Category: Statistics

Mol Ecol. 2021 Nov 12. doi: 10.1111/mec.16271. Online ahead of print.

ABSTRACT

Ecological adaptive differentiation alters both the species diversity and intraspecific genetic diversity in forests, thus affecting the stability of forest ecosystems. Therefore, knowledge of the genetic underpinnings of the ecological adaptive differentiation of forest species is critical for effective species conservation. In this study, single-nucleotide polymorphisms (SNPs) from population transcriptomes were used to investigate the spatial distribution of genetic variation in Liriodendron to assess whether environmental variables can explain genetic divergence. We examined the contributions of environmental variables to population divergence and explored the genetic underpinnings of local adaptation using a landscape genomic approach. Niche models and statistical analyses showed significant niche divergence between L. chinense and L. tulipifera, suggesting that ecological adaptation may play a crucial role in driving interspecific divergence. We detected a new fine-scale genetic structure in L. chinense, and divergence of the six groups occurred during the late Pliocene to early Pleistocene. Redundancy analysis (RDA) revealed significant associations between genetic variation and multiple environmental variables. Environmental association analyses identified 67 environmental association loci (EALs; nonsynonymous SNPs) that underwent interspecific or intraspecific differentiation, 28 of which were associated with adaptive genes. These 28 candidate adaptive loci provide substantial evidence for local adaptation in Liriodendron. Our findings reveal ecological adaptive divergence pattern between Liriodendron species and provide novel insight into the role of heterogeneous environments in shaping genetic structure and driving local adaptation among populations, informing future L. chinense conservation efforts.

PMID:34773328 | DOI:10.1111/mec.16271

Alcohol Clin Exp Res. 2021 Nov 13. doi: 10.1111/acer.14743. Online ahead of print.

ABSTRACT

BACKGROUND: Alcohol, insulin resistance (IR), and hepatitis C (HCV) are all significant contributors to adverse outcomes of chronic liver disease. Latinos are disproportionately affected by these risk factors. We investigated the relationship between alcohol use and insulin action in a large prospective Latino cohort with and without HCV.

METHODS: One hundred fifty-three non-diabetic Latinos (60 HCV+, 93 HCV-) underwent clinical evaluation and metabolic testing; 56 had repeat testing over a median follow-up of 1.5 years. Peripheral IR and hepatic IR were measured via steady-state plasma glucose (SSPG) and endogenous glucose production during a 2-step 240-minute insulin suppression test. Insulin secretion (IS) was measured using the graded glucose infusion test. Alcohol use was categorized as none, moderate (≤1 drink/day for women and ≤2 drinks/day for men), and heavy (not moderate). Multivariable models including HCV status assessed associations of alcohol use with baseline SSPG, hepatic IR and IS, and with changes in these parameters over time.

RESULTS: Overall, the median age was 44 years, 63.4% were male, 66.7% overweight/obese, and 31.9% had heavy lifetime alcohol use (60.4% moderate lifetime alcohol use). SSPG and IS were similar by levels of alcohol use at baseline and alcohol use was not statistically significantly associated with change in these measures over time. However, lifetime daily heavy alcohol use (vs not heavy, coef 2.4 μU-mg/kg-min-ml, p=0.04) and HCV status (coef 4.4 μU-mg/kg-min-ml, p=0.0003) were independently associated with higher baseline hepatic IR, and current heavy alcohol use was associated with greater change in hepatic IR in follow-up (coef 5.8 μU-mg/kg-min-ml, p=0.03).

CONCLUSIONS: In this Latino cohort, lifetime and current heavy alcohol use influenced hepatic IR and its change over time. Strategies to improve rates of alcohol cessation along with lifestyle modification and anti-HCV therapy to reduce metabolic risk are critical to prevent adverse liver and metabolic outcomes in Latinos.

PMID:34773280 | DOI:10.1111/acer.14743

Photodermatol Photoimmunol Photomed. 2021 Nov 12. doi: 10.1111/phpp.12753. Online ahead of print.

ABSTRACT

BACKGROUND: Photodynamic therapy (PDT) has become one of the most effective therapies for the treatment of actinic keratosis, allowing the removal of more than one lesion in a single session. However, the pain sustained by the patient during treatment and local skin reactions can limit its use.

