Adaptive estimation of the transition density of a particular hidden Markov chain

We study the following model of hidden Markov chain: $Y_i=X_i+\epsilon_i$, $i=1,...,n+1$ with $(X_i)$ a real-valued positive recurrent and stationary Markov chain and $(\epsilon_i)_{1\leq i\leq n+1}$ a noise independent of the sequence $(X_i)$ having a known distribution. We present an adaptive estimator of the transition density based on the quotient of a deconvolution estimator of the density of $X_i$ and an estimator of the density of $(X_i,X_{i+1})$. These estimators are obtained by contrast minimization and model selection. We evaluate the $L2$ risk and its rate of convergence for ordinary smooth and supersmooth noise with regard to ordinary smooth and supersmooth chains. Some examples are also detailed

Ten Simple Rules for Getting Help from Online Scientific Communities

The increasing complexity of research requires scientists to work at the intersection of multiple fields and to face problems for which their formal education has not prepared them. For example, biologists with no or little background in programming are now often using complex scripts to handle the results from their experiments; vice versa, programmers wishing to enter the world of bioinformatics must know about biochemistry, genetics, and other fields. In this context, communication tools such as mailing lists, web forums, and online communities acquire increasing importance. These tools permit scientists to quickly contact people skilled in a specialized field. A question posed properly to the right online scientific community can help in solving difficult problems, often faster than screening literature or writing to publication authors. The growth of active online scientific communities, such as those listed in Table S1, demonstrates how these tools are becoming an important source of support for an increasing number of researchers. Nevertheless, making proper use of these resources is not easy. Adhering to the social norms of World Wide Web communication—loosely termed “netiquette”—is both important and non-trivial. In this article, we take inspiration from our experience on Internet-shared scientific knowledge, and from similar documents such as “Asking the Questions the Smart Way” and “Getting Answers”, to provide guidelines and suggestions on how to use online communities to solve scientific problems

Thermoluminescence of zircon: a kinetic model

The mineral zircon, ZrSiO4, belongs to a class of promising materials for geochronometry by means of thermoluminescence (TL) dating. The development of a reliable and reproducible method for TL dating with zircon requires detailed knowledge of the processes taking place during exposure to ionizing radiation, long-term storage, annealing at moderate temperatures and heating at a constant rate (TL measurements). To understand these processes one needs a kinetic model of TL. This paper is devoted to the construction of such amodel. The goal is to study the qualitative behaviour of the system and to determine the parameters and processes controlling TL phenomena of zircon. The model considers the following processes: (i) Filling of electron and hole traps at the excitation stage as a function of the dose rate and the dose for both (low dose rate) natural and (high dose rate) laboratory irradiation. (ii) Time dependence of TL fading in samples irradiated under laboratory conditions. (iii) Short time annealing at a given temperature. (iv) Heating of the irradiated sample to simulate TL experiments both after laboratory and natural irradiation. The input parameters of the model, such as the types and concentrations of the TL centres and the energy distributions of the hole and electron traps, were obtained by analysing the experimental data on fading of the TL-emission spectra of samples from different geological locations. Electron paramagnetic resonance (EPR) data were used to establish the nature of the TL centres. Glow curves and 3D TL emission spectra are simulated and compared with the experimental data on time-dependent TL fading. The saturation and annealing behaviour of filled trap concentrations has been considered in the framework of the proposed kinetic model and comparedwith the EPR data associated with the rare-earth ions Tb3+ and Dy3+, which play a crucial role as hole traps and recombination centres. Inaddition, the behaviour of some of the SiOmn− centres has been compared with simulation results.

