Latent Process Research Articles

Chronic latent inflammatory processes of reproductive organs of dairy cows

The purpose of a long-term scientific and industrial study was to determine the prevalence of the symptom complex of chronic inflammatory processes of the organs of the reproductive tract with latent flow among dairy cows (n = 1159) of various dairy breeds kept in the conditions of industrial complexes. Research methods. An innovative methodological approach of complex in vivo gynecological examination was applied using echography (“Tringa vet”) and differential palpation diagnostics with a recording of data on anatomical areas of the reproductive tract “walls and cavity of the uterus – horns of the uterus – fallopian tubes and their potential patency – ovaries”. Based on the results of a comprehensive examination of unfertilized dairy cows, it was established that, on average, chronic latent endometritis was diagnosed in 52.95 ± 12.96 % of females, with fluctuations of 32.15–85.84 % in different herds (P < 0.05). A natural trend of increasing cases of chronic adhesive processes with loss of patency for gametes through the fallopian tube in the tissues in the “fallopian tube-ovary” anatomical region due to the increase in the number of chronic latent endometritis in the herd, namely the average number of salpingitis and ovosalpingitis 13.26 ± 2.27, was established. %, fluctuations ranged from 9.2 to 18.27 %. The use of an innovative lateralized approach to the palpation diagnostics of chronic adhesive lesions of the oviducts allowed for improvement in the prognosis of the effectiveness of the therapy of chronic salpingitis provided that bilateral lesions were detected, which characterize the irreversible infertility of the cow (37.93 ± 9.35 % of cases among all examined cows). The application of differential palpation diagnostics of the ovaries with the use of in vivo data visualization revealed the spread of hypotrophic lesions of the ovaries together with the course of chronic inflammatory pathologies of the uterine tissues (latent endometritis): 54.69 ± 19.80 % of cows were diagnosed with significantly pronounced hypotrophic processes in the functional tissues of the gonads, and 34.87 ± 19.59 % had moderate lesions. Experimental studies have shown a reliable trend of correlations between the spread of chronic latent inflammatory processes in various parts of the reproductive tract of dairy cows and the growth of clinical manifestations of hypotrophic phenomena in ovarian tissues, which gradually lose their generative and hormone-secreting function due to a decrease in cell trophism. The study results showed the need for further comprehensive analysis of the clinical condition of the organs of the reproductive system of dairy cows in vivo conditions.

Spectrum and frequency of non-motor disorders in Parkinson’s patients in the Kursk Region

Introduction. Parkinson's disease (PD) is characterized by a long period of latent neurodegenerative process. Some patients experience non-motor manifestations in the form of autonomic (constipation, dizziness, fainting, and sleep, smell, and urination disorders), mental (anxiety and depression), and cognitive disorders that occur several years before the onset of motor symptoms [1]. Purpose of the study: to determine the spectrum and frequency of non-motor disorders in patients with Parkinson’s disease in the Kursk Region, as well as to assess the degree of their severit y. Materials and method. We examined 32 patients (18 women and 14 men) with an established diagnosis of PD (mixed type), who were treated at the Kursk Regional Multidisciplinary Clinical Hospital. The age of the patients ranged from 59 to 76 years. The mean age was 69.03±5.71 years, and the duration of the disease was 6.93±8.08 years. Non-motor symptoms and their severity were assessed using the UPDRS (Part I) and the NMSS-PD. Sleep disturbances were assessed according to the Epworth Sleepiness Scale (ESS). The Hospital Anxiety and Depression Scale (HADS) was used to study the neuropsychological status of the patients. Cognitive status was assessed according to the MMSE, Frontal Assessment Battery, and the clock drawing test. Results. Non-motor symptoms occurred in 87.5 % of the patients in the study population and are an equally significant part of the PD clinical picture. These manifestations are most often represented by autonomic (87.5 %), mental (68.7 %), and cognitive (72 %) disorders, which have a different spectrum and frequency in this group of patients. Conclusion. Non-motor manifestations of PD require special attention from a neurologist. The clinical picture of such disorders is varied. Early detection and adequate correction of non-motor disorders contributes to the main goal of PD therapy — to improve the qualit y of life of patients.

