Passive target detection and tracking from electromagnetic field measurements

This paper presents a novel approach to the localization of moving targets in a complex environment based on the measurement of the perturbations induced by the target presence on an independently‐generated time‐varying electromagnetic field. Field perturbations are measured via a set of sensors deployed over the domain of interest and used to detect and track a possible target by resorting to a particle Bernoulli filter (PBF). To comply with real‐time operation, the PBF works along with an artificial neural network (ANN) model of the environment trained offline via finite elements (FEs). The performance of the proposed algorithm is assessed via simulation experiments.     

Use of an Online Extraction Technique Coupled to Liquid Chromatography for Determination of Caffeine in Coffee,Tea, and Cocoa

Caffeine is the most widely studied psychoactive molecule in history due to its many pharmacological activities and a high number of biological and physiological effects. In literature, there is a great number of applications that describe extraction, identification, and quantification of caffeine in foods and beverages. For this purpose, an extraction step is followed by an analytical technique for the identification and quantification of caffeine. This work proposes an innovative method in which sample preparation, separation, and detection steps are unified in a single step. To the best of our knowledge, this is the first report on the determination of caffeine in coffee, tea, and cocoa by means of an online extraction coupled to a liquid chromatographic system equipped with a photodiode array detector. The developed methodology was validated in terms of sensitivity, detection limits, accuracy, and precision. The advantages of this technique are (i) a significant reduction of analysis time (more than 70%) and of solvents used (the extraction step is integrated in the chromatographic analysis), (ii) the whole procedure is thus completely automated drastically reducing possible operator errors to occur, and (iii) easily realized by using a conventional monodimensional liquid chromatography system.     

Milk from cows fed a diet with a high forage:concentrate ratio improves inflammatory state,oxidative stress,and mitochondrial function in rats

Excessive energy intake may evoke complex biochemical processes characterized by inflammation, oxidative stress, and impairment of mitochondrial function that represent the main factors underlying noncommunicable diseases. Because cow milk is widely used for human nutrition and in food industry processing, the nutritional quality of milk is of special interest with respect to human health. In our study, we analyzed milk produced by dairy cows fed a diet characterized by a high forage:concentrate ratio (high forage milk, HFM). In view of the low n-6:n-3 ratio and high content of conjugated linoleic acid of HFM, we studied the effects of this milk on lipid metabolism, inflammation, mitochondrial function, and oxidative stress in a rat model. To this end, we supplemented for 4 wk the diet of male Wistar rats with HFM and with an isocaloric amount (82 kJ, 22 mL/d) of milk obtained from cows fed a diet with low forage:concentrate ratio, and analyzed the metabolic parameters of the animals. Our results indicate that HFM may positively affect lipid metabolism, leptin:adiponectin ratio, inflammation, mitochondrial function, and oxidative stress, providing the first evidence of the beneficial effects of HFM on rat metabolism.     

Unpatterned Bioactive Poly(Butylene 1,4-Cyclohexanedicarboxylate)-Based Film Fast Induced Neuronal-Like Differentiation of Human Bone Marrow-Mesenchymal Stem Cells

Herein, we present poly(butylene 1,4-cyclohexanedicarboxylate) (PBCE) films characterized by an unpatterned microstructure and a specific hydrophobicity, capable of boosting a drastic cytoskeleton architecture remodeling, culminating with the neuronal-like differentiation of human bone marrow-mesenchymal stem cells (hBM-MSCs). We have used two different filming procedures to prepare the films, solvent casting (PBCE) and compression-moulding (PBCE*). PBCE film had a rough and porous surface with spherulite-like aggregations (Ø = 10–20 μm) and was characterized by a water contact angle = 100°. PBCE* showed a smooth and continuous surface without voids and visible spherulite-like aggregations and was more hydrophobic (WCA = 110°). Both surface characteristics were modulated through the copolymerization of different amounts of ether-oxygen-containing co-units into PBCE chemical structure. We showed that only the surface characteristics of PBCE-solvent-casted films steered hBM-MSCs toward a neuronal-like differentiation. hBM-MSCs lost their canonical mesenchymal morphology, acquired a neuronal polarized shape with a long cell protrusion (≥150 μm), expressed neuron-specific class III β-tubulin and microtubule-associated protein 2 neuronal markers, while nestin, a marker of uncommitted stem cells, was drastically silenced. These events were observed as early as 2-days after cell seeding. Of note, the phenomenon was totally absent on PBCE* film, as hBM-MSCs maintained the mesenchymal shape and behavior and did not express neuronal/glial markers.     

