Multiplexing pH and Temperature in a Molecular Biosensor

Robust and reliable measurements in electrochemical biosensing of molecules are crucial for personalized medicine. Electrochemical sensors based on cytochrome P450 can detect the large majority of drugs commonly used in pharmacological treatments. The same cytochrome can detect different substrates; each of them changes the electrochemical response of the enzyme in a specific manner. Our system exploits the measure of electrical potential to identify the drug type, while current measurements decode the drug concentration. Since potential and current are affected by pH and temperature, and since variations occur in the patient samples, we propose a novel design for multiplexing biosensing with pH and temperature control, which ensures more precise measurements for drugs identification and their quantification

A Novel Multi-Working Electrode Potentiostat for Electrochemical Detection of Metabolites

A novel single-chip and multiplexed read-out circuit for multi-electrode electrochemical sensors, in standard 0.18 μm UMC CMOS technology, is presented. The circuit is a part of a fully-integrated biochip (in design) for the detection of multiple metabolites. The proposed topology is based on the potentiostat approach, and it is devoted to detect currents within the range of 250 pA - 650 nA for an electrode active area of 0.25 mm2. The need of multi-metabolites monitoring asks for a system with multi-working electrodes. In the proposed configuration, switches select one working electrode at each clock phase, while the others are short-circuited to the reference one, in order to nullify the injected current inside the counter. Low noise and low energy topology (50μW at 1.5V of voltage supply) is employed for the control amplifier. The linearity of the proposed read-out circuit allows accuracy better than 0.1%

Apigliano. Un villaggio bizantino e medievale in Terra d’Otranto. I Reperti

Vari articoli che trattano le ricerche sul sito del villaggio medievale abbandonato di Apigliano, Martano (LE

Progetto di ricerca archeologica a San Lorenzo "in Carminiano" (Foggia). L'avvio dell'indagine e i primi risultati

*Pasquale Favia:Il sito di S.Lorenzo: i caratteri insediativi(p.533-544);Le indagini non invasive(544-547);Il saggio I(548-550);Conclusioni(554-556

A Digital Architecture for the Real-Time Tracking of Wearing off Phenomenon in Parkinson’s Disease

Levodopa administration is currently the most common treatment to alleviate Parkinson’s Disease (PD) symptoms. Nevertheless, prolonged use of Levodopa leads to a wearing-off (WO) phenomenon, causing symptoms to reappear. To build a personalized treatment plan aiming to manage PD and its symptoms effectively, there is a need for a technological system able to continuously and objectively assess the WO phenomenon during daily life. In this context, this paper proposes a WO tracker able to exploit neuromuscular data acquired by a dedicated wireless sensor network to discriminate between a Levodopa benefit phase and the reappearance of symptoms. The proposed architecture has been implemented on a heterogeneous computing platform, that statistically analyzes neural and muscular features to identify the best set of features to train the classifier model. Eight models among shallow and deep learning approaches are analyzed in terms of performance, timing and complexity metrics to identify the best inference engine. Experimental results on five subjects experiencing WO, showed that, in the best case, the proposed WO tracker can achieve an accuracy of ~84%, providing the inference in less than 41 ms. It is possible by employing a simple fully-connected neural network with 1 hidden layer and 32 units

Spatio-Temporal Optimization of Perishable Goods’ Shelf Life by a Pro-Active WSN-Based Architecture

The waste in the perishable goods supply-chain has prompted many global organizations (e.g., FAO and WHO), to develop the Hazard Analysis and Critical Control Points (HACCP) protocol that ensures a high degree of food quality, minimizing the losses in all the stages of the farm-to-fork chain. It has been proven that good warehouse management practices improve the average life of perishable goods. The advances in wireless sensors network (WSN) technology offers the possibility of a “smart” storage organization. In this paper, a low cost reprogrammable WSN-based architecture for functional warehouse management is proposed. The management is based on the continuous monitoring of environmental parameters (i.e., temperature, light exposure and relative humidity), and on their combination to extract a spatial real-time prediction of the product shelf life. For each product, the quality decay is computed by using a 1st order kinetic Arrhenius model to the whole storage site area. It strives to identify, in a way compatible with the other products’ shelf lives, the position within the warehouse that maximizes the food expiration date. The shelf life computing and the “first-expired first-out” logistic problem are entrusted to a Raspberry Pi-based central unit, which manages a set of automated pallet transporters for the displacement of products, according to the computed shelf lives. The management unit supports several commercial light/temperature/humidity sensor solutions, implementing ZigBee, Bluetooth and HTTP-request interfaces. A proof of concept of the presented pro-active WSN-based architecture is also shown. Comparing the proposed monitoring system for the storage of e.g., agricultural products, with a typical one, the experimental results show an improvement of the expected expiration date of about 1.2 ± 0.5 days, for each pallet, when placed in a non-refrigerated environment. In order to stress the versatility of the WSN solution, a section is dedicated to the implemented system user interfaces that highlight detecting critical situations and allow timely automatic or human interventions, minimizing the latter

FPGA Implementation of a Single Step MFCV Estimator Based on EMG in Diabetic Neuropathy

This paper details the design and the hardware implementation of a real-time diagnostic system based on FPGA for the muscle fibre conduction velocity estimation (MFCV). The MFCV is considered as a principal monitoring index for diabetic neuropathy (DPN), as well as in muscle fatigue assessment, to evaluate the muscle fibre status. The FPGA platform evaluates the MFCV during dynamic contractions (e.g., gait), by exploiting a multichannel sensing system composed of 4 wireless surface EMG electrodes, placed in pair on each leg. Raw data are digitized and made binary to create two bitstreams for each monitored limb. Then, a comparison between the two-bit streamed EMGs extracted from the same leg is carried out. The comparison, which allows extracting the MFCV, exploits a computationally light version of the cross-correlation method. The overall architecture implemented and validated on an Altera Cyclone V FPGA is HPS-free and exploits 22.5% ALMs, 10,874 ALUTs, 9.81% registers, 3.36% block memory, and <2.7% of the total wires available on the platform. The choice of FPGA as computing system lies in the possibility to determine resource utilization, related timing constraints for a future real-time ASIC implementation in wearable applications. From the actual muscle contraction during gait (cyclical starting point of the computing), the system spends about 316 ms to acquire useful data and 47.5 ms (on average) to process the signal and provide the output, dynamically dissipating 28.6 mW. The accuracy of the tool evaluation has been evaluated proving the repeatability of the measurements by in vivo test. In this context, 1250 contractions from each subject involved in a protocolled 10-meter walk have been acquired (n=10 subjects evaluated). On average, the same MFCV estimation has been extracted on 1184/1250 contractions (standard deviation of 11 contractions), reaching an accuracy of 94.7%. These estimations fully match the physiological value range reported in literature