Inference Error Research Articles

Complete mitochondrial genome of Phoneutria depilata (Araneae, Ctenidae): New insights into the phylogeny and evolution of spiders

Spiders (Araneae) are the most abundant terrestrial predators and megadiverse on earth. In recent years, the mitochondrial genome of a great diversity of species has been sequenced, mainly for ecological and commercial purposes. These studies have uncovered the existence of a variety of mitochondrial genome rearrangements. However, there is poor genetic information in several taxonomic families of spiders. We have sequenced the complete genome of Phoneutria depilata (Ctenidae) and, based on this, extract the mitogenomes of other ctenid species from published transcriptomes to perform a comparative study among spider species to determine the relationship between the level of mitochondrial rearrangements and its possible relationship with molecular variability in spiders. Complete mitochondrial genomes of eighteen spiders (including eight Ctenidae species) were obtained by two different methodologies (sequencing and transcriptome extraction). Fifty-eight spider mitochondrial genomes were downloaded from the NCBI database for gene order analysis. After verifying the annotation of each mitochondrial gene, a phylogenetic and a gene order analysis from 76 spider mitochondrial genomes were carried out. Our results show a high rate of annotation error in the published spider mitochondrial genomes, which could lead to errors in phylogenetic inference. Moreover, to provide new mitochondrial genomes in spiders by two different methodologies to obtain them, our analysis identifies six different mitochondrial architectures among all spiders. Translocation or tandem duplication random loss (TDRL) events in tRNA genes were identified to explain the evolution of the spider mitochondrial genome. In addition, our findings provide new insights into spider mitochondrial evolution.

ADS-B-Based Spatiotemporal Alignment Network for Airport Video Object Segmentation

Video object segmentation (VOS) is the fundamental problem of vision-based intelligent transportation, and many VOS algorithms relying on inference from reference masks have been proposed. Due to the inherent defects of the inference strategy and the complex changes of targets, VOS methods that perform well on public datasets are usually ineffective in airport scenarios. We propose a spatiotemporal alignment network (STA-Net) that makes use of Automatic Dependent Surveillance-Broadcast (ADS-B) data as prior information to guide the long-term segmentation of aircraft. ADS-B is an airport-specific signal, which indicates the location of aircraft in real time. Based on ADS-B, we continuously generate new reference masks instead of using previous masks for inference, which greatly reduces the accumulation of inference errors. To achieve this, previous masks of each aircraft are aligned on the temporal domain based on the position information in ADS-B. All temporally-aligned masks are compared, and the one most similar to the current instant is reserved. This mask is both temporally and spatially aligned; hence it is a better reference mask for inference. Aligned masks are updated every time new ADS-B data arrive, so that they can support long-term inference. With the selected mask as a reference, aircraft of interest are segmented within a unified encoder-decoder framework over the long term. Experiments on a benchmark dataset and in a real airport scenario verify the effectiveness of the presented method.

Online inference of lane changing events for connected and automated vehicle applications with analytical logistic diffusion stochastic differential equation

Inferences of the lane changing behaviors (including lane changing initiation moment LCIM, cross-lane-mark moment CLMM, and lane changing duration LCD) of the surrounding vehicles and taking actions to avoid collision are essential for the safe operation of connected and autonomous vehicles (CAVs). The majority of current models rely on data-driven methods that need to be trained before deployment. Besides, an analytical and stochastic formulation of the lateral model which can generate CLMM and LCD distribution and adjust the parameters online is still lacking. To the best of our knowledge, currently, no method can provide an analytically stochastic lateral movement that explicitly considers the system noise and simultaneously outputs the information of lane changing initiation moment, cross-lane-mark moment, and lane changing duration in a real-time manner. To fill such a gap, a stochastic lateral trajectory framework with parsimonious parameters is established and an online simultaneous inference of LCIM, CLMM, and LCD is developed. The proposed method is tested with the highD and NGSIM datasets. The results show that 1) computational efficiency-wise, the algorithm takes milliseconds to run, which suggests promising prospects for field deployment; 2) the false negative and false positive errors of the LCIM inference are as low as 1.5%, indicating that the method is robust to the stochastic noise; 3) LCIM inference is insensitive to the lane width, which means the model is error tolerant when such information is lacking; and 4) the error of CLMM inference is within 2 sec, while most of the error of LCD locates within 4 sec, suggesting satisfactory performance.

