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The aim of this study is to evaluate the current practice of statistical analysis of eye data in clinical science papers published in British Journal of Ophthalmology ( BJO ) and to determine whether the practice of statistical analysis has improved in the past two decades. All clinical science papers (n=125) published in BJO in January-June 2017 were reviewed for their statistical analysis approaches for analysing primary ocular measure. We compared our findings to the results from a previous paper that reviewed BJO papers in 1995. Of 112 papers eligible for analysis, half of the studies analysed the data at an individual level because of the nature of observation, 16 (14%) studies analysed data from one eye only, 36 (32%) studies analysed data from both eyes at ocular level, one study (1%) analysed the overall summary of ocular finding per individual and three (3%) studies used the paired comparison. Among studies with data available from both eyes, 50 (89%) of 56 papers in 2017 did not analyse data from both eyes or ignored the intereye correlation, as compared with in 60 (90%) of 67 papers in 1995 (P=0.96). Among studies that analysed data from both eyes at an ocular level, 33 (92%) of 36 studies completely ignored the intereye correlation in 2017, as compared with in 16 (89%) of 18 studies in 1995 (P=0.40). A majority of studies did not analyse the data properly when data from both eyes were available. The practice of statistical analysis did not improve in the past two decades. Collaborative efforts should be made in the vision research community to improve the practice of statistical analysis for ocular data., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

ObjectiveTo identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2020.MethodA broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and image informatics. We only considered papers that have been published in journals providing at least three articles in the query response. Section editors then independently reviewed the titles and abstracts of preselected papers assessed on a three-point Likert scale. Papers were rated from 1 (do not include) to 3 (should be included) for each topical area (sensors, signals, and imaging informatics) and those with an average score of 2 or above were subsequently read and assessed again by two of the three co-editors. Finally, the top 14 papers with the highest combined scores were considered based on consensus.ResultsThe search for papers was executed in January 2021. After removing duplicates and conference proceedings, the query returned a set of 101, 193, and 529 papers for sensors, signals, and imaging informatics, respectively. We filtered out journals that had less than three papers in the query results, reducing the number of papers to 41, 117, and 333, respectively. From these, the co-editors identified 22 candidate papers with more than 2 Likert points on average, from which 14 candidate best papers were nominated after intensive discussion. At least five external reviewers then rated the remaining papers. The four finalist papers were found using the composite rating of all external reviewers. These best papers were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board.ConclusionsSensors, signals, and imaging informatics is a dynamic field of intense research. The four best papers represent advanced approaches for combining, processing, modeling, and analyzing heterogeneous sensor and imaging data. The selected papers demonstrate the combination and fusion of multiple sensors and sensor networks using electrocardiogram (ECG), electroencephalogram (EEG), or photoplethysmogram (PPG) with advanced data processing, deep and machine learning techniques, and present image processing modalities beyond state-of-the-art that significantly support and further improve medical decision making.

How to analyze data when there is violation of the positivity assumption? Several possible solutions exist in the literature. In this paper, we consider propensity score (PS) methods that are commonly used in observational studies to assess causal treatment effects in the context where the positivity assumption is violated. We focus on and examine four specific alternative solutions to the inverse probability weighting (IPW) trimming and truncation: matching weight (MW), Shannon's entropy weight (EW), overlap weight (OW), and beta weight (BW) estimators. We first specify their target population, the population of patients for whom clinical equipoise, that is, where we have sufficient PS overlap. Then, we establish the nexus among the different corresponding weights (and estimators); this allows us to highlight the shared properties and theoretical implications of these estimators. Finally, we introduce their augmented estimators that take advantage of estimating both the propensity score and outcome regression models to enhance the treatment effect estimators in terms of bias and efficiency. We also elucidate the role of the OW estimator as the flagship of all these methods that target the overlap population. Our analytic results demonstrate that OW, MW, and EW are preferable to IPW and some cases of BW when there is a moderate or extreme (stochastic or structural) violation of the positivity assumption. We then evaluate, compare, and confirm the finite-sample performance of the aforementioned estimators via Monte Carlo simulations. Finally, we illustrate these methods using two real-world data examples marked by violations of the positivity assumption., (© 2024 Wiley‐VCH GmbH.)

