The purpose of this paper is to test the Kuznets Environmental Curve (KEC) hypothesis for 10 MENA (Middle East and North Africa) countries during the period 1987-2017. To do this, the translogical functional form has been adopted to estimate the relationship between Toxic Gases (TG) emissions, energy consumption and GDP per capita. The results confirm the presence of KEC, GDP per capita and energy consumption have a positive influence on TG emissions, and the presence of a feedback relationship between GDP per capita and energy consumption. As a result, the environmental framework of the selected countries improves as their level of growth has become more advanced. In addition, to reduce TG emissions, MENA countries are expected to significantly increase the use of renewable energy and a more efficient energy policy.

Global Positioning System (GPS) stations located along coastal areas have the ability to measure tide gauge (TG) records by reflected signal reception from the sea water surface. In this study we used the GPS signal-to-noise ratio (SNR) data from the SEPT station (44.63 ⁰N, 124.05 ⁰W) located at South Beach, Oregon, USA, to estimate the TG records using only a few measurements. First, we derived the TG record from a GPS station (GPS-TG) and used traditional TG data from the National Water Level Observation Network (NWLON) sentinel station (Station ID: 9435380) located in Oregon for validation purposes because it was closest to the SEPT station. Our results show that the GPS-TG and NWLON-TG correlate well with the correlation coefficient (CC) of 0.942 and the root mean square (RMS) of their residuals was about 12.90 cm. The corresponding TG prediction by autoregressive moving average (ARMA-TG) and singular spectrum analysis (SSA-TG) models are evaluated for their effectiveness over the station. The comparative analysis demonstrates that the GPS-TG has improved correlation with ARMA-TG (CC of ~0.981 CC, RMS of ~4.80 cm), and SSA-TG (CC of ~0.998 CC, RMS of ~ 0.88 cm) compared to the NWLON-TG (CC of ~0.942 CC, RMS of ~12.90 cm) values. We believe the outcomes from this study contribute to a better understanding of the numerical modeling of TG records as well as other measurements based on reflectometry techniques.

Background: Angelica gigas Nakai, Korean dang-gui, has long been widely used in traditional treatment methods. There have been a number of studies of the health effects of A. gigas and related compounds, but studies addressing effects on blood triglycerides (TG) are lacking. To investigate the effects of A. gigas Nakai extract (AGNE) on TG in Korean subjects, we carried out a 12-week, randomized, double-blind, placebo-controlled clinical trial. Methods: Subjects who met the inclusion criterion (130 mg/dL ≤ fasting blood TG ≤ 200 mg/dL) were recruited for this study. One hundred subjects were assigned to the AGNE group (n=50) or the placebo group (n=50), who were given 1g/day of AGNE (as a gigas Nakai extract 200mg/d) in capsules, and the control group for 12 weeks. Outcomes were efficacy TG, lipid profiles, atherogenic index, and safety parameters were assessed initially for a baseline measurement and after 12 weeks. Results: After 12 weeks of supplementation, TG and very low-density lipoprotein cholesterol (VLDL-C) concentration and TG/HDL-C ratio in the AGNE group were significantly reduced compared to the placebo group (p <0.05). No significant changes in any safety parameter were observed. Conclusions: These results suggest that ingestion of AGNE may improve TG and be useful to manage or prevent hypertriglyceridemia.

Developmental toxicity testing urgently requires the implementation of human relevant new approach methodologies (NAMs) that better recapitulate the peculiar nature of human physiology during pregnancy, especially the placenta and the maternal/fetal interface, which represent a key stage for the human lifelong health. Fit-for-purpose NAMs for the placental-fetal interface are desirable to improve the biological knowledge of environmental exposure at molecular level and to reduce the high cost, time and ethical impact of animal studies. This article reviews the state of the art on the available in vitro (placental, fetal and amniotic cell-based systems) and in silico NAMs of human relevance for developmental toxicity testing purposes, as well as of the available Adverse Outcome Pathways related to developmental toxicity. The OECD TG 414 for the identification and assessment of deleterious effects of prenatal exposure to chemicals on developing organisms will be discussed to delineate the regulatory context and to better debate what is missing and needed in the context of the developmental origins of health and disease hypothesis to significantly improve this sector. Starting from this analysis, the development of a novel human feto-placental organ-on-chip platform will be introduced as an innovative alternative tool for developmental toxicity testing, considering possible implementation and validation strategies to overcome the limitation of the current animal studies and NAMs available in regulatory toxicology and in the biomedical field.