Simultaneous maintenance of emotions in affective working memory

ABSTRACT Affective Working Memory (AWM) is the ability to maintain an emotion after the emotion-eliciting stimulus is no longer present. Emotions are dynamic, and emotion-eliciting stimuli are encountered simultaneously and sequentially. Therefore, this research aimed to examine AWM when more than one emotion is being maintained. We aimed to re-examine previous findings, that people are better at maintaining positive than negative emotions in the context of dynamic presentations of multiple stimuli. We introduce a modified maintenance task, and present a novel metric that models the latent maintenance processes to acquire an accurate measure of AWM. Participants (N = 49) were asked to complete the study online. On each trial, participants were presented with a sequence of three images, and were asked to compare the intensity of the emotion elicited by image 1 to image 3, whilst maintaining the emotion elicited by image 2 to rate it at the end of the trial. The results showed that people are successful at simultaneously maintaining two emotions in AWM, and they replicate previous findings concerning the advantage of maintaining positive compared to negative emotions. Overall, the study highlights the complexity of AWM and provides insight into the processes involved in maintaining multiple emotions simultaneously.

A Bayesian survival treed hazards model using latent Gaussian processes.

Survival models are used to analyze time-to-event data in a variety of disciplines. Proportional hazard models provide interpretable parameter estimates, but proportional hazard assumptions are not always appropriate. Non-parametric models are more flexible but often lack a clear inferential framework. We propose a Bayesian treed hazards partition model that is both flexible and inferential. Inference is obtained through the posterior tree structure and flexibility is preserved by modeling the log-hazard function in each partition using a latent Gaussian process. An efficient reversible jump Markov chain Monte Carlo algorithm is accomplished by marginalizing the parameters in each partition element via a Laplace approximation. Consistency properties for the estimator are established. The method can be used to help determine subgroups as well as prognostic and/or predictive biomarkers in time-to-event data. The method is compared with some existing methods on simulated data and a liver cirrhosis dataset.

Natural and anthropogenic impacts on mercury accumulation in Xiaohai Lagoon, South China over the last 1200 years

Anthropogenic use and release of mercury (Hg) have profoundly affected the global Hg cycle since preindustrial times. However, it is often difficult to quantify the relative contributions of natural and anthropogenic factors to environmental Hg accumulation. Here, we have presented a 1200-year record of Hg deposition in a sediment core from the Xiaohai Lagoon (South China), in combination with multiple environmental indicators (e.g., geochemical elements, grain size and total organic carbon, etc.). Using principal component analysis (PCA) and stepwise regression analysis (SRA), we aimed to explore the latent processes governing the accumulation of Hg over time and to quantitatively assess the natural and anthropogenic impacts on Hg deposition over the last millennium in Xiaohai Lagoon. Our results have demonstrated that between ∼ 870 and ∼ 1860 CE, natural factors were the main drivers controlling Hg concentrations in the lagoon. These were directly driven by higher soil erosion and increased inputs of fine-grained matter. However, from 1860 to 2013 CE, enhanced anthropogenic activities played a significant role in Hg accumulation in Xiaohai Lagoon. Anthropogenic Hg fluxes increased significantly from ∼ 1860 CE, peaked several times during the 1860s to the 1950s, accelerated from the late 1950s to the early 2000s, and then declined gradually owing to the stringent environmental protection strategies and efficient pollutant control technologies. Our results have suggested that the increased anthropogenic Hg inputs between the 1860s and mid-1970s were mostly attributed to wars, the “Westernization Movement”, and global industrial activity, with a surge mainly after the 1980s dominated by industrial activities in China and numerous developing countries in Southeast Asia. This study has shown the natural and anthropogenic influences associated with mercury pollution through quantitative analysis and can deepen our understanding of the processes and mechanisms of mercury deposition in natural environments under the influence of human activities.

Conditional indirect effects of clarity on students’ information processing: disentangling sources of cognitive load

ABSTRACT The effect of instructor clarity on student learning has been explained using cognitive load theory, which stipulates that students have limited mental resources to devote to activities pertaining to learning. To date, the effect of teacher clarity on students’ cognitive burden has been studied in reference to students’ extraneous cognitive load as clear teachers design their instruction to be straightforward and free from distraction. However, few instructional communication studies have examined how clarity’s impact on extraneous cognitive load interacts with the intrinsic difficulty of students’ course lessons to conditionally impact students’ learning processes. This study utilized data from 221 students to estimate a latent first-stage conditional process model with teacher clarity predicting students’ deep processing of course material through students’ receiver apprehension, dependent upon the intrinsic difficulty of the course material (despite teaching). Results indicated that as teachers were generally clearer during instruction, students more deeply processed their course material because they experienced less receiver apprehension. However, this indirect effect was greater when students had more working memory availability (i.e., lower intrinsic cognitive load controlling for clear teaching).