Objective: To compare the estimated date of confinement of paper gestational wheels to the estimated date of confinement of APPs wheels using a standard last menstrual period., Methods: Obstetric providers were asked for their gestational wheels. The last menstrual period was set at Jan. 1, 2013, and the estimated date of confinement obtained was compared with the estimated date of confinement of Oct. 8th if the pregnancy completed 280 days. The process was performed on 20 electronic APPs downloadable to cell phones. The process was repeated for both for the leap year of 2012., Results: Thirty-one paper wheels from a variety of sources were collected. Ten wheels (35%) were consistent with the standard pregnancy duration of 280 days. Among the wheels surveyed, the largest discrepancy was 4 days short of 280 days. Two wheels gave an estimated date of confinement that differed from each other by 7 days. Wheels from the same source did not agree with each other. Twenty electronic gestational age calculators were examined. All 20 gave an estimated date of confinement of Oct. 8 consistent with 280 days. None of the paper gestational wheels but all of the APPs corrected for a leap year., Conclusion: In contrast to APPs gestational age calculators, the estimated date of confinement of the majority of paper wheels deviated from the standard pregnancy duration of 280 days. Precision in gestational age assessment is critical in a variety of clinical settings and heightened by the focus by payers and reporting agencies on elective deliveries before 39 weeks. The use of paper gestational age wheels should be abandoned., (Copyright © 2014 Mosby, Inc. All rights reserved.)

Background: As part of EUROCAT's surveillance of congenital anomalies in Europe, a statistical monitoring system has been developed to detect recent clusters or long-term (10 year) time trends. The purpose of this article is to describe the system for the identification and investigation of 10-year time trends, conceived as a "screening" tool ultimately leading to the identification of trends which may be due to changing teratogenic factors., Methods: The EUROCAT database consists of all cases of congenital anomalies including livebirths, fetal deaths from 20 weeks gestational age, and terminations of pregnancy for fetal anomaly. Monitoring of 10-year trends is performed for each registry for each of 96 non-independent EUROCAT congenital anomaly subgroups, while Pan-Europe analysis combines data from all registries. The monitoring results are reviewed, prioritized according to a prioritization strategy, and communicated to registries for investigation. Twenty-one registries covering over 4 million births, from 1999 to 2008, were included in monitoring in 2010., Conclusions: Significant increasing trends were detected for abdominal wall anomalies, gastroschisis, hypospadias, Trisomy 18 and renal dysplasia in the Pan-Europe analysis while 68 increasing trends were identified in individual registries. A decreasing trend was detected in over one-third of anomaly subgroups in the Pan-Europe analysis, and 16.9% of individual registry tests. Registry preliminary investigations indicated that many trends are due to changes in data quality, ascertainment, screening, or diagnostic methods. Some trends are inevitably chance phenomena related to multiple testing, while others seem to represent real and continuing change needing further investigation and response by regional/national public health authorities., (Copyright © 2011 Wiley-Liss, Inc.)

Finlay-Wilkinson regression is a popular method for modeling genotype-environment interaction in plant breeding and crop variety testing. When environment is a random factor, this model may be cast as a factor-analytic variance-covariance structure, implying a regression on random latent environmental variables. This paper reviews such models with a focus on their use in the analysis of multi-environment trials for the purpose of making predictions in a target population of environments. We investigate the implication of random versus fixed effects assumptions, starting from basic analysis-of-variance models, then moving on to factor-analytic models and considering the transition to models involving observable environmental covariates, which promise to provide more accurate and targeted predictions than models with latent environmental variables., (© 2024 The Author(s). Biometrical Journal published by Wiley‐VCH GmbH.)

