Biology subjects

Platt, J.

Publications and source records attributed to Platt, J..

3 recordsLinked to original sources

bioRxiv2026

When Time Online Tells Us Little: Duration of Social Media Use does not Influence Sleep Disruption or Psychopathology in Adolescents

Background: Adolescent mental health problems have risen alongside increasing sleep disruption and widespread social media use (SMU). However, the mechanisms linking these factors remain unclear. This study concurrently examined objectively measured sleep and SMU in relation to psychopathology to test whether SMU moderates the sleep-mental health association and to characterize day-to-day bidirectional relations among sleep, affect, mood, and SMU during the school year. Methods: Eighty-five adolescents (ages 12-18) completed 14 days of wrist actigraphy, smartphone-based ecological momentary assessments of mood and affect, and objective SMU duration tracking. Cross-sectional models tested whether SMU moderated associations between sleep and psychopathology, and multilevel daily models examined within-person lagged associations among sleep, SMU, and affect, controlling for age and sex. False discovery rate correction was applied. Results: Shorter average sleep duration and greater variability in sleep duration between weekdays and weekends were associated with higher psychopathology symptoms, primarily externalizing problems. SMU duration neither moderated nor directly predicted psychopathology or sleep. Within-person analyses showed that longer-than-usual sleep predicted more positive and less negative affect and lower next-day SMU, though these associations did not survive correction for multiple comparisons. Conclusions: Sleep disruption showed consistent associations with adolescent psychopathology, whereas the amount of time spent on social media did not. These findings suggest that duration-based measures of SMU may incompletely capture adolescents digital experiences and highlight the need for more granular, context-sensitive indicators of online behavior in understanding youth mental health.

neuroscience↗

bioRxiv2021

Reduced Dimension, Biophysical Neuron Models Constructed From Observed Data

Using methods from nonlinear dynamics and interpolation techniques from applied mathematics, we show how to use data alone to construct discrete time dynamical rules that forecast observed neuron properties. These data may come from from simulations of a Hodgkin-Huxley (HH) neuron model or from laboratory current clamp experiments. In each case the reduced dimension data driven forecasting (DDF) models are shown to predict accurately for times after the training period. When the available observations for neuron preparations are, for example, membrane voltage V(t) only, we use the technique of time delay embedding from nonlinear dynamics to generate an appropriate space in which the full dynamics can be realized. The DDF constructions are reduced dimension models relative to HH models as they are built on and forecast only observables such as V(t). They do not require detailed specification of ion channels, their gating variables, and the many parameters that accompany an HH model for laboratory measurements, yet all of this important information is encoded in the DDF model. As the DDF models use only voltage data and forecast only voltage data, they can be used in building networks with biophysical connections. Both gap junction connections and ligand gated synaptic connections among neurons involve presynaptic voltages and induce postsynaptic voltage response. Biophysically based DDF neuron models can replace other reduced dimension neuron models, say of the integrate-and-fire type, in developing and analyzing large networks of neurons. When one does have detailed HH model neurons for network components, a reduced dimension DDF realization of the HH voltage dynamics may be used in network computations to achieve computational efficiency and the exploration of larger biological networks.

biophysics↗

bioRxiv2020

Mono- and bi-allelic protein truncating variants in alpha-actinin 2 cause cardiomyopathy through distinct mechanisms

Alpha-actinin 2 (ACTN2) anchors actin within cardiac sarcomeres. The mechanisms linking ACTN2 mutations to myocardial disease phenotypes are unknown. Here, we characterize patients with novel ACTN2 mutations to reveal insights into the physiological function of ACTN2. Patient-derived iPSC-cardiomyocytes harboring ACTN2 protein-truncating variants were hypertrophic, displayed sarcomeric structural disarray, impaired contractility, and aberrant Ca2+-signaling. In heterozygous indel cells, the truncated protein incorporates into cardiac sarcomeres, leading to aberrant Z-disc structure. In homozygous stop-gain cells, affinity-purification mass spectrometry reveals an intricate ACTN2 interactome with sarcomere and sarcolemma-associated proteins. Loss of the C-terminus of ACTN2 disrupts interaction with ACTN1 and GJA1, two sarcolemma-associated proteins, that may lead to the clinical arrhythmic and relaxation defects. The causality of the stop-gain mutation was verified using CRISPR-Cas9 gene editing. Together, these data advance our understanding of the role of ACTN2 in the human heart and establish recessive inheritance of ACTN2 truncation as causative of disease.

genetics↗