This paper reports the identification of an extracellular acido-thermostable endo-chitinase (called ChiA-Hh59) from thermophilic Hydrogenophilus hirschii strain KB-DZ44. The ChiA-Hh59 was purified and characterized. The maximum chitinase activity recorded after 36-h of incubation at 60°C was 3000 U/ml. Pure enzyme was obtained after heat and acidic treatment, precipitation by ammonium sulphate and acetone, respectively, followed by sequential column chromatographies on Sephacryl S-200 and Mono Q-Sepharose. Based on MALDI–TOF/MS analysis, the purified enzyme is a monomer with a molecular mass of 59103.12-Da. The 22 residue NH₂-terminal sequence of the enzyme showed high homology with family-18 bacterial chitinases. The optimum pH and temperature values for chitinase activity were pH 5.0 and 85°C, respectively. The pure enzyme was completely inhibited by p-chloromercuribenzoic acid (p-CMB) and N-ethylmaleimide (NEM). The obtained results suggest that ChiA-Hh59 might be an endo-chitinase. The studied chitinase exhibited high activity towards colloidal chitin, chitin azure, glycol chitin, while it did not hydrolyse chitibiose and amylose. Its catalytic efficiency was higher than those of chitodextrinase and ChiA-65. Additionally, TLC analysis from chitin-oligosaccharides showed that ChiA-Hh59 acted as an endo-splitting enzyme. In conclusion, this chitinase may have great potential for the enzymatic degradation of chitin.

In Earth Observation (EO) process the data is assembled about planet Earth through remote detecting. The area, where most information about our planet is gathered, is space. The data accumulated at the Earth's end is extremely huge, accordingly requires a great deal of manual work by people to recover group and foresee information gathered. To limit human exertion, neural networks were introduced. Counterfeit neural systems have accomplished the human level picture grouping result. The issue of characterization of satellite pictures in the field of remote detection is commonly gained utilizing pixel-level, object-level, or scene-level. A Land spread characterization of satellite pictures using a Deep Learning strategy intends to accomplish uniform arrangement of land-structures. Utilizing a regulated learning procedure, the Convolutional Neural Network (CNN) model was created to group satellite pictures. This for the most part centers around the scene-level arrangement of satellite pictures utilizing a Deep Learning strategy. At scene-level, the ability of CNNs to arrange or aggregate a tremendous number of remotely detected picture information caught by different satellites has been examined utilizing various openly available datasets. Likewise, a trial dataset is utilized which has 0.5m goals and was adjusted further according to the necessity. Utilizing scene level arrangement results are acquired by ordering respective pictures into numerous semantic groups. A nitty-gritty survey and test are also performed to delineate and investigate how Deep Learning (DL) has been applied for remote picture evaluation tasks for scene-level grouping.

This research work predominantly centers around a scene-level arrangement, of satellite pictures utilizing a Deep Learning method. Additionally, this report presents an exhaustive investigation of ongoing advancement on different datasets and techniques accessible for scene grouping. The different methodologies were discussed by distinguishing some exploration holes in Deep Learning for satellite data.

At scene-level, the capacity of CNNs to order or gathering a colossal number of remotely detected information caught by different satellites had been examined and investigated widely utilizing various openly available datasets. A nitty-gritty inspection and test were carried out to depict and examine how DL has been applied for remote detecting picture examination tasks for the scene-level grouping. In the field of Remote Sensing, this examination covers the vast majority of the application and innovation in going from pre-training to the mapping of satellite pictures.

The search for thermo- and extremophilic microorganisms as talented sources for highly stable enzymes has gained growing attention in recent research. The literature indicates that several microorganisms that have the ability to live under extreme conditions, such as thermophilic hot springs, volcanic and geothermal regions, are endowed with unique features of considerable interest to various industrial applications and processes. In this context, a thermophilic bacteria Hydrogenophilus hirschii strain KB-DZ44 was isolated from Hammam Righa hot spring in Ain Defla (Algeria). Identification of the isolate was done according to morphological, physiological, and biochemical characteristics and 16S rDNA sequence similarity as well. The temperature range for growth was 40–80 °C (opt. temp. 60 °C), pH range was 6–12 (opt. pH 6.5–7.5), and NaCl range of 0 to 4 g/L (opt.1- 1.5 g/L). 16S rDNA sequence and blast analyses confirmed that the isolate belonging to the genus Hydrogenophilus. The sequence was submitted to GenBank with accession number (KY646164).

Severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) is responsible for the greatest pandemic of the 21st century. Since December 2019 until now more than nine millions have been infected by the virus and almost five hundred thousand people lost their lives for Coronavirus disease 2019 (COVID-19). Some mechanisms were associated to COVID-19 pathophysiology and specially in severe cases a specific dysregulation in the immune system could be identified, the cytokine storm. In that situation occurs a decrease in the anti-inflammatory and protective imune compounds and an increase in peripheral blood of pro-inflammatory, such as interleukin-2 (IL-2), IL-6, IL-10 and interferon-gamma (IFN-γ) that cause lung injury and worsens the patient's prognosis. So, this work aimed to review the role played by cytokine storm in SARS-CoV-2 infection, in order to to better understand the molecular events behind out-of-control cytokine response in severe COVID-19 patients.

COVID-19 (Coronavirus Disease 2019) is a pandemic infection caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Once in the body, COVID-19 can cause acute respiratory distress syndrome (ARDS) and multiorgan failure. The mandatory cellular structure for virus invasion is the membrane-bound form of angiotensin-converting enzyme 2 (ACE2). After SARS-CoV-2/ACE2 binding, this complex is internalized by the host cell, RNA is released and viral replication starts. ACE2 is part of the renin-angiotensin-system (RAS), that is critical in the physiological regulation of several body systems and specially in cardiovascular and blood pressure maintenance. The primary role of the positive RAS axis, by the ACE1 isoform, is to increase sympathetic nervous system tension, cause vasoconstriction, increase blood pressure, and promote inflammation, fibrosis, and myocardial hypertrophy. On the other hand, ACE2 acts in an opposite way to counteract these actions and maintain homeostasis. It is well established that RAS axis is directly related to the changes observed in hypertension pathophysiology. Thus, this work proposes to analyze the role of ACE2 in hypertension as a risk factor for COVID-19 and to evaluate the related emerging therapeutic strategies.

The objective of the present work is to cultivate the micro-alga "Spirulina platensis" of Burkina Faso origin in two types wastes water, olive mill wastes and the used engines oils in the area of Tizi-Ouzou, with a view to reduce pollution. In order to minimize the pollution caused by these wastes water, we opted to enrich them with the solution of fig wood ash as a source of alkalinity. The culture was carried out at 30 and 37° C. To be able to clean up these wastes water as much as possible, the recycling effect was studied. Some culture parameters (pH, phycocyanin level (%), Spirulina yields (%)), the analysis of functional groups and the antimicrobial activity of the obtained spirulina powders were determined.

The results obtained show that recycling has a positive effect on the culture of S.platensis in both natural media. A more or less significant production of phycocyanin was observed, in particular in the two recycled media at 30°C. These results state that temperature does not affect on the phycocyanin production.

Spirulina issued from the used oils of engines is dark brown in color and is characterized by a high level of pigments and impurities. On the other hand, that from olive mill wastes is blue-green in color, which reveals the presence of phycocyanin, the latter being the source of this color.

The two Spirulina powders from both media contain hydroxyl and carbonyl groups. Consequently, they are highly recommended as a source of carbon to use in agriculture.

The Multidrug-resistant Tuberculosis (MDR-TB) is considered a public health crisis and a threat to health security. The multi-resistant form can be treated and cured with second-line medications. However, second-line treatment options are limited and require long-term chemotherapy (up to two years of treatment) with drugs that, besides being expensive, are toxic. Thus, the need to find new antimicrobial agents to cope with this phenomenon increases. In this sense, the exploration of natural plants is valuable. Natural products from plants could be interesting alternatives. Some plant extracts are known to have antimycobacterial properties, and can be of great significance in therapeutic treatments. We carried out the study of antimycobacterial activity of Cissus incisa leaves. Three extracts were prepared from the leaves of this plant: hexane, chloroform methanol (1:1) and aqueous. Their antitubercular activities were evaluated using Alamar Blue assay. The hexane and chloroform/methanol extracts were the most active, with MIC values between 125-250 μg/mL. Until our knowledge, there are no studies to support the traditional use of this plant in relation to the mycobacterial infection diseases. This research contributes both to the knowledge of the Mexican flora, as well as the discovery of potential antibacterial agents derived from plants.

