model of reversible breakdown in hfo2 based on fractal patterns

We propose a model of the kinetics of reversible breakdown in metal-insulator-metal structures with afnia based on the growth of fractal patterns of defects when the insulator is subject to an external voltage. The probability that a defect is (or is not) generated and the position where it is generated depend on the electric field distribution. The new defect moves accordingly to fractal rules and attach to another defect in a tree branch. When the two electrodes sandwiching the insulating film are connected a conductive filament is formed and the breakdown takes place. The model is calibrated with experiments inducing metastable soft breakdown events in Pt/HfO2/Pt capacitors

Correlation between wearable inertial sensor data and standardised Parkinson's disease axial impairment measures using machine learning

Wearable sensors represent a valuable means for monitoring motion signs and symptoms of Parkinson’s disease (PD). In this paper, we explore the potential of a single inertial sensor to yield information correlated to the patient’s subjective perception of axial motion impairment during daily activities. This latter is expressed using as a relevant metric the sum of MDS-UPDRS items 2.11-2.13. Methods: thirty-one patients with PD were enrolled in this study, and asked to perform a timed-up-and-go test while wearing an inertial sensor on their thigh. Several time- and frequency-domain features were extracted from the inertial signals. They were fed to a random forest regression model for the prediction of the axial impairment metric. The model was optimized using 10-fold cross-validation and performance were assessed using leave-one-subject-out test. Results: Pearson correlation coefficient with the addressed metric of 0.76 (0.86) and mean absolute error of 1.70 (1.52) were obtained in patients under (not under) dopaminergic therapy. Moreover, moderate to strong correlations were found between the predicted score and some important disease progression, axial impairment, and motor performance metrics. Conclusion: a single wearable inertial sensor may be used for assessing motor disabilities of patients with PD

Objective assessment of walking impairments in myotonic dystrophy by means of a wearable technology and a novel severity index

Myotonic dystrophy type 1 (DM1) is a genetic inherited autosomal dominant disease characterized by multisystem involvement, including muscle, heart, brain, eye, and endocrine system. Although several methods are available to evaluate muscle strength, endurance, and dexterity, there are no validated outcome measures aimed at objectively evaluating qualitative and quantitative gait alterations. Advantageously, wearable sensing technology has been successfully adopted in objectifying the assessment of motor disabilities in different medical occurrences, so that here we consider the adoption of such technology specifically for DM1. In particular, we measured motor tasks through inertial measurement units on a cohort of 13 DM1 patients and 11 healthy control counterparts. The motor tasks consisted of 16 meters of walking both at a comfortable speed and fast pace. Measured data consisted of plantar-flexion and dorsi-flexion angles assumed by both ankles, so to objectively evidence the footdrop behavior of the DM1 disease, and to define a novel severity index, termed SI-Norm2, to rate the grade of walking impairments. According to the obtained results, our approach could be useful for a more precise stratification of DM1 patients, providing a new tool for a personalized rehabilitation approach

Visualization of Directional Beaming of Weakly Localized Raman from a Random Network of Silicon Nanowires

Disordered optical media are an emerging class of materials that can strongly scatter light. These materials are useful to investigate light transport phenomena and for applications in imaging, sensing and energy storage. While coherent light can be generated using such materials, its directional emission is typically hampered by their strong scattering nature. Here, the authors directly image Rayleigh scattering, photoluminescence and weakly localized Raman light from a random network of silicon nanowires via real-space microscopy and Fourier imaging. Direct imaging enables us to gain insight on the light transport mechanisms in the random material, to visualize its weak localization length and to demonstrate out-of-plane beaming of the scattered coherent Raman light. The direct visualization of coherent light beaming in such random networks of silicon nanowires offers novel opportunities for fundamental studies of light propagation in disordered media. It also opens venues for the development of next generation optical devices based on disordered structures, such as sensors, light sources, and optical switches

Molecular and computational diagnostic approach identifies FOXP3, ICOS, CD52 and CASP1 as the most informative biomarkers in acute graft versus host disease.

BACKGROUND: Acute graft-versus-host disease is a severe complication of allogeneic stem cell transplantation in which the functional immune cells of the donor recognize the recipient as foreign and mount an immunological attack. There is an urgent need for better diagnostic instruments for the assessment of acute graft-versus-host disease. In the present study, a novel bioinformatics framework was used to identify gene expression patterns associated with acute graft-versushost disease in patients undergoing allogeneic hematopoietic stem cell transplantation. DESIGN AND METHODS: Peripheral blood cells were collected prospectively from patients who did develop acute graftversus-host disease (YES) and from those who did not (NO). Gene expression profiling was performed using a panel of 47 candidate genes potentially involved in alloreactive responses. The entire population of YES/NO acute graft-versus-host disease patients formed the experimental validation set. Personalized modeling based on a gene selection technique was applied to identify the most significant mRNA transcripts, which were then used to profile individual data samples for training and testing the classification/prediction framework. RESULTS: A leave-one-out cross-validation procedure was performed to investigate the robustness of the classification framework producing the following results: 100% on the training dataset and 97% on the testing dataset. According to our integrated methodology, transcripts for FOXP3, ICOS, CD52 and CASP1, genes involved in immune alloreactive responses and participating in immune cell interactions, were identified as the most informative biomarkers in allogeneic stem cell transplant recipients experiencing acute graft-versus-host disease. CONCLUSIONS: This study demonstrates that the integrated methodology proposed is useful for the selection of valid gene targets for the diagnosis of acute graft-versus-host disease, producing satisfactory accuracy over independent clinical features of the allogeneic transplanted population

