Greenhouse gases in the Earth's atmosphere absorb the infrared radiation from the sun andrelease it. Much of the released heat reaches the earth, along with the heat of the sun that hasalready penetrated the atmosphere. Now both solar heat and radiated heat are absorbed by theearth and released. Some are reabsorbed by greenhouse gases to perpetuate the cycle. Theproblem is that the more of these gases exist, the more heat is prevented from escaping intospace and, therefore, the earth is heated. This increase in heat is called the greenhouse effect.

We are glad to invite you to participate in the workshop SIUSCI-01. SIUSCI-01 workshop series will be held at San Ignacio University (SIU), FL, Miami, USA. San Ignacio University is an innovative educational institution dedicated to the creation of the leaders of tomorrow accredited by the Accrediting Council for Independent Colleges and Schools (ACICS). The present workshop focus on topics of multidisciplinary sciences relevant to the interests of  SIU students and professors. SIUSCI-01 is also devoted to strength the collaborations and networking between SIU professors and students with other students and researchers in FL education system and worldwide. 

In this sense, SIUSci-01 is a workshop associated to and hosted online by the MOL2NET Conference Series on Multidisciplinary Sciences, MDPI Sciforum, Basel Switzerland. This means that all communications are going to be published online at Sciforum platform. All presentations will be peer reviewed and a DOI number will be assigned. MOL2NET conference of Sciforum is one of the platforms internationally recognized for scientific exchange. This annual edition is full of diversity in topics, approaches, and integration of disciplines, representing one of the common paradigms of modern science, interdisciplinary teamwork, and networking. I hope you will enjoy the program and the presentations.

Osteosarcoma (OS) is a rare genetic disease that represents 20% of all types of malignant and benign neoplasms of the bone, and 2% of pediatric cancers. Therefore, our aim in this study is to generate a consensus gene list associated with the pathogenicity of OS by using several theoretical approaches that let to propose new drivers associated to this sarcoma, and also possible biomarkers. Firstly, we evaluated the consensus between 9 prioritization strategies to early determine pathogenic genes related to OS. From these genes, we performed a communality analysis in the protein-protein interaction network further enrichment analysis. The consensus prioritized gene list consisted of 1295 genes. Our results revealed that consensus strategy proposes genes related to control in the cell cycle that describe the etiology of cancer in general, and prioritizes not only suppressors already described for OS such as RB1 and TP53, but also postulates new candidates that would help to describe its pathogenesis.

We can represent the brain as a Brain Connectome Network (BCN) formed by n_i brain cortex regions interacting with others (L_ij = 1) or not (L_ij = 0). The large number of links to be study and their complex connectivity patterns made difficult to select the appropriate topology in order to predict them with Artificial Neural Networks (ANNs) algorithms. In this context, Automated Machine Learning (AutoML) techniques may help non-experts to select, train, validate, and use automatically the correct algorithms. In this work, we developed a new ÁutoML Screening Model for ANN (SMANN) algorithm to solve this problem. We can quantify topological (connectivity) information of both the complex networks under study and a set of ANNs trained using Shannon measures. The SMANN model presented >85% of accuracy for 52690 outputs of 10 different ANNs for 52690 BCN links.

We used different experimental protocols to determine the mitoprotective activity (%P) of different carbon nanotubes (CNT) against mitochondrial swelling. The experimental conditions were combinations of the following factors:  different mitochondrial swelling assays using the MPT-inductor (Ca2+, Fe3+, H2O2) combined or not with a second MPT-inductor and swelling control assays using MPT-inhibitor (CsA, RR, EGTA), exposure time (0–600 s), and CNT concentrations (0–5 mg ml[1]1). Other factors changed were the CNT structural parameters CNT type (SW, SW + DW, MW), CNT functionalization type (H, OH, COOH). We also changed different physico-chemical properties of CNT properties like molecular weight/functionalization ratio (minW/maxW) or maximal and minimal diameter (Dmin/Dmax). Full paper published in: RSC Adv., 2015, 5, 103229–103245

