Estimating the Age and Mechanism of Boulder Transport Related with Extreme Waves Using Lichenometry

Tsunamis and storms cause considerable coastal flooding, numerous fatalities, destruction of structures, and erosion. The characterization of energy and frequency associated with each wave contribute to the risk assessment in coastal regions. Coastal boulder deposits represent a physical proof of extreme inundation and allow us to study the effects of marine floods further back in time than instrumental and historical records. Age estimation of these deposits is challenging due to lack of materials (such as sand, shells, corals, or organic matter) that retain information about the passage of time. Lichenometry, a simple age estimation method, which is cost effective, quick to apply, and nondestructive, is here proposed as a solution. A lichen growth model for a calcium-tolerant lichen species was developed and used to estimate the age of a boulder deposit related to extreme marine inundation(s) in Portugal. Estimated ages indicate several very recent events (\u3c 700 years) for most of the boulders’ stabilization and agree with results obtained with optically stimulated luminescence of marine sands found beneath boulders. Frequent and recent boulder transport implies a storm-origin for this deposit. These conclusions contrast with other works describing identical deposits that are attributed to paleo-tsunamis. This study presents a methodology using lichenometry as a successful alternative for age estimation in rocky coastal settings. These results offer an alternative explanation for coastal boulder deposits found on the west coast of Portugal

Efeitos da adubação orgânica e da época de colheita na qualidade da matéria-prima e nos rendimentos agrícola e de açúcar mascavo artesanal de duas cultivares de cana-de-açúcar (cana-planta).

Conduziu-se este trabalho com o objetivo de estudar os efeitos de três sistemas de adubação (30 t. ha-1 de esterco de curral, 3,5 t.ha-1 de esterco de galinha e adubação química - 120 kg.ha-1 de P2O5 e de K2O no plantio + 60 kg.ha-1 de N em cobertura) e três épocas de colheita da cana (julho, agosto e setembro de 2003), na qualidade da matéria-prima e nos rendimentos de colmos e de açúcar mascavo de duas cultivares de cana-de-açúcar (SP79-1011 e RB72454). O experimento foi instalado em área do Alambique JM, Perdões, MG. O delineamento experimental foi o de blocos casualizados, em esquema fatorial (2 x 3 x 3), com três repetições. Não houve efeito dos fertilizantes nos rendimentos de colmos e de açúcar mascavo das cultivares estudadas. Verificou-se efeito de épocas de colheita no rendimento de colmos, com destaque para os meses de agosto e setembro. No entanto, para rendimento de açúcar mascavo nenhuma diferença foi observada. Assim, nas condições deste trabalho, é viável a substituição da adubação química pela orgânica (esterco de curral ou de galinha), sem perdas na qualidade da matéria-prima e nos rendimentos de colmos e de açúcar mascavo artesanal, sendo que os meses de agosto e setembro foram os que proporcionaram matéria-prima de melhor qualidade e maiores rendimentos de colmos

Goats as Valuable Animal Model to Test the Targeted Glutamate Supplementation upon Antral Follicle Number, Ovulation Rate, and LH-Pulsatility

The potential effect of intravenous administration of glutamate on the ovarian activity and the LH secretion pattern, considering the anestrous yearling goat as an animal model, were assessed. In late April, yearling goats (n = 20) were randomly assigned to either (1) Glutamate supplemented (GLUT; n = 10, Live Weight (LW) = 29.6 ± 1.02 kg, Body Condition (BCS) = 3.4 ± 0.2 units; i.v. supplemented with 7 mg GLUT kg−1 LW) or (2) Non-supplemented (CONT; n = 10; LW = 29.2 ± 1.07 kg, BCS = 3.5 ± 0.2 units; i.v. saline). The oats were estrus-synchronized; blood sampling (6 h × 15 min) was carried out for LH quantification. Response variables included pulsatility (PULSE), time to first pulse (TTFP), amplitude (AMPL), nadir (NAD), and area under the curve (AUC) of LH. Ovaries were ultra-sonographically scanned to assess ovulation rate (OR), number of antral follicles (AF), and total ovarian activity (TOA = OR + AF). LH-PULSE was quantified with the Munro algorithm; significant treatment x time interactions were evaluated across time. The variables LW and BCS did not differ (p > 0.05) between the experimental groups. Nevertheless, OR (1.77 vs. 0.87 ± 0.20 units), TOA (4.11 vs. 1.87 ± 0.47 units) and LH-PULSE (5.0 vs. 2.2 pulses 6 h-1) favored (p < 0.05) to the GLUT group. Our results reveal that targeted glutamate supplementation, the main central nervous system neurotransmitter, arose as an interesting strategy to enhance the hypothalamic–hypophyseal–ovarian response considering the anestrous-yearling goat as an animal model, with thought-provoking while promising translational applications

A comparison of models for ductile fracture prediction in forging processes

The possibility of predicting ductile fracture plays an important role in the design of components by forging processes.Experimental observations showed that the nucleation, growth and coalescence of voids are the mechanisms thatcontrol the initiation and propagation of fracture and that these mechanisms are influenced in different ways by factorslike the hydrostatic stress, the equivalent stress or by the maximal principal stress. Many ductile fracture indicators, basedon some or all of those factors, are available and used in many practical situations in the design of those components. Inthis work a comparative work of many of those criteria was undertaken. Different criteria were chosen amongst the morepopular ones and from different groups, in which they may be classified, namely those based on micromechanics andthose based on the geometry of voids or their growth mechanisms. The criteria based on the Continuous Damage Mechanics,in which a coupling between plastic deformation and material degradation is taken into account and that include differentdamage evolution descriptions for traction or compressive stress states, give a more correct and clear localizationfor the fracture initiation site

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

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