Hierarchical Model Research Articles

Monitoring monthly mortality of maricultured Atlantic salmon (Salmo salar L.) in Scotland II. A hierarchical dynamic linear model

The sustainability of the salmon farming industry is being challenged by increased mortality rates. Scotland’s open-source salmon production data provides the possibility of developing an industry-wide mortality monitoring model, valuable for identifying and addressing unexpected increases in mortality without needing data sharing agreements across different companies. This study aimed to utilize these data to develop a hierarchical dynamic linear model (DLM) for monitoring monthly mortality of maricultured Atlantic salmon in Scotland. We evaluated whether considering the hierarchical structure present in the data (country, region, and site) would improve mortality predictions when compared to the production cycle level DLMs developed in a previous study. Our findings demonstrated that the hierarchical DLM outperformed the production cycle level DLMs, confirming the value of this more complex modelling approach. Nevertheless, the hierarchical model, like the production cycle level DLMs, exhibited some uncertainty in the mortality predictions. When mortality is higher than expected, site level warnings are generated, which can encourage producers and inspectors to further investigate the cause. Between 2015 and 2020, approximately 25% of the production cycles and 50% of the sites encountered at least one warning, with most warnings happening in the summer and autumn months. Additionally, the hierarchical model enabled monitoring mortality at multiple levels. This information is useful for various stakeholders as part of a monitoring system, offering insights into mortality trends at national, regional, and sites levels that may benefit from strategic resource management. Recommendations for model improvements include utilizing shorter data aggregation periods, such as weekly, which are not currently available as open-source data.

Swarming Insects May Have Finely Tuned Characteristic Reynolds Numbers

Over the last few years, there has been much effort put into the development and validation of stochastic models of the trajectories of swarming insects. These models typically assume that the positions and velocities of swarming insects can be represented by continuous jointly Markovian processes. These models are first-order autoregressive processes. In more sophisticated models, second-order autoregressive processes, the positions, velocities, and accelerations of swarming insects are collectively Markovian. Although it is mathematically conceivable that this hierarchy of stochastic models could be extended to higher orders, here I show that such a procedure would not be well-based biologically because some terms in these models represent processes that have the potential to destabilize insect flight dynamics. This prediction is supported by an analysis of pre-existing data for laboratory swarms of the non-biting midge Chironomus riparius. I suggest that the Reynolds number is a finely tuned property of swarming, as swarms may disintegrate at both sufficiently low and sufficiently high Reynolds numbers.

Mortality from type 2 diabetes mellitus across municipalities in Mexico

BackgroundOne in six Mexican adults’ lives with type 2 diabetes mellitus (T2DM), which is the third leading cause of death in the country. Analyzing the geographic distribution of T2DM mortality helps identify regions with higher mortality rates. This study aimed to examine the spatial patterns of mortality from type 2 diabetes mellitus (T2DM) across municipalities in Mexico and to analyze the main contextual factors linked to this cause of death in 2020.MethodsWe employed a spatial Bayesian hierarchical regression model to estimate the risk and probability of death from type 2 diabetes mellitus (T2DM) across Mexico’s municipalities.ResultsThe SMR results revealed geographic and age-specific patterns. Central Mexico and the Yucatán Peninsula exhibited the highest excess mortality rates. For the population under 50 years of age, municipalities in Oaxaca had the highest T2DM mortality rates, whereas those aged 50 years old and older had the highest rates in Tlaxcala and Puebla. Socioeconomic factors such as low levels of educational attainment, lack of health services, dietary deficiency, and marginalization were positively associated with increased T2DM mortality risk. By contrast, GDP per capita showed a negative association. High-risk areas for T2DM mortality were prominent along the south of the Pacific Coast, the Bajío, Central Mexico, and southern Yucatán for those under 50, and along a central strip extending to the Yucatán Peninsula for the older population. Significant uncertainties in mortality risk were identified, with Central Mexico, Oaxaca, Chiapas, and Tabasco showing high probabilities of excess risk for those under 50 years of age and extended risk areas along the Gulf of Mexico for those 50 years old and older.ConclusionsThe assessment and identification of spatial distribution patterns associated with T2DM mortality, and its main contextual factors, are crucial for informing effective public health policies aimed at reducing the impact of this chronic disease in Mexico.

