Long-term sequelae of SARS-CoV-2 two years following infection: exploring the interplay of biological, psychological, and social factors.

Background: Severe fatigue and cognitive complaints are frequently reported after SARS-CoV-2 infection and may be accompanied by depressive symptoms and/or limitations in physical functioning. The long-term sequelae of COVID-19 may be influenced by biomedical, psychological, and social factors, the interplay of which is largely understudied over time. We aimed to investigate how the interplay of these factors contribute to the persistence of symptoms after COVID-19.

How to evaluate uncertainty estimates in machine learning for regression?

As neural networks become more popular, the need for accompanying uncertainty estimates increases. There are currently two main approaches to test the quality of these estimates. Most methods output a density.

The effect of cardiovascular risk on disease progression in Parkinson's disease patients: An observational analysis.

Background: Currently available treatment options for Parkinson's disease are symptomatic and do not alter the course of the disease. Recent studies have raised the possibility that cardiovascular risk management may slow the progression of the disease.

Objectives: We estimated the effect of baseline cardiovascular risk factors on the progression of Parkinson's disease, using measures for PD-specific motor signs and cognitive functions.

Personalized monitoring of ambulatory function with a smartphone 2-minute walk test in multiple sclerosis.

Background: Remote smartphone-based 2-minute walking tests (s2MWTs) allow frequent and potentially sensitive measurements of ambulatory function.

Objective: To investigate the s2MWT on assessment of, and responsiveness to change in ambulatory function in MS.

Methods: One hundred two multiple sclerosis (MS) patients and 24 healthy controls (HCs) performed weekly s2MWTs on self-owned smartphones for 12 and 3 months, respectively.

autoRasch: An R Package to Do Semi-Automated Rasch Analysis.

The R package autoRasch has been developed to perform a Rasch analysis in a (semi-)automated way. The automated part of the analysis is achieved by optimizing the so-called (IPOQ-LL) or IPOQ-LL-DIF when differential item functioning (DIF) is included. These criteria measure the quality of fit on a pre-collected survey, depending on which items are included in the final instrument.

Specialized Versus Generic Allied Health Therapy and the Risk of Parkinson's Disease Complications.

Background: Specialized versus generic physiotherapy (PT) reduces Parkinson's disease (PD)-related complications. It is unclear (1) whether other specialized allied heath disciplines, including occupational therapy (OT) and speech and language therapy (S<), also reduce complications; (2) whether there is a synergistic effect among multiple specialized disciplines; and (3) whether each allied health discipline prevents specific complications.

Objectives: To longitudinally assessed whether the level of expertise (specialized vs.

Semi-automated Rasch analysis with differential item functioning.

Rasch analysis is a procedure to develop and validate instruments that aim to measure a person's traits. However, manual Rasch analysis is a complex and time-consuming task, even more so when the possibility of differential item functioning (DIF) is taken into consideration. Furthermore, manual Rasch analysis by construction relies on a modeler's subjective choices.

Development and validation of the patient-reported "Facial Function Scale" for facioscapulohumeral muscular dystrophy.

Purpose: Facial weakness and its functional consequences are an often underappreciated clinical feature of facioscapulohumeral muscular dystrophy (FSHD) by healthcare professionals and researchers. This is at least in part due to the fact that there are few adequate clinical outcome measures available.

Methods: We developed the Facial Function Scale, a Rasch-built questionnaire on the functional disabilities relating to facial weakness in FSHD.

Towards individualized monitoring of cognition in multiple sclerosis in the digital era: A one-year cohort study.

Background: Cognitive impairment is frequent in multiple sclerosis (MS), but reliable, sensitive and individualized monitoring in clinical practice is still limited. Smartphone-adapted tests may enhance the assessment of function as tests can be performed more frequently and within the daily living environment. The objectives were to prove reproducibility of a smartphone-based Symbol Digit Modalities Test (sSDMT), its responsiveness to relevant change in clinical cognitive outcomes, and develop an individual-based monitoring method for cognition.

Understanding the assumptions underlying Mendelian randomization.

With the rapidly increasing availability of large genetic data sets in recent years, Mendelian Randomization (MR) has quickly gained popularity as a novel secondary analysis method. Leveraging genetic variants as instrumental variables, MR can be used to estimate the causal effects of one phenotype on another even when experimental research is not feasible, and therefore has the potential to be highly informative. It is dependent on strong assumptions however, often producing biased results if these are not met.

Automatic Placenta Localization From Ultrasound Imaging in a Resource-Limited Setting Using a Predefined Ultrasound Acquisition Protocol and Deep Learning.

Placenta localization from obstetric 2-D ultrasound (US) imaging is unattainable for many pregnant women in low-income countries because of a severe shortage of trained sonographers. To address this problem, we present a method to automatically detect low-lying placenta or placenta previa from 2-D US imaging. Two-dimensional US data from 280 pregnant women were collected in Ethiopia using a standardized acquisition protocol and low-cost equipment.

Non-parametric synergy modeling of chemical compounds with Gaussian processes.

