The importance of oxygen in the winemaking process is widely known, as it affects the chemical aspects and therefore the organoleptic characteristics of the final product. Hence, it is evident the usefulness of a continuous and real-time measurements of the levels of oxygen in the various stages of the winemaking process, both for monitoring and for control. The WOW project has focused on the design and the development an innovative device for monitoring the oxygen levels in wine. This system is based on the use of an optical fiber to measure the luminescent lifetime variation of a reference metal/porphyrin complex, which decays in presence of oxygen. The developed technology results in a high sensitivity and low cost sensor head that can be employed for measuring the dissolved oxygen levels at several points inside a wine fermentation or aging tank. This system can be complemented with dynamic modeling techniques to provide predictive behavior of the nutrient evolution in space and time given few sampled measuring points for both process monitoring and control purposes. The experimental validation of the technology has been first performed in a controlled laboratory setup to attain calibration and study sensitivity with respect to different photo-luminescent compounds and alcoholic or non-alcoholic solutions, and then in an actual case study during a measurement campaign at a renown Italian winery.

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as analysis window. In this paper the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15 MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current's spectrogram with a significant reduction of the required computational resources.

Monitoring drought impacts in forest ecosystems is a complex process, because forest ecosystems are composed of different species with heterogeneous structural compositions. Even though forest drought status is a key control on the carbon cycle, very few indices exist to monitor and predict forest drought stress. The Forest Drought Indicator (ForDRI) is a new monitoring tool developed by the National Drought Mitigation Center (NDMC) to identify forest drought stress. ForDRI integrates 12 types of data, including satellite, climate, evaporative demand, ground water, and soil moisture, into a single hybrid index to estimate tree stress. The model uses Principal Component Analysis (PCA) to determine the contribution of each input variable based on its covariance in the historical records (2003–2017). A 15-year time series of 780 ForDRI maps at a weekly interval were produced. The ForDRI values at a 12.5km spatial resolution were compared with normalized weekly Bowen ratio data, a biophysically based indicator of stress, from nine AmeriFlux sites. There were strong and significant correlations between Bowen ratio data and ForDRI at sites that had experienced intense drought. In addition, tree ring annual increment data at eight sites in four eastern U.S. national parks were compared with ForDRI values at the corresponding sites. The correlation between ForDRI and tree ring increments at the selected eight sites during the summer season ranged between 0.46 and 0.75. Generally, the correlation between the ForDRI and normalized Bowen ratio or tree ring increment are reasonably good and indicate the usefulness of the ForDRI model for estimating drought stress and providing decision support on forest drought management.

The use of Hyperspectral (HS) and LiDAR acquisitions has a great potential to enhance mapping and monitoring practices of endangered grasslands habitats, beyond conventional botanical field surveys. In this study we assess the potentiality of Recursive Feature Elimination (RFE) in combination with Random Forest (RF) classification in extracting the main HS and LiDAR features needed to map selected Natura 2000 grasslands along Polish lowland river valleys, in particular alluvial meadows 6440, lowland hay meadows 6510 and xeric and calcareous grasslands 6120. We developed an automated RFE-RF system capable to combine the potentials of both techniques and applied it to multiple acquisitions. Several LiDAR-based products and different Spectral Indexes (SI) were computed and used as input in the system, with the aim of shedding light on the best-to-use features. Results showed a remarkable increase in classification accuracy when LiDAR and SI products are added to the HS dataset, strengthening in particular the importance of employing LiDAR in combination with HS. Using only the 24 optimal features selection generalized over the three study areas, strongly linked to the highly heterogeneous characteristics of the habitats and landscapes investigated, it was possible to achieve rather high classification results (K around 0.7-0.77 and habitats F1 accuracy around 0.8-0.85), indicating that the selected Natura 2000 meadows and dry grasslands habitats can be automatically mapped by airborne HS and LiDAR data. Similar approaches might be considered for future monitoring activities in the context of habitats protection and conservation

In this paper the authors describe the architecture of a multidisciplinary data acquisition and visualization platform devoted to the management of coastal environments. The platform integrates heterogeneous data acquisition sub-systems that can be roughly divided in two main categories: remote sensing systems and in-situ sensing systems. Remote sensing solutions include aerial and underwater remote data acquisition while in-situ sensing solutions include the use of RFID tracers, Wireless Sensor Networks and imaging techniques. All the data collected by these subsystems are stored, integrated and fused on a single platform that is also in charge of data visualization. This last task is carried out according to the paradigm of Augmented Virtuality which foresees the augmentation of a virtually reconstructed environment with data collected in the real world. The described solution proposes a novel holistic approach where different disciplines concur, with different data acquisition techniques, to a large scale definition of coastal dynamics, in order to better describe and face the coastal erosion phenomenon. The overall framework has been conceived by the so-called Team COSTE, a joint research team between the Universities of Pisa, Siena and Florence.

Background: The purpose was to develop a novel hypothetical method to increase the size of coronary arteries. Methods: In the long-term observation the coronary sizes were dilated in three unexpected scenarios. The coronary artery sizes were observed in patients with mitral stenosis (n=59) by angiogram prior to percutaneous balloon mitral valvuloplasty or valve replacement surgery for severe mitral stenosis. The coronaries of patients with patent ductus arteriosus who underwent surgical closure in the past (n=12) were examined by echocardiogram. Patients with renal failure on long-term dialysis through peripheral arterio-venous fistula without left ventricular hypertrophy (n=17) were studied by echocardiography. Normal age, weight and sex matched coronary sizes served as controls in the study. All these observations were made over a period of 11.5 years. Results: The sizes of coronaries in patients with mitral stenosis, patients who underwent closure for patent ductus arteriosus, and in patients on hemodialysis through arteriovenous fistulas were higher than normal controls (p<0.05, for all). A hypothetical model to increase the coronary sizes could be developed based on the analysis of the differential equations of Poiseuille’s. The proposed method is creating a peripheral arterio-venous fistula, which could be closed later electively by a percutaneous method/surgery. The closure time needs to be determined by experimental studies. The other methods could be a continuous exercise program or usage of beta-blockers. Conclusion: A novel hypothetical method of peripheral arteriovenous fistula formation could potentially increase the size of the coronaries, and this could be closed later.

Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. We cover especially unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

Placental growth factor (PlGF or PGF) is a member of the VEGF family, which is known to play a critical role in pathological angiogenesis, inflammation, and endothelial cell barrier function. However, the molecular mechanisms by which PlGF mediates its effects in non-proliferative diabetic retinopathy (DR) remain elusive. In this study, we performed transcriptome-wide profiling of differential gene expression for human retinal endothelial cells (HRECs) treated with PlGF antibody. The effect of antibody treatment on the samples was validated using trans-endothelial electric resistance (TEER), and western blot. A total of 3760 genes (1750 upregulated and 2010 downregulated) were found to be differentially expressed between the control and PlGF antibody treatment group. These differentially expressed genes (DEGs) were used for gene ontology and enrichment analysis to identify gene function, signal pathway, and interaction networks. The gene ontology results revealed that catalytic activity (GO:0003824) of molecular function, cell (GO:0005623) of the cellular component, and cellular process (GO:0009987) were among the most enriched biological processes. Pathways such as TGF-β, VEGF-VEGFR2, p53, apoptosis, pentose phosphate pathway, and ubiquitin-proteasome pathway, were among the most enriched, and TGF-β1 was identified as a primary upstream regulator. These data provide new insights into the underlying molecular mechanisms of PlGF in mediating biological functions, in relation to DR.

As the number of legal and illegal mining sites increase, integrative methods to evaluate the effects of mining pollution on Andes-Amazonia freshwater ecosystems are paramount. Here, we sampled water and sediments in 11 sites potentially affected by mining activities in the Napo province (Ecuador). The environmental impacts were evaluated using four lines of evidence (LOEs): water physico-chemical parameters; metal exposure concentrations; macroinvertebrate community response (AAMBI); and toxicity by conducting bioassays with Lactuca sativa and Daphnia magna. Overall, dissolved oxygen and total suspended solids were, under (&lt;80%) and above (&gt;130 mg/Ls) quality standards. Ag, Al, As, Cd, Cu, Fe, Mn, Pb and Zn in water and V, B and Cr in sediments were detected above quality standards. Nine out of eleven sites were classified as having bad environmental quality based on the AAMBI. Ranges of L. sativa seed germination in both water (37% to 70%) and sediment (0% to 65%), indicate significant toxicity. In 5 sites, neonates of D. magna showed a 25% reduction in survival compared to the control. Our integrated LOEs index ranked sites regarding their environmental degradation. Given the importance of the Andes-Amazon region, we recommend environmental impact monitoring of the mining expansion using multiple LOEs.

Blood cell analysis is a crucial diagnostic process in medical practice. In particular, detecting white blood cells (WBCs) is essential for diagnosing of many diseases. The manual screening of blood films is a time-consuming and subjective process, which can lead to inconsistencies and errors. Therefore, automated detection of blood cells can improve the accuracy and efficiency of the screening process. In this study, an explainable Vision Transformer (ViT) model was proposed for the automatic detection of WBCs from blood films. The proposed model utilizes the self-attention mechanism to extract relevant features from the input images and leverages transfer learning by incorporating pre-trained model weights to improve its performance. The proposed model achieved a classification accuracy of 99.40% for five distinct types of WBCs and exhibited potential in reducing the time required for manual screening of blood films by pathologists. Upon examination of the misclassified test samples, it was observed that incorrect predictions were correlated with the presence or absence of granules in the cell samples. To validate this observation, the dataset was divided into two classes, namely Granulocytes and Agranulocytes, and a secondary training process was conducted. The resulting ViT model trained for binary classification achieved an accuracy of 99.70%, recall of 99.54%, precision of 99.32%, and F-1 score of 99.43% during the test phase. To ensure the reliability of the ViT model's multi-class classification of WBCs, the pixel areas that the model focuses on in its predictions are visualized through the Score-CAM algorithm.

Very little is known about laboratory confirmed blood stream infections (LCBIs) in neonatal intensive care units (NICUs) in resource-limited settings. The aim of this cohort study was to determine the incidence, risk factors, and causative agents of LCBIs in a level-2 NICU in India. The diagnosis of LCBIs was established using the Centre for Disease Control, USA criteria. A predesigned questionnaire containing risk factors associated with LCBIs was filled-in. A total of 150 neonates (43% preterm) were included in the study. The overall incidence of LCBIs was 31%. The independent risk factors for LCBIs were: preterm neonates (relative risk (RR) 2.23), duration of NICU stay more than 14 days (RR 1.75), chorioamnionitis in the mother (RR 3.18), premature rupture of membrane in mothers (RR 2.32), neonate born through meconium-stained amniotic fluid (RR 2.32), malpresentation (RR 3.05), endotracheal intubation (RR 3.41), umbilical catheterization (RR 4.18), and ventilator-associated pneumonia (RR 3.17). The initiation of minimal enteral nutrition was protective from LCBIs (RR 0.22). The predominant causative organisms were gram-negative pathogens (58%). The results of the present study can be used to design antibiotic interventions to reduce LCBIs in resource-limited settings.

The neurovascular niche is crucial for constant blood supply and blood-brain barrier (BBB) function and is altered in a number of different neurological conditions, making this an intensely active field of research. Brain vasculature is unique for its tight association of endothelial cells with astrocytic endfeet processes. Separation of the vascular compartment by centrifugation-based methods confirmed enrichment of astrocytic endfeet processes, making it possible to study the entire vascular niche with such methods. Several centrifugation-based separation protocols are found in the literature; however, with some constraints which limit their applicability and the scope of the studies. Here, we describe and validate a protocol for physically separating the neurovascular niche from the parenchyma, which is optimized for smaller tissue quantities. Using endothelial, neuronal and astrocyte markers, we show that quantitative Western blot-based target detection can be performed of both the vascular and parenchymal fractions using as little as a single mouse brain hemisphere. Validation of our protocol in rodent stroke models by detecting changes in serum albumin signals and astrocyte activation, i.e. increased glial fibrillary acidic protein expression, between the ipsilateral and the lesion-free contralateral hemisphere demonstrates this protocol as a new way of detecting BBB breakdown and astrogliosis, respectively.

Innovations in industrial automation, information and communication technology (ICT), renewable energy, monitoring and sensing fields have been paving the way for smart devices, which can acquire and convey information to the internet, in every aspect of our lives. Since there is ever-increasing demand for large yet affordable production volumes for such devices, printed electronics has been attracting great attention in both industrial and academic research. In order to understand the potential and future prospects of the printed electronics, the present paper summarizes the basic principles and conventional approaches while providing the recent progresses in the fabrication and material technologies, applications and environmental impacts.

The construction of different roads, such as freeways, highways, major roads or minor roads must be accompanied by constant monitoring and evaluation of service delivery. Pavements are generally assessed by engineers in terms of the smoothness, surface condition, structural condition and surface safety. Pavement assessment is often conducted using the qualitative indices such as international roughness index (IRI), pavement condition index (PCI), structural condition index (SCI) and skid resistance value (SRV), which are used for smoothness assessment, surface condition assessment, structural condition assessment, and surface safety assessment, respectively. In this paper, Tehran-Qom Freeway in Iran has been selected as the case study and its smoothness and pavement surface conditions are assessed. At 2-km intervals, a 100-meter sample unit is selected in the slow-speed lane (totally, 118 sample units). In these sample units, the PCI is calculated after a visual inspection of the pavement and the recording of distresses. Then, in each sample unit, the average IRI is computed. The purpose of this study is to provide a method for estimating PCI based on IRI. The proposed theory was developed by Random Forest (RF), and Random Forest optimized by Genetic Algorithm (RF-GA) methods and these methods were validated using correlation coefficient (CC), scattered index (SI), and Willmott’s index of agreement (WI) criteria. The proposed method reduces costs, saves time and eliminates the safety risks.