Family communication and courageous coping in Korean adolescents and young adults: a cross-sectional study.

Adolescents and young adults (AYAs) with cancer are a vulnerable population during a critical developmental transition that can benefit from the adoption of courageous coping. Parental support is crucial in enhancing adjustment and coping skills. The linkage between parent-adolescent communication (PAC) and the use of courageous coping (UCC), however, remains unclear. This study examined the association between PAC and UCC and possible mediators of this relationship among Korean AYAs with cancer. In this cross-sectional, correlational study, self-report data were collected from 144 AYAs aged 11-26 years. A path analytic approach was employed using a hierarchical regression model to test for the direct relationship between PAC and UCC and the indirect effects of a proposed primary mediator (family cohesion) and two intermediary mediators (uncertainty of illness, hope) on PAC-UCC relationship. The mean age of the sample was 17 years (SD = 3.8), with 42% currently receiving cancer treatment. Greater PAC was significantly associated with increased UCC (mother figure: p = .0024, father figure: p = .0042). Increased family cohesion significantly mediated the PAC-UCC relationship, indicated by a diminished PAC-UCC relationship after controlling for family cohesion (mother: p = .2753; father: p = .8107). Mediated mediation models indicated that increased hope stemming from decreased uncertainty was the mechanism through which family cohesion impacted the PAC-UCC relationship. Findings underscore that greater PAC can facilitate UCC among Korean AYAs with cancer and provide insights for therapeutic parent-child communication. Results further demonstrate the complex mediating role of fostering family cohesion, reducing uncertainty, and fostering hope can play in the PAC-UCC relationship.

Prevalence and correlates of driving under the influence of alcohol and cannabis among Spanish adolescents.

Driving under the influence (DUI) of substances is the first cause of death among young populations. Unfortunately, there is a paucity of research looking at DUI risk factors in adolescents. This study aimed to estimate the past-year prevalence and correlates of DUI of alcohol (DUI-A), cannabis (DUI-C), and both (DUI-A+C) in adolescents who reported past-year alcohol and cannabis use. The study sample comprised 3,175 [(47.9 % females; Mage (SD) = 16.76 (.70)] Spanish adolescents from a national representative survey (ESTUDES) conducted by the Spanish Ministry of Health. Hierarchical regression models were conducted to identify correlates of DUI-A only, DUI-C only, and both behaviors, including sociodemographic, substance use and parental control variables. Past-year prevalence of DUI was 9.9 % among past-year alcohol and cannabis users. Past-year DUI of substances was more likely among males (15.4 %) relative to females (6 %) [χ2 = 73.39, p = <.001, Φ = .152]. Being male, reporting higher past-month days of cannabis use and cannabis-related problems were common risk factors for DUI-C and DUI-A+C. Risk factors of DUI-A were greater money availability for going out, higher past-month frequency of heavy drinking episodes and lower past-month cannabis use days. Specific correlates of DUI-A+C were being 18, past-year simultaneous alcohol and cannabis use, and earlier age of alcohol use initiation. There is a need to address DUI in substance use prevention in school settings. Screening should be particularly focused on adolescent substance users, while interventions should target attitudes and risk of substance use and driving.

Curious Choices: Infants' moment-to-moment information sampling is driven by their exploration history

Infants explore the world around them based on their intrinsically motivated curiosity. However, the cognitive mechanisms underlying such curiosity-driven exploratory behaviour remain largely unknown. Here, infants could freely explore two novel categories, triggering a new exemplar from a category by fixating on either of the two associated areas on a computer screen. This gaze-contingent design enabled us to distinguish between exploration – switching from one category to another – and exploitation – consecutively triggering exemplars from the same category. Data from 10 to 12-month-old infants (N = 68) indicated that moment-to-moment sampling choices were non-random but guided by the infants' exploration history. Self-generated sequences grouped into three clusters of brief yet explorative, longer exploitative, and overall more balanced sampling patterns. Bayesian hierarchical binomial regression models indicated that across sequence patterns, infants' longer trigger time, shorter looking time, and more gaze-shifting were associated with trial-by-trial decisions to disengage from exploiting one category and making an exploratory switch, especially after consecutively viewed stimuli of high similarity. These findings offer novel insights into infants' curiosity-driven exploration and pave the way for future investigations, also regarding individual differences.

