cssi/SciSciGPT-SciSciNet huggingface dataset:
Electrophysiologic Scar Substrate in Relation to VT: Noninvasive High‐Resolution Mapping and Risk Assessment with ECGI

Background Ischemic cardiomyopathy (ICM) can provide the substrate for ventricular tachycardia (VT). Objective To map noninvasively with high resolution the electrophysiologic (EP) scar substrate, identify its relationship to reentry circuits during VT, and stratify VT risk in ICM patients. Methods Noninvasive high-resolution epicardial mapping with electrocardiographic imaging (ECGI) was performed in 32 ICM patients (17 with clinical VT, 15 without VT). Abnormal scar EP substrate was determined based on electrogram (EGM) amplitude (as percentage of maximal peak-to-peak voltage over the entire ventricular epicardium; total scar [TS] Results Patients with VT had higher scar burden (TS: 51.0 ± 9.3% vs 36.5 ± 5.4%, P Conclusions ECGI noninvasively identified scar EP substrate that underlies abnormal conduction in ICM patients. It identified regions within the scar that aligned with critical elements of the reentry circuit during VT. ECGI can potentially be used for VT risk stratification in ICM patients.
Understanding Woman Abuse in Intimate Heterosexual Relationships: The Enduring Relevance of Feminist Ways of Knowing

Despite claims to the contrary, over the past 40 years, feminist scholars have made numerous empirical and theoretical contributions to the study of woman abuse in intimate heterosexual relationships. The main objective of this paper is to demonstrate that feminist ways of knowing continue to be relevant today. Another goal is to challenge some common myths about feminist modes of inquiry.
Combining CBR and Deep Learning to Generate Surprising Recipe Designs

This paper presents a dual-cycle CBR model in the domain of recipe generation. The model combines the strengths of deep learning and similarity-based retrieval to generate recipes that are novel and valuable (i.e. they are creative). The first cycle generates abstract descriptions which we call “design concepts” by synthesizing expectations from the entire case base, while the second cycle uses those concepts to retrieve and adapt objects. We define these conceptual object representations as an abstraction over complete cases on which expectations can be formed, allowing objects to be evaluated for surprisingness (the peak level of unexpectedness in the object, given the case base) and plausibility (the overall similarity of the object to those in the case base). The paper presents a prototype implementation of the model, and demonstrates its ability to generate objects that are simultaneously plausible and surprising, in addition to fitting a user query. This prototype is then compared to a traditional single-cycle CBR system.
Enduring a decade of drought: Patterns and drivers of vegetation change in a semi-arid grassland

Abstract This study evaluated patterns and drivers of vegetation change in a semi-arid grassland in southern Arizona across eleven years of extended drought and high temperatures, 2004–2014. Changes included declines in C4 perennial grass basal cover with patchy grass mortality, leaf litter increases, shrub declines, and increases in non-native grass Eragrostis lehmanniana . Linear mixed-effects models identified precipitation during January–June “extended spring” as the best predictor of grass basal cover, especially when plots were grouped by soil and topographic features. Models showed that a decrease in extended spring precipitation from 150 to 50 mm was associated with loss of one-quarter to one-half of plots' total grass cover. Association of grass declines with this novel season of drought is especially relevant because global circulation models predict steep declines in spring rainfall. Increasing E. lehmanniana dominance was also associated with native grass declines. There was little support over this time for predicted effects of livestock grazing or shrub encroachment. This study demonstrated how monitoring data from working landscapes can improve ecological understanding of drought. Findings also suggest managers could improve chances for sustaining resilience by responding to rainfall in multiple seasons, monitoring for mortality events, and establishing contingency plans for various types of drought.
An efficient video denoising method using decomposition approach for low-rank matrix completion

Denoising as one of the most significant task in video processing was studied widely in the literature. We propose an efficient video denoising method based on decomposition approach for matrix completion. A noisy video is processed in blockwise manner and for each processed block we find similar blocks in other frames. The similar blocks then will stack together and unreliable pixels will remove using fast matrix completion method [1]. We demonstrate the effectiveness of our algorithm in removing the mixed noise through the results. Our results also proved the effectiveness of our algorithm in removing noise from regular structures. We also compare with other denoising technique using matrix completion. Our method results in comparable performance with significantly lower computation complexity.
Abstract P5-11-10: Evaluation of the relationship between psychological distress and risk for breast cancer

