Paranoid Thinking, Suspicion, and Risk for Aggression: A Neurodevelopmental Perspective

This article represents an effort to extend our understanding of paranoia or suspicion and its development by integrating findings across clinical, developmental, and neuroscience literatures. We first define “paranoia” or paranoid thought and examine its prevalence across typically and atypically developing individuals and theoretical perspectives regarding its development and maintenance.We then briefly summarize current ideas regarding the neural correlates of adaptive, appropriately trusting interpersonal perception, social cognition, and behavior across development. Our focus shifts subsequently to examining in normative and atypical developmental contexts the neural correlates of several component cognitive processes thought to contribute to paranoid thinking: (a) attention bias for threat, (b) jumping to conclusions biases, and (c) hostile intent attribution biases. Where possible, we also present data regarding independent links between these cognitive processes and aggressive behavior. By examining data regarding the behavioral and neural correlates of varied cognitive processes that are likely components of a paranoid thinking style, we hope to advance both theoretical and empirical research in this domain

System Dynamics Modeling for Cancer Prevention and Control: A systematic review

Cancer prevention and control requires consideration of complex interactions between multilevel factors. System dynamics modeling, which consists of diagramming and simulation approaches for understanding and managing such complexity, is being increasingly applied to cancer prevention and control, but the breadth, characteristics, and quality of these studies is not known. We searched PubMed, Scopus, APA PsycInfo, and eight peer-reviewed journals to identify cancer-related studies that used system dynamics modeling. A dual review process was used to determine eligibility. Included studies were assessed using quality criteria adapted from prior literature and mapped onto the cancer control continuum. Characteristics of studies and models were abstracted and qualitatively synthesized. 32 studies met our inclusion criteria. A mix of simulation and diagramming approaches were used to address diverse topics, including chemotherapy treatments (16%), interventions to reduce tobacco or e-cigarettes use (16%), and cancer risk from environmental contamination (13%). Models spanned all focus areas of the cancer control continuum, with treatment (44%), prevention (34%), and detection (31%) being the most common. The quality assessment of studies was low, particularly for simulation approaches. Diagramming-only studies more often used participatory approaches. Involvement of participants, description of model development processes, and proper calibration and validation of models showed the greatest room for improvement. System dynamics modeling can illustrate complex interactions and help identify potential interventions across the cancer control continuum. Prior efforts have been hampered by a lack of rigor and transparency regarding model development and testing. Supportive infrastructure for increasing awareness, accessibility, and further development of best practices of system dynamics for multidisciplinary cancer research is needed

A Demographic Model of an Endangered Florida Native Bromeliad (Tillandsia utriculata)

The large, long-lived, epiphytic bromeliad Tillandsia utriculata is currently listed as state-endangered in Florida due to significant population reduction from predation by an invasive weevil, Metamasius callizona. We have developed a novel demographic model of a population of T. utriculata in Myakka River State Park (MRSP) in Sarasota, Florida using a stage-structured matrix model. Analysis of the model revealed conditions for population viability over a variety of parameter scenarios. Model analysis showed that without weevil predation the minimum germination rate required for population viability is low (4–16%), and that given a viable population at structural equilibrium we would expect to find 15 cm in flower or post-flowering each year. Additionally, the model presented here provides a basis for further analyses which explore specific conservation strategies

Transition to forefoot strike reduces load rates more effectively than altered cadence

Background Excessive vertical impacts at landing are associated with common running injuries. Two primary gait-retraining interventions aimed at reducing impact forces are transition to forefoot strike (FFS) and increasing cadence (CAD). The objective of this study was to compare the short- and long-term effects of 2 gait-retraining interventions aimed at reducing landing impacts. Methods A total of 39 healthy recreational runners using a rearfoot strike and a CAD of ≤170 steps/min were randomized into CAD or FFS groups. All participants performed 4 weeks of strengthening followed by 8 sessions of gait-retraining using auditory feedback. Vertical average load rates (VALR) and vertical instantaneous load rates were calculated from the vertical ground reaction force curve. Both CAD and foot strike angle were measured using 3-dimensional motion analysis and an instrumented treadmill at baseline and at 1 week, 1 month, and 6 months after retraining. Results Analysis of variance revealed that the FFS group had significant reductions in VALR (49.7%) and vertical instantaneous load rates (41.7%), and changes were maintained long term. Foot strike angle in the FFS group changed from 14.2° dorsiflexion at baseline to 3.4° plantarflexion, with changes maintained long term. The CAD group exhibited significant reduction only in VALR (16%) and only at 6 months. Both groups had significant and similar increases in CAD at all follow-ups (CAD, +7.2% to 173 steps/min; and FFS, +6.1% to 172 steps/min). Conclusion FFS gait-retraining resulted in significantly greater reductions in VALR and similar increases in CAD compared to CAD gait-retraining in the short and long term. CAD gait-retraining resulted in small reductions in VALR at only the 6-month follow-up

