Exploring the Relationship Between Utah\u27s Wages and Utah\u27s Real Estate Values

This paper uses a variety of multiple regression analysis techniques to attempt to answer whether a direct relationship exists between Utah\u27s employee wages and Utah\u27s residential real estate values. Unexpected declines in real estate values can have seriously negative impacts on businesses, individuals, and local governments in Utah. Conversely, unexpected increases represent missed opportunities. Researchers have used various statistical and mathematical methods to explain or predict changes in real estate values, but no method has consistently predicted values for a long period of time or across multiple geographical areas. This paper focuses on exploring the relationship between variables in Utah and uses a linear probability model with nine explanatory variables to attempt to explain trends in quarterly data from the Utah Housing Price Index over the last sixteen years. Initially, the regression returned promising numbers, but the results were misleading. Due to nonstationary data, high levels of autocorrelation, and other issues related to time-series data, the regression results were spurious, and no useable conclusions were drawn from the first model. In an attempt to correct for autocorrelation and the nonstationarity, the variables were transformed using a Prais-Winston transformation. Again, the results appeared promising. After multiple tests for stationarity and autocorrelation, however, the results were found to be autocorrelated and spurious. That being said, the time spent reading complex papers, gathering reliable data, researching advanced regression methods, transforming variables, re-specifying models and analyzing results has been a great help and will contribute to a solid statistical foundation in the future. Research opportunities are available in the future when higher level statistical methods are learned. A relation between Utah\u27s wages and Utah\u27s real estate values may exist, but the statistical methods necessary to create the proper model are beyond the scope of this paper

The New Challenge of Green Cosmetics: Natural Food Ingredients for Cosmetic Formulations

: Nowadays, much attention is paid to issues such as ecology and sustainability. Many consumers choose "green cosmetics", which are environmentally friendly creams, makeup, and beauty products, hoping that they are not harmful to health and reduce pollution. Moreover, the repeated mini-lock downs during the COVID-19 pandemic have fueled the awareness that body beauty is linked to well-being, both external and internal. As a result, consumer preferences for makeup have declined, while those for skincare products have increased. Nutricosmetics, which combines the benefits derived from food supplementation with the advantages of cosmetic treatments to improve the beauty of our body, respond to the new market demands. Food chemistry and cosmetic chemistry come together to promote both inside and outside well-being. A nutricosmetic optimizes the intake of nutritional microelements to meet the needs of the skin and skin appendages, improving their conditions and delaying aging, thus helping to protect the skin from the aging action of environmental factors. Numerous studies in the literature show a significant correlation between the adequate intake of these supplements, improved skin quality (both aesthetic and histological), and the acceleration of wound-healing. This review revised the main foods and bioactive molecules used in nutricosmetic formulations, their cosmetic effects, and the analytical techniques that allow the dosage of the active ingredients in the food

Determination of Ganciclovir in Plasma of Newborns with Congenital CMV Infection

Infections caused by human cytomegalovirus are important causes of fetal and neonatal morbidity and mortality. Ganciclovir ([(9-(1,3-dihydroxy-2- propoxymethyl) guanine, GCV) is a synthetic acyclic nucleoside which has shown activity against Cytomegalovirus. GCV treatment has been associated with serious toxic hematological effects such as neutropenia and leukopenia, thus drug monitoring is needed, especially in the case of newborns. The aim of the work is to develop and validate an HPLC method for the quantification of GCV in plasm

Exploiting the indole scaffold to design compounds binding to different pharmacological targets

Several indole derivatives have been disclosed by our research groups that have been collaborating for nearly 25 years. The results of our investigations led to a variety of molecules binding selectively to different pharmacological targets, specifically the type A γ-aminobutyric acid (GABAA) chloride channel, the translocator protein (TSPO), the murine double minute 2 (MDM2) protein, the A2B adenosine receptor (A2B AR) and the Kelch-like ECH-associated protein 1 (Keap1). Herein, we describe how these works were conceived and carried out thanks to the versatility of indole nucleus to be exploited in the design and synthesis of drug-like molecules

Childhood obesity: An overview of laboratory medicine, exercise and microbiome

In the last few years, a significant increase of childhood obesity incidence unequally distributed within countries and population groups has been observed, thus representing an important public health problem associated with several health and social consequences. Obese children have more than a 50% probability of becoming obese adults, and to develop pathologies typical of obese adults, that include type 2-diabetes, dyslipidemia and hypertension. Also environmental factors, such as reduced physical activity and increased sedentary activities, may also result in increased caloric intake and/or decreased caloric expenditure. In the present review, we aimed to identify and describe a specific panel of parameters in order to evaluate and characterize the childhood obesity status useful in setting up a preventive diagnostic approach directed at improving health-related behaviors and identifying predisposing risk factors. An early identification of risk factors for childhood obesity could definitely help in setting up adequate and specific clinical treatments

