Generalizing the Balance Heuristic Estimator in Multiple Importance Sampling.

In this paper, we propose a novel and generic family of multiple importance sampling estimators. We first revisit the celebrated balance heuristic estimator, a widely used Monte Carlo technique for the approximation of intractable integrals. Then, we establish a generalized framework for the combination of samples simulated from multiple proposals. Our approach is based on considering as free parameters both the sampling rates and the combination coefficients, which are the same in the balance heuristics estimator. Thus our novel framework contains the balance heuristic as a particular case. We study the optimal choice of the free parameters in such a way that the variance of the resulting estimator is minimized. A theoretical variance study shows the optimal solution is always better than the balance heuristic estimator (except in degenerate cases where both are the same). We also give sufficient conditions on the parameter values for the new generalized estimator to be better than the balance heuristic estimator, and one necessary and sufficient condition related to χ2 divergence. Using five numerical examples, we first show the gap in the efficiency of both new and classical balance heuristic estimators, for equal sampling and for several state of the art sampling rates. Then, for these five examples, we find the variances for some notable selection of parameters showing that, for the important case of equal count of samples, our new estimator with an optimal selection of parameters outperforms the classical balance heuristic. Finally, new heuristics are introduced that exploit the theoretical findings.

Risk and outcomes of COVID-19 in patients with multiple sclerosis.

BACKGROUND AND PURPOSE: Limited information is available on incidence and outcomes of COVID-19 in patients with multiple sclerosis (MS). This study investigated the risks of SARS-CoV-2 infection and COVID-19-related outcomes in patients with MS, and compared these with the general population. METHODS: A regional registry was created to collect data on incidence, hospitalization rates, intensive care unit admission, and death in patients with MS and COVID-19. National government outcomes and seroprevalence data were used for comparison. The study was conducted at 14 specialist MS treatment centers in Madrid, Spain, between February and May 2020. RESULTS: Two-hundred nineteen patients were included in the registry, 51 of whom were hospitalized with COVID-19. The mean age ± standard deviation was 45.3 ± 12.4 years, and the mean duration of MS was 11.9 ± 8.9 years. The infection incidence rate was lower in patients with MS than the general population (adjusted incidence rate ratio = 0.78, 95% confidence interval [CI] = 0.70-0.80), but hospitalization rates were higher (relative risk = 5.03, 95% CI = 3.76-6.62). Disease severity was generally low, with only one admission to an intensive care unit and five deaths. Males with MS had higher incidence rates and risk of hospitalization than females. No association was found between the use of any disease-modifying treatment and hospitalization risk. CONCLUSIONS: Patients with MS do not appear to have greater risks of SARS-CoV-2 infection or severe COVID-19 outcomes compared with the general population. The decision to start or continue disease-modifying treatment should be based on a careful risk-benefit assessment.

Stochastic Order and Generalized Weighted Mean Invariance.

In this paper, we present order invariance theoretical results for weighted quasi-arithmetic means of a monotonic series of numbers. The quasi-arithmetic mean, or Kolmogorov-Nagumo mean, generalizes the classical mean and appears in many disciplines, from information theory to physics, from economics to traffic flow. Stochastic orders are defined on weights (or equivalently, discrete probability distributions). They were introduced to study risk in economics and decision theory, and recently have found utility in Monte Carlo techniques and in image processing. We show in this paper that, if two distributions of weights are ordered under first stochastic order, then for any monotonic series of numbers their weighted quasi-arithmetic means share the same order. This means for instance that arithmetic and harmonic mean for two different distributions of weights always have to be aligned if the weights are stochastically ordered, this is, either both means increase or both decrease. We explore the invariance properties when convex (concave) functions define both the quasi-arithmetic mean and the series of numbers, we show its relationship with increasing concave order and increasing convex order, and we observe the important role played by a new defined mirror property of stochastic orders. We also give some applications to entropy and cross-entropy and present an example of multiple importance sampling Monte Carlo technique that illustrates the usefulness and transversality of our approach. Invariance theorems are useful when a system is represented by a set of quasi-arithmetic means and we want to change the distribution of weights so that all means evolve in the same direction.

