Digital Phenotypes of Mobile Keyboard Backspace Rates and Their Associations With Symptoms of Mood Disorder: Algorithm Development and Validation.

Background: Passive sensing through smartphone keyboard data can be used to identify and monitor symptoms of mood disorders with low participant burden. Behavioral phenotyping based on mobile keystroke data can aid in clinical decision-making and provide insights into the individual symptoms of mood disorders.

Objective: This study aims to derive digital phenotypes based on smartphone keyboard backspace use among 128 community adults across 2948 observations using a Bayesian mixture model.

Using a Novel Digital Go/No-Go to Dissociate Intra-subject Temporal Fluctuations in Reaction Time and Accuracy.

Impulsivity can be a risk factor for serious complications for those with mood disorders. To understand intra-individual impulsivity variability, we analyzed longitudinal data of a novel gamified digital Go/No-Go (GNG) task in a clinical sample (n=43 mood disorder participants, n=17 healthy controls) and an open-science sample (n=121, self-reported diagnoses). With repeated measurements within-subject, we disentangled two aspects of GNG: reaction time and accuracy in response inhibition (i.

Smartphone-derived Virtual Keyboard Dynamics Coupled with Accelerometer Data as a Window into Understanding Brain Health: Smartphone Keyboard and Accelerometer as Window into Brain Health.

We examine the feasibility of using accelerometer data exclusively collected during typing on a custom smartphone keyboard to study whether typing dynamics are associated with daily variations in mood and cognition. As part of an ongoing digital mental health study involving mood disorders, we collected data from a well-characterized clinical sample (N = 85) and classified accelerometer data per typing session into orientation (upright vs. not) and motion (active vs.

Residual serum fibrinogen as a universal biomarker for all serotypes of Myasthenia gravis.

Myasthenia Gravis (MG) is an autoimmune disease associated with severe neuromuscular weakness. Diagnostic confirmation of MG is typically delayed and secured in about 85% and 50% of patients with generalized and ocular MG, respectively with serum antibodies. We have identified a sensitive and specific diagnostic biomarker for various MG serotypes with quantitative proteomics.

The Relationship Between Myocardial Infarction and Estrogen Use: A Literature Review.

This thorough literature evaluation was prompted by significant research into the complex interactions between estrogen use and myocardial infarction (MI). Estrogen has fascinated researchers because of its possible cardioprotective benefits and its impact on cardiovascular health. In order to clarify the connection between estrogen use and the risk of MI, this review critically examines the body of prior evidence.

A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity.

The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders.

Associations between smartphone keystroke dynamics and cognition in MS.

Objective: Examine the associations between smartphone keystroke dynamics and cognitive functioning among persons with multiple sclerosis (MS).

Methods: Sixteen persons with MS with no self-reported upper extremity or typing difficulties and 10 healthy controls (HCs) completed six weeks of remote monitoring of their keystroke dynamics (i.e.

The Effects of Bipolar Disorder Risk on a Mobile Phone Keystroke Dynamics Based Biomarker of Brain Age.

Research by our group and others have demonstrated the feasibility of using mobile phone derived metadata to model mood and cognition. Given the effects of age and mood on cognitive performance, it was hypothesized that using such data a model could be built to predict chronological age and that differences between predicted age and actual age could be a marker of pathology. These data were collected via the ongoing BiAffect study.

Rituximab in Myasthenia Gravis - Where do we stand?

: Myasthenia gravis (MG) is an antibody-mediated disease with diverse serology and clinical presentation. Currently, MG is managed by untargeted immunomodulatory agents. About 15% patients are refractory to these therapies.

Effects of mood and aging on keystroke dynamics metadata and their diurnal patterns in a large open-science sample: A BiAffect iOS study.

Objective: Ubiquitous technologies can be leveraged to construct ecologically relevant metrics that complement traditional psychological assessments. This study aims to determine the feasibility of smartphone-derived real-world keyboard metadata to serve as digital biomarkers of mood.

Materials And Methods: BiAffect, a real-world observation study based on a freely available iPhone app, allowed the unobtrusive collection of typing metadata through a custom virtual keyboard that replaces the default keyboard.

Label-free proteomic analysis reveals large dynamic changes to the cellular proteome upon expression of the miRNA-23a-27a-24-2 microRNA cluster.

In deciphering the regulatory networks of gene expression controlled by the small non-coding RNAs known as microRNAs (miRNAs), a major challenge has been with the identification of the true mRNA targets by these RNAs within the context of the enormous numbers of predicted targets for each of these small RNAs. To facilitate the system-wide identification of miRNA targets, a variety of system wide methods, such as proteomics, have been implemented. Here we describe the utilization of quantitative label-free proteomics and bioinformatics to identify the most significant changes to the proteome upon expression of the miR-23a-27a-24-2 miRNA cluster.

Multiresponsive Tetradentate Phosphorescent Metal Complexes as Highly Sensitive and Robust Luminescent Oxygen Sensors: Pd(II) Versus Pt(II) and 1,2,3-Triazolyl Versus 1,2,4-Triazolyl.

Two Pd(II) complexes based on tetradentate chelate ligands with either a 1,2,4-triazolyl (Pd1) or 1,2,3-triazolyl (Pd2) unit were synthesized, and their structure-property relationships were studied. Both Pd1 and Pd2 are rare bright deep blue Pd(II) phosphors with contrasting properties. Pd1 displays stimuli-responsive luminescence in response to UV irradiation, concentration, or temperature change, which is ascribed to the facile switching of monomer to excimer emission.

A theorem proving approach for automatically synthesizing visualizations of flow cytometry data.

Background: Polychromatic flow cytometry is a popular technique that has wide usage in the medical sciences, especially for studying phenotypic properties of cells. The high-dimensionality of data generated by flow cytometry usually makes it difficult to visualize. The naive solution of simply plotting two-dimensional graphs for every combination of observables becomes impractical as the number of dimensions increases.

Automated parameter estimation for biological models using Bayesian statistical model checking.

Background: Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally.

Discovering rare behaviours in stochastic differential equations using decision procedures: applications to a minimal cell cycle model.

Stochastic Differential Equation (SDE) models are used to describe the dynamics of complex systems with inherent randomness. The primary purpose of these models is to study rare but interesting or important behaviours, such as the formation of a tumour. Stochastic simulations are the most common means for estimating (or bounding) the probability of rare behaviours, but the cost of simulations increases with the rarity of events.

Parameter discovery in stochastic biological models using simulated annealing and statistical model checking.

Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community.