Bax and p53 proteins are interesting therapeutic targets, as they signal apoptosis and prevent neoplasms. In most cancers, these proteins are downregulated to support tumor survival. A C-phycpcyanin (C-PC) is a phycobiliprotein with diverse biological activities and could upregulate Bax and P53 in tumor cells. To assess the interaction between C-PC and pro-apoptotic proteins, docking is a valuable screening tool avoiding time and resources spent on in vitro experiments if this interaction does not occur in silico. Using PRISM web server (algorithm based on structural matching) the interaction of the C-PC (PDB ID: 1GH0) was verified in its complete form (Full) or just its F chain with two proapoptotic proteins (p53- PDB ID: 1C26- and Bax- PDB ID: 1F16). The C-PC does not interact with p53, only Bax in its two forms with binding energy of -1.54 (Full) and -21.97 (chain F). UCSF Chimera (program for the interactive visualization and analysis of molecular structures) revealed that the F chain was connected to Bax by two Hydrogen bonds and 180 varied connections (polar, nonpolar, favorable and unfavorable). In silico analyzes showed preferential interaction of C-PC with Bax, without any interaction with p53. The higher binding energy with the F chain in relation to the complete structure of C-PC indicates greater stability of the protein complex. The docking demonstrated the feasibility of carrying out in vitro and in vivo studies to assess the possible stimulatory role of C-PC on Bax.

Map-making is an inherent cognitive capability of human beings to fulfill their basic needs and desire to explore. This art usually referred to as Cartography, has moved a long way from markings on the trees and cave stones as well as directions through celestial bodies using heuristics to today’s latest applications based on geodesy and web cartography running in real-time. The global satellite based navigation systems, which came into being in 1995 with their full operational capability, revolutionized the parallel developments in the satellite imaging technology. The NAVigation Satellite Timing And Ranging Global Positioning System (NAVSTAR GPS) from the United States of America and the Globalnaya navigatsionnaya sputnikovaya sistema (GLObal NAvigation Satellite System, GLONASS) from RUSSIA aided their military users and on availability to the cartographic community changed the way of surveying and mapping in different countries. The photogrammetric processing required for cartography purposes, topographic mapping, and photogrammetric product generation need high accuracy in both horizontal (planimetric) and vertical directions. The presented study covers various experimental sites in India at Dehradun site, Uttarakhand; Jaipur site, Rajasthan; Kendrapara site, Odisha; Chandigarh site, and Delhi site where the Differential GPS (DGPS) surveys in the static mode were carried out using Leica 500 series GPS, Trimble Net R9, and Trimble R7 Global Navigation Satellite System (GNSS) geodetic receivers. The horizontal and vertical precisions achieved in the post-processing of these collected GPS survey datasets using DGPS / DGNSS mode range from 2mm - 6mm majorly. The root mean square error (RMSE) attained for the satellite triangulation is better than a pixel resulting in the photogrammetric generation of significantly more accurate digital elevation models (DEM) as compared to the DEMs based on rational polynomial coefficients (RPC) alone and further improved orthoimages for the study sites using Cartosat-1 stereo pairs.

The pathophysiology of cancer is related to diverse cellular and molecular dysfunctions. Unregulated proliferation can occur due to changes in pathways such as MAP kinase (ERK is the effector kinase). Metastasis linked to the worst prognosis is related to the unregulated function of adhesion proteins (like E-cadherins/ ECAD). The C-phycocyanin (C-PC) is a photosynthetic pigment with several biological activities, with the potential to inhibit ERK and ECAD in tumors. In this work, we evaluated the possible interaction of C-PC with ERK and ECAD to deepen the knowledge about the anti-tumor mechanisms of C-PC. Using the PRISM web server (algorithm based on structural matching) the interaction of the C-PC was verified (PDB ID: 1GH0) in its complete form (Full) or F chain with ERK (PDB ID: 2ZOQ) and ECAD (PDB ID: 2O72). Full C-PC interacted with ECAD with -5.77 of binding energy (BE). The F chain interacted with ERK (-20.99 BE) and ECAD (-41.71 BE). UCSF Chimera program revealed that the F chain binding to ECAD by 179 varied connections (polar, nonpolar, favorable and unfavorable), without establishing Hydrogen bonds. The chain F binding to ERK by 5 Hydrogen bonds and 264 varied connections. The chain F of C- seems to have greater biological action (in relation to the complete structure), because it was linked to the two targets. The greater BE demonstrates the greater stability of the protein complex, so the greatest action must occur on ECAD. However, the interaction in ERK by 5 hydrogen bonds should favor the role of C-PC on this protein. Thus, the interaction of C-PC with ERK and ECAD demonstrates its potential to reduce proliferation and metastasis, encouraging in vitro studies.

Tuberculosis (TB) is a bacterial infection caused by the bacillus Mycobacterium tuberculosis. It usually affects the lungs, but it can also affect other sites (extrapulmonary TB).

In the “Global Tuberculosis Report 2018”, TB is recognized as one of the 10 main causes of death worldwide and the main cause of a single infectious agent (above HIV/AIDS).

By the other hand, medicinal plants have been widely used to treat this disease. Some extracts derived from plants are known to have antimycobacterial properties, and can be of great significance in therapeutic treatments. For that reason, we aimed to evaluate the antimycobacterial activity of Cissus incisa leaves on a sensible strain. Three extracts were prepared from collected leaves of this plant: hexane, chloroform methanol (1:1) and aqueous. The antitubercular activity was evaluated on M. Tuberculosis H37RV strain (ATCC), using Alamar Blue assay. The hexane and chloroform/methanol extracts were the most active (MIC=250 μg/mL). Until the best of our knowledge, there are no studies related to the traditional use of this plant for treat tuberculosis.

The present investigation will contribute to the discovery of potential antibacterial agents derived from plants, and to expand the use of C. incisa in traditional medicine.

Microalgae such as Spirulina (Arthrospira) platensis are the source of several bioactive compounds. C-phycocyanin (C-PC) is one of these compounds and has been widely used in the food industry. Caries disease is multifactorial and causes destruction of dental tissues. The lesions are sugar dependent, with sucrose being the most cariogenic sugar, which is metabolized by bacteria that cause caries. Thus, our objective was to verify through molecular docking the possible binding of C-PC with sucrose and thus create the possibility of adding C-PC in a mouthwash (which enhances the elimination of sucrose from the dental surface). We used the UFSC Chimera for removing heteroatoms from C-PC, AutoDock Tools for preparing protein (C-PC) and ligand (sucrose), AutoDock Vina for docking simulation, PyMol for viewing and converting files and, finally, the LigPlot+ to check the types of bonds between molecules. The C-PC and sucrose connected with a free bonding energy of -7.0 Kcal/mol. In addition, the molecules established 08 hydrogen bonds and 07 hydrophobic interactions. Our results demonstrate the potential for sucrose sequestration of C-PC and the possibility of adding C-PC in a mouthwash as an aid to dental cleaning and caries preventing.

For a long time, scientists have investigated in familiar world to identify novel microbial biocatalysts. However, the mountain soils has been shown as an almost entire reserve of novel enzymes with interesting properties for industrial and environmental applications. Bacillus sp. as a genus of facultative anaerobic bacteria, is widespread in nature. Many species of this genus produce different enzymes used in biodegradation, textiles, bakery, food industry, stationery, biopharmaceutical industries and in many other domains. Thus, the strain KA15 was isolated from Tikjda, in the Djurdjura Mountains, Algeria. The identification of this newly isolated bacterium was carried out using morphological, physiological, and biochemical characteristics. In addition, the 16S rDNA gene was also amplified and sequenced. All the data obtained with regards to the physiological and biochemical properties of the isolate, confirmed that the KA15 strain belonged to the Bacillus genus. The growth temperature was 8-45°C with an optimum Temperature at 25°C, and pH range was 5–8 with an optimum pH around 5. Moreover, the nucleotide sequence and blast analyses confirmed that the KA15 strain (access number GenBank MK874318) was closely related to those of the Bacillus strains. All the results obtained strongly suggested that this isolate should be assigned as Bacillus altitudinis strain KA15.

Looking into the literature and scientific forums, there isn’t any software that can explore the diversity of a database or a sequence subset by applying the similarity measures reported to delimit the twilight zone according all previously mentioned thresholds. So far, in order to retrieve several similarity measures like identity, similarity and scores in an all-vs-all pairwise sequence comparison, users should run previously software like needle (global alignment), water (local alignment), blast (local alignment) and even multiple sequence alignments (MSAs) tools (http://imed.med.ucm.es/Tools/sias.html), then results should be parsed to be presented in a nxn matrix. However, going through all these steps to get at the final similarity matrix require programming skills.

