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Introduction Modern warfare operations are volatile, highly complex environments, placing immense physiological, psychological, and cognitive demands on the warfighter. To maximize cognitive performance and warfighter resilience and readiness, training must address psychological stress to enhance performance. Resilience in the face of adversity is fundamentally rooted in an individual's psychophysiological stress response and optimized through decreased susceptibility to the negative impact of trauma exposure. The current project aims to optimize warfighter expertise, resilience, adaptability, and performance by utilizing a validated Full Dive Virtual Reality (FDVR) training platform to provide high-fidelity, safe, and scalable, controlled stress exposure in highly realistic simulated training scenarios with the most advanced, immersive technology available. Materials and Methods Following Institutional Review Board approval and consent, 2 operators were fitted with high-fidelity virtual reality headsets with hand and eye tracking, full-body haptic feedback suits, a 360° omnidirectional treadmill, and Food and Drug Administration (FDA) cleared biometric monitors. Following acclimation, operators were placed in an industrial fire scenario and instructed to respond as a firefighter and paramedic, to search for and resuscitate any casualties, extinguish the fire, and exfiltrate safely. Following initial acclimation and after each demonstration (n  = 2), 3 semistructured interviews asked operators their perceptions and experiences related to FDVR, focusing on usability, feasibility, and safety. Biometric data were continuously recorded using the Caretaker Medical VitalStream. Results Proof-of-concept (POC) testing proved that the FDVR training platform is usable, safe, and feasible. It creates an immersive environment with physiological responses to mimic realistic Mass Casualty Events (MCEs). Using a case study approach, transcript data were analyzed using thematic analysis. Three major themes emerged: Sensory deficits reduced realism, but sensory feedback improved fidelity, vestibular discord affected the virtual reality experience but only when the system did not respond naturally to operator movement after acclimation, and movement accommodations were made by operators to enhance usability, especially for fine motor movements. Biometric data analysis correlated timestamps from the VitalStream unit with operator responses to stress-inducing events (i.e. explosions, fires, and a deceased victim). Both operators exhibited significant physiological responses, including elevated heart rate, systolic blood pressure, and mean arterial pressure, particularly following explosions, encountering fire, and encountering the deceased victim within the training environment. Conclusions The FDVR training platform overcomes the obstacles of in-person simulation training and provides the closest to real-life experience available. It will allow warfighters to train with their teams in immersive environments that replicate the conditions in which they are expected to perform their duties. The POC demonstrated that physiological responses can be mapped to scenario events to allow tracking of stress responses, cognitive load, as well as performance, and decision-making of the warfighter. The POC only involved 2 operators, but served to prove that the platform was safe and effective. Future testing plans to include 200 warfighters in operational teams of 10 to 12 to further validate the training effectiveness of the FDVR platform. [ABSTRACT FROM AUTHOR]

Score-driven models have recently been introduced as a general framework to specify time-varying parameters of conditional densities. The score enjoys stochastic properties that make these models easy to implement and convenient to apply in several contexts, ranging from biostatistics to finance. Score-driven parameter updates have been shown to be optimal in terms of locally reducing a local version of the Kullback–Leibler divergence between the true conditional density and the postulated density of the model. A key limitation of such an optimality property is that it holds only locally both in the parameter space and sample space, yielding to a definition of local Kullback–Leibler divergence that is in fact not a divergence measure. The current paper shows that score-driven updates satisfy stronger optimality properties that are based on a global definition of Kullback–Leibler divergence. In particular, it is shown that score-driven updates reduce the distance between the expected updated parameter and the pseudo-true parameter. Furthermore, depending on the conditional density and the scaling of the score, the optimality result can hold globally over the parameter space, which can be viewed as a generalization of the monotonicity property of the stochastic gradient descent scheme. Several examples illustrate how the results derived in the paper apply to specific models under different easy-to-check assumptions, and provide a formal method to select the link function and the scaling of the score. [ABSTRACT FROM AUTHOR]

Jerome Wakefield criticizes my biostatistical analysis of the pathological—as statistically subnormal biological part-functional ability relative to species, sex, and age—for its lack of a harm clause. He first charges me with ignoring two general distinctions: biological versus medical pathology, and disease of a part versus disease of a whole organism. He then offers 10 counterexamples that, he says, are harmless dysfunctions but not medical disorders. Wakefield ends by arguing that we need a harm clause to explain American psychiatry's 1973 decision to declassify homosexuality. I reply, first, that his two distinctions are philosophic fantasies alien to medical usage, invented only to save his own harmful-dysfunction analysis (HDA) from a host of obvious counterexamples. In any case, they do not coincide with the harmless/harmful distinction. In reality, medicine admits countless chronic diseases that are, contrary to Wakefield, subclinical for most of their course, as well as many kinds of typically harmless skin pathology. As for his 10 counterexamples, no medical source he cites describes them as he does. I argue that none of his examples contradicts the biostatistical analysis: all either are not part-dysfunctions (situs inversus, incompetent sperm, normal-flora infection) or are indeed classified as medical disorders (donated kidney, Typhoid Mary's carrier status, latent tuberculosis or HIV, cherry angiomas). And if Wakefield's HDA fits psychiatry, the fact that it does not fit medicine casts doubt on psychiatry's status as a medical specialty. [ABSTRACT FROM AUTHOR]

