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Heavy-tailed continuous shrinkage priors, such as the horseshoe prior, are widely used for sparse estimation problems. However, there is limited work extending these priors to explicitly incorporate multivariate shrinkage for regressors with grouping structures. Of particular interest in this article, is regression coefficient estimation where pockets of high collinearity in the regressor space are contained within known regressor groupings. To assuage variance inflation due to multicollinearity we propose the group inverse-gamma gamma (GIGG) prior, a heavy-tailed prior that can trade-off between local and group shrinkage in a data adaptive fashion. A special case of the GIGG prior is the group horseshoe prior, whose shrinkage profile is dependent within-group such that the regression coefficients marginally have exact horseshoe regularization. We establish posterior consistency and posterior concentration results for regression coefficients in linear models and mean parameters in sparse normal means models. The full conditional distributions corresponding to GIGG regression can be derived in closed form, leading to straightforward posterior computation. We show that GIGG regression results in low mean-squared error across a wide range of correlation structures and within-group signal densities via simulation. We apply GIGG regression to data from the National Health and Nutrition Examination Survey for associating environmental exposures with liver functionality. [ABSTRACT FROM AUTHOR]

Sparse structure learning in high‐dimensional Gaussian graphical models is an important problem in multivariate statistical inference, since the sparsity pattern naturally encodes the conditional independence relationship among variables. However, maximum a posteriori (MAP) estimation is challenging under hierarchical prior models, and traditional numerical optimization routines or expectation–maximization algorithms are difficult to implement. To this end, our contribution is a novel local linear approximation scheme that circumvents this issue using a very simple computational algorithm. Most importantly, the condition under which our algorithm is guaranteed to converge to the MAP estimate is explicitly stated and is shown to cover a broad class of completely monotone priors, including the graphical horseshoe. Further, the resulting MAP estimate is shown to be sparse and consistent in the ℓ2$$ {\ell}_2 $$‐norm. Numerical results validate the speed, scalability and statistical performance of the proposed method. [ABSTRACT FROM AUTHOR]

Our goal is to provide a review of deep learning methods which provide insight into structured high-dimensional data. Merging the two cultures of algorithmic and statistical learning sheds light on model construction and improved prediction and inference, leveraging the duality and trade-off between the two. Prediction, interpolation, and uncertainty quantification can be achieved using probabilistic methods at the output layer of the model. Rather than using shallow additive architectures common to most statistical models, deep learning uses layers of semi-affine input transformations to provide a predictive rule. Applying these layers of transformations leads to a set of attributes (or, features) to which probabilistic statistical methods can be applied. Thus, the best of both worlds can be achieved: scalable prediction rules fortified with uncertainty quantification where sparse regularization finds the features. We review the duality between shallow and wide models such as principal components regression, and partial least squares and deep but skinny architectures such as autoencoders, multilayer perceptrons, convolutional neural net, and recurrent neural net. The connection with data transformations is of practical importance for finding good network architectures. By incorporating probabilistic components at the output level, the predictive uncertainty is allowed. We illustrate this idea by comparing plain Gaussian processes (GP) with partial least squares + Gaussian process (PLS + GP) and deep learning + Gaussian process (DL + GP). [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Avoidance of poison ivy plants is currently the primary approach to prevent the estimated 30–50 million annual poison ivy skin rash cases. The "leaves of three let it be" mnemonic device lacks specificity to differentiate poison ivy from other three‐leaflet native plants. This report demonstrated that poison ivy leaves show marked total leaf shape variability that likely confounds accurate poison ivy plant identification, and significantly undermines a poison ivy avoidance strategy for mitigating poison ivy rash cases. Therefore, there is an ongoing need to develop prophylactic medical procedures to prevent poison ivy rash that do not depend on human poison ivy plant identification. Summary: Urushiol is the natural product produced by poison ivy (Toxicodendron radicans) that is responsible for millions of cases of delayed contact allergenic dermatitis in North America each year. Avoidance of poison ivy plant material is the clinically recommended strategy for preventing urushiol‐induced dermatitis symptoms. However, poison ivy leaf shape is anecdotally notoriously variable, thereby confounding accurate poison ivy identification. This study focused on quantitative analyses of poison ivy and a common poison ivy look‐alike (American hog peanut) leaf shape variability in North America to empirically validate the high degree of poison ivy leaf shape plasticity.Poison ivy and American hog peanut iNaturalist.org records were scored for seven attributes of compound leaf shape that were combined to produce a total leaf complexity score. Both the mean and the distribution of poison ivy total leaf complexity scores were significantly greater than that of American hog peanut. Non‐metric multidimensional scaling analyses corroborated a high degree of poison ivy leaf shape variability relative to American hog peanut. A poison ivy accession producing frequent palmate penta‐leaflet compound leaves was evaluated using linear regression modeling.Poison ivy total leaf complexity was exceedingly variable across a given latitude or longitude. With that said, there was a small but significant trend of poison ivy total leaf complexity increasing from east to west. Palmate penta‐leaflet formation was significantly correlated with a stochastic leaflet deep‐lobing developmental process in one unusual poison ivy accession.The empirically‐validated poison ivy leaf shape hypervariability described in this report likely confounds accurate poison ivy identification, thereby likely resulting in many accidental urushiol‐induced clinical allergenic dermatitis cases each year. [ABSTRACT FROM AUTHOR]

