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This archaeometric study aims at characterizing the archaeological finds belonging to the lithic industry from La Calvera rock shelter (Camaleño, Cantabria) and at hypothesizing the possible provenance of each material. The site, located in the mountainous area of Picos de Europa National Park (more than 1000 m a.s.l.) close to the megalithic complex of Peña Oviedo, is characterized by the presence of hearths and charcoal remains, ancient pottery, and a rich lithic assemblage composed of siliceous rocks dating back to >8000 BP and linked to the first Holocene occupations of the Cantabrian Mountains. For the study of the rock shelter's lithic assemblage, a multi-analytical approach was used: SEM-EDS and XRD analyses were performed to define the microtextural characteristics of samples and to identify the amorphous/crystalline phases; physical and mechanical tests were conducted to define the petrophysical properties (density, porosity, imbibition and saturation indexes, mechanical strength) of the samples. Preliminary data of chemical compositions, obtained by portable XRF, are also presented. The results show that the different siliceous materials (quartzite, cherts, hyaline quartz) can be distinguished through the basic analytical techniques used here. In addition, most of the archaeological samples have mineralogical and petrographic features similar to the natural samples coming from nearby outcrops, corroborating the hypothesis of a local material supply. The presence of local sources of useful raw materials could have favoured the site's occupation. Finally, the diverse compositional and textural feature of the analysed materials result in different mechanical properties (porosity, density, hardness, workability), so they likely had different uses and technical functions. [ABSTRACT FROM AUTHOR]

Irisin is a myokine synthesized by skeletal muscle, which performs key actions on whole-body metabolism. Previous studies have hypothesized a relationship between irisin and vitamin D, but the pathway has not been thoroughly investigated. The purpose of the study was to evaluate whether vitamin D supplementation affected irisin serum levels in a cohort of 19 postmenopausal women with primary hyperparathyroidism (PHPT) treated with cholecalciferol for six months. In parallel, to understand the possible link between vitamin D and irisin, we analyzed the expression of the irisin precursor, Fndc5, in the C2C12 myoblast cell line treated with a biologically active form of vitamin D, 1α,25-dihydroxyvitamin D3 (1,25(OH)2D3). Our results demonstrate that vitamin D supplementation resulted in a significant increase in irisin serum levels (p = 0.031) in PHPT patients. In vitro, we show that vitamin D treatment on myoblasts enhanced Fndc5 mRNA after 48 h (p = 0.013), while it increased mRNAs of sirtuin 1 (Sirt1) (p = 0.041) and peroxisome proliferator-activated receptor γ coactivator 1α (Pgc1α) (p = 0.017) over a shorter time course. Overall, our data suggest that vitamin-D-induced modulation of Fndc5/irisin occurs through up-regulation of Sirt1, which together with Pgc1α, is an important regulator of numerous metabolic processes in skeletal muscle. [ABSTRACT FROM AUTHOR]

This paper deals with the "modern" plaster mortars based on air lime, hydraulic lime, and cement used between the 1950s and 1990s of the last century, taking, as a case study, a historical building of the Cagliari city whose foundations and ground floor are cut into in-situ limestone. Different plaster layers (i.e., arriccio and intonachino, paint), applied on the excavated limestone walls, were collected from cave-room. All samples were analysed by optical and electron (SEM-EDS) microscopy and X-ray diffractometry (XRD) in order to define their microstructures, textures and compositional features. In addition, real and bulk density, water and helium open porosity, water absorption kinetic, and saturation index were measured. By microscopic imaging analyses, the binder/aggregate ratio as vol.% was determined. Results revealed that cement mortars, composed mainly of C-S-H, C-A-H, and C-F-H phases, given their high hydraulicity, low open porosity, and a rigid behaviour, showed a good chemical but not physical–mechanical adherence, as they were often found detached from the substrate and frequently loaded with salt efflorescence. On the contrary, the hydraulic lime-based mortars, characterised by a binder composed of C-S-H and C-A-H phases and calcite derived from the portlandite carbonation, showed a greater affinity with limestone substrate and other plasters. Thus, they are more suitable to be used as a repair mortar, showing a long durability on the time. The thin air lime-based plasters (intonachino) showed a good adhesion to the substrate, exerting their coating function better than the harder, cement-based mortars. Lime-based wall paints have a good chemical adhesion and adaptability to the irregular surface of the substrate, due to low thickness of lime paint layers (<1 mm) that confers an elastic physical behaviour. [ABSTRACT FROM AUTHOR]

