The complementary role of imaging and tumor biomarkers in gynecological cancers: an update of the literature

Gynecological tumors, including endometrial, cervical and ovarian cancer, have increased in incidence over time. The widespread introduction of screening programs and advances in diagnostic imaging methods has lead to a progressive increase in gynecological cancer detection. Accurate diagnosis and proper monitoring of disease remain the primary target for a successful treatment. In the last years, knowledge about cancer biomarkers has considerably increased providing great opportunities for improving cancer detection and treatment. In addition, in the last few years there has been an important development of imaging techniques. Nowadays, a multimodal approach including the evaluation of serum tumor biomarkers combined with imaging techniques, seems to be the best strategy for assessing tumor presence, spread, recurrence, and/or the response to treatment in female cancer patients In this review we provide an overview of the application of biomarkers combined with novel imaging methods and highlight their roles in female cancer diagnosis and follow-up

Landslide forecasting and factors influencing predictability

Forecasting a catastrophic collapse is a key element in landslide risk reduction, but it is also a very difficult task owing to the scientific difficulties in predicting a complex natural event and also to the severe social repercussions caused by a false or missed alarm. A prediction is always affected by a certain error; however, when this error can imply evacuations or other severe consequences a high reliability in the forecast is, at least, desirable. <br><br> In order to increase the confidence of predictions, a new methodology is presented here. In contrast to traditional approaches, this methodology iteratively applies several forecasting methods based on displacement data and, thanks to an innovative data representation, gives a valuation of the reliability of the prediction. This approach has been employed to back-analyse 15 landslide collapses. By introducing a predictability index, this study also contributes to the understanding of how geology and other factors influence the possibility of forecasting a slope failure. The results showed how kinematics, and all the factors influencing it, such as geomechanics, rainfall and other external agents, are key concerning landslide predictability

Differential structure associated to axiomatic Sobolev spaces

The aim of this note is to explain in which sense an axiomatic Sobolev space over a general metric measure space (\ue0 la Gol'dshtein\u2013Troyanov) induces \u2013 under suitable locality assumptions \u2013 a first-order differential structure

Differential of metric valued Sobolev maps

We introduce a notion of differential of a Sobolev map between metric spaces. The differential is given in the framework of tangent and cotangent modules of metric measure spaces, developed by the first author. We prove that our notion is consistent with Kirchheim's metric differential when the source is a Euclidean space, and with the abstract differential provided by the first author when the target is R. We also show compatibility with the concept of co-local weak differential introduced by Convent and Van Schaftingen

Co-digestion of macroalgae for biogas production: an LCA-based environmental evaluation

Algae represent a favourable and potentially sustainable source of biomass for bioenergy-based industrial pathways in the future. The study, performed on a real pilot plant implemented in Augusta (Italy) within the frame of the BioWALK4Biofuels project, aims to figure out whether seaweed (macroalgae) cultivated in near-shore open ponds could be considered a beneficial aspect as a source of biomass for biogas production within the co-digestion with local agricultural biological waste. The LCA results confirm that the analysed A and B scenarios (namely the algae-based co-digestion scenario and agricultural mix feedstock scenario) present an environmental performance more favourable than that achieved with conventional non-renewable-based technologies (specifically natural gas - Scenario C). Results show that the use of seaweed (Scenario A) represent a feasible solution in order to replace classical biomass used for biofuel production from a land-based feedstock. The improvement of the environmental performances is quantifiable on 10% respect to Scenario B, and 38 times higher than Scenario

Quasi-Continuous Vector Fields on RCD Spaces

In the existing language for tensor calculus on RCD spaces, tensor fields are only defined m-a.e. In this paper we introduce the concept of tensor field defined \u20182-capacity-a.e.\u2019 and discuss in which sense Sobolev vector fields have a 2-capacity-a.e. uniquely defined quasi-continuous representative

Joint detection and classification of rockfalls in a microseismic monitoring network