Abstract: The broad clinical use of synthetic vascular grafts for vascular diseases is limited by their thrombogenicity and low patency rate, especially for vessels with a diameter inferior to 6 mm. Alternatives such as tissue-engineered blood grafts (TEBGs) have gained increasing interest. Among the different manufacturing approaches, 3D bioprinting presents numerous advantages and enables the fabrication of multi-scale, multi-material, and multicellular tissues with heterogeneous and functional intrinsic structures. Extrusion-, inkjet- and light-based 3D printing techniques have been used for the fabrication of TEBG out of hydrogels, cells, and/or solid polymers. This review discusses the state-of-the-art research on the use of 3D printing for TEBG with a focus on the biomaterials and deposition methods.

Background: Little is known about potential confounding factors influencing the humoral response in individuals having received the BNT162b2 vaccine. Methods: Blood samples from 231 subjects were collected before and 14, 28 and 42 days following COVID-19 vaccination with BNT162b2. Anti-Spike Receptor-Binding-Domain protein (anti-Spike/RBD) immunoglobulin G (IgG) antibodies were measured at each time-point. Impact of age, sex, childbearing age status, hormonal therapy, blood group, body mass index and past-history of SARS-CoV-2 infection were assessed by multivariable analyses. Results and Conclusions: In naïve subjects, the level of anti-Spike/RBD antibodies gradually increased following administration of the first dose to reach the maximal response at day 28 and then plateauing at day 42. In vaccinated subjects with previous SARS-CoV-2 infection, the plateau was reached sooner (i.e. at day 14). In the naïve population, age had a significant negative impact on anti-Spike/RBD titers at day 14 and 28 while lower levels were observed for males at day 42, when corrected for other confounding factors. BMI as well as B and AB blood groups had a significant impact in various subgroups on the early response at day 14 but no longer after. No significant confounding factors were highlighted in the previously infected group.

Current lines of research into mood disorders indicate that immune mediators participating in the pathophysiology of chronic somatic disorders have potent influences on brain functions, even when these mediators are produced in peripheral tissues. Elevated levels of circulating immune molecules have been consistently associated with depressive symptoms in a number of clinical populations and experimental models, to the extent that major depressive disorder (MDD) is now seen, at least in part, as a disorder of immunity. This paradigm has brought to the fore the use of anti-inflammatory therapies as adjunctive to standard antidepressant therapy with the hope to improve treatment efficacy, particularly in those cohorts that do not respond well to standard medication. Such new practice requires the availability of biomarkers to tailor these new therapies to those most likely to benefit but also clear mechanisms of action describing the interaction between peripheral immunity and brain function. These mechanisms are generally studied in preclinical models that try to recapitulate the human disease through peripherally induced sickness behaviour as the model for immune-induced MDD. After an appraisal of the data in rodent models and their adherence to the data in clinical cohorts, we propose a modified model of periphery-brain interaction that goes beyond the currently established view of interaction between peripheral cytokines and microglia cells as the driver of depression. Instead, we suggest that brain barriers are primary actors in the communication between body and brain and, as a consequence, in the pathophysiology of the disease. This model suggests novel biomarkers, novel targets for therapies as well as a novel mechanism for resistance to standard treatments.

Coincident changes in abundance and behavior pose a challenge for interpreting abundance data from monitoring programs. In the San Francisco Estuary, long-term monitoring documented the declines of many species including the anadromous Longfin Smelt (Spirinchus thaleichthys). We identified seasonal patterns in reginal presence of Longfin Smelt through its life cycle using monitoring data and generalized additive modelling. We then investigated the year-to-year variability in the seasonal patterns of presence using functional data analysis (FDA). FDA separated the variability due to population size from variability due to differences in timing of presence. We found that Longfin Smelt have consistent seasonal distribution patterns and that two trawl types were needed to accurately describe those patterns. After accounting for variability due to year-class strength, shifts in the timing of presence were evident in three regions. The most variable period for the upstream regions Suisun Bay and West Delta was for age-0 fish in summer and for the downstream region Central Bay was for age-0 fish in late fall. This manifested as a delay in the typical fall re-occupation of upstream regions that comprise the study area for another monitoring study (Fall Midwater Trawl). Thus, a portion of the recent reductions in Fall Midwater Trawl abundance of Longfin Smelt resulted from changes in behavior rather than a decline in abundance. The presence of multiple monitoring surveys allowed analysis of distribution from one data set to aid interpretation of patterns in abundance from another monitoring survey. This study highlights how identifying portions of the life cycle with the most and least variability in distribution can help inform the types of management strategies that will be most effective. It also illustrates an analytical method that can be used to address the problem of confounded effects of abundance and behavior on patterns in monitoring data.

Power disaggregation aims at determining the appliance-by-appliance electricity consumption leveraging upon a single meter only, which measures the entire power demand. Data-driven procedures based on Factorial Hidden Markov Models have been proven remarkable results on energy disaggregation. Nevertheless, those procedures have various weaknesses: there is a scalability problem as the number of devices to observe raises and the algorithmic complexity of the inference step is severe. DNN architectures, such as Convolutional Neural Networks, have demonstrated to be a viable solution to deal with FHMMs shortcomings. Nonetheless, there are two significant limitations: a complicated and time-consuming training system based on back-propagation has to be employed to estimates the neural architecture parameters, and large amounts of training data covering as many operation conditions as possible need to be collected to attain top performances. In this work, we aim to overcome those limitations by leveraging upon the unique and useful characteristics of the extreme learning machine technique, which is based on a collection of randomly chosen hidden units and analytically defined output weights. Experiment evaluation has been conducted using the UK-DALE corpus. We find that the suggested approach achieves similar performances to recently proposed ANN-based methods and outperforms FHMMs. Besides, our solution generalises well to unseen houses.

Lead pollutants mainly from urban agricultural practices, Leaded paints, Leaded fuels spillages, Lead-contaminated air, soils, and water sources pause a health threat to urban children in Uganda. Prolonged Lead exposure affects iron metabolism, by competitively blocking iron absorption leading to anemia. Blood Lead (BL) inhibits key enzymes ferrochelatase and aminolevulinic acid dehydrogenase (ALAD) involved in hemoglobin (Hb) biosynthesis even at very low levels. Lead poisoning and malaria infection geographically overlap, and both produce similar hematological outcome especially in children. Malaria parasites cause anemia by destroying parasitized red blood cells, therefore, co-existence of BL and malaria parasites infection worsens the anemia status of the host. This study aimed at expounding the extent of heme synthesis inhibition by BL levels among a study group of malaria positive children by measuring levels of free erythrocyte protoporphyrin (FEP) in blood samples. Briefly venous blood samples from 198 children were analyzed for malaria parasite densities by the thick smear method, hemoglobin (Hb) concentrations determined by the standard cyan methemoglobin method, BLL were analyzed on an atomic absorption spectrophotometer and FEP levels were fluorometrically measured. The results showed means of BLL (9.3 µg/dL), Hb (7.5 g/dL), FEP/Hb (8.3 µg/g) and parasite density (PD) (3.21×10³ parasites / µL) among the study group. A majority of 151/198 (76.3%) of the children were moderately anemic while 8/198 (4%) were severely anemic. There was weak correlation between PD and Hb (R²= -0.15, P-value < 0.001), as compared to one between FEP/Hb and Hb (R²= -0.6, P-value=0.001). The study concludes that BL is a significant contributor to malaria anemia and should be considered in the management of anemia in malaria-endemic areas.

The green biomass of horticultural plants contains valuable secondary metabolites (SM) which can potentially be extracted and sold. When exposed to stress, plants accumulate higher amounts of these SMs, making the extraction and commercialization even more attractive. We evaluated the potential for accumulating of the flavones cynaroside and graveobioside A in leaves of two bell pepper cultivars (Mavras and Stayer) when exposed to salt stress (100 mM NaCl), UVA/B excitation (UVA 4-5 W/m²; UVB 10-14 W/m² for 3 hours per day) or a combination of both stressors. HPLC analyses proved the enhanced accumulation of both metabolites under stress conditions. Cynaroside accumulation is effectively triggered by high-UV stress, whereas graveobioside A contents increase under salt stress. Highest contents were observed in plants exposed to combined stress. Effects of stress on overall plant performance differed significantly between treatments, with least negative impact on aboveground biomass found for high-UV stressed plants. The usage of two non-destructive instruments (Dualex and Multiplex) allowed us to gain insights in ontogenetical effects at the leaf level and temporal development of SM contents over time. Indices provided by those devices correlate fairly with amounts detected via HPLC (Cynaroside: R2 = 0.46 – 0.66; Graveobioside A: R2 = 0.51 – 0.71). The concentrations of both metabolites tend to decrease at leaf level during the ontogenetical development even under stress conditions. High-UV stress is a promising tool for enriching plant leaves with valuable SM without major effects on plant biomass. All data is available online [1].

Precision agriculture is a technology used by farmers to help food sustainability amidst growing population. One of the tools of precision agriculture is yield monitoring, which helps a farmer manage his production. Yield monitoring is usually done during harvest, however it could also be done early in the growing season. Early prediction of yield, specifically for fruit trees, aids the farmer in the marketing of their product and assists in managing production logistics such as labor requirement and storage needs. In this study, a machine vision system is developed to estimate fruit yield early in the season. The machine vision system uses a color camera to capture images of fruit trees during the full bloom period. An image segmentation algorithm based on an artificial neural network was developed to recognize and count the blossoms on the tree. The artificial neural network segmentation algorithm uses color information and position as input. The resulting correlation between the blossom count and the actual number of fruits on the tree shows the potential of this method to be used for early prediction of fruit yield.

Deck inclination and vertical displacements are among the most important technical parameters to evaluate the health status of a bridge and to verify its bearing capacity. Several methods, both conventional and innovative, are used for structural rotations and displacement monitoring; no one of these does allow, at the same time, precision, automation, static and dynamic monitoring without using high cost instrumentation. The proposed system uses a common laser pointer and image processing. The elastic line inclination is measured by analyzing the single frames of a HD video of the laser beam imprint projected on a flat target. For the image processing, a code was developed in Matlab® that provides instantaneous rotation and displacement of a bridge, charged by a mobile load. An important feature is the synchronization of the load positioning, obtained by a GNSS receiver or by a video. After the calibration procedures, a test was carried out during the movements of a heavy truck maneuvering on a bridge. Data acquisition synchronization allowed to relate the position of the truck on the deck to inclination and displacements. The inclination of elastic line was obtained with a precision of 0.01 mrad. The results demonstrate the suitability of the method for dynamic load tests, control and monitoring of bridges.

Stroke remains a major cause of death and disability in the United States and around the world. Solid safety and efficacy profiles of novel stroke therapeutics have been generated in the laboratory, but most failed in clinical trials. Investigations into the pathology and treatment of the disease remain a key research endeavor in advancing scientific understanding and clinical applications. In particular, cell-based regenerative medicine, specifically stem cells transplantation, may hold promise as stroke therapy because grafted cells and their components may recapitulate the growth and function of the neurovascular unit, which arguably represents the alpha and omega of stroke brain pathology and recovery. Recent evidence has implicated mitochondria, organelles with a central role in energy metabolism and stress response, in stroke progression. Recognizing that stem cells offer a source of healthy mitochondria, potentially transferrable into ischemic cells, may provide a new therapeutic tool. To this end, deciphering cellular and molecular processes underlying dysfunctional mitochondria may reveal innovative strategies for stroke therapy. Here, we review recent studies capturing the intimate participation of mitochondrial impairment in stroke pathology, and showcase promising methods of healthy mitochondria transfer into ischemic cells, to critically evaluate the potential of mitochondria-based stem cell therapy for stroke.

Antigen-based rapid diagnostic tests for Lymphatic filariasis do not come with quality control (QC) materials, and research and disease control programmes rely on stored positive plasma samples. This study was undertaken to evaluate the use of freeze-dried Wuchereria bancrofti antigen positive plasma samples to serve as QC materials for LF RDTs. 10 well characterized W. bancrofti positive samples were lyophilized and stored at 4°C, 28°C and 40°C. The samples were evaluated using the Filaria Test Strips before lyophilization and after one and three (3) months of storage. The sensitivity and stability of the lyophilized samples were evaluated. The results revealed a loss of sensitivity and stability with increasing temperature and duration of storage. The results are further discussed in terms of the use of Dried Blood Spot (DBS) in diagnostics studies on LF requiring quantitative assessments, and the need for thoughtful DBS preparation and storage.

