Adjusting to Mandatory Information Systems: Understanding Individual Adaptation to ERP Systems

Realising the benefits from information technology depends on how the systems are actually used. Although previous information systems (IS) research provides useful models for understanding individual acceptance, there is a limited understanding of the underlying adaptive process related to IS use, particularly in a mandatory context. This study argues that adaptation is a socially constructed process. Informed by the conceptual elements of coping theory, this study proposes an examination of the adaptive behaviours of enterprise resource planning (ERP) systems users. The fieldwork will be conducted in three organisations – one private, one public and one multinational – operating in Thailand. The multiple-case study design allows the scrutiny of contrasting patterns in the data. By taking an interpretive grounded theory approach, this study aims at producing an emergent and substantive theory that explains both the adaptive process and the complex interplay of individual and contextual factors that influences adaptive behaviours over time

Testing GeoGebra as an effective tool to improve the understanding of the concept of limit on engineering students

The impact GeoGebra on the teaching of the concept of limit was analyzed. Two groups of engineering students, studying differential calculus, served as control and test groups. The traditional teaching, based on examples solved by hand, was given to the control group while a series of activities involving the usage of the mathematical software GeoGebra were applied in an attempt of improving the degree of assimilation on the concept of limits

Expansion of the agricultural frontier in the largest South American Dry Forest: Identifying priority conservation areas for snakes before everything is lost

Conservation planning relies on integrating existing knowledge, social-environmental contexts, and potential threats to identify gaps and opportunities for action. Here we present a case study on how priority areas for conservation can be determined using existing information on biodiversity occurrence and threats. Specifically, our goals are: (1) to model the ecological niche of twelve endemic snake species in the Dry Chaco Forest, (2) to quantify the impact of the deforestation rates on their distributions, (3) to propose high priority areas for conservation in order to improve the actual protected area system, and (4) to evaluate the influence of the human footprint on the optimization of selected priority areas. Our results demonstrate that Argentinian Dry Chaco represent, on average, ~74% of the distribution of endemic snake species and deforestation has reduced suitable areas of all snake species in the region. Further, the current protected areas are likely insufficient to conserve these species as only very low percentages (3.27%) of snakes’ ranges occur within existing protected areas. Our models identified high priority areas in the north of the Chaco forest where continuous, well-conserved forest still exists. These high priority areas include transition zones within the foothill forest and areas that could connect patches of forest between the western and eastern Chaco forest. Our findings identify spatial priorities that minimize conflicts with human activities, a key issue for this biodiversity hotspot area. We argue that consultation with stakeholders and decision-makers are urgently needed in order to take concrete actions to protect the habitat, or we risk losing the best conservation opportunities to protect endemic snakes that inhabit the Argentinian Dry Chaco.Fil: Andrade Díaz, Soledad María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Bio y Geociencias del Noroeste Argentino. Universidad Nacional de Salta. Facultad de Ciencias Naturales. Instituto de Bio y Geociencias del Noroeste Argentino; ArgentinaFil: Sarquis, Juan Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto Nacional de Limnología. Universidad Nacional del Litoral. Instituto Nacional de Limnología; ArgentinaFil: Loiselle, Bette A.. University of Florida; Estados UnidosFil: Giraudo, Alejandro Raul. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto Nacional de Limnología. Universidad Nacional del Litoral. Instituto Nacional de Limnología; Argentina. University of Florida; Estados Unidos. Universidad Nacional del Litoral. Facultad de Humanidades y Ciencias; ArgentinaFil: Diaz Gomez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Bio y Geociencias del Noroeste Argentino. Universidad Nacional de Salta. Facultad de Ciencias Naturales. Instituto de Bio y Geociencias del Noroeste Argentino; Argentin

Caracterización estructural por Resonancia Magnética Nuclear de trioleina ozonizada