The peroxiredoxin (PRDX) family, a new family of proteins with a pivotal antioxidative function, is ubiquitously synthesized and abundantly identified in various organisms. In contrast to the intracellular localization of other family members (PRDX1/2/3/5/6), PRDX4 is the only known secretory form and protects against oxidative damage by scavenging reactive oxygen species in both the intracellular (especially the endoplasmic reticulum) compartments and the extracellular space. Recently, we generated unique human PRDX4 (hPRDX4) transgenic (Tg) mice on a C57BL/6J background and investigated the critical and diverse protective roles of PRDX4 against diabetes mellitus, atherosclerosis, insulin resistance, and nonalcoholic fatty liver disease (NAFLD) as well as evaluated its role in the intestinal function in various animal models. Our published data have shown that PRDX4 helps prevent the progression of metabolic syndrome by reducing local and systemic oxidative stress and synergistically suppressing steatosis, inflammatory reactions, and/or apoptotic activity. These observations suggest that Tg mice may be a useful animal model for studying the relevance of oxidative stress on inflammation and the dysregulation of lipid/bile acid/glucose metabolism upon the progression of human metabolic syndrome, and that specific accelerators of PRDX4 may be useful as therapeutic agents for ameliorating various chronic inflammatory diseases.

Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard names. As these names vary widely, the standardization of the nonstandard names in the Organs at Risk (OARs), Planning Target Volumes (PTVs), and 'Other' organs inside the area of interest is a vital problem. Prior works considered traditional machine learning approaches on structure sets with moderate success. This paper presents integrated deep learning methods applied to structure sets by integrating the multimodal data compiled from the radiotherapy centers administered by the US Veterans Health Administration (VHA) and the Department of Radiation Oncology at Virginia Commonwealth University (VCU). The de-identified radiation oncology data collected from VHA and VCU radiotherapy centers have 16,290 prostate structures. Our method integrates the heterogeneous (textual and imaging) multimodal data with Convolutional Neural Network (CNN)-based deep learning approaches like CNN, Visual Geometry Group (VGG) network, and Residual Network (ResNet). Our model presents improved results in prostate (RT) structure name standardization. Evaluation of our methods with macro-averaged F1 Score shows that our deep learning model with single-modal textual data usually performs better than the previous studies. We also experimented with various combinations of multimodal data (masked images, masked dose) besides textual data. The models perform well on the textual data alone, while the addition of imaging data shows that deep neural networks can achieve improved performance using information present in the other modalities. Additionally, using masked images and masked doses along with text leads to an overall performance improvement with the various CNN-based architectures than using all the modalities together. Undersampling the majority class leads to further performance enhancement. The VGG network on the masked image-dose data combined with CNNs on the text data performs the best and establishes the state-of-the-art in this domain.

This study focuses on mimicking constitutive phosphorylation in the N-terminus of the myosin regulatory light chain (S15D-RLC) as a rescue strategy for the mutation-induced cardiac dysfunction in transgenic (Tg) models of restrictive (RCM) and dilated (DCM) cardiomyopathy caused by mutations in myosin essential (ELC) or regulatory (RLC) light chains. S15D-RLC phosphomimetic was reconstituted in left ventricular papillary muscle (LVPM) fibers from two mouse models of cardiomyopathy, RCM-E143K ELC and DCM-D94A RLC, along with their corresponding Tg-ELC and Tg-RLC wild type (WT) mice. The beneficial effects of S15D-RLC in rescuing cardiac function were manifested by S15D-RLC-induced destabilization of the super-relaxed (SRX) state that was observed in both models of cardiomyopathy. S15D-RLC promoted a shift from the SRX state to the disordered relaxed (DRX) state, increasing the number of heads readily available to interact with actin and produce force. Additionally, S15D-RLC reconstituted fibers demonstrated significantly higher maximal isometric force per cross-section of muscle compared with reconstitution with WT-RLC protein. The effects of the phosphomimetic S15D-RLC were compared to those observed for Omecamtiv Mecarbil (OM), a myosin activator shown to bind to the catalytic site of cardiac myosin and increase myocardial contractility. A similar SRX↔DRX equilibrium shift was observed in OM=treated fibers as in S15D-RLC-reconstituted preparations. Additionally, treatment with OM resulted in significantly higher maximal pCa 4 force per cross-section of muscle fibers in both cardiomyopathy models. Our results suggest that both treatments with S15D-RLC and OM may improve the function of myosin motors and cardiac muscle contraction in RCM-ELC and DCM-RLC mice.