The detection of seismic events at regional and teleseismic distances is critical to Nuclear Treaty Monitoring. Traditionally, detecting regional and teleseismic events has required the use of an expensive multi-instrument seismic array; however in this work, we present DeepPick, a novel seismic detection algorithm capable of array-like performance from a single trace. We achieve this directly, by training our single-trace detector against labeled events from an array catalog, and by utilizing a deep temporal convolutional neural network. The training data consists of all arrivals in the International Seismological Centre Catalog for seven seismic arrays over a five year window from 1 Jan 2010 to 1 Jan 2015, yielding a total training set of 608,362 detections. The test set consists of the same seven arrays over a one year window from 1 Jan 2015 to 1 Jan 2016. We report our results by training the algorithm on six of the arrays and testing it on the seventh, so as to demonstrate the transportability and generalization of the technique to new stations. Detection performance against this test set is outstanding. Fixing a type-I error rate of 1%, the algorithm achieves an overall recall rate of 73% on the 141,095 array beam picks in the test set, yielding 102,394 correct detections. This is more than 4 times the 23,259 detections found in the analyst-reviewed single-trace catalogs over the same period, and represents an 8dB improvement in detector sensitivity over current methods. These results demonstrate the potential of our algorithm to significantly enhance the effectiveness of the global treaty monitoring network.

A non-Newtonian fluid model is used to investigate the two-dimensional pulsatile blood flow through a tapered artery with a stenosis. The mixed convection effects of heat and mass transfer are also taken into account. By applying non-dimensionalization and radial coordinate transformation, we simplify the system in a tube. Under the finite difference scheme, numerical solutions are calculated for velocity, temperature concentration, resistance, impedance, wall shear stress and shearing stress at the stenosis throat. Finally, Quantitative analysis is carried out.

A portable urea sensor for use in the fast flow condition was fabricated using porous polytetrafluoroethylene (PTFE) membranes coated with amine-functionalized parylene, parylene-A, by vapor deposition. To generate a specific electrochemical sensor signal from urea, the urea-hydrolyzing enzyme urease was immobilized on the parylene-A-coated PTFE membranes via chemical crosslinking using glutaraldehyde. The urease-immobilized membranes were assembled in a polydimethylsiloxane (PDMS) fluidic chamber, and a screen-printed carbon three-electrode system was used for electrochemical measurements. The success of urease immobilization was confirmed using fluorescence microscopy, scanning electron microscopy, and Fourier-transform infrared spectroscopy. The optimum concentration of urease for immobilization on the parylene-A-coated PTFE membranes was determined to be 48 mg/mL, and the optimum number of membranes in the PDMS chamber was found to be 8. Using these optimized conditions, we fabricated the urea biosensor and monitored urea samples under various flow rates ranging from 0.5 to 10 mL/min in the flow condition using chronoamperometry. To test the applicability of the sensor for physiological samples, we used it for monitoring urea concentration in the waste peritoneal dialysate of a patient with chronic renal failure, at a flow rate of 0.5 mL/min.

Despite the wide variety of variables commonly applied to measure different aspects of rehabilitation, the assessment and subsequent definition of indicators of environmental rehabilitation status are not simple tasks. The main challenges are comparing rehabilitated sites with target ecosystems as well as integrating individual environmental and eventually collinear variables into a single tractable measure of the state of a system before effective indicators that track rehabilitation may be modeled. For that, a consensus is lacking regarding which and how many variables need to be surveyed. Our approach considered ecological processes, vegetation structure, and community diversity from nonrehabilitated, rehabilitating and reference sites. We applied this approach to a curated set of 32 environmental variables retrieved from nonrevegetated, rehabilitating and reference sites associated with iron ore mines from the Urucum Massif, Mato Grosso do Sul, Brazil. By integrating variables from a single attribute or the entire set of variables into a single estimation of rehabilitation status, the proposed multivariate approach is straightforward and able to adequately address collinearity among variables. The proposed approach allows for the identification of biases towards single variables, surveys or analyses, which is necessary to rank environmental variables regarding their importance to the assessment. Furthermore, we show that bootstrapping permitted the detection of the minimum number of environmental variables necessary to achieve reliable estimations of the rehabilitation status. Finally, we show that the proposed variable integration enables the definition of environmental indicators for more comprehensive monitoring of mineland rehabilitation. Thus, the proposed multivariate ordination represents a powerful tool to outline the benefits of rehabilitating sites for the maintenance of biodiversity and ecosystem functions and services provided that sufficient environmental variables related to ecological processes, diversity and vegetation structure are gathered from nonrehabilitated, rehabilitating and reference study sites. By identifying deviations from predicted rehabilitation trajectories and providing assessments for environmental agencies, this proposed multivariate ordination increases the effectiveness of (mineland) rehabilitation.

The application of 3D LiDAR technology has become increasingly extensive in tunnel monitoring due to the large density and high accuracy of the acquired spatial data. The proposed processing method aims at circle tunnels and provides a clear workflow to automatically process raw point data and easily interpretable results to analyze tunnel health state. The proposed automatic processing method employs a series of algorithms to extract point cloud of a single tunnel segment without obvious noise from entire raw tunnel point cloud mainly by three steps: axis acquisition, segments extraction and denoising. Tunnel axis is extracted by fitting boundaries of the tunnel point cloud rejection in plane with RANSAC algorithm. With guidance of axis, the entire preprocessed tunnel point cloud is segmented by equal division to get a section of tunnel point cloud which corresponds to a single tunnel segment. Then the noise in every single point cloud segment is removed by clustering algorithm twice, based on distance and intensity. Finally, clean point clouds of tunnel segments are processed by effective deformation extraction processor to get ovality and three-dimensional deformation nephogram.

The present article is dealing with the state of question about building-energy monitoring systems used for data collection to estimate the Heat Lost Coefficient (HLC) with existing methods and so determinate buildings’ Envelope Energy Performance (EEP). In addition, the data requirements of HLC estimation methods are related with commonly used methods for fault detection, calibration and supervision of energy monitoring systems in buildings. Based on an extended review of experimental tests since 1978, a qualitative and quantitative analysis of Monitoring and Controlling System (MCS) specifications has been carried out. Although most actual Buildings Automation Systems (BAS) may measure the required parameters, further research is still needed to ensure that these data are accurate enough to rigorously apply the HLC estimation methods.

This research presents alternative solutions for an Energy Efficiency Management System (EEMS) serving as a framework for optimizing the energy consumption algorithm and lowering energy consumption. First, a monitoring Wireless Sensor and Actuator Network (WSAN) is used for sensing, measuring, gathering data, and modeling all the dynamic disturbance parameters of the rooms in the building. Second, integrated software for metering and controlling the processes of digital data flow is used. Third, an alternative solution is proposed to reduce energy consumption. The primary benefits of this system are real-time monitoring; rapid, alternative solutions; and the ability to make a prudent decision on how to lower energy consumption. The system shows instant and accumulated solutions for short and long-term time planning. The solutions identified can be implemented in the same buildings under the same circumstances. The universities of Majmaah and Philadelphia have buildings with similar infrastructure. The system was applied to the buildings at Philadelphia University. The results were generalized to both universities. After implementation, the energy consumption of the EEMS using WSAN (based on the monitoring was reduced up to 23% when compared to that of the initial state.

We report the development of an extrinsic self-healing coating system that shows no fluorescence from the intact coating, yellowish fluorescence in cracked regions, and greenish fluorescence in healed regions, thus allowing the separate monitoring of cracking and healing of coatings. This fluorescence monitoring self-healing system consisted of a top coating, an epoxy matrix resin containing mixed dye-loaded in single microcapsule. The dye-loaded microcapsules consisted of a poly(urea-formaldehyde) shell encapsulating a healing agent containing MAT-PDMS and styrene, a photo-initiator and a mixture of two dyes, one that fluoresces only in the solid state (DCM) and a second that fluoresces dramatically increased in the solid than solution state (4-TPAE). A mixture of the healing agent, photo-initiator and the two dyes was yellow due to fluorescence from DCM. On UV curing of this mixture, however, the color changed from yellow to green and the fluorescence intensity increased due to fluorescence from 4-TPAE in the solid state. When a self-healing coating embedded with microcapsules containing the DCM/4-TPAE dye mixture was scratched, the damaged region exhibited a yellowish color that changed to green after healing. Thus, the self-healing system reported here allows the separate monitoring of cracking and healing based on changes in fluorescence color.

Worldwide, one of the main causes of death among young adults is multiple trauma. In these pa-tients hemorrhagic shock represents the leading cause for worsening of the clinical status and for increased morbidity and mortality. This is due to a multifactorial complex involving cellular, bi-ological, and biophysical mechanisms. The most important mechanisms affecting clinical out-come are oxidative stress, the augmentation of pro-inflammatory status, immune deficiency, dis-ruptions in the coagulation cascade, imbalances in electrolyte and acid-base homeostasis. Poly-trauma patients in hemorrhagic shock need adequate fluid management to ensure hemodynamic stability that must consider not only the maintenance of adequate blood pressure, but also the ad-equate oxygenation of tissues for optimal cellular function. In the current clinical practice, fluid resuscitation in polytrauma patients uses a variety of widely studied pharmacological products, such as crystalloids, colloids, blood transfusions, and the infusion of other blood products. Alt-hough these products exist, an agreement was not reached on a standard administration protocol that could be generally applied for all patients. Moreover, numerous studies have reported a se-ries of adverse events related to fluid resuscitation and to the inadequate use of these products. This review aims at describing the impact the administration of all the solutions used in fluid re-suscitation might have on the cellular and pathophysiological mechanisms in the case of poly-trauma patients suffering from hemorrhagic shock.

This paper shows the results of applying high-resolution Unmanned Aircraft System (UAS) photogrammetric surveying on a large landslide. A real case study, where permanently installing GCPs could be complex, where natural shaped and formed land pose severe limitations in deploying ground targets with optimal geometric configuration. We analysed performances in terms of survey accuracy obtained by performing photogrammetric surveys through the Zenmuse P1 DJI optical camera and Phantom 4 Pro 2. In combination with DJI Matrice 300 UAS, the P1 camera allows direct georeferencing through GNSS observations in RTK mode. Photogrammetric surveys, performed through different georeferencing methods, have been compared. Several targets have been permanently installed on the ground over the maximum vegetation height to guarantee long-lasting reference over the years in the area, which is characterised by a diffuse short vegetation coverage. Multitemporal UAS surveys have been then compared using Digital Image Correlation (DIC) algorithms, and deformation maps have been produced. Afterwards, DIC results were compared with observations made by the GNSS ground-based permanent receivers resulting in a standard deviation of 0,077 m. Through results analysis, good accordance between ground-based GNSS observations and DIC analysis on the photogrammetric surveys have been identified for the same time span. To conclude, this type of landslide presents a moderate deformation speed; in such a case, effective deformations monitoring could be achieved using pseudo-direct georeferencing, which permitted a 0.24 m accuracy on the whole tested area.

Background: Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Also, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed. Methods: To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including N=229 African American and N=381 Hispanic/Latino participants, monocytes) and the Depression Genes and Networks study (DGN, N = 922 European ancestry participants, whole blood). We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African (N = 27,955) and Hispanic/Latino (N = 28,324) ancestry participants. Results: Our results revealed 24 suggestive signals (p < 1×10^(-4)) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study (N=802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN. Conclusions: These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.

The idea of using the lytic power of viruses against the malignant cells has been entertained for many decades. However, oncolytic viruses (OV) gained broad attention as an emerging anti-cancer therapy only recently with the successful implementation of the oncolytic herpesvirus to treat advanced melanoma. OVs offer an attractive therapeutic combination of tumor-specific cell lysis together with immune stimulation, yet the latter effect is less well studied. Nevertheless, OVs can be envisaged as potential in situ tumor vaccines. The therapeutic potential of OVs can be instigated further by using the molecular biological and biotechnological tools to modify the existing viruses for their optimal tumor selectivity and enhanced immune stimulation. Furthermore, OVs can be readily combined with other therapeutic agents to increase the efficacy of the existing therapeutic schemes. In this review, we discuss biotechnological advances in the development of therapeutic applications of OVs in Russia. Particular emphasis is made on the OV-mediated treatment of glioblastoma. In addition, we highlight the challenges of oncolytic virotherapy, and describe the strategies to optimize current approaches to improve clinical outcomes.

Background: The fulminant course of COVID-19, triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents with a high mortality rate and still lacks a causative treatment. C-reactive protein (CRP) has been shown to increase dramatically during the disease progression and correlates with deleterious outcomes. Selective CRP apheresis can reduce circulating CRP levels fast and effective. Methods: Seven hospitalized patients with documented severe COVID-19 progression, elevated CRP plasma levels (> 100 mg/L) and signs of respiratory failure were treated with CRP apheresis. 2-12 CRP apheresis sessions were performed generally in 24 h time intervals and depending on CRP plasma levels. Results: CRP apheresis reduced CRP plasma levels by up to 84% within a few hours, without exhibiting side effects in any patient. Despite signs of severe lung infiltration in all patients, only one patient died. The other patients showed improvements within the chest x-ray after CRP apheresis and were able to recover regardless of intubation and/or ECMO (4 patients). All remaining six patients were discharged from the hospital in good clinical condition. All patients had comorbidities. Conclusions: This case series presents a mortality rate of only 14%, which is dramatically lower than expected from the presented CRP levels as well as comorbidities and ventilation require-ments. Our clinical observations regarding the here presented seven patients support the hypoth-esis that CRP is a candidate to be therapeutically targeted in the early stage of severe COVID-19.

