Pressure Gradients Driving Ion Transport in the Topside Martian Atmosphere

An edited version of this paper was published by AGU. Copyright 2019 American Geophysical Union.Magnetic and thermal pressure gradient forces drive plasma flow in the topside ionosphere of Mars. Some of this flow can contribute to ion loss from the planet and thus affect atmospheric evolution. MAVEN measurements of the magnetic field, electron density, and electron temperature, taken over a 3‐year time period, are used to obtain averaged magnetic and thermal pressures in the topside ionosphere versus altitude, solar zenith angle, and latitude. Magnetic pressures are several times greater than thermal pressures for altitudes greater than about 300 km; that is, the plasma beta is less than one. The total pressure increases with altitude in the ionosphere and decreases with increasing solar zenith angle. Using these pressure patterns in the dayside ionosphere to estimate the pressure gradient force in the fluid momentum equation, we estimate horizontal day‐to‐night plasma flow speeds of a few kilometers per second near 400 km

MiR-144: A new possible therapeutic target and diagnostic/prognostic tool in cancers

MicroRNAs (miRNAs) are small and non-coding RNAs that display aberrant expression in the tissue and plasma of cancer patients when tested in comparison to healthy individuals. In past decades, research data proposed that miRNAs could be diagnostic and prognostic biomarkers in cancer patients. It has been confirmed that miRNAs can act either as oncogenes by silencing tumor inhibitors or as tumor suppressors by targeting oncoproteins. MiR-144s are located in the chromosomal region 17q11.2, which is subject to significant damage in many types of cancers. In this review, we assess the involvement of miR-144s in several cancer types by illustrating the possible target genes that are related to each cancer, and we also briefly describe the clinical applications of miR-144s as a diagnostic and prognostic tool in cancers

Measurements of Forbush decreases at Mars: both by MSL on ground and by MAVEN in orbit

The Radiation Assessment Detector (RAD), on board Mars Science Laboratory's (MSL) Curiosity rover, has been measuring ground level particle fluxes along with the radiation dose rate at the surface of Mars since August 2012. Similar to neutron monitors at Earth, RAD sees many Forbush decreases (FDs) in the galactic cosmic ray (GCR) induced surface fluxes and dose rates. These FDs are associated with coronal mass ejections (CMEs) and/or stream/corotating interaction regions (SIRs/CIRs). Orbiting above the Martian atmosphere, the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft has also been monitoring space weather conditions at Mars since September 2014. The penetrating particle flux channels in the Solar Energetic Particle (SEP) instrument onboard MAVEN can also be employed to detect FDs. For the first time, we study the statistics and properties of a list of FDs observed in-situ at Mars, seen both on the surface by MSL/RAD and in orbit detected by the MAVEN/SEP instrument. Such a list of FDs can be used for studying interplanetary CME (ICME) propagation and SIR evolution through the inner heliosphere. The magnitudes of different FDs can be well-fitted by a power-law distribution. The systematic difference between the magnitudes of the FDs within and outside the Martian atmosphere may be mostly attributed to the energy-dependent modulation of the GCR particles by both the pass-by ICMEs/SIRs and the Martian atmosphere

SWPT: An automated GIS-based tool for prioritization of sub-watersheds based on morphometric and topo-hydrological factors

© 2019 China University of Geosciences (Beijing) and Peking University The sub-watershed prioritization is the ranking of different areas of a river basin according to their need to proper planning and management of soil and water resources. Decision makers should optimally allocate the investments to critical sub-watersheds in an economically effective and technically efficient manner. Hence, this study aimed at developing a user-friendly geographic information system (GIS) tool, Sub-Watershed Prioritization Tool (SWPT), using the Python programming language to decrease any possible uncertainty. It used geospatial–statistical techniques for analyzing morphometric and topo-hydrological factors and automatically identifying critical and priority sub-watersheds. In order to assess the capability and reliability of the SWPT tool, it was successfully applied in a watershed in the Golestan Province, Northern Iran. Historical records of flood and landslide events indicated that the SWPT correctly recognized critical sub-watersheds. It provided a cost-effective approach for prioritization of sub-watersheds. Therefore, the SWPT is practically applicable and replicable to other regions where gauge data is not available for each sub-watershed

Squamous cell carcinoma of the tonsil managed by conventional surgery and postoperative radiation

BACKGROUND: The purpose of this study was to report the long-term outcome of patients with squamous cell cancer (SCC) of the tonsil managed by surgery followed by postoperative radiotherapy (PORT). METHODS: Eighty-eight patients treated between 1985 and 2005 were analyzed. Overall survival (OS), disease-specific survival (DSS), and recurrence-free survival (RFS) were determined by the Kaplan-Meier method. Factors predictive of outcome were determined by univariate and multivariate analysis. RESULTS: Forty-eight percent of patients had T3 to T4 disease and 75% had a positive neck. Five-year OS, DSS, and RFS were 66%, 82%, and 80%, respectively. The status of the neck was not predictive of outcome (DSS 80% for N0 vs 82% for N+; p = .97). Lymphovascular invasion was an independent predictor of OS, DSS, and RFS on multivariate analysis. CONCLUSION: Lymphovascular invasion but not pathological stage of the neck is an independent predictor of outcome in patients with tonsillar SCC. (c) 2014 Wiley Periodicals, Inc. Head Neck, 2014