Exploratory hypothesis tests can be more compelling than confirmatory hypothesis tests

ABSTRACT Preregistration has been proposed as a useful method for making a publicly verifiable distinction between confirmatory hypothesis tests, which involve planned tests of ante hoc hypotheses, and exploratory hypothesis tests, which involve unplanned tests of post hoc hypotheses. This distinction is thought to be important because it has been proposed that confirmatory hypothesis tests provide more compelling results (less uncertain, less tentative, less open to bias) than exploratory hypothesis tests. In this article, we challenge this proposition and argue that there are several advantages of exploratory hypothesis tests that can make their results more compelling than those of confirmatory hypothesis tests. We also consider some potential disadvantages of exploratory hypothesis tests and conclude that their advantages can outweigh the disadvantages. We conclude that exploratory hypothesis tests avoid researcher commitment and researcher prophecy biases, reduce the probability of data fraud, are more appropriate in the context of unplanned deviations, facilitate inference to the best explanation, and allow peer reviewers to make additional contributions at the data analysis stage. In contrast, confirmatory hypothesis tests may lead to an inappropriate level of confidence in research conclusions, less appropriate analyses in the context of unplanned deviations, and greater bias and errors in theoretical inferences.

Coded Distributed Computing With Predictive Heterogeneous User Demands: A Learning Auction Approach

Coded distributed computing(CDC) has shown great potentials to solve the unexpected delay caused by stragglers in distributed computing. In this paper, we focus on the auction design for efficient resource allocation in CDC. Specifically, we aim to design a learning auction mechanism to handle heterogeneous user demands and also to free users from the complexity of specifying valuations for resource combinations, which increases exponentially with the resource dimensions. The user demand type is heterogeneous according to different variation trends of the value with finish time and workload, which is modeled by deep learning. The platform would allocate resources according to the user value function. Then users do not need to consider the complex relationship between uncertain finish time and resource configuration in CDC. Due to the inference error of the learning model and the complexity of calculating uncertain finish time, the considered social welfare optimization problem is a non-linear and non-convex integer problem. Even worse, the typical VCG-based payment scheme cannot guarantee truthfulness with the inference error. In response to these difficulties, we transform the social welfare optimization problem into a mixed integer programming problem which already has efficient solutions. The social welfare gap caused by the inference error is analyzed theoretically. The relationship between the utility regret of reporting truthfully and the inference error is also analyzed. We prove that our mechanism satisfies incentive alignment and individual rationality. Extensive experiments show the superiority of our mechanism compared with existing ones.

Efficient and multifidelity terrain modeling for 3D large‐scale and unstructured environments

AbstractThe vast amount of data captured by robots in large‐scale unstructured environments brings computation and storage problems to terrain modeling methods. Considering the practical terrain geometry structures, we design a large‐scale terrain modeling framework for efficiently generating the compact and accurate terrain models from incomplete and uncertain measurements. The multiresolution elevation filtering is developed to generate terrain surfaces that preserve terrain details in the bumpy areas while achieving a sparse representation over the flat areas, which simultaneously reduces the computation time, storage space, and inference error. To further reduce the high computational complexity, we propose a computation decomposition strategy by adopting the Gaussian mixture model to partition the whole terrain surface into several subsurfaces, enabling the method to scale to much larger environments. For each subsurface, we train a Gaussian process model using the local data to adapt to the local terrain structure, concurrently resolving the data incompleteness and uncertainty. Furthermore, to provide a more informative terrain model, we efficiently derive terrain gradient through a closed‐form solution by treating the generated terrain models as continuous functions, enabling more robotics applications, such as the path planning. We perform extensive experiments to evaluate our framework, including the use of two medium‐sized terrain data sets to evaluate the overall and local performance, and two large‐scale glacier terrain and mountain terrain in the area of up to 9 km2 to validate the practical applications.