We evaluated the association of blood pressure with blood levels of cadmium, lead, and cadmium and lead together (cadmium+lead) in a representative sample of adolescents from Korea by use of 2010-2016 data from the Korean National Health and Nutrition Examination Survey (KNHANES). This cross-sectional study enrolled adolescents aged at 10-18 years-old who completed a health examination survey and had blood measurements of lead and cadmium. The association of adjusted mean differences in diastolic and systolic blood pressure with doubling of blood lead and cadmium were estimated by regression of blood pressure against log₂-transformed blood metals and their quartiles after covariate adjustment. Adjusted odds ratios for prehypertension were calculated for log₂-transformed blood levels of lead and cadmium and their quartiles. Our analysis of adolescents in Korea indicated that blood levels of lead and cadmium were not significantly associated with increased blood pressure or risk of pre-hypertension. However, the cadmium+lead level was associated with pre-hypertension. Previous studies showed that blood levels of lead and cadmium were associated with increased blood pressure and risk of hypertension in adult populations. We found no such effect in Korean adolescents, although the cadmium+lead level was associated with prehypertension. These differences may be because adolescents generally have lower levels of these blood metals or because adolescents only rarely have hypertension.

Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to decompose the whole loads in a household, which leads to low identification accuracy. In this paper, an NILM approach based on multi-feature integrated classification (MFIC) is explored, which combines some non-electrical features such as ON/OFF duration, usage frequency of appliances, and usage period to improve load differentiability. The implementation of MFIC algorithm is consistent with traditional event-based method. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. Simulation results using an open-access dataset demonstrate the effectiveness and high accuracy of MFIC algorithm, with the state-of-the-art NILM methods as benchmarks.

Effective maintenance of railways requires a comprehensive assessment of the actual condition of the construction materials involved. In this regard, Ground-Penetrating Radar (GPR) stands as a viable alternative to the invasive and time-consuming traditional techniques for the inspection of these infrastructures. This work reports the experimental activities carried out on a test-site area within a railway depot in Rome, Italy. Specifically, a 30 m-long railway section was divided into 10 sub-sections reproducing different various physical and structural conditions of the track-bed. In more detail, combinations of varying scenarios of fragmentation and fouling of the ballast were reproduced. The set-up was then investigated using different multi-frequency GPR horn antenna systems. The effects of the different physical conditions of ballast on the electromagnetic response of the material were analysed for each scenario using time- and frequency-domain signal processing techniques. Parallel to this, modelling was provided to estimate fouling content. Interpretation of results has proven the viability of the GPR method in detecting signs of decay at the network level, thereby proving this technique to be worthy for implementation in asset management systems.

This article proposes a monitoring system that allows to track transitions between different stages in the berry harvesting process (berry picking, waiting for transport, transport, and arrival to the packing) solely using information from temperature and vibration sensors located in the basket. The monitoring system assumes a characterization of the process based on Hidden Markov Models and uses the Viterbi algorithm to perform inference and estimate the most likely state trajectory. The obtained state trajectory estimate is then used to compute a potential damage indicator in real-time. The proposed methodology does not require information about the weight of the basket to identify each of the different stages, which makes it effective and more efficient than other alternatives available in the industry.

Platelets have long been associated with sustaining the balance between hemostasis and thrombosis. Platelets, however, are also involved in a wide range of biological activities, including inflammation, immunology, wound healing, cancer biology, and angiogenesis. Platelets' diverse roles are mediated by the expression of various adhesive and immune receptors and the secretion of a diverse array of bioactive proteins, ions stored in granules, and several lipid mediators. Platelets also release pro-inflammatory and anti-inflammatory, and angiogenic factors and shed microparticles into the bloodstream. The challenge for therapeutic intervention in non-hemostatic disease is identifying the factors that primarily inhibit specific targets implicated in platelets' complicated contribution to inflammation or tumor growth while leaving their hemostatic function intact. In addition, blood platelets are involved in infection and innate and adaptive immunity by mediating complicated vascular homeostasis via specialized receptors and granule release, RNA transfer, and mitochondrial secretion. Anti-platelet drugs/bioactive compounds are developed based on their platelet anti-aggregatory properties; however, very little information is available on their effects on non-hemostatic function. Therefore, a better understanding of the impact of the anti-platelet bioactive on the platelets' diverse roles and mechanisms may help develop new strategies and prevent CVD and other diseases. In this review, a comprehensive overview of platelet multifunctional roles in CVD and other diseases and dietary factors' modulatory effects are described.

There are numerous challenges involved in the diagnosis and treatment of lung cancer. Globally, majority of people suffers from cancerous disease involving throat cancer, lung cancer, stomach cancer, cancerous brain tumor. Among these the most common ones is the lung cancer or lung carcinoma. The leading cause of the lung cancer is the smoking. Around 80% to 90% of deaths are caused due to Non-small cell lung cancer (NSCLC). The inadequate diagnostic techniques and low chances for the survival of lung cancer patients results from the lack of an early prognosis and incompetency in traditional therapies. However, such challenges involved in the prognosis and treatment of lung cancer are on decline with the progression in magnetic nanoparticle (MNP) technology. Many break-through discoveries and inventions have been made in the field of cancer therapy by using magnetic nanoparticles. The implication of nanotechnology has led to the recent advancement in nanomedicine field. This has encouraged the improvement in different therapeutic and diagnosis strategies employing nanotechnology. The generation of immense technological benefits for nanoparticles systems has been accredited to its remarkable nanoscale physico-chemical properties. This in turns provides the early detection of lung cancer and active drugs delivery for an improved theranostics strategy. The present review provides a general idea of the current progression in the therapeutic and prognosis purpose of magnetic nanoparticles. Further, we disclose the development in the lung cancer theranostics by functionalization of magnetic nanoparticles. The established importance of magnetic nanoparticles in the theranostics centers for lung cancer has been revealed in this paper. The challenges existing in the theranostics of lung cancer are addressed through the functioning of magnetic nanoparticles in the process.

Marine invertebrates are model organisms in several areas of biological sciences, being a source of massive biological information. Although, the scientific relevance of marine invertebrates, the research with them can be limited for their tissue characteristics and troubles for the replication of physical and chemical properties of seawater. Thence, the main goal of this laboratory workflow is to provide a useful methodological approach to reduce the experimental limitations during the study of marine invertebrates. The present study describes experimental methodologies for the collection, transport, and maintenance of sessile tunicates. Also, an approach to observe and characterize, a diverse population of blood cells in marine invertebrates, by several cytological stains and electron microscopy. Lastly, suggestions and protocols to extract quality RNA from samples with high concentrations of salts, pigments, secondary metabolites, and polysaccharides. This methodological approach can be easily adapted to other marine invertebrates, moreover uses low-cost reagents and widely available laboratory equipment. Making possible the study of different types of marine animals in diverse locations.

Condition monitoring of industrial robots has the potential to decrease downtimes in highly automated production systems. We suggest a new health indicator based on vibration data measurements and compare its performance with state-of-the-art health indicators regarding different criteria. This evaluation is based on different data sets from robot test rigs. We find that the proposed health indicator can detect different faults, has low temperature sensitivity and works in instationary velocity regimes. A discussion of the validity of the results rounds up our contribution.

Glioblastoma is one of the most difficult tumor types to treat with conventional therapy options like tumor debulking, chemo and radiotherapy. Immunotherapeutic agents like oncolytic viruses, immune checkpoint inhibitors and chimeric antigen receptor T cells have revolutionized cancer therapy, but their success in glioblastoma remains limited and further optimization of immunotherapies is needed. Several oncolytic viruses have demonstrated ability to infect tumors and trigger anti-tumor immune responses in malignant glioma patients. Leading the pack, oncolytic herpesvirus, first in its class, awaits an approval for treating malignant glioma from MHLW, the federal authority of Japan. Nevertheless, some major hurdles like the blood brain barrier, immunosuppressive tumor microenvironment, and tumor heterogeneity can engender suboptimal efficacy in malignant glioma. In this review, we discuss the current status of malignant glioma therapies with a focus on oncolytic viruses in clinical trials. Furthermore, we discuss the obstacles faced by oncolytic viruses in malignant glioma patients and strategies that are being used to overcome these limitations to 1) optimize delivery of oncolytic viruses beyond the blood brain barrier; 2) trigger inflammatory immune responses in and around tumors; and 3) use of multimodal therapies in combination to tackle tumor heterogeneity, with an end goal of optimizing the therapeutic outcome of oncolytic virotherapy.

IoT system becomes hot topic nowadays for smart home, IoT helps devices to communicate together without human intervention inside home, so it is offering many challenges. A new smart home IoT platform powered using electromagnetic energy harvesting is proposed in this paper. It contains a high gain transmitted antenna array and efficient circular polarized array rectenna system to harvest enough power from any direction to increase life time of the batteries used in IoT system. Optimized energy consumption, the software with adopting the Zigbee protocol of the sensor node and low power microcontroller are used to operate in lower power modes. The proposed system has an 84.6 days lifetime which is approximately 10 times the lifetime for similar system. On the other hand, the proposed power management circuit operated at 0.3 V DC to boost the voltage to ~3.7V from radio frequency energy harvesting and manage battery level to increase the batteries lifetime. A predictive indoor environment monitoring system is designed based on a novel hybrid system to provide a non-static plan, approve energy consumption and avoiding failure of sensor nodes in smart home.

Different alcoholic beverages can have different effects on blood alcohol concentrations (BAC) and neurotoxicity even if equalized for alcohol content by volume. Anecdotal evidence suggested that natural wine is metabolized differently from conventional wines. This triple-blind study compared the BAC of 55 healthy male subjects after consuming the equivalent of 2 units of alcohol of a natural or conventional wine over 3 mins in two separate sessions one week apart. BAC was measured using a professional breathalyzer every 20 mins after consumption for 2 hrs. The BAC curves in response to the two wines diverged significantly at twenty minutes, at forty minutes and also at their maximum concentrations (peaks), with the natural wine inducing a lower BAC than the conventional wine (T20 0.40 vs. 0.46 [p<0.0002], T40 0.49 vs. 0.53 [p<0.0015], peak 0.52 vs. 0.56 [p<0.0002]). These differences are likely related to the development of different amino acids and antioxidants in the two wines during their production. This in turn may affect the kinetics of alcohol absorption and metabolism. Other contributing factors may also include pesticide residues, differences in dry extract content and the use of indigenous or selected yeasts. Further studies are needed to fully understand why natural wines are metabolized differently from conventional wines.

Generating a range-wide population status of the diamondback terrapin (Malaclemys terrapin spp.) is challenging due to a combination of species ecology and behavior, and limitations associated with traditional sampling methods. Visual counting of emergent heads offers an efficient, non-invasive and promising method for generating abundance estimates of terrapin populations across broader spatial scales and can be used to explain spatial variation in population size. We conducted repeated visual head count surveys at 38 predetermined sites along the shoreline of Wellfleet Bay in Wellfleet, Massachusetts. We analyzed the count data using a hierarchical modeling framework designed specifically to analyze repeated count data: the so-called N-mixture model. This approach allows for simultaneous modeling of imperfect detection to generate estimates of true terrapin abundance. We found detection probability was lowest when skies were overcast and when wind speed was highest. Site specific abundance varied but we found that abundance estimates were, on average, higher in unexposed sites compared to exposed sites. We demonstrate the utility of pairing visual head counts and N-mixture models as an efficient method for estimating terrapin abundance and show how the approach can be used to identifying environmental factors that influence detectability and distribution.

This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e. data with sampling rates lower than the AC base frequency. We first review the many degrees of freedom of these approaches, what has already been done in literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported MAE and F$_1$-scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10\,s, a large field of view, the usage of GAN losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.

Plasma and serum are the most widely used blood-derived biofluids for metabolomics and lipidomics assays, but the isolation of these products from blood may introduce additional bias as indicated by the fact that many analytes that are present at high concentrations in blood cells cannot be measured and evaluated in those samples. Of particular concern, variable hemolysis during the pre-processing of blood products could compromise accurate and reproducible quantification. Compared with plasma or serum, whole blood may be a better alternative due to simplicity of processing. In this study, we provide a comprehensive method for quantification of the whole blood sphingolipidome and the concentrations were compared with those from plasma. Combining a single-phase extraction method with liquid-chromatography high resolution mass spectrometry (R=120, 000), assisted by alkaline hydrolysis, we were able to identify and simultaneously quantify more than 150 sphingolipids. Furthermore, most of sphingolipids remained stable after a freeze/thaw cycle. Whole blood contained a higher concentration of most sphingolipids than corresponding plasma. Moreover, individual variations in the levels of sphingolipids were lower for whole blood than plasma. These findings demonstrate that whole blood could be a better alternative to plasma, and potentially guide the evaluation of sphinglipidome for biomarker discovery.