Introduction: Women at increased risk for breast cancer (due to family history or having a pathogenic germline mutation in a cancer causing gene) have greater distress levels, which can impact quality of life and screening behavior. The goal of our study was to evaluate distress related to different risk factors for breast cancer and examine the stability of this distress over time. Methods: Women at increased risk for breast cancer who enrolled in the High-Risk Breast Program (HRBP) at the University of Vermont Medical Center were eligible for participation. Women with strong family history, a genetic mutation or atypia on biopsy (benign breast disease, BBD) who completed the Impact of Event Scale (IES) at least once were identified. The IES is a self-reported measure designed to measure subjective distress within the past seven days in relation to a stressor. The scale is based on a two-factor intrusion-avoidance model. Linear regression was used to examine change in distress over time and to compare distress levels between risk groups. Both risk measures and distress scores were examined using bivariate plots and normal probability plots. Pearson correlation coefficients and least squares regression coefficients were obtained along with 95% confidence intervals. In addition to treating the risk estimates as continuous measures, these estimates were grouped into low or average risk ( =5%) over five years. Using the ordinal classification into three risk groups, analysis of variance was conducted to detect group differences in average psychological distress using a 5% significance level. Results: The cohort comprised 344 women at increased risk for breast cancer due to a strong family history (80%), a genetic mutation (9%) or benign breast disease (16%). Mean IES score was 16.8 (CI 15.5, 18.2), and indicates high distress levels in this cohort. IES scores decreased over time (p Conclusion: We demonstrated that distress is similar among different risk categories and that distress decreases over time; patterns not previously recognized. Women with benign breast disease have IES scores similar to women with strong family histories of breast cancer. Women with multiple risk factors are also noted to have higher scores. Women with genetic mutations appear to have the highest distress levels. These findings further our understanding of cancer related distress. Targeting women with highest distress levels may improve both quality of life and screening adherence. Citation Format: Vennard K, Crocker A, Ogheneruona A, Cuke M, Wood M. Evaluation of the relationship between psychological distress and risk for breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P5-11-10.
Sexual size dimorphism of group-living Mongolian gerbils Meriones unguicutalus (Muridae: Gerbillinae)

Polygynous mammals often have male-biased sexual size dimorphism due to male–male competition for mates. However, fecundity selection may enlarge female body size. The Mongolian gerbil Meriones ung...
No girls allowed?: Fluctuating boundaries between gay men and straight women in gay public space:

How are boundaries between sexual identities constructed and maintained through interaction? I draw on ethnographic observation in Philadelphia gay bars popular among heterosexual patrons and suppl...
F3: Simulating spatiotemporal forest change from field inventory, remote sensing, growth modeling, and management actions

Abstract Contemporary forest management requires highly-detailed, spatially-contiguous, multi-temporal, and scenarios-comparable forest conditions. Field inventories and individual-tree models often contain highly-detailed data and allow for long-term complex scenarios comparison, but the information is only at sampled locations and lacks complete spatial coverage. Forest landscape models (FLMs) provide landscape-level spatiotemporal data, but the details that are important to land managers are often lost in the generalized outputs. We developed a modeling framework, F3, to integrate FIA (Forest Inventory and Analysis) plots, the Forest Vegetation Simulator (FVS), and FastEmap (Field And SatelliTe for Ecosystem MAPping) to simulate spatiotemporal forest change under natural succession and vegetation management. F3 extrapolates the details of forest inventory plots and individual-tree model outputs to a spatially-contiguous landscape by fusing tree-list field measurements, individual tree growth and yield models, remote sensing and environmental geospatial datasets. F3 allows for area-specific management action simulations. F3 compares FVS results with field measurements for temporal accuracy assessment and uses a leave-one-out cross-validation for spatial accuracy assessment. F3 adopts parallel computation techniques to implement the modeling in an automatic and efficient manner. The proof of concept of F3 was demonstrated in Tahoe National Forest (TNF) showing spatiotemporal changes on six forest structural metrics (quadratic mean diameter, basal area, biomass, habitat suitability index, canopy cover, and coarse woody debris) under natural succession, regeneration-cut, and thinning scenarios for the years 2014–2114 at a 30 m resolution. F3 can be used for initializing FLMs and for analyzing a wide range of ecosystem services; however, the under-representation of certain forest types in the FIA plot data set, the modeling bias from FVS, and choice of FastEmap covariates contribute to major uncertainties in the framework.
Reaction Path Models for Polyatomic Reaction Dynamics-- from Transition State Theory to Path Integrals

