Notes

[Health detective associated with workers uncovered within a hadrontherapy center].
For GO2 and GO3, despite the significant anti-biofouling effect, a detrimental impact on desalination performance was observed. The high content of large sheets in GO2 and small sheet stacking in GO3 produced an unfavorable impact on the water flow. Therefore, the synergistic effect due to the presence of large- and small-sized GO sheets and high content of OH-functional groups (GO1) made it possible to balance the performance of the membrane.Salivary glands (SGs) are of the utmost importance for maintaining the health of the oral cavity and carrying out physiological functions such as mastication, protection of teeth, perception of food taste, and speech [...].Aptamers are short fragments of nucleic acids, DNA or RNA that have the ability to bind selected proteins with high specificity and affinity. These properties allow them to be used as an element of biosensors for the detection of specific proteins, including viral ones, which makes it possible to design valuable diagnostic tools. The influenza virus causes a huge number of human and animal deaths worldwide every year, and contributes to remarkable economic losses. In addition, in 2020, a new threat appeared-the SARS-Cov-2 pandemic. Both disease entities, especially in the initial stage of infection, are almost identical in terms of signs and symptoms. Therefore, a diagnostic solution is needed that will allow distinguishing between both pathogens, with high sensitivity and specificity; it should be cheap, quick and possible to use in the field, for example, in a doctor's office. All the mentioned properties are met by aptasensors in which the detection elements are specific aptamers. We present here the latest developments in the construction of various types of aptasensors for the detection of influenza virus. Aptasensor operation is based on the measurement of changes in electric impedance, fluorescence or electric signal (impedimetric, fluorescence and electrochemical aptasensors, respectively); it allows both qualitative and quantitative determinations. The particularly high advancement for detecting of influenza virus concerns impedimetric aptasensors.This study aims to enable effective breast ultrasound image classification by combining deep features with conventional handcrafted features to classify the tumors. In particular, the deep features are extracted from a pre-trained convolutional neural network model, namely the VGG19 model, at six different extraction levels. The deep features extracted at each level are analyzed using a features selection algorithm to identify the deep feature combination that achieves the highest classification performance. Furthermore, the extracted deep features are combined with handcrafted texture and morphological features and processed using features selection to investigate the possibility of improving the classification performance. Fenretinide The cross-validation analysis, which is performed using 380 breast ultrasound images, shows that the best combination of deep features is obtained using a feature set, denoted by CONV features that include convolution features extracted from all convolution blocks of the VGG19 model. In pCONV and morphological features can achieve effective breast ultrasound image classifications that increase the capability of detecting malignant tumors and reduce the potential of misclassifying benign tumors.The Internet of Things (IoT) is an emerging paradigm that enables many beneficial and prospective application areas, such as smart metering, smart homes, smart industries, and smart city architectures, to name but a few. These application areas typically comprise end nodes and gateways that are often interconnected by low power wide area network (LPWAN) technologies, which provide low power consumption rates to elongate the battery lifetimes of end nodes, low IoT device development/purchasing costs, long transmission range, and increased scalability, albeit at low data rates. However, most LPWAN technologies are often confronted with a number of physical (PHY) layer challenges, including increased interference, spectral inefficiency, and/or low data rates for which cognitive radio (CR), being a predominantly PHY layer solution, suffices as a potential solution. Consequently, in this article, we survey the potentials of integrating CR in LPWAN for IoT-based applications. First, we present and discuss a detailed list of different state-of-the-art LPWAN technologies; we summarize the most recent LPWAN standardization bodies, alliances, and consortia while emphasizing their disposition towards the integration of CR in LPWAN. We then highlight the concept of CR in LPWAN via a PHY-layer front-end model and discuss the benefits of CR-LPWAN for IoT applications. A number of research challenges and future directions are also presented. This article aims to provide a unique and holistic overview of CR in LPWAN with the intention of emphasizing its potential benefits.Cardiovascular diseases are the leading causes of mortality worldwide. Among them, hypertension and its pathological complications pose a major risk for the development of other cardiovascular diseases, including heart failure and stroke. Identifying novel and early stage biomarkers of hypertension and other cardiovascular diseases is of paramount importance in predicting and preventing the major morbidity and mortality associated with these diseases. Biomarkers of such diseases or predisposition to their development are identified by changes in a specific indicator's expression between healthy individuals and patients. These include changes in protein and microRNA (miRNA) levels. Protein profiling using mass spectrometry and miRNA screening utilizing microarray and sequencing have facilitated the discovery of proteins and miRNA as biomarker candidates. In this review, we summarized some of the different, promising early stage protein and miRNA biomarker candidates as well as the currently used biomarkers for hypertension and other cardiovascular diseases. Although a number of promising markers have been identified, it is unlikely that a single biomarker will unambiguously aid in the classification of these diseases. A multi-marker panel-strategy appears useful and promising for classifying and refining risk stratification among patients with cardiovascular disease.
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