Law enforcement agencies continue to grow in the use of spatial analysis to assist in identifying patterns of outcomes. Despite the critical nature of proper resource allocation for mental health incidents, there has been little progress in statistical modeling of the geo-spatial nature of mental health events in Little Rock, Arkansas. In this article, we provide insights into the spatial nature of mental health data from Little Rock, Arkansas between 2015 and 2018, under a supervised spatial modeling framework. We provide evidence of spatial clustering and identify the important features influencing such heterogeneity via a spatially informed hierarchy of generalized linear, tree-based, and spatial regression models, viz. the Poisson regression model, the random forest model, the spatial Durbin error model, and the Manski model. The insights obtained from these different models are presented here along with their relative predictive performances. The inferential tools developed here can be used in a broad variety of spatial modeling contexts and have the potential to aid both law enforcement agencies and the city in properly allocating resources. We were able to identify several built-environment and socio-demographic measures related to mental health calls while noting that the results indicated that there are unmeasured factors that contribute to the number of events. [ABSTRACT FROM AUTHOR]

Amidst the proliferation of community- and place-based, several innovative measurement tools have become more readily available for criminological and criminal justice researchers. The current study illustrates the utility of two novel data sources – Google transportation data and municipal infrastructure files – as a means of extending studies focused on racial and ethnic segregation's effect on crime to include critical insights from environmental criminology regarding neighborhood boundary permeability. In doing so, we utilize data from over 120 block groups in Little Rock, Arkansas that include measures of Black isolation and boundary permeability: walk times to adjacent neighborhoods and thru streets captured in city infrastructure files. Our findings reveal that both segregation and neighborhood boundary permeability affect crime independently and net of key structural and spatial covariates, but that boundary permeability conditions the effect of segregation on crime. We conclude by discussing how the integration of newer and under-utilized measurement tools advances long-standing research on segregation and crime by operationalizing key theoretical concepts that have remained difficult to include using more standard secondary databases [ABSTRACT FROM AUTHOR]

False negative rates of severe acute respiratory coronavirus 2 diagnostic tests, together with selection bias due to prioritized testing can result in inaccurate modeling of COVID‐19 transmission dynamics based on reported "case" counts. We propose an extension of the widely used Susceptible‐Exposed‐Infected‐Removed (SEIR) model that accounts for misclassification error and selection bias, and derive an analytic expression for the basic reproduction number R0 as a function of false negative rates of the diagnostic tests and selection probabilities for getting tested. Analyzing data from the first two waves of the pandemic in India, we show that correcting for misclassification and selection leads to more accurate prediction in a test sample. We provide estimates of undetected infections and deaths between April 1, 2020 and August 31, 2021. At the end of the first wave in India, the estimated under‐reporting factor for cases was at 11.1 (95% CI: 10.7,11.5) and for deaths at 3.58 (95% CI: 3.5,3.66) as of February 1, 2021, while they change to 19.2 (95% CI: 17.9, 19.9) and 4.55 (95% CI: 4.32, 4.68) as of July 1, 2021. Equivalently, 9.0% (95% CI: 8.7%, 9.3%) and 5.2% (95% CI: 5.0%, 5.6%) of total estimated infections were reported on these two dates, while 27.9% (95% CI: 27.3%, 28.6%) and 22% (95% CI: 21.4%, 23.1%) of estimated total deaths were reported. Extensive simulation studies demonstrate the effect of misclassification and selection on estimation of R0 and prediction of future infections. A R‐package SEIRfansy is developed for broader dissemination. [ABSTRACT FROM AUTHOR]

