Inflammatory bowel disease (IBD), a disorder characterized by chronic inflammation of the gastrointestinal (GI) tract and a range of adverse health effects including diarrhea, abdominal pain, ...vomiting, and bloody stools, affects nearly 3.1 million genetically susceptible adults in the United States today. Although the etiology of IBD remains unclear, genetics, stress, diet, and gut microbiota dysbiosis- especially in immunocompromised individuals- have been identified as possible causes of disease. Although previous research has largely focused on the role of bacteria in IBD pathogenesis, recently observed alterations of fungal load and biodiversity in the GI tract of afflicted individuals suggest interkingdom interactions amongst different gut microbial communities, particularly between bacteria and fungi. These discoveries point to the potential utilization of treatment approaches such as antibiotics, antifungals, probiotics, and postbiotics that target both bacteria and fungi in managing IBD. In this review, we discuss the impact of specific fungi on disease pathogenesis, with a focus on the highly virulent genus Candida and how the presence of certain co-enzymes impacts its virulence. In addition, we evaluate current gut microbiome-based therapeutic approaches with the intention of better understanding the mechanisms behind novel therapies.
We design a sequential Monte Carlo scheme for the dual purpose of Bayesian inference and model selection. We consider the application context of urban mobility, where several modalities of transport ...and different measurement devices can be employed. Therefore, we address the joint problem of online tracking and detection of the current modality. For this purpose, we use interacting parallel particle filters, each one addressing a different model. They cooperate for providing a global estimator of the variable of interest and, at the same time, an approximation of the posterior density of each model given the data. The interaction occurs by a parsimonious distribution of the computational effort, with online adaptation for the number of particles of each filter according to the posterior probability of the corresponding model. The resulting scheme is simple and flexible. We have tested the novel technique in different numerical experiments with artificial and real data, which confirm the robustness of the proposed scheme.
Statistical signal processing applications usually require the estimation of some parameters of interest given a set of observed data. These estimates are typically obtained either by solving a ...multi-variate optimization problem, as in the maximum likelihood (ML) or maximum a posteriori (MAP) estimators, or by performing a multi-dimensional integration, as in the minimum mean squared error (MMSE) estimators. Unfortunately, analytical expressions for these estimators cannot be found in most real-world applications, and the Monte Carlo (MC) methodology is one feasible approach. MC methods proceed by drawing random samples, either from the desired distribution or from a simpler one, and using them to compute consistent estimators. The most important families of MC algorithms are the Markov chain MC (MCMC) and importance sampling (IS). On the one hand, MCMC methods draw samples from a proposal density, building then an ergodic Markov chain whose stationary distribution is the desired distribution by accepting or rejecting those candidate samples as the new state of the chain. On the other hand, IS techniques draw samples from a simple proposal density and then assign them suitable weights that measure their quality in some appropriate way. In this paper, we perform a thorough review of MC methods for the estimation of static parameters in signal processing applications. A historical note on the development of MC schemes is also provided, followed by the basic MC method and a brief description of the rejection sampling (RS) algorithm, as well as three sections describing many of the most relevant MCMC and IS algorithms, and their combined use. Finally, five numerical examples (including the estimation of the parameters of a chaotic system, a localization problem in wireless sensor networks and a spectral analysis application) are provided in order to demonstrate the performance of the described approaches.
Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is classifier chains, in which ...the predictions of individual classifiers are cascaded along a chain, thus taking into account inter-label dependencies and improving the overall performance. Several varieties of classifier chain methods have been introduced, and many of them perform very competitively across a wide range of benchmark datasets. However, scalability limitations become apparent on larger datasets when modelling a fully cascaded chain. In particular, the methods׳ strategies for discovering and modelling a good chain structure constitute a major computational bottleneck. In this paper, we present the classifier trellis (CT) method for scalable multi-label classification. We compare CT with several recently proposed classifier chain methods to show that it occupies an important niche: it is highly competitive on standard multi-label problems, yet it can also scale up to thousands or even tens of thousands of labels.
•A study of multi-output classification as graphical models.•An empirical comparison of existing strategies for modelling dependency among outputs.•A novel scalable approach based on a hill climbing heuristic: the classifier trellis.•An empirical cross-fold comparison with other methods.•A connection to structured output prediction and a comparison in a segmentation task.