Here, we present SeqDivA, a python-based tool with a friendly GUI allowing non-expert users to run alignment algorithms (water, needle and blast) to compare all vs all protein, DNA and RNA sequences. SeqDivA provides similarity, identity and bit-score matrixes to explore the diversity/homology of the sequences, enabling the delimitation of the twilight zone. The resulting matrixes are visualized using dot plot-like graphs representing pairwise similarity measures (identities, similarity and bit-scores). SeqDivA also allows redundancy reduction by exploring amino acid identities from global alignments and can be connected to the output of software simulating related sequences with a known evolutionary history i.e. ROSE [1] and INDELible [2] in order to get subsets of homologous sequences at different identities or bit-scores ranges. The software can be freely downloaded at https://github.com/eancedeg/SeqDivA. The software was published as part of the paper published at https://doi.org/10.3390/biom10010026

1- Stoye, J., D. Evers, and F. Meyer, Rose: generating sequence families. Bioinformatics, 1998. 14(2): p. 157-163.

2- Fletcher, W. and Z. Yang, INDELible: a flexible simulator of biological sequence evolution. Mol Biol Evol, 2009. 26(8): p. 1879-88

The Streptococcus mutans is one of the main causes of pathologies such as caries. To generate its effects, S.mutans has several mechanisms, including adhesion between microorganisms and microorganisms in tooth enamel. Adhesins are surface or cell wall proteins of S. mutans fundamental to the adhesion process. Thus, blocking this type of protein could represent an inhibition or deceleration of the caries process. The Rosemary (R. officinalis L.) can promote several pharmacological effects due to the interaction between the molecules of the plant and the organic systems. The objective of this study was to verify the possible interaction between Rosemary and adhesion protein (antigen I / II) of S. mutans through molecular docking. First, we prepare the receptor (adhesin) by removing the heteroatoms using the UCSF Chimera. Then, we made several preparations in the receptor and in the ligand (Rosemary) using AutoDock Tools. Finally, the docking simulation took place in AutoDock Vina. The V-region of S. mutans antigen I/II (adhesin) and Alecrim connected with a free binding energy of -8.0 Kcal/mol. The molecules established 08 hydrogen bonds and 06 hydrophobic interactions. Considering the anti-microbial action of rosemary associated with its binding potential in adhesins demonstrated in the docking, Rosemary can be a potent aid in combating caries in dental products.

An extracellular acido-thermostable chitinase (called ChiA-Ba43) was hyper-produced and purified to homogeneity from a newly isolated Bacillus altitudinis strain KA15. This strain exhibited the highest chitinase activity (about 10,000 U/mL) after 46 h of incubation in an optimized meduim. Pure enzyme was obtained after ammonium sulphate precipitation (30-60%), followed by sequential column chromatographies on Sephacryl S-200 HR and Mono Q-Sepharose. The purified enzyme is a monomer with a molecular mass of 43,190.05 Da as determined by matrix assisted laser desorption ionization time-of-flight/mass spectrometry (MALDI-TOF/MS). The sequence of the 27 NH₂-terminal residues of ChiA-Ba43 showed high homology with other Bacillus species. Optimal activity was recorded at pH 4.0-5.5 and 85°C. The pure enzyme was inhibited by p-chloromercuribenzoic acid (p-CMB) and N-ethylmaleimide (NEM). Interstingly, ChiA-Ba43 showed higher activity towards colloidal chitin, chitin azure, glycol chitin, glycol chitosane, chitotriose, and chito-oligosaccharide, while it did not hydrolyse chitibiose and amylose. Moreover, ChiA-Ba43 acted as an endo-splitting enzyme as showed by thin-layer chromatography (TLC) from enzymatic catalyzed hydrolysis of chitin-oligosaccharides and colloidal chitin. More interstingly, ChiA-Ba43 showed a high level of catalytic efficiency compared to chitinases ChiA-Mt45, ChiA-Hh59, Chitodextrinase^®, N-acetyl-β-glucosaminidase^®, and ChiA-65. Thanks to its biochemical properties, ChiA-Ba43 may be used for the bioconversion of chitinous waste on an industrial scale.

The Ribonuclease III (RNase III) enzymatic family is implied in many important biological processes from the bacteria until humans. In this sense, they have been selected as drug-target candidates for drug development. Although RNase III members show a high degree of sequence diversity, they generally share common structural and some degree of enzymatic activity. The accessory domains are key determinants for both the substrate selectivity and the biological function of each RNase III type, however, their role is still under study. The in vitro enzymatic activity of three RNase III members from class I (E. coli RNase III, Schizosaccharomyces pombe Pac1 and Saccharomyces cerevisiae Rntp1) and the human Drosha placed in class II was tested against four different substrates. These two RNase III classes encompass members having different domain organization. Enzymatic activity differences were found among members of the class I, which get increased when the human Drosha (class II) was included in the test. The substrate promiscuity of the E. coli RNase III was corroborated in vivo through its expression in S. cerevisiae, as reported previously, but was extended here to Pichia pastoris. The new biochemical data integrated with previous studies confirmed that RNases III substrate specificity is highly influenced by its protein structure architecture. This fact also allowed drawing evolutionary links between RNase III members from their structural and substrate specificity differences. More details about the results can be consulted at DOI: 10.2174/1381612822666160511150416.

Besides COVID-19 can severely affect the health of those infected, this pandemic is also causing several problems in society, such as economic and psychological issues. Thus, preventing new cases is important to minimized these problems. This work developing an app (SMStopCorona) that sending text messages (Short Message Service – SMS) in automated and tailored way, with guidance about prevention and general health tips, aiming to welcome the population about coping pandemic. A multidisciplinary team from Instituto Federal Sul-rio-grandense (IFSul) and Hospital Escola/Universidade Federal de Pelotas (HE/UFPel) prepared the messagens to be sent and registered it in the app. About 500 people from the southern region of Brazil signed up to receive these messages, and then they were imported into the app. SMStopCorona then automatically sends a daily orientation to each person registered via SMS. Preliminary results indicated good acceptance in receiving these messages by registered people. Several people responded to the messages received, thanking for the care and guidance they had received via SMS text message.

The Sars-CoV-2 or new coronavirus is responsible for the coronavirus disease-19 (COVID-19) that causes the current and serious pandemic. There are still no vaccines or well-defined treatments for this disease, making it urgent to investigate molecules capable of preventing and/or treating COVID-19. One of the most important proteins for coronavirus is the Spike. This protein is responsible for the fusion of the virus with the host cell to initiate the pathology. Spike blockade could prevent viral fusion with human cells and act preventively and/or therapeutically. The Rosemary (R. officinalis L.) already had antiviral effects on the Human Immunodeficiency Virus type 1, thus having the potential to inhibit the proliferation of Sars-CoV-2. Our objective was to verify the possible interaction between Rosemary and Spike protein through molecular docking. We prepare the receptor (Spike) by removing the heteroatoms using the UCSF Chimera and we made others preparations in the receptor and in the ligand (Rosemary) using AutoDock Tools. For the docking simulation we used the AutoDock Vina. The Spike of coronavirus and Alecrim connected with a free binding energy of -6.5 Kcal/mol. The molecules established 06 hydrogen bonds and 05 hydrophobic interactions. Considering the antiviral action of Rosemary associated with its binding potential in Spike demonstrated in the docking on this research, Rosemary can be a potent aid against the new coronavirus.

To represent the molecular structure of most nanomaterials by a molecular graph or even simplified molecular input-line entry system (SMILES) is very problematic. Databases that contain a large number of different nanomaterials are not established up to now. Nevertheless, large collections of the behavior of individual nanomaterials under different experimental conditions are available. Namely, the experimental conditions are a tool to define how to influence nanomaterial in order to obtain some attractive effect on different targets such as cells, organisms, or chemical-technological processes. Traditional SMILES provide special codes related to the molecular structure which can be used to build up traditional QSPR/QSAR models. Quasi-SMILES is an extension of the traditional SMILES by means of considering additional codes that reflect experimental conditions. The quasi-SMILES were applied to build up models for different endpoints related to nanomaterials such as mutagenic potential of multiwalled carbon nanotubes (MWCNTs) [1]; cytotoxicity for metal oxide nanoparticles [2,3]; cytotoxicity of MWCNTs [4]; solubility of fullerenes C₆₀ and C₇₀ in various solvents [5]; cell viability of human lung and skin cells exposed to different metal oxide nanomaterials [6]; mutagenic potential of silver nanoparticles [7]. Most probably, quasi-SMILES will find many others applications in the nearest future, e.g. by using the CORAL software [1-7] (http://www.insilico.eu/coral).