Biostatistics plays a pivotal role in developing, interpreting and drawing conclusions from clinical, biological and epidemiological data. However, the improper application of statistical methods can lead to erroneous conclusions and misinterpretations. This paper provides a comprehensive examination of the most frequent mistakes encountered in the biostatistical analysis process. We identified and elucidated 10 common errors in biostatistical analysis. These include using the wrong metric to describe data, misinterpreting P -values, misinterpreting the 95% confidence interval, misinterpreting the hazard ratio as an index of prognostic accuracy, ignoring the sample size calculation, misinterpreting analysis by strata in randomized clinical trials, confusing correlation and causation, misunderstanding confounders and mediators, inadequately codifying variables during the data collection, and bias arising when group membership is attributed on the basis of future exposure in retrospective studies. We discuss the implications of these errors and propose some practical strategies to mitigate their impact. By raising awareness of these pitfalls, this paper aims to enhance the rigor and reproducibility of biostatistical analyses, thereby fostering more robust and reliable biomedical research findings. [ABSTRACT FROM AUTHOR]

Biometric technology encompasses a proliferating array of data forms, applications, and stakeholders but has raised numerous social and ethical concerns. This article examines contending perceptions of biometrics by developing a three-way framework of science–society orderings, drawn from social studies of biometrics and wider science studies literature. By analysing documentary sources and participant observation data through this framework, the article identifies a series of distinct normative interpretations or imaginaries of biometrics. It is argued that these imaginaries, described, respectively, as 'public good', 'collective control', and 'societal risks', project contending normative framings of science–society relations. These imaginaries were also however found to reflexively encompass perceived challenges, giving rise to practices that I term imaginative politics. These findings raise the need for science policy studies to consider the distinction between imagining and realizing in greater depth and to consider more profoundly the politics of science–society co-production. [ABSTRACT FROM AUTHOR]

Simulation studies are powerful tools in epidemiology and biostatistics, but they can be hard to conduct successfully. Sometimes unexpected results are obtained. We offer advice on how to check a simulation study when this occurs, and how to design and conduct the study to give results that are easier to check. Simulation studies should be designed to include some settings in which answers are already known. They should be coded in stages, with data-generating mechanisms checked before simulated data are analysed. Results should be explored carefully, with scatterplots of standard error estimates against point estimates surprisingly powerful tools. Failed estimation and outlying estimates should be identified and dealt with by changing data-generating mechanisms or coding realistic hybrid analysis procedures. Finally, we give a series of ideas that have been useful to us in the past for checking unexpected results. Following our advice may help to prevent errors and to improve the quality of published simulation studies. [ABSTRACT FROM AUTHOR]

Correlation among multiple phenotypes across related individuals may reflect some pattern of shared genetic architecture: individual genetic loci affect multiple phenotypes (an effect known as pleiotropy), creating observable relationships between phenotypes. A natural hypothesis is that pleiotropic effects reflect a relatively small set of common "core" cellular processes: each genetic locus affects one or a few core processes, and these core processes in turn determine the observed phenotypes. Here, we propose a method to infer such structure in genotype-phenotype data. Our approach, sparse structure discovery (SSD) is based on a penalized matrix decomposition designed to identify latent structure that is low-dimensional (many fewer core processes than phenotypes and genetic loci), locus-sparse (each locus affects few core processes), and/or phenotype-sparse (each phenotype is influenced by few core processes). Our use of sparsity as a guide in the matrix decomposition is motivated by the results of a novel empirical test indicating evidence of sparse structure in several recent genotype-phenotype datasets. First, we use synthetic data to show that our SSD approach can accurately recover core processes if each genetic locus affects few core processes or if each phenotype is affected by few core processes. Next, we apply the method to three datasets spanning adaptive mutations in yeast, genotoxin robustness assay in human cell lines, and genetic loci identified from a yeast cross, and evaluate the biological plausibility of the core process identified. More generally, we propose sparsity as a guiding prior for resolving latent structure in empirical genotype-phenotype maps. [ABSTRACT FROM AUTHOR]

The article titled "Acknowledgments Referees 2023" expresses gratitude to the referees of the journal Biometrics for their diligent and thorough reviews, which have made the journal possible. The referees' work is challenging and time-consuming, and they receive no material reward for their efforts. The article acknowledges and thanks the individuals who support the referee system used in the review process of Biometrics. [Extracted from the article]

Motivation Cell label annotation is a challenging step in the analysis of single-cell RNA sequencing (scRNA-seq) data, especially for tissue types that are less commonly studied. The accumulation of scRNA-seq studies and biological knowledge leads to several well-maintained cell marker databases. Manually examining the cell marker lists against these databases can be difficult due to the large amount of available information. Additionally, simply overlapping the two lists without considering gene ranking might lead to unreliable results. Thus, an automated method with careful statistical testing is needed to facilitate the usage of these databases. Results We develop a user-friendly computational tool, EasyCellType, which automatically checks an input marker list obtained by differential expression analysis against the databases and provides annotation recommendations in graphical outcomes. The package provides two statistical tests, gene set enrichment analysis and a modified version of Fisher's exact test, as well as customized database and tissue type choices. We also provide an interactive shiny application to annotate cells in a user-friendly graphical user interface. The simulation study and real-data applications demonstrate favorable results by the proposed method. Availability and implementation https://biostatistics.mdanderson.org/shinyapps/EasyCellType/ ; https://bioconductor.org/packages/devel/bioc/html/EasyCellType.html. Supplementary information Supplementary data are available at Bioinformatics Advances online. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel cryptographic primitive named reusable group fuzzy extractor (RGFE) allowing any member of a group to extract and reproduce random strings from a fuzzy and non-uniform source of high entropy (called fingerprint). Any group member can anonymously generate a random string for the group using his fingerprint and can be traced when needed, whereas other members can reproduce the string using their own fingerprints. Moreover, a fingerprint can be repeatedly used to generate multiple random strings. Basing on RGFE, we present group-shared Bitcoin wallet, which can be used by a group of users to receive or spend coins via biometrics in a traceable way. [ABSTRACT FROM AUTHOR]