The reaction path Hamiltonian model for the dynamics of general polyatomic systems is reviewed. Various dynamical treatments based on it are discussed, from the simplest statistical approximations (e.g., transition state theory, RRKM, etc.) to rigorous path integral computational approaches that can be applied to chemical reactions in polyatomic systems. Examples are presented which illustrate this “menu” of dynamical possibilities.
Kainic Acid: Insights into Its Receptor-Mediated Neurotoxic Mechanisms

Historically, kainic acid (KA) was selected as a potential exitotoxin because the evidence at the time was consistent with the notion that, as a conformationally restricted analogue of L-glutamate, it was a potent agonist at glutamate receptors (Olney et al., 1974; Coyle and Schwarcz, 1976). Since the first reports of the perikaryal-specific neurotoxic action of intracerebrally injected KA, it has become increasingly apparent that the mechanism of its neurotoxic effects is complex. Consequently, it has been our strategy for clarifying the mechanism of neurotoxicity of KA to focus on receptor specific interactions of the drug. We have felt that this approach might lead to a better understanding of the proximate physiologic events that result in perikaryal-specific neuronal degeneration. Furthermore, the effects of KA might be distinguished from other receptor-specific excitatory amino acid analogues (Watkins and Evans, 1981) such as N-methyl-D-aspartic acid (NMDA) and quisqualic acid as well as more generalized consequences of excessive stimulation of the broad class of the acidic amino acid receptors. These studies have provided evidence of the unique physiologic, pharmacologic and toxicologic properties associated with activation of receptors for KA.
Managing migraine associated with sensitization.

Abstract The majority of migraineurs seeking secondary or tertiary medical care experience throbbing pain and cutaneous allodynia during the course of migraine. Underlying the origin of these symptoms are peripheral and central trigeminovascular neurons, whose cell bodies are located in the trigeminal ganglion and the spinal dorsal horn, respectively. The development of throbbing in the initial phase of migraine is mediated by sensitization of peripheral trigeminovascular neurons, whereas the development of cutaneous allodynia later in the attack is propelled by sensitization of central trigeminovascular neurons which, unfortunately, are not equipped to respond to triptans directly. Triptans appear to act presynaptically in the dorsal horn, such as to inhibit signal transmission from peripheral to central trigeminovascular neurons. Reining in the central neurons using triptan treatment is possible as long as their excitability remains driven by incoming signals from the meninges, but not after they develop autonomous activity. Accordingly, attacks with allodynia can be effectively terminated, provided that the patient vigilantly resorts to triptan therapy before or soon after the onset of allodynia, but not after allodynia has become firmly established. On the other hand, allodynic patients who missed the critical window for effective triptan therapy can still be rendered pain‐free using an intravenous infusion of non-steroidal anti-inflammatory drugs.
Probabilistic Wind and Solar Power Predictions

The added value of probabilistic wind and solar power predictions with respect to deterministic forecasting is demonstrated. A review of the current state-of-the-science approaches to generate probabilistic predictions and uncertainty quantification is provided, follow by an example of a comparison of probabilistic vs. deterministic estimates based on real data.
Controllability Gramian, Symmetry Structures, and Application of Circulant Networks

The Chap. 6 considered a group of first order agents, adopting a weighted consensus-type coordination protocol over a Circulant network. The chapter examined the controllability properties of a Circulant network.
Computers Versus Brains: Game Is Over or More to Come?

