Comparison of IOL Power Prediction Accuracy between Two Swept-Source OCT Biometry Devices.

PURPOSE: To compare intraocular lens (IOL) power prediction accuracy of the Eyestar 900 (EyeS900) and the IOLMaster 700 (IOLM700) based on estimated and measured posterior corneal power DESIGN: Retrospective, inter-instrument reliability study METHODS: Setting: Institutional. PARTICIPANTS: 225 eyes of 225 cataract surgery patients. MEASUREMENTS: Patients underwent measurements by both devices preoperatively. MAIN OUTCOME MEASURES: Spherical Equivalent Prediction Error (SEQ-PE), spread of the SEQ-PE (Precision) and the absolute SEQ-PE (accuracy) of each device using Barrett Universal II (BUII) formula with either estimated posterior keratometry (E-PK) or measured posterior keratometry (M-PK). RESULTS: Trimmed mean SEQ-PEs of EyeS900 E-PK, EyeS900 M-PK, IOLM700 E-PK and IOLM700 M-PK were 0.03, 0.08, 0.02 and 0.08 D, respectively with no significant differences between EyeS900 E-PK and IOLM700 E-PK (p=0.31) as well as between EyeS900 M-PK and IOLM700 M-PK (p=0.31). Statistically significant SEQ-PE differences were found when E-PK and M-PK were compared, regardless of the device used, showing hyperopic SEQ-PE in M-PK calculations. Excellent correlation and agreement in SEQ-PE were found between the devices for both E-PK (p<0.001,r=0.848, mean bias:+0.01 D, 95% LOA of -0.32 to +0.34 D) and M-PK (p<0.001,r=0.776, mean bias:-0.01 D, 95% LOA of -0.42 to +0.39 D). No significant differences were found comparing absolute SEQ-PE and precision of the devices. CONCLUSION: The Eyestar 900 and the IOLMaster 700 show comparable IOL power prediction accuracy by the BUII formula using either estimated or measured posterior keratometry. An adjusted lens factor may be required for BUII when utilizing measured posterior keratometry in both devices.

Accuracy of Toric Intraocular Lens Calculations Using Estimated Versus Measured Posterior Corneal Astigmatism.

PURPOSE: To compare the prediction accuracy of toric intraocular lens calculations using estimated vs measured posterior corneal astigmatism (PCA). DESIGN: Retrospective case series. METHODS: A total of 110 eyes of 110 patients with uncomplicated toric intraocular lens implantation were included in this study. Predicted postoperative refractive astigmatism was calculated with the Barrett Toric Calculator using the estimated PCA (E-PCA), the measured IOLMaster 700 PCA (I-PCA), and the measured Pentacam PCA (P-PCA). Refractive astigmatism prediction errors (RA-PEs), including their trimmed (tr-) centroid (mean vector), spread (precision), tr-mean absolute RA-PE (accuracy), and percentage within a certain threshold, were determined using vector analysis and compared between groups. SETTING: University Eye Clinic, Maastricht University Medical Center+, the Netherlands. RESULTS: The tr-centroid RA-PEs of the E-PCA (0.02 diopter [D] at 82.2°), the I-PCA (0.08 D at 35.5°), and the P-PCA (0.09 D at 69.1°) were significantly different from each other (P < .01), but not significantly different from zero (P = .75, P = .05, and P = .05, respectively). The E-PCA had the best precision (tr-mean 0.40 D), which was not significantly lower than the I-PCA (0.42 D, P = .53) and P-PCA (0.43 D, P = .06). The E-PCA also had the best accuracy (0.40 D), which was not significantly different from the I-PCA (0.42 D, P = .26) and significantly better than the P-PCA (0.44 D, P < .01). The precision and accuracy of the I-PCA did not significantly differ from those of the P-PCA. There were no statistically significant differences in the percentage of eyes within a certain absolute RA-PE threshold. CONCLUSIONS: The Barrett Toric Calculator using the E-PCA, I-PCA, or P-PCA showed a comparable prediction of postoperative refractive astigmatism in standard clinical practice.