Beta-Blocker Use in Older Hospitalized Patients Affected by Heart Failure and Chronic Obstructive Pulmonary Disease: An Italian Survey From the REPOSI Register

Beta (β)-blockers (BB) are useful in reducing morbidity and mortality in patients with heart failure (HF) and concomitant chronic obstructive pulmonary disease (COPD). Nevertheless, the use of BBs could induce bronchoconstriction due to β2-blockade. For this reason, both the ESC and GOLD guidelines strongly suggest the use of selective β1-BB in patients with HF and COPD. However, low adherence to guidelines was observed in multiple clinical settings. The aim of the study was to investigate the BBs use in older patients affected by HF and COPD, recorded in the REPOSI register. Of 942 patients affected by HF, 47.1% were treated with BBs. The use of BBs was significantly lower in patients with HF and COPD than in patients affected by HF alone, both at admission and at discharge (admission, 36.9% vs. 51.3%; discharge, 38.0% vs. 51.7%). In addition, no further BB users were found at discharge. The probability to being treated with a BB was significantly lower in patients with HF also affected by COPD (adj. OR, 95% CI: 0.50, 0.37–0.67), while the diagnosis of COPD was not associated with the choice of selective β1-BB (adj. OR, 95% CI: 1.33, 0.76–2.34). Despite clear recommendations by clinical guidelines, a significant underuse of BBs was also observed after hospital discharge. In COPD affected patients, physicians unreasonably reject BBs use, rather than choosing a β1-BB. The expected improvement of the BB prescriptions after hospitalization was not observed. A multidisciplinary approach among hospital physicians, general practitioners, and pharmacologists should be carried out for better drug management and adherence to guideline recommendations

Beneficial Effects of Polydeoxyribonucleotide (PDRN) in an In Vitro Model of Fuchs Endothelial Corneal Dystrophy

Fuchs endothelial corneal dystrophy (FECD) is a bilateral, hereditary syndrome characterized by progressive irreversible injury in the corneal endothelium; it is the most frequent cause for corneal transplantation worldwide. Oxidative stress induces the apoptosis of corneal endothelial cells (CECs), and has a crucial function in FECD pathogenesis. The stimulation of the adenosine A2A receptor (A2Ar) inhibits oxidative stress, reduces inflammation and modulates apoptosis. Poly-deoxyribonucleotide (PDRN) is a registered drug that acts through adenosine A2Ar. Thus, the goal of this study was to assess the effect of PDRN in an in vitro FECD model. Human Corneal Endothelial Cells (IHCE) were challenged with H2O2 (200 µM) alone or in combination with PDRN (100 µg/mL), PDRN plus ZM241385 (1 µM) as an A2Ar antagonist, and CGS21680 (1 µM) as a well-known A2Ar agonist. H2O2 reduced the cells’ viability and increased the expression of the pro-inflammatory markers NF-κB, IL-6, IL-1β, and TNF-α; by contrast, it decreased the expression of the anti-inflammatory IL-10. Moreover, the pro-apoptotic genes Bax, Caspase-3 and Caspase-8 were concurrently upregulated with a decrease of Bcl-2 expression. PDRN and CGS21680 reverted the negative effects of H2O2. Co-incubation with ZM241385 abolished the effects of PDRN, indicating that A2Ar is involved in the mode of action of PDRN. These data suggest that PDRN defends IHCE cells against H2O2-induced damage, potentially as a result of its antioxidant, anti-inflammatory and antiapoptotic properties, suggesting that PDRN could be used as an FECD therapy

Ultrathin silicon nanowires for optical and electrical nitrogen dioxide detection

The ever-stronger attention paid to enhancing safety in the workplace has led to novel sensor development and improvement. Despite the technological progress, nanostructured sensors are not being commercially transferred due to expensive and non-microelectronic compatible materials and processing approaches. In this paper, the realization of a cost-effective sensor based on ultrathin silicon nanowires (Si NWs) for the detection of nitrogen dioxide (NO2 ) is reported. A modification of the metal-assisted chemical etching method allows light-emitting silicon nanowires to be obtained through a fast, low-cost, and industrially compatible approach. NO2 is a well-known dangerous gas that, even with a small concentration of 3 ppm, represents a serious hazard for human health. We exploit the particular optical and electrical properties of these Si NWs to reveal low NO2 concentrations through their photoluminescence (PL) and resistance variations reaching 2 ppm of NO2 . Indeed, these Si NWs offer a fast response and reversibility with both electrical and optical transductions. Despite the macro contacts affecting the electrical transduction, the sensing performances are of high interest for further developments. These promising performances coupled with the scalable Si NW synthesis could unfold opportunities for smaller sized and better performing sensors reaching the market for environmental monitoring

A machine learning-based approach to optimize repair and increase yield of embedded flash memories in automotive systems-on-chip

Nowadays, Embedded Flash Memory cores occupy a significant portion of Automotive Systems-on-Chip area, therefore strongly contributing to the final yield of the devices. Redundancy strategies play a key role in this context; in case of memory failures, a set of spare word- and bit-lines are allocated by a replacement algorithm that complements the memory testing procedure. In this work, we show that replacement algorithms, which are heavily constrained in terms of execution time, may be slightly inaccurate and lead to classify a repairable memory core as unrepairable. We denote this situation as Flash memory false fail. The proposed approach aims at identifying false fails by using a Machine Learning approach that exploits a feature extraction strategy based on shape recognition. Experimental results carried out on the manufacturing data show a high capability of predicting false fails