Impairment in recognition memory may be associated with near-term risk for suicide attempt in a high-risk sample

IntroductionPrior work has implicated several neurocognitive domains, including memory, in patients with a history of prior suicide attempt. The current study evaluated whether a delayed recognition test could enhance prospective prediction of near-term suicide outcomes in a sample of patients at high-risk for suicide. Methods132 Veterans at high-risk for suicide completed a computer-based recognition memory test including semantically-related and -unrelated words. Outcomes were coded as actual suicide attempt (ASA), other suicide-related event (OtherSE) such as aborted/interrupted attempt or preparatory behavior, or neither (noSE), within 90 days after testing. ResultsReduced performance was a significant predictor of upcoming ASA, but not OtherSE, after controlling for standard clinical variables such as current suicidal ideation and history of prior suicide attempt. However, compared to the noSE reference group, the OtherSE group showed a reduction in the expected benefit of semantic relatedness in recognizing familiar words. A computational model, the drift diffusion model (DDM), to explore latent cognitive processes, revealed the OtherSE group had decreased decisional efficiency for semantically-related compared to semantically-unrelated familiar words. LimitationsThis study was a secondary analysis of an existing dataset, involving participants in a treatment trial, and requires replication; ~10 % of the sample was excluded from analysis due to failure to master the practice tasks and/or apparent noncompliance. ConclusionImpairments in recognition memory may be associated with near-term risk for suicide attempt, and may provide a tool to improve prediction of when at-risk individuals may be transitioning into a period of heightened risk for suicide attempt.

A Continuous Volatility Forecasting Model Based on Neural Differential Equations and Scale-Similarity.

Volatility forecasting is a problem in finance that attracts the attention of both academia and industry. While existing approaches typically utilize a discrete-time latent process that governs the volatility to forecast its future level, volatility is considered to evolve continuously, which makes discrete-time modeling inevitably lose some critical information about the evolution of volatility. In this article, a novel neural-network-based model, Continuous Volatility Forecasting Model, CVFM is proposed to tackle this problem. First, CVFM introduces a continuous-time latent process, whose evolution is modeled with neural differential equations (NDEs), to govern volatility, which effectively captures the continuous evolutionary behavior of volatility in a data-driven way. Second, a scale-similarity-based mechanism is designed to calibrate the evolution equation of the latent process with real-world observations in the absence of high-frequency data. CVFM is tested on six real-world stock index datasets. The main experimental results show that CVFM can significantly outperform existing models in terms of both forecasting accuracy and high-volatility recognition.

An efficient data-driven approximation to the stochastic differential equations with non-global Lipschitz coefficient and multiplicative noise

<abstract><p>This paper studied the numerical approximation of the stochastic differential equations driven by non-global Lipschitz drift coefficient and multiplicative noise. An efficient data-driven method, called extended continuous latent process flow, was proposed for the underlying problem. Compared with the piecewise construction of a variational posterior process used in the classical continuous latent process flow developed by Deng et al. <sup>[<xref ref-type="bibr" rid="b13">13</xref>]</sup>, the principle idea of our method was to derive a variational lower bound by constructing a posterior latent process conditional on all information over the whole time interval to maximize the log-likelihood generated by the observations, which reduces the computational cost and, thus, provides a convenient way to approximate the considered equation. Particularly, our new method showed a better approximation to the underlying equation than the classical drift-$ \theta $ discretization scheme through numerical error comparison. Numerical experiments were finally reported to demonstrate the effectiveness and generalization performance of the proposed method.</p></abstract>

Adapting Few-shot Classification via In-process Defense.

