How Should Governments Address High Levels of Natural Radiation and Radon--Lessons from the Chernobyl Nuclear Accident and Ramsar, Iran

The authors discuss the high levels of natural background radiation in Ramsar, Iran, and offer data indicating that this has had little effect on the health of Ramsar\u27s inhabitants. The authors then examine the implications their research could have for public health policy

Green synthesis of silver nanoparticles (AgNPs) by Lallemantia royleana leaf Extract: Their Bio-Pharmaceutical and catalytic properties

The study of the silver nanoparticles (AgNPs) synthesis based-green methods become more interesting recently due to their low-cost preparation, eco-friendly and non-toxic precursors. The present study approved the ability of the Lallemantia royleana (Benth. in Wall.) Benth. leaf extract for the synthesis of AgNPs for the first time. The synthesized AgNPs were physico-chemical characterized using ultraviolet–visible spectroscopy (UV–Vis), X-ray diffraction (XRD), Fourier Transform-Infrared Spectroscopy (FT-IR), zeta potential and transmission electron microscopy (TEM) analysis. The total phenols, flavonoids, anthocyanin, tannin contents, antioxidant, antimicrobial, anti-inflammatory, anti-arthritic and cytotoxic activities of L. royleana leaf extract and the synthesized AgNPs were investigated. The biocatalytic activity of prepared AgNPs was assessed on methylene blue as a pollutant organic dye. The TEM examination showed that the synthesized AgNPs were predominantly spherical with some mixed shapes and crystalline with average size 34.47 ± 1.6 nm, and showed a localized surface plasmon resonance (LSPR) peak at 425 nm. The zeta potential value was −24.1 mV indicating the stability of produced AgNPs. The new prepared AgNPs have lower total phenols, flavonoids, anthocyanin, tannin contents than L. royleana leaf extract. In addition, the new prepared AgNPs demonstrated the higher DPPH radical scavenging activity (87 %) and the ABTS radical scavenging activity (77 %) at the maximum prepared concentration of 250 μg mL−1 as compared to the L. royleana leaf extract (62 % and 58 %, respectively). The produced AgNPs also exhibited the higher antimicrobial activity against both the Gram-positive (Staphylococcus aureus and Bacillus cereus) and the Gram-negative (Escherichia coli and Shigella flexneri) bacteria and the Candida strains (Candida glabrata and Candida albicans) as compared to the L. royleana leaf extract. The resulting AgNPs indicated a dose-dependent anti-inflammatory effect on human red-blood cell (RBC) membrane stabilization assay and had more activity (72 %) compared to the L. royleana leaf extract (61 %) at 250 µg mL−1. The prepared AgNPs showed promising in vitro anti-arthritic activity evaluated by 73 % compared to 58 % in case of L. royleana leaf extract. The new produced AgNPs showed the higher cytotoxic effect against the human hepatoma (Hep-G2) and the human breast (MCF-7) cancer cells compared to the L. royleana leaf extract with 79.3 % and 77.2 % at 250 µg/mL, respectively. The obtained results revealed also that the green synthesized AgNPs were capable to catalyze MB dye. Therefore, the obtained results provide a promising route of the green synthesis of AgNPs using L. royleana leaf extract with considerable biopharmaceuticals and catalytic applications

Antioxidants: Positive or Negative Actors?

The term "antioxidant" is one of the most confusing definitions in biological/medical sciences. In chemistry, "antioxidant" is simply conceived "a compound that removes reactive species, mainly those oxygen-derived", while in a cell context, the conceptual definition of an antioxidant is poorly understood. Indeed, non-clinically recommended antioxidants are often consumed in large amounts by the global population, based on the belief that cancer, inflammation and degenerative diseases are triggered by high oxygen levels (or reactive oxygen species) and that through blocking reactive species production, organic unbalances/disorders can be prevented and/or even treated. The popularity of these chemicals arises in part from the widespread public mistrust of allopathic medicine. In fact, reactive oxygen species play a dual role in dealing with different disorders, since they may contribute to disease onset and/or progression but may also play a key role in disease prevention. Further, the ability of the most commonly used supplements, such as vitamins C, E, selenium, and herbal supplements to decrease pathologic reactive oxygen species is not clearly established. Hence, the present review aims to provide a nuanced understanding of where current knowledge is and where it should go.Antoni Sureda acknowledges the support of Institute of Health Carlos III (Project CIBEROBNCB12/03/30038). Natália Martins thank to Portuguese Foundation for Science and Technology (FCT–Portugal) for the Strategic project ref. UID/BIM/04293/2013 and “NORTE2020-Programa Operacional Regional do Norte” (NORTE-01-0145-FEDER-000012)

