Spatio-temporal patterns of recent and future climate extremes in the eastern Mediterranean and Middle East region

Recent and future changes in temperature and precipitation climate extremes are estimated using the Hadley Centre PRECIS ("Providing REgional Climates for Impacts Studies") climate model for the eastern Mediterranean and Middle East region. The area of interest is considered vulnerable to extreme climate events as there is evidence for a temperature rise while precipitation tends to decline, suggesting likely effects on vital socioeconomic sectors in the region. Observations have been obtained for the recent period (1961–1990) and used to evaluate the model output. The spatial distribution of recent temporal trends in temperature indicates strong increasing in minimum temperature over the eastern Balkan Peninsula, Turkey and the Arabian Peninsula. The rate of warming reaches 0.4–0.5 °C decade⁻¹ in a large part of the domain, while warming is expected to be strongest in summer (0.6–0.7 °C decade⁻¹) in the eastern Balkans and western Turkey. The trends in annual and summer maximum temperature are estimated at approximately 0.5 and 0.6 °C decade⁻¹ respectively. Recent estimates do not indicate statistically significant trends in precipitation except for individual sub-regions. Results indicate a future warming trend for the study area over the last 30 years of the 21st century. Trends are estimated to be positive and statistically significant in nearly the entire region. The annual trend patterns for both minimum and maximum temperature show warming rates of approximately 0.4–0.6 °C decade⁻¹, with pronounced warming over the Middle Eastern countries. Summer temperatures reveal a gradual warming (0.5–0.9 °C decade⁻¹) over much of the region. The model projects drying trends by 5–30% in annual precipitation towards the end of the 21st century, with the number of wet days decreasing at the rate of 10–30 days year⁻¹, while heavy precipitation is likely to decrease in the high-elevation areas by 15 days year⁻¹

A high-resolution RNA expression atlas of Retinitis Pigmentosa genes in the human and mouse retinas

PURPOSE. Retinitis pigmentosa (RP) is one of the leading causes of visual handicap in the world population and is characterized by high genetic heterogeneity. The study of the disease mechanisms and the development of efficient therapeutic approaches have mostly relied on the availability of animal models for this condition, so far. Nevertheless, little information is available about the RNA expression profiles of RP genes in the human retina. An expression atlas of 34 known RP genes in human and murine retinas was generated to overcome this lack of information. METHODS. Appropriate templates were retrieved for 34 RP genes that were used to perform RNA in situ hybridization studies on human and murine adult eyes. RESULTS. Most of the genes displayed similar patterns between human and mouse retina. Different expression patterns were observed for the CNGB1, USH2A, and FSCN2 genes, compared with those in previously reported profiles. In addition, different expression profiles were detected for the RPGR, CA4, PAP1, RGR, and RLBP1 genes in human and mouse retinas. CONCLUSIONS. The first gene expression atlas has been generated of RP genes in human and murine retinas. Differences observed in the expression patterns of some genes in humans and mice, will open new perspectives on the function of these genes and their putative roles in disease pathogenesis

A new nonlinear lifting-line method for aerodynamic analysis and deep learning modeling of small UAVs

In this work, a computationally efficient and high-precision nonlinear aerodynamic configuration analysis method is presented for both design optimization and mathematical modeling of small unmanned aerial vehicles (UAVs). First, we have developed a novel nonlinear lifting line method which (a) provides very good match for the pre- and poststall aerodynamic behavior in comparison to experiments and computationally intensive tools, (b) generates these results in order of magnitudes less time in comparison to computationally intensive methods such as computational fluid dynamics (CFD). This method is further extended to a complete configuration analysis tool that incorporates the effects of basic fuselage geometries. Moreover, a deep learning based surrogate model is developed using data generated by the new aerodynamic tool that can characterize the nonlinear aerodynamic performance of UAVs. The major novel feature of this model is that it can predict the aerodynamic properties of UAV configurations by using only geometric parameters without the need for any special input data or pre-process phase as needed by other computational aerodynamic analysis tools. The obtained black-box function can calculate the performance of a UAV over a wide angle of attack range on the order of milliseconds, whereas CFD solutions take several days/weeks in a similar computational environment. The aerodynamic model predictions show an almost 1-1 coincidence with the numerical data even for configurations with different airfoils that are not used in model training. The developed model provides a highly capable aerodynamic solver for design optimization studies as demonstrated through an illustrative profile design example

The tumor suppressor protein OPCML potentiates anti-EGFR and anti-HER2 targeted therapy in HER2-positive ovarian and breast cancer.

OPCML is a tumor suppressor gene that is frequently inactivated in ovarian cancer and many other cancers by somatic methylation. We have previously shown that OPCML exerts its suppressor function by negatively regulating a spectrum of receptor tyrosine kinases (RTKs), such as ErbB2/HER2, FGFR1 and EphA2, thus attenuating their related downstream signaling. The physical interaction of OPCML with this defined group of RTKs is a prerequisite for their downregulation. Overexpression/gene amplification of EGFR and HER2 is a frequent event in multiple cancers including ovarian and breast cancers. Molecular therapeutics against EGFR/HER2 or EGFR only, such as lapatinib and erlotinib respectively, were developed to target these receptors but resistance often occurs in relapsing cancers. Here we show that, though OPCML interacts only with HER2 and not with EGFR, the interaction of OPCML with HER2 disrupts the formation of the HER2-EGFR heterodimer and this translates into a better response to both lapatinib and erlotinib in HER2-expressing ovarian and breast cancer cell lines. Also, we show that high OPCML expression is associated with better response to lapatinib therapy in breast cancer patients and better survival in HER2-overexpressing ovarian cancer patients, suggesting that OPCML co-therapy could be a valuable sensitizing approach to RTK inhibitors