The current exponential increase of spatiotemporally explicit data streams from satellite-based Earth observation missions offers promising opportunities for global vegetation monitoring. Intelligent ...sampling through active learning (AL) heuristics provides a pathway for fast inference of essential vegetation variables by means of hybrid retrieval approaches, i.e., machine learning regression algorithms trained by radiative transfer model (RTM) simulations. In this study we summarize AL theory and perform a brief systematic literature survey about AL heuristics used in the context of Earth observation regression problems over terrestrial targets. Across all relevant studies it appeared that: (i) retrieval accuracy of AL-optimized training data sets outperformed models trained over large randomly sampled data sets, and (ii) Euclidean distance-based (EBD) diversity method tends to be the most efficient AL technique in terms of accuracy and computational demand. Additionally, a case study is presented based on experimental data employing both uncertainty and diversity AL criteria. Hereby, a a simulated training data base by the PROSAIL-PRO canopy RTM is used to demonstrate the benefit of AL techniques for the estimation of total leaf carotenoid content (Cxc) and leaf water content (Cw). Gaussian process regression (GPR) was incorporated to minimize and optimize the training data set with AL. Training the GPR algorithm on optimally AL-based sampled data sets led to improved variable retrievals compared to training on full data pools, which is further demonstrated on a mapping example. From these findings we can recommend the use of AL-based sub-sampling procedures to select the most informative samples out of large training data pools. This will not only optimize regression accuracy due to exclusion of redundant information, but also speed up processing time and reduce final model size of kernel-based machine learning regression algorithms, such as GPR. With this study we want to encourage further testing and implementation of AL sampling methods for hybrid retrieval workflows. AL can contribute to the solution of regression problems within the framework of operational vegetation monitoring using satellite imaging spectroscopy data, and may strongly facilitate data processing for cloud-computing platforms.
Many applications in signal processing require the estimation of some parameters of interest given a set of observed data. More specifically, Bayesian inference needs the computation of a-posteriori ...estimators which are often expressed as complicated multi-dimensional integrals. Unfortunately, analytical expressions for these estimators cannot be found in most real-world applications, and Monte Carlo methods are the only feasible approach. A very powerful class of Monte Carlo techniques is formed by the Markov Chain Monte Carlo (MCMC) algorithms. They generate a Markov chain such that its stationary distribution coincides with the target posterior density. In this work, we perform a thorough review of MCMC methods using multiple candidates in order to select the next state of the chain, at each iteration. With respect to the classical Metropolis–Hastings method, the use of multiple try techniques foster the exploration of the sample space. We present different Multiple Try Metropolis schemes, Ensemble MCMC methods, Particle Metropolis–Hastings algorithms and the Delayed Rejection Metropolis technique. We highlight limitations, benefits, connections and differences among the different methods, and compare them by numerical simulations.
The current nutritional composition of the “American diet” (AD; also known as Western diet) has been linked to the increasing incidence of chronic diseases, including inflammatory bowel disease ...(IBD), namely Crohn disease (CD).
This study investigated which of the 3 major macronutrients (protein, fat, carbohydrates) in the AD has the greatest impact on preventing chronic inflammation in experimental IBD mouse models.
We compared 5 rodent diets designed to mirror the 2011–2012 “What We Eat in America” NHANES. Each diet had 1 macronutrient dietary source replaced. The formulated diets were AD, AD-soy-pea (animal protein replaced by soy + pea protein), AD-CHO (“refined carbohydrate” by polysaccharides), AD-fat redistribution of the ω-6:ω-3 (n–6:n–3) PUFA ratio; ∼10:1 to 1:1, and AD-mix (all 3 “healthier” macronutrients combined). In 3 separate experiments, 8-wk-old germ-free SAMP1/YitFC mice (SAMP) colonized with human gut microbiota (“hGF-SAMP”) from CD or healthy donors were fed an AD, an AD-“modified,” or laboratory rodent diet for 24 wk. Two subsequent dextran sodium sulfate–colitis experiments in hGF-SAMP (12-wk-old) and specific-pathogen-free (SPF) C57BL/6 (20-wk-old) mice, and a 6-wk feeding trial in 24-wk-old SPF SAMP were performed. Intestinal inflammation, gut metagenomics, and MS profiles were assessed.
