A qualitative analysis was carried out on the interaction of drug-protein and its role in neural complex networks of six brain regions. The data was extracted from a database (chEMBL). In addition, Shannon's entropy was calculated and the data was processed in a statistical software.

In this project, a qualitative study of the interaction of different proteins and drugs for the treatment of Alzheimer's disease was carried out, using the chEMBL database to observe the bioassays that have been applied for these drugs. The Shannon entropy of the drug protein interaction was calculated according to the test conditions.

For the development of the project different activities were carried out, starting with the analysis of databases downloaded from ChEMBL. These databases collect information on thousands of drugs that are used to treat several diseases. In this case, we used bases related to Plasmodium which causes malaria in humans. Additionally, a compilation of information on multiple nanoparticles was analyzed. Finally, with help of Excel application and a statistical package named STATISTICA, we found a computational model that can help us to select more effective drug-nanoparticles pairs instead of wasting resources and time creating many samples.

Methods for pairwise ortholog detection (POD) usually relies on alignment-based (AB) similarity measures. However, AB algorithms are still limited in POD since they may fail in the presence of certain evolutionary and genetic events. In this sense, POD is an open field in bioinformatics demanding either constant improvements in existing methods or new effective scaling algorithms to deal with Big Data.

In a previous paper, we developed a Big Data supervised POD approach considering several AB pairwise gene features and the low ortholog pair ratios found between two proteomes (Galpert, del Río et al. 2015). Although the higher sensitivity achieved for our supervised POD models in relation to classical POD methodologies, when were comparatively evaluated on the Saccharomycete yeast benchmark dataset (Salichos and Rokas 2011); they were implemented in MapReduce framework and tested on a single yeast genome pair.

In (Galpert, Fernández et al. 2018) (https://doi.org/10.1186/s12859-018-2148-8), we propose some improvements to our supervised POD approach by i) surveying the incorporation of alignment-free pairwise similarity measures ii) evaluating other classifiers under the Big Data Spark platform and iii) extending the test set to other related Saccharomycete yeast proteomes.

The area of ​​aesthetics has grown widely in the last few years and its treatments gained popularity throughout the world. The imposition of beauty standards pre-established by society, directly influence personal satisfaction and quality of life of individuals, contributing to the beauty market grow considerably in different areas. From this perspective, the objective of this study was to evaluate the profile of the clients served, as well as the popularity of specific treatments in aesthetic clinics located in the city of Barra do Garças, Mato Grosso state, Brazil. Were evaluated thirty-three individuals attending aesthetic clinics (mean age: 25 years, mean weight: 66 kg, mean height: 1.64 m), of both sexes. Participants were informed of all study objectives and, after agreeing, signed a free and informed consent form. The study showed that the population served is mostly single (84.8%) and female (84.8%). As for the aesthetic procedures, the most accomplished were Laser (39.39%), skin cleansing (33.3%) and lymphatic drainage (12.12%). The procedures performed less frequently were: microneedle (3.03%), hydrolipo (3.03%), botox (3.03%), pelling (3.03%) and introderma (3.03%). This study showed that, although the number of aesthetic procedures is growing, the female audience is still the one that most looks for aesthetic procedures, as well as showing that singles seek more for procedures when compared to married individuals.

Defining the target proteins of new anti-melanoma compounds is a crucial task in Medicinal Chemistry. In this sense, chemists carry out preclinical assays with a high number of combinations of experimental conditions (cj). In fact, ChEMBL database contains outcomes of 327480 different anti-melanoma activity preclinical assays for 1031 different chemical compounds (317,6 assays per compound). These assays cover different combinations of c_j of biological activity parameters (c₀), proteins (c₁), drug targets (c₂), cells (c₃) and 5 organisms of assay (c₄) and/or organisms of the target (c₄), etc. In this work, we report a PTML for this data set with high Specificity and Sensitivity .

Wildfires are a natural phenomenon in the environment, which allow certain species of plants and trees to germinate (NPS, n.d.). However, in the last decade,the behavior of wildfires has drastically become more deadly and intense because of greenhouse gasemission and anthropogenic activity (Abatzoglou & Williams, 2016). Climate change has extended the fire season and has encouraged winds to spread across land rapidly, making it almost impossible to suppress flames (Forest Services, 2015).As the wildfire intensity increases, the elderly are primary victims, and the hardest to locate because of lack of transportation and wildfire preparedness (Cahalan & Renne, 2007). This issue gains prevalence as baby boomers begin to age and retire at home alone (AARP, n.d.). Nationally, there is also a shortage of trained firefighters to combat the growing strength of flames, adding to the difficulty in fire suppression (NFPA, 2015). The first step in fighting the growing intensity and damage related to wildfires is to initiate a national carbon tax to encourage green energy investment, and whichrevenue would be used to fund a National Elderly and Disabled evacuation planfor the most at-risk populations. Additionally, allowing an inmate rehabilitation program will provide an employment of an additional 8,000 non-violent inmate firefighters, which will drastically help the national firefighter shortage, while also reducing recidivism. As the United States prepares to exit from the Paris Agreement, it is imperative that states, and Congress take initiative to regulate the scalingcontributions of fossil fuel emission in order to control the rising danger of wildland forest fires.

