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BackgroundExisting interventions for co-occurring depression and cannabis use often do not treat both disorders simultaneously and can result in higher rates of symptom relapse. Traditional in-person interventions are often difficult to obtain due to financial and time limitations, which may further prevent individuals with co-occurring depression and cannabis use from receiving adequate treatment. Digital interventions can increase the scalability and accessibility for these individuals, but few digital interventions exist to treat both disorders simultaneously. Targeting transdiagnostic processes of these disorders with a digital intervention—specifically positive valence system dysfunction—may yield improved access and outcomes. ObjectiveRecent research has highlighted a need for the inclusion of individuals with lived experiences to assist in the co-design of interventions to enhance scalability and relevance of an intervention. Thus, the purpose of this study is to describe the process of eliciting feedback from individuals with elevated depressed symptoms and cannabis use and co-designing a digital intervention, Amplification of Positivity—Cannabis Use Disorder (AMP-C), focused on improving positive valence system dysfunction in these disorders. MethodsTen individuals who endorsed moderate to severe depressive symptoms and regular cannabis use (2-3×/week) were recruited online via Meta ads. Using a mixed methods approach, participants completed a 1-hour mixed methods interview over Zoom (Zoom Technologies Inc) where they gave their feedback and suggestions for the development of a mental health app, based on an existing treatment targeting positive valence system dysfunction, for depressive symptoms and cannabis use. The qualitative approach allowed for a broader investigation of participants’ wants and needs regarding the engagement and scalability of AMP-C, and the quantitative approach allowed for specific ratings of intervention components to be potentially included. ResultsParticipants perceived the 13 different components of AMP-C as overall helpful (mean 3.9-4.4, SD 0.5-1.1) and interesting (mean 4.0-4.9, SD 0.3-1.1) on a scale from 1 (not at all) to 5 (extremely). They gave qualitative feedback for increasing engagement in the app, including adding a social component, using notifications, and being able to track their symptoms and progress over time. ConclusionsThis study highlights the importance of including individuals with lived experiences in the development of interventions, including digital interventions. This inclusion resulted in valuable feedback and suggestions for improving the proposed digital intervention targeting the positive valence system, AMP-C, to better match the wants and needs of individuals with depressive symptoms and cannabis use.

Abstract Animals are exposed to many microbes in their environment, some of which have been shown to colonize various tissues including the intestine. The composition of the intestinal microbiota affects many aspects of the host’s physiology and health. Despite this, very little is known about whether host behavior contributes to the colonization. We approach this question in the nematode C. elegans, which feeds on bacteria and also harbors an intestinal microbiome. We examined the behavior of C. elegans towards CeMbio, a simplified microbiome consisting of twelve strains that represent the bacteria found in the animal’s natural environment. We observed that C. elegans raised on E. coli shows a strong preference for three members of CeMbio (Lelliottia amnigena JUb66, Enterobacter hormaechei CEent1, and Pantoea nemavictus BIGb0393) compared to E. coli. Previously, these three bacterial strains have been shown to support faster C. elegans development time than E. coli OP50 and are low colonizers compared to eight other members of CeMbio. We then used gas chromatography coupled to mass spectrometry to identify that these three bacteria release isoamyl alcohol, a previously described C. elegans chemoattractant. We suggest that C. elegans seeks bacteria that release isoamyl alcohol and support faster growth.

In value-based decision making, options are selected according to subjective values assigned by the individual to available goods and actions. Despite the importance of this faculty of the mind, the neural mechanisms of value assignments, and how choices are directed by them, remain obscure. To investigate this problem, we used a classic measure of utility maximization, the Generalized Axiom of Revealed Preference, to quantify internal consistency of food preferences in Caenorhabditis elegans, a nematode worm with a nervous system of only 302 neurons. Using a novel combination of microfluidics and electrophysiology, we found that C. elegans food choices fulfill the necessary and sufficient conditions for utility maximization, indicating that nematodes behave as if they maintain, and attempt to maximize, an underlying representation of subjective value. Food choices are well-fit by a utility function widely used to model human consumers. Moreover, as in many other animals, subjective values in C. elegans are learned, a process we find requires intact dopamine signaling. Differential responses of identified chemosensory neurons to foods with distinct growth potentials are amplified by prior consumption of these foods, suggesting that these neurons may be part of a value-assignment system. The demonstration of utility maximization in an organism with a very small nervous system sets a new lower bound on the computational requirements for utility maximization and offers the prospect of an essentially complete explanation of value-based decision making at single neuron resolution in this organism.