OBJECTIVES: To determine the efficacy and safety of combined PDT (daylight PDT followed by conventional PDT) vs. conventional PDT 12 weeks after treatment.

METHODS: The study was performed as a randomized, single-center, non-inferiority clinical trial with two parallel groups. A total of 51 patients with grade I and II AKs on the scalp or face were randomized. 25 patients received one session of combined PDT (combPDT) and 26 patients received one session of conventional PDT (cPDT). The primary endpoint was the reduction of AKs, 12 weeks after treatment. The secondary endpoint was the reduction in pain and local skin reaction.

RESULTS: The reduction rate of grade I and II AKs was similar in combPDT and cPDT, showing no statistically significant differences between both groups, 76.67% vs. 86.63% [p = 0.094] and 80.48% vs. 83.08% [p = 0.679], respectively. However, pain was significantly lower in the combPDT group (2.56 vs. 5, p < 0.01), as were local skin reactions.

CONCLUSIONS: CombPDT has proven to be as effective as cPDT for the treatment of grade I and II AKs located on the scalp and face. Furthermore, combPDT has been shown to be considerably more tolerable than cPDT, causing only mild local skin reactions.

PMID:34773302 | DOI:10.1111/phpp.12753

Vet Rec. 2021 Nov 12:e974. doi: 10.1002/vetr.974. Online ahead of print.

ABSTRACT

OBJECTIVE: Transmissable venereal tumour (TVT) is a tumour transplanted by physical contact between dogs. Lesions typically affect the genitalia. TVT is not considered enzootic in the United Kingdom (UK), with cases seen in imported dogs. We sought to determine the patient characteristics, temporal and spatial distribution and country of origin of affected dogs in the UK.

METHODS: Electronic pathology records (EPRs) from four UK veterinary diagnostic laboratories collected between 2010 and 2019 were searched for the terms ‘venereal’ or ‘TVT’. Reports were reviewed for statements confirming a TVT and descriptive statistics collated.

RESULTS: Of 182 EPRs matching the search terms, a diagnosis of TVT was confirmed in 71. Country of origin was noted in 36 cases (50.7%) with Romania being the most common (n = 29). Cases were reported in each UK constituent country, with the majority being in England (64, 90.1%). The incidence of TVT diagnosis increased over the last decade (z = 2.78, p = 0.005).

CONCLUSIONS/DISCUSSION: The incidence of TVT diagnosed in the UK is increasing. The majority of cases were known to have been imported. Autochthonous transmission cannot be excluded due to study design. Vets are encouraged to carefully examine the genitalia of dogs imported to the UK from countries with enzootic TVT.

PMID:34773267 | DOI:10.1002/vetr.974

Stat Med. 2021 Nov 12. doi: 10.1002/sim.9250. Online ahead of print.

ABSTRACT

The medium-throughput mRNA abundance platform NanoString nCounter has gained great popularity in the past decade, due to its high sensitivity and technical reproducibility as well as remarkable applicability to ubiquitous formalin fixed paraffin embedded (FFPE) tissue samples. Based on RCRnorm developed for normalizing NanoString nCounter data and Bayesian LASSO for variable selection, we propose a fully integrated Bayesian method, called RCRdiff, to detect differentially expressed (DE) genes between different groups of tissue samples (eg, normal and cancer). Unlike existing methods that often require normalization performed beforehand, RCRdiff directly handles raw read counts and jointly models the behaviors of different types of internal controls along with DE and non-DE gene patterns. Doing so would avoid efficiency loss caused by ignoring estimation uncertainty from the normalization step in a sequential approach and thus can offer more reliable statistical inference. We also propose clustering-based strategies for DE gene selection, which do not require any external dataset and are free of any arbitrary cutoff. Empirical evidence of the attractiveness of RCRdiff is demonstrated via extensive simulation and data examples.

PMID:34773277 | DOI:10.1002/sim.9250

Med Phys. 2021 Nov 12. doi: 10.1002/mp.15345. Online ahead of print.