Psychosocial Work Factors, Job Stress, and Self-Rated Health Among Hotel Housekeepers.

Hotel housekeeping is widely recognized as a poor-quality job due to its high demands and limited resources. Hotel housekeepers (HHs) face both hard physical work and mentally demanding conditions, yet psychosocial factors in this feminized and precarious occupation remain under-researched. To address this gap, this study examines HHs' exposure to psychosocial factors at work and their impact on job stress and self-rated health. A cross-sectional survey of a random sample of 926 HHs in the Balearic Islands (Spain) assessed job stress, self-rated health, psychosocial factors (job demands and resources), and sociodemographic variables using the Copenhagen Psychosocial Questionnaire II (COPSOQ-II) and the National Health Survey. Descriptive analysis and hierarchical linear regression models were applied. The prevalence of job stress was 61.1% (95% confidence interval [CI] = [57.8%, 64.1%]), while the prevalence of poor self-rated health was 59.9% (95% CI = [56.6%, 62.9%]). Hotel housekeepers were highly exposed to job demands such as intense work pace, job-specific stressors, work-life conflict, and emotional demands; highly available job resources were role clarity, task meaning, and social support. Regression models revealed work pace, work-life conflict, nationality, and weak leader support as key predictors of job stress; and work-life conflict and leadership quality as key predictors of self-rated health. Although considered an eminently physical job, psychosocial work factors play a key role in explaining HHs' job stress and self-rated health. Occupational health professionals should design workplace interventions to reduce work pace, mitigate work-life conflict, and enhance resources such as leader support, sense of community, and leadership quality.

Zelenirstat Inhibits N-Myristoyltransferases to Disrupt Src Family Kinase Signalling and Oxidative Phosphorylation Killing Acute Myeloid Leukemia Cells.

Acute myeloid leukemia (AML) is a hematological malignancy with limited treatment options and a high likelihood of recurrence after chemotherapy. We studied N-myristoylation, the myristate modification of proteins linked to survival signaling and metabolism, as a potential therapeutic target for AML. N-myristoylation is catalyzed by two N-myristoyltransferases (NMTs), NMT1 and NMT2, with varying expressions in AML cell lines and patient samples. We identified NMT2 expression as a marker for AML patient survival, and low NMT2 expression was associated with poor outcomes. We used the first-in-class pan-NMT inhibitor, zelenirstat, to investigate the role of N-myristoylation in AML. Zelenirstat effectively inhibits myristoylation in AML cell lines and patient samples, leading to degradation of Src family kinases (SFKs), induction of endoplasmic reticulum (ER) stress, apoptosis, and cell death. Zelenirstat was well tolerated in vivo and reduced the leukemic burden in an ectopic AML cell line and in multiple orthotopic AML patient-derived xenograft models. The leukemia stem cell (LSC)-enriched fractions of the hierarchical OCI-AML22 model were highly sensitive to myristoylation inhibition. Zelenirstat also impairs mitochondrial complex I and oxidative phosphorylation, which are critical for LSC survival. These findings suggest that targeting N-myristoylation with zelenirstat represents a novel therapeutic approach for AML, with promise in patients with currently poor outcomes.

Spatio-temporal data fusion for the analysis of in situ and remote sensing data using the INLA-SPDE approach

We propose a Bayesian hierarchical model to address the challenge of spatial misalignment in spatio-temporal data obtained from in situ and satellite sources. The model is fit using the INLA-SPDE approach, which provides efficient computation. Our methodology combines the different data sources in a “fusion” model via the construction of projection matrices in both spatial and temporal domains. Through simulation studies, we demonstrate that the fusion model has superior performance in prediction accuracy across space and time compared to standalone “in situ” and “satellite” models based on only in situ or satellite data, respectively. The fusion model also generally outperforms the standalone models in terms of parameter inference. Such a modeling approach is motivated by environmental problems, and our specific focus is on the analysis and prediction of harmful algae bloom (HAB) events, where the convention is to conduct separate analyses based on either in situ samples or satellite images. A real data analysis shows that the proposed model is a necessary step towards a unified characterization of bloom dynamics and identifying the key drivers of HAB events.