Quantifying the influence of stimulation protocols on neural network connectivity inference to optimize rapid network measurements.

Connectivity is key to understanding neural circuit computations. However, estimating in vivo connectivity using recording of activity alone is challenging. Issues include common input and bias errors in inference, and limited temporal resolution due to large data requirements. Perturbations (e.g. stimulation) can improve inference accuracy and accelerate estimation. However, optimal stimulation protocols for rapid network estimation are not yet established. Here, we use neural network simulations to identify stimulation protocols that minimize connectivity inference errors when using generalized linear model inference. We find that stimulation parameters that balance excitatory and inhibitory activity minimize inference error. We also show that pairing optimized stimulation with adaptive protocols that choose neurons to stimulate via Bayesian inference may ultimately enable rapid network inference.

Topology Detection in Power Distribution Networks: A PMU Based Deep Learning Approach

This paper proposes a novel data driven framework for detecting topology transitions in a distribution network. The framework analyzes data from phasor measurement units (PMUs) and relies on the fact that changes in network topology results in changes in the structure and admittance of the network. Using voltage and current phasors recorded by PMUs, the proposed method approximates network parameters using an ensemble-based deep learning model and thus, it does not require any knowledge of network parameters and load models. Using the prediction error of the proposed model, a connectivity matrix which shows the status of switches is constructed. In contrast to other methods, this proposed framework does not require a library of voltage and current transients associated with possible network transitions. It can also detect simultaneous switching actions and is robust to noise and load variations. The model yields a lower error detection rate, and its performance is validated using a modified version of the IEEE 33 bus network and a real feeder located in Queensland, Australia, under full and partial observability conditions. The proposed model has also been compared with another data driven method in terms of inference time and error detection rates.

Comparison of Three Methods for Skeleton Age Estimation.

To find the appropriate method for age estimation for different ages and sexes. The costal cartilage, sternum and pubic symphysis of 91 unknowns from 2000 to 2020 from the Forensic Department of the Criminal Investigation Team of Shanghai Public Security Bureau were collected. Costal cartilage, sternal and pubic symphysis inferences were used to estimate the age, and the consistency between the estimated results and the actual physiological age of the unknowns was tested. The accuracy of age estimation of different samples was compared, and the relationship between accuracy and age and sex was analyzed. Using the costal cartilage method, the inference errors of males, females and the whole population under 40 years old were (0.608±2.298) years, (0.429±1.867) years and (0.493±2.040) years, while those over 40 years old were (-1.707±3.770) years, (-3.286±4.078) years and (-2.625±4.029) years. The differences between different age groups in these three populations were statistically significant (P<0.05). Using the sternum method, the inference errors of males and females under the age of 40 were (0.921±3.019) years and (0.452±1.451) years, while those over the age of 40 were (-5.903±5.088) years and (-1.429±2.227) years. The differences between different age groups in males and females were statistically significant (P<0.05). Using the pubic symphysis method, the inference errors of males and females under 40 years old were (-0.204±1.876) years and (0.238±2.477) years, while those over 40 years old were (1.500±2.156) years and (-2.643±4.270) years. The differences between different age groups in males and females were statistically significant (P<0.05). Using the sternum method and pubic symphysis method for age estimation of over 40 years old, the difference between different sexes was statistically significant (P<0.05). All three methods of age estimation are stable and effective and more accurate for people under 40 years old. For age estimation of unknowns over 40 years old, the pubic symphysis method is preferred in males and the sternum method is preferred in females.