Purpose: To use a combination of partial least squares regression and a machine learning approach to predict intraocular lens (IOL) tilt using preoperative biometry data., Setting: Kepler University Clinic Linz, Linz, Austria., Design: Prospective single-center study., Methods: Optical coherence tomography, autorefraction, and subjective refraction were performed at baseline and 8 weeks after cataract surgery. In analysis I, only 1 eye per patient was included and a tilt prediction model was generated. In analysis II, a pairwise comparison between right and left eyes was performed., Results: In analysis I, 50 eyes of 50 patients were analyzed. Difference in amount, orientation, and vector from preoperative to postoperative lens tilt was -0.13 degrees, 2.14 degrees, and 1.20 degrees, respectively. A high predictive power (variable importance for projection [VIP]) for postoperative tilt prediction was found for preoperative tilt (VIP = 2.2), pupil decentration (VIP = 1.5), lens thickness (VIP = 1.1), axial eye length (VIP = 0.9), and preoperative lens decentration (VIP = 0.8). These variables were applied to a machine learning algorithm resulting in an out of bag score of 0.92 degrees. In analysis II, 76 eyes of 38 patients were included. The difference of preoperative to postoperative IOL tilt of right and left eyes of the same individual was statistically relevant., Conclusions: Postoperative IOL tilt showed excellent predictability using preoperative biometry data and a combination of partial least squares regression and a machine learning algorithm. Preoperative lens tilt, pupil decentration, lens thickness, axial eye length, and preoperative lens decentration were found to be the most relevant parameters for this prediction model., (Copyright © 2024 Published by Wolters Kluwer on behalf of ASCRS and ESCRS.)

Due to migration, terror‐threats and the viral pandemic, various EU member states have re‐established internal border control or even closed their borders. European Association for Biometrics (EAB), a non‐profit organisation, solicited the views of its members on ways which biometric technologies and services may be used to help with re‐establishing open borders within the Schengen area while at the same time mitigating any adverse effects. From the responses received, this position paper was composed to identify ideas to re‐establish free travel between the member states in the Schengen area. The paper covers the contending needs for security, open borders and fundamental rights as well as legal constraints that any technological solution must consider. A range of specific technologies for direct biometric recognition alongside complementary measures are outlined. The interrelated issues of ethical and societal considerations are also highlighted. Provided a holistic approach is adopted, it may be possible to reach a more optimal trade‐off with regards to open borders while maintaining a high‐level of security and protection of fundamental rights. European Association for Biometrics and its members can play an important role in fostering a shared understanding of security and mobility challenges and their solutions. [ABSTRACT FROM AUTHOR]

An assessment is made the biological and economical chemical indicators of 11 samples Burley tobacco, which 10 new promising hydrides in first generation and standard Pliska 2002 variety. As a result, the study found that the most favorable biometric indicators is presented Hybrid 1595A. With the short vegetative period is characterized Hybrid 1594, and the longest such – standard Pliska 2002 variety. All new selection lines excel at length of the vegetative period standard Pliska 2002 variety. The highest yield is represented by Hybrid 1595A, followed by Hybrid 1593. The highest percentage of first grade is derived from Hybrid 1593А, which gives the lowest percentage of third grade. With favorable ratio of grades is formed and Hybrid 1593. All new lines superior the standard variety Pliska 2002, to as yield, so and percentage of first grade. The studied hybrids, according to their biometric indicators, are grouped into two clusters, and two main components influence the clustering of the hybrids and explain 77.17% of the total variation. Depending on their economic indicators, the hybrids are arranged in two clusters, and in them, too, two main components are the cause of 89.27% of the total variation. Complex assessment of biological and economic indicators shows that with the best parameters differs Hybrid 1593, and work with it should continue with production testing. All new lines superior the standard variety Pliska 2002, to as yield, so and percentage of first grade, which is an indicator of the success of the selection work. [ABSTRACT FROM AUTHOR]