Damped reaction field method and the accelerated convergence of the real space Ewald summation.

In this paper, we study a general theoretical framework which allows us to approximate the real space Ewald sum by means of effective force shifted screened potentials, together with a self term. Using this strategy it is possible to generalize the reaction field method, as a means to approximate the real space Ewald sum. We show that this method exhibits faster convergence of the Coulomb energy than several schemes proposed recently in the literature while enjoying a much more sound and clear electrostatic significance. In terms of the damping parameter of the screened potential, we are able to identify two clearly distinct regimes of convergence. First, a reaction field regime corresponding to the limit of small screening, where effective pair potentials converge faster than the Ewald sum. Second, an Ewald regime, where the plain real space Ewald sum converges faster. Tuning the screening parameter for optimal convergence occurs essentially at the crossover. The implication is that effective pair potentials are an alternative to the Ewald sum only in those cases where optimization of the convergence error is not possible.

Validating hidden Markov models for seabird behavioural inference.

Understanding animal movement and behaviour can aid spatial planning and inform conservation management. However, it is difficult to directly observe behaviours in remote and hostile terrain such as the marine environment. Different underlying states can be identified from telemetry data using hidden Markov models (HMMs). The inferred states are subsequently associated with different behaviours, using ecological knowledge of the species. However, the inferred behaviours are not typically validated due to difficulty obtaining 'ground truth' behavioural information. We investigate the accuracy of inferred behaviours by considering a unique data set provided by Joint Nature Conservation Committee. The data consist of simultaneous proxy movement tracks of the boat (defined as visual tracks as birds are followed by eye) and seabird behaviour obtained by observers on the boat. We demonstrate that visual tracking data is suitable for our study. Accuracy of HMMs ranging from 71% to 87% during chick-rearing and 54% to 70% during incubation was generally insensitive to model choice, even when AIC values varied substantially across different models. Finally, we show that for foraging, a state of primary interest for conservation purposes, identified missed foraging bouts lasted for only a few seconds. We conclude that HMMs fitted to tracking data have the potential to accurately identify important conservation-relevant behaviours, demonstrated by a comparison in which visual tracking data provide a 'gold standard' of manually classified behaviours to validate against. Confidence in using HMMs for behavioural inference should increase as a result of these findings, but future work is needed to assess the generalisability of the results, and we recommend that, wherever feasible, validation data be collected alongside GPS tracking data to validate model performance. This work has important implications for animal conservation, where the size and location of protected areas are often informed by behaviours identified using HMMs fitted to movement data.

Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study.

The parotid glands are the largest of the major salivary glands. They can harbour both benign and malignant tumours. Preoperative work-up relies on MR images and fine needle aspiration biopsy, but these diagnostic tools have low sensitivity and specificity, often leading to surgery for diagnostic purposes. The aim of this paper is (1) to develop a machine learning algorithm based on MR images characteristics to automatically classify parotid gland tumours and (2) compare its results with the diagnoses of junior and senior radiologists in order to evaluate its utility in routine practice. While automatic algorithms applied to parotid tumours classification have been developed in the past, we believe that our study is one of the first to leverage four different MRI sequences and propose a comparison with clinicians. In this study, we leverage data coming from a cohort of 134 patients treated for benign or malignant parotid tumours. Using radiomics extracted from the MR images of the gland, we train a random forest and a logistic regression to predict the corresponding histopathological subtypes. On the test set, the best results are given by the random forest: we obtain a 0.720 accuracy, a 0.860 specificity, and a 0.720 sensitivity over all histopathological subtypes, with an average AUC of 0.838. When considering the discrimination between benign and malignant tumours, the algorithm results in a 0.760 accuracy and a 0.769 AUC, both on test set. Moreover, the clinical experiment shows that our model helps to improve diagnostic abilities of junior radiologists as their sensitivity and accuracy raised by 6 % when using our proposed method. This algorithm may be useful for training of physicians. Radiomics with a machine learning algorithm may help improve discrimination between benign and malignant parotid tumours, decreasing the need for diagnostic surgery. Further studies are warranted to validate our algorithm for routine use.