Examining the synergistic effects of a cognitive control video game and a home-based, self-administered non-invasive brain stimulation on alleviating depression: the DiSCoVeR trial protocol.

Enhanced behavioral interventions are gaining increasing interest as innovative treatment strategies for major depressive disorder (MDD). In this study protocol, we propose to examine the synergistic effects of a self-administered home-treatment, encompassing transcranial direct current stimulation (tDCS) along with a video game based training of attentional control. The study is designed as a two-arm, double-blind, randomized and placebo-controlled multi-center trial (ClinicalTrials.gov: NCT04953208). At three study sites (Israel, Latvia, and Germany), 114 patients with a primary diagnosis of MDD undergo 6 weeks of intervention (30 × 30 min sessions). Patients assigned to the intervention group receive active tDCS (anode F3 and cathode F4; 2 mA intensity) and an action-like video game, while those assigned to the control group receive sham tDCS along with a control video game. An electrode-positioning algorithm is used to standardize tDCS electrode positioning. Participants perform their designated treatment at the clinical center (sessions 1-5) and continue treatment at home under remote supervision (sessions 6-30). The endpoints are feasibility (primary) and safety, treatment efficacy (secondary, i.e., change of Montgomery-Åsberg Depression Rating Scale (MADRS) scores at week six from baseline, clinical response and remission, measures of social, occupational, and psychological functioning, quality of life, and cognitive control (tertiary). Demonstrating the feasibility, safety, and efficacy of this novel combined intervention could expand the range of available treatments for MDD to neuromodulation enhanced interventions providing cost-effective, easily accessible, and low-risk treatment options.ClinicalTrials.gov: NCT04953208.

BitterMatch: recommendation systems for matching molecules with bitter taste receptors.

Bitterness is an aversive cue elicited by thousands of chemically diverse compounds. Bitter taste may prevent consumption of foods and jeopardize drug compliance. The G protein-coupled receptors for bitter taste, TAS2Rs, have species-dependent number of subtypes and varying expression levels in extraoral tissues. Molecular recognition by TAS2R subtypes is physiologically important, and presents a challenging case study for ligand-receptor matchmaking. Inspired by hybrid recommendation systems, we developed a new set of similarity features, and created the BitterMatch algorithm that predicts associations of ligands to receptors with ~ 80% precision at ~ 50% recall. Associations for several compounds were tested in-vitro, resulting in 80% precision and 42% recall. The encouraging performance was achieved by including receptor properties and integrating experimentally determined ligand-receptor associations with chemical ligand-to-ligand similarities.BitterMatch can predict off-targets for bitter drugs, identify novel ligands and guide flavor design. The novel features capture information regarding the molecules and their receptors, which could inform various chemoinformatic tasks. Inclusion of neighbor-informed similarities improves as experimental data mounts, and provides a generalizable framework for molecule-biotarget matching.

Modeling dependency structures in 450k DNA methylation data.

MOTIVATION: DNA methylation has been shown to be spatially dependent across chromosomes. Previous studies have focused on the influence of genomic context on the dependency structure, while not considering differences in dependency structure between individuals. RESULTS: We modeled spatial dependency with a flexible framework to quantify the dependency structure, focusing on inter-individual differences by exploring the association between dependency parameters and technical and biological variables. The model was applied to a subset of the Finnish Twin Cohort study (N = 1611 individuals). The estimates of the dependency parameters varied considerably across individuals, but were generally consistent across chromosomes within individuals. The variation in dependency parameters was associated with bisulfite conversion plate, zygosity, sex and age. The age differences presumably reflect accumulated environmental exposures and/or accumulated small methylation differences caused by stochastic mitotic events, establishing recognizable, individual patterns more strongly seen in older individuals. AVAILABILITY AND IMPLEMENTATION: The twin dataset used in the current study are located in the Biobank of the National Institute for Health and Welfare, Finland. All the biobanked data are publicly available for use by qualified researchers following a standardized application procedure (https://thl.fi/en/web/thl-biobank/for-researchers). A R-script for fitting the dependency structure to publicly available DNA methylation data with the software used in this article is provided in supplementary data. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Erratum: Corridengum to: Self-Rated Smell Ability Enables Highly Specific Predictors of COVID-19 Status: A Case-Control Study in Israel.

