Deep-seated gravitational slope deformations in central Sardinia: insights into the geomorphological evolution

In this study, we analyse deep-seated gravitational slope deformations (DSGSDs) in central Sardinia. The area is characterised by plateaus with a prominent limestone scarp overlying metamorphites. A comprehensive mapping of structural, karst, fluvial, and slope morphologies in Pardu and Ulassai valleys is presented herein. The uplift linked to the Plio-Pleistocene tectonic activity leads to high-slope topography, which favours gravitational processes, such as DSGSDs and rock-avalanches. Although DSGSD is a common phenomenon in the relief of the central Mediterranean region, it has never been studied in Sardinia. We describe the kinematic models and geomorphological evolution of DSGSD in Sardinia for the first time. The application of light detection and ranging, high-resolution unmanned aerial vehicle photogrammetry, and geological, structural, and geomorphological surveys enabled a depth morphometric analysis and the development of interpretative three-dimensional models. The geo-structural setting and high relief energy associated with recent upliftment are the major controlling factors of DSGSDs

Epigenetic mechanisms in oral cancer: new diagnostic and therapeutic strategies

Complexity and heterogeneity are frequently present during the development and progression of carcinogenesis and, in the last 15 years, significant progress made in clinical research underlines the role of some epigenetic mechanisms. The most important characteristics of the epigenetic concept are that these events are reversible, not related to modifications in the structure of DNA and may drive fundamental cell signaling alterations1. Among these systems of communication in normal and pathological conditions, also microbiome and staminal cells2 seem to be important. These new profiles of pathological communication develop novel diagnostic, prognostic and therapeutic tool

Partial purification and MALDI-TOF MS analysis of UN1, a tumor antigen membrane glycoprotein.

UN1 is a membrane glycoprotein that is expressed in immature human thymocytes, a subpopulation of peripheral T lymphocytes, the HPB acute lymphoblastic leukemia (ALL) T-cell line and fetal thymus. We previously reported the isolation of a monoclonal antibody (UN1 mAb) recognizing the UN1 protein that was classified as "unclustered" at the 5th and 6th International Workshop and Conference on Human Leukocyte Differentiation Antigens. UN1 was highly expressed in breast cancer tissues and was undetected in non-proliferative lesions and in normal breast tissues, indicating a role for UN1 in the development of a tumorigenic phenotype of breast cancer cells. In this study, we report a partial purification of the UN1 protein from HPB-ALL T cells by anion-exchange chromatography followed by immunoprecipitation with the UN1 mAb and MALDI-TOF MS analysis. This analysis should assist in identifying the amino acid sequence of UN

World diabetes day 2017 focus: antepartum hyperglycaemia (or gestational prediabetes) is more in women below 40 years

Prediabetes is a state of hyperglycaemia that is above normal, but below blood glucose threshold for diabetes mellitus. As part of the theme for World Diabetes Day of 2017, it has been articulated by the International Diabetes Federation, that up to 16% of births may be affected by this condition and it is estimated that 50% of the affected antenatal patients are under 30 years old. The notion of gestational prediabetes, hyperglycaemia in pregnancy as a clinical condition is yet to be seriously discussed in regards to prediabetes in pregnancy. Instead, it seems to be subsumed in the discourse of gestational diabetes mellitus. This subsuming position is evident in the fact that even in discussion of management challenges of the ‘hyperglycaemia in pregnancy’ condition, blood glucose thresholds for gestational diabetes refers to higher versus lower cut-offs, but never refers to prediabetes

The Development of a Short Version of the SIMS Using Machine Learning to Detect Feigning in Forensic Assessment

In the present study, we applied machine learning techniques to evaluate whether the Structured Inventory of Malingered Symptomatology (SIMS) can be reduced in length yet maintain accurate discrimination between consistent participants (i.e., presumed truth tellers) and symptom producers. We applied machine learning item selection techniques on data from Mazza et al. (2019c) to identify the minimum number of original SIMS items that could accurately distinguish between consistent participants, symptom accentuators, and symptom producers in real personal injury cases. Subjects were personal injury claimants who had undergone forensic assessment, which is known to incentivize malingering and symptom accentuation. Item selection yielded short versions of the scale with as few as 8 items (to differentiate between consistent participants and symptom producers) and as many as 10 items (to differentiate between consistent and inconsistent participants). The scales had higher classification accuracy than the original SIMS and did not show the bias that was originally reported between false positives and false negatives

Machine learning approach using MLP and SVM algorithms for the fault prediction of a centrifugal pump in the oil and gas industry