[Educational intervention on organ and tissue donation at UMAE No. 34]. / Intervención educativa sobre donación de órganos y tejidos en la UMAE No. 34.

Background: The lack of information associated with donation is devastating for those patients in need of a transplant and requires a solution based on changing social perception through educational interventions. Objective: Determine the level of knowledge of the general population after an educational intervention about organ and tissue donation at the Hospital de Cardiología UMAE No. 34. Methods: Educational intervention study with measurement before and after, prospective. Instrument validated using the Kuder-Richardson formula with a reliability coefficient of 0.74. The study population was made up of the general population in the waiting rooms at UMAE No. 34, only the associations with values of p < 0.05 were considered statistically significant. Results: 266 evaluation instruments were applied to 133 participants. The educational intervention contributed to an increase in the level of knowledge (p = 0.0001). The level of knowledge after the intervention was higher in the younger participants (p = 0.013) and in those with a university studies (p = 0.0001). The relation between age and the level of subsequent knowledge showed favorable significance in the intention to donate in younger participants with high subsequent knowledge (p = 0.046). Conclusions: An educational intervention on donation of organs and tissues for transplant purposes is an effective strategy to increase and reinforce the knowledge of the general population.

Introducción: la falta de información relacionada con la donación de órganos y tejidos resulta devastadora para aquellos pacientes en necesidad de un trasplante, y requiere de una solución basada en el cambio de percepción social mediante intervenciones educativas. Objetivo: determinar el nivel de conocimiento de la población general posterior a una intervención educativa sobre la donación de órganos y tejidos en el Hospital de Cardiología No. 34. Métodos: estudio de intervención educativa con medición antes y después, prospectivo. Instrumento validado mediante fórmula de Kuder-Richardson con coeficiente de fiabilidad de 0.74. La población de estudio se conformó por la población general en las salas de espera de la UMAE No. 34. Las asociaciones con valores de p < 0.05 se consideraron estadísticamente significativas. Resultados: se aplicaron 266 instrumentos de evaluación en 133 participantes. La intervención educativa contribuyó a aumentar el nivel de conocimiento (p = 0.0001). El nivel de conocimiento posterior a la intervención fue mayor en los participantes jóvenes (p = 0.013) y en aquellos con estudios universitarios (p = 0.0001). La relación entre la edad y el nivel de conocimiento posterior mostró significancia favorable en la intención de donación en jóvenes con conocimiento posterior alto (p = 0.046). Conclusiones: una intervención educativa sobre la donación de órganos y tejidos con fines de trasplantes es una estrategia eficaz para aumentar y reforzar el conocimiento de la población general.

Dimethyl fumarate-related immune and transcriptional signature is associated with clinical response in multiple sclerosis-treated patients.