Federated Learning in Medical Imaging:Part I: Toward Multicentral Health Care Ecosystems

With recent developments in medical imaging facilities, extensive medical imaging data are produced every day. This increasing amount of data provides an opportunity for researchers to develop data-driven methods and deliver better health care. However, data-driven models require a large amount of data to be adequately trained. Furthermore, there is always a limited amount of data available in each data center. Hence, deep learning models trained on local data centers might not reach their total performance capacity. One solution could be to accumulate all data from different centers into one center. However, data privacy regulations do not allow medical institutions to easily combine their data, and this becomes increasingly difficult when institutions from multiple countries are involved. Another solution is to use privacy-preserving algorithms, which can make use of all the data available in multiple centers while keeping the sensitive data private. Federated learning (FL) is such a mechanism that enables deploying large-scale machine learning models trained on different data centers without sharing sensitive data. In FL, instead of transferring data, a general model is trained on local data sets and transferred between data centers. FL has been identified as a promising field of research, with extensive possible uses in medical research and practice. This article introduces FL, with a comprehensive look into its concepts and recent research trends in medical imaging

Federated Learning in Medical Imaging:Part II: Methods, Challenges, and Considerations

Federated learning is a machine learning method that allows decentralized training of deep neural networks among multiple clients while preserving the privacy of each client's data. Federated learning is instrumental in medical imaging due to the privacy considerations of medical data. Setting up federated networks in hospitals comes with unique challenges, primarily because medical imaging data and federated learning algorithms each have their own set of distinct characteristics. This article introduces federated learning algorithms in medical imaging and discusses technical challenges and considerations of real-world implementation of them

Efecto de la aplicación foliar de selenio y zinc para aumentar los rendimientos cuantitativos y cualitativos de colza en diferentes fechas de siembra

The sowing date is an important factor for expanding the cultivated area of rapeseed and affects seed yield, oil content, and fatty acid compounds. Micronutrient elements play an important role in improving the vegetative and reproductive growth of the plant, especially under conditions of biological and environmental stresses. A two-year experiment (2014-2016) was performed to study the response of rapeseed genotypes to foliar application of micronutrients on different sowing dates. The treatments were arranged as a factorial-split plot in a randomized complete block design with three replicates. Three sowing dates of 7 (well-timed sowing date), 17, and 27 (delayed sowing dates) October and two levels of foliar application with pure water (control), selenium (1.5%), zinc (1.5%), and selenium+zinc (1.5%) were factorial in the main plots and five genotypes of SW102, Ahmadi, GKH2624, GK-Gabriella, and Okapi were randomized in the subplots (a total of 30 treatments). Seed yield, oil yield and content, oleic acid, and linoleic acid were reduced when rapeseeds were cultivated on 17 and 27 October, while the contents in palmitic, linolenic, and erucic acids, and glucosinolate increased (p < 0.01). a selenium+zinc treatment improved seed yield, oil content and yield (p < 0.01). The oil quality increased due to increased contents of oleic and linoleic acids under the selenium+zinc treatment (p < 0.01). The GK-Gabriella and GKH2624 genotypes are recommended to be sown on well-timed (7 October) and delayed sowing dates (17 and 27 October) and treated with selenium+zinc due to the higher oil yield, linoleic and oleic acids.La fecha de siembra es un factor importante para expandir el área cultivada de colza que afecta el rendimiento de la semilla, el contenido de aceite y la composición en ácidos grasos. Los micronutrientes juegan un papel importante en la mejora del crecimiento vegetativo y reproductivo de la planta, especialmente en condiciones de estrés biológico y ambiental. Se realizó un experimento de dos años (2014-2016) para estudiar la respuesta de los genotipos de colza a la aplicación foliar de micronutrientes en diferentes fechas de siembra. Los tratamientos se organizaron como una parcela dividida factorial en un diseño de bloques completos al azar con tres repeticiones. Tres fechas de siembra del 7 (fecha de siembra en el momento oportuno), 17 y 27 (fechas de siembra retrasadas) de octubre y dos niveles de aplicación foliar con agua pura (control), selenio (1,5%), zinc (1,5%) y selenio + zinc (1.5%) fueron factoriales en las parcelas principales y cinco genotipos de SW102, Ahmadi, GKH2624, GK-Gabriella y Okapi fueron aleatorizados en las subparcelas (un total de 30 tratamientos). El rendimiento de semilla, el contenido y rendimiento de aceite, los ácidos grasos oleico y linoleico se redujeron cuando se cultivaron semillas de colza los días 17 y 27 de octubre, mientras que los contenidos de los ácidos grasos palmítico, linolénico y erúcico y glucosinolato aumentaron (p <0,01). El tratamiento con selenio + zinc mejoró el rendimiento de semillas, el contenido de aceite y el rendimiento (p <0,01). La calidad del aceite aumentó debido al mayor contenido de ácidos oleico y linoleico bajo tratamiento con selenio + zinc (p <0.01). Se recomiendan los genotipos GK-Gabriella y GKH2624 sembrados en fechas oportunas (7 de octubre) y tardía (17 y 27 de octubre) y tratados con selenio + zinc, respectivamente, debido al mayor rendimiento de aceite y contenido de los ácidos linoleico y oleico