References

Toropov AA, Toropova AP. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. Chemosphere 2015; 124 (1): 40-46. DOI: 10.1016/j.chemosphere.2014.10.067

Toropova AP, Toropov AA, Rallo R, Leszczynska D, Leszczynski J. Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions. Ecotoxicol Environ Saf. 2015; 112: 39-45. DOI: 10.1016/j.ecoenv.2014.10.003

Ahmadi S. Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria. Chemosphere 2020; 242: 125192. DOI: 10.1016/j.chemosphere.2019.125192

Trinh TX, Choi J-S, Jeon H, Byun H-G, Yoon T-H, Kim J. Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells. Chem. Res. Toxicol, 2018; 31 (3): 183-190. DOI: 10.1021/acs.chemrestox.7b00303

Toropova AP, Toropov AA. QSPR and nano-QSPR: What is the difference? J Mol Struct. 2019; 1182: 141-149. DOI: 10.1016/j.molstruc.2019.01.040

Choi J-S, Trinh TX, Yoon T-H, Kim J, Byun H-G. Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials. Chemosphere 2019; 217: 243-249. DOI: 10.1016/j.chemosphere.2018.11.014

Toropov AA, Toropova AP. The Correlation Contradictions Index (CCI): Building up reliable models of mutagenic potential of silver nanoparticles under different conditions using quasi-SMILES. Sci Total Environ. 2019: 681: 102-109. DOI: 10.1016/j.scitotenv.2019.05.114

The enzymatic activity classification of the GH-70 enzymes is a challenge in Bioinformatics due to the high diversity of these sequences. From the 501 sequences reported when we accessed Cazy.org, just 58 were labeled into 6 EC number classes. In this paper we propose a semi-supervised classification algorithm based on the k-mers frequency descriptors with k equals to 2, 3, 4, 5 and 6 as alignment-free measures extracted from the sequences. The high dimensionality of the k-mers ( vectors and the increasing number of sequences lead to the application of big data Spark classifiers such as the ones in Apache MLlib. Specifically, the K-means clustering applied in an iterative way yields multiple results that can be ensemble in a semi-supervised second-round clustering step capable of detecting groups of similar sequences including the labeled and the unlabeled ones. Finally, external measures validate the ensemble clustering for the labeled sequences. Further improvements in the clustering and ensemble steps could raise the quality of classification.

The Streptococcus mutans is one of the main etiologic agents of multifactorial disease of caries. Adhesins like the antigen I / II are proteins of S. mutans and play an important role in caries lesions, even being a target for anti-cavity vaccines. C-phycocyanin (C-PC) is a blue-green heteroprotein which acts as a photosynthetic pigment for cyanobacteria such as Spirulina (Arthrospira) platensis and it has antibacterial effects. Thus, the C-PC could block the adhesins and represent an inhibition the caries process. Verifying the possible docking beween C-PC and adhesion protein antigen I/II of S. mutans in the main goal of this study. We used the PRISM web server (algorithm based on structural matching) for to check the interaction of the C-PC in its complete form (Full) or F chain with adhesion protein (antigen I / II) of S. mutans. The Full C-PC did not interact with the adhesion, while the F chain interacted with a binding energy of -17.12establishing 01 Hydrogen bridge and 139 varied connections (polar, nonpolar, favorable and unfavorable). Thus, the interaction of F chain with adhesion protein of S. mutans indicates a possible anticaries role of C-PC, encouraging continuity of studies in this area.

Natural products have been the mainstay of chemotherapy for years. Drug resistance in tumor cells is a major cause of failure in chemotherapy treatment. Several factors support this resistance and one of the main factors is the presence of drug efflux pumps such as ABCB1 transporters. The search for molecules capable of blocking the efflux by ABCB1 is important to increase the success of chemotherapy treatment. The Allicin (ALC) is one of the most biologically active compounds in freshly crushed garlic and the propolis-benzofuran B (PBB), a bioactive natural product isolated from honeybee propolis resin. ALC and PBB have already demonstrated anti-tumor effects, so they could block ABCB1. Comparing the binding pattern of ALC and PBB with ABCB1 through molecular docking was the main objective of this study. The docking simulation by AutoDock Vina demonstrated a binding energy with the ABCB1 more negative for PBB (-9.2 Kcal/mol) and less negative for ALC (-4.7 Kcal/mol). ALC linked to an extracellular region of ABCB1, whereas PBB bounded close to the nucleotide binding domain (intracellular portion) of ABCB1. The ALC established 02 and PBB 03 Hydrogen bonds with ABCB1. The number of hydrophobic interactions with ABCB1 was the same (10 interactions) for both molecules. Considering the more negative binding energy and the higher number of hydrogen bonds (evidence of greater affinity and stability) PBB appears to be a better ABCB1 inhibitor. However, it is necessary to continue in vitro and in vivo studies.

Natural products comprise several medicines. The curcumin (CRC) is a constituent of the spice turmeric and part of the mixture of compounds referred to as curcuminoids. The Piperin (PPR) is the major pungent alkaloid present in the fruits of Piper nigrum L. Both have several biological activities, including anti-inflammatory. The inflammatory process is important for tissue homeostasis, however its imbalance is related to several pathologies. Cyclooxygenase-2 (COX-2) participates in the inflammatory process by converting arachidonic acid into prostaglandins (inflammatory mediators). Understanding the connection pattern, at the molecular level, of CRC and PPR with COX-2 can assist in the development of research on natural anti-inflammatories. Comparing the binding pattern of CRC and PPR with COX-2 through molecular docking was the main objective of this study. The docking simulation by AutoDock Vina demonstrated a binding energy with the COX-2 quite similar: for CRC -8.8 Kcal/mol and for PPR -9.0 Kcal/mol. In addition, the binding site at COX-2 was also similar for CRC and PPR. The number of Hydrogen bonds and hydrophobic interactions established with COX-2 was exactly the same for CRC and PPR (01 Hydrogen bond and 12 hydrophobic interaction), however the COX-2 amino acids involved in these bonds were not the same. The energy patterns, location and types of binding established with COX-2 were similar for CRC and PPR, so the anti-inflammatory properties may be similar. In this sense, it would be important to continue in vitro and in vivo studies with both molecules.

A large part of electricity consumption in hospitals is spent on air conditioning equipment. This is due to the fact most equipment is old and has low energy efficiency. In addition, in some places there are no rules of use the air conditioning and it can be used at temperatures that do not generate thermal comfort for patients, also causing greater energy consumption. The present work develops a non-invasive system for control and automation of conciliated air, seeking energy efficiency and low cost of implementation, combined with sustainability and thermal comfort. Using the same infrared communication principle as conventional remote controls, a drive module with a Wi-Fi connection was developed, connected to a database that contains the equipment information. The activation and control can be done remotely. The drive module is composed of a power supply, an IR LED and a microcontroller with Wi-Fi connection. The database used is PostgreSQL and to perform the communication between these two systems, a web page was created with login and password, where the user can start the equipment and also configure operating hours so that they work autonomously. Until now, tests have been carried out on air conditioning units in the split model, commanding them remotely, showing that the system is capable of accomplishing its purpose. Future studies will evaluate if this systems will reduce the energy costs.

FTIR-ATR, DFT Studies of Adapalene 0.1% and Benzoyl peroxide 2.5% gel and its Efficacy in the Treatment of Acne Vulgaris

Padmavathi R¹*, Rajamannan B², Gunasekaran S³, Ramkumar G.R⁴, Sankari G⁵, Muthu S⁶

1 Department of Physics, Meenakshi Sundararajan Engineering College, Kodambakkam, Chennai, 600024, TN, India.

Pathmavati@gmail.com

2 Department of Engineering physics, FEAT Annamalai University, Annamalai Nagar, 608002,

Chidambaram, TN, India. rajamannan1@gmail.com

3 Research and development St. Peter’s Institute of Higher Education and Research, St. Peter’s

University, Avadi, Chennai, 600054, TN, India. deanresearchspu@gmail.com

4 Department of Physics, C. Kandaswaminaidu College for Men, Anna Nagar East, Chenna-600102,

TN, India. gr.ramkumaar@yahoo.com

5 Department of Physics, Meenakshi College for Women's, Kodambakkam, Chennai-600024, TN, India.

gsankarik@gmail.com

6 Department of physics, Govt. Thirumagal Mills College, Gudiyatham-632602, Vellore, TN, India.

mutgee@gmail.com

Correspondence: pathmavati@gmail.com

Abstract

Adapalene 0.1% and Benzoyl Peroxide 2.5 % (ADP+BPO) is a topical gel used in the treatment of acne vulgaris. In this study, the experimental and theoretical spectroscopic studies of the gel ADP + BPO using FTIR-ATR technique were carried out. The geometrical parameters and spectroscopic analysis were performed using DFT methods by RHF/6-31G basis sets. The optimized geometrical parameters, vibrational wavenumbers of the normal modes, Mulliken atomic charges, HOMO-LUMO, MESP, etc., of the compound, were also calculated. Including, the present study is to evaluate the safety and efficacy of the (ADP+BPO) fixed-dose combination, in a few teenage boys and girls (both male and female) for the treatment of Acne vulgaris. In diagnosing all diseases, the blood serves as the important metabolic transport system in every human body. Instead of analyzing blood to diagnose Acne Vulgaris, hair is a tissue sample used to detect acne vulgaris using FTIR-ATR spectroscopic technique. The Biomolecular changes due to disease, acne vulgaris present in the blood, hair that looks like blood will also reflect the same Biomolecular changes present in the blood. A single scalp hair was collected from 20 male and female subjects between the ages group of 20-25. On a daily basis, a combination of ADP+BPO was applied on the face for three months. After the medication, FTIR spectra of the Acne vulgaris, hair samples (Post-treatment) were recorded in the mid-infrared region, and the biomarkers for the acne vulgaris samples with specific peaks during the before treatment (Pre-treatment) were analyzed and compared with, hair samples of healthy subjects. Some of the biomarkers such as R₁ = I _3264/2864, R₂ = I _1633/2864, R₃ = I _1516/2864, and R4 = I _1454/2864 were used as diagnostic parameters, the significance of the intensity ratio results were estimated using dependent t-test statistic method. For the statistical interpretation, a low p-value (p<0.05) is taken as statistically significant.