Background and objective: Dimethyl fumarate (DMF) is an immunomodulatory drug approved for the therapy of multiple sclerosis (MS). The identification of response biomarkers to DMF is a necessity in the clinical practice. With this aim, we studied the immunophenotypic and transcriptomic changes produced by DMF in peripheral blood mononuclear cells (PBMCs) and its association with clinical response. Material and methods: PBMCs were obtained from 22 RRMS patients at baseline and 12 months of DMF treatment. Lymphocyte and monocyte subsets, and gene expression were assessed by flow cytometry and next-generation RNA sequencing, respectively. Clinical response was evaluated using the composite measure "no evidence of disease activity" NEDA-3 or "evidence of disease activity" EDA-3 at 2 years, classifying patients into responders (n=15) or non-responders (n=7), respectively. Results: In the whole cohort, DMF produced a decrease in effector (TEM) and central (TCM) memory T cells in both the CD4+ and CD8+ compartments, followed by an increase in CD4+ naïve T cells. Responder patients presented a greater decrease in TEM lymphocytes. In addition, responder patients showed an increase in NK cells and were resistant to the decrease in the intermediate monocytes shown by non-responders. Responder patients also presented differences in 3 subpopulations (NK bright, NK dim and CD8 TCM) at baseline and 4 subpopulations (intermediate monocytes, regulatory T cells, CD4 TCM and CD4 TEMRA) at 12 months. DMF induced a mild transcriptional effect, with only 328 differentially expressed genes (DEGs) after 12 months of treatment. The overall effect was a downregulation of pro-inflammatory genes, chemokines, and activators of the NF-kB pathway. At baseline, no DEGs were found between responders and non-responders. During DMF treatment a differential transcriptomic response was observed, with responders presenting a higher number of DEGs (902 genes) compared to non-responders (189 genes). Conclusions: Responder patients to DMF exhibit differences in monocyte and lymphocyte subpopulations and a distinguishable transcriptomic response compared to non-responders that should be further studied for the validation of biomarkers of treatment response to DMF.

[Donation in the Cardiology Hospital No. 34]. / Donación en el Hospital de Cardiología No. 34.

BACKGROUND: Chronic degenerative diseases have become a challenge for the Mexican health system. Faced with this problem, health institutions have focused on the therapeutic potential of organ and tissue donation and transplantation. OBJECTIVE: To analyze the experience of the donation program and to identify areas of opportunity at the Hospital de Cardiología No. 34 (Hospital of Cardiology Number 34), in Monterrey, Nuevo León, Mexico. MATERIAL AND METHODS: Observational, cross-sectional, and retrospective study. The study population was made up by deaths and successful interviews. Only groupings with values of p < 0.05 were considered statistically significant. RESULTS: A global of 1947 deaths were registered and a total of 210 interviews were carried out; 83 (39.5%) family members accepted to donate and 127 (60.5%) refused to donate. Only 3 associations between variables had significant statistical value. The year was an important determinant in the increase in effective donations (p = 0.010), and so was the month of the year (p = 0.037), obtaining more positive interviews in the second half of the year; finally, the shift also contributed to the family response (p = 0.012), with the morning shift being the best shift to conduct a successful family interview. CONCLUSIONS: It is imperative to conduct studies that analyze and describe the experience of donation programs to promote and encourage the value of donation.

INTRODUCCIÓN: las enfermedades crónico-degenerativas se han convertido en un desafío para el sistema de salud mexicano. Frente a este problema, las instituciones sanitarias se han enfocado en el potencial terapéutico de la donación y el trasplante de órganos y tejidos. OBJETIVO: analizar la experiencia del programa de donación e identificar áreas de oportunidad en el Hospital de Cardiología No. 34, en Monterrey, Nuevo León, México. MATERIAL Y MÉTODOS: estudio observacional, transversal y retrospectivo. La población de estudio se conformó por defunciones y entrevistas exitosas. Únicamente agrupaciones con valores de p < 0.05 se consideraron estadísticamente significativas. RESULTADOS: se registró un global de 1947 defunciones y se efectuaron en total 210 entrevistas; 83 (39.5%) disponentes secundarios aceptaron donar y 127 (60.5%) se negaron. Solo tres asociaciones entre variables tuvieron valor estadístico significativo. El año fue un determinante importante en el incremento de las donaciones efectivas (p = 0.010) y también lo fue el mes del año (p = 0.037), pues se obtuvieron más entrevistas positivas en el segundo semestre del año; finalmente, el turno también contribuyó en la respuesta familiar (p = 0.012) y fue el turno matutino el mejor para hacer una entrevista familiar exitosa. CONCLUSIONES: es imperativo llevar a cabo estudios que analicen y describan la experiencia del programa de donación para promover y fomentar el valor de la donación.