The demand for cost-effective, reliable and safe machinery operation requires accurate fault detection and classification to achieve an efficient maintenance strategy and increase performance. Furthermore, in strategic sectors such as the oil and gas industry, fault prediction plays a key role to extend component lifetime and reduce unplanned equipment thus preventing costly breakdowns and plant shutdowns. This paper presents the preliminary development of a simple and easy to implement machine learning (ML) model for early fault prediction of a centrifugal pump in the oil and gas industry. The data analysis is based on real-life historical data from process and equipment sensors mounted on the selected machinery. The raw sensor data, mainly from temperature, pressure and vibrations probes, are denoised, pre-processed and successively coded to train the model. To validate the learning capabilities of the ML model, two different algorithms-the Support Vector Machine (SVM) and the Multilayer Perceptron (MLP)-are implemented in KNIME platform. Based on these algorithms, potential faults are successfully recognized and classified ensuring good prediction accuracy. Indeed, results from this preliminary work show that the model allows us to properly detect the trends of system deviations from normal operation behavior and generate fault prediction alerts as a maintenance decision support system for operatives, aiming at avoiding possible incoming failures

Combustion synthesis and spark plasma sintering of apatite-tricalcium phosphate nanocomposites

A processing route consisting of Spark Plasma Sintering (SPS) of precursor powders prepared by Solution Combustion Synthesis (SCS) is proposed for the first time for the fabrication of bulk nanostructured biphasic calcium phosphates. The apatite phase content in the product obtained by SCS was maximized using a fuel to oxidizer ratio of 1.1. After a post-synthesis air-annealing step conducted a 700 °C/3 h, powders consisted of 83 wt.% of carbonated apatite, with average crystallite size less than 70 nm, and β- and α-TCP (tricalcium phosphate), as secondary phases. Detailed structural analyses evidenced that the original nanostructure was retained after sintering at 900 °C, with the obtainment of nearly 91% dense, apatite-rich, biphasic bioceramics, with grains size of about 100 nm. The developed nanostructured biphasic material is expected to possess a higher resorption rate than standard microcrystalline hydroxyapatite, which makes it preferable for bone tissue regeneration

Submarine Geomorphology of the Southwestern Sardinian Continental Shelf (Mediterranean Sea): Insights into the Last Glacial Maximum Sea-Level Changes and Related Environments

During the lowstand sea-level phase of the Last Glacial Maximum (LGM), a large part of the current Mediterranean continental shelf emerged. Erosional and depositional processes shaped the coastal strips, while inland areas were affected by aeolian and fluvial processes. Evidence of both the lowstand phase and the subsequent phases of eustatic sea level rise can be observed on the continental shelf of Sardinia (Italy), including submerged palaeo-shorelines and landforms, and indicators of relict coastal palaeo-environments. This paper shows the results of a high-resolution survey on the continental shelf off San Pietro Island (southwestern Sardinia). Multisensor and multiscale data\u2014obtained by means of seismic sparker, sub-bottom profiler chirp, multibeam, side scan sonar, diving, and uncrewed aerial vehicles\u2014made it possible to reconstruct the morphological features shaped during the LGM at depths between 125 and 135 m. In particular, tectonic controlled palaeo-cliffs affected by landslides, the mouth of a deep palaeo-valley fossilized by marine sediments and a palaeo-lagoon containing a peri-littoral thanatocenosis (18,983 \ub1 268 cal BP) were detected. The Younger Dryas palaeo-shorelines were reconstructed, highlighted by a very well preserved beachrock. The coastal paleo-landscape with lagoon-barrier systems and retro-littoral dunes frequented by the Mesolithic populations was reconstructed

The effect of prime-site occupancy on the hepatitis C virus NS3 protease structure.

We recently reported a new class of inhibitors of the chymotrypsin-like serine protease NS3 of the hepatitis C virus. These inhibitors exploit the binding potential of the S′ site of the protease, which is not generally used by the natural substrates. The effect of prime-site occupancy was analyzed by circular dichroism spectroscopy and limited proteolysis-mass spectrometry. Generally, nonprime inhibitors cause a structural change in NS3. Binding in the S′ site produces additional conformational changes with different binding modes, even in the case of the NS3/4A cofactor complex. Notably, inhibitor binding either in the S or S′ site also has profound effects on the stabilization of the protease. In addition, the stabilization propagates to regions not in direct contact with the inhibitor. In particular, the N-terminal region, which according to structural studies is endowed with low structural stability and is not stabilized by nonprime inhibitors, was now fully protected from proteolytic degradation. From the perspective of drug design, P-P′ inhibitors take advantage of binding pockets, which are not exploited by the natural HCV substrates; hence, they are an entry point for a novel class of NS3/4A inhibitors. Here we show that binding of each inhibitor is associated with a specific structural rearrangement. The development of a range of inhibitors belonging to different classes and an understanding of their interactions with the protease are required to address the issue of the most likely outcome of viral protease inhibitor therapy, that is, viral resistanc

Anomaly Detection for Diagnosing Failures in a Centrifugal Compressor Train

Predicting machine failures is of the utmost importance in industrial systems as it can turn expensive crashes and repair costs into affordable maintenance costs. To this end, this paper presents preliminary work for detecting failures in a centrifugal compressor train based on sensorial data. We show the detection capabilities of a two-step process consisting of: (1) a preprocessing step to reduce the dimensionality of the input data using Principal Component Analysis, and (2) an anomaly detection step using the Mahalanobis distance to detect anomalous observations on the sensors' data. The experiments using real-world data demonstrate the feasibility of our approach and the ability of the method to detect the failures eight days in advance