Intermittent Behavior of the Separated Boundary Layer along the Suction Surface of a Low Pressure Turbine Blade under Periodic Unsteady Flow Conditions

The paper experimentally and theoretically studies the effects of periodic unsteady wake flow and aerodynamic characteristics on boundary layer development, separation and re-attachment along the suction surface of a low pressure turbine blade. The experiments were carried out at Reynolds number of 110,000 (based on suction surface length and exit velocity). For one steady and two different unsteady inlet flow conditions with the corresponding passing frequencies, intermittency behaviors were experimentally and theoretically investigated. The current investigation attempts to extend the intermittency unsteady boundary layer transition model developed in previously to the LPT cases, where separation occurs on the suction surface at a low Reynolds number. The results of the unsteady boundary layer measurements and the intermittency analysis were presented in the ensemble-averaged and contour plot forms. The analysis of the boundary layer experimental data with the flow separation, confirms the universal character of the relative intermittency function which is described by a Gausssian function

Cytosine-5 RNA Methylation Regulates Neural Stem Cell Differentiation and Motility

Loss-of-function mutations in the cytosine-5 RNA methylase NSUN2 cause neurodevelopmental disorders in humans, yet the underlying cellular processes leading to the symptoms that include microcephaly remain unclear. Here, we show that NSUN2 is expressed in early neuroepithelial progenitors of the developing human brain, and its expression is gradually reduced during differentiation of human neuroepithelial stem (NES) cells in vitro. In the developing $\textit{Nsun2}$$^{-/-}$ mouse cerebral cortex, intermediate progenitors accumulate and upper-layer neurons decrease. Loss of NSUN2-mediated methylation of tRNA increases their endonucleolytic cleavage by angiogenin, and 5' tRNA fragments accumulate in $\textit{Nsun2}$$^{-/-}$ brains. Neural differentiation of NES cells is impaired by both $\textit{NSUN2}$ depletion and the presence of angiogenin. Since repression of NSUN2 also inhibited neural cell migration toward the chemoattractant fibroblast growth factor 2, we conclude that the impaired differentiation capacity in the absence of NSUN2 may be driven by the inability to efficiently respond to growth factors.This work was funded by Cancer Research UK (CR-UK C10701/A15181 ), Worldwide Cancer Research ( 15-0168 ), the Medical Research Council (MRC MR/M01939X/1 ), the European Research Council (ERC 310360 ), and EMBO. Research in M.F.'s laboratory was supported by a core support grant from the Wellcome Trust and MRC to the Wellcome Trust-Medical Research Cambridge Stem Cell Institute

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression

Importance Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. Objectives To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system. Design, Setting, and Participants This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. Main Outcomes and Measures Accuracy and generalizability of prognostic systems. Results A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. Conclusions and RelevanceThese findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.Question Can a transition to psychosis be predicted in patients with clinical high-risk states or recent-onset depression by optimally integrating clinical, neurocognitive, neuroimaging, and genetic information with clinicians' prognostic estimates? Findings In this prognostic study of 334 patients and 334 control individuals, machine learning models sequentially combining clinical and biological data with clinicians' estimates correctly predicted disease transitions in 85.9% of cases across geographically distinct patient populations. The clinicians' lack of prognostic sensitivity, as measured by a false-negative rate of 38.5%, was reduced to 15.4% by the sequential prognostic model. Meaning These findings suggest that an individualized prognostic workflow integrating artificial and human intelligence may facilitate the personalized prevention of psychosis in young patients with clinical high-risk syndromes or recent-onset depression.</p

Threatened fishes of the world: Pseudophoxinus anatolicus (Hanko 1924) (Cyprinidae), Central Anatolia, Turkey

Being an endemic and endangered fish species in Anatolia, P. anatolicus restricted to Lakes Akgol, Beysehir, Cavuscu, Akkaya Dam and Eregli marshes as well as some other small marshes in Central Anatolia region, Turkey. Current population of P. anatolicus tends to decrease in the habitats. A detailed study of current population status, biology, ecology and life history of P. anatolicus is required. It should also be included into national threatened fish category

Performance of a salt gradient solar pond with reflective covered surface and derivation of analytic functions for air and soil temperatures for isparta region

6th International Conference of the Balkan-Physical-Union -- AUG 22-26, 2006 -- Istanbul, TURKEYWOS: 000246647900188In this study, salt gradient solar pond having a surface area of 3.5x3.5 m(2) and depth of 2 m has been constructed in Isparta/Turkiye and a computer software, capable of giving the temporal temperature variation at any point inside or outside a insulated solar pond at any time is presented. Hourly average values of air temperature and daily average soil temperature for the site were used as input parameters. These parameters have been calculated from analytical functions derived by using the hourly and daily temperature values for air and soil data obtained from the local meteorological station in Isparta region, respectively. The results obtained using analytic functions were in good agreement with the experimental results. Performance of the pond has been investigated theoretically and experimentally.Balkan Phys Union, Turkish Phys Soc, Istanbul Univ, Yildiz Tech Univ, Bogaz Univ, Dogus Univ, European Phys Soc, Govt Istanbul, Istanbul Metropolitan Municipal, Turkish Atomic Energy Author, Sci & Technol Res Council Turkey, United Natl Educ Sci & Cultutal Org, NEL Electroni