Most few-shot learning methods employ either adaptive approaches or parameter amortization techniques. However, their reliance on pre-trained models presents a significant vulnerability. When an attacker's trigger activates a hidden backdoor, it may result in the misclassification of images, profoundly affecting the model's performance. In our research, we explore adaptive defenses against backdoor attacks for few-shot learning. We introduce a specialized stochastic process tailored to task characteristics that safeguards the classification model against attack-induced incorrect feature extraction. This process functions during forward propagation and is thus termed an "in-process defense." Our method employs an adaptive strategy, effectively generating task-level representations, enabling rapid adaptation to pre-trained models, and proving effective in few-shot classification scenarios for countering backdoor attacks. We apply latent stochastic processes to approximate task distributions and derive task-level representations from the support set. This task-level representation guides feature extraction, leading to backdoor trigger mismatching and forming the foundation of our parameter defense strategy. Benchmark tests on Meta-Dataset reveal that our approach not only withstands backdoor attacks but also shows an improved adaptation in addressing few-shot classification tasks.

A spatio-temporal model of multi-marker antimalarial resistance.

The emergence and spread of drug-resistant Plasmodium falciparum parasites have hindered efforts to eliminate malaria. Monitoring the spread of drug resistance is vital, as drug resistance can lead to widespread treatment failure. We develop a Bayesian model to produce spatio-temporal maps that depict the spread of drug resistance, and apply our methods for the antimalarial sulfadoxine-pyrimethamine. We infer from genetic count data the prevalences over space and time of various malaria parasite haplotypes associated with drug resistance. Previous work has focused on inferring the prevalence of individual molecular markers. In reality, combinations of mutations at multiple markers confer varying degrees of drug resistance to the parasite, indicating that multiple markers should be modelled together. However, the reporting of genetic count data is often inconsistent as some studies report haplotype counts, whereas some studies report mutation counts of individual markers separately. In response, we introduce a latent multinomial Gaussian process model to handle partially reported spatio-temporal count data. As drug-resistant mutations are often used as a proxy for treatment efficacy, point estimates from our spatio-temporal maps can help inform antimalarial drug policies, whereas the uncertainties from our maps can help with optimizing sampling strategies for future monitoring of drug resistance.

Understanding Psoriasis as a Biobehavioral Network Disease

Psoriasis is a common immune-mediated inflammatory disease that can often be associated with psychiatric problems such as depression and anxiety. Although psychiatric disorders were initially considered secondary, the high prevalence suggests that common pathophysiological mechanisms may be involved in the development of psoriasis and psychiatric disorders. The shared neuroendocrine and immune mediators weave a web of networks with bidirectional pathways. Biopsychosocial patterns of psoriasis include psychological and behavioral consequences influencing personal social networks, psychological dispositions, and brain-skin psychoimmunological network patterns, which sums in a network of networks. The pathodynamics of other organ diseases like diabetes, liver diseases, internal organ tumors, and latent long-term inflammatory processes (chronic tonsillitis, prostatitis, abscesses and inflammations in the gums, chronic sinusitis) influence psoriasis. Vice versa, psoriasis might cause a pathological impact on other organ systems via networked connections, like arthritis or psychological dispositions. Treatment of psoriasis needs networking through the cooperation of dermatology, rheumatology, and psychiatry and by combining different therapies.

Latent Gaussian Processes Based Graph Learning for Urban Traffic Prediction

Latent Gaussian Processes Based Graph Learning for Urban Traffic Prediction

Latent Autoregressive Student-t Prior Process Models to Assess Impact of Interventions in Time Series

With the advent of the “Internet of Things” (IoT), insurers are increasingly leveraging remote sensor technology in the development of novel insurance products and risk management programs. For example, Hartford Steam Boiler’s (HSB) IoT freeze loss program uses IoT temperature sensors to monitor indoor temperatures in locations at high risk of water-pipe burst (freeze loss) with the goal of reducing insurances losses via real-time monitoring of the temperature data streams. In the event these monitoring systems detect a potentially risky temperature environment, an alert is sent to the end-insured (business manager, tenant, maintenance staff, etc.), prompting them to take remedial action by raising temperatures. In the event that an alert is sent and freeze loss occurs, the firm is not liable for any damages incurred by the event. For the program to be effective, there must be a reliable method of verifying if customers took appropriate corrective action after receiving an alert. Due to the program’s scale, direct follow up via text or phone calls is not possible for every alert event. In addition, direct feedback from customers is not necessarily reliable. In this paper, we propose the use of a non-linear, auto-regressive time series model, coupled with the time series intervention analysis method known as causal impact, to directly evaluate whether or not a customer took action directly from IoT temperature streams. Our method offers several distinct advantages over other methods as it is (a) readily scalable with continued program growth, (b) entirely automated, and (c) inherently less biased than human labelers or direct customer response. We demonstrate the efficacy of our method using a sample of actual freeze alert events from the freeze loss program.