Abstract 5031: Improved deconvolution of combined bulk and single-cell RNA-sequencing data

Abstract Identifying how cell types and their abundances evolve during tumor progression is critical to understanding the mechanisms and identifying predictors of metastasis. Single-cell RNA sequencing (scRNA-seq) has been especially promising in resolving heterogeneity of expression programs at the single cell level but is not always available, for example for large cohort studies or longitudinal analysis of archived samples. In such cases, cell subpopulations must be inferred by deconvolution, a process that can infer single-cell genomic data from bulk data but has limited ability to resolve fine clonal structure. We extend our previous bulk genomic deconvolution tool, Robust and Accurate Deconvolution (RAD), to establish a new method, scRAD, that can use reference scRNA-seq to interpret sample collections for which only bulk RNA-seq is available for some samples, e.g., clonally resolving archived primary (PRM) tissues using scRNA-seq from metastases (METs). We preprocess scRNA-seq data to accurately represent gene expression profiles, yielding a signature matrix S then extend our RAD method via a regularization term to deconvolve bulk data while maximizing consistency with S. We validate our method on semi-synthetic data derived from human PRM breast cancer cases and bone and ovary METs, showing that scRAD improves inference of single-cell gene expression profiles and their frequencies relative to the prior RAD with random initialization or initialization using the single-cell matrix S (Table 1). We then apply scRAD to a collection of paired PRM and MET tumors to quantify progression changes in common cell types. One-sided Kaplan-Meier analysis shows that tumors inferred to increase the mast cell fraction from PRM to MET exhibit lower overall survival (p&amp;lt;0.05), consistent with the role of mast cells in metastatic growth and propagation. Tumors that show increased macrophage cell fraction from PRM to MET show improved overall survival (p&amp;lt;0.04), consistent with the role of immune infiltration in survival. mean square error (MSE) of gene expression and mixture fraction inference on semi-simulated data Method RAD with random initialization RAD with random initialization RAD with random initialization RAD initialized with S RAD initialized with S RAD initialized with S scRAD scRAD scRAD Sample number 2 4 8 2 4 8 2 4 8 Gene Expression MSE 0.41 0.28 0.37 0.31 0.27 0.37 0.22 0.15 0.13 Mixture fraction MSE 0.58 0.82 0.59 0.39 0.83 0.58 0.19 0.20 0.22 Citation Format: Haoyun Lei, Xiaoyan A. Guo, Yifeng Tao, Kai Ding, Xuecong Fu, Steffi Oesterreich, Adrian V. Lee, Russell Schwartz. Improved deconvolution of combined bulk and single-cell RNA-sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5031.

Maternal major depression disorder misclassification errors: Remedies for valid individual- and population-level inference.

Individual and population level inference about risk and burden of MDD, particularly maternal MDD, is often made using case‐finding tools that are imperfect and prone to misclassification error (i.e. false positives and negatives). These errors or biases are rarely accounted for and lead to inappropriate clinical decisions, inefficient allocation of scarce resources, and poor planning of maternal MDD prevention and treatment interventions. The argument that the use of existing maternal MDD case‐finding instruments results in misclassification errors is not new; in fact, it has been argued for decades, but by and large its implications and particularly how to correct for these errors for valid inference is unexplored. Correction of the estimates of maternal MDD prevalence, case‐finding tool sensitivity and specificity is possible and should be done to inform valid individual and population‐level inferences.

Micromobility Trip Origin and Destination Inference Using General Bikeshare Feed Specification Data

Emerging micromobility services (e.g., e-scooters) have a great potential to enhance urban mobility but more knowledge on their usage patterns is needed. The General Bikeshare Feed Specification (GBFS) data are a possible source for examining micromobility trip patterns, but efforts are needed to infer trips from the GBFS data. Existing trip inference methods are usually based on the assumption that the vehicle identity (ID) of a micromobility option (e-scooter or e-bike) does not change, and so they cannot deal with data with vehicle IDs that change over time. This paper proposes a comprehensive package of algorithms to infer origin–destination (OD) pairs from GBFS data with static vehicle ID and unlinked trip origins and destinations from GBFS data with resetting and dynamic vehicle ID. The algorithms were implemented in Washington D.C. by analyzing one week (last week of February 2020) of GBFS data published by six vendors, and the inference accuracy of the proposed algorithms are evaluated by R-squared, mean absolute error, and sum absolute error. It is found that the R-squared measure is larger than 0.9 and the MAE measure is less than 2 when the algorithms are evaluated with a 400 m × 400 m grid. The absolute errors are relatively larger in the downtown area, and the inference error is relatively high during early morning and early nighttime. The accuracy of the trip inference algorithms is sufficiently high for most practical applications.