This article revises and extends Leland and Thurmaier’s (2004a, 2004b) City—County Consolidation (C3) model by synthesizing it with Johnson’s (2004) Theory of Local Constitutional Change (LCC) and Hughes and Lee’s (2002) Evolutionary Consolidation Model (ECM). The result, we find, is a more general model of local government consolidation. Our model is applicable to a wider variety of consolidation types and incorporates a full consideration of varied charter development processes. Ultimately, this allows for acknowledgment of the possibility that consolidation attempts may be halted prior to a referendum campaign and that those attempts may reflect either conflicts of interest or consensual efforts at problem-solving. We focus specifically on Indiana after enactment of the 2006 Government Modernization Act. After enactment of the act, Indiana experienced seven consolidation efforts from 2008 to 2012. Examination of these efforts provides a robust comparative case study of consolidation efforts occurring during a narrow timeframe and under a common institutional context. The study not only illustrates the suitability of our revised and extended model, but it also confirms a number of Leland and Thurmaier’s (2005) findings from their reassessment of the C3 model.

Background: Major depressive disorder (MDD) is a complex disorder that affects nearly 264 million people worldwide. Structural brain abnormalities in multiple neuroanatomical networks have been implicated in the etiology of MDD, but the degree to which MDD affects brain structure during early to late adulthood is unclear. Methods: We examined morphometry of brain regions commonly implicated in MDD, including the amygdala, hippocampus, anterior cingulate gyrus, lateral orbitofrontal gyrus, subgenual cortex, and insular cortex subregions, from early to late adulthood. Harmonized measures for gray matter (GM) volume and cortical thickness of each region were estimated cross-sectionally for 305 healthy controls (CTLs) and 247 individuals with MDD (MDDs), collated from four research cohorts. We modeled the nonlinear associations of age with GM volume and cortical thickness using generalized additive modeling and tested for age-dependent group differences. Results: Overall, all investigated regions exhibited smaller GM volume and thinner cortical measures with increasing age. Compared to age matched CTLs, MDDs had thicker cortices and greater GM volume from early adulthood until early middle age (average 35 years), but thinner cortices and smaller GM volume during and after middle age in the lateral orbital gyrus and all insular subregions. Deviations of the MDD and CTL models for both GM volume and cortical thickness in these regions started as early as age 18. Conclusions: The analyses revealed that brain morphometry differences between MDDs and CTLs are dependent on age and brain region. The significant age-by-group interactions in the lateral orbital frontal gyrus and insular subregions make these regions potential targets for future longitudinal studies of MDD.

Background: Association among local hemodynamic parameters and their implications in development of acute coronary syndrome (ACS) have not been fully investigated.Methods: A total of 216 lesions in ACS patients undergoing coronary CT angiography (CCTA) before 1–24 months from ACS event were analyzed. High-risk plaque on CCTA was defined as a plaque with ≥2 of low-attenuation plaque, positive remodeling, spotty calcification, and napkin-ring sign. With the use of computational fluid dynamics analysis, fractional flow reserve (FFR) derived from CCTA (FFRCT) and local hemodynamic parameters including wall shear stress (WSS), axial plaque stress (APS), pressure gradient (PG) across the lesion, and delta FFRCT across the lesion (ΔFFRCT) were obtained. The association among local hemodynamics and their discrimination ability for culprit lesions from non-culprit lesions were compared.Results: A total of 66 culprit lesions for later ACS and 150 non-culprit lesions were identified. WSS, APS, PG, and ΔFFRCT were strongly correlated with each other (all p < 0.001). This association was persistent in all lesion subtypes according to a vessel, lesion location, anatomical severity, high-risk plaque, or FFRCT ≤ 0.80. In discrimination of culprit lesions causing ACS from non-culprit lesions, WSS, PG, APS, and ΔFFRCT were independent predictors after adjustment for lesion characteristics, high-risk plaque, and FFRCT ≤ 0.80; and all local hemodynamic parameters significantly improved the predictive value for culprit lesions of high-risk plaque and FFRCT ≤ 0.80 (all p < 0.05). The risk prediction model for culprit lesions with FFRCT ≤ 0.80, high-risk plaque, and ΔFFRCT had a similar or superior discrimination ability to that with FFRCT ≤ 0.80, high-risk plaque, and WSS, APS, or PG; and the addition of WSS, APS, or PG into ΔFFRCT did not improve the model performance.Conclusions: Local hemodynamic indices were significantly intercorrelated, and all indices similarly provided additive and independent predictive values for ACS risk over high-risk plaque and impaired FFRCT.