Keywords: Acne Vulgaris, ADP+BPO, FTIR-ATR, DFT, Scalp hair tissues, Biomarker.

We provided a microservice developed in Nodejs connected to Google Cloud services. From a photograph of the ID obtained through a mobile device, its content is analyzed to extract its information. The Vision AI API uses AutoML Vision, which is capable of interpreting text. The information returned by the Vision AI service is processed in the microservice and the confidential information is anonymized, to be stored in a MySQL database server. The microservice is part of a mobile application to register the entry of citizens to a government institution.

One of the main difficulties in the world is the inadequate disposal of waste, which generates several environmental and social problems, such as pollution, flooding and damage to public health. The main goal of this project is to develop a robot prototype to collect waste in an automated way, generating efficiency and agility. The LEGO Mindstorm EV3 kit was used to build the robot, using color and presence sensors.
The robot follows a certain path, defined by a black line detected with the color sensor. Upon detecting an object in its front, the robot collects and loads
the waste until the end of the route that was defined as appropriate place for disposal, marked by a gate. After the end of the route, the process is automatically restarted. The robot performs the proposed tasks as expected. This robot prototype contributes to the reduction of the aforementioned problem, collecting the waste and disposing of it in the appropriate place. This project is a robot prototype that can be developed for real situations using other robotic resources.

Diabetes mellitus is a common chronic metabolic disease that constitutes a risk factor to patient infected by Covid-19. Aldose reductase enzyme catalyzes the metabolic glucose-sorbitol conversion for the polyol pathway in diabetic conditions. The accumulated sorbitol in the tissues has been reported to be responsible for diabetic complications such as cataracts, retinopathy, neuropathy, and nephropathy. Inhibitors of aldose reductase are considered as therapeutic target in prophylaxis and treatment of these affections. A particular case, have been studies the flavonoids isolated from fruits, vegetables and medicinal plants. In the current paper we development a Generalized Linear QSAR model using MATLAB and molecular descriptor implemented in DRAGON software. A result shows a model adjusted whit R² of 0.948. The model was extensively validated according to OECD regulatory principles by mean of internal and external validation exercises. In addition, the applicability domain was obtained to warranty the trustworthy of the predictions. Due to it predictive power (R²ext = 0,943) the model was used to predict the ALR2 inhibition by the flavonoids reported in Jatopha gossypifolia L. The ellagic acid was the most promising metabolite (pIC₅₀ predicted = 12.69), which is into the applicability domain and have drug-like properties for oral administration. Finally, we can conclude that proposed tools are useful to the quick and economic identification of drug potential candidates against ALR2 in diabetic complications.

Type 2 Diabetes Mellitus (DM) is one of the relevant changes observed in Metabolic Syndrome (MS), which in turn is inherent to obesity. In extreme cases of obesity, bariatric surgery is indicated, reflecting directly on weight loss and improving metabolic rates. Scientific studies show a clinical improvement in glycemic values a few days after surgery, which disassociates the improvement presented, from the weight loss condition. The reversal of type 2 diabetes would be associated with changes in the entero-insular axis resulting from duodendo-jejunal exclusion, which would trigger an increase in the production of incretins such as GLP1 (Glucagon-like peptide-1) and a decrease in GIP (glucose-dependent insulinotropic polypeptide) reflecting directly in the body's sensitization to insulin. A bibliographic survey was carried out through scientific articles searched on the various research platforms such as Scielo and Google academic to verify the real causes of the clinical improvement of post-bariatric glycemic indexes. The restructuring of the gastrointestinal tract excluding the jejunal duodenum facilitates the action of incretins, modulating their production and improving metabolic rates. The gastroplasty with the best therapeutic results for DM was Roux Y derivation, but the patient must undergo changes in habits, such as healthy eating and physical activity.

The alcohol consumed during pregnancy can result in damage to the fetus, which is irreversible, as well as damage to the pregnant woman. So, the alcohol crosses the placenta and reaches the fetus, including amniotic fluid, which acts as a reservoir for alcohol, affecting the brain and heart. In this sense, since alcohol is the most consumed drug worldwide, the main objective of the research was to explore the current knowledge about how the alcohol ingested by the pregnant woman interacts with the organism of the fetus and what are the consequences of this interaction. We used research of different bibliographic through scientific articles searched on the various research platforms. Fetal Alcohol Syndrome (FAS) is the most serious disorder in the spectrum of fetal alcoholic disorders, and causes a complex clinical condition. The effects result in interference with brain formation, yet, resulting in congenital changes, malformation of the central nervous system, growth retardation and impaired cognitive and behavioral development. Therefore, it is concluded that the intake of alcoholic beverages, regardless of the dosage during pregnancy and breastfeeding, constitutes an extremely harmful and unacceptable behavior.

Vitamin D3 (VitD) has several biological activities and some studies demonstrate its possible correlation with better prognosis in COVID-19 disease. The Sars-CoV-2 or new coronavirus is responsible for the current and serious pandemic of COVID-19. One of the main proteins involved in the infection process of the host cell is the Spike protein and its blocking could have preventive and therapeutic actions. The objective of this work was to verify the ability of binding, through molecular docking, VitD with the Spike protein. Docking performed with AutoDockVina demonstrated that the binding between VitD and Spike occurs with a binding energy of -7.1 Kcal/mol and through 12 hydrophobic connections. So, at the molecular level, VitD's interaction with Spike could inhibit this viral protein and act as an Anti-Sars-CoV molecule. To confirm this evidence demonstrated by the docking, it is necessary to continue the studies in vitro and in vivo.

Inflammatory processes, although important for tissue homeostasis, can suffer imbalances and be related to several diseases. An important enzyme involved in the mediation of inflammatory processes is cyclooxygenase 2 (COX-2), which generates prostaglandins from arachidonic acid. In this context, this enzyme is an important drug target and its inhibition is a mechanism present in several anti-inflammatory drugs. The chlorogenic acid (CHLORO) could act in an inhibitory way on COX-2 and there are reports of anti-inflammatory actions of this molecule. To evaluate the possible inhibitory power on COX-2 of chlorogenic acid through molecular docking was the objective of this study. The docking simulation by AutoDock Vina demonstrated a binding energy with the COX-2 of -9.5 Kcal/mol. Besides that, COX-2 and CHLORO stablished 09 Hydrogen bonds and 06 hydrophobic interactions. The binding energy and types of binding established with COX-2 can demonstrate the anti-inflammatory properties of the CHLORO. In this sense, it would be important to continue in vitro and in vivo studies.

Vegan cheese alternatives are gaining new interest for the agro-industrial market due to the improving request of animal friendly products. The present research focused a preliminary development of a vegan cheese production based on Lupinus mutabilis derivates. The vegan cheese have been produced removing lupanine. Microbiological analysis, moisture content, protein amount, and organoleptic analysis have been performed respecting several nacional, and international guidilines. All microbiological data are compliant with international regulations ranges regarding molds, yeasts, and coliforms. Moisture content was 59.15%, and the protein amount 40%. The organoleptic analysis, based on a 5-point hedonic scale, shown a average of 4.3, which reveal a very acceptable results. All these preliminary results showing a promising lupine-based food, which give an interisting vegan cheese alternative.

In this paper, we address the challenge of land use and land cover classification (LULC) using Convolution Neural Networks (CNN) on existing remote sensing datasets and compare the obtained results in an Indian Urban area context. This paper showcases the theoretical and experimental study of various large-scale, high-resolution remote sensing datasets. An image retrieval dataset is used to perform image classification and promising results are found. Also, a small-scale dataset is used and modified from a high-resolution large-scale data as per the requirement. Different datasets with different dimensions and spectral bands are used for the study. The results and comparisons of various datasets are tabulated. From a dataset point of view, classification or categorization techniques can be developed and assessed by making use of image retrieval datasets but this doesn't work the other way around.
This paper also provides the literature with standard outcomes for future research on datasets for Machine Learning based image classification especially in terms of reducing memory consumption of computers and fastening the process of execution. The resulting classification system finds its use in a large number of Earth observation applications.