ABSTRACT

PURPOSE: Upper airway segmentation on MR images is a prerequisite step for quantitatively studying the anatomical structure and function of the upper airway and surrounding tissues. However, the complex variability of intensity and shape of anatomical structures and of different modes of image acquisition commonly used in this application makes automatic upper airway segmentation challenging. In this paper, we develop and test a comprehensive deep-learning-based segmentation system for use on MR images to address this problem.

MATERIAL & METHODS: In our study, both static and dynamic MRI data sets are utilized including 58 axial static 3D MRI studies, 22 mid-retropalatal dynamic 2D MRI studies, 21 mid-retroglossal dynamic 2D MRI studies, 36 mid-sagittal dynamic 2D MRI studies, and 23 isotropic dynamic 3D MRI studies, involving a total of 160 subjects and over 20,000 MRI slices. Samples of static and 2D dynamic MRI data sets were randomly divided into training, validation, and test sets by an approximate ratio of 5:2:3. Considering that the variability of annotation data among 3D dynamic MRIs was greater than for other MRI data sets, we increased the ratio of training data for these data to improve the robustness of the model. We designed a unified framework consisting of the following procedures. For static MRI, a generalized region of interest (GROI) strategy is applied to localize the partitions of nasal cavity and other portions of upper airway in axial data sets as two separate sub-objects. Subsequently, the two sub-objects are segmented by two separate 2D U-Nets. The two segmentation results are combined as the whole upper airway structure. The generalized ROI strategy is also applied to other MRI modes. To minimize false positive and false negative rates in the segmentation results, we employed a novel loss function based explicitly on these rates to train the segmentation networks. An inter-reader study is conducted to test the performance of our system in comparison to human variability in ground truth (GT) segmentation of these challenging structures.

RESULTS: The proposed approach yielded mean Dice coefficients of 0.84±0.03, 0.89±0.13, 0.84±0.07, and 0.86±0.05 for static 3D MRI, mid-retropalatal/ mid-retroglossal 2D dynamic MRI, mid-sagittal 2D dynamic MRI, and isotropic dynamic 3D MRI, respectively. The quantitative results show excellent agreement with manual delineation results. The inter-reader study results demonstrate that the segmentation performance of our approach is statistically indistinguishable from manual segmentations considering the inter-reader variability in GT.

CONCLUSIONS: The proposed method can be utilized for routine upper airway segmentation from static and dynamic MR images with high accuracy and efficiency. The proposed approach has the potential to be employed in other dynamic MRI-related applications, such as lung or heart segmentation. This article is protected by copyright. All rights reserved.

PMID:34773260 | DOI:10.1002/mp.15345

Curr Osteoporos Rep. 2021 Nov 13. doi: 10.1007/s11914-021-00711-w. Online ahead of print.

ABSTRACT

PURPOSE OF REVIEW: Statistical models of shape and appearance have increased their popularity since the 1990s and are today highly prevalent in the field of medical image analysis. In this article, we review the recent literature about how statistical models have been applied in the context of osteoporosis and fracture risk estimation.

RECENT FINDINGS: Recent developments have increased their ability to accurately segment bones, as well as to perform 3D reconstruction and classify bone anatomies, all features of high interest in the field of osteoporosis and fragility fractures diagnosis, prevention, and treatment. An increasing number of studies used statistical models to estimate fracture risk in retrospective case-control cohorts, which is a promising step towards future clinical application. All the reviewed application areas made considerable steps forward in the past 5-6 years. Heterogeneities in validation hinder a thorough comparison between the different methods and represent one of the future challenges to be addressed to reach clinical implementation.

PMID:34773211 | DOI:10.1007/s11914-021-00711-w

Atten Percept Psychophys. 2021 Nov 12. doi: 10.3758/s13414-021-02330-0. Online ahead of print.