Psychometric Analysis and Structural Invariance of the Life Orientation Test-Revised (LOT-R) Across Genders in a Latin American Sample

Dispositional optimism is associated with various health and well-being outcomes, underscoring the importance of having valid assessment tools. The Life Orientation Test-Revised (LOT-R) is widely used for this purpose, but its factorial structure is still debated. This study aimed to validate the LOT-R’s factorial model and assess its Spanish version’s psychometric properties with a sample of 687 participants. Bayesian and DWLS Confirmatory factor analysis favored a Hierarchical model and a Bifactor MC model. All items showed good discrimination and correlation. The optimism factor had a reliability of 0.72, while the pessimism factor was 0.6. The criterion validity of the two factors was established through correlation analysis with the PANAS scale. Metric invariance across gender was not achieved, and items 4 and 5 exhibited differential item functioning (DIF), suggesting that gender differences on this scale may be attributed to variations in item interpretation rather than true differences in the underlying construct. The factorial structure of the LOT-R indicates the presence of a general factor encompassing both optimism and pessimism. While the optimism-related items exhibit strong psychometric properties and can be reliably used as a standalone factor, the pessimism-related items fail to meet the required reliability thresholds for independent use.

Studying the Implementation of Equity Projects in Science Education in Divisive Political Contexts

ABSTRACTThe implementation of equity‐oriented reforms is never simply a technical matter: it involves directly engaging with the norms and politics responsible for reproducing inequitable opportunities and outcomes, and with efforts to promote educational justice. To date, there has been little research on how leaders in science education navigate the political environments of schooling to engage in equity work in their local contexts. The current political divisions within and among states regarding teaching about racial equity provide an important and timely context for such study. This study examines equity projects that science education leaders engage in and how these relate to recently passed legislation in several states regarding teaching about race and its ongoing role in shaping American society and institutions. It relies on survey data from science education leaders in 33 states, focusing on their familiarity with and involvement in different kinds of equity projects in science education, along with their perceptions of what supports and what hinders their equity‐focused work. Employing a mixed methods approach, including descriptive analysis, hierarchical linear modeling, and thematic analysis, we found that engagement in equity projects varied widely across and within states. However, in states with laws promoting equity, leaders were engaged in more racial equity‐related projects than in states with legislation that restrict discussion of matters of race, so‐called “gag orders”. These findings underscore the significance of the broader political environments in shaping science education leadership and present opportunities for researchers in the field to support leaders in navigating them.

Estimation of HIV prevalence at the ZIP code-level in Atlanta, Georgia: Bayesian prediction modeling using passive surveillance data and social determinants of disease spreading

ObjectiveThis study aims to predict the number of undiagnosed HIV cases at the ZIP Code-level in Atlanta, Georgia, based on publicly available information. Study designStatistical modeling. MethodsWe fitted a Bayesian hierarchical Binomial model to county-level estimates of the passive-surveillance-system. The denominator was the true total HIV cases arising from a Negative Binomial distribution. The trial probability, known as ascertainment probability, depended on socio-economic determinants of HIV, retained via feature-selection algorithms. Data were obtained from CDC's HIV report for End of the HIV Epidemic and the American Community Survey. The prediction model was assessed out-of-sample in Georgia counties. We combined socio-economic data with the posterior predictive distribution of the coefficients to predict the mean ascertainment probability and total HIV cases at the ZIP Code-level. These estimates were spatially smoothed and aggregated at the county-level for secondary validations. ResultsThe county-level model showed good mixing properties and predictive accuracy. The mean ascertainment probability calibrated to the ZIP Code-level varied from 78.4% (95% credible interval: 24.4%–99.3%) to 93.8% (95%CI: 80.6%–99.8%). Further, the predicted undiagnosed HIV cases ranged between 12 (95%CI: 6–19; ZIP Code 30322) to 1603 (95%CI 1209–1968; ZIP Code 30318). ConclusionsOur findings provide a more complete picture of the relative burden of HIV across ZIP codes. Such information can be used by Local Health Departments to identify underserved areas and allocate resources accordingly. Furthermore, our methodological approach can be applied to complement the information obtained from passive surveillance, especially when more resource-intensive approaches are not available or are unfeasible to employ.

