Resting state functional thalamic connectivity abnormalities in patients with post-stroke sleep apnoea: a pilot case-control study

OBJECTIVE: Sleep apnoea is common after stroke, and has adverse effects on the clinical outcome of affected cases. Its pathophysiological mechanisms are only partially known. Increases in brain connectivity after stroke might influence networks involved in arousal modulation and breathing control. The aim of this study was to investigate the resting state functional MRI thalamic hyper connectivity of stroke patients affected by sleep apnoea (SA) with respect to cases not affected, and to healthy controls (HC). PATIENTS AND METHODS: A series of stabilized strokes were submitted to 3T resting state functional MRI imaging and full polysomnography. The ventral-posterior-lateral thalamic nucleus was used as seed. RESULTS: At the between groups comparison analysis, in SA cases versus HC, the regions significantly hyper-connected with the seed were those encoding noxious threats (frontal eye field, somatosensory association, secondary visual cortices). Comparisons between SA cases versus those without SA, revealed in the former group significantly increased connectivity with regions modulating the response to stimuli independently to their potentiality of threat (prefrontal, primary and somatosensory association, superolateral and medial-inferior temporal, associative and secondary occipital ones). Further significantly functionally hyper connections were documented with regions involved also in the modulation of breathing during sleep (pons, midbrain, cerebellum, posterior cingulate cortices), and in the modulation of breathing response to chemical variations (anterior, posterior and para-hippocampal cingulate cortices). CONCLUSIONS: Our preliminary data support the presence of functional hyper connectivity in thalamic circuits modulating sensorial stimuli, in patients with post-stroke sleep apnoea, possibly influencing both their arousal ability and breathing modulation during sleep

Ensemble of deep convolutional neural networks for automatic pavement crack detection and measurement

Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-the-art methods were compared. To evaluate the efficiency of crack detection methods, three parameters were considered: precision (Pr), recall (Re) and F1 score (F1). For the two public databases of pavement images, the proposed method obtained the highest values of the three evaluation parameters: for the CFD database, Pr = 0.9552, Re = 0.9521 and F1 = 0.9533 (which reach values up to 0.5175 higher than the values obtained on the same database with the other methods), for the AigleRN database, Pr = 0.9302, Re = 0.9166 and F1 = 0.9238 (which reach values up to 0.7313 higher than the values obtained on the same database with the other methods). The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement

ROADS—Rover for Bituminous Pavement Distress Survey: An Unmanned Ground Vehicle (UGV) Prototype for Pavement Distress Evaluation

Maintenance has a major impact on the financial plan of road managers. To ameliorate road conditions and reduce safety constraints, distress evaluation methods should be efficient and should avoid being time consuming. That is why road cadastral catalogs should be updated periodically, and interventions should be provided for specific management plans. This paper focuses on the setting of an Unmanned Ground Vehicle (UGV) for road pavement distress monitoring, and the Rover for bituminOus pAvement Distress Survey (ROADS) prototype is presented in this paper. ROADS has a multisensory platform fixed on it that is able to collect different parameters. Navigation and environment sensors support a two-image acquisition system which is composed of a high-resolution digital camera and a multispectral imaging sensor. The Pavement Condition Index (PCI) and the Image Distress Quantity (IDQ) are, respectively, calculated by field activities and image computation. The model used to calculate the I-ROADS index from PCI had an accuracy of 74.2%. Such results show that the retrieval of PCI from image-based approach is achievable and values can be categorized as "Good"/"Preventive Maintenance", "Fair"/"Rehabilitation", "Poor"/"Reconstruction", which are ranges of the custom PCI ranting scale and represents a typical repair strategy

Automatic crack detection on road pavements using encoder-decoder architecture

Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low-level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms

Materials study to implement a 3D printer system to repair road pavement potholes