Previous studies have evaluated the marginal effect of various factors on the risk of severe maternal morbidity (SMM) using regression approaches. We add to this literature by utilizing a Bayesian network (BN) approach to understand the joint effects of clinical, demographic, and area-level factors. We conducted a retrospective observational study using linked birth certificate and insurance claims data from the Arkansas All-Payer Claims Database (APCD), for the years 2013 through 2017. We used various learning algorithms and measures of arc strength to choose the most robust network structure. We then performed various conditional probabilistic queries using Monte Carlo simulation to understand disparities in SMM. We found that anemia and hypertensive disorder of pregnancy may be important clinical comorbidities to target in order to reduce SMM overall as well as racial disparities in SMM. [ABSTRACT FROM AUTHOR]

The current study focuses on criminal incidents reported to the National Incident Based Reporting System for the state of Arkansas, USA, in 2016. Arkansas law enforcement agencies are mandated to report their crime data to the Arkansas Crime Information Center (ACIC). The current study attempts to showcase the advantages that will be gained by the collection of address-identified NIBRS data throughout Arkansas and for other states that follow suit. In particular, we compare (1) statewide NIBRS data that is publicly available to (2) the address-level data for the city of Little Rock. To illustrate this variation, we use Arkansas as an example of the spatial variation in crime occurrence at a macro-level then move toward meso and micro-level agency-based analyses. [ABSTRACT FROM AUTHOR]

The spatial elements of crime occurrence and the identification of crime generators/attractors have remained a prominent area of research. We focus on the utility of the 80-20 rule and the labeling of risky facilities in crime forecasting models with risk terrain modeling (RTM). We first examine whether the rule holds across types of crime generating places including liquor stores, department stores, hotels/motels, restaurants/bars, and apartment complexes. Next, we use our findings to test whether conducting preliminary analyses to identify risky facilities increases the predictive power of RTM versus using all possible facilities. When restricting the RTM approach to only risky facilities, results were more accurate than a traditional RTM approach. Findings and implications are nested in the utilization of the wider body of environmental criminology research to increase our understanding of where crime is likely to occur. [ABSTRACT FROM AUTHOR]

Summary: Since the advent of the horseshoe priors for regularisation, global–local shrinkage methods have proved to be a fertile ground for the development of Bayesian methodology in machine learning, specifically for high‐dimensional regression and classification problems. They have achieved remarkable success in computation and enjoy strong theoretical support. Most of the existing literature has focused on the linear Gaussian case; for which systematic surveys are available. The purpose of the current article is to demonstrate that the horseshoe regularisation is useful far more broadly, by reviewing both methodological and computational developments in complex models that are more relevant to machine learning applications. Specifically, we focus on methodological challenges in horseshoe regularisation in non‐linear and non‐Gaussian models, multivariate models and deep neural networks. We also outline the recent computational developments in horseshoe shrinkage for complex models along with a list of available software implementations that allows one to venture out beyond the comfort zone of the canonical linear regression problems. [ABSTRACT FROM AUTHOR]

Summary: Since the advent of the horseshoe priors for regularisation, global–local shrinkage methods have proved to be a fertile ground for the development of Bayesian methodology in machine learning, specifically for high‐dimensional regression and classification problems. They have achieved remarkable success in computation and enjoy strong theoretical support. Most of the existing literature has focused on the linear Gaussian case; for which systematic surveys are available. The purpose of the current article is to demonstrate that the horseshoe regularisation is useful far more broadly, by reviewing both methodological and computational developments in complex models that are more relevant to machine learning applications. Specifically, we focus on methodological challenges in horseshoe regularisation in non‐linear and non‐Gaussian models, multivariate models and deep neural networks. We also outline the recent computational developments in horseshoe shrinkage for complex models along with a list of available software implementations that allows one to venture out beyond the comfort zone of the canonical linear regression problems. [ABSTRACT FROM AUTHOR]