Evaluating parental experiences in teaching children with learning disabilities in resource rooms of public schools in Amman

BackgroundThe education of children with learning disabilities in public schools’ resource rooms remains a vital area of concern, particularly in Amman, where diverse educational needs and varying levels of resource allocation pose significant challenges. Despite the establishment of resource rooms, there is a notable lack of comprehensive data on parental satisfaction with these services. Previous research has indicated that the effectiveness of such educational settings largely depends on the quality of instruction, the adequacy of educational resources, and the extent of parental involvement and satisfaction. However, there is a significant gap in understanding how these factors specifically impact parents’ perceptions in the context of Amman’s public schools.AimThis study aimed to address this gap by evaluating the perspectives of parents of students with learning disabilities in resource rooms within Amman’s public schools, focusing on their satisfaction with educational services, including instruction quality, resource adequacy, and facility accessibility.MethodsA cross-sectional study design was utilized, involving a structured questionnaire administered to 200 parents of children attending resource rooms in 33 public schools across Amman. The questionnaire included Likert-scale questions and demographic queries to assess parents’ perspectives on the educational services provided. Data were analyzed using SPSS version 27 to compute descriptive statistics, correlation coefficients, and hierarchical linear modeling (HLM) to account for clustering effects.ResultsThe majority of respondents were female (60%, n = 120), and the largest age group was 31–40 years (45%, n = 90). Most parents held a Bachelor’s degree (50%, n = 100) and were employed (75%, n = 150). The predominant age group of the children was 11–15 years (50%, n = 100), with dyslexia being the most common type of learning disability (40%, n = 80). Parents rated the quality of instruction at a mean of 3.5 (SD = 1.2), adequacy of educational materials at 3.3 (SD = 1.1), and overall satisfaction with the program at 3.6 (SD = 1.0). Accessibility of facilities was rated lower at 2.8 (SD = 1.3). This interpretation is based on the lower mean score, which indicates a less favorable perception of accessibility, and the relatively high standard deviation, which reflects variability in parental experiences. HLM analysis revealed significant positive relationships between quality of instruction (β = 0.75, SE = 0.05, t = 15.00, p &amp;lt; 0.01), adequacy of educational materials (β = 0.70, SE = 0.04, t = 17.50, p &amp;lt; 0.01), and accessibility of facilities (β = 0.65, SE = 0.06, t = 10.83, p &amp;lt; 0.01) with overall parental satisfaction.ConclusionThe study underscores the importance of high-quality instruction, sufficient educational materials, and improved facility accessibility in enhancing parental satisfaction with resource room programs in Amman. This conclusion is supported by the findings from both the hierarchical regression analysis and the descriptive statistics, demonstrating that these factors are crucial for the effective support of children with learning disabilities.

Factors Associated with Dietary Diversity in Community-Dwelling Brazilian Older Adults.

Older populations are at high risk of nutritional inadequacy and monotonous diets, and assessing dietary diversity can be a practical measure to indicate groups at nutritional risk. We aimed to explore the dietary diversity of older adults enrolled in primary health care services in Brazil and to evaluate its associated factors. In this cross-sectional study, we evaluated the dietary diversity score (DDS) of 581 participants (≥60 years) registered in primary care services. All foods mentioned in a 24 h food recall were classified into 10 groups, and factors associated with the DDS were analyzed using hierarchical linear regression models in two blocks: (1) sociodemographic and (2) health conditions and lifestyle. The mean DDS was 5.07 (±1.34), and 67.5% of the sample reached the minimum dietary diversity (≥5 groups). In the final model, income, previous diagnosis of cancer, and sporadic intake of alcohol were positively associated with DDS. In contrast, cognitive decline, sedentary lifestyle, and anorexia of aging were negatively associated with DDS. These findings show that the entire structural, economic, and social system needs to facilitate access to quality food, adequate places and conditions for the practice of physical activity, and policies regarding tobacco and alcohol abuse, in addition to nutritional guidance.

