A transfer-learning approach to feature extraction from cancer transcriptomes with deep autoencoders

Publicado en Lecture Notes in Computer Science.The diagnosis and prognosis of cancer are among the more challenging tasks that oncology medicine deals with. With the main aim of fitting the more appropriate treatments, current personalized medicine focuses on using data from heterogeneous sources to estimate the evolu- tion of a given disease for the particular case of a certain patient. In recent years, next-generation sequencing data have boosted cancer prediction by supplying gene-expression information that has allowed diverse machine learning algorithms to supply valuable solutions to the problem of cancer subtype classification, which has surely contributed to better estimation of patient’s response to diverse treatments. However, the efficacy of these models is seriously affected by the existing imbalance between the high dimensionality of the gene expression feature sets and the number of sam- ples available for a particular cancer type. To counteract what is known as the curse of dimensionality, feature selection and extraction methods have been traditionally applied to reduce the number of input variables present in gene expression datasets. Although these techniques work by scaling down the input feature space, the prediction performance of tradi- tional machine learning pipelines using these feature reduction strategies remains moderate. In this work, we propose the use of the Pan-Cancer dataset to pre-train deep autoencoder architectures on a subset com- posed of thousands of gene expression samples of very diverse tumor types. The resulting architectures are subsequently fine-tuned on a col- lection of specific breast cancer samples. This transfer-learning approach aims at combining supervised and unsupervised deep learning models with traditional machine learning classification algorithms to tackle the problem of breast tumor intrinsic-subtype classification.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Provenance of archaeological limestone with EPR spectroscopy: The case of the Cypriote-type statuettes

The present work demonstrates the potential of EPR spectroscopy as a useful technique in provenance investigation of archaeological finds of limestone. The case of the small, Cypriote-type limestone statuettes found in most major Archaic sanctuaries of the Eastern Mediterranean is used as an illustrative application. Ancient and modern limestone quarries of Cyprus, Samos, Rhodes and Egypt were sampled in order to form a reference data bank for the likely places of origin. Samples were also taken from statuettes exhibited in the archaeological museums of Nicosia (2 samples), Samos (14 samples) and Copenhagen (National Archaeological Museum, 19 samples). All quarry and archaeological samples were analysed with EPR spectroscopy. The quarry samples from Rhodes were easily distinguished from the other quarry samples and were not treated further because they produce material of low quality and compactness. A detailed study of the EPR spectroscopy results leads to the determination of a number of parameters, which separate the reference group of Samos from those of Cyprus and Egypt. The structure of the EPR spectra in the region around g=2.0000 is characteristic for these different quarrying areas. Diagrams where each quarry area is represented by a field were drawn and the archaeological samples were plotted on them. All the analysed statuettes (except for one, which is most probably of Samian limestone) appear to be carved in Cypriote limestone. Consequently, the results of this research offer a decisive argument in favour of the Cypriote origin for statuettes of this type found in the Aegean. © 2004 Elsevier Ltd. All rights reserved

Neutron activation and X-ray analysis of "Thapsos Class" vases. An attempt to identify their origin

Instrumental neutron activation analysis and X-ray techniques have been applied for the determination of 24 major, minor and trace elements (Al, Ca, Cc, Co, Cr, Cs, Eu, Hf, Fe, K, La, Lu, Na, Rb, Sb, Sc, Si, Sm, Ta, Tb, Th, Ti, Yb and Zn) in three or four different groups of vases (Protocorinthian, Thapsos Class, Late Geometric Corinthian and Aigion Crater). A close agreement for all elements examined between the pottery specimens of all groups was found. The matching in chemical composition of the four groups of vases strongly suggests the same origin for all of them. © 1980 Academic Press Inc. (London) Ltd

Platelet-activating factor (PAF) receptor expression is associated with histopathological stage and grade and patients' survival in gastric adenocarcinoma

Platelet activating factor (PAF) has been considered as potent inflammatory lipid mediator that exerts its actions by binding to PAF receptor (PAFR). PAF/PAFR system has been implicated in several pathophysiological states, including tumor progression, angiogenesis and metastasis. Te present study aimed to evaluate the clinical significance of PAFR expression in gastric adenocarcinoma. PAFR protein expression was assessed immunohistochemically on 54 gastric adenocarcinoma tissue samples and was analyzed in relation with clinicopathological parameters, tumor proliferative capacity and patients' survival. PAFR was abundantly expressed in all gastric adenocarcinoma cases examined. Increased PAFR expression was significantly more frequently observed in well/moderately compared to poorly differentiated gastric adenocarcinoma cases (p=0.011). PAFR expression was significantly increased in intestinal- compared to diffuse-type cases (p=0.020). Elevated PAFR expression was significantly associated with smaller tumor size, absence of lymph node and organ metastasis and low tumor histopathological stage (p=0.025, p<0.001, p=0.009 and p<0.001, respectively). Additionally, patients presenting elevated PAFR expression had significantly longer survival times compared to those with low PAFR expression (log-rank test, p<0.001). Tese results support an important potential role of PAFR signalling in gastric malignant disease progression and render further research in this field a necessity

Optimizing the Efficiency of Machine Learning Techniques

© 2020, Springer Nature Singapore Pte Ltd. The prediction of judicial decisions based on historical datasets in the legal domain is a challenging task. To answer the question about how the court will render a decision in a particular case has remained an important issue. Prior studies conducted on the prediction of judicial case decisions have datasets with limited size by experimenting less efficient set of predictors variables applied to different machine learning classifiers. In this work, we investigate and apply more efficient sets of predictors variables with a machine learning classifier over a large size legal dataset for court judgment prediction. Experimental results are encouraging and depict that incorporation of feature selection technique has significantly improved the performance of predictive classifier

Emergency response, intervention, and societal recovery in Greece and Turkey after the 30th October 2020, M-W=7.0, Samos (Aegean Sea) earthquake

On 30 October 2020, an M-w = 7.0 earthquake struck the Eastern Aegean Sea with considerable impact on Samos Island in Greece and the area of Izmir in Turkey. It was the most lethal seismic event in 2020 worldwide, and the largest and most destructive in the Aegean Sea since the 1955 earthquake that also affected both countries. The Civil Protection authorities in Greece and Turkey were effectively mobilized responding to the earthquake emergency. The main response actions comprised initial announcements of the earthquake and first assessment of the impact, provision of civil protection guidelines through emergency communication services, search and rescue operations,medical care, set up of emergency shelters and provisions of essential supplies, psychological support, as well as education, training activities and financial support to the affected population. From the comparison of the Civil Protection framework and the implemented response actions, it is seen that actions at both sides of the eastern Aegean Sea, followed a single-hazard approach in disaster management with similar response activities coordinated by a main Civil Protection agency, which was in close cooperation with the respective authorities at a national, regional and local level. Based on the presented information, it is concluded that the post-earthquake response and emergency management were satisfactory in both countries, with valuable lessons learnt ahead of the next major earthquake. To this end, many aspects can be further addressed to enhance community resilience and introduce a multi-hazard approach in (natural and man-made) disaster management

The Levant

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