[This corrects the article DOI: 10.1093/ofid/ofaa589.].

Self-Rated Smell Ability Enables Highly Specific Predictors of COVID-19 Status: A Case-Control Study in Israel.

BACKGROUND: Clinical diagnosis of coronavirus disease 2019 (COVID-19) is essential to the detection and prevention of COVID-19. Sudden onset of loss of taste and smell is a hallmark of COVID-19, and optimal ways for including these symptoms in the screening of patients and distinguishing COVID-19 from other acute viral diseases should be established. METHODS: We performed a case-control study of patients who were polymerase chain reaction-tested for COVID-19 (112 positive and 112 negative participants), recruited during the first wave (March 2020-May 2020) of the COVID-19 pandemic in Israel. Patients reported their symptoms and medical history by phone and rated their olfactory and gustatory abilities before and during their illness on a 1-10 scale. RESULTS: Changes in smell and taste occurred in 68% (95% CI, 60%-76%) and 72% (95% CI, 64%-80%) of positive patients, with odds ratios of 24 (range, 11-53) and 12 (range, 6-23), respectively. The ability to smell was decreased by 0.5 ± 1.5 in negatives and by 4.5 ± 3.6 in positives. A penalized logistic regression classifier based on 5 symptoms had 66% sensitivity, 97% specificity, and an area under the receiver operating characteristics curve (AUC) of 0.83 on a holdout set. A classifier based on degree of smell change was almost as good, with 66% sensitivity, 97% specificity, and 0.81 AUC. The predictive positive value of this classifier was 0.68, and the negative predictive value was 0.97. CONCLUSIONS: Self-reported quantitative olfactory changes, either alone or combined with other symptoms, provide a specific tool for clinical diagnosis of COVID-19. A simple calculator for prioritizing COVID-19 laboratory testing is presented here.

Onset, duration and unresolved symptoms, including smell and taste changes, in mild COVID-19 infection: a cohort study in Israeli patients.

OBJECTIVES: To characterize longitudinal symptoms of mild coronavirus disease 2019 (COVID-19) patients for a period of 6 months, to potentially aid in disease management. METHODS: Phone interviews were conducted with 103 patients with mild COVID-19 in Israel over a 6-month period (April 2020 to October 2020). Patients were recruited via social media and word to mouth and were interviewed up to 4 times, depending on reports of their unresolved symptoms. Inclusion criteria required participants to be residents of Israel aged 18 years or older, with positive COVID-19 real-time PCR results and nonsevere symptoms. The onset, duration, severity and resolution of symptoms were analysed. RESULTS: A total of 44% (45/103), 41% (42/103), 39% (40/103) and 38% (39/103) of patients experienced headache, fever, muscle ache and dry cough as the first symptom respectively. Smell and taste changes were experienced at 3.9 ± 5.4 and 4.6 ± 5.7 days (mean ± standard deviation (SD)) after disease onset respectively. Among prevalent symptoms, fever had the shortest duration (5.8 ± 8.6 days), and taste and smell changes were the longest-lasting symptoms (17.2 ± 17.6 and 18.9 ± 19.7 days; durations censored at 60 days). Longer recovery of the sense of smell correlated with the extent of smell change. At the 6-month follow-up, 46% (47/103) of the patients had at least one unresolved symptom, most commonly fatigue (22%, 23/103), smell and taste changes (15%, 15/103 and 8%, 8/103 respectively) and breathing difficulties (8%, 8/103). CONCLUSIONS: Long-lasting effects of mild COVID-19 manifested in almost half of the participants reporting at least one unresolved symptom after 6 months.

Better-than-chance classification for signal detection.