A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation

In regions with lack of hydrological and hydraulic data, a spatial flood modeling and mapping is an opportunity for the urban authorities to predict the spatial distribution and the intensity of the flooding. It helps decision-makers to develop effective flood prevention and management plans. In this study, flood inventory data were prepared based on the historical and field surveys data by Sari municipality and regional water company of Mazandaran, Iran. The collected flood data accompanied with different variables (digital elevation model and slope have been considered as topographic variables, land use/land cover, precipitation, curve number, distance to river, distance to channel and depth to groundwater as environmental variables) were applied to novel hybridized model based on neural network and swarm intelligence-grey wolf algorithm (NN-SGW) to map flood-inundation. Several confusion matrix criteria were used for accuracy evaluation by cutoff-dependent and independent metrics (e.g., efficiency (E), positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUC)). The accuracy of the flood inundation map produced by the NN-SGW model was compared with that of maps produced by four state-of-the-art benchmark models: random forest (RF), logistic model tree (LMT), classification and regression trees (CART), and J48 decision tree (J48DT). The NN-SGW model outperformed all benchmark models in both training (E = 90.5%, PPV = 93.7%, NPV = 87.3%, AUC = 96.3%) and validation (E = 79.4%, PPV = 85.3%, NPV = 73.5%, AUC = 88.2%). As the NN-SGW model produced the most accurate flood-inundation map, it can be employed for robust flood contingency planning. Based on the obtained results from NN-SGW model, distance from channel, distance from river, and depth to groundwater were identified as the most important variables for spatial prediction of urban flood inundation. This work can serve as a basis for future studies seeking to predict flood susceptibility in urban areas using hybridized machine learning (ML) models and can also be applied in other urban areas where flood inundation presents a pressing challenge, and there are some problems regarding required model and availability of input data

Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides

The robustness of landslide prediction models has become a major focus of researchers worldwide. We developed two novel hybrid predictive models that combine the self-organizing, deep-learning group method of data handling (GMDH) with two swarm intelligence optimization algorithms, i.e., cuckoo search algorithm (CSA) and whale optimization algorithm (WOA) for spatially explicit prediction of landslide susceptibility. Eleven landslide-causing factors and 334 historic landslides in a 31,340 km2 landslide-prone area in Iran were used to produce geospatial training and validation datasets. The GMDH model was employed to develop a basic predictive model that was then restructured and its parameters were optimized using the CSA and WOA algorithms, yielding the novel hybrid GMDH-CSA and GMDH-WOA models. The hybrid models were validated and compared to the standalone GMDH model by calculating the area under the receiver operating characteristic (AUC) curve and root mean square error (RMSE). The results demonstrated that the hybrid models overcame the computational shortcomings of the basic GMDH model and significantly improved landslide susceptibility prediction (GMDH-CSA, AUC = 0.909 and RMSE = 0.089; GMDH-WOA, AUC = 0.902 and RMSE = 0.129; standalone GMDH, AUC = 0.791 and RMSE = 0.226). Further, the hybrid models were more robust than the standalone GMDH model, showing consistently excellent performance when the training and validation datasets were changed. Overall, the swarm intelligence-optimized models, but not the standalone model, identified the best trade-offs among objectives, accuracy, and robustness

Observational Diagnostics of Gas Flows: Insights from Cosmological Simulations

Galactic accretion interacts in complex ways with gaseous halos, including galactic winds. As a result, observational diagnostics typically probe a range of intertwined physical phenomena. Because of this complexity, cosmological hydrodynamic simulations have played a key role in developing observational diagnostics of galactic accretion. In this chapter, we review the status of different observational diagnostics of circumgalactic gas flows, in both absorption (galaxy pair and down-the-barrel observations in neutral hydrogen and metals; kinematic and azimuthal angle diagnostics; the cosmological column density distribution; and metallicity) and emission (Lya; UV metal lines; and diffuse X-rays). We conclude that there is no simple and robust way to identify galactic accretion in individual measurements. Rather, progress in testing galactic accretion models is likely to come from systematic, statistical comparisons of simulation predictions with observations. We discuss specific areas where progress is likely to be particularly fruitful over the next few years.Comment: Invited review to appear in Gas Accretion onto Galaxies, Astrophysics and Space Science Library, eds. A. J. Fox & R. Dave, to be published by Springer. Typos correcte

Gas Accretion and Star Formation Rates

Cosmological numerical simulations of galaxy evolution show that accretion of metal-poor gas from the cosmic web drives the star formation in galaxy disks. Unfortunately, the observational support for this theoretical prediction is still indirect, and modeling and analysis are required to identify hints as actual signs of star-formation feeding from metal-poor gas accretion. Thus, a meticulous interpretation of the observations is crucial, and this observational review begins with a simple theoretical description of the physical process and the key ingredients it involves, including the properties of the accreted gas and of the star-formation that it induces. A number of observations pointing out the connection between metal-poor gas accretion and star-formation are analyzed, specifically, the short gas consumption time-scale compared to the age of the stellar populations, the fundamental metallicity relationship, the relationship between disk morphology and gas metallicity, the existence of metallicity drops in starbursts of star-forming galaxies, the so-called G dwarf problem, the existence of a minimum metallicity for the star-forming gas in the local universe, the origin of the alpha-enhanced gas forming stars in the local universe, the metallicity of the quiescent BCDs, and the direct measurements of gas accretion onto galaxies. A final section discusses intrinsic difficulties to obtain direct observational evidence, and points out alternative observational pathways to further consolidate the current ideas.Comment: Invited review to appear in Gas Accretion onto Galaxies, Astrophysics and Space Science Library, eds. A. J. Fox & R. Dav\'e, to be published by Springe