Effect of short-term versus long-term grassland management and seasonal variation in organic and conventional dairy farming on the composition of bulk tank milk

Bulk tank milk from 28 dairy farms was sampled every second month for 2 yr to assess the effects of grassland management, production system and season on milk fatty acid (FA) composition, concentrations of fat-soluble vitamins, Se, and milk sensory quality. Grassland management varied in terms of time since establishment. Short-term grassland management (SG) was defined as establishment or reseeding every fourth year or more often, and long-term grassland management (LG) was defined as less frequent establishment or reseeding. Fourteen organic (ORG) dairy farms with either short-term or long-term grassland management were paired with 14 conventional (CON) farms with respect to grassland management. Within ORG farms, SG farms differed from LG farms in herbage botanical composition, but not in concentrate FA concentrations, dry matter intake, or milk yield. Within CON farms, herbage composition, concentrate FA concentrations, dry matter intake, and milk yield showed no or insignificant variations. The ORG farms differed from CON farms in herbage botanical composition, concentrate FA concentrations, concentrate intake, and milk yield. Compared with ORG-LG farms, ORG-SG farms produced milk fat with higher proportions of C10:0 and C12:0 associated with higher herbage proportions of legumes (Fabaceae) and lower proportions of other dicotyledon families. Compared with milk from CON farms, milk fat from ORG farms had higher proportions of most saturated FA and all n-3 FA, but lower proportions of C18:0 and C18:1 cis-9 associated with higher forage proportion and differences in concentrations of FA in concentrates. Compared with the outdoor-feeding periods, the indoor feeding periods yielded milk fat with higher proportions of most short-chain and medium-chain FA and lower proportions of most C18-FA associated with grazing and higher forage proportions. Milk concentrations of α-tocopherol and β-carotene were lower during the grazing periods. Inclusion of fishmeal in organic concentrates may explain higher Se concentrations in organically produced milk. Milk sensory quality was not affected in this study. In conclusion, grassland management had minor effects on milk composition, and differences between ORG farms and CON farms may be explained by differences in concentrate intake and concentrate FA concentrations. Milk produced on ORG farms versus CON farms and milk produced during the outdoor versus indoor feeding periods had potential health benefits due to FA composition. In contrast, the higher milk-fat proportions of saturated FA in milk from ORG farms may be perceived as negative for human health

Intestinal stem cells lacking the Math1 tumour suppressor are refractory to Notch inhibitors

Intestinal cells are constantly produced from a stem cell reservoir that gives rise to proliferating transient amplifying cells, which subsequently differentiate into one of the four principal cell types. Signalling pathways, including the Notch signalling pathway, coordinate these differentiation processes and their deregulation may cause cancer. Pharmacological inhibition through γ-secretase inhibitors or genetic inactivation of the Notch signalling pathway results in the complete loss of proliferating crypt progenitors due to their conversion into post-mitotic goblet cells. The basic helix–loop–helix transcription factor Math1 is essential for intestinal secretory cell differentiation. Because of the critical roles of both Math1 and Notch signalling in intestinal homeostasis and neoplastic transformation, we sought to determine the genetic hierarchy regulating the differentiation of intestinal stem cells into secretory cells. In this paper, we demonstrate that the conversion of intestinal stem cells into goblet cells upon inhibition of the Notch signalling pathway requires Math1

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Bias in MRI Measurements of Apparent Diffusion Coefficient and Kurtosis: Implications for Choice of Maximum Diffusion Encoding

Tissue water diffusion is non-Gaussian and the expressions used to calculate diffusion parameters are approximations which introduce systematic errors dependent on the maximum diffusion encoding, diffusion time, etc. This study aimed at characterizing biases in estimates of both apparent diffusion coefficient and kurtosis, and determines their dependence on these parameters. Similar to the approach of several previous studies, Taylor expansion of the diffusion signal was used to calculate biases. Predicted errors were compared with data from one volunteer. Predicted errors agreed well with the measured errors and also the published diffusion tensor imaging measurements. The equations derived predict biases in measured diffusion parameters and explain much of the discrepancy between measurements obtained with different acquisition protocols. The equations may also be used to choose appropriate diffusion encoding for diffusion weighted, tensor, and kurtosis imaging

Open Problems on Central Simple Algebras

We provide a survey of past research and a list of open problems regarding central simple algebras and the Brauer group over a field, intended both for experts and for beginners.Comment: v2 has some small revisions to the text. Some items are re-numbered, compared to v