Liver metastases (mts) from colorectal cancer (CRC) can have different responses to chemotherapy in the same patient. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts. 22 radiomic features (RF) were computed on pretreatment portal CT scans following a manual segmentation of mts. RFs were extracted from 7x7 Region of Interests (ROIs) that moved across the image by step of 2 pixels. Liver mts were classified as non-responder (R-) if their largest diameter increased more than 3 mm after 3 months of treatment and responder (R+), otherwise. Features selection (FS) was performed by a genetic algorithm and classification by a Support Vector Machine (SVM) classifier. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values were evaluated for all lesions in the training and validation sets, separately. On the training set, we obtained sensitivity of 86%, specificity of 67%, PPV of 89% and NPV of 61%, while, on the validation set, we reached a sensitivity of 73%, specificity of 47%, PPV of 64% and NPV of 57%. Specificity was biased by the low number of R- lesions on the validation set. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance— to personalize treatment of patients with metastastic colorectal cancer, based on the likelihood of response to chemotherapy of each liver metastasis.

The aim of the study is to present a new Convolutional Neural Network (CNN) based system for the automatic segmentation of the colorectal cancer. The algorithm implemented consists of several steps: a pre-processing to normalize and highlights the tumoral area, the classification based on CNNs, and a post-processing aimed at reducing false positive elements. The classification is performed using three CNNs: each of them classifies the same regions of interest acquired from three different MR sequences. The final segmentation mask is obtained by a majority voting. Performances were evaluated using a semi-automatic segmentation revised by an experienced radiologist as reference standard. The system obtained Dice Similarity Coefficient (DSC) of 0.60, Precision (Pr) of 0.76 and Recall (Re) of 0.55 on the testing set. After applying the leave-one-out validation, we obtained a median DSC=0.58, Pr=0.74, Re=0.54. The promising results obtained by this system, if validated on a larger dataset, could strongly improve personalized medicine.

In this study we investigated whether combining external visualizations with extreme case reasoning may facilitate developing of conceptual understanding about wave optics. For purposes of answering our research question we conducted a pretest-posttest quasi-experiment which included 179 students from a first year introductory physics course at the University of Zagreb, Croatia. Students who were guided through extreme case reasoning in their wave optics seminars significantly outperformed their peers who received conventional teaching treatment. Findings from our study suggest that combining external visualizations with extreme case reasoning facilitates development of visually rich internal representations which are a good basis for performing mental simulations about wave optics phenomena. In addition, it has been also found that many students use the “ closer to the source implicates greater effect ” p-prim when reasoning about certain relationships, such as the relationship between fringes’ dimension and slits-screen separation.

We propose a framework for designing observers possessing global convergence properties and desired asymptotic behaviors for the state estimation of nonlinear systems. The proposed scheme consists in combining two given continuous-time observers: One, denoted as global, ensures (approximate) convergence of the estimation error for any initial condition ranging in some prescribed set, while the other, denoted as local, guarantees a desired local behavior. We make assumptions on the properties of these two observers, and not on their structures, and then explain how to unite them as a single scheme using hybrid techniques. Two case studies are provided to demonstrate the applicability of the framework. Finally, a numerical example is presented.

We study the design of state observers for nonlinear networked control systems (NCSs) affected by disturbances and measurement noise, via an emulation-like approach. That is, given an observer designed with a specific stability property in the absence of communication constraints, we implement it over a network, and we provide sufficient conditions on the latter to preserve the stability property of the observer. In particular, we provide a bound on the maximum allowable transmission interval (MATI) that guarantees an input-to-state stability (ISS) property for the corresponding estimation error system. The stability analysis is trajectory-based, utilizes small-gain arguments, and exploits a persistently exciting property of the scheduling protocols. This property is key in our analysis and allows us to obtain significantly larger MATI bounds in comparison to the ones found in the literature. Our results hold for a general class of NCSs; however, we show that these results are also applicable to NCSs implemented over a specific physical network called WirelessHART (WH). The latter is mainly characterized by its multihop structure, slotted communication cycles, and the possibility to simultaneously transmit over different frequencies. We show that our results can be further improved by taking into account the intrinsic structure of the WH–NCS model. That is, we explicitly exploit the model structure in our analysis to obtain an even tighter MATI bound that guarantees the same ISS property for the estimation error system. Finally, to illustrate our results, we present analysis and numerical simulations for a class of Lipschitz nonlinear systems and high-gain observers.