Investigating the reliability of molecular estimates of evolutionary time when substitution rates and speciation rates vary

BackgroundAn accurate timescale of evolutionary history is essential to testing hypotheses about the influence of historical events and processes, and the timescale for evolution is increasingly derived from analysis of DNA sequences. But variation in the rate of molecular evolution complicates the inference of time from DNA. Evidence is growing for numerous factors, such as life history and habitat, that are linked both to the molecular processes of mutation and fixation and to rates of macroevolutionary diversification. However, the most widely used methods rely on idealised models of rate variation, such as the uncorrelated and autocorrelated clocks, and molecular dating methods are rarely tested against complex models of rate change. One relationship that is not accounted for in molecular dating is the potential for interaction between molecular substitution rates and speciation, a relationship that has been supported by empirical studies in a growing number of taxa. If these relationships are as widespread as current evidence suggests, they may have a significant influence on molecular dates.ResultsWe simulate phylogenies and molecular sequences under three different realistic rate variation models—one in which speciation rates and substitution rates both vary but are unlinked, one in which they covary continuously and one punctuated model in which molecular change is concentrated in speciation events, using empirical case studies to parameterise realistic simulations. We test three commonly used “relaxed clock” molecular dating methods against these realistic simulations to explore the degree of error in molecular dates under each model. We find average divergence time inference errors ranging from 12% of node age for the unlinked model when reconstructed under an uncorrelated rate prior using BEAST 2, to up to 91% when sequences evolved under the punctuated model are reconstructed under an autocorrelated prior using PAML.ConclusionsWe demonstrate the potential for substantial errors in molecular dates when both speciation rates and substitution rates vary between lineages. This study highlights the need for tests of molecular dating methods against realistic models of rate variation generated from empirical parameters and known relationships.

Meaning and Purpose: Using Phylogenies to Investigate Human History and Cultural Evolution

Phylogenies are increasingly being used to investigate human history, diversification and cultural evolution. While using phylogenies in this way is not new, new modes of analysis are being applied to inferring history, reconstructing past states, and examining processes of change. Phylogenies have the advantage of providing a way of creating a continuous history of all current populations, and they make a large number of analyses and hypothesis tests possible even when other forms of historical information are patchy or nonexistent. In common with approaches taken in other historical sciences, phylogenetics is a way of reconstructing past and processes using the traces left in the present day. Trees, based on DNA, language, cultural traits, or other evidence, are now sprouting all over the academic landscape. The increasing use of phylogenetic analysis to understand human cultural evolution has been embraced by some, and scorned by others. The purpose of this article is not to review methods and applications of phylogenetic analyses, nor to consider the growing field of cultural phylogenetics, but, more broadly, to explore how we interpret phylogenies as narratives about human diversification. The first half of the article deals with meaning: phylogenies are often interpreted as histories, but a bifurcating tree is at best an abstract representation of history, and its connections to past events and processes is dependent on the data used, the assumptions made in the analysis, and the degree to which nodes in the tree (where one lineage splits into two) can be connected to change and movement in real populations. The second half of the article explores the purpose of phylogenies: a tree does not have to be a literal history of human lineages in order to be useful for investigating processes of human diversification. Phylogenies should not be read as accurate records of history, but as a way of exploring plausible explanations for current patterns of diversity. Phylogenies provide important information that can be used to test ideas about human diversity, and can help to guard against errors of inference arising from statistical artifacts.

