A cardiotoxicity dataset for breast cancer patients.

This dataset is a result of the collaboration between the University of A Coruña and the University Hospital of A Coruña. It contains information about 531 women diagnosed with HER2+ breast cancer, treated with potentially cardiotoxic oncologic therapies. These treatments can cause cardiovascular adverse events, including cardiac systolic dysfunction, the development of which has important clinical and prognostic implications.

Effectiveness of non-pharmaceutical interventions in nine fields of activity to decrease SARS-CoV-2 transmission (Spain, September 2020-May 2021).

Background: We estimated the association between the level of restriction in nine different fields of activity and SARS-CoV-2 transmissibility in Spain, from 15 September 2020 to 9 May 2021.

Methods: A stringency index (0-1) was created for each Spanish province ( = 50) daily. A hierarchical multiplicative model was fitted.

Modeling the number of people infected with SARS-COV-2 from wastewater viral load in Northwest Spain.

The quantification of the SARS-CoV-2 RNA load in wastewater has emerged as a useful tool to monitor COVID-19 outbreaks in the community. This approach was implemented in the metropolitan area of A Coruña (NW Spain), where wastewater from a treatment plant was analyzed to track the epidemic dynamics in a population of 369,098 inhabitants. Viral load detected in the wastewater and the epidemiological data from A Coruña health system served as main sources for statistical models developing.

Cure models to estimate time until hospitalization due to COVID-19: A case study in Galicia (NW Spain).

A short introduction to survival analysis and censored data is included in this paper. A thorough literature review in the field of cure models has been done. An overview on the most important and recent approaches on parametric, semiparametric and nonparametric mixture cure models is also included.

Estimating lengths-of-stay of hospitalised COVID-19 patients using a non-parametric model: a case study in Galicia (Spain).

Estimating the lengths-of-stay (LoS) of hospitalised COVID-19 patients is key for predicting the hospital beds' demand and planning mitigation strategies, as overwhelming the healthcare systems has critical consequences for disease mortality. However, accurately mapping the time-to-event of hospital outcomes, such as the LoS in the intensive care unit (ICU), requires understanding patient trajectories while adjusting for covariates and observation bias, such as incomplete data. Standard methods, such as the Kaplan-Meier estimator, require prior assumptions that are untenable given current knowledge.

Nonparametric covariate hypothesis tests for the cure rate in mixture cure models.

In lifetime data, like cancer studies, there may be long term survivors, which lead to heavy censoring at the end of the follow-up period. Since a standard survival model is not appropriate to handle these data, a cure model is needed. In the literature, covariate hypothesis tests for cure models are limited to parametric and semiparametric methods.

Analysis of interval-grouped data in weed science: The binnednp Rcpp package.

Weed scientists are usually interested in the study of the distribution and density functions of the random variable that relates weed emergence with environmental indices like the hydrothermal time (HTT). However, in many situations, experimental data are presented in a grouped way and, therefore, the standard nonparametric kernel estimators cannot be computed.Kernel estimators for the density and distribution functions for interval-grouped data, as well as bootstrap confidence bands for these functions, have been proposed and implemented in the binnednp package.

Challenges and Future Trends for Microarray Analysis.

The current situation in microarray data analysis and prospects for the future are briefly discussed in this chapter, in which the competition between microarray technologies and high-throughput technologies is considered under a data analysis view. The up-to-date limitations of DNA microarrays are important to forecast challenges and future trends in microarray data analysis; these include data analysis techniques associated with an increasing sample sizes, new feature selection methods, deep learning techniques, covariate significance testing as well as false discovery rate methods, among other procedures for a better interpretability of the results.

ROC Curves for the Statistical Analysis of Microarray Data.

A receiver operating characteristic (ROC) curve is a graphical plot that illustrates the diagnostic ability of a binary classifier as a function of its discrimination threshold. This chapter is an overview on the use of ROC curves for microarray data. The notion of ROC curve and its motivation is introduced in Subheading 1.

Bootstrap testing for cross-correlation under low firing activity.

A new cross-correlation synchrony index for neural activity is proposed. The index is based on the integration of the kernel estimation of the cross-correlation function. It is used to test for the dynamic synchronization levels of spontaneous neural activity under two induced brain states: sleep-like and awake-like.