Hedycium coronarium (Zingiberaceae) is widespread in many countries in the tropical region. The essential oil has been investigated in the last decade, and some biological activities have been performed. The chemical composition has been studied on several samples belonging to many different countries, and the main compounds of the H. coronarium rhizomes oil are 1,8-cineole (11-56%), β-pinene (10-39%), and α-pinene (6-13%). Several preliminary studies concerning biological activities have been performed, and some promising results have been identified regarding the antiophidian, allelopathic, antimicrobial, larvicidal, and pupicidal activities. In the present mini-review 11 articles have been selected, and analyzed in order to elucidate the chemical composition, and pharmacological data of H. coronarium essential oil, some trends for new researches have been also proposed. The H. coronarium rhyzome essential, probably due to the presence of 1,8-cineole, deserve additional investigation in order to verify its potential as bioherbicide or natural-based antivenom treatment.

The applying of radical polymerization techniques is an important economic and theoretical task of chemical technology. Hence, the elaboration of the radical polymerization processes has both theoretical and practical significance. The ability of the CORAL software to build up robust models for transfer constants in radical polymerization is demonstrated. The Monte Carlo technique is applied in the CORAL software as the principle of development of the models. For molecular fragments represented by simplified molecular input-line entry system (SMILES), correlation weights were selected that maximize the correlation coefficient between the logarithm of the chain transfer constants in radical polymerization log (1/Cs) and the descriptor calculated from the correlation weights for the training set (n = 25). This correlation was preserved for the external validation set in three optimization samples by the Monte Carlo method.

Consumers are aware of the dangers arising from the use of synthetic antioxidants and antimicrobials in the agrifood industry, demanding safer and “greener” alternatives. In this study, the antioxidant activity of commercial essential oils through DPPH method, their antimicrobial effects against the bacterium Pseudomonas syringae and the phytopathogenic fungus Fusarium oxysporum by means of the standardized disk method were determined. Clove along with winter savory, cinnamon and oregano essential oils as well as carvacrol showed the highest antioxidant activity comparable to reference standards. Wintergreen essential oil was the most potent inhibitor against P. syringae growth at the highest doses (20 and 10 μL). Oregano essential oil and its main component carvacrol were able to stop the bacterium growth even at the lowest treatment (1 μL). Cinnamon, oregano and peppermint essential oils inhibited F. oxysporum development at all doses (20, 10 and 5 μL) assayed. In general, most of the essential oils displayed more antifungal than antibacterial and antioxidant activities.

Actualmente existe una gran rivalidad en cuanto a las técnicas de medición destructivas y a las
técnicas de medición no destructivas. Mientras unas optan por la destrucción de la muestra para
que el resultado de dicho proceso pueda ser analizado por un experto, las otras optan por un
análisis empı́rico y repetible, que otorga la posibilidad de realizar numerosos tipos de análisis
sobre una misma muestra.

En cuanto a las técnicas de medición no destructiva destaca la espectroscopia de infrarrojo
cercano, técnica que se encarga de analizar el espectro electromagnético en busca de los enlaces de
carbono pertenecientes a la sustancia a examinar. Ésta recibe ese nombre debido a que la región
del espectro que analiza se sitúa muy próxima a la luz visible, concretamente entre los 700 nm y
los 2500 nm.

Los dispositivos usados para llevar a cabo dichas mediciones son los espectrofotómetros. Estos
dispositivos tienen un tamaño y coste considerables, por lo que su uso está reducido a localizaciones
como los laboratorios.
Para solucionar este problema, se han creado los espectrofotómetros portátiles, que tienen
un tamaño y coste reducido. El mayor inconveniente es que el softoware para lidiar con ellos es
inexistente o está desactualizado.
El objetivo del proyecto es la creación de una aplicación para dispositivos móviles que permi-
ta la comunicación con un espectrofotómetro portátil.

Se ha llevado a cabo una prueba práctica para comprobar el correcto funcionamiento del
sistema. Esta estaba situada en el ámbito de las cervezas. Consistı́a en reconocer si una cerveza
de tipo ”lagger” tenı́a alcohol o no a partir de una muestra.
Se ha llevado a cabo un estudio del arte en el que se han detectado numerosos estudios acerca
del tema. La gran diferencia es que se ha hecho uso de un espectrofotómetro de laboratorio, lo que
otorga mucha más precisión y alcanza un mayor rango de medición que el dispositivo usado para
llevar a cabo este experimento.

En cuanto a la base de datos generada, se han tenido en cuenta 36 cervezas de distintas
graduaciones y marcas. Tan solo se han usado cervezas “puras”, es decir, cervezas que carecı́an de
sustancias como limón o tequila, ya que la composición de las mismas podrı́a afectar a la medición.
A mayores, se han realizado 3 mediciones por cerveza para hacer la base de datos más robusta.

Existen una infinidad de aplicaciones posibles haciendo uso de esta tecnologı́a.

El cribado y el modelado in silico son actividades críticas para la reducción de los costes
experimentales. También aceleran notablemente la investigación y refuerzan el marco teórico,
permitiendo así a los investigadores cuantificar numéricamente la importancia de un
determinado subconjunto de información. Por ejemplo, en campos como el cáncer y otras
enfermedades de alta prevalencia, es crucial disponer de un método de predicción fiable. El
objetivo de este trabajo es clasificar las secuencias peptídicas en base a su actividad anti-
angiogénica para entender los principios subyacentes a través del aprendizaje automático. En
primer lugar, las secuencias peptídicas fueron convertidas en tres tipos de descriptores
moleculares numéricos basados en la composición de aminoácidos. Se realizaron diferentes
experimentos con los descriptores y se fusionaron para obtener resultados de línea de base para
el desempeño de los modelos, particularmente de cada subconjunto de descriptores
moleculares. Se aplicó un proceso de selección de características para reducir la dimensionalidad
del problema y eliminar las características ruidosas, que están muy presentes en los problemas
biológicos. Después de un robusto diseño experimental de aprendizaje de máquina en igualdad
de condiciones, se superó estadística y significativamente el mejor modelo antiangiogénico
previamente publicado con un modelo lineal generalizado (glmnet), alcanzando un valor medio
de AUC superior a 0,96 y con una precisión de 0,86 con 200 descriptores moleculares, mezclados
de los tres grupos. De acuerdo con nuestros hallazgos, los péptidos de actividad antiangiogénica
están fuertemente asociados con las secuencias de aminoácidos SP, LSL, PF, DIT, PC, GH, RQ,
QD, TC, SC, AS, CLD, ST, MF, GRE, IQ, CQ y HG.

This research explores the use of artificial intelligence to simulate how cities will grow
vertically. By learning how cities have evolved in the recent past, genetic algorithms can
successfully simulate vertical urban growth. The research was applied to buildings 130 meters
and taller in the Minato Ward of Tokyo in 2015. An evolutionary computer model was built
from a standard genetic algorithm, using historical and economic data, which then simulated
future growth for the 2016 to 2019 period. The results obtained matched the area of study’s
real vertical growth for the study period, with a 85.7% accuracy for the number of buildings,
73.7% for their average heights, and 96.3% for the likelihood of new construction projects
happening within a mapped area. By learning how a city evolved in the past, the model
replicated the future vertical growth of a city center.

En este trabajo presentamos un sistema de predicción de movimiento de buques basado en
un modelo Deep Learning. Hemos registrado durante varios años el movimiento de múltiples buques
de carga en el Puerto Exterior de Punta Langosteira (A Coruña, España) y hemos creado un modelo
Deep Learning que clasifica el movimiento del barco dadas sus dimensiones, el estado del mar y las
condiciones meteorológicas. Para explotar el modelo hemos creado un sistema que permite gestionar
modelos de aprendizaje automático y mostrar sus salidas en una aplicación web. El frontend del
sistema se implementó utilizando Node-RED y el backend usa una arquitectura basada en
microservicios y está dividido en dos microservicios: uno responsable de obtener los datos de entrada
del modelo y otro responsable de manejar el ciclo de vida del modelo y proporcionar las salidas de este
en forma de un servicio REST. Monitorizar el movimiento de un buque atracado es una tarea difícil y
costosa y los operadores portuarios no tienen medios para predecir si el buque va a exceder los límites
establecidos para los diferentes movimientos. Usando nuestro modelo en el sistema que hemos creado
obtenemos una herramienta que, haciendo uso del sistema de predicción meteorológico de Puertos del
Estado, es capaz de predecir el comportamiento de un buque amarrado con 72 horas de antelación. Esto
ayudará a coordinar las operaciones del buque, minimizando el impacto económico y físico que la olas,
mareas y viento tienen sobre los buques.