ABSTRACT

Increasing evidence demonstrates that observers can learn the likely location of salient singleton distractors during visual search. To date, the reduced attentional capture at high-probability distractor locations has typically been examined using so called compound search, in which by design a target is always present. Here, we explored whether statistical distractor learning can also be observed in a visual detection task, in which participants respond target present if the singleton target is present and respond target absent when the singleton target is absent. If so, this allows us to examine suppression of the location that is likely to contain a distractor both in the presence, but critically also in the absence, of a priority signal generated by the target singleton. In an online variant of the additional singleton paradigm, observers had to indicate whether a unique shape was present or absent, while ignoring a colored singleton, which appeared with a higher probability in one specific location. We show that attentional capture was reduced, but not absent, at high-probability distractor locations, irrespective of whether the display contained a target or not. By contrast, target processing at the high-probability distractor location was selectively impaired on distractor-present displays. Moreover, all suppressive effects were characterized by a gradient such that suppression scaled with the distance to the high-probability distractor location. We conclude that statistical distractor learning can be examined in visual detection tasks, and discuss the implications for attentional suppression due to statistical learning.

PMID:34773244 | DOI:10.3758/s13414-021-02330-0

Sci Rep. 2021 Nov 12;11(1):22203. doi: 10.1038/s41598-021-01290-7.

ABSTRACT

APACHE IVa provides typically useful and accurate predictions on in-hospital mortality and length of stay for patients in critical care. However, there are factors which may preclude APACHE IVa from reaching its ceiling of predictive accuracy. Our primary aim was to determine which variables available within the first 24 h of a patient’s ICU stay may be indicative of the APACHE IVa scoring system making occasional but potentially illuminating errors in predicting in-hospital mortality. We utilized the publicly available multi-institutional ICU database, eICU, available since 2018, to identify a large observational cohort for our investigation. APACHE IVa scores are provided by eICU for each patient’s ICU stay. We used Lasso logistic regression in an aim to build parsimonious final models, using cross-validation to select the penalization parameter, separately for each of our two responses, i.e., errors, of interest, which are APACHE falsely predicting in-hospital death (Type I error), and APACHE falsely predicting in-hospital survival (Type II error). We then assessed the performance of the models with a random holdout validation sample. While the extremeness of the APACHE prediction led to dependable predictions for preventing either type of error, distinct variables were identified as being strongly associated with the two different types of errors occurring. These included a primary set of predictors consisting of mean SpO2 and worst lactate for predicting Type I errors, and worst albumin and mean heart rate for Type II. In addition, a secondary set of predictors including changes recorded in care limitations for the patient’s treatment plan, worst pH, whether cardiac arrest occurred at admission, and whether vasopressor was provided for predicting Type I error; age, whether the patient was ventilated in day 1, mean respiratory rate, worst lactate, worst blood urea nitrogen test, and mean aperiodic vitals for Type II. The two models also differed in their performance metrics in their holdout validation samples, in large part due to the lower prevalence of Type II errors compared to Type I. The eICU database was a good resource for evaluating our objective, and important recommendations are provided, particularly identifying key variables that could lead to APACHE prediction errors when APACHE scores are sufficiently low to predict in-hospital survival.

PMID:34772961 | DOI:10.1038/s41598-021-01290-7

Nat Commun. 2021 Nov 12;12(1):6570. doi: 10.1038/s41467-021-26357-x.

ABSTRACT

The Earth’s climate system is a classical example of a multiscale, multiphysics dynamical system with an extremely large number of active degrees of freedom, exhibiting variability on scales ranging from micrometers and seconds in cloud microphysics, to thousands of kilometers and centuries in ocean dynamics. Yet, despite this dynamical complexity, climate dynamics is known to exhibit coherent modes of variability. A primary example is the El Niño Southern Oscillation (ENSO), the dominant mode of interannual (3-5 yr) variability in the climate system. The objective and robust characterization of this and other important phenomena presents a long-standing challenge in Earth system science, the resolution of which would lead to improved scientific understanding and prediction of climate dynamics, as well as assessment of their impacts on human and natural systems. Here, we show that the spectral theory of dynamical systems, combined with techniques from data science, provides an effective means for extracting coherent modes of climate variability from high-dimensional model and observational data, requiring no frequency prefiltering, but recovering multiple timescales and their interactions. Lifecycle composites of ENSO are shown to improve upon results from conventional indices in terms of dynamical consistency and physical interpretability. In addition, the role of combination modes between ENSO and the annual cycle in ENSO diversity is elucidated.

PMID:34772916 | DOI:10.1038/s41467-021-26357-x