Inulin is an essential part of the family of complex carbohydrates, which actively participates in industrial applications, since it is an active ingredient of several vegetative species of tubers and roots. In developing countries, there are no inulin production industry from biomass. This carbohydrate is only imported as active ingredient of pharmaceutical drugs and quantification in already elaborated products. These are the reasons, why it is extremely important the development of inulin industrial production. This research aims to design a technological process to produce inulin from garlic biomass (Allium sativum), starting from the state of the art analysis of the field, which prioritizes the study of inulin extraction from garlic by solid-liquid extraction at laboratory scale, using water as solvent. The influence of the variables water / garlic ratio: 2, 3 and 4 ml water / g garlic and the temperature: 30, 55 and 80 °C on the extraction process, during 45 minutes, with constant agitation was assessed. The refining obtained from the filtration of the extract was quantified; the results indicate that garlic contains about 18% of inulin and the best extraction conditions are: temperature at 80 °C and water / garlic ratio of 4. A pilot-scale sustainable technology was designed and simulated with SuperPro Designer software from a raw material input capacity of 10 kg/batch. A comparative analysis was made on the feasibility and possibilities of implementation of the different flow works.

Ochroma pyramidale, is a species recognized for its economic and ecological importance, widely used in forest plantation programs. The aim of this work was to evaluate seed quality indicators of Ochroma pyramidale in three sites in the Amazon region of Ecuador for reforestation purpose in degraded areas. The results indicated that the seeds of the species in the three study sites are of good quality, expressed through the germinative capacity, germination energy, useful value and germinative vigor, although in San Juan they presented higher values as a reflection of their vitality and exuberant nature.

Hypertension is a health problem of high prevalence worldwide. Because it is an important cardiovascular risk factor, the development of new drugs that are more effective and with fewer side effects is extremely important. Recent studies have shown that several natural compounds have good antihypertensive activity by inhibiting the angiotensin II converting enzyme (ACE), which makes them good candidates the prototype for the development of new drugs. Based on this perspective, this work proposes to evaluate the solubility (partition coefficient and water solubility) of the natural compounds oleroupein, guanosine, epicatechin 3-O-gallate, mirtilin and ligandstroside, through the software ALOGPS 2.1, and observe their interaction with the ACE, through molecular docking, with the software Autodock 4.2, aiming to corroborate the experimental data widely described in the literature. it was observed that all the compounds involved in the study had adequate partition coefficient and water solubility to interact with aqueous (biological fluids) and liposoluble (plasma membrane) surfaces. It was also observed, through the molecular docking study, that all the compounds interacted attractively with the active site of the enzyme, forming intermolecular interactions with the amino acids of the site and with the zinc ion, which is of extreme importance for the enzyme to convert angiotensin I in angiotensin II. Among the compounds involved in the study, epicatechin 3-O-gallate showed the most stable interaction with the active site, with energy at -8.02 kcal / mol. The theoretical results developed in this work allowed a better view, at a molecular level, of the interactions between several natural compounds with the active site of ACE. It can be observed that the polar groups of the compounds are of extreme importance for the interaction of the zinc ion and for its biological activities.

In this report are used two datasets involving the main antidiabetic enzymes targets α-amylase and α-glucosidase. The prediction of α-amylase and α -glucosidase inhibitory activity as Antidiabetic Agents is carried out using LDA and classification trees (CT). A large data set of 640 compounds for α-amylase and 1546 compounds in the case of α-glucosidase are selected to develop the tree model. In the case of CT-J48 have the better classification model performances for both targets with values above 80- 90% for the training and prediction sets, correspondingly. The best model shows an accuracy higher than 95% for training set; the model was also validated using 10-fold cross-validation procedure and through a test set achieving accuracies values of 85.32% and 86.80%, correspondingly. The main descriptors that influence the inhibitory activity of the antidiabetic enzymes are interpreted. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better or goodness results. Finally, we can say that, the present results provided a double target approach for increasing the estimation of antidiabetic chemicals identification aimed by double-way workflow in virtual screenings pipelines

In this work, we used Perturbation Theory (PT) techniques to define a linear model for metabolic pathway networks of >40 organisms compiled by Barabasis’ group. We calculated PT operators for 150000 pairs of nodes (metabolites) using Markov linear indices f_k. The linear CPTML model obtained predicts network topology with values of accuracy, specificity, and sensitivity in the range of 85-100% in both training and external validation data series.