Quantifying interictal intracranial EEG to predict focal epilepsy

Intracranial EEG (IEEG) is used for 2 main purposes, to determine: (1) if epileptic networks are amenable to focal treatment and (2) where to intervene. Currently these questions are answered qualitatively and sometimes differently across centers. There is a need for objective, standardized methods to guide surgical decision making and to enable large scale data analysis across centers and prospective clinical trials. We analyzed interictal data from 101 patients with drug resistant epilepsy who underwent presurgical evaluation with IEEG. We chose interictal data because of its potential to reduce the morbidity and cost associated with ictal recording. 65 patients had unifocal seizure onset on IEEG, and 36 were non-focal or multi-focal. We quantified the spatial dispersion of implanted electrodes and interictal IEEG abnormalities for each patient. We compared these measures against the 5 Sense Score (5SS), a pre-implant estimate of the likelihood of focal seizure onset, and assessed their ability to predict the clinicians choice of therapeutic intervention and the patient outcome. The spatial dispersion of IEEG electrodes predicted network focality with precision similar to the 5SS (AUC = 0.67), indicating that electrode placement accurately reflected pre-implant information. A cross-validated model combining the 5SS and the spatial dispersion of interictal IEEG abnormalities significantly improved this prediction (AUC = 0.79; p<0.05). The combined model predicted ultimate treatment strategy (surgery vs. device) with an AUC of 0.81 and post-surgical outcome at 2 years with an AUC of 0.70. The 5SS, interictal IEEG, and electrode placement were not correlated and provided complementary information. Quantitative, interictal IEEG significantly improved upon pre-implant estimates of network focality and predicted treatment with precision approaching that of clinical experts.Comment: 25 pages, 4 Figures, 1 tabl

JWST mirror and actuator performance at cryo-vacuum

The James Webb Space Telescope (JWST) telescope’s Secondary Mirror Assembly (SMA) and eighteen Primary Mirror Segment Assemblies (PMSAs) are each actively controlled in rigid body position via six hexapod actuators. Each of the PMSAs additionally has a radius of curvature actuator. The mirrors are stowed to the mirror support structure to survive the launch environment and then must be deployed 12.5 mm to reach the nominally deployed position before the Wavefront Sensing & Control (WFSC) alignment and phasing process begins. JWST requires testing of the full optical system in a Cryogenic Vacuum (CV) environment before launch. The cryo vacuum test campaign was executed in Chamber A at the Johnson Space Center (JSC) in Houston Texas. The test campaign consisted of an ambient vacuum test, a cooldown test, a cryo stable test at 65 Kelvin, a warmup test, and finally a second ambient vacuum test. Part of that test campaign was the functional and performance testing of the hexapod actuators on the flight mirrors. This paper will describe the testing that was performed on all 132 hexapod and radius of curvature actuators. The test campaign first tests actuators individually then tested how the actuators perform in the hexapod system. Telemetry from flight sensors on the actuators and measurements from external metrology devices such as interferometers, photogrammetry systems and image analysis was used to demonstrate the performance of the JWST actuators. The mirror move commanding process was exercised extensively during the JSC CV test and many examples of accurately commanded moves occurred. The PMSA and SMA actuators performed extremely well during the JSC CV test, and we have demonstrated that the actuators are fully functional both at ambient and cryo temperatures and that the mirrors will go to their commanded positions with the accuracy needed to phase and align the telescope

Estimating the Redshift Distribution of Faint Galaxy Samples

We present an empirical method for estimating the underlying redshift distribution N(z) of galaxy photometric samples from photometric observables. The method does not rely on photometric redshift (photo-z) estimates for individual galaxies, which typically suffer from biases. Instead, it assigns weights to galaxies in a spectroscopic subsample such that the weighted distributions of photometric observables (e.g., multi-band magnitudes) match the corresponding distributions for the photometric sample. The weights are estimated using a nearest-neighbor technique that ensures stability in sparsely populated regions of color-magnitude space. The derived weights are then summed in redshift bins to create the redshift distribution. We apply this weighting technique to data from the Sloan Digital Sky Survey as well as to mock catalogs for the Dark Energy Survey, and compare the results to those from the estimation of photo-z's derived by a neural network algorithm. We find that the weighting method accurately recovers the underlying redshift distribution, typically better than the photo-z reconstruction, provided the spectroscopic subsample spans the range of photometric observables covered by the photometric sample.Comment: 14 pages, 9 figures, submitted to MNRA