The AD-soy-pea diet resulted in lower histology scores mean ± SD (56.1% ± 20.7% reduction) in all feeding trials and IBD mouse models than did other diets (P < 0.05). Compared with the AD, the AD-soy-pea correlated with increased abundance in Lactobacillaceae and Leuconostraceae (1.5–4.7 log2 and 3.0–5.1 log2 difference, respectively), glutamine (6.5 ± 0.8 compared with 3.9 ± 0.3 ng/μg stool, P = 0.0005) and butyric acid (4:0; 3.3 ± 0.5 compared with 2.54 ± 0.4 ng/μg stool, P = 0.006) concentrations, and decreased linoleic acid (18:2n–6; 5.4 ± 0.4 compared with 8.6 ± 0.3 ng/μL plasma, P = 0.01).
Replacement of animal protein in an AD by plant-based sources reduced the severity of experimental IBD in all mouse models studied, suggesting that similar, feasible adjustments to the daily human diet could help control/prevent IBD in humans.
Magnetic hysteresis loops areas and hyperthermia on magnetic nanoparticles have been studied with the aim of providing reliable and reproducible methods of measuring the specific absorption rate ...(SAR).
The SAR of Fe3O4 nanoparticles with two different mean sizes, and Ni1−xZnxFe2O4 ferrites with 0 ≤ x ≤ 0.8 has been measured with three approaches: static hysteresis loops areas, dynamic hysteresis loops areas and hyperthermia of a water solution. For dynamic loops and thermometric measurements, specific experimental setups have been developed, that operate at comparable frequencies (≈ 69kHz and ≈ 100kHz respectively) and rf magnetic field peak values (up to 100mT). The hyperthermia setup has been fully modelled to provide a direct measurement of the SAR of the magnetic nanoparticles by taking into account the heat exchange with the surrounding environment in non-adiabatic conditions and the parasitic heating of the water due to ionic currents.
Dynamic hysteresis loops are shown to provide an accurate determination of the SAR except for superparamagnetic samples, where the boundary with a blocked regime could be crossed in dynamic conditions. Static hysteresis loops consistently underestimate the specific absorption rate but can be used to select the most promising samples.
A means of reliably measure SAR of magnetic nanoparticles by different approaches for hyperthermia applications is presented and its validity discussed by comparing different methods.
This work fits within the general subject of metrological traceability in medicine with a specific focus on magnetic hyperthermia. This article is part of a Special Issue entitled "Recent Advances in Bionanomaterials" Guest Editor: Dr. Marie-Louise Saboungi and Dr. Samuel D. Bader.
•SAR measurementsin non-adiabatic conditions with detailed modelling of the heat exchange mechanisms.•Dynamic hysteresis loops measurements at magnetic field frequency and intensity comparable to hyperthermia conditions.•Measurement of power losses by static loops, dynamic loops and thermal effects on two model systems (Fe3O4, Ni-Zn ferrites).
During the last decade, soundscape research has become one of the most active topics in Acoustics. This work provides a nonlinear variable selection analysis over the well-known dataset ...'emo-soundscapes'. Namely, we provide a selection of the soundscape indicators using a nonlinear and nonparametric model as a regression tree method. Modern techniques (proposed in the literature) have been used, first for ranking the variables and then for choosing the effective number of features. We have also compared and discussed our results with those provided previously in the literature. This study, based on modern techniques in selecting the effective number of variables, confirms the result presented in previous recent work (but based on a linear model) that very parsimonious models should be considered (in the case of a nonlinear model, it is based on very few variables, from 2 to 4, depending on the output). All the results are obtained by analyzing a single dataset. As future research works, we plan to extend our study by also considering alternative datasets.
The evaluation of the biological effects of therapeutic hyperthermia in oncology and the precise quantification of thermal dose, when heating is coupled with radiotherapy or chemotherapy, are active ...fields of research. The reliable measurement of hyperthermia effects on cells and tissues requires a strong control of the delivered power and of the induced temperature rise. To this aim, we have developed a radiofrequency (RF) electromagnetic applicator operating at 434 MHz, specifically engineered for in vitro tests on 3D cell cultures. The applicator has been designed with the aid of an extensive modelling analysis, which combines electromagnetic and thermal simulations. The heating performance of the built prototype has been validated by means of temperature measurements carried out on tissue-mimicking phantoms and aimed at monitoring both spatial and temporal temperature variations. The experimental results demonstrate the capability of the RF applicator to produce a well-focused heating, with the possibility of modulating the duration of the heating transient and controlling the temperature rise in a specific target region, by simply tuning the effectively supplied power.
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