We show that prediction performance for global-local shrinkage regression can overcome two major dificulties of global shrinkage regression: (i) the amount of relative shrinkage is monotone in the singular values of the design matrix and (ii) the shrinkage is determined by a single tuning parameter. Specifically, we show that the horseshoe regression, with heavy-tailed component-specific local shrinkage parameters, in conjunction with a global parameter providing shrinkage towards zero, alleviates both these dificulties and consequently, results in an improved risk for prediction. Numerical demonstrations of improved prediction over competing approaches in simulations and in a pharmacogenomics data set confirm our theoretical findings. [ABSTRACT FROM AUTHOR]

We propose a new prior for ultra-sparse signal detection that we term the "horseshoe+ prior." The horseshoe+ prior is a natural extension of the horseshoe prior that has achieved success in the estimation and detection of sparse signals and has been shown to possess a number of desirable theoretical properties while enjoying computational feasibility in high dimensions. The horseshoe+ prior builds upon these advantages. Our work proves that the horseshoe+ posterior concentrates at a rate faster than that of the horseshoe in the Kullback-Leibler (K-L) sense. We also establish theoretically that the proposed estimator has lower posterior mean squared error in estimating signals compared to the horseshoe and achieves the optimal Bayes risk in testing up to a constant. For one-group global-local scale mixture priors, we develop a new technique for analyzing the marginal sparse prior densities using the class of Meijer-G functions. In simulations, the horseshoe+ estimator demonstrates superior performance in a standard design setting against competing methods, including the horseshoe and Dirichlet- Laplace estimators. We conclude with an illustration on a prostate cancer data set and by pointing out some directions for future research. [ABSTRACT FROM AUTHOR]

Aim: Malnutrition has been reported in the literature to be adversely associated with outcomes in paediatric malignancies. Our objective in this paper was to evaluate malnutrition as a potential predictor for adverse outcomes in febrile neutropenia associated with haematological malignancies.Methods: A prospective observational study was performed in a tertiary care teaching hospital of Kolkata, India. Forty-eight participants, suffering from haematological malignancy, were included. Participants were included if they experienced at least one episode of febrile neutropenia. For children aged <5 years, weight for height, height for age and weight for age were used as criteria for defining malnutrition, while body mass index for age was used in children ≥5 years. A total of 162 episodes of febrile neutropenia were studied.Results: Thirty patients (30/48, 62.5%) included in the study had malnutrition. In bivariate analyses at patient level, there is a strong association between malnutrition and death (odds ratio (OR) 7.286, 95% confidence interval (CI) 0.838-63.345, one-tailed P = 0.044), and life-threatening complications show a moderate trend towards significance (OR 3.333, 95% CI 0.791-14.052, one-tailed P = 0.084). Survival functions were significantly different between malnourished and non-malnourished children (log rank test χ(2)  = 4.609, degree of freedom = 1, P = 0.032). Wasting was associated with life-threatening complications in children aged <5 years (OR 14, 95% CI 1.135-172.642, one-tailed P = 0.036). Logistic regression analyses at episode level revealed that phase of treatment and respiratory system involvement were significant predictors of death, while malnutrition was not.Conclusion: Malnutrition may be a potential predictor of mortality in febrile neutropenia. [ABSTRACT FROM AUTHOR]

The specificity and potency of venom components give them a unique advantage in developing various pharmaceutical drugs. Though venom is a cocktail of proteins, rarely are the synergy and association between various venom components studied. Understanding the relationship between various components of venom is critical in medical research. Using meta-analysis, we observed underlying patterns and associations in the appearance of the toxin families. For Crotalus, Dis has the most associations with the following toxins: PDE; BPP; CRL; CRiSP; LAAO; SVMP P-I and LAAO; SVMP P-III and LAAO. In Sistrurus venom, CTL and NGF have the most associations. These associations can predict the presence of proteins in novel venom and understand synergies between venom components for enhanced bioactivity. Using this approach, the need to revisit the classification of proteins as major components or minor components is highlighted. The revised classification of venom components is based on ubiquity, bioactivity, the number of associations, and synergies. The revised classification can be expected to trigger increased research on venom components, such as NGF, which have high biomedical significance. Using hierarchical clustering, we observed that the genera's venom compositions were similar, based on functional characteristics rather than phylogenetic relationships. [ABSTRACT FROM AUTHOR]