Do gender quotas increase political knowledge?

AbstractDo gender quotas increase political knowledge? While some studies suggest that quotas can positively impact women's political engagement and participation, others find null or negative effects. This paper focuses on Western Europe and argues that the implementation of quotas serves as an attention and consciousness‐raising event, potentially enhancing awareness of the political sphere. To investigate this, I propose a novel research design that capitalizes on the (quasi) exogenous shock resulting from the introduction of gender quotas. By influencing symbolic representation, quotas may enhance women's sense of empowerment, equality and willingness to engage in politics. Furthermore, the impact is expected to be more pronounced among younger women due to the heightened political socialization experienced during adolescence. Thus, the institutional change brought about by quotas is anticipated to particularly boost political knowledge among (younger) women and subsequently narrow the gender gap. To examine this hypothesis, I analyse data from 1992 to 2018 from 12 countries, of which six implemented gender quotas. Using a hierarchical Bayesian model, I assess respondents' answers to knowledge questions. The findings indicate that the introduction of gender quotas in parliamentary systems has a positive effect on reducing the gender gap in political knowledge among younger individuals, while the effects are statistically insignificant for older citizens.

Priority-Elastic net for binary disease outcome prediction based on multi-omics data

BackgroundHigh-dimensional omics data integration has emerged as a prominent avenue within the healthcare industry, presenting substantial potential to improve predictive models. However, the data integration process faces several challenges, including data heterogeneity, priority sequence in which data blocks are prioritized for rendering predictive information contained in multiple blocks, assessing the flow of information from one omics level to the other and multicollinearity.MethodsWe propose the Priority-Elastic net algorithm, a hierarchical regression method extending Priority-Lasso for the binary logistic regression model by incorporating a priority order for blocks of variables while fitting Elastic-net models sequentially for each block. The fitted values from each step are then used as an offset in the subsequent step. Additionally, we considered the adaptive elastic-net penalty within our priority framework to compare the results.ResultsThe Priority-Elastic net and Priority-Adaptive Elastic net algorithms were evaluated on a brain tumor dataset available from The Cancer Genome Atlas (TCGA), accounting for transcriptomics, proteomics, and clinical information measured over two glioma types: Lower-grade glioma (LGG) and glioblastoma (GBM).ConclusionOur findings suggest that the Priority-Elastic net is a highly advantageous choice for a wide range of applications. It offers moderate computational complexity, flexibility in integrating prior knowledge while introducing a hierarchical modeling perspective, and, importantly, improved stability and accuracy in predictions, making it superior to the other methods discussed. This evolution marks a significant step forward in predictive modeling, offering a sophisticated tool for navigating the complexities of multi-omics datasets in pursuit of precision medicine’s ultimate goal: personalized treatment optimization based on a comprehensive array of patient-specific data. This framework can be generalized to time-to-event, Cox proportional hazards regression and multicategorical outcomes. A practical implementation of this method is available upon request in R script, complete with an example to facilitate its application.

High-Dimensional Bayesian Semiparametric Models for Small Samples: A Principled Approach to the Analysis of Cytokine Expression Data.

In laboratory medicine, due to the lack of sample availability and resources, measurements of many quantities of interest are commonly collected over a few samples, making statistical inference particularly challenging. In this context, several hypotheses can be tested, and studies are not often powered accordingly. We present a semiparametric Bayesian approach to effectively test multiple hypotheses applied to an experiment that aims to identify cytokines involved in Crohn's disease (CD) infection that may be ongoing in multiple tissues. We assume that the positive correlation commonly observed between cytokines is caused by latent groups of effects, which in turn result from a common cause. These clusters are effectively modeled through a Dirichlet Process (DP) that is one of the most popular choices as nonparametric prior in Bayesian statistics and has been proven to be a powerful tool for model-based clustering. We use a spike-slab distribution as the base measure of the DP. The nonparametric part has been included in an additive model whose parametric component is a Bayesian hierarchical model. We include simulations that empirically demonstrate the effectiveness of the proposed testing procedure in settings that mimic our application's sample size and data structure. Our CD data analysis shows strong evidence of a cytokine gradient in the external intestinaltissue.