Aims Statistical shape models (SSMs) of cardiac anatomy provide a new approach for analysis of cardiac anatomy. In adults, specific cardiac morphologies associate with cardiovascular risk factors and early disease stages. However, the relationships between morphology and risk factors in children remain unknown. We propose an SSM of the paediatric left ventricle to describe its morphological variability, examine its relationship with biometric parameters and identify adverse anatomical remodelling associated with obesity. Methods and results This cohort includes 2631 children (age 10.2 ± 0.6 years), mostly Western European (68.3%) with a balanced sex distribution (51.3% girls) from Generation R study. Cardiac magnetic resonance short-axis cine scans were segmented. Three-dimensional left ventricular (LV) meshes are automatically fitted to the segmentations to reconstruct the anatomies. We analyse the relationships between the LV anatomical features and participants' body surface area (BSA), age, and sex, and search for features uniquely related to obesity based on body mass index (BMI). In the SSM, 19 modes described over 90% of the population's LV shape variability. Main modes of variation were related to cardiac size, sphericity, and apical tilting. BSA, age, and sex were mostly correlated with modes describing LV size and sphericity. The modes correlated uniquely with BMI suggested that obese children present with septo-lateral tilting (R 2 = 4.0%), compression in the antero-posterior direction (R 2 = 3.3%), and decreased eccentricity (R 2 = 2.0%). Conclusions We describe the variability of the paediatric heart morphology and identify anatomical features related to childhood obesity that could aid in risk stratification. Web service is released to provide access to the new shape parameters. [ABSTRACT FROM AUTHOR]

The use of fingerprint continues to increase constantly with the propagation of biometric authentication technologies. Fingerprint is today the most widely used biometric modality for human verification and identification, due to its practical use and its discriminative structure that allows to different identities to be easily distinguished. Unfortunately, this emergence has been accompanied by a number of problems and challenges related to identity theft and to security issues in general. To address these concerns, many approaches have been proposed in which only few of them were able to reach an acceptable level of both security and performance. In this paper, we propose a new fingerprint template protection scheme that enhances the security of protected system while preserving performance. The proposed approach is a minutiae-based technique that performs fingerprint matching in a transformed space using irreversible minutiae tetrahedrons. Using the original Fingerprint Verification Competition (FVC) protocol, the provided experimental results on FVC2002 DB1, DB2 and DB3 fingerprint databases have shown satisfactory recognition rates. Our results are compared to some existing techniques that use the same protocol of test. We have proved as well that the proposed scheme meets the requirements of revocability, unlinkability and irreversibility. [ABSTRACT FROM AUTHOR]

Prof. Chasan-Taber would do her readers a favor if she told them that grant-writing requires talent, that everyone must decide at some point whether they have enough to justify cultivating it, and then giving frank but kind advice about how to make that decision. This review assignment was timely because I just re-submitted a proposal to the US government's National Institutes of Health (NIH) and writing it was ... difficult. [Extracted from the article]

Distance covariance is a powerful new dependence measure that was recently introduced by Székely et al. and Székely and Rizzo. In this work, the concept of distance covariance is extended to measuring dependence between a covariate vector and a right‐censored survival endpoint by establishing an estimator based on an inverse‐probability‐of‐censoring weighted U‐statistic. The consistency of the novel estimator is derived. In a large simulation study, it is shown that induced distance covariance permutation tests show a good performance in detecting various complex associations. Applying the distance covariance permutation tests on a gene expression dataset from breast cancer patients outlines its potential for biostatistical practice. [ABSTRACT FROM AUTHOR]

While racial/ethnic differences in fetal growth have been documented, few studies have examined whether they vary by exogenous factors, which could elucidate underlying causes. The purpose of this study was to characterize longitudinal fetal growth patterns by maternal sociodemographic, behavioral, and clinical factors and examine whether associations with maternal race/ethnicity varied by these other predictors. Between 2016 and 2019, pregnant women receiving prenatal care at NYU Langone Health (New York, New York) were invited to participate in a birth cohort study. Women completed questionnaires, and clinical data were abstracted from ultrasound examinations. Maternal characteristics were assessed in relation to fetal biometric measures throughout pregnancy using linear mixed models. Maternal race/ethnicity was consistently associated with fetal biometry: Black, Hispanic, and Asian women had fetuses with smaller head circumference, abdominal circumference, and biparietal diameter than White women. The associations between race/ethnicity and fetal growth varied by nativity for Asian women, such that the disparity between Asian and White women was much greater for US-born women than for foreign-born women. However, associations for Black and Hispanic women did not vary by nativity. While race/ethnicity-specific fetal growth standards have been proposed, additional work is needed to elucidate what could be driving these differences, including factors that occur in parallel and differentially affect fetal growth. [ABSTRACT FROM AUTHOR]