Fingerprints have been widely used and accepted as an effective method of human identification. This biometric tool aids in criminal investigations for personal identity for over a century. Whilst the Automatic Fingerprint Identification System (AFIS) has bolstered security efforts, it has also opened doors to potential scams, affecting both civilian and law enforcement operations. Despite extensive research on fingerprint authentication issues, very little attention has been given to addressing the problem of fingerprint alteration or obfuscation. Fraudsters, with the guidance of experts, have developed new techniques to obscure their fingerprints intentionally. Fingerprint obfuscation is the deliberate alteration of fingerprint patterns with the aim of concealing their true identity, raising concerns amongst security and investigative organizations. The objective of the current communication is to highlight the numerous techniques used for obfuscation, forgery and alteration of fingerprints in humans. It further accentuates the need for identification and interpretation of these altered fingerprints and recommends notifying law enforcement agencies of potential threats., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Causal inference methods have been developed for longitudinal observational study designs where confounding is thought to occur over time. In particular, one may estimate and contrast the population mean counterfactual outcome under specific exposure patterns. In such contexts, confounders of the longitudinal treatment-outcome association are generally identified using domain-specific knowledge. However, this may leave an analyst with a large set of potential confounders that may hinder estimation. Previous approaches to data-adaptive model selection for this type of causal parameter were limited to the single time-point setting. We develop a longitudinal extension of a collaborative targeted minimum loss-based estimation (C-TMLE) algorithm that can be applied to perform variable selection in the models for the probability of treatment with the goal of improving the estimation of the population mean counterfactual outcome under a fixed exposure pattern. We investigate the properties of this method through a simulation study, comparing it to G-Computation and inverse probability of treatment weighting. We then apply the method in a real-data example to evaluate the safety of trimester-specific exposure to inhaled corticosteroids during pregnancy in women with mild asthma. The data for this study were obtained from the linkage of electronic health databases in the province of Quebec, Canada. The C-TMLE covariate selection approach allowed for a reduction of the set of potential confounders, which included baseline and longitudinal variables., (© 2019 The International Biometric Society.)

Germination and biometric parameters (number and length of needles, root length, trunk height, root neck diameter, and fractional seedling mass) of pine ( Pinus sylvestris L.) and spruce ( Picea abies L. Karst) seedlings with a closed root system are evaluated. The seedlings are grown on a mix of peat with growing media prepared of pulp and paper mill waste (PPW) conversed by worms ( Eisenia foctida) using chlorine-free pulp bleaching technology. The growing media includes active sludge; mixes of soil, bark, and woodchip product slivers ground together in a crusher; and coniferous sawdust. The share of (PPW) media is 20, 50 and 100 vol %. The experimental results have shown that in most cases the parameters of pine and spruce seedlings grown on these media in small volumes (20%) reach the parameters of the control samples represented by the seedlings grown on fertilized peat. When a growing medium is used in small amounts, the aboveground/subterraneous ratio of the seedlings is also similar to the control values. The more the growing medium is applied, the lower the parameters of the seedlings are in comparison with the control figures. This is probably caused by the offset of the nitrogen status of the growing media to nitrate nitrogen, which is not typical of natural forest soils. [ABSTRACT FROM AUTHOR]

The area of small irregular microscopic objects can be estimated by projecting the image of the objects on to tracing paper, making a tracing using a fibre tipped sketch pen, eluting the ink from the paper, and estimating the colour photometrically. Similarly treated spots of known area are used as standards. The method can also be used to measure the lengths of lines that are too complicated to be measured by other means.

When causal effects are to be estimated from observational data, we have to adjust for confounding. A central aim of covariate selection for causal inference is therefore to determine a set that is sufficient for confounding adjustment, but other aims such as efficiency or robustness can be important as well. In this paper, we review six general approaches to covariate selection that differ in the targeted type of adjustment set. We discuss and illustrate their advantages and disadvantages using causal diagrams. Moreover, the approaches and different ways of implementing them are compared empirically in an extensive simulation study. We conclude that there are considerable differences between the approaches but none of them is uniformly best, with performance depending on the chosen adjustment method as well as the true confounding structure. Any prior structural knowledge on the causal relations is helpful to choose the most appropriate method., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)