This study deals with the mortars and subordinately rocks collected from the archaeological site of Cap de Forma, that is a "Bien de Interés Cultural" located on a cape along the southeastern coast of Menorca (Balearic Islands, Spain). Cap de Forma consists of different structures belonging to different periods and civilization phases: a fortified settlement of Talaiotic age, built in cyclopean technique and including three rooms and a rainwater cistern; a nearby necropolis of tombs (cuevas) excavated into the cliff; a more recent site occupancy testified by plastering of the cistern; a house-fort (pecheña casa-quartel), a lookout point of the 17th–18th century. Compositional features and mineralogy of mortars and rocks were investigated by optical microscopy and X-ray diffraction. Physical properties (density, porosity, water saturation and water saturation coefficients and mechanical strength) were also determined. This work is aimed at characterizing these archaeologic remains to understand the building technique, the choice of raw materials and possibly their provenance, taking into account the age and civilization they belong to. Results indicate that cocciopesto-rich mortars were used in the cistern watertight and other ancient structures linking from a Roman age. The cocciopesto seems to derive from local pottery even if some evidence would suggest the contrary, whereas the source of the binder is definitely the local Mg-rich limestone. The house-fort was plastered with gypsum-based mortars in the 17th–18th century. The most likely source of raw gypsum was the island of Mallorca where some quarries were opened in the same period. Mechanical and physical tests reveal a strong state of decay that requires conservation actions. This work sheds light on a poorly studied monument, better constraining the different phases of its occupation. Some interesting questions, such as the cocciopesto provenance, are still open. [ABSTRACT FROM AUTHOR]

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg. [ABSTRACT FROM AUTHOR]

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients. [ABSTRACT FROM AUTHOR]

The Antas site (SW Sardinia, Italy) is of fundamental cultural importance because it testifies the presence of Nuragic, Punic and Roman civilizations from the second millennium to the third century BC. This work focuses on the Punic and the Roman temples and aims to define their conservation state and provenance of construction materials through their minero-petrographic and physical-mechanical characterization. In addition, artificial geomaterials used in restoration works comprising a partial anastylosis and a consolidation intervention on the monument, were investigated to evaluate the aesthetic, petrographic and petrophysical compatibility with the original materials. The results indicate that Punic builders preferred to use a porous sandstone coming from at least few kilometres away from the site. By contrast, Roman builders opted for the use of the less porous and harder local metadolostones, more difficult to quarry and to hew but promptly available in the surrounding area. The Roman temple still preserves decorative architectural elements (as the Pronao threshold and the mosaic tesserae) whose source is definitely not local, suggesting the import of these materials. As regards artificial materials, a new material was found within the Punic temple consisting of a sandstone-like rock (i.e., lime based sandy-conglomeratic geomaterial) and characterized by higher mechanical strength and lower porosity. [ABSTRACT FROM AUTHOR]

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings. [ABSTRACT FROM AUTHOR]

This paper reports the findings of an archaeometric study performed on 14 architectural earthenwares from the archaeological site of S. Omobono, located in the historic center of Rome (Italy). The archaeological site, accidentally discovered in 1937, includes the remains of a sacred area previously occupied by two temples, one of which was converted into the church of S. Omobono, in 1575. The samples, dated between the 7th and the 6th century BC, belong to different sectors of the site. Their petrographic, mineralogical and geochemical characterization was performed by optical microscopy (OM), X-ray powder diffraction (XRPD), X-ray fluorescence (XRF), electron probe micro-analysis (EPMA), and Raman spectroscopy (RS). The compositional data obtained were also subjected to the principal component analysis (PCA) in order to highlight similarities and differences among the samples. By combining geochemical and petrographic data, we were able to identify several different fabrics. Furthermore, the study provided valuable information on the firing temperatures of some samples and the provenance of the raw materials, by analyzing the chemical composition of clinopyroxenes present as non-plastic inclusions. [ABSTRACT FROM AUTHOR]