Global warming is defined as an increase in the average temperature of the Earth’s atmosphere. Climate change is an effect of global warming that cause drastic changes in the weather. Volcanic eruptions, solar radiation, and movement of crustal plates are some of the natural causes of climate change. Additionally, modern lifestyle is a substantial contributor to climate change. Global warming influenced by society is caused through an increase in greenhouse gases. Some of these gases include water vapor, carbon dioxide, methane, fluorinated gases and nitrous oxide. These gases warm the Earth’s atmosphere by trapping heat. Fluorinated gases have the highest warming potential followed by nitrous oxide, methane, carbon dioxide and water vapor. In the environment, water vapor is released in enormous amounts followed by carbon dioxide, methane, nitrous oxide and fluorinated gases. Water vapor is a special gas because it has a low warming potential but is released in such high amounts, therefore, it has the highest warming effect. The carbon cycle circulates and transforms carbon back and forth between living species and the environment. Animal agriculture, transportation, and water utilities disrupt the carbon cycle by releasing stored carbon. As a result of global warming the following occur: intense heat, droughts, hurricanes, fires, floods, and rising of the sea level. Researchers created two experiments, the first experiment tested the warming potential of greenhouse gases at a constant rate. In contrast, the second experiment tested the warming potential of greenhouse gases at the rate they are released in the environment. In order to conduct these experiments greenhouses, ice sculptures, lights and thermometers were used. The results stated fluorinated gases had the greatest warming potential, while gases were distributed at a constant rate and carbon dioxide had the greatest warming potential when release in correlation to the environment. In conclusion, global warming is caused by greenhouse gases that are released by modern lifestyle. If global warming continues there will be fatal outcomes and congress has the power to prevent this by imposing a law in which methane is taxed.

Natural disasters are an inevitable annual occurrence. Every year there is billions of dollars in damages and hundreds of casualties. Out of all the natural disasters in the United States, floods are the most prevalent. FEMA is the government agency that is charge of managing disaster in the nation. The main problem with FEMA is the time that they take to respond to natural disasters and increased spending. Currently many homes and public infrastructures such as roads, levies and bridges in disaster prone areas are ill equipped to handle natural disasters such as floods. Flood insurance companies often times underpay policy holders in claims or sometimes do not pay them at all.

This paper offers proposed legislative reformations as answers to these problems. The proposed legislative reformation for FEMA is to readjust their budget for more spending on the National Flood Program while saving $1.5 billion overall. In addition to readjusting the budget the proposed reformation also includes increasing training for natural disaster relief and responsiveness by 25%. Another proposed reformation is to eliminate flood insurance companies from the National Flood Program. Reinforcing homes by elevating them or rebuilding them and making public infrastructure more flood and storm resistant is another proposed reformation.

With the passing of the proposed reformations, there will be significant positive financial and social outcomes. The cost of property and infrastructure damage will decrease by more than half with the reinforcement of infrastructure. The amount of casualties will also decrease by more than half as well. As a result of FEMA becoming more efficient and the elimination of insurance from the National Flood Program, responses to natural disasters will be quicker and homeowners will see less damages to their homes and more money received on claims much quicker.

Melanoma is known as a malignant transformation of the melanocyte. Worldwide Skin melanoma has an incidence of 1% in men and 0.9% in women in countries and of 0.7% in men and 0.6% in women in developing countries. In Mexico, skin melanoma has an incidence of 1.3% of all cancer patients. Although there is a high number of antineoplastic drugs, the rate of mortality of this disease is highly. Collecting information in the database of CHEMBL we concluded the little information it has about the target protein related to drug under test. There were 327480 preclinical test compounds and only 242 described the related protein. Knowing this fact could drastically changes the compound.

The methodology used was basically a "statistical methodology", they were collected all melanoma-related compounds registered in CHEMBL, a manually curated chemical database of bioactive molecules with pharmaceutical-like properties. The number of test compounds found was 327480, of which a sample of 243 having the related target protein was taken. It was obtained Shannon entropy of each variable of the drug. In addition to calculating the entropy of the proteins with the FASTA format, PseAAC was used: Generating pseudo amino acid composition to obtain the lambda factor, which was also calculated entropy. The method used was the comparison of all the information on sequence, test conditions, entropies of the new drug with respect to the drug values ​​of reference. The STATISTICA software was used to obtain the desired model.

From the present investigation it has been possible to obtain a predictive model that allows to recognize the biological activity of the new drug against the "j" conditions of a drug of reference in melanoma from a base of 243 proteins. Most of the drugs are unknown the related target protein, with this model we try to recognize the probability that one of the 243 base proteins is related to drugs that are unknown this variable.