Background: Understanding the synergetic and antagonistic effects of combinations of drugs and toxins is vital for many applications, including treatment of multifactorial diseases and ecotoxicological monitoring. Synergy is usually assessed by comparing the response of drug combinations to a predicted non-interactive response from reference (null) models. Possible choices of null models are Loewe additivity, Bliss independence and the recently rediscovered Hand model.

Methodology of the DCCSS later fatigue study: a model to investigate chronic fatigue in long-term survivors of childhood cancer.

Background: A debilitating late effect for childhood cancer survivors (CCS) is cancer-related fatigue (CRF). Little is known about the prevalence and risk factors of fatigue in this population. Here we describe the methodology of the Dutch Childhood Cancer Survivor Late Effect Study on fatigue (DCCSS LATER fatigue study).

Potential mechanisms of the fatigue-reducing effect of cognitive-behavioral therapy in cancer survivors: Three randomized controlled trials.

Objective: Fatigue is a common symptom among cancer survivors that can be successfully treated with cognitive-behavioral therapy (CBT). Insights into the working mechanisms of CBT are currently limited. The aim of this study was to investigate whether improvements in targeted cognitive-behavioral variables and reduced depressive symptoms mediate the fatigue-reducing effect of CBT.

Spectral Ranking of Causal Influence in Complex Systems.

Similar to natural complex systems, such as the Earth's climate or a living cell, semiconductor lithography systems are characterized by nonlinear dynamics across more than a dozen orders of magnitude in space and time. Thousands of sensors measure relevant process variables at appropriate sampling rates, to provide time series as primary sources for system diagnostics. However, high-dimensionality, non-linearity and non-stationarity of the data are major challenges to efficiently, yet accurately, diagnose rare or new system issues by merely using model-based approaches.

Probabilistic Modelling of Gait for Robust Passive Monitoring in Daily Life.

Passive monitoring in daily life may provide valuable insights into a person's health throughout the day. Wearable sensor devices play a key role in enabling such monitoring in a non-obtrusive fashion. However, sensor data collected in daily life reflect multiple health and behavior-related factors together.

Estimating the Effect of Early Treatment Initiation in Parkinson's Disease Using Observational Data.

Background: Both patients and physicians may choose to delay initiation of dopamine replacement therapy in Parkinson's disease (PD) for various reasons. We used observational data to estimate the effect of earlier treatment in PD. Observational data offer a valuable source of evidence, complementary to controlled trials.

Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study.

Background: Wearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies to date have been conducted in highly controlled laboratory environments.

Objective: This paper aims to assess whether sensor-based analysis of real-life gait can be used to objectively and remotely monitor motor fluctuations in PD.

Methods: The Parkinson@Home validation study provides a new reference data set for the development of digital biomarkers to monitor persons with PD in daily life.

Semi-automated Rasch analysis using in-plus-out-of-questionnaire log likelihood.

Rasch analysis is a popular statistical tool for developing and validating instruments that aim to measure human performance, attitudes and perceptions. Despite the availability of various software packages, constructing a good instrument based on Rasch analysis is still considered to be a complex, labour-intensive task, requiring human expertise and rather subjective judgements along the way. In this paper we propose a semi-automated method for Rasch analysis based on first principles that reduces the need for human input.

Identification and validation of risk factors for antisocial behaviour involving police.

Adult antisocial behaviour has precursors in childhood and adolescence and is most successfully treated using childhood interventions. The aim of this study was to identify and validate robust risk factors for antisocial behaviour involving police contact in a data-driven, hypothesis-free framework. Antisocial behavior involving police contact (20/25% incidence) as well as 554 other behavioural and environmental measures were assessed in the longitudinal general population Estonian Children Personality Behaviour and Health Study sample (n=872).

Investigating the effect of dependence between conditions with Bayesian Linear Mixed Models for motif activity analysis.

Motivation: Cellular identity and behavior is controlled by complex gene regulatory networks. Transcription factors (TFs) bind to specific DNA sequences to regulate the transcription of their target genes. On the basis of these TF motifs in cis-regulatory elements we can model the influence of TFs on gene expression.

Additive Dose Response Models: Defining Synergy.

In synergy studies, one focuses on compound combinations that promise a synergistic or antagonistic effect. With the help of high-throughput techniques, a huge amount of compound combinations can be screened and filtered for suitable candidates for a more detailed analysis. Those promising candidates are chosen based on the deviance between a measured response and an expected non-interactive response.

Measuring Parkinson's disease over time: The real-world within-subject reliability of the MDS-UPDRS.

Background: An important challenge in Parkinson's disease research is how to measure disease progression, ideally at the individual patient level. The MDS-UPDRS, a clinical assessment of motor and nonmotor impairments, is widely used in longitudinal studies. However, its ability to assess within-subject changes is not well known.

Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies.

Computational modeling plays an important role in modern neuroscience research. Much previous research has relied on statistical methods, separately, to address two problems that are actually interdependent. First, given a particular computational model, Bayesian hierarchical techniques have been used to estimate individual variation in parameters over a population of subjects, leveraging their population-level distributions.