Best unbiased graphical estimators of location-scale distribution parameters: application to the Pozzuoli's bradyseism earthquake data

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CXCL16/CXCR6 axis drives microglia/macrophages phenotype in physiological conditions and plays a crucial role in glioma

Microglia are patrolling cells that sense changes in the brain microenvironment and respond acquiring distinct phenotypes that can be either beneficial or detrimental for brain homeostasis. Anti-inflammatory microglia release soluble factors that might promote brain repair; however, in glioma, anti-inflammatory microglia dampen immune response and promote a brain microenvironment that foster tumor growth and invasion. The chemokine CXCL16 is expressed in the brain, where it is neuroprotective against brain ischemia, and it has been found to be over-expressed in glioblastoma (GBM). Considering that CXCL16 specific receptor CXCR6 is diffusely expressed in the brain including in microglia cells, we wanted to investigate the role of CXCL16 in the modulation of microglia cell activity and phenotype, and in the progression of glioma. Here we report that CXCL16 drives microglia polarization toward an anti-inflammatory phenotype, also restraining microglia polarization toward an inflammatory phenotype upon LPS and IFN? stimulation. In the context of glioma, we demonstrate that CXCL16 released by tumor cells is determinant in promoting glioma associated microglia/macrophages (GAMs) modulation toward an anti-inflammatory/pro-tumor phenotype, and that cxcr6ko mice, orthotopically implanted into the brain with GL261 glioma cells,survive longer compared to wild-type mice. We also describe that CXCL16/CXCR6 signaling acts directly on mouse glioma cells, as well as human primary GBM cells, promoting tumor cell growth, migration and invasion. All together these data suggest that CXCL16 signaling could represent a good target to modulate microglia phenotype in order to restrain inflammation or to limit glioma progression

Smooth Lasso Estimator for the Function-on-Function Linear Regression Model

A new estimator, named as S-LASSO, is proposed for the coefficient function of a functional linear regression model where values of the response function, at a given domain point, depends on the full trajectory of the covariate function. The S-LASSO estimator is shown to be able to increase the interpretability of the model, by better locating regions where the coefficient function is zero, and to smoothly estimate non-zero values of the coefficient function. The sparsity of the estimator is ensured by a functional LASSO penalty whereas the smoothness is provided by two roughness penalties. The resulting estimator is proved to be estimation and pointwise sign consistent. Via an extensive Monte Carlo simulation study, the estimation and predictive performance of the S-LASSO estimator are shown to be better than (or at worst comparable with) competing estimators already presented in the literature before. Practical advantages of the S-LASSO estimator are illustrated through the analysis of the well known \textit{Canadian weather} and \textit{Swedish mortality dat

Functional clustering methods for resistance spot welding process data in the automotive industry

Quality assessment of resistance spot welding (RSW) joints of metal sheets in the automotive industry is typically based on costly and lengthy off-line tests that are unfeasible on the full production, especially on large scale. However, the massive industrial digitalization triggered by the industry 4.0 framework makes available, for every produced joint, on-line RSW process parameters, such as, in particular, the so-called dynamic resistance curve (DRC), which is recognized as the full technological signature of the spot welds. Motivated by this context, the present paper means to show the potentiality and the practical applicability to clustering methods of the functional data approach that avoids the need for arbitrary and often controversial feature extraction to find out homogeneous groups of DRCs, which likely pertain to spot welds sharing common mechanical and metallurgical properties. We intend is to provide an essential hands-on overview of the most promising functional clustering methods, and to apply the latter to the DRCs collected from the RSW process at hand, even if they could go far beyond the specific application hereby investigated. The methods analyzed are demonstrated to possibly support practitioners along the identification of the mapping relationship between process parameters and the final quality of RSW joints as well as, more specifically, along the priority assignment for off-line testing of welded spots and the welding tool wear analysis. The analysis code, that has been developed through the software environment R, and the DRC data set are made openly available online at https://github.com/unina-sfere/funclustRSW

Neuromuscular junction as an entity of nerve-muscle communication

One of the crucial systems severely affected in several neuromuscular diseases is the loss of effective connection between muscle and nerve, leading to a pathological non-communication between the two tissues. The neuromuscular junction (NMJ) represents the critical region at the level of which muscle and nerve communicate. Defects in signal transmission between terminal nerve endings and muscle membrane is a common feature of several physio-pathologic conditions including aging and Amyotrophic Lateral Sclerosis (ALS). Nevertheless, controversy exists on whether pathological events beginning at the NMJ precede or follow loss of motor units. In this review, the role of NMJ in the physio-pathologic interplay between muscle and nerve is discussed