Microsamples (collections usually less than 50 µL) have been introduced in pre-clinical, clinical, and research settings to overcome obstacles in sampling via traditional venipuncture. However, venipuncture remains the sampling gold standard for metabolic phenotyping of blood. This pre-sents several challenges in metabolic phenotyping workflows: accessibility for individuals in ru-ral and remote underserved areas (due to the need for trained personnel), the unamenable nature to frequent sampling protocols in longitudinal research (for its invasive nature), and sample col-lection difficulty in the young and elderly. Furthermore, venous sample stability may be compro-mised when temperate conditions necessary for cold-chain transport are beyond control. Alter-natively, research utilising microsamples extends phenotyping possibilities to inborn errors of metabolism, therapeutic drug monitoring, nutrition, as well as sport and anti-doping. Although the application of microsamples in metabolic phenotyping exists, it is still in its infancy, with whole blood being overwhelmingly the primary biofluid collected through the collection method of dried blood spots. Research into metabolic phenotyping of microsamples is limited; however, with advances in commercially available microsampling devices, common barriers such as volumetric inaccuracies and the ‘haematocrit effect’ in dried blood spot microsampling can be overcome. In this review, we provide an overview of the common uses and workflows for mi-crosampling in metabolic phenotyping research. We discuss the advancements in technologies, highlighting key considerations and remaining knowledge gaps for employment of microsamples in metabolic phenotyping research. Supporting the translation of research from the ‘bench to the community’.

Over past decades, a frequent co-morbidity of cerebrovascular pathology and Alzheimer's disease pathology has been observed. Numerous published studies indicate that preservation of healthy cerebrovascular endothelium can be an important therapeutic target. By incorporating appropriate drug(s) into biomimetic (lipid cubic-phase) nanocarriers, one obtains a multitasking combination therapeutic which targets certain cell-surface scavenger receptors, mainly class B type 1 (i.e., SR-BI), and crosses the blood-brain barrier. This targeting allows for various Alzheimer’s-related cell types to be simultaneously searched out for localized drug treatment in vivo.

Background: A malfunction of the innate immune response in COVID-19 is associated with eosinopenia particularly in more severe cases. This study tested the hypothesis that this eosinopenia is COVID-19 specific and is associated with systemic activation of eosinophils.. Methods: Blood of 15 healthy controls and 75 adult patients with suspected COVID-19 at the ER were included before PCR testing and analyzed by point-of-care automated flow cytometry (CD10, CD11b, CD16 and CD62L) in the absence or presence of a formyl peptide (fNLF). 45 SARS-CoV-2 PCR positive patients were grouped based on disease severity. PCR negative patients with proven bacterial (n=20) or other viral (n=10) infections were used as disease controls. Eosinophils were identified with the use of the FlowSom algorithm. Results: Low blood eosinophil numbers (<100 cells/microL; p<0.005) were found both in patients with COVID-19 and with other infectious diseases albeit less pronounced. Two discrete eosinophil populations were identified in healthy controls both before and after activation with fNLF based on the expression of CD11b. Before activation, the CD11bbright population consisted of 5.4% (CI95% = 3.8, 13.4) of total eosinophils. After activation, this population of CD11bbright cells comprised nearly half the population (42.21%, CI95% = 35.9, 54.1). Eosinophils in COVID-19 had a similar percentage of CD11bbright cells before activation (7.6%, CI95% = 4.5, 13.6), but were clearly refractory to activation with fNLF as a much lower percentage of cells end up in the CD11bbright fraction after activation (23.7%, CI95% = 18.5, 27.6; p<0.001). Conclusion: Low eosinophil numbers in COVID-19 are associated with refractoriness in responsiveness to fNLF, which is likely caused by migration of fully functional cells to the tissue. Such homing might be beneficial as eosinophils have been implicated in viral killing.

Introduction: multiple sclerosis (MS) is the most common immune-mediated chronic neurodegenerative disease of the central nervous system (CNS), affecting young people. Due to the permanent disability, cognitive impairment, and the enormous detrimental impact MS can exert on the patients’ health-related quality of life. It is of great importance to recognise it in time and commence adequate treatment at an early stage. The currently used disease-modifying therapies (DMT) aim to reduce disease activity and thus halt disability development, which in current clinical practice are monitored by clinical and imaging parameters but not by biomarkers found in blood and/or the cerebrospinal fluid (CSF). Both clinical and radiological measures routinely used to monitor disease activity lack information on the fundamental pathophysiological features and mechanisms of MS. Furthermore, they lag behind the disease process itself. By the time a clinical relapse becomes evident or a new lesion appears on the MRI scan, potentially irreversible damage has already occurred in the CNS. In recent years several biomarkers that previously have been linked to other neurological and immunological diseases have received increased attention in MS. Additionally, other novel, potential biomarkers with prognostic and diagnostic properties have been detected in the CSF and blood of MS patients. Areas covered: in this review, we summarise the most up to date knowledge and research conducted on the already known and most promising new biomarker candidates found in the CSF and blood of MS patients. Author commentary: the current diagnostic criteria of MS rely on three pillars; MRI imaging, clinical events and the presence of oligoclonal bands in the CSF (which was reinstated into the diagnostic criteria by the most recent revision). Even though the most recent McDonald criteria made the diagnosis of MS faster than the prior iteration, it is still not an infallible diagnostic toolset, especially at the very early stage of clinically isolated syndrome. Together with the gold standard MRI and clinical measures, ancillary blood and CSF biomarkers may not just improve diagnostic accuracy and speed but very well may become agents to monitor therapeutic efficacy and make even more personalised treatment in MS a reality in the near future. The major disadvantage of these biomarkers in the past has been the need to obtain CSF to measure them. However, the recent advances in extremely sensitive immunoassays made their measurement possible from peripheral blood even when present only in minuscule concentrations. This should mark the beginning of a new biomarker research and utilisation era in MS.

The authors determined mass concentrations of 12 elements (As, Cd, Cr, Cu, Mn, Ni, Pb, Se, Sr, Tl, V, Zn) in blood and urine taken from 100 rural children living in unremarkable condition in the West Ural region of the Russian Federation. We applied inductively coupled plasma mass spectrometry in conformity with Methodical Guidelines 4.1. 3230-14 (FR.1.31.2014.17064) designed by us and detailed in the paper. The article contains setting parameters for Agilent 7500cx quadrupole inductively coupled plasma mass spectrometer . The authors present grounds for an optimal scheme of samples preparation which helps to eliminate "matrix" effect and explain why it is advisable to use reaction/collision functioning mode as it enables interference overlaps suppression.To prepare whole blood samples for analysis, we used acid dissolution in concentrated nitric acid followed by centrifugation. The arithmetic mean (AM) of element content in blood amounted to 0.34 µg/l (As); 0.39 µg/l (Cd); 6.41 µg/l (Cr); 866.11 µg/l (Сu); 13.73 µg/l (Mn); 5.86 µg/l (Ni); 23.65 µg/l (Pb); 87.43 µg/l (Se); 33.54 µg/l (Sr); 0.04 µg/l (Tl); 0.16 µg/l (V); 4713.48 µg/l (Zn). The validity of the results was confirmed by means of SERONORM ^TM Whole Blood L2 standard samples (Norway). Urine samples were directly analyzed after 1/10 (V/V) dilution with 1% nitric acid solution. The arithmetic mean of element content of urine corresponded to 18.99 µg/l (As); 0.12 µg/l (Cd); 1.91 µg/l (Cr); 13.28 µg/l (Сu); 0.96 µg/l (Mn); 1.84 µg/l (Ni); 0.83 µg/l (Pb); 22.55 µg/l (Se); 239.09 µg/l (Sr); 0.16 µg/l (Tl); 0.68 µg/l (V); 270.56 µg/l (Zn). The validity of the results was confirmed by analyzing the SERONORM ^TM standard urine samples (Norway). Regional differences from levels found in other countries included higher concentrations of Cr, Mn and Ni in blood and lower levels of Se and Zn levels in West Ural children. Urine concentrations of West Ural children showed higher levels of As and Sr.

Cancer is the second biggest cause of death in children. With the development of chemotherapy, there is a substantial increase in overall survival rate over recent 30 years. However, the overall mortality rate in children with cancer still remains 25%, and many survivors experience a decline in overall quality of life and long-term adverse effects caused by treatments. Although cancer cells share common characteristics, pediatric cancers are different from adult cancers in their prevalence, mutation load, and drug response. Therefore, there is an urgent unmet need to develop therapeutic approaches specifically designed for children with cancer. Nanotechnology can potentially overcome the deficiencies of conventional methods of administering chemotherapy and ultimately improve clinical outcomes. The nanoparticle-based drug delivery systems can decrease the toxicity of therapy, provide a sustained or controlled drug release, improve pharmacokinetic properties of loading contents, and achieve a targeted drug delivery with achievable modifications. Further, therapeutic approaches based on combining nanoformulated drugs with novel immunotherapeutic agents are emerging. In this review, we discuss the recently developed nanotechnology-based strategies for treating blood and solid pediatric cancers.

P-glycoprotein (Pgp) determines resistance to a broad spectrum of drugs in glioblastoma multiforme (GB) because it is highly expressed in GB stem cells and in brain-blood barrier (BBB), the peculiar endothelium surrounding brain. Inhibiting Pgp activity in BBB and GB is still an open challenge. Here, we tested the efficacy of a small library of tetrahydroisoquinoline derivatives with an EC₅₀ for Pgp < 50 nM, in primary human BBB cells and in patients-derived GB, from which we isolated differentiated/adherent cells (AC, i.e. Pgp-negative/doxorubicin-sensitive cells) and stem cells (neurospheres, NS, i.e. Pgp-positive/doxorubicin-resistant cells). At 1 nM, 3 compounds increased the delivery of doxorubicin, a typical substrate of Pgp, across BBB monolayer, without altering expression and activity of other transporters. The compounds increased the drug accumulation within NS, restoring necrosis, apoptosis and reduction in cell viability induced by doxorubicin. In co-culture systems, the compounds added to the luminal face of BBB increased the delivery of doxorubicin to NS growing under BBB and rescued the drug’s cytotoxicity. Our work identified new ligands of Pgp active at low nanomolar concentrations, that effectively reduce Pgp activity in BBB and GB, and can improve chemotherapy efficacy in this tumor.

FoxP3 is a transcription factor essential for the differentiation and function of T regulatory cells (Tregs). There are two major subsets of Tregs: natural Tregs (nTregs) generated in thymus and inducible Tregs (iTregs) produced in peripheral immune system. It has been documented that iTreg development is dependent on soluble mediators including interleukin 2 (IL2), transforming growth factor β (TGFβ) and all-trans-retinoic acid (ATRA). In our experiments we performed a gene expression array, followed by Real-time PCR experiments, to study the expression of genes regulated by 1,25-dihydroxyvitamin D (1,25D) or ATRA in cells of myeloid origin. Our experiments revealed that ATRA alone, but also a cocktail of mediators consisting of IL2, TGFβ and ATRA, upregulate the expression of FOXP3 gene in lymphoid cells, but also in normal and leukemic myeloid cells. The FoxP3 expression is followed by a phenotypic changes in cells of myeloid origin. Our results indicate that signaling pathways which are used in the late stages of T cell differentiation, are also active in the cells of myeloid lineage

The switch from intermittently scanned continuous glucose monitoring (isCGM) to real-time (rt) CGM could improve glycemic management in suboptimal controlled type 1 diabetes patients, but long-term study is lacking. We evaluated retrospectively the ambulatory glucose profile (AGP) in such patients after switching from Free Style libre 1 (FSL1) to Dexcom G4 (DG4) over 1 year. Patients (n=21, 43±15 years, BMI 25±5, HbA1c 8.1±1.0%) had severe hypoglycemia and/or HbA1c≥8%. AGP metrics (time-in-range (TIR) 70-180 mg/dL, time-below-range (TBR)<70 mg/dL or <54 mg/dL, glucose coefficient of variation (%CV), time-above-range (TAR) >180 mg/dL or >250 mg/dL, glucose management indicator (GMI), average glucose) were collected the last 3 months of FSL1 use (M0) and of DG4 for 3, 6 (M6) and 12 (M12) months of use. Values were means ± standard deviation or medians [Q1;Q3]. At M12 versus M0, the higher TIR (50±17 vs. 45±16, P=0.036), and lower TBR<70 mg/dL (2.5 [1.6;5.5] vs. 7.0 [4.5;12.5], P=0.0007), TBR<54 mg/dL (0.7 [0.4;0.8] vs. 2.3 [0.8;7.0], P=0.007) and %CV (39±5 vs. 45±8, P=0.0009), evidenced a long-term effectiveness of the switch. Compared to M6, TBR<70mg/dL decreased, %CV remained stable, while the improvement on hyperglycemia exposure decreased (higher GMI, TAR and average glucose). This switch was a relevant therapeutic option, though a loss of benefit on hyperglycemia stressed the need for optimized management of threshold alarms. Nevertheless, few patients attained the recommended values for AGP metrics, and the reasons why some patients are “responders” vs “non-responders” warrant to be investigated.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized mainly by the gradual decay in neuronal function as a consequence of diverse degenerating events primarily including mitochondria dysfunction and cascades of neuro-immune reactions. Besides the acquired harmful reactive oxygen species (ROS), neurotoxins, and amyloid-beta (Aβ) and TAU pathologies in neurons, accumulating evidence with time underlined the roles of cytokines and growth factors in the AD pathogenesis. It may help us in evaluating the propensities and specific mechanism(s) of cytokines and factors impacting neuron upon apoptotic decline. Proinflammatory cytokines often induce inflammation in AD and AD-like pathogenesis in response to the apoptotic scenarios where some growth factors are involved in cytokinetic reactions to activate microglia and causing inflammation in AD. In this report, we comprehensively reviewed role of cytokines and chemokines in immune response to AD and neuropsychiatry. We provided insights into the neuroinflammation and the role of diverse factors including the pro-/anti-inflammatory cytokines, APP, TAU phosphorylation, glycation end products, complement system, and the role of glial cells. Also, we discussed the pathogenic and protective role of macrophage migration inhibitory factors, choroid plexus-, neurotrophic- and hematopoietic -related growth factors in AD. We further shed light on the availability and accessibility of the cytokines across the blood-brain barrier in AD pathophysiology. Taken together, the emerging role of these factors in AD pathology emphasized the importance of building novel strategies for an effective therapeutic/neuropsychiatric management of AD in clinics.