Abstract Background Simulation of a left ventricle has become a critical facet of evaluating therapies and operations that interact with cardiac performance. The ability to simulate a wide range of possible conditions, changes in cardiac performance, and production of nuisances at transition points enables evaluation of precision medicine concepts that are designed to function through this spectrum. Ventricle models have historically been based on biomechanical analysis, with model architectures constituted of continuous states and not conducive to deterministic processing. Producing a finite-state machine governance of a left ventricle model would enable a broad range of applications: physiological controller development, experimental left ventricle control, and high throughput simulations of left ventricle function. Methods A method for simulating left ventricular pressure-volume control utilizing a preload, afterload, and contractility sensitive computational model is shown. This approach uses a logic-based conditional finite state machine based on the four pressure-volume phases that describe left ventricular function. This was executed with a physical system hydraulic model using MathWorks’ Simulink® and Stateflow tools. Results The approach developed is capable of simulating changes in preload, afterload, and contractility in time based on a patient’s preload analysis. Six pressure–volume loop simulations are presented to include a base-line, preload change only, afterload change only, contractility change only, a clinical control, and heart failure with normal ejection fraction. All simulations produced an error of less than 1 mmHg and 1 mL of the absolute difference between the desired and simulated pressure and volume set points. The acceptable performance of the fixed-timestep architecture in the finite state machine allows for deployment to deterministic systems, such as experimental systems for validation. Conclusions The proposed approach allows for personalized data, revealed through an individualized clinical pressure–volume analysis, to be simulated in silico. The computational model architecture enables this control structure to be executed on deterministic systems that govern experimental left ventricles. This provides a mock circulatory system with the ability to investigate the pathophysiology for a specific individual by replicating the exact pressure–volume relationship defined by their left ventricular functionality; as well as perform predictive analysis regarding changes in preload, afterload, and contractility in time.

Background: Risk stratification is crucial to improve tailored therapy in patients with suspected coronary artery disease (CAD). This study investigated the ability of targeted proteomics to predict presence of high-risk plaque or absence of coronary atherosclerosis in patients with suspected CAD, defined by coronary computed tomography angiography (CCTA). Methods: Patients with suspected CAD (n = 203) underwent CCTA. Plasma levels of 358 proteins were used to generate machine learning models for the presence of CCTA-defined high-risk plaques or complete absence of coronary atherosclerosis. Performance was tested against a clinical model containing generally available clinical characteristics and conventional biomarkers. Findings: A total of 196 patients with analyzable protein levels (n = 332) was included for analysis. A subset of 35 proteins was identified predicting the presence of high-risk plaques. The developed machine learning model had fair diagnostic performance with an area under the curve (AUC) of 0·79 ± 0·01, outperforming prediction with generally available clinical characteristics (AUC = 0·65 ± 0·04, p

Working with various groups within CENS, we are developing, testing, and will be soon using acoustic sensor arrays to identify, track, and study the social interactions of various bird species. There has been good technical progress towards this goal, especially in the localization and recognition areas. We are currently collecting data and testing prototype systems at three sites, the James Reserve and the Hastings Reserve in California, and the Montes Azules Biopshere Reserve in Chiapas Mexico.

During the period of 2008 to 2012, four consolidation attempts occurred under Indiana’s Government Modernization Act: two proposing city–county consolidation and two proposing town–township consolidation. The town–township mergers received overwhelming support, with more than 70% of voters approving consolidation in each case. The city–county mergers, on the other hand, were each defeated by a margin of approximately two to one. In this paper, we conduct a comparative case study of the four consolidation proposals using Leland and Thurmaier’s (2004) C3 model as an analytical framework. The results of our study indicate that greater demographic diversity and the potential for large cost shifts from urban to rural customers stimulated opposition to the city–county consolidations. The successful town–township consolidations were characterized by more homogeneous populations and modest and predictable fiscal impacts. We find no evidence that pre-existing functional consolidation or strong elite advocacy on behalf of consolidation leads to greater support for the referendum.

Aim: The purpose of this study was to document in people with type 2 diabetes (T2DM) in Barbados, attitudes and beliefs that may result in psychological insulin resistance. Methods: A representative, population-based, sample of 175 eligible people with T2DM 25 years of age and over was surveyed by telephone. The 20-item insulin treatment appraisal scale (ITAS) was administered (score range 20 to 100 for positive to negative perceptions). Results: 117 people participated (67% response rate, 32% male, mean age 66 years, 90% Black, 22% on insulin). Of non-responders, 52 were not contactable and 6 were difficult to communicate with. Negative perceptions about insulin use included – meant a worsening of diabetes (68%), would worry family (63%), feared self-injection (58%), meant a failure in self-management (57%), injections were painful (54%), would be seen as being sicker (46%), increased hypoglycaemia risk (38%), required effort (34%), causes weight gain (27%), causes a deterioration in health (14%), and would have to give up enjoyable activities (10%). Positive perceptions were – helps good glycaemic control (78%), would prevent complications (61%) and improves health (58%). Mean total ITAS score (61.6, SD = 7.7) was lower for those on insulin compared to those not on insulin (53.7 vs. 63.8, p