. In addition, liver steatosis was more pronounced in the ob/ob mice than in db/db mice. We also found very distinct inflammatory profiles between ob/ob and db/db mice, with a more pronounced inflammatory tone in the liver for ob/ob mice as compared to a higher inflammatory tone in the (subcutaneous) adipose tissue for db/db mice. When analyzing the gut microbiota composition, we found that the quantity of 19 microbial taxa was in some way affected by the genotype. Furthermore, we also show that serum LPS concentration, hepatic bile acid content, and cecal short-chain fatty acid profiles were differently affected by the two genotypes. Conclusion: Taken together, our results elucidate potential mechanisms implicated in the development of an obese or a diabetic state in two genetic models characterized by an altered leptin signaling. We propose that these differences could be linked to specific inflammatory tones, serum LPS concentration, bile acid metabolism, short-chain fatty acid profile, and gut microbiota composition

Serum Biomarker Profile Associated With High Bone Turnover and BMD in Postmenopausal Women

Early diagnosis of the onset of osteoporosis is key to the delivery of effective therapy. Biochemical markers of bone turnover provide a means of evaluating skeletal dynamics that complements static measurements of BMD by DXA. Conventional clinical measurements of bone turnover, primarily the estimation of collagen and its breakdown products in the blood or urine, lack both sensitivity and specificity as a reliable diagnostic tool. As a result, improved tests are needed to augment the use of BMD measurements as the principle diagnostic modality. In this study, the serum proteome of 58 postmenopausal women with high or low/normal bone turnover (training set) was analyzed by surface enhanced laser-desorption/ionization time-of-flight mass spectrometry, and a diagnostic fingerprint was identified using a variety of statistical and machine learning tools. The diagnostic fingerprint was validated in a separate distinct test set, consisting of serum samples from an additional 59 postmenopausal women obtained from the same Mayo cohort, with a gap of 2 yr. Specific protein peaks that discriminate between postmenopausal patients with high or low/normal bone turnover were identified and validated. Multiple supervised learning approaches were able to classify the level of bone turnover in the training set with 80% sensitivity and 100% specificity. In addition, the individual protein peaks were also significantly correlated with BMD measurements in these patients. Four of the major discriminatory peaks in the diagnostic profile were identified as fragments of interalpha-trypsin-inhibitor heavy chain H4 precursor (ITIH4), a plasma kallikrein-sensitive glycoprotein that is a component of the host response system. These data suggest that these serum protein fragments are the serum-borne reflection of the increased osteoclast activity, leading to the increased bone turnover that is associated with decreasing BMD and presumably an increased risk of fracture. In conjunction with the identification of the individual proteins, this protein fingerprint may provide a novel approach to evaluate high bone turnover states

La voce di chi sparisce, la narrativa di Tiziana Plebani

La scrittura di Tiziana Pleban