The aim of this study was to analyze whether the respiratory variation in ECG standard lead II R-wave amplitude (ΔRDII) could be used to assess intravascular volume status following inferior vena cava (IVC) clamping. This clamping causes an acute decrease in cardiac output during liver transplantation (LT). We retrospectively compared ΔRDII and related variables before and after IVC clamping in 34 recipients. Receiver operating characteristic (ROC) curve and area under the curve (AUC) analyses were used to derive a cutoff value of ΔRDII for predicting pulse pressure variation (PPV). After IVC clamping, cardiac output significantly decreased while ΔRDII significantly increased (P = 0.002). The cutoff value of ΔRDII for predicting a PPV >13% was 16.9% (AUC: 0.685) with a sensitivity of 57.9% and specificity of 77.6% (95% confidence interval 0.561 – 0.793, P = 0.015). Frequency analysis of ECG also significantly increased in the respiratory frequency band (P = 0.016). Although significant changes in ΔRDII during vena cava clamping were found at norepinephrine doses < 0.1 μg/kg/min (P = 0.014), such changes were not significant at norepinephrine doses > 0.1 μg/kg/minP = 0.093). ΔRDII could be a noninvasive dynamic parameter in LT recipients presenting with hemodynamic fluctuation. Based on our data, we recommended cautious interpretation of ΔRDII may be requisite according to vasopressor administration status.

In light of the globally increasing prevalence of diet-related non-communicable diseases (NCDs), new scalable and non-invasive dietary monitoring techniques are urgently needed. Automatically collected digital receipts from loyalty cards have the potential to serve as an objective and automatically traceable digital biomarker for individual food choice behavior and do not require patients to manually log each individual meal item. Until recently, such electronic purchase records were hard to collect for researchers and were only validated in national empirical studies. Multiple quantitative indicators for purchase quality have been suggested, but so far no comparison has validated the potential of these alternative indicators to discriminate between health-beneficial and -detrimental food choices. With the introduction of the General Data Privacy Regulation in the European Union, millions of consumers gained the right to access their purchase data in a machine-readable form, representing a historic chance to leverage purchase data for scalable monitoring of food choices. This study hence is the first study comparing the calibration capacity and validating the discrimination potential of previously suggested purchase indicators for the nutritional quality of purchased groceries, incl. HEI-2015, HETI, GPQI, and FSA-NPS DI. To assess the indicators' potential, 464 study participants were asked to complete a validated food frequency questionnaire (FFQ) and to donate their digital receipts from the loyalty card programs of the two leading Swiss grocery retailers, representing 69\% of the national grocery market. 89 participants fulfilled the eligibility criteria, i.e. completed the FFQ and were frequent users of the loyalty card systems. Compared to absolute food and nutrient intake, correlations between density-based relative food and nutrient intake and food purchase data are stronger. Counterintuitively, although the frameworks of the HETI and the GPQI are centered around food groups, both indicators do not capture food group intake such as vegetables or sweets very well. The FSA-NPS DI has the best calibration and discrimination performance in classifying participants' consumption of nutrients and food groups, and seems to be a superior indicator to estimate nutritional quality of a user's diet based on digital receipts from grocery purchases in Switzerland.

The development of an underground gas storage (UGS) project and its subsequent management must ensure technical feasibility, commercial value and long-term efficiency. The UGS industry has borrowed much of its knowledge from other disciplines (primarily oil and gas reservoir engineering), but it has also developed its own technology. This paper provides a methodological approach based on current practices and available methods for designing and safely operating a UGS (including the so-called “delta-pressure” option to enhance UGS performance) and highlights what is special in UGS compared to oil and gas reservoirs.

This article presents and discusses analytical data on the scientific publication record from 1910 to 2020 on two topics: "climate" and "climate change/global warming/climate emergency". The goal is to visualize how the publication record on these two topics has evolved over time, from different classification perspectives (year, country, source and organization). Three hypotheses are tested using data collected from Web of Science and various graphical representations of the data. It is found that research output related to the Earth’s contemporary changing climate overtook that of general climate research in 2011, and the publication ratio has been expanding in the last decade. There are significant differences in the publication countries and sources between the two topics, and conversely less significant differences in terms of organizations publishing these works. Differentiation factors that affect the level of research output and engagement on the climate challenge include: island versus landlocked nations, specialized versus general scientific journals, academic versus institutional organizations. The future of the publication records is discussed, such as the emergence of new terms to refer to the climate challenge, such as “climate emergency”.

Focusing geophysics to improve recovery factor of hydrocarbon reservoirs adds value and contributes toward ZERO carbon footprint by increasing the recovery factor by of 30-40 % and thus reducing the cost/carbon emission per produced barrel. Thus, the Enhanced Oil Recovery (EOR) market is expected to grow more than 3.5% annually. This will be even more fueled by the Green-House-Gas (GHG) reduction and subsequent CO2 injection into the reservoirs as they are being produced. Presently, geophysics only ac-counts for a small percentage of this market, thus its growth is inevitable since more deterministic observation lead higher operating efficiency. Imaging the fluids (hydro-carbon, water, and CO2) is a key component to optimized production and injection. We designed a novel electromagnetic (EM) acquisition system that combines mul-ti-physics fluid imaging and acquires surface and borehole data with high fidelity. Borehole calibration is needed to upscale reservoir data and parameters to measurement scale. Multiple electromagnetic methods are used as well as microseismics in one layout for Exploration and Production (E & P) problems. Multi-components in electromagnetics allows resolving oil and water-bearing zones equally well while achieving the best ac-curacy suitable for repeat measurements. Because sedimentary basins are intrinsically anisotropic, considering 3-dimensional anisotropy is essential from measurement and 3D modeling viewpoint. Thus, the results have the better subsurface images. Here, we combine hardware design, methodology, 3D modeling, processing, and interpretations into an integrated technology and demonstrate the success with verifiable case histories.

Several common and functional genes are known to contribute to responsiveness to blood pressure (BP) therapy. BP therapy is typically guided by algorithms that do not include a patient’s genetic information. This study aimed to determine the impact of a multi-organ genetic panel on BP response to pharmacotherapy. Eighty-six patients completed one study visit consisting of a buccal swab collection, measurement of office BP, and a medical chart review for BP history. Genes analyzed included those that encode for one drug metabolizing enzyme, renal Na+ handling, vascular, and cardiac function. Relationships between genotype and control of BP (<140/<90), ∆ systolic BP, ∆ diastolic BP, and ∆ mean arterial BP were assessed. SLC12A3 resulted in a significant association between the target drug and the functional genotype for BP control (<140/<90 cut off) (p<0.05). Conversely, three of five renal genotypes were associated with BP control using 120/80 as a cut-off (p<0.05). Three of four cardiac genotypes were associated with the BP control at <140/<90, with one being statistically significant (position 49 of ADRB1). Only one vascular genotype was predictive of blood pressure control at <140/<90. We found a significant drop in mean BP from baseline in six genes, three important in the diuretic response and three in β-blockade (p<0.05 on target drug vs. not). These results demonstrate that a multi-gene panel for renal Na+ handling, vascular function, and cardiac output may influence the BP response to therapy, but larger studies with more statistical power are needed.

In contrast to normal cells, tumor cells of multiple entities overexpress the Heat Shock Protein 70 (Hsp70) not only in the cytosol, but also present it on their plasma membrane in a tumor-specific manner. Furthermore, membrane-Hsp70 positive tumor cells actively release Hsp70 into lipid microvesicles termed exosomes into the blood. Due to conformational changes of Hsp70 in the lipid environment, most commercially available antibodies fail to detect membrane-bound and exosomal Hsp70. To fill this gap and to assess the role of exosomal Hsp70 in the circulation as a potential tumor biomarker, we established the novel complete Hsp70 (compHsp70) sandwich ELISA using two monoclonal antibodies (mAbs) that are able to recognize both, free and lipid-associated Hsp70 on the cell surface of viable tumor cells and exosomes. The epitopes of the mAbs cmHsp70.1 (aa 451-461) and cmHsp70.2 (aa 614-623) that are conserved among different species reside in the substrate-binding domain of Hsp70, with measured affinities of 0.42 nM and 0.44 nM, respectively. Validation of the compHsp70 ELISA revealed a high intra- and inter-assay precision, linearity in a concentration range of 1.56 to 25 ng/ml, high recovery rates of ‘spiked’ liposomal Hsp70 (>84%), comparable values between human serum and plasma samples, and no interference by food intake or age of the donors. Hsp70 concentrations in the circulation of patients with glioblastoma, squamous cell or adeno non-small cell lung carcinoma (NSCLC) at diagnosis were significantly higher than those of healthy volunteers. Hsp70 concentrations dropped concomitantly with the decrease in viable tumor mass on irradiation of patients with approximately 20 Gy (range 18 – 22.5 Gy) or after completion of radiotherapy (60 - 70 Gy). In summary, the compHsp70 ELISA presented herein provides a highly sensitive and reliable tool for measuring free and exosomal Hsp70 in liquid biopsies of tumor patients, levels of which can be used as a predictive tumor-specific biomarker, risk assessment and for monitoring therapeutic outcome.

Coral reefs are declining worldwide due to global changes in the marine environment. The increasing frequency of massive bleaching events in the tropics is highlighting the need to better understand the stages of coral physiological responses to extreme conditions. Moreover, like many other coastal regions, coral reef ecosystems are facing additional localized anthropogenic issues such as nutrient loading, increased turbidity, and coastal development. Different strategies have been developed to measure the health status of a damaged reef, ranging from the resolution of individual polyps to an entire coral community, but techniques for measuring coral physiology in situ are not yet widely implemented. For instance, while there are many studies of the coral holobiont response in single or limited-number multiple stressor experiments, they provide only partial insights to metabolic performance under more complex temporally and spatially variable natural conditions. Here, we discuss the current status of coral reefs and their global and local stressors in the context of experimental techniques that measure core processes in coral metabolism (respiration, photosynthesis, and biocalcification) in situ, and their role in indicating the health status of colonies and communities. We highlight the need to improve the capability of in situ studies in order to better understand the resilience and stress response of corals under multiple global and local scale stressors.

Conditional generative adversarial networks (CGANs) learn a mapping from conditional input to observed image and perform tasks in image generation, manipulation and translation. In-situ monitoring uses sensors to obtain real-time information of additive manufacturing (AM) processes that relate to process stability and part quality. Understanding the correlations between process inputs and in-situ process signatures through machine learning can enable experimental-driven predictions of future process inputs. In this research, in-situ data obtained during a metallic powder bed fusion AM process is mapped with a CGAN. A single build of two turbine blades is monitored using EOSTATE Exposure OT, a near-infrared optical tomography system of the EOS M290 system. Layerwise images generated from the in-situ monitoring system were paired with a conditional image that labeled the specimen cross-section, laser-scan stripe overlap and z-distance to part surfaces. A CGAN was trained using the turbine blade data set and employed to generate new in-situ layerwise images for unseen conditional inputs.

This paper presents a device for measuring and maintaining the constant water level in a tank. The device uses a capacity sensor to measure the water level. The sensor has a DC voltage output proportional to the water level in the tank. This voltage can be used in other automation too. The water level, in percentages, is displayed on a vu meter with 8 LEDs. The circuit for maintaining the constant level consists in a comparator with hysteresis. The level of the water is adjustable, using a reference voltage from a potentiometer (marked in percentages). The level of the water is compared with the prescribed level and the difference between them commands the start/stop of the water pump which supplies the tank through a relay. The device is powered by 220 V and is isolated from the environment.

In reservoir engineering, one of the main sources of information for the characterization of reservoir and well parameters is well testing. Among the unconventional well testing methodologies, Harmonic Well Testing (HPT) is appealing from an economic standpoint because it could provide well performance and reservoir behavior monitoring without having to interrupt field production. Recorded pressure analysis is performed in the frequency domain by adopting a derivative approach similar to conventional well testing. To this end, pressure and rate data must be decomposed into harmonic components. Test interpretability can be significantly improved if pressure data are detrended prior to interpretation, filtering out non periodic events such as discontinuous production from neighboring wells and flow regime variations which did not respect the designed test periodicity. Therefore, detrending offers the possibility of overcoming the limitation of HPT applicability due to the difficulty of imposing a regularly pulsing rate for the whole test duration (typically lasting several days). This makes HPT attractive for well performance monitoring, especially in gas storage fields. In this paper, the application of different detrending methodologies to synthetic HPT pressure data generated in different reservoir and operational scenarios is presented and discussed. Moreover, a real case application is also presented.