Biometric systems depending on 'one-modal biometrics' do not meet up with the required performance necessities for huge user appliances, owing to certain issues like 'noisy data, intra-class variations, restricted degrees of freedom, spoof attacks and unacceptable error rates'. This work tends to discover a multimodal biometric recognition (MBR) model that includes three main phases like '(i) pre-processing, (ii) segmentation, (iii) feature extraction and (iv) classification'. Initially, the images are pre-processed and those pre-processed images are subjected to segmentation. In this context, segmentation is carried out using the Otsu thresholding model. The segmented images are then subjected to a feature extraction process. This work exploits local feature extraction, where 'Gabor filter features, Zernibe moment features and proposed local binary pattern features' are extracted. Subsequently, the fusion framework is developed, which has enhanced classification abilities with minimal dimension for MBR. As the next process, recognition takes place by the optimized neural network (NN) model. As a novelty, the training of NN is carried out using a new modified dragonfly algorithm by selecting the optimal weight. Finally, analysis is carried out for validating the betterment of the presented model in terms of different measures. [ABSTRACT FROM AUTHOR]

With the rise of advanced biometric technologies, the surveilling of populations who do not match racial and gender norms has increased. Modern-day biometrics make assumptions about gender and race based on skin color, facial structure, body type, and body parts, which are encoded in predictive algorithms and other AI-driven systems. Growing empirical evidence points to the obstacles this poses for trans and non-binary individuals in several spheres, including border security, healthcare, and social media. Drawing on autoethnographic vignettes, semi-structured interviews, and survey responses, we look to the increased use of binary-based biometric technologies and automatic gender recognition (AGR), which rely on outmoded understandings of gender as static, measurable, and physiological. Our ethnographic data demonstrate how trans and non-binary bodies are forced to bend to these systems; meanwhile these technologies and algorithms increasingly extract data on trans and non-binary users, which may then be used as challenge sets to refine their accuracy. [ABSTRACT FROM AUTHOR]

Human affect recognition (HAR) using images of facial expression and electrocardiogram (ECG) signal plays an important role in predicting human intention. This system improves the performance of the system in applications like the security system, learning technologies and health care systems. The primary goal of our work is to recognize individual affect states automatically using the multilayered binary structured support vector machine (MBSVM), which efficiently classify the input into one of the four affect classes, relax, happy, sad and angry. The classification is performed efficiently by designing an efficient support vector machine (SVM) classifier in multilayer mode operation. The classifier is trained using the 8-fold cross-validation method, which improves the learning of the classifier, thus increasing its efficiency. The classification and recognition accuracy is enhanced and also overcomes the drawback of 'facial mimicry' by using hybrid features that are extracted from both facial images (visual elements) and physiological signal ECG (signal features). The reliability of the input database is improved by acquiring the face images and ECG signals experimentally and by inducing emotions through image stimuli. The performance of the affect recognition system is evaluated using the confusion matrix, obtaining the classification accuracy of 96.88%. [ABSTRACT FROM AUTHOR]

The article focuses on daily dose of ω-3 polyunsaturated fatty acids (PUFA) improved by decreased impaired β-cell function and fetal biometrics. Topics discussed include Placental insufficiency-induced intrauterine growth restriction (IUGR) in muscle-centric fetal programming, risk of hypertension caused by decreased nutrients and recovery of glucose homeostasis in IUGR fetus through increased glucose metabolism.

Electroencephalogram (EEG) plays an essential role in analysing and recognizing brain-related diseases. EEG has been increasingly used as a new type of biometrics in person identification and verification systems. These EEG-based systems are important components in applications for both police and civilian works, and both areas process a huge amount of EEG data. Storing and transmitting these huge amounts of data are significant challenges for data compression techniques. Lossy compression is used for EEG data as it provides a higher compression ratio (CR) than lossless compression techniques. However, lossy compression can negatively influence the performance of EEG-based person identification and verification systems via the loss of information in the reconstructed data. To address this, we propose introducing performance measures as additional features in evaluating lossy compression techniques for EEG data. Our research explores if a common value of CR exists for different systems using datasets with lossy compression that could provide almost the same system performance with those using datasets without lossy compression. We performed experiments on EEG-based person identification and verification systems using two large EEG datasets, CHB MIT Scalp and Alcoholism, to investigate the relationship between standard lossy compression measures and our proposed system performance measures with the two lossy compression techniques, discrete wavelet transform—adaptive arithmetic coding and discrete wavelet transform—set partitioning in hierarchical trees. Our experimental results showed a common value of CR exists for different systems, specifically, 70 for person identification systems and 50 for person verification systems. [ABSTRACT FROM AUTHOR]

This essay provides an account of how to distinguish between health and pathology of trait tokens in medical theory. It (1) proposes to distinguish between two health/pathology concepts-health/pathology pertaining to survival and health/pathology pertaining to reproduction. It (2) defines measures for survival-efficiency and reproduction-efficiency of performances of physiological functions. It (3) provides an account of how, using the efficiency measures, to draw the line between health and pathology. The account draws, but seeks to improve, on Christopher Boorse's biostatistical theory. In relation to that theory, the suggested account has the advantages (1) that it defines efficiency and (2) that it harmonizes with judgments in medical theory in cases of common diseases and "normal aging." Furthermore, the essay argues against a competing idea of how to improve on the biostatistical theory, advocated by Peter Schwartz and Daniel Hausman. [ABSTRACT FROM AUTHOR]