InfraRob is a research project funded by the European Commission's research programme Horizon 2020 that aims to maintain integrity, performance, and safety of the road infrastructure through autonomous robotized solutions and modularization. A specific task of the project is focused on the development of a system 3D printer able to extrude a specific mixture for filling in small cracks and potholes, to be integrated with an existing small autonomous carrier. The first step of the research deals with the definition of the optimal parameters of the system 3D printer/mixture, by studying in parallel the material design and the printer design. This paper presents the study performed on a mixture chosen among those commonly used for road potholes repair. The mixture is studied to achieve and balance the different conflicting performances: consistence, flowability homogeneity, and internal structure. In addition to the basic components, the use of special additives has also been explored to improve the plasticity and adhesivity of the mixture. The first phase of tests is conducted to define the main printing controls: i) Extrudability control: materials for 3D printing need to have an acceptable degree of extrudability, which is related to the capacity of a material to pass continuously through the printing head; ii) Flowability control, to ensure the mixture can be easy-pumpable in the delivery system and easy-usable on the crack or the pothole to be filed-in; iii) Setting time control: printing material requires a certain setting time to maintain a consistent flow rate for good extrudability, thus appropriate additives are needed to control the setting time. The second phase includes in situ tests to verify the compaction of the mixture under the traffic loads. The paper presents the results of the lab and in situ tests, and the features of the chosen mix, suitable to be managed by the 3D printer

Sonographic knowledge of occiput position to decrease failed operative vaginal delivery: a systematic review and meta-analysis of randomized controlled trials

Objective: This study aimed to assess the efficacy of sonographic assessment of fetal occiput position before operative vaginal delivery to decrease the number of failed operative vaginal deliveries. Data Sources: The search was conducted in MEDLINE, Embase, Web of Science, Scopus, ClinicalTrial.gov, Ovid, and Cochrane Library as electronic databases from the inception of each database to April 2021. No restrictions for language or geographic location were applied. Study Eligibility Criteria: Selection criteria included randomized controlled trails of pregnant women randomized to either sonographic or clinical digital diagnosis of fetal occiput position during the second stage of labor before operative vaginal delivery. Methods: The primary outcome was failed operative vaginal delivery, defined as a failed fetal operative vaginal delivery (vacuum or forceps) extraction requiring a cesarean delivery or forceps after failed vacuum. The summary measures were reported as relative risks or as mean differences with 95% confidence intervals using the random effects model of DerSimonian and Laird. An I2 (Higgins I2) >0% was used to identify heterogeneity. Results: A total of 4 randomized controlled trials including 1007 women with singleton, term, cephalic fetuses randomized to either the sonographic (n=484) or clinical digital (n=523) diagnosis of occiput position during the second stage of labor before operative vaginal delivery were included. Before operative vaginal delivery, fetal occiput position was diagnosed as anterior in 63.5% of the sonographic diagnosis group vs 69.5% in the clinical digital diagnosis group (P=.04). There was no significant difference in the rate of failed operative vaginal deliveries between the sonographic and clinical diagnosis of occiput position groups (9.9% vs 8.2%; relative risk, 1.14; 95% confidence interval, 0.77–1.68). Women randomized to sonographic diagnosis of occiput position had a significantly lower rate of occiput position discordance between the evaluation before operative vaginal delivery and the at birth evaluation when compared with those randomized to the clinical diagnosis group (2.3% vs 17.7%; relative risk, 0.16; 95% confidence interval, 0.04–0.74; P=.02). There were no significant differences in any of the other secondary obstetrical and perinatal outcomes assessed. Conclusion: Sonographic knowledge of occiput position before operative vaginal delivery does not seem to have an effect on the incidence of failed operative vaginal deliveries despite better sonographic accuracy in the occiput position diagnosis when compared with clinical assessment. Future studies should evaluate how a more accurate sonographic diagnosis of occiput position or other parameters can lead to a safer and more effective operative vaginal delivery technique

Bioactive potential of minor italian olive genotypes from apulia, sardinia and abruzzo

This research focuses on the exploration, recovery and valorization of some minor Italian olive cultivars, about which little information is currently available. Autochthonous and unexplored germplasm has the potential to face unforeseen changes and thus to improve the sustainability of the whole olive system. A pattern of nine minor genotypes cultivated in three Italian regions has been molecularly fingerprinted with 12 nuclear microsatellites (SSRs), that were able to unequivocally identify all genotypes. Moreover, some of the principal phenolic compounds were determined and quantified in monovarietal oils and the expression levels of related genes were also investigated at different fruit developmental stages. Genotypes differed to the greatest extent in the content of oleacein (3,4-DHPEA-EDA) and total phenols. Thereby, minor local genotypes, characterized by stable production and resilience in a low-input agro-system, can provide a remarkable contribution to the improvement of the Italian olive production chain and can become very profitable from a socio-economic point of view