Application of Multi-Input Multi-Output Brain-Computer Interface in Cognitive Assessment for Alzheimer’s Disease

This research aims to augment human cognitive abilities, particularly memory function, through the development of innovative prosthetics. The hippocampus, a crucial brain region involved in memory encoding and recall, has been the focal point of numerous studies seeking to develop prosthetics that can restore or enhance memory function in individuals with impairments such as Alzheimer’s disease. This review examines two studies that delve into the development and application of hippocampal neural prosthetics, highlighting the potential of these prosthetics to revolutionize the treatment of memory impairments. One study utilized static neural stimulation patterns to enhance memory for specific information content, while another developed a nonlinear Multiple Input Multiple Output (MIMO) dynamic model to predict and stimulate neural activity patterns during memory encoding. Both studies demonstrated significant improvements in memory performance. Additionally, this paper discusses the clinical significance and application prospects of hippocampal neural prosthetics, including the potential for implantable prosthetics and the widespread applicability of MIMO stimulation. Furthermore, the paper explores advanced modeling and classification techniques for neural ensembles and visual memory decoding, including Volterra-type hierarchical modeling, optimal multi-unit stimulation patterns, and a sparse classification model for decoding visual memories. Finally, the paper presents the implementation and evaluation of neural prosthesis systems, including an application-specific integrated circuit (ASIC) design for a hippocampal prosthesis and a high-performance, scalable system architecture for real-time estimation of neural activity. These advancements hold promise for future research and applications in neural prosthetics for memory enhancement.

Modeling Groundwater Levels in California's Central Valley by Hierarchical Gaussian Process and Neural Network Regression

AbstractModeling groundwater levels continuously across California's Central Valley (CV) hydrological system is challenging due to low‐quality well data which is sparsely and noisily sampled across time and space. The lack of consistent well data makes it difficult to evaluate the impact of 2017 and 2019 wet years on CV groundwater following a severe drought during 2012–2015. A novel machine learning method is formulated for modeling groundwater levels by learning from a 3D lithological texture model of the CV aquifer. The proposed formulation performs multivariate regression by combining Gaussian processes (GP) and deep neural networks (DNN). The hierarchical modeling approach constitutes training the DNN to learn a lithologically informed latent space where non‐parametric regression with GP is performed. We demonstrate the efficacy of GP‐DNN regression for modeling non‐stationary features in the well data with fast and reliable uncertainty quantification, as validated to be statistically consistent with the empirical data distribution from 90 blind wells across CV. We show how the model predictions may be used to supplement hydrological understanding of aquifer responses in basins with irregular well data. Our results indicate that on average the 2017 and 2019 wet years in California were largely ineffective in replenishing the groundwater loss caused during previous drought years.

Leveraging synthetic aperture radar (SAR) with the National Water Model (NWM) to improve above-normal flow prediction in ungauged basins

Abstract Effective flood prediction supports developing proactive risk management strategies, but its application in ungauged basins faces tremendous challenges due to limited/no streamflow record. This study investigates the potential for integrating streamflow derived from synthetic aperture radar (SAR) data and U.S. National Water Model (NWM) reanalysis estimates to develop improved predictions of above-normal flow (ANF) over the coterminous US. Leveraging the SAR data from the Global Flood Detection System to estimate the antecedent conditions using principal component regression, we apply the spatial-temporal hierarchical model (STHM) using NWM outputs for improving ANF prediction. Our evaluation shows promising results with the integrated model, STHM-SAR, significantly improving NWE, especially in 60% of the sites in the coastal region. Spatial and temporal validations underscore the model’s robustness, with SAR data contributing to explained variance by 24% on average. This approach not only improves NWM prediction, but also uniquely combines existing remote sensing data with national-scale predictions, showcasing its potential to improve hydrological modeling, particularly in regions with limited stream gauges.