Predicting soil property distribution from a catena in the digital environment has been explored by many researchers with only slightly better than modest results. In this study, the landform recognition algorithm “geomorphons” in the GRASS GIS environment was explored to determine if this landscape model could improve predictions of soil properties. For 74 borings on the Wabash glacial moraine in Wells County, Indiana, measurements were made for: A horizon thickness, depth to chroma 2 features, effervescence, dense glacial till, carbonate concretions, and autochthonous platy structure. A digital elevation model (DEM) generated from light detection and ranging (LiDAR) data was used for the study site. The geomorphons algorithm was used to generate 10 original landforms: “flat”, “footslope”, “summit”, “ridge”, “shoulder”, “spur”, “slope”, “hollow”, “valley”, and “depression” that were aggregated to new landforms coinciding with slope positions: “toeslope”, “footslope”, “backslope”, “shoulder”, “summit”, and “depression” recognized by soil surveyors. Linear Discriminant Analysis (LDA) and Multinomial Logistics Regression Analysis (MLR) were used to aggregate the measured soil properties into the landform groups. The aggregation of geomorphons groups improved the MRL predictions to 83% accuracy. Also, the aggregation of geomorphons to five landforms to predict soil property distribution on the landscape gave promising results for the low-relief and relatively flat area of northeast Indiana. To test if the true mean value of each soil property for each landform was reliable for generalizing population characteristics, relative standard error (RSE) was calculated as a proportion of standard error to population mean from a bootstrap estimation. The range of RSE values for all soil properties and landforms was between ~ 0.7% and ~ 19%. Since the estimates of the measured soil properties all have RSE values of less than 25%, they can be considered reliable. [ABSTRACT FROM AUTHOR]

Summary Diffuse large B cell lymphoma (DLBCL) is the most common form of blood cancer and is characterized by a striking degree of genetic and clinical heterogeneity. This heterogeneity poses a major barrier to understanding the genetic basis of the disease and its response to therapy. Here, we performed an integrative analysis of whole-exome sequencing and transcriptome sequencing in a cohort of 1,001 DLBCL patients to comprehensively define the landscape of 150 genetic drivers of the disease. We characterized the functional impact of these genes using an unbiased CRISPR screen of DLBCL cell lines to define oncogenes that promote cell growth. A prognostic model comprising these genetic alterations outperformed current established methods: cell of origin, the International Prognostic Index comprising clinical variables, and dual MYC and BCL2 expression. These results comprehensively define the genetic drivers and their functional roles in DLBCL to identify new therapeutic opportunities in the disease. [ABSTRACT FROM AUTHOR]

Middle-age is a critical period of rapid changes in brain function that presents an opportunity for early diagnostics and intervention for neurodegenerative conditions later in life. Hearing loss is one such early indicator linked to many comorbidities later in life. However, current clinical tests fail to capture hearing difficulties for ∼10% of middle-aged adults seeking help at hearing clinics. Cochlear neural degeneration (CND) could play a role in these hearing deficits, but our current understanding is limited by the lack of objective diagnostics and uncertainty regarding its perceptual consequences. Here, using a cross-species approach, we measured envelope following responses (EFRs) - neural ensemble responses to sound originating from the peripheral auditory pathway - in young and middle-aged adults with normal audiometric thresholds, and compared these responses to young and middle-aged Mongolian gerbils, where CND was histologically confirmed. We observed near identical changes in EFRs across species that were associated with CND. Perceptual effects measured as behavioral readouts showed deficits in the most challenging listening conditions and were associated with CND. Additionally, pupil-indexed listening effort increased even at moderate task difficulties where behavioral outcomes were matched. Our results reveal perceptual deficits in middle-aged adults driven by CND and increases in listening effort, which may result in increased listening fatigue and conversational disengagement.