Sentient Spaces: Intelligent Totem Use Case in the ECSEL FRACTAL Project

The objective of the FRACTAL project is to create a novel approach to reliable edge computing. The FRACTAL computing node will be the building block of scalable Internet of Things (from Low Computing to High Computing Edge Nodes). The node will also have the capability of learning how to improve its performance against the uncertainty of the environment. In such a context, this paper presents in detail one of the key use cases: an Internet-of-Things solution, represented by intelligent totems for advertisement and wayfinding services, within advanced ICT-based shopping malls conceived as a sentient space. The paper outlines the reference scenario and provides an overview of the architecture and the functionality of the demonstrator, as well as a roadmap for its development and evaluation

Interpreting physical performance in professional soccer match-play: Should we be more pragmatic in our approach?

Academic and practitioner interest in the physical performance of male professional soccer players in the competition setting determined via time-motion analyses has grown substantially over the last four decades leading to a substantial body of published research and aiding development of a more systematic evidence-based framework for physical conditioning. Findings have forcibly shaped contemporary opinions in the sport with researchers and practitioners frequently emphasising the important role that physical performance plays in match outcomes. Time-motion analyses have also influenced practice as player conditioning programmes can be tailored according to the different physical demands identified across individual playing positions. Yet despite a more systematic approach to physical conditioning, data indicate that even at the very highest standards of competition, the contemporary player is still susceptible to transient and end-game fatigue. Over the course of this article, the author suggests that a more pragmatic approach to interpreting the current body of time-motion analysis data and its application in the practical setting is nevertheless required. Examples of this are addressed using findings in the literature to examine: a) the association between competitive physical performance and ‘success’ in professional soccer, b) current approaches to interpreting differences in time-motion analysis data across playing positions and, c) whether data can realistically be used to demonstrate the occurrence of fatigue in match-play. Gaps in the current literature and directions for future research are also identified

Perinatal death by bile acid levels in intrahepatic cholestasis of pregnancy: a systematic review

Background: Intrahepatic cholestasis of pregnancy (ICP) is characterized by the elevation of total bile acids (TBAs). The primary concern in women with ICP is the increased risk of stillbirth. ICP is generally considered as “mild” when TBA levels range from 10 to 39 µmol/L and “severe” with levels greater than 40 µmol/L, although levels of TBA ≥100 µmol/L have been also considered as a further threshold of severity. Objective: To quantify the association between different severities of ICP (TBA 10–39, 40–99, and ≥100 µmol/L) and perinatal death. Data sources: Medline, Embase, Scopus, Web of Sciences, and ClinicalTrial.gov were searched from the inception of each database to February 2019. Methods of study selection: Randomized, cohort, case-control, or case series studies reporting maternal and perinatal outcomes on women with ICP by the three prespecified TBA levels (10–39, 40–99, and ≥100 µmol/L) were included. We excluded multiple gestations and trials which included an intervention. The analysis was performed with Pearson chi-square and Fisher’s exact test as appropriate. Continuous outcomes were compared using metaregression with inverse variance weighting using reported sample sizes and standard deviations. Pairwise comparisons used a Bonferroni correction to control for multiple testing. Tabulation, integration, and results: Six articles including 1280 singleton pregnancies affected by ICP were included in the systematic review. Out of the 1280 singleton pregnancies affected by ICP included, 118 had ICP with TBA ≥100 µmol/L. Perinatal death was more common in women with TBA ≥100 µmol/L (0.4% for TBA 10-39 μmol/L versus 0.3% for TBA 40-99 μmol/L versus 6.8% for TBA ≥ 100 μmol/L, p <.0001). Of the 8 perinatal deaths in the TBA ≥100 µmol/L group, 3 occurred ≥34 weeks. TBA ≥100 µmol/L increased the risk of spontaneous preterm birth (PTB) (5.4% versus 8.6% versus 18.2% respectively, p <.0001) and iatrogenic PTB (10.8% versus 21.6% versus 35.8% respectively, p<.0001) as well as meconium-stained amniotic fluid (9.0% versus 18.4% versus 31.6% respectively, p <.0001). Conclusions: Maternal TBA ≥100 µmol/L is associated with a 6.8% incidence of perinatal death, most of which (5.9% overall) are stillbirths, while TBA <100 µmol/L are associated with an incidence of perinatal death of 0.3%. It may be reasonable to consider late preterm delivery (at about 35–36 weeks) in women with TBA ≥100 µmol/L