BET-Inhibitor I-BET762 and PARP-Inhibitor Talazoparib Synergy in Small Cell Lung Cancer Cells

Small cell lung cancer (SCLC) is an aggressive type of lung cancer with high mortality that is caused by frequent relapses and acquired resistance. Despite that several target-based approaches with potential therapeutic impact on SCLC have been identified, numerous targeted drugs have not been successful in providing improvements in cancer patients when used as single agents. A combination of targeted therapies could be a strategy to induce maximum lethal effects on cancer cells. As a starting point in the development of new drug combination strategies for the treatment of SCLC, we performed a mid-throughput screening assay by treating a panel of SCLC cell lines with BETi or AKi in combination with PARPi or EZH2i. We observed drug synergy between I-BET762 and Talazoparib, BETi and PARPi, respectively, in SCLC cells. Combinatorial efficacy was observed in MYCs-amplified and MYCs-wt SCLC cells over SCLC cells with impaired MYC signaling pathway or non-tumor cells. We indicate that drug synergy between I-BET762 and Talazoparib is associated with the attenuation HR-DSBR process and the downregulation of various players of DNA damage response by BET inhibition, such as CHEK2, PTEN, NBN, and FANCC. Our results provide a rationale for the development of new combinatorial strategies for the treatment of SCLC.     

Graphene nanoplatelets composite membranes for thermal comfort enhancement in performance textiles

The advent of 2D nanostructured materials as advanced fillers for polymer matrix composites has opened the doors to a plethora of new industrial applications requiring both electric and thermal management. Unique properties, in fact, can arise from accurate selection and processing of 2D fillers and their matrix. Here, we report an innovative family of nanocomposite membranes based on polyurethane (PU) and graphene nanoplatelets (GNPs), designed to improve thermal comfort in functional textiles. GNP particles were thoroughly characterized (through Raman, atomic force microscopy, high-resolution TEM, scanning electron microscope), and showed high crystallinity (I_D/I_G = 0.127), low thickness (D50 < 6–8 layers), and high lateral dimensions (D50 ≈ 3 μm). When GNPs were loaded (up to 10% wt/wt) into the PU matrix, their homogeneous dispersion resulted in an increase of the in-plane thermal conductivity of composite membranes up to 471%. The thermal dissipation of membranes, alone or coupled with cotton fabric, was further evaluated by means of an ad hoc system designed to simulate a human forearm. The results obtained provide a new strategy for the preparation of membranes suitable for technical textiles, with improved thermal comfort.     

Hybrid and optical packet switching supporting different service classes in data center network

Photonic Network Communications - Optical packet switching is a prominent technology proposing not only a reduction in the energy consumption by the elimination of numerous...     

Indoor levels of volatile organic compounds at Florentine museum environments in Italy

Indoor Air Quality monitoring in cultural institutions is of particular concern to protect these places and the cultural heritage content. An indoor monitoring campaign was performed in three museums in Florence (Italy) to determine the occurrence and levels of volatile organic compounds (VOCs). VOCs of interest included BTEX (benzene, toluene, ethylbenzene, xylenes), terpenes, aldehydes, organic acids, and cyclic volatile methyl siloxanes (cVMS). The most abundant VOCs in all samples analyzed were BTEX, which were strictly related to the traffic source, followed by siloxanes and terpenes. Among BTEX, toluene was always the most abundant followed by xylenes, ethylbenzene, and benzene. cVMS in exhibition rooms with the presence of visitors showed higher values compared to samples collected when the museums were closed. Terpenes showed not only the influence of vegetation-biogenic sources surrounding a museum but could also be related to the wood used for the construction of showcases and furniture and the use of cleaning products. Data obtained also showed the presence of organic acids and aldehydes whose source can be traced back to exhibits themselves and wood-based furniture. Assessing the levels of organic acids in museums is important because, over time, it can cause deterioration of the artifacts.     

A multi-tone central divided difference frequency tracker with adaptive process noise covariance tuning

The problem of real-time frequency estimation of nonstationary multi-harmonic signals is important in many applications. In this paper, we propose a novel multi-frequency tracker based on a state-space representation of the signal with Cartesian filters and the second-order central divided difference filter (CDDF), which improves the performance of the extended Kalman filter (EKF) by using Stirling's interpolation method to approximate the mean and covariance of the state vector. A crucial element of the method is the adaptive scaling of the process noise covariance matrix appearing in the filter equations, as a function of the innovation sequence, which tunes the accuracy-reactivity trade-off of the filter. The proposed solution is evaluated against two approaches from the literature, namely the factorized adaptive notch filter (FANF) and the extended Kalman filter frequency tracker (EKFFT). Several experiments emphasize the estimation accuracy of the proposed method as well as the improved robustness with respect to initial errors and input signal complexity. The presented method appears to be particularly efficient with rapidly varying frequencies, thanks to the update mechanism that adjusts the filter parameters based on the amplitude of the estimation error.