Hedonic and environmental quality: A hybrid model of product differentiation

In this paper, we analyze how strategic competition between a green firm and a browncompetitor develops when their products are differentiated along two dimensions: hedonicquality and environmental quality. The former dimension refers to the pure (intrinsic) per-formance of the good, whereas the latter dimension has a positional content: buying greengoods satisfies the consumer’s desire to be portrayed as a socially worthy citizen. We con-sider the case in which these quality dimensions are in conflict with each other so that thehigher the hedonic quality of a good, the lower the corresponding environmental quality.We characterize the equilibrium configurations and discuss the policy implications derivingfrom our analysis

Ramipril for claudication?

This ACE inhibitor can help patients with peripheral artery disease walk longer while remaining pain free

Sum-Product Network structure learning by efficient product nodes discovery

Sum-Product Networks (SPNs) are recently introduced deep probabilistic models providing exact and tractable inference. SPNs have been successfully employed in several application domains, from computer vision to natural language processing, as accurate density estimators. However, learning their structure and parameters from high dimensional data poses a challenge in terms of time complexity. Classical SPNs structure learning algorithms work by repeating several times two high cost operations: determining independencies among random variables (RVs)-introducing product nodes-and finding sub-populations among samples-introducing sum nodes. Even one of the simplest greedy structure learner, LearnSPN, scales quadratically in the number of the variables to determine RVs independencies. In this work, we investigate the trade-off between accuracy and efficiency when employing approximate but fast procedures to determine independencies among RVs. We introduce and evaluate sub-quadratic procedures based on a random subspace approach and leveraging entropy as a proxy criterion to split independent RVs. Experimental results on many benchmark datasets for density estimation show that LearnSPN-like structure learners, when equipped by our splitting procedures, provide reduced learning and/or inference times, generally containing the degradation of inference accuracy. Ultimately, we provide an empirical confirmation of a "no free lunch" when learning the structure of SPNs

Anthropogenic modifications to the drainage network of Rome (Italy). The case study of the Aqua Mariana

Rome is characterized by millennia of urbanization. Long lasting geomorphological investigations have allowed the geomorphological description of the city centre and the valorisation of its geomorphological heritage. In this paper the spatial change of the hydrographic network in historical times is illustrated, with some examples showing how deep has been, and still it is, the link between the historical-cultural development and the natural geomorphological and hydrological characteristics of the Roman territory. In particular, the most relevant human interventions on the drainage network, in the southern area of the city centre, have been investigated. Before the land-use modifications of Roman-age, this area was drained by the most important left tributary of the Tiber River within the city walls, the Nodicus River, more recently known as Aqua Mariana. This stream has undergone many anthropogenic modifications and diversions during the centuries, and its original path is known only downstream of the San Giovanni Basilica. According to geomorphological, archaeological and geological evidences, it is possible to hypothesize that the dimension of the pre-urbanization drainage basin, as known and reconstructed in the available literature, should have been until now underestimated

Random sum-product networks: A simple and effective approach to probabilistic deep learning

Sum-product networks (SPNs) are expressive probabilistic models with a rich set of exact and efficient inference routines. However, in order to guarantee exact inference, they require specific structural constraints, which complicate learning SPNs from data. Thereby, most SPN structure learners proposed so far are tedious to tune, do not scale easily, and are not easily integrated with deep learning frameworks. In this paper, we follow a simple “deep learning” approach, by generating unspecialized random structures, scalable to millions of parameters, and subsequently applying GPU-based optimization. Somewhat surprisingly, our models often perform on par with state-of-the-art SPN structure learners and deep neural networks on a diverse range of generative and discriminative scenarios. At the same time, our models yield well-calibrated uncertainties, and stand out among most deep generative and discriminative models in being robust to missing features and being able to detect anomalies

An Outlook on Uterine Neoplasms: From Hormonal and DNA Damaging to Cervical and Endometrial Cancer Development and Minimally Invasive Management.