On Natural and Transcendental Illusions in a Kantian-Pragmatist Philosophical Anthropology

Abstract The covid-19 pandemic and the increasingly polarized political situation in many countries today have highlighted the significance of various humanly natural intellectual mistakes, cognitive biases, and widespread inferential errors. This essay examines, at a philosophical meta-level, the relation between our natural epistemic errors and the kind of humanly unavoidable transcendental illusion analyzed by Immanuel Kant in the Transcendental Dialectic of the First Critique. While both kinds of illusion are usually primarily discussed in an epistemological context, my approach is not exclusively epistemic. Rather, my main argument extends the broadly Kantian critique of reason – as a critical via negativa focusing on what is epistemically “wrong” about us, as the kind of cognitive agents we human beings naturally are – into a pragmatically enriched investigation of “the negative” as constitutive of the human condition, that is, our lack of sufficiently critical epistemic and ethical capacities, including our propensity to both cognitive error and moral evil. Our being inescapably inclined to manifest such negativities in our epistemic and ethical lives, and our insufficient awareness of doing so, are, arguably, “transcendental facts” about us, to be critically examined in terms of a Kantian-cum-pragmatist philosophical anthropology. This significantly enriches our picture of transcendental philosophy.

Bayesian identification of structural coefficients in causal models and the causal false-positive risk of confounders and colliders in linear Markovian models

BackgroundCausal inference has seen an increasing popularity in medical research. Estimation of causal effects from observational data allows to draw conclusions from data when randomized controlled trials cannot be conducted. Although the identification of structural causal models (SCM) and the calculation of structural coefficients has received much attention, a key requirement for valid causal inference is that conclusions are drawn based on the true data-generating model.MethodsIt remains widely unknown how large the probability is to reject the true structural causal model when observational data from it is sampled. The latter probability – the causal false-positive risk – is crucial, as rejection of the true causal model can induce bias in the estimation of causal effects. In this paper, the widely used causal models of confounders and colliders are studied regarding their causal false-positive risk in linear Markovian models. A simulation study is carried out which investigates the causal false-positive risk in Gaussian linear Markovian models. Therefore, the testable implications of the DAG corresponding to confounders and colliders are analyzed from a Bayesian perspective. Furthermore, the induced bias in estimating the structural coefficients and causal effects is studied.ResultsResults show that the false-positive risk of rejecting a true SCM of even simple building blocks like confounders and colliders is substantial. Importantly, estimation of average, direct and indirect causal effects can become strongly biased if a true model is rejected. The causal false-positive risk may thus serve as an indicator or proxy for the induced bias.ConclusionWhile the identification of structural coefficients and testable implications of causal models have been studied rigorously in the literature, this paper shows that causal inference also must develop new concepts for controlling the causal false-positive risk. Although a high risk cannot be equated with a substantial bias, it is indicative of the induced bias. The latter fact calls for the development of more advanced risk measures for committing a causal type I error in causal inference.

Premature commitment to uncertain decisions during human NMDA receptor hypofunction

Making accurate decisions based on unreliable sensory evidence requires cognitive inference. Dysfunction of n-methyl-d-aspartate (NMDA) receptors impairs the integration of noisy input in theoretical models of neural circuits, but whether and how this synaptic alteration impairs human inference and confidence during uncertain decisions remains unknown. Here we use placebo-controlled infusions of ketamine to characterize the causal effect of human NMDA receptor hypofunction on cognitive inference and its neural correlates. At the behavioral level, ketamine triggers inference errors and elevated decision uncertainty. At the neural level, ketamine is associated with imbalanced coding of evidence and premature response preparation in electroencephalographic (EEG) activity. Through computational modeling of inference and confidence, we propose that this specific pattern of behavioral and neural impairments reflects an early commitment to inaccurate decisions, which aims at resolving the abnormal uncertainty generated by NMDA receptor hypofunction.