EXPLORING LINKS BETWEEN COGNITIVE AGING AND FEATURES OF WORKING MEMORY VIA COMPUTATIONAL MODELING

Abstract With an aging population, understanding cognitive aging is essential for developing interventions to support independence and well-being. Additionally, there is a need for sensitive measures that can differentiate subtle cognitive decline related to neurodegenerative disease from age-related changes. In particular, Alzheimer’s Disease and Related Dementia (ADRD) increases in prevalence with age and involves progressive cognitive decline. The Color Shapes task (CST) is a visual working memory task that has been shown to be sensitive to early indicators for ADRD. CST involves feature binding (e.g., mental representation of features into a unified whole) that is impaired in people with ADRD. In an experiment, we manipulated different CST properties to study whether drift diffusion modeling (DDM) can account for individual differences in latent decision-making processes generating manifest CST performance scores. DDM allows us to quantify theoretically meaningful cognitive parameters that underlie task performance captured in reaction times and accuracies. 68 U.S. community-residing adults (28-80 years) completed daily smartphone assessments across 8 days with experimentally manipulated versions of the CST. We implemented a single-step Bayesian estimation of experimental conditions contrasts, latent DDM parameters, and their associations. We show how individual differences in experimental contrasts were related to age in terms of cognitive (drift rate), non-cognitive (non-decision time), and meta-cognitive (boundary separation) parameters. The results highlight the benefits of using cognitive process models with optimized cognitive tasks to delineate features of cognitive aging for ADRD research.

Fitting latent non-Gaussian models using variational Bayes and Laplace approximations

–Latent Gaussian models (LGMs) are perhaps the most commonly used class of models in statistical applications. Nevertheless, in areas ranging from longitudinal studies in biostatistics to geostatistics, it is easy to find datasets that contain inherently non-Gaussian features, such as sudden jumps or spikes, that adversely affect the inferences and predictions made using an LGM. These datasets require more general latent non-Gaussian models (LnGMs) that can handle automatically these non-Gaussian features. However, fast implementation and easy-to-use software are lacking, preventing the broad applicability of LnGMs. In this paper, we derive variational Bayes algorithms for fast and scalable inference of LnGMs. The approximation leads to an LGM that downweights extreme events in the latent process, reducing their influence and leading to more robust inferences. It can be applied to a wide range of models, such as autoregressive processes for time series, simultaneous autoregressive models for areal data, and spatial Matérn models. To facilitate Bayesian inference, we introduce the ngvb package, where LGMs implemented in R-INLA can be easily extended to LnGMs by adding a single line of code.

Mixtures of factor analysers with censored responses and external covariates: An application to educational data from Peru.

Analysing data from educational tests allows governments to make decisions for improving the quality of life of individuals in a society. One of the key responsibilities of statisticians is to develop models that provide decision-makers with pertinent information about the latent process that educational tests seek to represent. Mixtures of factor analysers (MtFA) have emerged as a powerful device for model-based clustering and classification of high-dimensional data containing one or several groups of observations with fatter tails or anomalous outliers. This paper considers an extension of MtFA for robust clustering of censored data, referred to as the MtFAC model, by incorporating external covariates. The enhanced flexibility of including covariates in MtFAC enables cluster-specific multivariate regression analysis of dependent variables with censored responses arising from upper and/or lower detection limits of experimental equipment. An alternating expectation conditional maximization (AECM) algorithm is developed for maximum likelihood estimation of the proposed model. Two simulation experiments are conducted to examine the effectiveness of the techniques presented. Furthermore, the proposed methodology is applied to Peruvian data from the 2007 Early Grade Reading Assessment, and the results obtained from the analysis provide new insights regarding the reading skills of Peruvian students.