Uterine neoplasms are common tumors, formed by endometrial and cervical cancers; endometrial cancer is the fourth most frequently diagnosed cancer in developed countries and the eighth leading cause of cancer death in women, and cervical cancer is the second most common cancer in women worldwide and is a leading cause of cancer-related death in women in underdeveloped countries. Cervical cancer arises by HPV DNA damaging; in fact cervical cancer starts in the cells on the surface of the cervix, exposed to viral infective agents, as HPV, founded in 80% of patients affected by cervical cancer. Thus, more than 99% of cervical uterine cancer cases show HPV presence. Nevertheless, Endometrial cancer involves cancerous growth of the endometrium, and increasing evidence indicates that different biological and genetic factors play relevant roles its onset so as carcinogenesis generally develops by hormonal modifications. Both tumors can be safely and feasibly managed from minimally invasive surgical techniques till to endoscopic radical operations, such as hysterectomy, bilateral salpingo- oophorectomy, pelvic and para-aortic lymphadenectomy for surgical treatment. The authors reviewed several excellent reviews and studies in the area of hormonal, viral and genetical risk factors associated with endometrial and cervical cancer risk and development, analyzing the area of biologic markers, all papers dealing with serum and plasma markers involved in uterine cancer detection, development, progression and minimally invasive treatment

The hierarchical response of human corneal collagen to load

Fibrillar collagen in the human cornea is integral to its function as a transparent lens of precise curvature, and its arrangement is now well-characterised in the literature. While there has been considerable effort to incorporate fibrillar architecture into mechanical models of the cornea, the mechanical response of corneal collagen to small applied loads is not well understood. In this study the fibrillar and molecular response to tensile load was quantified using small and wide angle X-ray scattering (SAXS/WAXS), and digital image correlation (DIC) photography was used to calculate the local strain field that gave rise to the hierarchical changes. A molecular scattering model was used to calculate the tropocollagen tilt relative to the fibril axis and changes associated with applied strain. Changes were measured in the D-period, molecular tilt and the orientation and spacing of the fibrillar and molecular networks. These measurements were summarised into hierarchical deformation mechanisms, which were found to contribute at varying strains. The change in molecular tilt is indicative of a sub-fibrillar “spring-like” deformation mechanism, which was found to account for most of the applied strain under physiological and near-physiological loads. This deformation mechanism may play an important functional role in tissues rich in fibrils of high helical tilt, such as skin and cartilage

Sufentanil sublingual tablet system. From rationale of use to clinical practice

The control of post-operative pain in Italy and other western countries is still suboptimal. In recent years, the Sufentanil Sublingual Tablet System (SSTS; Zalviso; AcelRx Pharmaceuticals, Redwood City, CA, USA), which is designed for patient-controlled analgesia (PCA), has entered clinical practice. SSTS enables patients to manage moderate-to-severe acute pain during the first 72 postoperative hours directly in the hospital setting. However, the role of SSTS within the current framework of options for the management of post-operative pain needs to be better established. This paper presents the position on the use of SSTS of a multidisciplinary group of Italian Experts and provides protocols for the use of this device

Feasibility of sub-dermal soft tissue deformation assessment using B-mode ultrasound for pressure ulcer prevention

Pressure Ulcer (PU) prevention remains a main public health issue. The physio-pathology of this injury is not fully understood, and a satisfactory therapy is currently not available. Recently, several works suggested that mechanical strains are responsible of deformation-induced damage involved in the initiation of Deep Tissue Injury (DTI). A better assessment of the internal behavior could allow to enhance the modeling of the transmission of loads into the different structures composing the buttock. A few studies focused on the experimental in vivo buttock deformation quantification using Magnetic Resonance Imaging (MRI), but its use has important drawbacks. In clinical practice, ultrasound imaging is an accessible, low cost, and real-time technic to study the soft tissue. The objective of the present work was to show the feasibility of using B-mode ultrasound imaging for the quantification of localised soft-tissue strains of buttock tissues during sitting. An original protocol was designed, and the intra-operator reliability of the method was assessed. Digital Image Correlation was used to compute the displacement field of the soft tissue of the buttock during a full realistic loading while sitting. Reference data of the strains in the frontal and sagittal planes under the ischium were reported for a population of 7 healthy subjects. The average of shear strains over the region of interest in the fat layer reached levels up to 117% higher than the damage thresholds previously quantified for the muscular tissue in rats. In addition, the observation of the muscles displacements seems to confirm previous results which already reported the absence of muscular tissue under the ischium in the seated position, questioning the assumption commonly made in Finite Element modeling that deep tissue injury initiates in the muscle underlying the bone.This work was supported by the Fondation de l'Avenir (grant number AP-RM-2016-030), by la Fondation des Arts et Métiers and the Fond de dotation Clinatec. The authors are also grateful to the ParisTech BiomecAM chair program on subject-specific musculoskeletal modeling