Hierarchical Algorithms for Causality Retrieval in Atrial Fibrillation Intracavitary Electrograms.

Multichannel intracavitary electrograms (EGMs) are acquired at the electrophysiology laboratory to guide radio frequency catheter ablation of patients suffering from atrial fibrillation. These EGMs are used by cardiologists to determine candidate areas for ablation (e.g., areas corresponding to high dominant frequencies or complex fractionated electrograms). In this paper, we introduce two hierarchical algorithms to retrieve the causal interactions among these multiple EGMs. Both algorithms are based on Granger causality, but other causality measures can be easily incorporated. In both cases, they start by selecting a root node, but they differ on the way in which they explore the set of signals to determine their cause-effect relationships: either testing the full set of unexplored signals (GS-CaRe) or performing a local search only among the set of neighbor EGMs (LS-CaRe). The ensuing causal model provides important information about the propagation of the electrical signals inside the atria, uncovering wavefronts and activation patterns that can guide cardiologists towards candidate areas for catheter ablation. Numerical experiments, on both synthetic signals and annotated real-world signals, show the good performance of the two proposed approaches.

Accelerating MCMC algorithms.

Markov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heavy requests on understanding the nature of the target, but it also potentially induces a lengthy exploration of this target, with a requirement on the number of simulations that grows with the dimension of the problem and with the complexity of the data behind it. Several techniques are available toward accelerating the convergence of these Monte Carlo algorithms, either at the exploration level (as in tempering, Hamiltonian Monte Carlo and partly deterministic methods) or at the exploitation level (with Rao-Blackwellization and scalable methods). This article is categorized under: Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC)Algorithms and Computational Methods > AlgorithmsStatistical and Graphical Methods of Data Analysis > Monte Carlo Methods.

Immunophenotype and Transcriptome Profile of Patients With Multiple Sclerosis Treated With Fingolimod: Setting Up a Model for Prediction of Response in a 2-Year Translational Study.

BACKGROUND: Fingolimod is a functional sphingosine-1-phosphate antagonist approved for the treatment of multiple sclerosis (MS). Fingolimod affects lymphocyte subpopulations and regulates gene expression in the lymphocyte transcriptome. Translational studies are necessary to identify cellular and molecular biomarkers that might be used to predict the clinical response to the drug. In MS patients, we aimed to clarify the differential effects of fingolimod on T, B, and natural killer (NK) cell subsets and to identify differentially expressed genes in responders and non-responders (NRs) to treatment. MATERIALS AND METHODS: Samples were obtained from relapsing-remitting multiple sclerosis patients before and 6 months after starting fingolimod. Forty-eight lymphocyte subpopulations were measured by flow cytometry based on surface and intracellular marker analysis. Transcriptome sequencing by next-generation technologies was used to define the gene expression profiling in lymphocytes at the same time points. NEDA-3 (no evidence of disease activity) and NEDA-4 scores were measured for all patients at 1 and 2 years after beginning fingolimod treatment to investigate an association with cellular and molecular characteristics. RESULTS: Fingolimod affects practically all lymphocyte subpopulations and exerts a strong effect on genetic transcription switching toward an anti-inflammatory and antioxidant response. Fingolimod induces a differential effect in lymphocyte subpopulations after 6 months of treatment in responder and NR patients. Patients who achieved a good response to the drug compared to NR patients exhibited higher percentages of NK bright cells and plasmablasts, higher levels of FOXP3, glucose phosphate isomerase, lower levels of FCRL1, and lower Expanded Disability Status Scale at baseline. The combination of these possible markers enabled us to build a probabilistic linear model to predict the clinical response to fingolimod. CONCLUSION: MS patients responsive to fingolimod exhibit a recognizable distribution of lymphocyte subpopulations and a different pretreatment gene expression signature that might be useful as a biomarker.