There has been raging discussion and debate around the quality of COVID death data in South Asia. According to WHO, of the 5.5 million reported COVID-19 deaths from 2020-2021, 0.57 million (10%) were contributed by five low and middle income countries (LMIC) countries in the Global South: India, Pakistan, Bangladesh, Sri Lanka and Nepal. However, a number of excess death estimates show that the actual death toll from COVID-19 is significantly higher than the reported number of deaths. For example, the IHME and WHO both project around 14.9 million total deaths, of which 4.5-5.5 million were attributed to these five countries in 2020-2021. We focus our gaze on the COVID-19 performance of these five countries where 23.5% of the world population lives in 2020 and 2021, via a counterfactual lens and ask, to what extent the mortality of one LMIC would have been affected if it adopted the pandemic policies of another, similar country? We use a Bayesian semi-mechanistic model developed by Mishra et al. (2021) to compare both the reported and estimated total death tolls by permuting the time-varying reproduction number (Rt) across these countries over a similar time period. Our analysis shows that, in the first half of 2021, mortality in India in terms of reported deaths could have been reduced to 96 and 102 deaths per million compared to actual 170 reported deaths per million had it adopted the policies of Nepal and Pakistan respectively. In terms of total deaths, India could have averted 481 and 466 deaths per million had it adopted the policies of Bangladesh and Pakistan. On the other hand, India had a lower number of reported COVID-19 deaths per million (48 deaths per million) and a lower estimated total deaths per million (80 deaths per million) in the second half of 2021, and LMICs other than Pakistan would have lower reported mortality had they followed India's strategy. The gap between the reported and estimated total deaths highlights the varying level and extent of under-reporting of deaths across the subcontinent, and that model estimates are contingent on accuracy of the death data. Our analysis shows the importance of timely public health intervention and vaccines for lowering mortality and the need for better coverage infrastructure for the death registration system in LMICs., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Kundu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Enteropathy-associated T cell lymphoma (EATL) is a lethal, and the most common, neoplastic complication of celiac disease. Here, we defined the genetic landscape of EATL through whole-exome sequencing of 69 EATL tumors. SETD2 was the most frequently silenced gene in EATL (32% of cases). The JAK-STAT pathway was the most frequently mutated pathway, with frequent mutations in STAT5B as well as JAK1 , JAK3 , STAT3 , and SOCS1 We also identified mutations in KRAS , TP53 , and TERT Type I EATL and type II EATL (monomorphic epitheliotropic intestinal T cell lymphoma) had highly overlapping genetic alterations indicating shared mechanisms underlying their pathogenesis. We modeled the effects of SETD2 loss in vivo by developing a T cell-specific knockout mouse. These mice manifested an expansion of γδ T cells, indicating novel roles for SETD2 in T cell development and lymphomagenesis. Our data render the most comprehensive genetic portrait yet of this uncommon but lethal disease and may inform future classification schemes., (© 2017 Moffitt et al.)

Hepatosplenic T-cell lymphoma (HSTL) is a rare and lethal lymphoma; the genetic drivers of this disease are unknown. Through whole-exome sequencing of 68 HSTLs, we define recurrently mutated driver genes and copy-number alterations in the disease. Chromatin-modifying genes, including SETD2, INO80 , and ARID1B , were commonly mutated in HSTL, affecting 62% of cases. HSTLs manifest frequent mutations in STAT5B (31%), STAT3 (9%), and PIK3CD (9%), for which there currently exist potential targeted therapies. In addition, we noted less frequent events in EZH2, KRAS , and TP53 SETD2 was the most frequently silenced gene in HSTL. We experimentally demonstrated that SETD2 acts as a tumor suppressor gene. In addition, we found that mutations in STAT5B and PIK3CD activate critical signaling pathways important to cell survival in HSTL. Our work thus defines the genetic landscape of HSTL and implicates gene mutations linked to HSTL pathogenesis and potential treatment targets. Significance: We report the first systematic application of whole-exome sequencing to define the genetic basis of HSTL, a rare but lethal disease. Our work defines SETD2 as a tumor suppressor gene in HSTL and implicates genes including INO80 and PIK3CD in the disease. Cancer Discov; 7(4); 369-79. ©2017 AACR. See related commentary by Yoshida and Weinstock, p. 352 This article is highlighted in the In This Issue feature, p. 339 ., (©2017 American Association for Cancer Research.)