Abstract Creating machines that can think and act just like humans has fascinated humanity for millennia. Breakthroughs of scientific and technological developments in the past century, which demonstrate rapidly accelerating pace in recent decades, allow us now to build computers and robots that parallel or even surpass human abilities in some respects. This is the era of new Artificial Intelligence (AI) with Deep Learning (DL) exploiting very big databases and requiring massive computational resources. It is a fundamental question whether the new AI could benefit from lessons learnt from human brains and human intelligence, or machine intelligence may develop better without considering human experiences and constraints. In this chapter we analyze various aspects of biological and artificial intelligence. We introduce a balanced approach based on the concepts of complementarity and multistability as manifested in human brain operation and cognitive processing. This approach provides insights into key principles of intelligence in biological brains and helps building more powerful artificially intelligent devices.
A Comprehensive Ignition System Model for Spark Ignition Engines

In the present paper, a comprehensive ignition system model (VTF ignition model) accounting for the practical module and working mechanism of a spark plug was developed, aiming to provide enhanced capability for the 3D combustion simulation of spark ignition engines. In this model, an electrical circuitry model is used to represent the ignition coil, spark plug, and air column. The air column is represented by a set of Lagrangian particles that move with the local flow field. Flame propagation is directly calculated using SAGE model with a reduced isooctane reaction mechanism. The new ignition system model is further implemented into CONVERGE through user defined functions and is verified by comparing with the conventional DPIK model. It is found that the VTF ignition model predicts slower combustion than the DPIK model, mainly due to more realistic energy deposit method and energy discharging rate. Furthermore, the VTF model also has the capability of predicting the arc motion and restrike phenomena associated with spark ignition processes. It is expected that with more validation with experiments, the new VTF model has the great potential to better serve the needs of engine combustion simulation.Copyright © 2018 by ASME
Essentials of public service: An introduction to contemporary public administration

One of the first steps Master of Public Administration (MPA) and Master of Public Policy (MPP) students take is enrolling in some form of an introductory course. Such a class is typically designed ...
An Anisotropic Damage Model for Metal Fatigue

This paper presents the development of an anisotropic fatigue damage model to assess residual life of engineering components based on theory of Damage Mechanics (DM). This is achieved by extending the application of an anisotropic model of Damage Mechanics recently proposed by Chow and Wang (Chow and Wang, 1987a,b,c, 1988; Wang, 1987). The effect of plastic damage on fatigue damage is taken into account by establishing the required evolution equations of plastic damage. The failure criterion is based on the postulation that a macro-crack is formed when overall equivalent damage Z, which includes fatigue damage Zf and plastic damage ZP, reaches a critical value Zcr.
Immersogeometric analysis of moving objects in incompressible flows

Abstract We deploy the immersogeometric approach for tracking moving objects. The method immerses objects into non-boundary-fitted meshes and weakly enforces Dirichlet boundary conditions on the object boundaries. The object motion is driven by the integrated surface force and external body forces. A residual-based variational multiscale method is employed to stabilize the finite element formulation for incompressible flows. Adaptively refined quadrature rules are used to better capture the geometry of the immersed boundaries by accurately integrating the intersected background elements. Treatment for the freshly-cleared nodes (i.e. background mesh nodes that are inside the object at one time step, but are in the fluid domain at the next time step) is considered. We assess the accuracy of the method by analyzing object motion in different flow structures including objects freely dropping in viscous fluids and particle focusing in unobstructed and obstructed micro-channels. We show that key quantities of interest are in very good agreements with analytical, numerical and experimental solutions. We also show a much better computational efficiency of this framework than current commercial codes using adaptive boundary-fitted approaches. We anticipate deploying this framework for applications of particle inertial migration in microfluidic channels.
Natural Carbon By‐Products for Transparent Heaters: The Case of Steam‐Cracker Tar