A rockfall (RF) is a ubiquitous geohazard that is difficult to monitor or predict and poses a significant risk for people and transportation in several hilly and mountainous environments. The seismic signal generated by RF carries abundant physical and mechanical information. Thus, signals can be used by researchers to reconstruct the event location, onset time, volume and trajectory, and develop an efficient early warning system. Therefore, the precise automatic detection and classification of RF events are important objectives for scientists, especially in seismic monitoring arrays. An algorithm called DESTRO (DEtection and STorage of ROckfalls) aimed at combining seismic event automatic detection and classification was implemented ad hoc within the MATLAB environment. In event detection, the STA/LTA (short-time-average through long-time-average) method combined with other parameters, such as the minimum duration of an RF and the minimum interval time between two continuous seismic events is used. Furthermore, nine significant features based on the frequency, amplitude, seismic waveform, duration and multiple station attributes are newly proposed to classify seismic events in a RF environment. In particular, a three-step classification method is proposed for the discrimination of five different source types: RFs, earthquakes (EQs), tremors, multispike events (MSs) and subordinate MS events. Each component (vertical, east–west and north–south) at each station within the monitoring network is analysed, and a three-step classification is performed. At a given time, the event series detected from each component are integrated and reclassified component by component and station by station into a final event-type series as an output result. By this algorithm, a case study of the seven-month-long seismic monitoring of a former quarry in Central Italy was investigated by means of four triaxial velocimeters with continuous acquisition at a sampling rate of 200 Hz. During this monitoring period, a human-induced RF simulation was performed, releasing 95 blocks (in which 90 blocks validated) of different sizes from the benches of the quarry. Consequently, 64.9 per cent of EQs within 100 km were confirmed in a one-month monitoring period, 88 blocks in the RF simulation were classified correctly as RF events and 2 blocks were classified as MSs given their small energy. Finally, an ad hoc section of the algorithm was designed specifically for RF classification combined with EQ recognition. The algorithm could be applied in slope seismic monitoring to monitor the dynamic states of rock masses, as well as in slope instability forecasting and risk evaluation in EQ-prone areas

A framework for temporal and spatial rockfall early warning using micro-seismic monitoring

Rockfall risk is usually characterized by a high frequency of occurrence, difficulty in prediction (given high velocity, lack of noticeable forerunners, abrupt collapse, and complex mechanism), and a relatively high potential vulnerability, especially against people and communication routes. Considering that larger rockfalls and rockslides are generally anticipated by an increased occurrence of events, in this study, a framework based on microseismic monitoring is introduced for a temporal and spatial rockfall early warning. This approach is realized through the detection, classification, and localization of all the rockfalls recorded during a 6-month-long microseismic monitoring performed in a limestone quarry in central Italy. Then, in order to provide a temporal warning, an observable quantity of accumulated energy, associated to the rockfall rolling and bouncing and function of the number and volume of events in a certain time window, has been defined. This concept is based on the material failure method developed by Fukuzono-Voight. As soon as the first predicted time of failure and relative warning time are declared, all the rockfalls occurred in a previous time window can be located in a topographic map to find the rockfall susceptible area and thus to complement the warning with spatial information. This methodology has been successfully validated in an ex post analysis performed in the aforementioned quarry, where a large rockfall was forecasted with a lead time of 3 min. This framework provides a novel way for rockfall spatiotemporal early warning, and it could be helpful for activating traffic lights and closing mountain roads or other transportation lines using the knowledge of the time and location of a failure. Since this approach is not based on the detection of the triggering events (like for early warnings based on rainfall thresholds), it can be used also for earthquake-induced failures

GB-InSAR monitoring and observational method for landslide emergency management: the Montaguto earthflow (AV, Italy)

Abstract. On 10 March 2010, because of the heavy rainfall in the preceding days, the Montaguto landslide (Southern Italy) reactivated, affecting both state road 90 Delle Puglie and the Rome–Bari railway. A similar event occurred on May 2005 and on September 2009. As a result, the National Civil Protection Department (DPC) started an accurate monitoring and analysis program. A monitoring project using the GB-InSAR (ground-based interferometric synthetic aperture radar) system was emplaced to investigate the landslide kinematics, plan urgent safety measures for risk mitigation and design long-term stabilization work.Here, we present the GB-InSAR monitoring system results and its applications in the observational method (OM) approach. GB-InSAR is an established instrument for long-term campaigns aimed at early warning and monitoring during construction works. Our paper further develops these aspects in that it highlights how the OM based on the GB-InSAR technique can produce savings in terms of cost and time in engineering projects without compromising safety. This study focuses on the key role played by the monitoring activities during the design and planning activities, with special reference to the emergency phase