Anaplasma phagocytophilum (Ap) , agent of human anaplasmosis, is an intracellular bacterium that causes the second most common tick-borne illness in North America. To address the lack of a genetic system for these pathogens, we used random Himar1 transposon mutagenesis to generate a library of Ap mutants capable of replicating in human promyelocytes (HL-60 cells). Illumina sequencing identified 1195 non-randomly distributed insertions. As the density of mutants was non-saturating, genes without insertions were either essential for Ap , or spared randomly. To resolve this question, we applied a biostatistical method for prediction of essential genes. Since the chances that a transposon was inserted into genomic TA dinucleotide sites should be the same for all loci, we used a Markov chain Monte Carlo model to estimate the probability that a non-mutated gene was essential for Ap. Predicted essential genes included those coding for structural ribosomal proteins, enzymes involved in metabolism, components of the type IV secretion system, antioxidant defense molecules and hypothetical proteins. We have used an in silico post-genomic approach to predict genes with high probability of being essential for replication of Ap in HL-60 cells. These results will help target genes to investigate their role in the pathogenesis of human anaplasmosis. [ABSTRACT FROM AUTHOR]

Introduction: The Roche Cobas c513 (c513) is a dedicated stand-alone high throughput HbA1c analyzer. We evaluated the performance and the difference in turnaround times (TAT) of the c513 against our Cobas 8000 c502 (c502). Methods: We assessed the linearity and precision of the c513, and its agreement (Deming regression and Bland–Altman analysis) with the c502 assay. We compared TAT for these analyzers for a single run of 50 samples and for all samples run over 2 comparable time periods. Results: The c513 assay was linear from 4.4–18.3% HbA1c. Interassay precision (CV%) was 1.2 and 0.8 at HbA1c levels of 5.7 and 10.5%, respectively. The c513 assay showed excellent concordance with the c502 assay (r = 0.997) with no significant difference between methods by Bland–Altman analysis (mean difference = 0.021% HbA1c, P = 0.1422). The c513 took 17 min to analyze 50 samples, compared to 40 min on the c502. Over comparable 2-month periods, 90% of samples requiring HbA1c tests only were completed under 25 min (c513) vs 30–35 min (c502). For tubes sharing complete blood count (CBC) testing with HbA1c, the 90th percentile TAT was 35–40 min (c513) compared to 45–50 min (c502). Conclusion: The c513 assay performs well with excellent correlation to the c502 assay. The improved TAT of the c513 is suitable when there are demands for rapid HbA1c results and it may forestall requests for point-of-care testing. It is also attractive to sites with heavy workloads with a claimed throughput of 400 tests / h. [ABSTRACT FROM AUTHOR]

Biometric information is unique to a human, so it would be desirable to use the biometric characteristic as the private key in a cryptographic system to protect data security and privacy. In this paper, we introduce a notion called two-factor decryption (TFD). Informally speaking, a TFD scheme is a variant of the public-key encryption (PKE) scheme. In a TFD scheme, messages are encrypted under public keys as that in a standard PKE scheme, but both private keys (i.e. the first factor) and biometric inputs (i.e. the second factor) are required to decrypt the ciphertexts and obtain the underlying plaintexts. We first describe a framework of TFD, and then define a formal security model for TFD. Thereafter, we present a generic construction on TFD based on the cryptographic primitives of linear sketch and functional encryption (FE) with certain properties and analyse its security. In addition, we give instantiations of TFD by applying concrete FE schemes into the generic construction and show their applications. [ABSTRACT FROM AUTHOR]

A control theory perspective on determination of optimal dynamic treatment regimes is considered. The aim is to adapt statistical methodology that has been developed for medical or other biostatistical applications to incorporate powerful control techniques that have been designed for engineering or other technological problems. Data tend to be sparse and noisy in the biostatistical area and interest has tended to be in statistical inference for treatment effects. In engineering fields, experimental data can be more easily obtained and reproduced and interest is more often in performance and stability of proposed controllers rather than modeling and inference per se. We propose that modeling and estimation should be based on standard statistical techniques but subsequent treatment policy should be obtained from robust control. To bring focus, we concentrate on A‐learning methodology as developed in the biostatistical literature and H∞‐synthesis from control theory. Simulations and two applications demonstrate robustness of the H∞ strategy compared to standard A‐learning in the presence of model misspecification or measurement error. [ABSTRACT FROM AUTHOR]

Vulvodynia is a disabling condition in which pelvic floor muscles' (PFM) hypertonicity plays an important role. To evaluate biometric changes in PFM in women with vulvodynia undergoing kinesiotherapy treatment protocol (KTP). A single-blinded randomized controlled trial of 57 women with vulvodynia randomly assigned to either KTP + amitriptyline or amitriptyline alone (controls) for treatment. Four-dimensional translabial ultrasound assessed PFM regarding symphysis-levator distance at rest, anorectal angle at rest, excursion of the levator plate angle, and levator hiatal narrowing. Volunteers underwent a vaginal examination for a cotton swab test (CST), fulfillment of Friedrich criteria score and PFM power of contraction, and completed a diary of sexual pain and frequency of vaginal intercourse. Outcomes were assessed at baseline and after 8 weeks of treatment. Primary outcomes were differences in biometric parameters assessed by four-dimensional translabial ultrasound after treatment, between groups. Secondary outcomes were changes in clinical variables (CST, Friedrich criteria, PFM power of contraction, frequency of intercourse, and intensity of sexual pain) between groups and correlation analysis between biometric parameters and clinical variables. Only the KTP group had statistically significant changes in biometric parameters after treatment (symphysis-levator distance: 0.22 ± 0.2, 95% CI = 0.1–0.4, P =.008; levator hiatal narrowing: −0.33 ± 0.2, 95% CI = −1 to −0.2, P =.04). Comparisons between groups showed that symphysis-levator distance (0.3, 95% CI = 0.2–0.6, P =.005) and excursion of levator plate angle (4.9, 95% CI = −0.4 to 10.1, P =.02) improved significantly after KTP treatment. Clinical variables showed greater improvement in the group treated with KTP for CST (difference of −3.7, 95% CI = −7 to −0.4, P =.01), Friedrich criteria (difference of −1.9, 95% CI = −3.2 to −0.6, P =.003), PFM power of contraction (0.3, 95% CI = 0.1–0.6, P =.05) and intensity of sexual pain (reduction of 1.7, 95% CI = −3.1 to −0.2, P =.01). Some clinical and biometric variables correlated positively, for example, frequency of vaginal intercourse and anorectal angle (P =.04; r = 0.25), or inversely, for example, pain intensity at CST and anorectal angle (P =.004, r = −0.31). This study provides evidence on efficiency of a physical therapy protocol for improvement of symptoms of vulvodynia and hypertonicity changes. This pilot study suggests that KTP for women with vulvodynia promoted significant changes in PFM biometric measures, consistent with alterations in hypertonicity and clinical improvement. Bardin MG, Giraldo PC, Martinho N. Pelvic Floor Biometric Changes Assessed by 4D Translabial Ultrassound in Women With Vulvodynia Submitted to Physical Therapy: A Pilot Study of a Randomized Controlled Trial. J Sex Med 2020;17:2236–2246. [ABSTRACT FROM AUTHOR]