Since the dawn of neuroscience, and for almost 100 years, information processing in the brain was attributed to neurons. Thanks to advances in imaging techniques and fluorescent markers, it was possible to prove that astrocytes, a type of glial cell, are also relevant for information processing [Araque, Parpura, Sanzgiri, & Haydon, 1999]. It is not yet known exactly what the interactions between neurons and astrocytes look like, nor when they take place, or through what mechanisms. What is known is that in areas of the brain where complex cognitive processes take place, such as the hippocampus or the cortex, "tripartite synapses" occur to process information, involving two neurons and an astrocyte.

From the point of view of Artificial Intelligence we have studied for the first time the influence of artificial astrocytes on information processing, through the construction of Artificial NeuroAstrocyte Networks that include artificial astrocyte modulation algorithms [Porto, Araque, Rabuñal, Dorado, & Pazos, 2007; Porto-Pazos et al., 2011; Pastur-Romay, Porto-Pazos, Cedron, & Pazos, 2015, Mesejo et al., 2015; Pastur-Romay, 2018]. In this way, Neuroscience and Artificial Intelligence come together, collaborating in their study and prospecting for progress. The confluence of both specialties takes place by extracting information on mechanisms of astrocytic modulation tested in laboratories in a replicable and refutable manner [http://www.cajal.csic.es/departamentos/araque-almendros/araque-almendros.html] as have been shown to be phenomena of potentiation, depression or effects on cannabinoid receptors induced by this type of glial cell for regulation of synapses. With each phenomenon to be simulated, a mathematical and computational model implemented in silico has been proposed, which has allowed to solve classification and regression problems.

ANAN (Artificial Neuron-Astrocytic Networks) have been compared with multi-layer artificial neuron networks (ANN). To carry out these tests, we have developed an opensource simulation tool for high performance computers, called Energlia [Cedron, 2019]. This tool allows, through a web access, to be used from any place and to optimize the memory management and increase the speed of execution and training, besides a simple interface that allows to introduce the data in a very intuitive way and without using complex commands.

In addition to the design of neuroglial algorithms that model different possibilities of astrocyte-neuron interaction/modulation, parallel networks of astrocytes and neurons such as those existing in the biological brain have also been designed [Blanco, 2015]. So far, significant improvements have been found in the accuracy of ANAN results with respect to NNAs, especially contrasted in classification problems, providing veracity, replicability and support to these studies, conceiving these findings as a guide stone for further research under the premise of a performance improvement with the introduction of astrocytes to classical artificial neuronal networks.

These algorithms can be easily transferred to solve, for example, medical problems for disease diagnosis, analysis of medical signals or images, home automation, security, object identification, etc. In short, provide a new method of machine learning that can facilitate data analysis in any area.

En enfermedades complejas y multifactoriales, como el cáncer de colon, en el que se realizan
diferentes conjuntos de pruebas complementarias durante todo el periodo de convalecencia de
un paciente, la cantidad de datos derivados de cada paciente es ingente y de fuentes
heterogéneas. Si, además, tenemos en cuenta que se conoce el componente genético
hereditario de la misma, se pueden incluir en el análisis también pruebas derivadas de familiares
de primer grado. Es por esto que se hace necesario el desarrollo de técnicas de integración de
datos que permitan analizar en conjunto la mayor cantidad de datos posibles para intentar
incrementar la interpretabilidad de la enfermedad. Este conocimiento, además de mejorar la
calidad asistencial también se espera que incrementará la tasa de éxito en la fase de
reclutamiento y el control de la evolución en los ensayos clínicos y, por tanto, mejorará la
efectividad de probar nuevos fármacos quimioterápicos o permitirá mejorar la posibilidad de
administrar fármacos de manera personalizada gracias a la utilización de técnicas de docking
molecular que medirán el grado de afinidad entre los fármacos aprobados por FDA y
quimiotecas propias con 60.000 moléculas de la plataforma InnoPharma y las dianas proteicas
que son codificadas por los genes implicados en el cáncer colorrectal.

The current work is proposing a very simple and fast python script to classify polyps in
colonoscopy images using Fastai deep learning. This work is the optimized version of the
previous tool: GitHub repository CNN4Polyps about colonoscopy polyp detection
(classification + localization into an image) with convolutional neural network - CNNs
(https://github.com/muntisa/Colonoscopy-polyps-detection-with-CNNs) using Keras. I
demonstrated that simple CNNs or VGG16 transfer learning could be used with a GPU to
create good classifiers able to detect a polyp in colonoscopy images.
Jeremy Howard and the team are proposing a faster python package for deep learning models
as a free course at Fast.ai (https://course.fast.ai/). Google Colab free virtual computers
(https://colab.research.google.com) with GPU support was used to run all the calculations.
The current dataset was generated with the previous project CNN4Polyps starting from a
public dataset: 910 images for training and 302 images for validation. All the models were
saved in the project folder.
The current script (Figure 1) demonstrated the ability to create a very accurate classifier for
medical imaging with an accuracy of 0.99 using resnet50 transfer learning fine tuning, only
5-10 lines of code, free GPU hardware (Nvidia K80), and free fastai package (based on
PyTorch library). The scripts to train the model or to make predictions are available at
https://github.com/muntisa/Fastai-Colon-Polyps.

Acknowledgements
I gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this
research (https://developer.nvidia.com/academic_gpu_seeding). In addition, the authors would like to
acknowledge support from the Galician Network for Colorectal Cancer Research (REGICC) (Ref. ED431D
2017/23). This work is also supported by “Collaborative Project in Genomic Data Integration (CICLOGEN)”
PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical
Research and Innovation 2013−2016 and the European Regional Development Funds (FEDER).

Estamos en una era en la que la que la investigación necesita realizar una cantidad
muy elevada de cálculos para poder confirmar las hipótesis de un estudio y poder
tener unas conclusiones sólidas. Aunque la tecnología avanza a pasos agigantados no
sigue el ritmo que requieren los investigadores para cubrir sus necesidades y poder
tener todo el poder de cómputo que ellos demandan.
En este trabajo se exponen diferentes problemas que necesitan una gran capacidad de
cálculo y que no se puede llevar a cabo por un único ordenador. Para resolverlos se
necesita de un cluster, una agrupación de máquinas, que en conjunto puedan sumar la
potencia necesario para poder tener un tiempo de respuesta aceptable. En el trabajo
aparece detallado cómo es la infraestructura de estas características y cuáles son las
necesidades que demandan los investigadores.
Para finalizar se muestran unas gráficas de cómo el grupo RNASA-IMEDIR está
empleando el cluster del que disponen.

