Modelling Driving Forces of Urban Growth with Fuzzy Sets and GIS

Urban growth occurs in conjunction with a series of decision-making processes and is, on the whole, not deterministic but rather is the outcome of competing local demands and uncontrolled, chaotic processes. Fuzzy sets theory is ideally suited to treat the complexity and uncertainties in the decision-making process. This chapter presented an example of how fuzzy sets can be applied to model urban growth driving forces within geographical information system environment. The mathematical models for measuring, computing and defining 10 fuzzy urban growth factors to form fuzzy driving forces of urban growth in Riyadh City, Saudi Arabia, were discussed. Four factors were considered as the driving forces for urban growth in Riyadh City: the transport support factor, urban agglomeration and attractiveness factor, topographical constraints factor, and planning policies and regulations factor. The urban growth factors were established using fuzzy set theory, which quantified the effect of distance decay using fuzziness. This approach is a transparent method of interpreting the curve of distance decay using linguistic variables. This feature does not exist in the linear, negative exponential or inverse power functions. The results indicate that fuzzy accessibility and fuzzy urban density factors are capable of mimicking and representing the uncertainty in the behaviour of the human decision-making process in land development in a very efficient manner

Rainfall-Altitude Relationship in Saudi Arabia

Relations between rainfall and the altitude of the terrain can have significant implications for rainfall-runoff studies in hydrology. The aim of this paper is to report on a study of relationships between annual and seasonal rainfall and the altitude of the terrain in Saudi Arabia (SA) using global ordinary least square (OLS) and local geographically weighted regression (GWR) methods. Results using the OLS method showed a significant correlation between altitude and spring rainfall, with a coefficient of determination of , but no significant correlation for the annual and other seasons’ rainfalls. The relationships were more pronounced when GWR local analysis was performed with coefficients of determination of , 0.64, 0.83, 0.82, and 0.71 for annual, winter, spring, summer, and fall rainfalls, respectively. There was some variation in the parameter estimates derived with GWR, but the majority of the estimates indicated a positive association. Results from this study corroborate those of selected other studies in which rainfall and altitude were found to be correlated spatially. The authors concluded that the use of a nonstationary local model such as GWR enabled them to provide a deeper explanation of relations between rainfall and the altitude of the terrain than a global model such as OLS in terms of spatial estimation and prediction

Spatial Optimization of Urban Cellular Automata Model

Although cellular automata (CA) offer a modelling framework and set of techniques for modelling the dynamic processes of urban growth, determining the optimal value of weights or parameters for elements or factors of urban CA models is challenging. This chapter demonstrates the implementation of a calibration module in a fuzzy cellular urban growth model (FCUGM) for optimizing the weights and parameters of an urban CA model using three types of algorithms: (i) genetic algorithm (GA), (ii) parallel simulated annealing (PSA) and (iii) expert knowledge (EK). It was found that the GA followed by EK produced better and more accurate and consistent results compared with PSA. This suggests that the GA was able to some extent to understand the urban growth process and the underlying relationship between input factors in a way similar to human experts. It also suggests that the two algorithms (GA and EK) have similar agreement about the efficiency of scenarios in terms of modelling urban growth. In contrast, the results of the PSA do not show results corresponding to those of the GA or EK. This suggests that the complexity of the urban process is beyond the algorithm’s capability or could be due to being trapped in local optima. With this satisfactory calibration of the FCUGM for the urban growth of Riyadh city in Saudi Arabia by using CALIB-FCUGM, these calibrated parameters can be passed into the SIM-FCUGM to simulate the spatial patterns of urban growth of Riyadh

UU/UA Dinucleotide Frequency Reduction in Coding Regions Results in Increased mRNA Stability and Protein Expression

