On the optimal usage of labelled examples in semi-supervised multi-class classification problems

In recent years, the performance of semi-supervised learning has been theoretically investigated. However, most of this theoretical development has focussed on binary classification problems. In this paper, we take it a step further by extending the work of Castelli and Cover [1] [2] to the multi-class paradigm. Particularly, we consider the key problem in semi-supervised learning of classifying an unseen instance x into one of K different classes, using a training dataset sampled from a mixture density distribution and composed of l labelled records and u unlabelled examples. Even under the assumption of identifiability of the mixture and having infinite unlabelled examples, labelled records are needed to determine the K decision regions. Therefore, in this paper, we first investigate the minimum number of labelled examples needed to accomplish that task. Then, we propose an optimal multi-class learning algorithm which is a generalisation of the optimal procedure proposed in the literature for binary problems. Finally, we make use of this generalisation to study the probability of error when the binary class constraint is relaxed

Cutaneous Apocrine Adenocarcinoma of the Groin With Extramammary Paget Disease: Response to Dual HER2 Blockade With Trastuzumab and Pertuzumab

Adenocarcinoma apocrino cutáneo; Enfermedad de Paget extramamaria; TratamientoAdenocarcinoma apocri cutani; Malaltia de Paget extramamària; TractamentCutaneous apocrine adenocarcinoma; Extramammary Paget's disease; TreatmentEl carcinoma apocrino cutáneo es una neoplasia maligna rara que se asocia a mal pronóstico. La enfermedad de Paget extramamaria (EPEM) invasiva es una forma rara de adenocarcinoma que se observa principalmente en las áreas donde se encuentran las glándulas apocrinas y tiene un pronóstico peor que su estadio in situ con un riesgo mayor de neoplasias subyacentes. La diseminación epidérmica pagetoide es poco común en los carcinomas apocrinos. Se han descrito muy pocos casos que asocien estas dos entidades y se desconoce su histogénesis. Entre un 20 y un 60% de EPEM sobreexpresa HER-2/neu (c-erbB-2), con pocos casos descritos de respuesta favorable a trastuzumab (anticuerpo monoclonal recombinante contra HER-2), solo o en combinación con paclitaxel. La sobreexpresión de HER-2/neu conduce a una proliferación aumentada de las células tumorales en la EPEM, como en los cánceres de mama HER2 positivos, de comportamiento más agresivo y más recurrències

Approaching Sentiment Analysis by Using Semi-supervised Learning of Multidimensional Classifiers

Sentiment Analysis is defined as the computational study of opinions, sentiments and emotions expressed in text. Within this broad field, most of the work has been focused on either Sentiment Polarity classification, where a text is classified as having positive or negative sentiment, or Subjectivity classification, in which a text is classified as being subjective or objective. However, in this paper, we consider instead a real-world problem in which the attitude of the author is characterised by three different (but related) target variables: Subjectivity, Sentiment Polarity, Will to Influence, unlike the two previously stated problems, where there is only a single variable to be predicted. For that reason, the (uni-dimensional) common approaches used in this area yield suboptimal solutions to this problem. In order to bridge this gap, we propose, for the first time, the use of the novel multi-dimensional classification paradigm in the Sentiment Analysis domain. This methodology is able to join the different target variables in the same classification task so as to take advantage of the potential statistical relations between them. In addition, and in order to take advantage of the huge amount of unlabelled information available nowadays in this context, we propose the extension of the multi-dimensional classification framework to the semi-supervised domain. Experimental results for this problem show that our semi-supervised multi-dimensional approach outperforms the most common Sentiment Analysis approaches, concluding that our approach is beneficial to improve the recognition rates for this problem, and in extension, could be considered to solve future Sentiment Analysis problems

Cooperative and Escaping Mechanisms between Circulating Tumor Cells and Blood Constituents

Metastasis is the leading cause of cancer-related deaths and despite measurable progress in the field, underlying mechanisms are still not fully understood. Circulating tumor cells (CTCs) disseminate within the bloodstream, where most of them die due to the attack of the immune system. On the other hand, recent evidence shows active interactions between CTCs and platelets, myeloid cells, macrophages, neutrophils, and other hematopoietic cells that secrete immunosuppressive cytokines, which aid CTCs to evade the immune system and enable metastasis. Platelets, for instance, regulate inflammation, recruit neutrophils, and cause fibrin clots, which may protect CTCs from the attack of Natural Killer cells or macrophages and facilitate extravasation. Recently, a correlation between the commensal microbiota and the inflammatory/immune tone of the organism has been stablished. Thus, the microbiota may affect the development of cancer-promoting conditions. Furthermore, CTCs may suffer phenotypic changes, as those caused by the epithelial–mesenchymal transition, that also contribute to the immune escape and resistance to immunotherapy. In this review,we discuss the findings regarding the collaborative biological events among CTCs, immune cells, and microbiome associated to immune escape and metastatic progression

On the optimal usage of labelled examples in semi-supervised multi-class classification problems

In recent years, the performance of semi-supervised learning has been theoretically investigated. However, most of this theoretical development has focussed on binary classification problems. In this paper, we take it a step further by extending the work of Castelli and Cover [1] [2] to the multi-class paradigm. Particularly, we consider the key problem in semi-supervised learning of classifying an unseen instance x into one of K different classes, using a training dataset sampled from a mixture density distribution and composed of l labelled records and u unlabelled examples. Even under the assumption of identifiability of the mixture and having infinite unlabelled examples, labelled records are needed to determine the K decision regions. Therefore, in this paper, we first investigate the minimum number of labelled examples needed to accomplish that task. Then, we propose an optimal multi-class learning algorithm which is a generalisation of the optimal procedure proposed in the literature for binary problems. Finally, we make use of this generalisation to study the probability of error when the binary class constraint is relaxed

Variabilidad de la frecuencia cardíaca: investigación y aplicaciones prácticas para el control de los procesos adaptativos en el deporte

This review examines the main applications and limitations of heart rate monitors in sports. The key issues, that researchers and trainers should consider, have been exposed in order to optimize their use in daily training control through monitoring the heart rate variability. These concern the reliability and validity; and methodological considerations for registration and data statistical interpretation. Given its limitations, this work shows the usefulness of the main indicators of parasympathetic activity (i.e. rMSSD) which monitored at rest for sort periods, 3-4 times week, could improve control processes adaptation to training, both physiological and psychological, in individual and team sports. Overall, the analysis shows that resting heart could be a useful to assess the adaptive response and performance in this context.Esta revisão examina as principais aplicações e limitações de monitores de frequência cardíaca no esporte. Questões-chave que ambos os pesquisadores e formadores deve considerar, foram expostos a fim de optimizar a sua utilização no controle treinamento diário através de monitoramento da variabilidade da freqüência cardíaca. Estas dizem respeito a confiabilidade ea validade; e considerações metodológicas fundamentais para o registro e interpretação estatística dos dados. Dadas as suas limitações, este estudo mostra a utilidade dos principais indicadores da actividade parassimpático (i.e. rMSSD), que monitorado por períodos curtos, 3-4 vezes por semana, poderia melhorar a adaptação de processos de controlo ao treinamento, tanto fisiológicos e psicológicos, em esportes individuais e coletivos. Da mesma forma, a análise da freqüência cardíaca em repouso poderia ser uma ferramenta útil para avaliar a resposta do organismo e reforçar o desempenho no contexto esportivo.Esta revisión analiza las principales aplicaciones y limitaciones de los monitores de frecuencia cardiaca en el ámbito deportivo. Los aspectos clave, que tanto investigadores como entrenadores deberían considerar, han sido expuestos con el fin de optimizar su uso en el control diario del entrenamiento mediante la monitorización de la variabilidad de la frecuencia cardíaca. Estos se refieren a la fiabilidad y validez; así como consideraciones metodológicas fundamentales para su registro e interpretación. Teniendo en cuenta sus limitaciones, este trabajo manifiesta la utilidad de los principales indicadores de actividad parasimpática (i.e. rMSSD) donde monitorizados en reposo durante cortos periodos, 3-4 ves por semana, podrían mejorar los procesos de control de adaptación al entrenamiento, tanto fisiológicos como psicológicos, en deportes individuales y colectivos. En conjunto, el análisis muestra que la frecuencia cardíaca en reposo podría ser una herramienta útil para valorar la respuesta del organismo y maximizar el rendimiento deportivo

Observación automatizada: la variabilidad de la frecuencia cardíaca y su relación con las variables psicológicas determinantes del rendimiento en nadadores jóvenes

La correcta interpretación de los marcadores de rendimiento desde una perspectiva psicofisiológica es importante para el desarrollo de los atletas en etapas formativas. Este trabajo contiene dos objetivos. El primero es analizar la relación de la variabilidad de la frecuencia cardíaca (VFC) con las variables psicológicas: calidad de sueño, autoestima y esta- dos de ánimo en jóvenes nadadores de una escuela profesional. El segundo objetivo es estudiar la relación de la VFC y determinantes psicológicos con el rendimiento. Esta se compuso 9 nadadores (11.7 ± 1.4 años) basada en un método de muestreo a propósito. Las mediciones fueron efectuadas una vez por semana durante tres semanas de entrenamiento. El análisis es- tadístico muestra como la dimensión de ansiedad correlacionó negativa- mente con el logaritmo natural de banda de alta frecuencia (Ln HF) y posi- tivamente con el logaritmo natural de la banda de muy baja frecuencia (Ln VLF) de forma significativa. No se encontraron relaciones para las varia- bles autoestima y calidad de sueño. El tiempo en la prueba de rendimiento fue asociado negativamente con indicadores de la actividad parasimpática. Estos resultados sugieren que la VFC podría ser una herramienta válida pa- ra la predicción del rendimiento y mejora de la interpretación de la ansie- dad.Correct interpretation of performance markers from a psycho- physiological perspective is important in young developing athletes. This study had two objectives. The first was to analyze the relationship between heart rate variability (HRV) and the psychological variables sleep quality, self-esteem, and mood states in young swimmers from a professional swimming club. The second was to study the relationship between per- formance and HRV and psychological determinants. The sample was composed of nine swimmers (11.7±1.4 years) base on purposive sampling method. Data were collected once a week during training sessions for 3 weeks. The statistical analysis showed that anxiety was negatively correlat- ed with the high frequency component of HRV (Ln HF) and positively correlated with the very low frequency component (Ln LVF). No signifi- cant correlations were observed for self-esteem or sleep quality. Perfor- mance in a 200-m freestyle event was negatively correlated with the para- sympathetic HRV indices. Our results suggest that HRV could be a valid tool for predicting performance and improving interpretation of psycho- metric tests

Approaching Sentiment Analysis by Using Semi-supervised Learning of Multidimensional Classifiers

Sentiment Analysis is defined as the computational study of opinions, sentiments and emotions expressed in text. Within this broad field, most of the work has been focused on either Sentiment Polarity classification, where a text is classified as having positive or negative sentiment, or Subjectivity classification, in which a text is classified as being subjective or objective. However, in this paper, we consider instead a real-world problem in which the attitude of the author is characterised by three different (but related) target variables: Subjectivity, Sentiment Polarity, Will to Influence, unlike the two previously stated problems, where there is only a single variable to be predicted. For that reason, the (uni-dimensional) common approaches used in this area yield suboptimal solutions to this problem. In order to bridge this gap, we propose, for the first time, the use of the novel multi-dimensional classification paradigm in the Sentiment Analysis domain. This methodology is able to join the different target variables in the same classification task so as to take advantage of the potential statistical relations between them. In addition, and in order to take advantage of the huge amount of unlabelled information available nowadays in this context, we propose the extension of the multi-dimensional classification framework to the semi-supervised domain. Experimental results for this problem show that our semi-supervised multi-dimensional approach outperforms the most common Sentiment Analysis approaches, concluding that our approach is beneficial to improve the recognition rates for this problem, and in extension, could be considered to solve future Sentiment Analysis problems

Extracellular vesicle-miRNAs as liquid biopsy biomarkers for disease identification and prognosis in metastatic colorectal cancer patients

We would like to extend our gratitude to the all the patients and the healthy volunteers who participated in the study, as well as the University of Granada, Biomedicine PhD program. This work was supported by Roche Spain, the PhD grant from the University of Granada (DdMP) (2014) and the PhD grant from the Spanish Government (ARM) (FPU) 2014, REF FPU14/05461.Disseminated disease is present in ≈50% of colorectal cancer patients upon diagnosis, being responsible for most of cancer deaths. Addition of biological drugs, as Bevacizumab, to chemotherapy, has increased progression free survival and overall survival of metastatic colorectal cancer (mCRC) patients. However, these benefits have been only reported in a small proportion of patients. To date, there are not biomarkers that could explain the heterogeneity of this disease and would help in treatment selection. Recent findings demonstrated that microRNAs (miRNAs) play an important role in cancer and they can be encapsulated with high stability into extracellular vesicles (EVs) that are released in biological fluids. EVs can act as cell-to-cell communicators, transferring genetic information, such as miRNAs. In this context, we aimed to investigate serum EV associated miRNAs (EV-miRNAs) as novel non-invasive biomarkers for the diagnosis and prognosis of Bevacizumab-treated mCRC patients. We observed that baseline miRNA-21 and 92a outperformed carcinoembryonic antigen levels in the diagnosis of our 44 mCRC patients, compared to 17 healthy volunteers. In addition, patients who died presented higher levels of miRNA-92a and 222 at 24 weeks. However, in the multivariate Cox analysis, higher levels of miRNA-222 at 24 weeks were associated with lower overall survival. Altogether, these data indicate that EV-miRNAs have a strong potential as liquid biopsy biomarkers for the identification and prognosis of mCRC.Roche SpainUniversity of Granada (DdMP)Spanish Government REF FPU14/0546