Despite recent developments in monitoring and visualizing construction progress, the data exchange between construction jobsite and office lacks automation and real-time recording. To address this issue, a near real-time construction work inspection system called iVR is proposed; this system integrates 3D scanning, extended reality, and visual programming to visualize interactive onsite inspection for indoor activities and provide numeric data. iVR comprises five modules: iVR-location finder (finding laser scanner located in the construction site) iVR-scan (capture point cloud data of jobsite indoor activity), iVR-prepare (processes and convert 3D scan data into a 3D model), iVR-inspect (conduct immersive visual reality inspection in construction office), and iVR-feedback (visualize inspection feedback from jobsite using augmented reality). An experimental lab test is conducted to validate the applicability of iVR process; it successfully exchanges required information between construction jobsite and office in a specific time. This system is expected to assist Engineers and workers in quality assessment, progress assessments, and decision-making which can realize a productive and practical communication platform, unlike conventional monitoring or data capturing, processing, and storage methods, which involve storage, compatibility, and time-consumption issues.

(1) Background: High blood pressure is one of the symptoms of stroke, the Self Care Deficit theory by Dorothea Orem focuses on a person's ability to care for himself independently so that the ability to maintain his health and well-being is achieved. There are currently 600 million hypertensive patients worldwide, of which 3 million die every year. Uncontrolled high blood pressure can lead to a 7-fold increase in the chances of stroke. The existence of this condition can be minimized in patients through several innovative interventions, one of which is foot massage. This study aims to determine the effectiveness of stretching and foot massage with lavender oil in stabilizing high blood pressure in stroke patients.(2) Methods: The method used in this research is descriptive. The subjects of this case study were selected according to the inclusion and exclusion criteria of the journal Evidence-Based. Subjects included intervention patients and control patients. Foot massage therapy with lavender oil is done 2 times a day for 10-15 minutes using the effleurage technique after showering in the morning and evening. Stretching is done 2 times a day every 10-15 minutes.(3) Results: The results of the analysis showed that there was a change in blood pressure after the application of stretching and foot massage with lavender oil. From the initial examination, the results showed that the blood pressure of 170/100 mmHg decreased to 140/90 mmHg and the pain scale decreased from 4 to 0.

The present work aimed to validate a low-cost passive monitoring procedure. For its validation, the monitoring of atmospheric organic pollutants - polycyclic aromatic hydrocarbons (PAH) was carried out in a capital of the central-western region of Brazil. The sensors were fixed on poles intended for electrification during the dry season. After 15 days, samples were extracted by solvent extraction and analysed by High-Resolution Gas Chromatography with Flame Ionization Detector (HRGC-FID). For the validation of the procedure, PAHs monitored and standardised by the American Environmental Agency (EPA), a benchmark for environmental monitoring of air quality by several countries, were analysed. The results demonstrated that the low-cost passive monitoring method was effective in the quantification of PAH in the environment-air, capable of being used by countries that do not have many resources for monitoring air quality.

Background and Objectives: The IPED study showed that a smartphone-based event recorder increased the number of patients in whom an ECG was captured during symptoms over five-fold to more than 55% at 90 days compared to standard care [1] and concluded that this safe, non-invasive and easy to use device should be considered part of on-going care to all patients presenting acutely with unexplained palpitations or pre-syncope. This study reports the process of establishing a smartphone palpitation and pre-syncope service. Materials and Methods: A clinical Standard Operating Procedure (SOP) was devised, and funding was secured through a business case for the purchase of 40 AliveCor devices in the first instance. The clinic was launched on 22nd July 2019. Results: Between 22nd July 2019 and 31st October 2019, 68 patients seen in the ED with palpitations or pre-syncope were referred to SPACC. 30 were male and 38 female and mean age was 45.8 (SD 15.1) with a range from 18 to 80 years. 50 (74%) patients underwent full investigation. 7 (11%) patients were deemed on first assessment to have non-cardiac palpitations and were not fitted with the device. All patients who underwent full investigation achieved symptomatic rhythm correlation most with sinus rhythm, ventricular ectopics or bigeminy. A symptomatic cardiac dysrhythmia was detected in 6 (8.8%) patients. 3 patients had supraventricular tachycardia; SVT (4%), 2 had atrial fibrillation (3%) and 1 atrial flutter (2%). Qualitative feedback from the SPACC team suggested several areas where improvement to the clinic could be made. Conclusion: We believe a smartphone palpitation service based in ambulatory care is simple to implement and is effective at detecting cardiac dysrhythmia in ED palpitation patients.

Survivors of severe brain injury may require care in a neurointensive care unit (neuro-ICU), where the brain is vulnerable to secondary brain injury. Thus, there is a need for noninvasive, bedside, continuous cerebral blood flow monitoring approaches in the neuro-ICU. Our goal is to address this need through combined measurements of EEG and functional optical spectroscopy (EEG-Optical) instrumentation and analysis to provide a complementary fusion of data about brain activity and function. The present case demonstrates in a patient with traumatic brain injury, noninvasive cerebral blood flow transients can be recorded that correlate with gold-standard invasive measurements and with the frequency content changes in the EEG data during clinical care.

CXCR4 is a chemokine receptor crucial in tumor progression, although the angiogenic role of CXCR4 in oral squamous cell carcinoma (OSCC) has not been investigated. Here we show that CXCR4 is crucial for tumor angiogenesis thereby supports tumor survival in OSCC. Immunohistochemistry on human clinical specimens revealed that CXCR4 and a tumor vasculature marker CD34 were co-distributed in tumor vessels in human OSCC specimens. To ask the effects of CXCR4 inhibition, we treated the OSCC-xenografted mice with AMD3100, so-called plerixafor, an antagonist of CXCR4. Notably, we found a unique pathophysiological structure defined as Tumor Angiogenic Inhibition Triggered Necrosis (TAITN) induced by the CXCR4 antagonism. Treatment with AMD3100 increased necrotic area with the induction of hypoxia-inducible factor-1α in the xenografted tumors, suggesting that AMD3100-induced TAITN was involved in hypoxia and ischemia. Taken together, we demonstrated that CXCR4 plays a crucial role in tumor angiogenesis required for OSCC progression, whereas TAITN induced by CXCR4 antagonism could be an effective anti-angiogenic therapeutic strategy in OSCC treatment.

Student attendance serves many other important purposes aside from monitoring. In certain universities, the attendance of students in a course is also used as one of the requirements for students to be allowed to sit for the final examination. Traditionally, among most Malaysian Institutions of Higher Learning (IHL), attendance recording is usually done using pen and paper, or uses simple web-based system that is time consuming and difficult for faculty periodic monitoring. To address the identified drawbacks, this research aims to develop a Smart Attendance for Faculty Monitoring System using the Bluetooth Low Energy (BLE) technology to assist faculty in recording, managing and monitoring students’ attendance and class schedules effectively. The system is developed for Android-based devices using an agile methodology consists of iteration and incremental approaches. Thus, to evaluate the effectiveness of the system, a survey was conducted on 140 respondents involving lecturers and students of Kolej Universiti Poly-Tech MARA (KUPTM). Respondents were selected using purposive sampling. The descriptive analysis showed that 87.9% of the respondents strongly agreed that the system is effective in assisting lecturers to record attendance, manage class schedules and student attendance as well as to assist faculty in monitoring students’ absenteeism.

Hypertension is a common and growing medical problem that leads to enormous cardiovascular and kidney disease worldwide. While many drugs exist to treat hypertension, there is large individual variation in how a given individual responds to different agents, which contributes to dismal rates of hypertension control. While demographic factors predict which drugs may work better in certain individuals, a great degree of this variation has a genetic basis. In recent years, genome wide association studies have begun to identify specific gene variants that predict drug response to particular agents. This review identifies the major genetic variants influencing antihypertensive response that have emerged from this growing body of work. For novel genetic variants without a previously known biologic basis in blood pressure, it is crucial to validate initial findings in subsequent studies. This information may eventually lead to a more personalized approach to hypertension management that will improve blood pressure control and patient outcomes. The integration of this large amount of data and its real world application will be highly challenging, but strategies to accomplish this are discussed.

In this paper, we investigated transducer placement strategies for detecting crack in primary aircraft structures using ultrasonic Structural Health Monitoring (SHM). The approach developed is for an expected damage location based on fracture mechanics, for example fatigue crack growth in a high stress location. To assess the performance of the developed approach, finite-element (FE) modelling of a damage tolerant aluminum fuselage has been performed by introducing an artificial crack at a rivet hole into the structural FE model and assessing its influence on the Lamb wave propagation, compared to a baseline measurement simulation. The efficient practical sensor position was determined from the largest change in area that is covered by reflected and missing wave scatter using an additive color model. Blob detection algorithms were employed in order to determine the boundaries of this area and to calculate the blob centroid. To demonstrate that the technique can be generalized, the results from different crack lengths and from tilted crack are also presented.

Swift and diligent resilience response is mandatory in sustainable geo-distributed ecosystems. The real-time geo-spatial resilience requires agility in millions of parallel and distributed data processing tasks on data acquired from regional condition monitoring(RCM) systems. These tasks include expiditous resolution of complex sustainability conflict sets, promptly anomalies characterization in chaos sets, and resilience response uniqueness. This work is an archetype of a paragon geo-resilience support system(GRSS) for regional sustainability using a novel melioration in DADO production machine. The proposed expert system capitalized the synergic strengths of RETE, TREAT, LEAPS and GATOR networks was designed and implemented as a synthetic DADO machine(SDM). The generic architecture of DADO machine was improved in this work by enrichment in rule set conditions and solution set for regional scale resilience rule set conditions. The condition left-hand side(LHS) X equal to the solution set 2X for right-hand side (RHS) was the goal achieved by working memory(WM) optimization and conflict resolution strategy(CRS) in alpha and beta networks rules. A round-trip time of 80.2 seconds for first event response set using 1492 segment size and sequence number 360,000 with maximum packets at a single geospatial structure was 21 packets/sec was a noticeable landmark in this work. LEAPS and Concurrent-read algorithm for GATOR cluster networks in the proposed synthetic DADO machine architecture was the overall implementation that enabled urban scale resilient system practically possible on physical SHM deployment.

Wearable sensors for human physiological monitoring have attracted tremendous interest from researchers in recent years. However, most of the research was only done in simple trials without any significant analytical algorithms. This study provides a way of recognizing human motion by combining textile stretch sensors based on single-walled carbon nanotubes (SWCNTs) and spandex fabric (PET/SP) and machine learning algorithms in a realistic applications. In the study, the performance of the system will be evaluated by identification rate and accuracy of the motion standardized. This research aims to provide a realistic motion sensing wearable products without unnecessary heavy and uncomfortable electronic devices.

Ecosystem surveillance monitoring is critical to managing natural resources and especially so under changing environments. Despite this importance, the design and implementation of monitoring programs across large temporal and spatial scales has been hampered by the lack of appropriately standardised methods and data streams. To address this gap, we outline a surveillance monitoring method based on permanent plots and voucher samples suited to rangeland environments around the world that is repeatable, cost-effective, appropriate for large-scale comparisons and adaptable to other global biomes. The method provides comprehensive data on vegetation composition and structure along with soil attributes relevant to plant growth, delivered as a combination of modules that can be targeted for different purposes or available resources. Plots are located in a stratified design across vegetation units, landforms and climates to enhance continental and global comparisons. Changes are investigated through revisits. Vegetation is measured to inform on composition, cover and structure. Samples of vegetation and soils are collected and tracked by barcode labels and stored long-term for subsequent analysis. Technology is used to enhance the accuracy of field methods, including differential GPS r plot locations, instrument based Leaf Area Index (LAI) measures, and three dimensional photo-panoramas for advanced analysis. A key feature of the method is the use of electronic field data collection to enhance data delivery into a publicly-accessible database.Our method is pragmatic, whilst still providing consistent data, information and samples on key vegetation and soil attributes. The method is operational and has been applied at more than 704 field locations across the Australian rangelands as part of the Ecosystem Surveillance program of the Terrestrial Ecosystem Research Network (TERN). The methodology enables continental analyses, and has been tested in communities broadly representative of rangelands globally, with components being applicable to other biomes. Here we also recommend the consultative process and guiding principles that drove the development of this method as an approach for development of the method into other biomes. The consistent, standardised and objective method enables continental, and potentially global analyses than were not previously possible with disparate programs and datasets.

My hypothesis is that the signs and symptoms of Long COVID can be explained by a shortage of blood in the body and a resulting deficient blood flow through nearly all organs. This shortage arises through damage to the blood-producing organs during the acute phase, while the breakdown of blood continues as normal, after an initial increase. In order to ensure the perfusion of organs that are directly necessary for survival, the body takes the emergency measure of diverting blood from other organs and tissues. The perfusion of the blood-producing organs is also affected by this distribution measure, which hinders the smooth recovery of the total blood volume. The body is stuck in this vicious circle: a shortage of circulating blood hinders the recovery of blood production. This explains the long duration of Long COVID. My proposed treatment of Long COVID focuses on the recovery of the correct volume of blood in the body of the right composition by the very careful administration of donor blood products under continuous expert supervision. A trial treatment can be performed in any hospital without much additional preparations, and has a lower associated risk for the patient than analysing the total blood. A diagnosis ex juvantibus, by therapeutic response, is therefore preferable, and will result in the healing process starting earlier. Indications in blood laboratory values of a shortage of blood are a high serum ferritin due to internal breakdown of blood and values for haematocrit and albumin at reciprocal extremes of the reference ranges due to a stagnation of blood production.