In the present study ozonized triolein with 739 mmolequiv/ kg peroxide index is characterized by NMR.The triolein and ozonized triolein show very similar 1H NMR spectra except for the resonances at δ 9.74 ppm, which correspond to aldehydic protons and δ 5.14 ppm (ozonides methylic protons). Other new signal assignments are based on the connectivities provided by the proton scalar coupling constants δ 2.41 ppm (methylenic group allylic to aldehydic protons and ozonides methynic protons) and δ 1.67 ppm (methylenic protons in position with respect to ozonides methylic protons). From the 13C and 1H-13C spectrum of the ozonized triolein, the presence of ozonides was confirmed by the signals δ 104.2 and 104.3 ppm, respectively. Other new signals in δ 43.9 ppm confirm the presence of methylenic carbon ozonides. From the structural elucidation of ozonated triglycerides, relevant chemical information about ozonated vegetable oil can be found .En el presente estudio ha sido caracterizada por RMN la trioleina ozonizada con índice de peróxidos de 739 mmolequiv/ kg. La trioleina y la trioleina ozonizada muestran espectros muy similares exceptuando los valores de las resonancias δ 9,74 ppm de los protones aldehídicos, y δ 5,14 ppm (protones metínicos de los ozónidos). Otras nuevas asignaciones fueron basadas en las conectividades obtenidas por las constantes de acoplamiento escalar como δ 2,41 ppm (grupo metilénico alilico a los protones aldehídicos y protones metínicos de los ozónidos) y δ 1,67 ppm (protones metilénicos en posición βcon respecto a los protones metínicos de los ozónidos). En los espectros 13C y 1H-13C de la trioleina ozonizada la presencia de ozónidos fue atribuida, respectivamente, por las señales δ 104,2 y δ 104,3 ppm. Una nueva señal en δ 43,9 ppm confirma la presencia de carbono metilénico de ozónidos. Estos resultados indican que la elucidación estructural de triglicéridos ozonizados, ofrece información química relevante relacionada con los aceites vegetales ozonizados

Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds

[Abstract] Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that measures the probability of a drug being active under certain conditions (protein, cell line, organism, etc.). In this paper, we used a linear discriminant analysis and neural network to train and compare PT and non-PT models. All the explored models have an accuracy of 89.19–95.25% for training and 89.22–95.46% in validation sets. PTML-based strategies have similar accuracy but generate simplest models. Therefore, they may become a versatile tool for predicting antisarcoma compounds.Ministerio de Economía y Competitividad; CTQ2016-74881-PMinisterio de Economía y Competitividad; UNLC08-1E-002Ministerio de Economía y Competitividad; UNLC13-13-3503Xunta de Galicia; ED431C 2018/49Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/23Gobierno Vasco; IT1045-16Instituto de Salud Carlos III; PI17/0182

A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing

[Abstract] Osteosarcoma is the most common type of primary malignant bone tumor. Although nowadays 5-year survival rates can reach up to 60–70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. We developed a multi-objective algorithm for the repurposing of new anti-osteosarcoma drugs, based on the modeling of molecules with described activity for HOS, MG63, SAOS2, and U2OS cell lines in the ChEMBL database. Several predictive models were obtained for each cell line and those with accuracy greater than 0.8 were integrated into a desirability function for the final multi-objective model. An exhaustive exploration of model combinations was carried out to obtain the best multi-objective model in virtual screening. For the top 1% of the screened list, the final model showed a BEDROC = 0.562, EF = 27.6, and AUC = 0.653. The repositioning was performed on 2218 molecules described in DrugBank. Within the top-ranked drugs, we found: temsirolimus, paclitaxel, sirolimus, everolimus, and cabazitaxel, which are antineoplastic drugs described in clinical trials for cancer in general. Interestingly, we found several broad-spectrum antibiotics and antiretroviral agents. This powerful model predicts several drugs that should be studied in depth to find new chemotherapy regimens and to propose new strategies for osteosarcoma treatment.Universidad de Las Américas (Quito, Ecuador); ENF.RCA.18.01Gobierno Vasco; IT1045-16)-2016–202

Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis

[Abstract] Osteosarcoma is the most common subtype of primary bone cancer, affecting mostly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its molecular etiology has still not been determined with precision. Therefore, we applied a consensus strategy with the use of several bioinformatics tools to prioritize genes involved in its pathogenesis. Subsequently, we assessed the physical interactions of the previously selected genes and applied a communality analysis to this protein–protein interaction network. The consensus strategy prioritized a total list of 553 genes. Our enrichment analysis validates several studies that describe the signaling pathways PI3K/AKT and MAPK/ERK as pathogenic. The gene ontology described TP53 as a principal signal transducer that chiefly mediates processes associated with cell cycle and DNA damage response It is interesting to note that the communality analysis clusters several members involved in metastasis events, such as MMP2 and MMP9, and genes associated with DNA repair complexes, like ATM, ATR, CHEK1, and RAD51. In this study, we have identified well-known pathogenic genes for osteosarcoma and prioritized genes that need to be further explored.Instituto Carlos III; PI17/01826Xunta de Galicia; ED431C 2018/49Xunta de Galicia; ED431G/0

Indicadores para medir la capacidad creativa, de diseño e innovación en México: Programa Mexicano de Diseño 2018