Functional Neural Network Control Chart

In many Industry 4.0 data analytics applications, quality characteristic data acquired from manufacturing processes are better modeled as functions, often referred to as profiles. In practice, there are situations where a scalar quality characteristic, referred to also as the response, is influenced by one or more variables in the form of functional data, referred to as functional covariates. To adjust the monitoring of the scalar response by the effect of this additional information, a new profile monitoring strategy is proposed on the residuals obtained from the functional neural network, which is able to learn a possibly nonlinear relationship between the scalar response and the functional covariates. An extensive Monte Carlo simulation study is performed to assess the performance of the proposed method with respect to other control charts that appeared in the literature before. Finally, a case study in the railway industry is presented with the aim of monitoring the heating, ventilation and air conditioning systems installed onboard passenger trains

FEM simulation of a crack propagation in a round bar under combined tension and torsion fatigue loading

An edge crack propagation in a steel bar of circular cross-section undergoing multiaxial fatigue loads is simulated by Finite Element Method (FEM). The variation of crack growth behaviour is studied under axial and combined in phase axial+torsional fatigue loading. Results show that the cyclic Mode III loading superimposed on the cyclic Mode I leads to a fatigue life reduction. Numerical calculations are performed using the FEM software ZENCRACK to determine the crack path and fatigue life. The FEM numerical predictions have been compared against corresponding experimental and numerical data, available from literature, getting satisfactory consistencyN/

Crack propagation calculations in aircraft engines by coupled FEM-DBEM approach

New generation jet engines are subject to severe reduced fuel consumption requirements. This usually leads to thin components in which damage issues such as thermomechanical fatigue, creep and crack propagation can be quite important. The combination of stresses due to centrifugal loads and thermal stresses usually leads to mixed-mode loading. Consequently, a suitable crack propagation tool must be able to predict mixed-mode crack propagation of arbitrarily curved cracks in three-dimensional space. To tackle this problem a procedure has been developed based on a combined FEM (Finite Element Method) - DBEM (Dual Boundary Element Method) approach. Starting from a three-dimensional FEM mesh for the uncracked structure a subdomain is identified, in which crack initiation and propagation are simulated by DBEM. Such subdomain is extracted from the FEM domain and imported, together with its boundary conditions (calculated by a previous thermal-stress FEM analysis), in a DBEM environment, where a linear elastic crack growth calculation is performed. Once the crack propagation direction is determined a new crack increment can be calculated and for the new crack front the procedure can be repeated until failure. The proposed procedure allows to also consider the spectrum effects and the creep effects: both conditions relieve residual stresses that the crack encounters during its propagation. The procedure has been tested on a gas turbine vane, getting sound results, and can be made fully automatic, thanks to in house made routines needed to facilitate the data exchange between the two adopted codes

Stress Analysis of an Endosseus Dental Implant by BEM and FEM

In this work the Boundary Element Method (BEM) and the Finite Element Method (FEM) have been used for an elastic-static analysis of both a Branemark dental implant and a generic conic threaded implant, modelled either in the complete mandible or in a mandibular segment, under axial and lateral loading conditions. Two different hypotheses are considered with reference to degree of osteo-integration between the implant and the mandibular bone: perfect and partial osteointegration. The BEM analysis takes advantage of the submodelling technique, applied on the region surrounding the implant. Such region is extracted from the overall mandible and the boundary conditions for such submodel are obtained from the stress analysis realised on the complete mandible. The obtained results provide the localisation of the most stressed areas at the bone-implant interface and at the mandibular canal (containing the alveolar nerve) which represent the most critical areas during mastication. This methodology, enriched with the tools necessary for the numerical mandible reconstruction, is useful to realise sensitivity analysis of the stress field against a variation of the localisation, inclination and typology of the considered implant, in order to assess the optimal implant conditions for each patient under treatment. Due to the high flexibility in the pre- and post-processing phase and accuracy in reproducing superficial stress gradients, BEM is more efficient than FEM in facing this kind of problem, especially when a linear elastic constitutive material law is adopted