Petrographic & microstratigraphic analysis of mortar-based building materials from the Temple of Venus, Pompeii.

Recipes for mortar-based building materials may change over time and differ in various construction and restoration phases. They normally reflect craftsmen’s knowledge, availability of raw materials, and also the importance of the building in which they are found. The present research focuses on mortar-based materials from several construction and renovation phases of the Temple of Venus, at Pompeii, Italy, in order to identify any changes over time in production recipes

Copper deficiency-associated myelopathy in cryptogenic hyperzincemia: A case report

Copper deficiency syndrome is an underestimated cause of posterior myelitis. We describe the case of a 41-year-old woman, who developed a subacute ataxic paraparesis associated with low back pain. Her 3T spine MRI showed a thin hyperintense FS-Echo T2 longitudinally extensive lesion involving the posterior columns of the cervical cord (from C2 to C6). An extensive diagnostic work-up excluded other causes of myelopathy and blood tests pointed out hypocupremia and mild hyperzincemia. Patients affected by this rare form of oligoelement deficiency typically develop progressive posterior column dysfunction with sensory ataxia and spasticity, sometimes associated with sensori-motor polyneuropathy. Clinical and radiological characteristics of posterior myelopathy due to copper deficiency are briefly reviewed. Physicians should be aware of this condition since a prompt introduction of copper supplementation can avoid progression of the neurological damage. (www.actabiomedica.it)

Ileocecal reservoir reconstruction after total mesorectal excision: functional results of the long-term follow-up

Background: The aim of this study is to obtain functional results of the long-term follow-up after TME and ileocecal interposition as rectal replacement. Methods: The study included patients operated on between March 1993 and August 1997 who received an ileocecal interposition as rectal replacement. Follow-up was carried out 3 and 5 years postoperatively. For statistical analysis, the paired t-test, rank test (Wilcoxon), and chi-square or Fisher's exact test were applied; level of significance, P<0.05. Results: Forty-four patients were included in the studies. Of these, five were not available and four patients could not be evaluated (dementia 1, radiation proctitis 1, fistula 1, pouchitis 1). Seventeen patients died during the observation period; 12 died of the disease. Recurrence of the disorder occurred in 2 of 35 patients (5.7%); 26 and 18 patients, 3 and 5 years postoperatively, respectively remained in the study. At 5 years, 78% of the patients were continent; mean stool frequency was 2.5±1.6 per day. Conclusions: Functional results and subjective assessment of ileocecal interposition were constant at 3 and 5 years postoperatively. If construction of a colonic J-pouch is not possible due to lack of colonic length, especially after prior colonic resections, the ileocecal interpositional reservoir may offer an alternative to rectal replacemen

Hardware Implementation of the Spot Payload for Orbiting Objects Detection Using Star Sensors

Space debris issue has become an attractive challenge for many applications in the framework of Space Situational Awareness (SSA) and Space Surveillance and Tracking (SST). The Star sensor image on-board Processing for orbiting Objects deTection (SPOT) fits in this field as an innovative space based autonomous and versatile system for Resident Space Objects’ optical detection via star sensors and for different Earth orbits scenarios. This system is planned to be a payload for an In-Orbit Validation (IOV) activity in the next future. The purpose of this paper is to show the architecture of the SPOT system together with its implementation on a System on Chip (SoC)/Field Programmable Gate Array (FPGA) space representative board. The SPOT algorithms involve several layers of filters which are relatively expensive in terms of computational latency, limiting their applicability to real-time image processing applications. This work presents the design and implementation of SPOT algorithm on the Zynq-7000 SoC using Xilinx FPGA and ARM CPU. Algorithms have been modelled with Simulink and implemented on FPGA using Xilinx system generator with aiming to optimize both processing time and area usage. A Hardware-In-the-Loop (HIL) setup was developed as well, to verify the performances and robustness of the SPOT algorithms and simulating critical scenario by using real night sky images from acquisition campaig

A fully automated pipeline for a robust conjunctival hyperemia estimation

Purpose: Many semi-automated and fully-automated approaches have been proposed in literature to improve the objectivity of the estimation of conjunctival hyperemia, based on image processing analysis of eyes’ photographs. The purpose is to improve its evaluation using faster fully-automated systems and independent by the human subjectivity. Methods: In this work, we introduce a fully-automated analysis of the redness grading scales able to completely automatize the clinical procedure, starting from the acquired image to the redness estimation. In particular, we introduce a neural network model for the conjunctival segmentation followed by an image processing pipeline for the vessels network segmentation. From these steps, we extract some features already known in literature and whose correlation with the conjunctival redness has already been proved. Lastly, we implemented a predictive model for the conjunctival hyperemia using these features. Results: In this work, we used a dataset of images acquired during clinical practice.We trained a neural network model for the conjunctival segmentation, obtaining an average accuracy of 0.94 and a corresponding IoU score of 0.88 on a test set of images. The set of features extracted on these ROIs is able to correctly predict the Efron scale values with a Spearman’s correlation coefficient of 0.701 on a set of not previously used samples. Conclusions: The robustness of our pipeline confirms its possible usage in a clinical practice as a viable decision support system for the ophthalmologists

Prediction of vascular aging based on smartphone acquired PPG signals

Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) \u2013 the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking \u2013 was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results

Prediction of vascular aging based on smartphone acquired PPG signals

Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) – the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking – was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results