Functional two-way analysis of variance and bootstrap methods for neural synchrony analysis.

Background: Pairwise association between neurons is a key feature in understanding neural coding. Statistical neuroscience provides tools to estimate and assess these associations. In the mammalian brain, activating ascending pathways arise from neuronal nuclei located at the brainstem and at the basal forebrain that regulate the transition between sleep and awake neuronal firing modes in extensive regions of the cerebral cortex, including the primary visual cortex, where neurons are known to be selective for the orientation of a given stimulus.

Bursting thalamic responses in awake monkey contribute to visual detection and are modulated by corticofugal feedback.

The lateral geniculate nucleus is the gateway for visual information en route to the visual cortex. Neural activity is characterized by the existence of two firing modes: burst and tonic. Originally associated with sleep, bursts have now been postulated to be a part of the normal visual response, structured to increase the probability of cortical activation, able to act as a "wake-up" call to the cortex.

Cross nearest-spike interval based method to measure synchrony dynamics.

A new synchrony index for neural activity is defined in this paper. The method is able to measure synchrony dynamics in low firing rate scenarios. It is based on the computation of the time intervals between nearest spikes of two given spike trains.

Influence of social support on older adults with cognitive impairment, depressive symptoms, or both coexisting.

The influence of social support dimensions (the extent of contact with others, the satisfaction with contacts, and the availability of help if sick or disabled) in elderly people with cognitive impairment (COG), depressive symptoms (DEP), or the co-occurrence of these symptoms (COG-DEP) was assessed in a cross-sectional analysis of a representative sample of 579 individuals aged 65 years and older. A lower extent of contact was related to COG (OR: 2.26).

Antagonist muscle co-activation during straight walking and its relation to kinematics: insight from young, elderly and Parkinson's disease.

Increased antagonist muscle co-activation of the lower limb during walking seems to be an adaptive process to the physiological changes of aging, in order to gain joint stability. In the healthy subjects this view seems to be reinforced by the fact that the co-activation index (CAI) increases when the gait is faster. The few reports on antagonist co-activation in Parkinson's disease (PD) patients indicate that they have larger co-activation than the healthy elderly, supporting the idea of the stabilization role of CAI during gait, as postural instability is a cardinal feature of PD.

Lasso logistic regression, GSoft and the cyclic coordinate descent algorithm: application to gene expression data.

Statistical methods generating sparse models are of great value in the gene expression field, where the number of covariates (genes) under study moves about the thousands while the sample sizes seldom reach a hundred of individuals. For phenotype classification, we propose different lasso logistic regression approaches with specific penalizations for each gene. These methods are based on a generalized soft-threshold (GSoft) estimator.

A random effect multiplicative heteroscedastic model for bacterial growth.

Background: Predictive microbiology develops mathematical models that can predict the growth rate of a microorganism population under a set of environmental conditions. Many primary growth models have been proposed. However, when primary models are applied to bacterial growth curves, the biological variability is reduced to a single curve defined by some kinetic parameters (lag time and growth rate), and sometimes the models give poor fits in some regions of the curve.

Different sources of nitric oxide mediate neurovascular coupling in the lateral geniculate nucleus of the cat.

Understanding the link between neuronal responses (NRs) and metabolic signals is fundamental to our knowledge of brain function and it is a milestone in our efforts to interpret data from modern non invasive optical techniques such as fMRI, which are based on the close coupling between metabolic demand of active neurons and local changes in blood flow. The challenge is to unravel the link. Here we show, using spectrophotometry to record oxyhaemoglobin and methemoglobin (surrogate markers of cerebral flow and nitric oxide levels respectively) together with extracellular neuronal recordings in vivo and applying a multiple polynomial regression model, that the markers are able to predict up about 80% of variability in NR.

Evaluating the ability of tree-based methods and logistic regression for the detection of SNP-SNP interaction.

Most common human diseases are likely to have complex etiologies. Methods of analysis that allow for the phenomenon of epistasis are of growing interest in the genetic dissection of complex diseases. By allowing for epistatic interactions between potential disease loci, we may succeed in identifying genetic variants that might otherwise have remained undetected.