Hercegovački pršut as a traditional dry-cured smoked ham (prosciutto) produced by using an open fire that can be potentially contaminated with polycyclic aromatic hydrocarbons (PAHs) and can pose a health risk for consumers. The aim of this research was to identify the types and concentrations of 16 PAHs in 34 samples of traditionally smoked prosciutto. Out of 16 investigated PAHs, identified in the EPA (Environmental Protection Agency) list of priority pollutants, 14 compounds were detected. Average levels of cancerogenic benzo[a]pyrene (BaP) and PAH4 (benzo[a]anthracene (BaA), benzo[b]fluoranthene (BbF), chrysene (Chry), and benzo[a]pyrene (BaP)) ranged from <LOQ (level of quantification) to 5.08 μg/kg and 0.45 μg/kg to 22.67 μg/kg. Two analyzed samples exceeded currently prescribed values according to the Bosnia and Herzegovina legislation for BaP concentrations and one sample for PAH4 content. PAH16 concentrations were on average between 2.92 μg/kg and 87.6 μg/kg. The highest PAH concentrations were found in samples from the Herzegovina-Neretva canton. The results of the research highlight the importance of standardizing smoking procedures and manufacturing practice, in the production of Hercegovački pršut, in order to reduce the PAH content.

Abstract In this article, we describe the Cayley graphs of pseudo-unitary homogeneous semigroups with zero 0, which are 0-disjoint unions of their subsets indexed by a groupoid.

This document presents a new complete standalone system for a recognition of sleep apnea using signals from the pressure sensors placed under the mattress. The developed hardware part of the system is tuned to filter and to amplify the signal. Its software part performs more accurate signal filtering and identification of apnea events. The overall achieved accuracy of the recognition of apnea occurrence is 91%, with the average measured recognition delay of about 15 seconds, which confirms the suitability of the proposed method for future employment. The main aim of the presented approach is the support of the healthcare system with the cost-efficient tool for recognition of sleep apnea in the home environment.

The ballistocardiography is a technique that measures the heart rate from the mechanical vibrations of the body due to the heart movement. In this work a novel noninvasive device placed under the mattress of a bed estimates the heart rate using the ballistocardiography. Different algorithms for heart rate estimation have been developed.

Methods based exclusively on heart rate hardly allow to differentiate between physical activity, stress, relaxation, and rest, that is why an additional sensor like activity/movement sensor added for detection and classification. The response of the heart to physical activity, stress, relaxation, and no activity can be very similar. In this study, we can observe the influence of induced stress and analyze which metrics could be considered for its detection. The changes in the Root Mean Square of the Successive Differences provide us with information about physiological changes. A set of measurements collecting the RR intervals was taken. The intervals are used as a parameter to distinguish four different stages. Parameters like skin conductivity or skin temperature were not used because the main aim is to maintain a minimum number of sensors and devices and thereby to increase the wearability in the future.

Background Immune-related diarrhoea/colitis (ir-D/C) is a common adverse event of immune checkpoint inhibitor (ICI) therapy. Guidelines recommend corticosteroid (CS) treatment; however, the average treatment duration for ir-D/C remains poorly defined. Methods All advanced melanoma patients treated with ICI therapy at the Royal Marsden Hospital between 2011 and 2016 were reviewed to identify ir-D/C cases alongside clinical variables. Results 117 any-grade ir-D/C episodes occurred in 109 (21%) patients out of a total of 519 patients treated (ipilimumab=77 episodes, anti-PD1=17 (nivolumab or pembrolizumab), ipilimumab and nivolumab=23 (ipi+nivo)) (seven patients had ir-D/C more than once on different lines of treatment) and >/=grade 3 ir-D/C occurred most frequently (63/519 patients (12%) vs 29/519 (5%) grade 1, and 25/519 (5%) grade 2). Median onset (days) of all-grade ir-D/C after starting ICI therapy was 41 for ipilimumab (IQR 24 to 59, n=77), 91 for anti-PD1 (IQR 46 to 355, n=17) and 45 for ipi+nivo (IQR 24 to 67, n=23). In 71/117 (61%) patients, ir-D/C episodes were treated with CS (17% grade 2; 79% grade 3/4): 54 being steroid-responsive; 17 being steroid-refractory and received additional anti-tumor necrosis factor (TNF) treatment. Median grade 3 ir-D/C CS duration was similar across treatments, averaging 58 days. Median overall CS duration (days) was longer in the grade 3/4 D/C steroid-refractory group (94 vs 45 days). Infection developed in 11/71 (15%) CS recipients and in 6/17 (35%) anti-TNF recipients. In 65/117 (55%) patients, ir-D/C episodes were investigated with flexible sigmoidoscopy. Of these patients, 38/65 (58%) had macroscopic colitis and 12/65 (18%) had microscopic colitis. The steroid-refractory group had more macroscopic changes, 13/17 (76%), than the steroid-responsive group, 22/41 (54%). Conclusion Rates of grade 3 ir-D/C were higher than reported in clinical trials. The 58-day median duration of CS therapy for grade 3 ir-D/C places a significant number of patients at risk of complications. We demonstrate that microscopic colitis is an important subgroup, advocating biopsies in ir-D/C even with macroscopically normal bowel.