The estimated accuracy of a classifier is a random quantity with variability. A common practice in supervised machine learning, is thus to test if the estimated accuracy is significantly better than chance level. This method of signal detection is particularly popular in neuroimaging and genetics. We provide evidence that using a classifier's accuracy as a test statistic can be an underpowered strategy for finding differences between populations, compared to a bona fide statistical test. It is also computationally more demanding than a statistical test. Via simulation, we compare test statistics that are based on classification accuracy, to others based on multivariate test statistics. We find that the probability of detecting differences between two distributions is lower for accuracy-based statistics. We examine several candidate causes for the low power of accuracy-tests. These causes include: the discrete nature of the accuracy-test statistic, the type of signal accuracy-tests are designed to detect, their inefficient use of the data, and their suboptimal regularization. When the purpose of the analysis is the evaluation of a particular classifier, not signal detection, we suggest several improvements to increase power. In particular, to replace V-fold cross-validation with the Leave-One-Out Bootstrap.

Detection and accurate false discovery rate control of differentially methylated regions from whole genome bisulfite sequencing.

With recent advances in sequencing technology, it is now feasible to measure DNA methylation at tens of millions of sites across the entire genome. In most applications, biologists are interested in detecting differentially methylated regions, composed of multiple sites with differing methylation levels among populations. However, current computational approaches for detecting such regions do not provide accurate statistical inference. A major challenge in reporting uncertainty is that a genome-wide scan is involved in detecting these regions, which needs to be accounted for. A further challenge is that sample sizes are limited due to the costs associated with the technology. We have developed a new approach that overcomes these challenges and assesses uncertainty for differentially methylated regions in a rigorous manner. Region-level statistics are obtained by fitting a generalized least squares regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions. We develop an inferential approach, based on a pooled null distribution, that can be implemented even when as few as two samples per population are available. Here, we demonstrate the advantages of our method using both experimental data and Monte Carlo simulation. We find that the new method improves the specificity and sensitivity of lists of regions and accurately controls the false discovery rate.

Selection-Corrected Statistical Inference for Region Detection With High-Throughput Assays.

Scientists use high-dimensional measurement assays to detect and prioritize regions of strong signal in spatially organized domain. Examples include finding methylation enriched genomic regions using microarrays, and active cortical areas using brain-imaging. The most common procedure for detecting potential regions is to group neighboring sites where the signal passed a threshold. However, one needs to account for the selection bias induced by this procedure to avoid diminishing effects when generalizing to a population. This paper introduces pin-down inference, a model and an inference framework that permit population inference for these detected regions. Pin-down inference provides non-asymptotic point and confidence interval estimators for the mean effect in the region that account for local selection bias. Our estimators accommodate non-stationary covariances that are typical of these data, allowing researchers to better compare regions of different sizes and correlation structures. Inference is provided within a conditional one-parameter exponential family per region, with truncations that match the selection constraints. A secondary screening-and-adjustment step allows pruning the set of detected regions, while controlling the false-coverage rate over the reported regions. We apply the method to genomic regions with differing DNA-methylation rates across tissue. Our method provides superior power compared to other conditional and non-parametric approaches.

Cell lineage analysis in human brain using endogenous retroelements.

Somatic mutations occur during brain development and are increasingly implicated as a cause of neurogenetic disease. However, the patterns in which somatic mutations distribute in the human brain are unknown. We used high-coverage whole-genome sequencing of single neurons from a normal individual to identify spontaneous somatic mutations as clonal marks to track cell lineages in human brain. Somatic mutation analyses in >30 locations throughout the nervous system identified multiple lineages and sublineages of cells marked by different LINE-1 (L1) retrotransposition events and subsequent mutation of poly-A microsatellites within L1. One clone contained thousands of cells limited to the left middle frontal gyrus, whereas a second distinct clone contained millions of cells distributed over the entire left hemisphere. These patterns mirror known somatic mutation disorders of brain development and suggest that focally distributed mutations are also prevalent in normal brains. Single-cell analysis of somatic mutation enables tracing of cell lineage clones in human brain.

