To study associations across tumor types between genome-wide loss of heterozygosity (gLOH) and alterations in homologous recombination repair (HRR)-associated genes beyond BRCA1 and BRCA2.
Genomic ...profiling using a targeted next-generation sequencing assay examining 324-465 genes (FoundationOne, FoundationOne Heme, and FoundationOne CDx; Foundation Medicine, Inc.) was performed in a cohort of 160,790 samples across different tumor types. Zygosity predictions and gLOH status were calculated and linked with alterations in 18 HRR-associated genes (BRCA1, BRCA2, PALB2, BARD1, ATR, ATRX, ATM, BAP1, RAD51B, RAD51C, RAD51D, BRIP1, NBN, CHEK1, CHEK2, FANCA, FANCC, MRE11) and other genomic features, using Fisher's exact test and Mann-Whitney U tests.
We identified a strong correlation between elevated gLOH and biallelic alterations in a core set of HRR-associated genes beyond BRCA1 and BRCA2, such as BARD1, PALB2, FANCC, RAD51C, and RAD51D (particularly in breast, ovarian, pancreatic, and prostate cancer). Monoallelic/heterozygous alterations in HRR-associated genes were not associated with elevated gLOH. gLOH was also independently associated with TP53 loss. Co-occurrence of TP53 loss and alterations in HRR-associated genes, and combined loss of TP53-PTEN or TP53-RB1, was associated with a higher gLOH than each of the events separately.
Biallelic alterations in core HRR-associated genes are frequent, strongly associated with elevated gLOH, and enriched in breast, ovarian, pancreatic, and prostate cancer. This analysis could inform the design of the next generation of clinical trials examining DNA repair-targeting agents, including PARP inhibitors.
Se abordó teóricamente la relación entre derechos humanos y responsabilidad social universitaria mediante el estudio de la iniciativa “Escuela para Migrantes: Educación para el Ejercicio de Derechos ...y Responsabilidades”, un proyecto de vinculación con el medio de la Universidad Austral de Chile, ejecutado por la Facultad de Ciencias Jurídicas y Sociales. Esta iniciativa se consideró como una práctica socialmente responsable bajo los supuestos teóricos de la responsabilidad social, aplicables a la gestión universitaria. Se realizó una investigación de carácter cualitativo para conformar una base de información a partir de interacciones mediante conversaciones con informantes clave y revisión de fuentes bibliográficas.
Given recent regulatory inquiries into the derivative-trading practices of mutual funds, we examine their detailed option holdings to assess how mutual funds employ options, what funds use options, ...and how that affects performance and risk. Mutual funds’ use of options appears consistent with income generation and hedging motives, is systematically related to experience, education, and gender characteristics of portfolio managers, and does not lead to performance benefits, on average. Instead, certain uses of options lead to underperformance. We document no permanent or temporary aggressive risk taking by options users, finding instead that some funds use options to effectively lower risk.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Many methods have been proposed to mine fuzzy association rules from databases with crisp values in order to help decision-makers make good decisions and tackle new types of problems. However, most ...real-world problems present a certain degree of imprecision. Various studies have been proposed to mine fuzzy association rules from imprecise data but they assume that the membership functions are known in advance and it is not an easy task to know a priori the most appropriate fuzzy sets to cover the domains of the variables. In this paper, we propose FARLAT-LQD, a new fuzzy data-mining algorithm to obtain both suitable membership functions and useful fuzzy association rules from databases with a wide range of types of uncertain data. To accomplish this, first we perform a genetic learning of the membership functions based on the 3-tuples linguistic representation model to reduce the search space and to learn the most adequate context for each fuzzy partition, maximizing the fuzzy supports and the interpretability measure GM3M in order to preserve the semantic interpretability of the obtained membership functions. Moreover, we propose a new algorithm based on the Fuzzy Frequent Pattern-growth algorithm, called FFP-growth-LQD, to efficiently mine the fuzzy association rules from inaccurate data considering the learned membership functions in the genetic process. The results obtained over 3 databases of different sizes and kinds of imprecisions demonstrate the effectiveness of the proposed algorithm.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
We find closed-form expressions for the variance and the third moment of the number of hires in the assistant hiring algorithm, as well as asymptotic values for higher moments of this variable.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