The article provides associate professor Lauri Goldkind's commentary, and insights about social work and artificial intelligence, and discusses potential implications for future research, practitioners, and policy makers. Recommendations and list of references also is given.

An important tool to evaluate the performance of any design is an optimal benchmark proposed by O'Quigley and others (2002. Non-parametric optimal design in dose finding studies. Biostatistics3, 51-56) that provides an upper bound on the performance of a design under a given scenario. The original benchmark can only be applied to dose finding studies with a binary endpoint. However, there is a growing interest in dose finding studies involving continuous outcomes, but no benchmark for such studies has been developed. We show that the original benchmark and its extension by Cheung (2014. Simple benchmark for complex dose finding studies. Biometrics70, 389-397), when looked at from a different perspective, can be generalized to various settings with several discrete and continuous outcomes. We illustrate and compare the benchmark's performance in the setting of a dose finding Phase I clinical trial with a continuous toxicity endpoint and a Phase I/II trial with binary toxicity and continuous efficacy endpoints. We show that the proposed benchmark provides an accurate upper bound in these contexts and serves as a powerful tool for evaluating designs. [ABSTRACT FROM AUTHOR]

In the chapters on regression models (chapters 17-19), basic ideas are introduced with one predictor before introducing models with multiple covariates. I have used the second edition of this book, published in 2000 by Duxbury, as the textbook in my introductory courses for many years and have been looking forward to reading the third edition, wondering how different it would be. The second part, probability, contains four chapters, in contrast to three chapters in the second edition. [Extracted from the article]

The article features Professor Maria Blettner and her career in radiation epidemiology. Topics discussed include her educational background, reason that her stay at the National Cancer Institute (NCI) was considered particularly groundbreaking, and her contributions to methodologic fundamentals in epidemiology. Her awards received are also mentioned.

Objectives We simultaneously investigated the role of three hypotheses regarding age-related differences in face processing: perceptual degradation, impaired holistic processing, and an interaction between the two. Methods Young adults (YA) aged 20–33-year olds, middle-age adults (MA) aged 50–64-year olds, and older adults (OA) aged 65–82-year olds were tested on the context congruency paradigm, which allows measurement of face-specific holistic processing across the life span (Meinhardt-Injac, Persike & Meinhardt, 2014. Acta Psychologica , 151 , 155–163). Perceptual degradation was examined by measuring performance with faces that were not filtered (FSF), with faces filtered to preserve low spatial frequencies (LSF), and with faces filtered to preserve high spatial frequencies (HSF). Results We found that reducing perceptual signal strength had a greater impact on MA and OA for HSF faces, but not LSF faces. Context congruency effects were significant and of comparable magnitude across ages for FSF, LSF, and HSF faces. By using watches as control objects, we show that these holistic effects reflect face-specific mechanisms in all age groups. Discussion Our results support the perceptual degradation hypothesis for faces containing only HSF and suggest that holistic processing is preserved in aging even under conditions of reduced signal strength. [ABSTRACT FROM AUTHOR]

Many research studies aim to draw causal inferences using data from large, nationally representative survey samples, and many of these studies use propensity score matching to make those causal inferences as rigorous as possible given the non-experimental nature of the data. However, very few applied studies are careful about incorporating the survey design with the propensity score analysis, which may mean that the results do not generate population inferences. This may be because few methodological studies examine how to best combine these methods. Furthermore, even fewer of them investigate different non-response mechanisms. This study examines methods for handling survey weights in propensity score matching analyses of survey data under different non-response mechanisms. Our main conclusions are: (i) whether the survey weights are incorporated in the estimation of the propensity score does not impact estimation of the population treatment effect, as long as good population treated-comparison balance is achieved on confounders, (ii) survey weights must be used in the outcome analysis, and (iii) the transferring of survey weights (i.e., assigning the weights of the treated units to the comparison units matched to them) can be beneficial under certain non-response mechanisms. [ABSTRACT FROM AUTHOR]