UU and UA dinucleotides are rare in mammalian genes and may offer natural selection against endoribonuclease-mediated mRNA decay. This study hypothesized that reducing UU and UA (UW) dinucleotides in the mRNA-coding sequence, including the codons and the dicodon boundaries, may promote resistance to mRNA decay, thereby increasing protein production. Indeed, protein expression from UW-reduced coding regions of enhanced green fluorescent protein (EGFP), luciferase, interferon-α, and hepatitis B surface antigen (HBsAg) was higher when compared to the wild-type protein expression. The steady-state level of UW-reduced EGFP mRNA was higher and the mRNA half-life was also longer. Ectopic expression of the endoribonuclease, RNase L, did not reduce the wild type or UW-reduced mRNA. A mutant form of the mRNA decay-promoting protein, tristetraprolin (TTP/ZFP36), which has a point mutation in the zinc-finger domain (C124R), was used. The wild-type EGFP mRNA but not the UW-reduced mRNA responded to the dominant negative action of the C124R ZFP36/TTP mutant. The results indicate the efficacy of the described rational approach to formulate a general scheme for boosting recombinant protein production in mammalian cells

p16INK4A Positively Regulates Cyclin D1 and E2F1 through Negative Control of AUF1

/pRB/E2F pathway, a key regulator of the critical G1 to S phase transition of the cell cycle, is universally disrupted in human cancer. However, the precise function of the different members of this pathway and their functional interplay are still not well defined. -dependent manner, and several of these genes are also members of the AUF1 and E2F1 regulons. We also present evidence that E2F1 mediates p16-dependent regulation of several pro- and anti-apoptotic proteins, and the consequent induction of spontaneous as well as doxorubicin-induced apoptosis. is also a modulator of transcription and apoptosis through controlling the expression of two major transcription regulators, AUF1 and E2F1

Alternative polyadenylation variants of the RNA binding protein, HuR: abundance, role of AU-rich elements and auto-Regulation

The RNA-binding protein, HuR, is involved in the stabilization of AU-rich element-containing mRNAs with products that are involved in cell-cycle progression, cell differentiation and inflammation. We show that there are multiple polyadenylation variants of HuR mRNA that differ in their abundance, using both bioinformatics and experimental approaches. A polyadenylation variant with distal poly(A) signal is a rare transcript that harbors functional AU-rich elements (ARE) in the 3′UTR. A minimal 60-nt region, but not a mutant form, fused to reporter-3′UTR constructs was able to downregulate the reporter activity. The most predominant and alternatively polyadenylated mature transcript does not contain the ARE. HuR itself binds HuR mRNA, and upregulated the activity of reporter from constructs fused with ARE-isoform and the HuR ARE. Wild-type tristetraprolin (TTP), but not the zinc finger mutant TTP, competes for HuR binding and upregulation of HuR mRNA. The study shows that the HuR gene codes for several polyadenylation variants differentially regulated by AU-rich elements, and demonstrates an auto-regulatory role of HuR

Effect of biochar modified with magnetite nanoparticles and HNO\u3csub\u3e3\u3c/sub\u3e for efficient removal of Cr(VI) from contaminated water: A batch and column scale study

© 2020 Elsevier Ltd Chromium (Cr) poses serious consequences on human and animal health due to its potential carcinogenicity. The present study aims at preparing a novel biochar derived from Chenopodium quinoa crop residues (QBC), its activation with magnetite nanoparticles (QBC/MNPs) and strong acid HNO3 (QBC/Acid) to evaluate their batch and column scale potential to remove Cr (VI) from polluted water. The QBC, QBC/MNPs and QBC/Acid were characterized with SEM, FTIR, EDX, XRD as well as point of zero charge (PZC) to get an insight into their adsorption mechanism. The impact of different process parameters including dose of the adsorbent (1–4 g/L), contact time (0–180 min), initial concentration of Cr (25–200 mg/L) as well as solution pH (2–8) was evaluated on the Cr (VI) removal from contaminated water. The results revealed that QBC/MNPs proved more effective (73.35–93.62-%) for the Cr (VI) removal with 77.35 mg/g adsorption capacity as compared with QBC/Acid (55.85–79.8%) and QBC (48.85–75.28-%) when Cr concentration was changed from 200 to 25 mg/L. The isothermal experimental results follow the Freundlich adsorption model rather than Langmuir, Temkin and Dubinin-Radushkevich adsorption isotherm models. While kinetic adsorption results were well demonstrated by pseudo second order kinetic model. Column scale experiments conducted at steady state exhibited excellent retention of Cr (VI) by QBC, QBC/MNPs and QBC/Acid at 50 and 100 mg Cr/L. The results showed that this novel biochar (QBC) and its modified forms (QBC/Acid and QBC/MNPs) are applicable with excellent reusability and stability under acidic conditions for the practical treatment of Cr (VI) contaminated water

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Background Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator.Background Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator