Salivary proteomic biomarkers of oral squamous cell carcinoma

Objectives. The aim of the present study is to investigate the presence of proteomic signatures of Oral Squamous Cell Carcinoma (OSCC) in saliva and their use as potential biomarkers for early and non-invasive diagnosis, as well as prognostication. Methods. Saliva from 45 OSCC patients and 30 healthy controls was analysed by SELDI-TOF mass spectrometry and ProteinChip\uae technology. Proteomic profiles were tested with differential expression analysis and fold change of protein peaks, principal component analysis, Spearman rank correlation test and hierarchical clustering in order to identify a list of peaks of interest representative of controls, N- and N+ cases. Those peaks were used in a supervised artificial neural network in order to classify samples according to the following conditions: controls vs OSCC, controls vs N-, and controls vs N+. Results. When compared with controls, four peaks (i.e. 6913, 11948, 13287 and 27280 m/z) were significantly altered in both N- group and N+ group; four peaks (i.e. 3353, 3433, 3482 and 4136 m/z) were selectively altered in Ngroup; eight peaks were selectively altered in N+ group (i.e. 4038, 7133, 11755, 13746, 13841, 14264, 16807, 17127 m/z). Those peaks were capable to classify 100% of cases and controls, thus being potential diagnostic and prognostic biomarkers for OSC

Smart Analogue Sampler for the Optical Module of a Cherenkov Neutrino Detector

A transient waveform sampler/recorder IC has been developed and realized in AMS C35B4 technology. This chip has been designed to fit the needs of a proposal for a front-end architecture for the readout of the anode signal of the photomultipliers in an underwater neutrino telescope. The design is based around a 3 channels x 32 cells switched capacitor array unit sampling its voltage inputs at 200MHz external clock rate and transferring the stored analogue voltage samples to its single analogue output at 1/10th of the sampling rate. This unit is replicated inside the ASIC providing 4 independent analogue sampling queues for signal transients up to 32 x 5 ns and a fifth unit storing transients up to 128 x 5 ns. A micro-pipelined unit, based on Muller C-gates, controls the 5 independent samplers. This paper briefly summarizes the complete front-end architecture and discusses in more detail the internal structure of the ASIC and its first functional tests

Reliability of spring recession curve analysis as a function of the temporal resolution of the monitoring dataset

Mountain springs represent one of the largest and most precious sources of potable water in Italy, necessary to meet the water needs of the population. Optimizing the present and future management strategies of mountain groundwater resources has become increasingly necessary. The accuracy and frequency of the flow rate (Q) measurements determine and restrict the processes that can be studied using spring hydrograph and recession curve analysis. Therefore, to properly define mountain aquifers’ hydrogeological properties, it turns out important to highlight the variation of the error in the estimation of the hydrogeological parameters as the time interval of sampling varies. In this paper, recession curve analysis was performed on two different mountain springs (Spring 1 and Spring 2) of north-western Italy, firstly considering available 4-h resolution measuring data and subsequently by resampling data to simulate longer sampling intervals of 1, 3, 7, 15, and 30 days. The resulting distribution of errors introduced by longer acquisition intervals underlined how the percentage error increases with increasing acquisition interval. For obtaining an adequate estimation of mountain aquifer hydrodynamic parameters, in place of continuous hourly data, 1-day and 3-day sampling intervals with associated errors respectively lower than 5% and 10% were found to be valid

Groundwater heat pump systems diffusion and groundwater resources protection

Geothermal Energy, being a clean and sustainable source of energy, is gaining importance worldwide due to various reasons. Geothermal power can be generated throughout the year on twenty four hour basis as it's not much dependent on ambient temperature and weather conditions. Recently there is an increased interest in exploitation of low enthalpy geothermal resources for other applications such as geothermal space heating and cooling for domestic, industrial and commercial applications.GroundWater Heat Pump systems (GWHPs) extract water from one or more wells, pass it through a heat exchanger or a heat pump, which either extracts heat from, or rejects heat, and discharge water back into the aquifer or nearby surface water.This reinjection disturbs the natural aquifer temperature, producing a local temperature anomalies (cold or heat plume) known as the thermal affected zone (TAZ).Moreover, it is important to know if the TAZ can interfere with downgradient pre-existing plants or subsurface infrastructure or with the plant itself (thermal feedback). It is then important to know, even before constructing a GWHP system, the future TAZ extent around the planned injection point.Due to these risks, the increasing number of GWHP systems enforces the need for new criteria to develop subsurface energy policies that allow planning their spatial distribution. To obtain these sustainability criteria, the results of different dedicated studies are here proposed, in order to optimize the design and operation of GWHP systems

Impact of district heating and groundwater heat pump systems on the primaryenergy needs in urban areas

This work is focused on the planning of rational heating systems for urban areas. From the sustainability viewpoint, district heating is an important option to supply heat to the users in urban areas. The energy convenience of such option depends on the annual energy request, the population density and the efficiency in heat production. Among the alternative technologies, geothermal heat pumps (both open loop and closed loop heat pumps) play a crucial role. This paper aims to propose a procedure to select which users in an urban area should be connected with a district heating network and which ones should be heated through an alternative technology, in order to reach a globally optimal system from the energy viewpoint. The procedure proposes district heating as the initial choice for all the users. The users are then progressively disconnected to the network, according with the primary energy required to supply them heat, and the alternative technology is considered for disconnected users. Here, ground water heat pump is considered as the alternative technology. The total primary energy request is assumed as the objective function to be minimized. To reach this result, the exergetic cost of heat supplied through heat pumps system must be evaluated. Such evaluation is not trivial, as it must include proper analysis of both the district heating network and the alternative system. In the case of densely populated areas, an additional consideration is necessary: the subsurface thermal degradation caused by heat pump installations may affect the performances of surrounding installations. This impact is calculated through a thermo-fluid dynamic model of the subsurface. The application to an Italian town is considered as a test case. The optimal configuration of the overall urban heating system is obtained. This configuration corresponds to the minimum primary energy request to supply heat to all the users (those connected to the network and those using an alternative heating system)

FIGARO: reinForcement learnInG mAnagement acRoss the computing cOntinuum

The widespread adoption of Artificial Intelligence applications to analyze data generated by Internet of Things sensors leads to the development of the edge computing paradigm. Deploying applications at the periphery of the network effectively addresses cost and latency concerns associated with cloud computing. However, it generates a highly distributed environment with heterogeneous devices, opening the challenges of how to select resources and place application components. Starting from a state-of-the-art design-time tool, we present in this paper a novel framework based on Reinforcement Learning, named FIGARO (reinForcement learnInG mAnagement acRoss the computing cOntinuum). It handles the runtime adaptation of a computing continuum environment, dealing with the variability of the incoming load and service times. To reduce the training time, we exploit the design-time knowledge, achieving a significant reduction in the violations of the response time constraint