Summary: The nested case‐control and case‐cohort designs are two main approaches for carrying out a substudy within a prospective cohort. This article adapts multiple imputation (MI) methods for handling missing covariates in full‐cohort studies for nested case‐control and case‐cohort studies. We consider data missing by design and data missing by chance. MI analyses that make use of full‐cohort data and MI analyses based on substudy data only are described, alongside an intermediate approach in which the imputation uses full‐cohort data but the analysis uses only the substudy. We describe adaptations to two imputation methods: the approximate method (MI‐approx) of White and Royston (2009) and the "substantive model compatible" (MI‐SMC) method of Bartlett et al. (2015). We also apply the "MI matched set" approach of Seaman and Keogh (2015) to nested case‐control studies, which does not require any full‐cohort information. The methods are investigated using simulation studies and all perform well when their assumptions hold. Substantial gains in efficiency can be made by imputing data missing by design using the full‐cohort approach or by imputing data missing by chance in analyses using the substudy only. The intermediate approach brings greater gains in efficiency relative to the substudy approach and is more robust to imputation model misspecification than the full‐cohort approach. The methods are illustrated using the ARIC Study cohort. Supplementary Materials provide R and Stata code. [ABSTRACT FROM AUTHOR]

Summary: The box‐and‐whiskers plot is an extraordinary graphical tool that provides a quick visual summary of an observed distribution. In spite of its many extensions, a really suitable boxplot to display circular data is not yet available. Thanks to its simplicity and strong visual impact, such a tool would be especially useful in all fields where circular measures arise: biometrics, astronomy, environmetrics, Earth sciences, to cite just a few. For this reason, in line with Tukey's original idea, a Tukey‐like circular boxplot is introduced. Several simulated and real datasets arising in biology are used to illustrate the proposed graphical tool. [ABSTRACT FROM AUTHOR]

Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron-Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches. [ABSTRACT FROM AUTHOR]

Nursing home bed-hold policies provide continuity of care for Medicaid beneficiaries by paying nursing homes to reserve beds so residents can return to their facility of occupancy following an acute hospitalization. In 2001, Michigan implemented bed-hold policies in nursing homes. We investigated the impact of these policies on mortality and hospitalizations using 1999-2004 quarterly data from nursing homes in Michigan and nursing homes in 11 states that did not implement such policies. Synthetic Control has been used to estimate the effects of policies by accounting for changes over time unrelated to the intervention. Synthetic Control is intended for scalar continuous outcome at each period, and assumes a single treated unit and multiple control units. We propose a Bayesian procedure to overcome these limitations. It imputes the outcomes of nursing homes in Michigan if they were not exposed to the policy by matching to non-exposed nursing homes that are associated with the exposed ones in the pre-policy period. Because sampling from a Bayesian model is computationally challenging, we describe an approximation procedure that can be implemented using existing software. Our approach can be applied to other studies that examine the impact of policies. [ABSTRACT FROM AUTHOR]

Summary: We consider a research scenario motivated by integrating multiple sources of information for better knowledge discovery in diverse dynamic biological processes. Given two longitudinal high‐dimensional datasets for a group of subjects, we want to extract shared latent trends and identify relevant features. To solve this problem, we present a new statistical method named as joint principal trend analysis (JPTA). We demonstrate the utility of JPTA through simulations and applications to gene expression data of the mammalian cell cycle and longitudinal transcriptional profiling data in response to influenza viral infections. [ABSTRACT FROM AUTHOR]

Evaluation of the body chemical composition of beef cattle can only be measured postmortem and those data cannot be used in real production scenarios to adjust nutritional plans. The objective of this study was to develop multiple linear regression equations from in vivo measurements, such as ultrasound parameters [backfat thickness (uBFT, mm), rump fat thickness (uRF, mm), and ribeye area (uLMA, cm2)], shrunk body weight (SBW, kg), age (AG, d), hip height (HH, m), as well as from postmortem measurements (composition of the 9th to 11th rib section) to predict the empty body and carcass chemical composition for Nellore cattle. Thirty-three young bulls were used (339 ± 36.15 kg and 448 ± 17.78 d for initial weight and age, respectively). Empty body chemical composition (protein, fat, water, and ash in kg) was obtained by combining noncarcass and carcass components. Data were analyzed using the PROC REG procedure of SAS software. Mallows' Cp values were close to the ideal value of number of independent variables in the prediction equations plus one. Equations to predict chemical components of both empty body and carcass using in vivo measurements presented higher R2 values than those determined by postmortem measurements. Chemical composition of the empty body using in vivo measurements was predicted with R2 > 0.73. Equations to predict chemical composition of the carcass from in vivo measurements showed R2 lower (R2 < 0.68) than observed for empty body, except for the water (R2 = 0.84). The independent variables SBW, uRF, and AG were sufficient to predict the fat, water, energy components of the empty body, whereas for estimation of protein content the uRF, HH, and SBW were satisfactory. For the calculation of the ash, the SBW variable in the equation was sufficient. Chemical compounds from components of the empty body of Nellore cattle can be calculated by the following equations: protein (kg) = 47.92 + 0.18 × SBW - 1.46 × uRF - 30.72 × HH (R² = 0.94, RMSPE = 1.79); fat (kg) = 11.33 + 0.16 × SBW + 2.09 × uRF - 0.06 × AG (R² = 0.74, RMSPE = 4.18); water (kg) = - 34.00 + 0.55 × SBW + 0.10 × AG - 2.34 × uRF (R² = 0.96, RMSPE = 5.47). In conclusion, the coefficients of determination (for determining the chemical composition of the empty body) of the equations derived from in vivo measures were higher than those of the equations obtained from rib section measurements taken postmortem, and better than coefficients of determination of the equations to predict the chemical composition of the carcass. [ABSTRACT FROM AUTHOR]

