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1. Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing: A Machine Learning Approach With Robust Feature Selection

   Prerna Chikersal, Afsaneh Doryab ( January 2021 , ACM transactions on humanrobot interaction)

   null (Ed.)

   We present a machine learning approach that uses data from smartphones and fitness trackers of 138 college students to identify students that experienced depressive symptoms at the end of the semester and students whose depressive symptoms worsened over the semester. Our novel approach is a feature extraction tech- nique that allows us to select meaningful features indicative of depressive symptoms from longitudinal data. It allows us to detect the presence of post-semester depressive symptoms with an accuracy of 85.7% and change in symptom severity with an accuracy of 85.4%. It also predicts these outcomes with an accuracy of >80%, 11–15 weeks before the end of the semester, allowing ample time for pre-emptive interventions. Our work has significant implications for the detection of health outcomes using longitudinal behavioral data and limited ground truth. By detecting change and predicting symptoms several weeks before their onset, our work also has implications for preventing depression. 

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2. Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing: A Machine Learning Approach With Robust Feature Selection

   Prerna Chikersal, Afsaneh Doryab ( January 2021 , ACM transactions on humanrobot interaction)

   null (Ed.)
3. Evolution of Force Chains Explains the Onset of Strain Stiffening in Fiber Networks

   https://doi.org/10.1115/1.4055586  

   Sarkar, Mainak ; Notbohm, Jacob ( November 2022 , Journal of Applied Mechanics)

   Abstract Fiber networks are the primary structural components of many biological structures, including the cell cytoskeleton and the extracellular matrix. These materials exhibit global nonlinearities, such as stiffening in extension and shear, during which the fibers bend and align with the direction of applied loading. Precise details of deformations at the scale of the fibers during strain stiffening are still lacking, however, as prior work has studied fiber alignment primarily from a qualitative perspective, which leaves incomplete the understanding of how the local microstructural evolution leads to the global mechanical behavior. To fill this gap, we studied how axial forces are transmitted inside the fiber network along paths called force chains, which continuously evolve during the course of deformation. We performed numerical simulations on two-dimensional networks of random fibers under uniaxial extension and shear, modeling the fibers using beam elements in finite element software. To quantify the force chains, we identified all chains of connected fibers for which the axial force was larger than a preset threshold and computed the total length of all such chains. To study the evolution of force chains during loading, we computed the derivative of the total length of all force chains with respect to the applied engineering strain. Results showed that the highest rate of evolution of force chains coincided with the global critical strain for strain stiffening of the fiber network. Therefore, force chains are an important factor connecting understanding of the local kinematics and force transmission to the macroscale stiffness of the fiber network. 

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4. The Emergence and Evolution of Problematic Properties: Onset, Persistence, Aggravation, and Desistance

   https://doi.org/10.1007/s10940-022-09542-z  

   O’Brien, Daniel T. ; Ristea, Alina ; Tucker, Riley ; Hangen, Forrest ( March 2022 , Journal of Quantitative Criminology)

   Objectives Scholars and practitioners have paid increasing attention to problematic properties, but little is known about how they emerge and evolve. We examine four phenomena suggested by life-course theory that reflect stability and change in crime and disorder at properties: onset of issues; persistence of issues; aggravation to more serious types of issues; and desistance of issues. We sought to identify the frequency and dynamics of each. Methods We analyze how residential parcels (similar to properties) in Boston, MA shifted between profiles of crime and disorder from 2011 to 2018. 911 dispatches and 311 requests provided six measures of physical disorder, social disorder, and violence for all parcels. K-means clustering placed each parcel into one of six profiles of crime and disorder for each year. Markov chains quantified how properties moved between profiles year-to-year. Results Onset was relatively infrequent and more often manifested as disorder than violence. Pathways of aggravation led from less serious profiles to a mixture of violence and disorder. Desistance was more likely to occur as de-escalations along these pathways then complete cessation of issues. In neighborhoods with above-average crime, persistence was more prevalent whereas desistance less often culminated in cessation, even relative to local expectations. Conclusions The results offer insights for further research and practice attentive to trends of crime and disorder at problematic properties. It especially speaks to the understanding of stability and change; the role of different types of disorder; and the toolkit needed for problem properties interventions. 

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5. A model for focal seizure onset, propagation, evolution, and progression

   https://doi.org/10.7554/eLife.50927  

   Liou, Jyun-you ; Smith, Elliot H ; Bateman, Lisa M ; Bruce, Samuel L ; McKhann, Guy M ; Goodman, Robert R ; Emerson, Ronald G ; Schevon, Catherine A ; Abbott, LF ( March 2020 , eLife)

   We developed a neural network model that can account for major elements common to human focal seizures. These include the tonic-clonic transition, slow advance of clinical semiology and corresponding seizure territory expansion, widespread EEG synchronization, and slowing of the ictal rhythm as the seizure approaches termination. These were reproduced by incorporating usage-dependent exhaustion of inhibition in an adaptive neural network that receives global feedback inhibition in addition to local recurrent projections. Our model proposes mechanisms that may underline common EEG seizure onset patterns and status epilepticus, and postulates a role for synaptic plasticity in the emergence of epileptic foci. Complex patterns of seizure activity and bi-stable seizure end-points arise when stochastic noise is included. With the rapid advancement of clinical and experimental tools, we believe that this model can provide a roadmap and potentially an in silico testbed for future explorations of seizure mechanisms and clinical therapies. 

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