Investigating and correcting plasma DNA sequencing coverage bias to enhance aneuploidy discovery.

Pregnant women carry a mixture of cell-free DNA fragments from self and fetus (non-self) in their circulation. In recent years multiple independent studies have demonstrated the ability to detect fetal trisomies such as trisomy 21, the cause of Down syndrome, by Next-Generation Sequencing of maternal plasma. The current clinical tests based on this approach show very high sensitivity and specificity, although as yet they have not become the standard diagnostic test. Here we describe improvements to the analysis of the sequencing data by reducing GC bias and better handling of the genomic repeats. We show substantial improvements in the sensitivity of the standard trisomy 21 statistical tests, which we measure by artificially reducing read coverage. We also explore the bias stemming from the natural cleavage of plasma DNA by examining DNA motifs and position specific base distributions. We propose a model to correct this fragmentation bias and observe that incorporating this bias does not lead to any further improvements in the detection of fetal trisomy. The improved bias corrections that we demonstrate in this work can be readily adopted into existing fetal trisomy detection protocols and should also lead to improvements in sub-chromosomal copy number variation detection.

Summarizing and correcting the GC content bias in high-throughput sequencing.

GC content bias describes the dependence between fragment count (read coverage) and GC content found in Illumina sequencing data. This bias can dominate the signal of interest for analyses that focus on measuring fragment abundance within a genome, such as copy number estimation (DNA-seq). The bias is not consistent between samples; and there is no consensus as to the best methods to remove it in a single sample. We analyze regularities in the GC bias patterns, and find a compact description for this unimodal curve family. It is the GC content of the full DNA fragment, not only the sequenced read, that most influences fragment count. This GC effect is unimodal: both GC-rich fragments and AT-rich fragments are underrepresented in the sequencing results. This empirical evidence strengthens the hypothesis that PCR is the most important cause of the GC bias. We propose a model that produces predictions at the base pair level, allowing strand-specific GC-effect correction regardless of the downstream smoothing or binning. These GC modeling considerations can inform other high-throughput sequencing analyses such as ChIP-seq and RNA-seq.

Reconstructing visual experiences from brain activity evoked by natural movies.

Quantitative modeling of human brain activity can provide crucial insights about cortical representations [1, 2] and can form the basis for brain decoding devices [3-5]. Recent functional magnetic resonance imaging (fMRI) studies have modeled brain activity elicited by static visual patterns and have reconstructed these patterns from brain activity [6-8]. However, blood oxygen level-dependent (BOLD) signals measured via fMRI are very slow [9], so it has been difficult to model brain activity elicited by dynamic stimuli such as natural movies. Here we present a new motion-energy [10, 11] encoding model that largely overcomes this limitation. The model describes fast visual information and slow hemodynamics by separate components. We recorded BOLD signals in occipitotemporal visual cortex of human subjects who watched natural movies and fit the model separately to individual voxels. Visualization of the fit models reveals how early visual areas represent the information in movies. To demonstrate the power of our approach, we also constructed a Bayesian decoder [8] by combining estimated encoding models with a sampled natural movie prior. The decoder provides remarkable reconstructions of the viewed movies. These results demonstrate that dynamic brain activity measured under naturalistic conditions can be decoded using current fMRI technology.

Effects of productivity and disturbance on species richness: a neutral model.

Productivity and disturbance are major determinants of species diversity, and results from theoretical models predict that species richness should peak at intermediate levels of both factors. Such "unimodal" responses have been documented in many field and laboratory studies and have usually been attributed to differences among species in competitive ability and/or trade-offs between competitive ability and tolerance to disturbance. Here we show that most documented patterns of disturbance-richness and productivity-richness relationships, as well as the observed interactions between the two factors, can be explained by a simple neutral model where all species are ecologically identical and lack trade-offs in species characteristics. This finding suggests that current neutral theories can be extended to explain patterns of species responses to productivity and disturbance.