Recent Opioid Use Impedes Range Adaptation in Reinforcement Learning in Human Addiction

BackgroundDrugs like opioids are potent reinforcers thought to co-opt value-based decisions by overshadowing other rewarding outcomes, but how this happens at a neurocomputational level remains elusive. Range adaptation is a canonical process of fine-tuning representations of value based on reward context. Here, we tested whether recent opioid exposure impacts range adaptation in opioid use disorder, potentially explaining why shifting decision making away from drug taking during this vulnerable period is so difficult. MethodsParticipants who had recently (<90 days) used opioids (n = 34) or who had abstained from opioid use for ≥ 90 days (n = 20) and comparison control participants (n = 44) completed a reinforcement learning task designed to induce robust contextual modulation of value. Two models were used to assess the latent process that participants engaged while making their decisions: 1) a Range model that dynamically tracks context and 2) a standard Absolute model that assumes stationary, objective encoding of value. ResultsControl participants and ≥90-days-abstinent participants with opioid use disorder exhibited choice patterns consistent with range-adapted valuation. In contrast, participants with recent opioid use were more prone to learn and encode value on an absolute scale. Computational modeling confirmed the behavior of most control participants and ≥90-days-abstinent participants with opioid use disorder (75%), but a minority in the recent use group (38%), was better fit by the Range model than the Absolute model. Furthermore, the degree to which participants relied on range adaptation correlated with duration of continuous abstinence and subjective craving/withdrawal. ConclusionsReduced context adaptation to available rewards could explain difficulty deciding about smaller (typically nondrug) rewards in the aftermath of drug exposure.

Mixed-Variable Global Sensitivity Analysis for Knowledge Discovery and Efficient Combinatorial Materials Design

Abstract Global Sensitivity Analysis (GSA) is the study of the influence of any given input on the outputs of a model. In the context of engineering design, GSA has been widely used to understand both individual and collective contributions of design variables on the design objectives. So far, global sensitivity studies have often been limited to design spaces with only quantitative (numerical) design variables. However, many engineering systems also contain, if not only, qualitative (categorical) design variables in addition to quantitative design variables. In this paper, we integrate Latent Variable Gaussian Process (LVGP) with Sobol’ analysis to develop the first metamodel-based mixed-variable GSA method. Through numerical case studies, we validate and demonstrate the effectiveness of our proposed method for mixed-variable problems. Furthermore, while the proposed GSA method is general enough to benefit various engineering design applications, we integrate it with multi-objective Bayesian optimization (BO) to create a sensitivity-aware design framework in accelerating the Pareto front design exploration for metal-organic framework (MOF) materials with many-level combinatorial design spaces. Although MOFs are constructed only from qualitative variables that are notoriously difficult to design, our method can utilize sensitivity analysis to navigate the optimization in the many-level large combinatorial design space, greatly expediting the exploration of novel MOF candidates.

Computational modelling of reinforcement learning and functional neuroimaging of probabilistic reversal for dissociating compulsive behaviours in gambling and cocaine use disorders.

Individuals with cocaine use disorder or gambling disorder demonstrate impairments in cognitive flexibility: the ability to adapt to changes in the environment. Flexibility is commonly assessed in a laboratory setting using probabilistic reversal learning, which involves reinforcement learning, the process by which feedback from the environment is used to adjust behavior. It is poorly understood whether impairments in flexibility differ between individuals with cocaine use and gambling disorders, and how this is instantiated by the brain. We applied computational modelling methods to gain a deeper mechanistic explanation of the latent processes underlying cognitive flexibility across two disorders of compulsivity. We present a re-analysis of probabilistic reversal data from individuals with either gambling disorder (n = 18) or cocaine use disorder (n = 20) and control participants (n = 18), using a hierarchical Bayesian approach. Furthermore, we relate behavioural findings to their underlying neural substrates through an analysis of task-based functional magnetic resonanceimaging (fMRI) data. We observed lower 'stimulus stickiness' in gambling disorder, and report differences in tracking expected values in individuals with gambling disorder compared to controls, with greater activity during reward expected value tracking in the cingulate gyrus and amygdala. In cocaine use disorder, we observed lower responses to positive punishment prediction errors and greater activity following negative punishment prediction errors in the superior frontal gyrus compared to controls. Using a computational approach, we show that individuals with gambling disorder and cocaine use disorder differed in their perseverative tendencies and in how they tracked value neurally, which has implications for psychiatric classification.