Vivimos en una era en la que todo –desde una vivienda hasta una pareja– se busca online
y se elige en función de imágenes. Disponemos de una tecnologı́a que permite determinar las
mejores fotografı́as de un conjunto, desde la perspectiva de la calidad o el valor estético. Esto
permite mostrar, desde un primer momento, la mejor imagen de un producto a tus clientes o
de un acontecimiento a tus amigos.
Para conseguir llegar al punto actual de definición de esta tecnologı́a han hecho falta 17
años de trabajo de dos grupos de investigación. Desde la Universidade da Coruña presentamos
nuestra solución tecnológica a este programa, contando con la total aceptación de la Univer-
sidade de Coimbra para gestionar su comercialización.
Esta solución puede ser adaptada a un número inmenso de áreas, empleando distintos tipos
de imágenes (fotografı́as en general, imágenes de casas, de rostros, de paisajes, etc.) y diferentes
tipos de criterios de ordenación (valor estético, calidad fotográfica, interés para compradores
de casas, etc.). Además, puede adaptarse a un público objetivo (gusto estético de hombres de
18 a 25 años, por ejemplo) o a un individuo particular.
Se trata de una tecnologı́a versátil, que aprende a partir de ejemplos, a través de los que se
obtiene una huella estética. Ası́, para determinar el impacto publicitario de una imagen inmo-
biliaria se empleará un conjunto muy amplio de imágenes reales (3.000 imágenes extraı́das de
portales inmobiliarios), que serán evaluadas por un grupo de personas (30 personas por ima-
gen) según su interés como imagen de un inmueble. Esto proporcionará un impacto medio de
cada una de estas 3.000 fotografı́as. A partir de esta información, la tecnologı́a podrá estimar
el impacto comercial de cualquier otra imagen inmobiliaria.
En los últimos años hemos obtenido resultados experimentales de éxito en sucesivas versio-
nes de esta tecnologı́a, aplicada a tareas como la determinación de autorı́a en obras pictóricas;
la detección de obras pictóricas en colecciones de fotografı́as; la clasificación de fotografı́as
según su carácter profesional o amateur; varios test psicológicos; la complejidad perceptible
de la imagen y el valor estético medio de un conjunto de imágenes. La versión actual de la
tecnologı́a permite el aprendizaje de tendencias estéticas con resultados muy superiores a las
versiones anteriores y que se destacan muy significativamente de otros resultados del estado
del arte.
Nuestra intención es crear una spin-off de la Universidade da Coruña dedicada a explotar
comercialmente esta tecnologı́a en sus múltiples usos. Si bien es muy amplio el conjunto de pro-
ductos comerciales que se pueden llegar a desarrollar, pretendemos comenzar con el desarrollo
de un módulo de evaluación estética que permitirá la ordenación de imágenes de inmuebles
(casas, pisos, etc.), según una mezcla de calidad fotográfica y valor estético. Este producto se
destinará a portales de compraventa especializados en mercado inmobiliario, tanto especı́ficos
(Idealista, Fotocasa) como generalistas (Milanuncios y Vibbo). En estos portales la calidad
fotográfica de los anuncios es muy diversa, en especial cuando es realizado por particulares.
Nuestra solución ordenará un conjunto de fotografı́as de forma que las primeras presentenmayor impacto comercial.
El cliente del sistema será el portal, que lo integrará en su funcionamiento a través de
nuestro servicio web. Se cobrará por el uso del sistema, con precios ajustados al volumen de
trabajo y condiciones de exclusividad del servicio. Dada nuestra ventaja tecnológica, la úni-
ca competencia existente para un producto de estas caracterı́sticas serı́a la realización de las
mismas tareas por parte de personal humano, con un coste mucho mayor. El portal podrá
incluirlo como ventaja competitiva sin coste para el cliente (Idealista), o bien ofrecerlo como
servicio a cambio de un coste muy reducido (Milanuncios). En este último caso, al ofrecer el
servicio se le podrá indicar al usuario final una estimación del impacto que esta ordenación
de las imágenes tendrá en el número de accesos a su anuncio (p.ej. “Milanuncios ha detectado
que cambiando el orden de las fotografı́as de este inmueble se pueden conseguir un 120 % más
de visualizaciones en su anuncio”).
La adaptación de esta tecnologı́a al mercado de compraventa inmobiliaria será un primer
paso, buscando el mercado con mayor valor añadido. Se pretende adaptar al sistema para
otros mercados de compraventa (automóvil, electrónica, etc.). También se planifica abordar la
internacionalización de esta tecnologı́a después de explorar el mercado nacional.
Otras aplicaciones que se abordarán posteriormente son:
Aplicaciones de ordenación estética de productos para empresas de comercio electrónico.
En este caso se podrán ordenar los productos de una tienda online en función de su valor
estético.
Huellas estéticas de un un target determinado (mujeres adolescentes, hombres de 40-55
años de poder adquisitivo alto...), tanto para las tiendas online comentadas anteriormen-
te, como para poder hacer estimaciones del impacto de imagen publicitaria de forma
rápida.
Huella estética personal. En este caso, el usuario final será el que permita emplear su
huella personal en Internet, de forma que, contenidos como búsquedas de imágenes,
productos de tiendas online, etc. se muestren según su estética propia.
Creación de filtros para mejorar las imágenes de forma automática de forma que tengan
más impacto (sean más interesantes / atractivas / de mayor calidad).

En esta conferencia se hace una revisión de la infraestructura necesaria en un centro médico
para la gestión de imagen médica y datos de historia radiológica.
Se estudian los retos que plantea los sistemas de almacenamiento de imagen médica,
conocidos como PACS, de cara a los retos de acceso transparente y seguro, tanto para médicos
como para pacientes. Esto incluye temas de privacidad, tele-radiología, sistemas en la nube y
seguridad de datos.
Se profundiza en las diferencias entre grandes centros de referencia y pequeñas clínicas, ya
que tienen una problemática común, pero situaciones radicalmente distintas.
En la última parte se estudian los parámetros de importancia para la visualización de las
imágenes médicas y los procesos de calibración, de cara al desarrollo de herramientas de
inteligencia artificial, tanto software como hardware, que faciliten la mejor visualización por
parte del radiólogo para realizar su diagnóstico.

Desde una perspectiva científica, la identificación de un conjunto de métricas
relacionadas con el estilo y el valor estético de una imagen es una contribución
especialmente importante para la comprensión del fenómeno estético y la experiencia
visual, proporcionando contribuciones claras en los campos de la estética, la percepción
visual y la teoría del arte, reconocimiento de patrones, computación gráfica y visión
artificial. Creemos que una predicción del valor estético o la complejidad de un objeto
gráfico, incluso cuando no es perfecto, es útil en la disposición y clasificación de las
imágenes, ya que la complejidad perceptiva, así como el valor estético, constituyen
valores subjetivos que son determinantes. En diversas decisiones humanas, como el
comportamiento frente a diferentes tipos de objetos, desde obras de arte a páginas
web, diseños publicitarios o productos comerciales.
Nuestra investigación propone un acercamiento a la obtención de una medida
computacional del valor estético de una imagen a través de criterios como el estilo, la
complejidad o la belleza. Para esto se diseñarán y realizarán diferentes experimentos,
con los siguientes objetivos:
- Validación de características objetivas: mediante la identificación de autoría de
obras de arte y la diferenciación entre pinturas y fotografías.
- Validación de la percepción estética del clasificador mediante test
psicológicos.
- Validación de características subjetivas: predicción de la percepción de
complejidad.
En estas tareas se incluye la determinación de un conjunto de métricas óptimo para
medir la complejidad estética, utilizando los métodos de compresión JPEG y fractal, la
ley de Zipf y la dimensión fractal, además de los filtros de detección de bordes Canny y
Sobel sobre un modelo de color HSV. Los resultados obtenidos se comparan con otras
aproximaciones y con las percepciones de humanos.
Los resultados muestran que es posible predecir la complejidad percibida por humanos
a partir de una serie de métricas de la imagen relacionadas con la complejidad y se
demuestra la relevancia de las métricas de estimación de complejidad basadas en el
error de compresión en clasificaciones de estilo y complejidad.
Se pretende dar continuidad al trabajo realizado con el estudio de otras arquitecturas
clasificadoras y con la búsqueda y estudio de nuevas características, así como la mejora
de las actuales, para lograr una mejor solución para los problemas de clasificación y
clasificación de imágenes de acuerdo con criterios estéticos.

En los últimos años se ha producido una gran proliferación de pequeños sensores,
wareables, estaciones de control... destinados a la medición de las más diversas variables. En
paralelo, a lo largo de los últimos años, la sociedad también ha empezado a ser más consciente y
a preocuparse más por el impacto que ciertos componentes ambientales pueden llegar a tener en
su salud. Esto ha llevado a que actualmente se han comenzado a tomar ciertas medidas de control
ante la especial sensibilización causada por todos aquellos aspectos relacionados directa o
indirectamente con temas relativos a la salud.
Este trabajo pretende unir ambas vertientes mediante un sistema de obtención de valores
ambientales de radiactividad basado en el empleo de dispositivos de bajo coste y una aplicación
web que permitirá la consulta de forma sencilla de los mismos y almacenar dichos datos para, en
un futuro, analizar los mismos y facilitar el desarrollo de herramientas para la toma de decisiones.
Como campo de trabajo se ha optado por la monitorización de los niveles medioambientales
de radón, debido a su especial incidencia en España en general y en Galicia en particular, debido
a su geología fuertemente ligada con el granito, fuente principal de las emisiones de radón.
El radón (Rn) es un gas radioactivo natural incoloro, sin olor o sabor, que ocurre en formas
diferentes, todas ellas con el idéntico número atómico pero diferente masa atómica, denominadas
isótopos. A medida que transcurre el tiempo, el radón se degrada perdiendo masa atómica ̧
emitiendo radiación y transformándose en el proceso en otro elemento radioactivo. Esto se repite
cíclicamente hasta que se alcanza un estado estable, en este caso hasta que el radón se transforma
en plomo. La radiación emitida son partículas alfa, partículas beta y rayos gama, que puede
suponer un problema en cuanto a la exposición prolongada a la misma. El radón ha sido
declarado carcinógeno humano por la Environmental Protection Agency de Estados Unidos
(USEPA) y la Agencia Internacional de Investigación en Cáncer (IARC). En España, los estudios
pioneros en cuanto a la toma de mediciones ambientales datan de finales de los años 80. Estos
estudios pusieron en relevancia las elevadas concentraciones de radón en varias regiones, entre
otras Galicia.
La herramienta planteada permite la gestión de dispositivos de monitorización
facilitando su geolocalización y el acceso remoto a los valores de medición. Asímismo permite
el establecimiento de alertas cuando los valores medios o puntuales exceden ciertos umbrales
preestablecidos.