Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.

Background and Aims: Continuous glucose monitoring (CGM) devices could be useful for real-time management of diabetes therapy. In particular, CGM information could be used in real time to predict future glucose levels in order to prevent hypo-/hyperglycemic events. This article proposes a new online method for predicting future glucose concentration levels from CGM data. Methods: The predictor is implemented with an artificial neural network model (NNM). The inputs of the NNM are the values provided by the CGM sensor during the preceding 20 min, while the output is the prediction of glucose concentration at the chosen prediction horizon (PH) time. The method performance is assessed using datasets from two different CGM systems (nine subjects using the Medtronic [Northridge, CA] Guardian® and six subjects using the Abbott [Abbott Park, IL] Navigator®). Three different PHs are used: 15, 30, and 45 min. The NNM accuracy has been estimated by using the root mean square error (RMSE) and prediction delay. Results: The RMSE is around 10, 18, and 27 mg/dL for 15, 30, and 45 min of PH, respectively. The prediction delay is around 4, 9, and 14 min for upward trends and 5, 15, and 26 min for downward trends, respectively. A comparison with a previously published technique, based on an autoregressive model (ARM), has been performed. The comparison shows that the proposed NNM is more accurate than the ARM, with no significant deterioration in the prediction delay

Leveraging machine learning to predict rail corrugation level from axle-box acceleration measurements on commercial vehicles

Rail corrugation is a prominent degradative problem in the health monitoring of railway systems. Monitoring process is dependent on use of a diagnostic trolley, which is expensive and needs the track to be out-of-service. Alternatively, in-service rail vehicles with Axle-Box Acceleration measurement systems installed, have shown success in detecting rail corrugation levels based on physical models, albeit with limitations. Extending this approach, we build a Machine Learning model, represented by a tuned Random Forest regressor, trained on collected accelerometer signals along with other offline and/or static features. We also propose a method to engineer acceleration-based features which nullifies the aggregated acceleration vibrations inherited from the other rail due to dynamically coupled vibrations between the left and right rails. The resulting model is able to recreate the moving RMS irregularity profile at bandwidth 100-300 mm, especially in highly corrugated sections, with an R-2 score of 0.97-0.98. The results show that the suggested data-driven approach outperforms a state-of-the-art model-based benchmark

Aqueye optical observations of the Crab Nebula pulsar

We observed the Crab pulsar in October 2008 at the Copernico Telescope in Asiago - Cima Ekar with the optical photon counter Aqueye (the Asiago Quantum Eye) which has the best temporal resolution and accuracy ever achieved in the optical domain (hundreds of picoseconds). Our goal was to perform a detailed analysis of the optical period and phase drift of the main peak of the Crab pulsar and compare it with the Jodrell Bank ephemerides. We determined the position of the main peak using the steepest zero of the cross-correlation function between the pulsar signal and an accurate optical template. The pulsar rotational period and period derivative have been measured with great accuracy using observations covering only a 2 day time interval. The error on the period is 1.7 ps, limited only by the statistical uncertainty. Both the rotational frequency and its first derivative are in agreement with those from the Jodrell Bank radio ephemerides archive. We also found evidence of the optical peak leading the radio one by ~230 microseconds. The distribution of phase-residuals of the whole dataset is slightly wider than that of a synthetic signal generated as a sequence of pulses distributed in time with the probability proportional to the pulse shape, such as the average count rate and background level are those of the Crab pulsar observed with Aqueye. The counting statistics and quality of the data allowed us to determine the pulsar period and period derivative with great accuracy in 2 days only. The time of arrival of the optical peak of the Crab pulsar leads the radio one in agreement with what recently reported in the literature. The distribution of the phase residuals can be approximated with a Gaussian and is consistent with being completely caused by photon noise (for the best data sets).Comment: 7 pages, 7 figures. Accepted for publication in Astronomy and Astrophysic

A study of the factors affecting flange-climb derailment in railway vehicles

Avoiding flange climb derailment is one main issue with ensuring the running safety of railway vehicles. This paper discusses the different causes that can lead to the derailment of a railway wheel, particularly in the light of different derailment criteria used by the standards or proposed by various researchers. Furthermore the paper presents two case studies, one for a vehicle with solid axles and one for a bogie with independently rotating wheels, reporting a description of the derailment case and discussing the causes that led to derailment, by making combined use of measurements and numerical simulation. Based on these exemplary cases, some conclusions are drawn concerning the validity of the derailment criteria presently used by the standards in force

PD-L1 ≥ 50% lung cancer refractory to PD-1 inhibition: The role of salvage chemo-immunotherapy combination

Novel treatment strategies incorporating PD-1/PD-L1 inhibitors in the first-line setting of advanced non-small-cell lung cancer (NSCLC) provided relevant improvements in survival outcomes. Among NSCLC patients with PD-L1 tumor proportion score ≥50%, identifying the ones to be addressed to pembrolizumab monotherapy or chemo-immunotherapy combinations is a matter of debate, taking into account the risks of overtreatment and toxicity. Here we report the clinical stories of four NSCLC patients with PD-L1 tumor proportion score ≥50% and good performance status, sharing high tumor burden including serosal involvement. After having rapidly progressed on first-line PD-1/PD-L1 inhibitors, they achieved major clinical and radiological response to pembrolizumab-chemotherapy combination. These cases prove the feasibility and effectiveness of salvage chemo-immunotherapy in pembrolizumab-refractory NSCLC patients

Inositol(s) from Bench to Bedside in Endocrinology and Gynecology

The family of inositol(s) is a primordial group of ubiquitary molecules, which appeared at the beginning of evolution of life. Nature has used inositol(s) for several biological functions exploiting minimal changes in the structure. This family plays a pivotal role in regulating many metabolic pathways and hormonal signalling, and its essential function is well known in reproduction process. Plenty of experimental and clinical data have shown that inositols play a pivotal role, as drugs, in treating several pathologies such as PCOS, metabolic syndrome, and gestational diabetes; these natural molecules allow avoiding congenital anomalies and they are very effective in improving assisted reproduction technology (ART); moreover, they have demonstrated promising anticancer activities as shown in numerous studies. This special issue will take in consideration reviews, original research articles, short communications, and any scientific contribution providing both new insights from old data and updated results from experimental or clinical studies, thus pushing forward the boundaries of knowledge of inositol(s) in endocrinology and gynecology