Assessment of ability realization using the 4th version of the Spinal Cord Independence Measure.

Context: Change in ability realization reflects the main contribution of rehabilitation to improvement in the performance of daily activities after spinal cord lesions (SCL).

Objective: To adapt a Spinal Cord Ability Realization Measurement Index (SCI-ARMI) formula to the new Spinal Cord Independence Measure version 4 (SCIM4).

Methods: Using data from 156 individuals for whom American Spinal Injury Association Motor Score (AMS) and SCIM4 scores were collected, we obtained an estimate for the highest possible SCIM4 given the patient's AMS value, using the 95th percentile of SCIM4 values at discharge from rehabilitation (SCIM95) for patients with any given AMS at discharge.

Spherical equivalent prediction analysis in intraocular lens power calculations using Eyetemis: a comprehensive approach.

Purpose: To compare 2 different datasets, using Eyetemis, an online analytical tool designed for assessing the spherical equivalent prediction errors (SEQ-PEs) of intraocular lens (IOL) power calculation formulas after cataract surgery.

Setting: Institutional.

Design: Retrospective case series.

Discussion on "Optimal test procedures for multiple hypotheses controlling the familywise expected loss" by Willi Maurer, Frank Bretz, and Xiaolei Xun.

We discuss three issues. In the first part, we discuss the criteria emphasized by Maurer, Bretz, and Xun, warning that it modifies the per comparison error rate that does not address the concerns raised by multiple testing. In the second part, we strengthen the optimality results developed in the paper, based on our recent results.

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.

Developing a length of stay prediction model for newborns, achieving better accuracy with greater usability.

Background: One in ten newborn children is born prematurely. The elongated length of stay (LOS) of these children in the Neonatal Intensive Care Unit (NICU) has important implications on hospital occupancy figures, healthcare and management costs, as well as the psychology of parents. In order to allow accurate planning and resource allocation, this study aims to create a generalizable and robust model to predict the NICU LOS of preterm newborns.

A novel performance scoring quantification framework for stress test set-ups.

Stress tests, e.g., the cardiac stress test, are standard clinical screening tools aimed to unmask clinical pathology.

Decoding human spontaneous spiking activity in medial temporal lobe from scalp EEG.

Linking scalp electroencephalography (EEG) signals and spontaneous firing activity from deep nuclei in humans is not trivial. To examine this, we analyzed simultaneous recordings of scalp EEG and unit activity in deeply located sites recorded overnight from patients undergoing pre-surgical invasive monitoring. We focused on modeling the within-subject average unit activity of two medial temporal lobe areas: amygdala and hippocampus.

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.

The role of models in the covid-19 pandemic.

Mathematical and statistical models have played an important role in the analysis of data from COVID-19. They are important for tracking the progress of the pandemic, for understanding its spread in the population, and perhaps most significantly for forecasting the future course of the pandemic and evaluating potential policy options. This article describes the types of models that were used by research teams in Israel, presents their assumptions and basic elements, and illustrates how they were used, and how they influenced decisions.

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.

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.].

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.

Reliability Validity and Responsiveness of the Spinal Cord Independence Measure 4th Version in a Multicultural Setup.

Objective: To examine the fourth version of the Spinal Cord Independence Measure for reliability and validity.

Design: Partly blinded comparison with the criterion standard Spinal Cord Independence Measure III, and between examiners and examinations.

Setting: A multicultural cohort from 19 spinal cord injury units in 11 countries.

Hypotheses on a tree: new error rates and testing strategies.

We introduce a multiple testing procedure that controls global error rates at multiple levels of resolution. Conceptually, we frame this problem as the selection of hypotheses that are organized hierarchically in a tree structure. We describe a fast algorithm and prove that it controls relevant error rates given certain assumptions on the dependence between the -values.

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.

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.

Exploration in the Presence of Mother in Typically and Non-typically Developing Pre-walking Human Infants.

In previous phenotyping studies of mouse and rat exploratory behavior we developed a computational exploratory data analysis methodology including videotaping, tracking, preparatory methods for customized data analysis, a methodology for improving the replicability of results across laboratories, and algorithmic design for exposing the natural reference places (origins) used by animals during exploration. We then measured the animals' paths in reference to these origins, revealing robust, highly replicable modules termed excursions, which are performed from the origin into the environment and back to the origin. Origin-related exploration has been claimed to be phylogenetically conserved across the vertebrates.

Multi-domain potential biomarkers for post-traumatic stress disorder (PTSD) severity in recent trauma survivors.

Contemporary symptom-based diagnosis of post-traumatic stress disorder (PTSD) largely overlooks related neurobehavioral mechanisms and relies entirely on subjective interpersonal reporting. Previous studies associating biomarkers with PTSD have mostly used symptom-based diagnosis as the main outcome measure, disregarding the wide variability and richness of PTSD phenotypical features. Here, we aimed to computationally derive potential biomarkers that could efficiently differentiate PTSD subtypes among recent trauma survivors.

Novel Alzheimer's disease subtypes identified using a data and knowledge driven strategy.

The population of adults with Alzheimer's disease (AD) varies in needs and outcomes. The heterogeneity of current AD diagnostic subgroups impedes the use of data analytics in clinical trial design and translation of findings into improved care. The purpose of this project was to define more clinically-homogeneous groups of AD patients and link clinical characteristics with biological markers.

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.

Hierarchical Data-Driven Analysis of Clinical Symptoms Among Patients With Parkinson's Disease.

Mutations in the LRRK2 and GBA genes are the most common inherited causes of Parkinson's disease (PD). Studies exploring phenotypic differences based on genetic status used hypothesis-driven data-gathering and statistical-analyses focusing on specific symptoms, which may influence the validity of the results. We aimed to explore phenotypic expression in idiopathic PD (iPD) patients, G2019S-LRRK2-PD, and GBA-PD using a data-driven approach, allowing screening of large numbers of features while controlling selection bias.

Current and Potential Approaches for Defining Disease Signatures: a Systematic Review.

Identifying disease signatures in order to facilitate accurate diagnosis/treatment has been the focus of research efforts in the last decade. However, the term "disease signature" has not been properly defined, resulting in inconsistencies between studies, as well as limited ability to fully utilize the tools/information available in the evolving field of healthcare big data. Research was conducted according to the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines.

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.