An Opportunistic Power Control Scheme for Mitigating User Location Tracking Attacks in Cellular Networks

Cellular networks have been successfully evolved over the decades. Especially, Long-Term Evolution (LTE) has been exceedingly successful and the security threats against LTE systems have increased rapidly. Particularly, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tracking</i> LTE user devices has been shown to be effective as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">temporary</i> user identifiers (IDs) are easily extracted and used to locate targeted devices by passive eavesdroppers. We notice that naive approaches, such as frequent updates of temporary user IDs, are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">insufficient</i> to mitigate user-tracking attacks since the new and old temporary IDs for the same user device are easily <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">linkable</i> by adversaries who can measure the wireless channel characteristics between the user device and herself. In this paper, we propose an opportunistic uplink power control scheme to minimize the probability of successful user tracking by an adversary whose location is unknown. We devise the notion of average inference error probability in order to measure the level of users’ location privacy. Moreover, we derive the closed-form expression of the approximated average inference error probability and formulate an optimization problem to maximize the average inference error probability under a constraint of an allowable power budget for each user. Against a passive adversary, our proposed power control scheme effectively degrades an adversary’s inference ability by 50% when 10 users are scheduled in each transmission time slot, which will lead to almost 100% inference error at the adversary over multiple time slots.

Strong replica symmetry for high-dimensional disordered log-concave Gibbs measures

Abstract We consider a generic class of log-concave, possibly random, (Gibbs) measures. We prove the concentration of an infinite family of order parameters called multioverlaps. Because they completely parametrize the quenched Gibbs measure of the system, this implies a simple representation of the asymptotic Gibbs measures, as well as the decoupling of the variables in a strong sense. These results may prove themselves useful in several contexts. In particular in machine learning and high-dimensional inference, log-concave measures appear in convex empirical risk minimization, maximum a-posteriori inference or M-estimation. We believe that they may be applicable in establishing some type of ‘replica symmetric formulas’ for the free energy, inference or generalization error in such settings.

Prevalence, classification and factors associated with hyponatremia in patients hospitalized in the clinical service of Hospital José Carrasco Arteaga, Cuenca, 2019

BACKGROUND: Hyponatremia is the most frequent hydroelectrolytic disorder; it has multiple etiologies, and it is important to study in order to define an adequate treatment. Thus the aim of this study is to determine the prevalence, classification and factors associated with hyponatremia in patients hospitalized in the clinical service of Hospital José Carrasco Arteaga. METHODS: an observational, cross-sectional, analytical study was carried out with a sample of 347 patients, calculated with 95% confidence, 10% prevalence, inference error of 3%, in a population of 1 426 patients. Data was obtained from the institutional medical system (AS400) and analyzed with PSPP software. To determine the prevalence, hyponatremia was considered with sodium less than 135mEq/l, which was corrected according to glucose value. We calculated the prevalence ratio with a 95% confidence interval, statistical significance was estimated when p value was &lt;0.05. RESULTS: the average age was 62 years, the female sex represented 53.3% of the sample. The prevalence of hyponatremia was 25.9%. According to severity, mild forms represented 72.22%, moderate 16.67% and severe 11.11%; in terms of osmolarity, 98.89% were hypotonic. Associated factors with hyponatremia were: decompensated hypothyroidism (PR: 3.39, 95% CI = 2.42 - 4.75, p= &lt; 0.001), liver cirrhosis (PR: 3.18, 95% CI = 2.25 - 4.48, p= &lt; 0.001), kidney failure (PR: 2.46, 95% CI = 1.70 - 3.55, p= &lt; 0.001), disorders that produce third space: sepsis or pancreatitis (PR: 2.00, 95% CI = 1.29 - 3.09, p = 0.006) and use of diuretics (PR: 1.86, 95% CI = 1.29 - 2.68, p = 0.001). CONCLUSION: a quarter of the hospitalized patients presented hyponatremia, the mild form was the most frequent. Hyponatremia had a statistically significant association with decompensated hypothyroidism, liver cirrhosis, kidney failure, disorders that produce third space and use of diuretics.