Identifying Incident Casual Factors to Improve Aviation Transportation Safety: Proposing a Deep Learning Approach

Aviation is a complicated transportation system, and safety is of paramount importance because aircraft failure often involves casualties. Prevention is clearly the best strategy for aviation transportation safety. Learning from past incident data to prevent potential accidents from happening has proved to be a successful approach. To prevent potential safety hazards and make effective prevention plans, aviation safety experts identify primary and contributing factors from incident reports. However, safety experts’ review processes have become prohibitively expensive nowadays. The number of incident reports is increasing rapidly due to the acceleration of advances in information technologies and the growth of the commercial and private aviation transportation industries. Consequently, advanced text mining algorithms should be applied to help aviation safety experts facilitate the process of incident data extraction. This paper focuses on constructing deep-learning-based models to identify causal factors from incident reports. First, we prepare the data sets used for training, validation, and testing with approximately 200,000 qualified incident reports from the Aviation Safety Reporting System (ASRS). Then, we take an open-source natural language model, which is well trained with a large corpus of Wikipedia texts, as the baseline and fine-tune it with the texts in incident reports to make it more suited to our specific research task. Finally, we build and train an attention-based long short-term memory (LSTM) model to identify primary and contributing factors in each incident report. The solution we propose has multilabel capability and is automated and customizable, and it is more accurate and adaptable than traditional machine learning methods in extant research. This novel application of deep learning algorithms to the incident reporting system can efficiently improve aviation safety

The biomechanical analysis of three plating fixation systems for periprosthetic femoral fracture near the tip of a total hip arthroplasty

<p>Abstract</p> <p>Background</p> <p>A variety of techniques are available for fixation of femoral shaft fractures following total hip arthroplasty. The optimal surgical repair method still remains a point of controversy in the literature. However, few studies have quantified the performance of such repair constructs. This study biomechanically examined 3 different screw-plate and cable-plate systems for fixation of periprosthetic femoral fractures near the tip of a total hip arthroplasty.</p> <p>Methods</p> <p>Twelve pairs of human cadaveric femurs were utilized. Each left femur was prepared for the cemented insertion of the femoral component of a total hip implant. Femoral fractures were created in the femurs and subsequently repaired with Construct A (Zimmer Cable Ready System), Construct B (AO Cable-Plate System), or Construct C (Dall-Miles Cable Grip System). Right femora served as matched intact controls. Axial, torsional, and four-point bending tests were performed to obtain stiffness values.</p> <p>Results</p> <p>All repair systems showed 3.08 to 5.33 times greater axial stiffness over intact control specimens. Four-point normalized bending (0.69 to 0.85) and normalized torsional (0.55 to 0.69) stiffnesses were lower than intact controls for most comparisons. Screw-plates provided either greater or equal stiffness compared to cable-plates in almost all cases. There were no statistical differences between plating systems A, B, or C when compared to each other (p > 0.05).</p> <p>Conclusions</p> <p>Screw-plate systems provide more optimal mechanical stability than cable-plate systems for periprosthetic femur fractures near the tip of a total hip arthroplasty.</p

Fragile three-dimensionality in the quasi-one-dimensional cuprate PrBa_2Cu_4O_8

In this article we report on the experimental realization of dimensional crossover phenomena in the chain compound PrBa$_2$Cu$_4$O$_8$ using temperature, high magnetic fields and disorder as independent tuning parameters. In purer crystals of PrBa$_2$Cu$_4$O$_8$, a highly anisotropic three-dimensional Fermi-liquid state develops at low temperatures. This metallic state is extremely susceptible to disorder however and localization rapidly sets in. We show, through quantitative comparison of the relevant energy scales, that this metal/insulator crossover occurs precisely when the scattering rate within the chain exceeds the interchain hopping rate(s), i.e. once carriers become confined to a single conducting element.Comment: 12 pages, 5 figures, published at http://www.iop.org/EJ/article/1367-2630/8/9/172/njp6_9_172.htm