Exercise, immune system, nutrition, respiratory and cardiovascular diseases during COVID-19: A complex combination

Coronaviruses (CoVs) represent a large family of RNA viruses that can infect different living species, posing a global threat to human health. CoVs can evade the immune response, replicate within the host, and cause a rapid immune compromise culminating in severe acute respiratory syndrome. In humans, the immune system functions are influenced by physical activity, nutrition, and the absence of respiratory or cardiovascular diseases. This review provides an in-depth study between the interactions of the immune system and coronaviruses in the host to defend against CoVs disease

Development of an integrated genome informatics, data management and workflow infrastructure: a toolbox for the study of complex disease genetics.

The genetic dissection of complex disease remains a significant challenge. Sample-tracking and the recording, processing and storage of high-throughput laboratory data with public domain data, require integration of databases, genome informatics and genetic analyses in an easily updated and scaleable format. To find genes involved in multifactorial diseases such as type 1 diabetes (T1D), chromosome regions are defined based on functional candidate gene content, linkage information from humans and animal model mapping information. For each region, genomic information is extracted from Ensembl, converted and loaded into ACeDB for manual gene annotation. Homology information is examined using ACeDB tools and the gene structure verified. Manually curated genes are extracted from ACeDB and read into the feature database, which holds relevant local genomic feature data and an audit trail of laboratory investigations. Public domain information, manually curated genes, polymorphisms, primers, linkage and association analyses, with links to our genotyping database, are shown in Gbrowse. This system scales to include genetic, statistical, quality control (QC) and biological data such as expression analyses of RNA or protein, all linked from a genomics integrative display. Our system is applicable to any genetic study of complex disease, of either large or small scale.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Forcing Versus Feedback: Epidemic Malaria and Monsoon Rains in Northwest India

Malaria epidemics in regions with seasonal windows of transmission can vary greatly in size from year to year. A central question has been whether these interannual cycles are driven by climate, are instead generated by the intrinsic dynamics of the disease, or result from the resonance of these two mechanisms. This corresponds to the more general inverse problem of identifying the respective roles of external forcings vs. internal feedbacks from time series for nonlinear and noisy systems. We propose here a quantitative approach to formally compare rival hypotheses on climate vs. disease dynamics, or external forcings vs. internal feedbacks, that combines dynamical models with recently developed, computational inference methods. The interannual patterns of epidemic malaria are investigated here for desert regions of northwest India, with extensive epidemiological records for Plasmodium falciparum malaria for the past two decades. We formulate a dynamical model of malaria transmission that explicitly incorporates rainfall, and we rely on recent advances on parameter estimation for nonlinear and stochastic dynamical systems based on sequential Monte Carlo methods. Results show a significant effect of rainfall in the inter-annual variability of epidemic malaria that involves a threshold in the disease response. The model exhibits high prediction skill for yearly cases in the malaria transmission season following the monsoonal rains. Consideration of a more complex model with clinical immunity demonstrates the robustness of the findings and suggests a role of infected individuals that lack clinical symptoms as a reservoir for transmission. Our results indicate that the nonlinear dynamics of the disease itself play a role at the seasonal, but not the interannual, time scales. They illustrate the feasibility of forecasting malaria epidemics in desert and semi-arid regions of India based on climate variability. This approach should be applicable to malaria in other locations, to other infectious diseases, and to other nonlinear systems under forcing

Statistical Inference for Multi-Pathogen Systems

There is growing interest in understanding the nature and consequences of interactions among infectious agents. Pathogen interactions can be operational at different scales, either within a co-infected host or in host populations where they co-circulate, and can be either cooperative or competitive. The detection of interactions among pathogens has typically involved the study of synchrony in the oscillations of the protagonists, but as we show here, phase association provides an unreliable dynamical fingerprint for this task. We assess the capacity of a likelihood-based inference framework to accurately detect and quantify the presence and nature of pathogen interactions on the basis of realistic amounts and kinds of simulated data. We show that when epidemiological and demographic processes are well understood, noisy time series data can contain sufficient information to allow correct inference of interactions in multi-pathogen systems. The inference power is dependent on the strength and time-course of the underlying mechanism: stronger and longer-lasting interactions are more easily and more precisely quantified. We examine the limitations of our approach to stochastic temporal variation, under-reporting, and over-aggregation of data. We propose that likelihood shows promise as a basis for detection and quantification of the effects of pathogen interactions and the determination of their (competitive or cooperative) nature on the basis of population-level time-series data