Summary: There is an increasing need to construct a risk‐prediction scoring system for survival data and identify important risk factors (e.g., biomarkers) for patient screening and treatment recommendation. However, most existing methodologies either rely on strong model assumptions (e.g., proportional hazards) or only handle binary outcomes. In this article, we propose a flexible method that simultaneously selects important risk factors and identifies the optimal linear combination of risk factors by maximizing a pseudo‐likelihood function based on the time‐dependent area under the receiver operating characteristic curve. Our method is particularly useful for risk evaluation and recommendation of optimal subsequent treatments. We show that the proposed method has desirable theoretical properties, including asymptotic normality and the oracle property after variable selection. Numerical performance is evaluated on several simulation data sets and an application to hepatocellular carcinoma data. [ABSTRACT FROM AUTHOR]

Summary: Survival data collected from a prevalent cohort are subject to left truncation and the analysis is challenging. Conditional approaches for left‐truncated data could be inefficient as they ignore the information in the marginal likelihood of the truncation times. Length‐biased sampling methods may improve the estimation efficiency but only when the underlying truncation time is uniform; otherwise, they may generate biased estimates. We propose a semiparametric method for left‐truncated data under the Cox model with no parametric distributional assumption about the truncation times. Our approach is to make inference based on the conditional likelihood augmented with a pairwise likelihood, which eliminates the truncation distribution, yet retains the information about the regression coefficients and the baseline hazard function in the marginal likelihood. An iterative algorithm is provided to solve for the regression coefficients and the baseline hazard function simultaneously. By empirical process and U‐process theories, it has been shown that the proposed estimator is consistent and asymptotically normal with a closed‐form consistent variance estimator. Simulation studies show substantial efficiency gain of our estimator in both the regression coefficients and the cumulative baseline hazard function over the conditional approach estimator. When the uniform truncation assumption holds, our estimator enjoys smaller biases and efficiency comparable to that of the full maximum likelihood estimator. An application to the analysis of a chronic kidney disease cohort study illustrates the utility of the method. [ABSTRACT FROM AUTHOR]

Summary: Somatic mutations are the driving forces for tumor development, and recent advances in cancer genome sequencing have made it feasible to evaluate the association between somatic mutations and cancer‐related traits in large sample sizes. However, despite increasingly large sample sizes, it remains challenging to conduct statistical analysis for somatic mutations, because the vast majority of somatic mutations occur at very low frequencies. Furthermore, cancer is a complex disease and it is often accompanied by multiple traits that reflect various aspects of cancer; how to combine the information of these traits to identify important somatic mutations poses additional challenges. In this article, we introduce a statistical approach, named as SOMAT, for detecting somatic mutations associated with multiple cancer‐related traits. Our approach provides a flexible framework for analyzing continuous, binary, or a mixture of both types of traits, and is statistically powerful and computationally efficient. In addition, we propose a data‐adaptive procedure, which is grid‐search free, for effectively combining test statistics to enhance statistical power. We conduct an extensive study and show that the proposed approach maintains correct type I error and is more powerful than existing approaches under the scenarios considered. We also apply our approach to an exome‐sequencing study of liver tumor for illustration. [ABSTRACT FROM AUTHOR]

Summary: Epidemiologic studies and disease prevention trials often seek to relate an exposure variable to a failure time that suffers from interval‐censoring. When the failure rate is low and the time intervals are wide, a large cohort is often required so as to yield reliable precision on the exposure‐failure‐time relationship. However, large cohort studies with simple random sampling could be prohibitive for investigators with a limited budget, especially when the exposure variables are expensive to obtain. Alternative cost‐effective sampling designs and inference procedures are therefore desirable. We propose an outcome‐dependent sampling (ODS) design with interval‐censored failure time data, where we enrich the observed sample by selectively including certain more informative failure subjects. We develop a novel sieve semiparametric maximum empirical likelihood approach for fitting the proportional hazards model to data from the proposed interval‐censoring ODS design. This approach employs the empirical likelihood and sieve methods to deal with the infinite‐dimensional nuisance parameters, which greatly reduces the dimensionality of the estimation problem and eases the computation difficulty. The consistency and asymptotic normality of the resulting regression parameter estimator are established. The results from our extensive simulation study show that the proposed design and method works well for practical situations and is more efficient than the alternative designs and competing approaches. An example from the Atherosclerosis Risk in Communities (ARIC) study is provided for illustration. [ABSTRACT FROM AUTHOR]

Summary: Phosphorylated proteins provide insight into tumor etiology and are used as diagnostic, prognostic, and therapeutic markers of complex diseases. However, pre‐analytic variations, such as freezing delay after biopsy acquisition, often occur in real hospital settings and potentially lead to inaccurate results. The objective of this work is to develop statistical methodology to assess the stability of phosphorylated proteins under short‐time cold ischemia. We consider a hierarchical model to determine if phosphorylation abundance of a protein at a particular phosphorylation site remains constant or not during cold ischemia. When phosphorylation levels vary across time, we estimate the direction of the changes in each protein based on the maximum overall posterior probability and on the pairwise posterior probabilities, respectively. We analyze a dataset of ovarian tumor tissues that suffered cold‐ischemia shock before the proteomic profiling. Gajadhar et al. (2015) applied independent clusterings for each patient because of the high heterogeneity across patients, while our proposed model shares information allowing conclusions for the entire sample population. Using the proposed model, 15 out of 32 proteins show significant changes during 1‐hour cold ischemia. Through simulation studies, we conclude that our proposed methodology has a higher accuracy for detecting changes compared to an order restricted inference method. Our approach provides inference on the stability of these phosphorylated proteins, which is valuable when using these proteins as biomarkers for a disease. [ABSTRACT FROM AUTHOR]