66 páginas.Este trabajo de investigación debe entenderse como un primer ejercicio para intentar responder la siguiente pregunta: ¿podemos medir a partir de indicadores existentes el desempeño de México en relación con la creatividad, el diseño y la innovación? Primero, se presenta el concepto de “ciudad creativa”, así como algu¬nos ejemplos internacionales que sirven para contextualizar el problema. En segundo lugar, se revisan algunas propuestas existentes para medir la creatividad y el diseño vinculados a la innovación. Luego, se describe la metodología y el proceso de investigación utilizado para hacer un aná¬lisis inicial sobre el desempeño de México en relación con su capacidad y creatividad, de diseño y de innovación. Antes de concluir, los autores proponemos una serie de recomendaciones que deberían formar parte de un programa que impulse el diseño en México, y que a su vez permita disponer, en el tiempo, de más datos para medir su impacto económico y social en la capacidad de innovación de nuestro país. Por supuesto que estas recomendaciones deben considerarse como ilustrativas y conven¬dría que sirvieran como base para que gobierno, profesionales del diseño, empresas y ciudadanos emprendieran un diálogo en la elaboración de un Programa Mexicano de Diseño que pudiera implementarse; y es que es necesario aprovechar la voluntad y la curiosidad que ha despertado entre la comunidad política, empresarial y académica, que la Ciudad de México haya sido designada como “capital mundial del diseño” para el 2018. Por último, se plantean algunas conclusiones y tareas pendientes para el futuro

OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine

[Abstract] Breast cancer (BC) is the leading cause of cancer-related death among women and the most commonly diagnosed cancer worldwide. Although in recent years large-scale efforts have focused on identifying new therapeutic targets, a better understanding of BC molecular processes is required. Here we focused on elucidating the molecular hallmarks of BC heterogeneity and the oncogenic mutations involved in precision medicine that remains poorly defined. To fill this gap, we established an OncoOmics strategy that consists of analyzing genomic alterations, signaling pathways, protein-protein interactome network, protein expression, dependency maps in cell lines and patient-derived xenografts in 230 previously prioritized genes to reveal essential genes in breast cancer. As results, the OncoOmics BC essential genes were rationally filtered to 140. mRNA up-regulation was the most prevalent genomic alteration. The most altered signaling pathways were associated with basal-like and Her2-enriched molecular subtypes. RAC1, AKT1, CCND1, PIK3CA, ERBB2, CDH1, MAPK14, TP53, MAPK1, SRC, RAC3, BCL2, CTNNB1, EGFR, CDK2, GRB2, MED1 and GATA3 were essential genes in at least three OncoOmics approaches. Drugs with the highest amount of clinical trials in phases 3 and 4 were paclitaxel, docetaxel, trastuzumab, tamoxifen and doxorubicin. Lastly, we collected ~3,500 somatic and germline oncogenic variants associated with 50 essential genes, which in turn had therapeutic connectivity with 73 drugs. In conclusion, the OncoOmics strategy reveals essential genes capable of accelerating the development of targeted therapies for precision oncology.Instituto de Salud Carlos III; PI17/0182

Prediction of Breast Cancer Proteins Involved in Immunotherapy, Metastasis, and RNA-Binding Using Molecular Descriptors and Artifcial Neural Networks

[Abstract] Breast cancer (BC) is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disruption, ethnicity and environmental determinants are involved. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design. This work is proposing accurate prediction classifer for BC proteins using six sets of protein sequence descriptors and 13 machine-learning methods. After using a univariate feature selection for the mix of fve descriptor families, the best classifer was obtained using multilayer perceptron method (artifcial neural network) and 300 features. The performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980±0.0037, and accuracy of 0.936±0.0056 (3-fold cross-validation). Regarding the prediction of 4,504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1. This powerful model predicts several BC-related proteins that should be deeply studied to fnd new biomarkers and better therapeutic targets. Scripts can be downloaded at https://github.com/muntisa/ neural-networks-for-breast-cancer-proteins.This work was supported by a) Universidad UTE (Ecuador), b) the 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) - “A way to build Europe”; c) the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and “Drug Discovery Galician Network” Ref. ED431G/01 and the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23); d) the Spanish Ministry of Economy and Competitiveness for its support through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union; e) the Consolidation and Structuring of Competitive Research Units - Competitive Reference Groups (ED431C 2018/49), funded by the Ministry of Education, University and Vocational Training of the Xunta de Galicia endowed with EU FEDER funds; f) research grants from Ministry of Economy and Competitiveness, MINECO, Spain (FEDER CTQ2016-74881-P), Basque government (IT1045-16), and kind support of Ikerbasque, Basque Foundation for Science; and, g) Sociedad Latinoamericana de Farmacogenómica y Medicina Personalizada (SOLFAGEM)Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/49Gobierno Vasco; IT1045-1