With the rush of metal consumption in the last decades and the expected raw material demand driven by the clean and digital transition, a growing concern has emerged about the decline of ore grades. ...Research of the effect of ore grade decline on energy consumption during the processing of metals has conventionally been addressed using historical data and LCA analyses. This paper provides another approach using a computational model developed with specialized software, HSC Chemistry, to analyse this relationship using gold as a case study. Gold was selected as it is a precious metal widely used in various applications, from jewellery to electronic circuits and will be key for digitalizing the economy. Considering all mineral processing stages, it was verified that the specific energy and associated environmental impact would experience exponential growth as ore grade in the mines decreases. As one of the most energy intensive stages is comminution, fuelled by electricity, the associated environmental impact is very much dependent on the electricity mix of the producing country. This approach allows for an evaluation of the future production's environmental impact for gold.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Understanding solar energy has become crucial for the development of modern societies. For this reason, significant effort has been placed on building models of solar resource assessment. Here, we ...analyzed satellite imagery and solar radiation data of three years (2012, 2013, and 2014) to build seven predictive models of the solar energy obtained at different altitudes above sea level. The performance of four machine learning algorithms was evaluated using four evaluation metrics, MBE, R2, RMSE, and MAPE. Random Forest showed the best performance in the model with data obtained at altitudes below 800 m.a.s.l. The results achieved by the algorithm were: 4.89, 0.82, 107.25, and 41.08%, respectively. In general, the differences in the results of the machine learning algorithms in the different models were not very significant; however, the results provide evidence showing that the estimation of solar radiation from satellite images anywhere on the planet is feasible.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Knowing the behavior of solar energy is imperative for its use in photovoltaic systems; moreover, the number of weather stations is insufficient. This study presents a method for the integration of ...solar resource data: images and datasets. For this purpose, variables are extracted from images obtained from the GOES-13 satellite and integrated with variables obtained from meteorological stations. Subsequently, this data integration was used to train solar radiation prediction models in three different scenarios with data from 2012 and 2017. The predictive ability of five regression methods was evaluated, of which, neural networks had the highest performance in the scenario that integrates the meteorological variables and features obtained from the images. The analysis was performed using four evaluation metrics in each year. In the 2012 dataset, an R
2
of 0.88 and an RMSE of 90.99 were obtained. On the other hand, in the 2017 dataset, an R
2
of 0.92 and an RMSE of 40.97 were achieved. The model integrating data improves performance by up to 4% in R
2
and up to 10 points less in the level of dispersion according to RMSE, with respect to models using separate data.
Malignant melanoma (MM) is the most aggressive form of skin cancer, with increasing incidence worldwide. To date, there are no suitable clinical diagnostic, prognostic or predictive biomarkers for ...MM. Our data highlight the potential of metabolomic characterization of cancer stem cell‐ or serum‐derived exosomes using high‐resolution mass spectrometry for the discovery of clinically useful MM biomarkers.
Malignant melanoma (MM) is the most aggressive and life‐threatening form of skin cancer. It is characterized by an extraordinary metastasis capacity and chemotherapy resistance, mainly due to melanoma cancer stem cells (CSCs). To date, there are no suitable clinical diagnostic, prognostic or predictive biomarkers for this neoplasia. Therefore, there is an urgent need for new MM biomarkers that enable early diagnosis and effective disease monitoring. Exosomes represent a novel source of biomarkers since they can be easily isolated from different body fluids. In this work, a primary patient‐derived MM cell line enriched in CSCs was characterized by assessing the expression of specific markers and their stem‐like properties. Exosomes derived from CSCs and serums from patients with MM were characterized, and their metabolomic profile was analysed by high‐resolution mass spectrometry (HRMS) following an untargeted approach and applying univariate and multivariate statistical analyses. The aim of this study was to search potential biomarkers for the diagnosis of this disease. Our results showed significant metabolomic differences in exosomes derived from MM CSCs compared with those from differentiated tumour cells and also in serum‐derived exosomes from patients with MM compared to those from healthy controls. Interestingly, we identified similarities between structural lipids differentially expressed in CSC‐derived exosomes and those derived from patients with MM such as the glycerophosphocholine PC 16:0/0:0. To our knowledge, this is the first metabolomic‐based study aimed at characterizing exosomes derived from melanoma CSCs and patients' serum in order to identify potential biomarkers for MM diagnosis. We conclude that metabolomic characterization of CSC‐derived exosomes sets an open door to the discovery of clinically useful biomarkers in this neoplasia.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
PDF