Introduction: Blood transfusion involves the transfer of blood from donors to patients. A blood transfusion is carried out every 2 seconds in the US. It is made up of about 29000 units of red blood cells and is transfused every day in the US. When blood transfusion is done correctly, it can result in the saving of lives and the improvement of healthcare. However, it may also lead to immediate, late, delayed, and chronic complications. No previous studies have been conducted in Qatar to address this issue. Methods: This is a cross-sectional study intended to determine the knowledge, attitude, and practice toward blood transfusion among healthcare providers at Hamad Medical Corporation (HMC), which is the principal healthcare provider in Qatar. Participants between 18 and 25 years of age were selected for the research study. A 10-item online questionnaire that people can fill out on their own will be used to get the data needed for the analysis and meet the study's goals. Results: the analysis has indicated that facing negative reactions after blood transfusion and being worried about getting affected by any infection have a small positive association, with the specific values coming in at r = 0.317, p = 0.000. Fever after blood transfusion and feeling like refusing blood transfusion have a significant and moderate positive correlation, with the specific values coming in at r = 0.630 and p = 0.000. Conclusion: The findings of this study have helped us figure out how healthcare providers feel, what they know, and what they do during a blood transfusion.

The oil and gas industry faces increasing pressure to remove people from dangerous offshore environments. Robots present a cost-effective and safe method for inspection, repair and maintenance of topside and marine offshore infrastructure. In this work, we introduce a new immobile multi-sensing robot, the Limpet, which is designed to be low-cost and highly manufacturable, and thus can be deployed in huge collectives for monitoring offshore platforms. The Limpet can be considered an instrument, where in abstract terms, an instrument is a device that transforms a physical variable of interest (measurand) into a form that is suitable for recording (measurement). The Limpet is designed to be part of the ORCA (Offshore Robotics for Certification of Assets) Hub System, which consists of the offshore assets and all the robots (UAVs, drones, mobile legged robots etc.) interacting with them. The Limpet comprises the sensing aspect of the ORCA Hub System. We integrated the Limpet with Robot Operating System (ROS), which allows it to interact with other robots in the ORCA Hub System. In this work, we demonstrate how the Limpet can be used to achieve real-time condition monitoring for offshore structures, by combining remote sensing with signal processing techniques. We show an example of this approach for monitoring offshore wind turbines. We demonstrate the use of four different communication systems (WiFi, serial, LoRa and optical communication) for the condition monitoring process. By processing the sensor data on-board, we reduce the information density of our transmissions, which allows us to substitute short-range high-bandwidth communication systems with low-bandwidth long-range communication systems. We train our classifier offline and transfer its parameters to the Limpet for online classification, where it makes an autonomous decision based on the condition of the monitored structure.

This article discusses the results of a series of experiments on pulse ultrasonic cure monitoring of carbon fiber reinforced plastics applied to the pultrusion process. The aim of this study is to validate the hypothesis that pulse ultrasonic cure monitoring can be applied a) for profiles having small cross sections such as 7x0.5 mm and b) within the environment of the pultrusion process. Ultrasonic transducers are adhesively bonded to the pultrusion tool as actuators and sensors. The time-of-flight and the amplitude of an ultrasonic wave are analyzed to deduce the current curing state of the epoxy matrix. The experimental results show that ultrasonic cure monitoring is indeed applicable even to very thin cross sections. However, significant challenges can be reported when the techniques are used during the pultrusion process.

Cerebral blood flow (CBF) decreases across the lifespan, and chronic conditions such as dementia and stroke accelerate this decline. Impaired CBF results in reduced delivery of oxygen and nutrients, which can damage the brain over time. Thus, there is a need to identify lifestyle interventions including diet and exercise to maintain CBF with aging and in the presence of chronic disease. In the present study, we used transcranial Doppler ultrasound to record middle cerebral artery velocity (MCAv), a surrogate measure of CBF, during moderate-intensity exercise in sedentary, cognitively-normal older adults (N = 90). A multiple linear regression model (F(4, 85) = 3.21, p = 0.02) showed self-reported omega-3 supplement use significantly moderated the association between age and mean exercising MCAv in these individuals (p = 0.01). Older age was associated with lower exercising MCAv in the group not taking omega-3 supplements, while exercising MCAv showed no decline with increasing age in the group who reported omega-3 supplement use. These findings suggest omega-3 supplementation may have an important role in the preservation of CBF with aging.

One of the key geophysical technologies for the energy industry during energy transition to zero footprint is fluid imaging. Knowledge of fluid distribution allows better, more optimized production reducing thus CO2 footprint per barrel produced and for CO2 storage the knowledge of where stored fluids go is mandatory to monitor reservoir seals. Electromagnetic is the preferred way to image fluid due to its strong coupling to the fluid resistivity. Unfortunately, acquiring and interpreting the data takes too long to contribute significantly to field operation and cost optimization. Using artificial intelligence and Cloud based data acquisition we can reduce the operational feedback to near real time and for the interpretation to close to 24 h. This then opens new door for the usefulness of this technology from exploration, monitoring and allows the application envelope to be enlarged to much noisier environment where real time acquisition can be optimized based on the acquired data.

Age-in-place can reduce the progress of dementia syndrome and improve the quality of life of the sufferers and their families. This work proposes an innovative information home support system to be established at the houses of people suffering from dementia. The main innovation of the system are its transparent character that overcomes the need for training of the sufferer, as well as the exploitation of a well-known technique for dementia, namely music therapy. It addresses the need of people suffering from dementia as well as their familiars to be recognized by them and have better interaction and collaboration. The system offers a ubiquitous recognition system, just by using smart devices like smart-phones or smart-wristbands. When a familiar person is detected in the house, then a sound file is reproduced on smart speakers placed in the house, in order to trigger the memory, based on the principles of music therapy. The system reproduced the appropriate time at 100% of the cases and the benefits of music therapy are evident daily. To the best of the authors' knowledge, this is the first system of its kind ever reported in the literature.

Androgens are key for pubertal development of the mammalian testis, a phenomenon tightly linked to Sertoli cell maturation. In this review, we discuss how androgen signalling affects Sertoli cell function and morphology by concomitantly inhibiting some processes and promoting others that contribute jointly to the completion of spermatogenesis. We focus on the molecular mechanisms that underlie AMH inhibition by androgens at puberty, as well as on the role androgens have on Sertoli cell tight junction formation and maintenance and, consequently, on its effect on proper germ cell differentiation and meiotic onset during spermatogenesis.

Understanding the relationships between land cover/urban structure patterns and air pollutants is key to sustainable urban planning and development. In this study, we employ a mobile monitoring method to collect PM_2.5 and BC data in the city of Philadelphia, PA during the summer of 2019 and apply the Structure of Urban Landscapes (STURLA) methodology to examine relationships between urban structure and atmospheric pollution. We find that, while PM_2.5 and BC vary by STURLA class, many of the differences in pollutant concentrations between classes are not significant. However, we also find that the proportions in which STURLA components are present throughout the urban landscape can be used to predict urban air pollution. Among frequently sampled STURLA classes, gpl hosted the highest PM_2.5 concentrations on average (16.60 _± 4.29 µg/m³), while tgbwp hosted the highest BC concentrations (2.31 _± 1.94 µg/m³). Furthermore, STURLA combined with machine learning modeling was able to correlate PM_2.5 (R²= 0.68, RMSE 2.82 µg/m³) and BC (R² = 0.64, RMSE 0.75 µg/m³) concentrations with the urban landscape and spatially interpolate concentrations where sampling did not take place. These results demonstrate the efficacy of the STURLA methodology in modeling relationships between air pollution and land cover/urban structure patterns.

Internet of Things (IoT) opens new horizons by enabling automated procedures without human interaction using IP connectivity. IoT deals with devices, called things which are represented as any item from our daily life that is enhanced with computing or communication facilities. Among various mobile communications, Zigbee communication is broadly used in controlling or monitoring applications due to its low data rate and low power consumption. Securing IoT systems have been the main concern for the research community. In this paper, different security-threats of Zigbee networks in IoT platform have been addressed to predict the potential security threats of Zigbee protocol and a Security Improvement Framework (SIF) has been designed for intelligent monitoring in an office environment. Our proposed SIF can predict and protect various potential malicious attacks in the Zigbee network and respond accordingly through a notification to the system administrator. This framework (SIF) is designed to make automated decisions immediately based on real-time data which are defined by the system administrator. Finally, the designed SIF has been implemented in an office security system as a case study for real-time monitoring. This office security system is evaluated based on the capacity of detecting potential security attacks. The evaluation results show that the proposed SIF is capable of detecting and protecting several potential security attacks efficiently enabling more secure way of intelligent monitoring.

The nondestructive testing of reinforced concrete chimneys, especially the high ones, is an important element of the assessment of their condition, making it possible to forecast their safe service lifespan. Industrial chimneys are often exposed to the strong action of acidic substances – they are adversely affected by the flue gas condensate on the inside and by acid precipitation on the outside. Initially, this results in the corrosion of the shell concrete and then in the corrosion of the reinforcing steel. During the service life of such chimneys their condition should monitored in order to prevent structural failures and indicate the most endangered parts of the structure. Owing to thermographic surveys one can monitor the hazards leading to the degradation of the chimney structure, which is particularly vital when due to the character of the production process the chimney cannot be put out of operation. The methods for the interpretation of results from thermovision studies to determine the safety and durability of industrial chimneys are shown.

Background: Hypertension is a condition of a person's blood pressure being at a level above normal, hypertension treatment can be done herbally using avocado leaves. Avocado leaves can lower blood pressure because they contain active substances, namely flavonoids and quercetin. This research was conducted in the working area of the Tilango Health Center. Objective: This study aims to determine the effect of giving boiled water of avocado leaves on reducing blood pressure in the elderly with hypertension. Methods: Design This study used a two-group quasi-experimental pre-test - post-test design. The sample is the elderly who have hypertension, amounting to 125 people with purposive sampling technique. The research instrument used a questionnaire. Results: Statistical test using T-Test on blood pressure before and after the intervention was given a significance value of 0.000 (p-value < 0.05 ). Conclusion : This study concludes that there is a significant effect between giving avocado leaf boiled water on reducing blood pressure in the elderly with hypertension in the working area of the Tilango Health Center.

Wireless sensor networks can provide a cheap and flexible infrastructure to support the measurement of noise pollution. However, the processing of the gathered data is challenging to implement on resource-constrained nodes, because each node has its own limited power supply, low-performance and low-power micro-controller unit and other limited processing resources, as well as limited amount of memory. We propose a sensor node for monitoring of indoor ambient noise. The sensor node is based on a hardware platform with limited computational resources and utilizes a number of simplifications to approximate more complex and costly signal processing stage. Furthermore, to reduce the communication between the sensor node and a sink node, as well as the power consumed by the IEEE 802.15.4 (ZigBee) transceiver, we perform digital A-weighting filtering and non-calibrated calculation of the sound pressure level on the node. According to experimental results, the proposed sound level meter can accurately measure the noise levels of up to 100~dB, with the mean difference of less than 2~dB compared to Class 1 sound level meter. The proposed device can continuously monitor indoor noise for several days. Despite the limitations of the used hardware platform, the presented node is a promising low-cost and low-power solution for indoor ambient noise monitoring.

Oscillometric blood pressure (BP) devices currently estimate a single point but do not identify fluctuations in BP or distinguish them from variations in response to physiological properties. In this paper, to analyze BP normality based on oscillometric measurements, we use statistical approaches including kurtosis, skewness, Kolmogorov-Smirnov, and correlation tests. Then, to mitigate uncertainties, we use a deep neural network (DNN) to determine the confidence limits (CLs) of BP measurements based on their normality. The proposed DNN regression model decreases the standard deviation of error (SDE) of the mean error (ME) and the mean absolute error (MAE) and reduces the uncertainty of the CLs and SDEs of the proposed technique. We validate the normality of the distribution of the BP estimation distribution which fits the Gaussian distribution very well. We use a rank test in the DNN regression model to demonstrate the independence of the artificial SBP and DBP estimations. First, we perform statistical tests to verify the normality of the BP measurements for individual subjects. The proposed methodology provides accurate BP estimations and reduces the uncertainties associated with the CLs and SDEs based on the DNN regression estimator.

An innovative wireless sensor network (WSN) based on Ultra-Wide Band (UWB) technology for 3D accurate superficial monitoring of ground deformations, as landslides and subsidence, is proposed. The system has been designed and developed as part of an European Life+ project, called Wi-GIM (Wireless Sensor Network for Ground Instability Monitoring). The details of the architecture, the localization via wireless technology and data processing protocols are described. The flexibility and accuracy achieved by the UWB two-way ranging technique is analysed and compared with the traditional systems, such as robotic total stations (RTSs), Ground-based Interferometric Synthetic Aperture Radar (GB-InSAR), highlighting the pros and cons of the UWB solution to detect the surface movements. An extensive field trial campaign allows the validation of the system and the analysis of its sensitivity to different factors (e.g., sensor nodes inter-visibility, effects of the temperature, etc.). The Wi-GIM system represents a promising solution for landslide monitoring and it can be adopted in conjunction with traditional systems or as an alternative in areas where the available resources are inadequate. The versatility, easy/fast deployment and cost-effectiveness, together with the good accuracy, make the Wi-GIM system a possible solution for municipalities that cannot afford expensive/complex systems to monitor potential landslides in their territory.