Steam-cracker tar (SCT) is a by-product of ethylene production that is in massive quantities globally (>150 × 106 tons per year). With few useful applications, the production of unwanted SCT leads to the need for its costly disposal or burning at the boiler plant. The discovery of new uses for SCT would therefore bring both economic and environmental benefits, although, to date, efforts toward employing SCT in diverse applications have been limited, and progress is further hampered by a lack of understanding of the material itself. Although complex and highly heterogeneous in nature, the molecular composition of SCT has the potential to serve as a diverse and tunable feedstock for wide-ranging applications. Here, a simple solution-processing method for SCT that allows its conductivity and optical properties to be controlled over orders of magnitude is reported. Here, by way of example, the focus is on the production of transparent conductive thin films, which exhibit a wide range of transparencies (23-93%) and sheet resistances (2.5 Ω □-1 to 1.2 kΩ □-1 ) that are tuned by a combination of solution concentration and thermal annealing. As transparent Joule heaters, even without optimization, these SCT devices show competitive performance compared to established technologies such as those based on reduced graphene oxide, and surpass the temperature stability limit of other materials. Furthermore, it is demonstrated that laser annealing can be used to process the SCT films and directly pattern transparent heaters on an arbitrary substrate. These results highlight the potential of SCT as a feedstock material for electronic applications and suggest that broader classes of either naturally occurring carbon or produced carbonaceous by-products could prove useful in a range of applications.
Factors Influencing Infection Rates after Open Hand Fractures.

Introduction Open hand fractures are anecdotally reported to have lower infection rates than open long bone fractures. Although a 3-hour rule for antibiotic administration and a 6-hour rule for operative debridement have historically been upheld as ideal management for open fractures, other factors may be more influential in the development of infection. The purpose of this study was to investigate factors associated with open hand fracture infections. Materials and Methods We retrospectively reviewed 67 patients with 107 open hand fractures between 2012 and 2017. Time from injury to antibiotic administration and operative debridement, modified Gustilo–Anderson classification, and patient characteristics including age, smoking status, and presence of chronic disease were examined for each patient. Outcome parameters were the development of infection and fracture union. Results The overall rate of infection was 9% (6 of 67 patients). No type 1 or type 2 fractures developed infection in contrast to 12.2% of type 3 fractures. Patients who received antibiotics in less than 3 hours and underwent debridement in less than 6 hours did not have lower infection or nonunion rates than those who did not. The association between the modified Gustilo–Anderson classification and the development of infection or nonunion was statistically significant. Conclusion Factors including time to antibiotics, time to operative debridement, smoking status, and chronic disease comorbidities were not predictive of either infection or nonunion in open hand fractures. Fracture type as defined by a modified Gustilo–Anderson classification was the factor most strongly related to the development of infection or nonunion in these fractures.
E-043 Automated large vessel occlusion by artificial intelligence improves stroke workflow metrics: 1st 100 patient experience in a hub and spoke stroke system

Introduction Large vessel occlusion (LVO) acute ischemic stroke is a time sensitive disease process. Technology that could shorten the time to mechanical thrombectomy (MT) holds great promise, but experience and data on these applications are sparse. We describe our initial experience with Artificial Intelligence (AI) for automated LVO detection (aLVOd) in a hub and spoke network. Methods This is a retrospective chart review of consecutive code stroke cases at a comprehensive stroke center and two spoke hospitals. Viz AI was implemented from May 2019 to December 2019, during which time 100 consecutive LVO alerts were evaluated. Baseline clinical characteristics were collected on all aLVO alerts and from historical LVO controls taken to the angiography suite for intervention from the 6 months prior to AI implementation. Details of medical treatment, CT/CTA interpretation by blinded Neuroradiologists (confirmed LVO, normal, or significant cerebrovascular finding without LVO), final diagnosis, results of MT, clinical outcomes, and stroke workflow metrics were collected. Categorical variables were compared with Pearson chi-squared testing and continuous variables with Wilcoxon rank sum testing. Results A total of 680 code strokes were evaluated during the AI study period. The average age and NIHSS score of the 100 aLVOd patients were 69 (IQR 57, 80) and 4 (IQR 1, 13.75), respectively. There were no significant differences in baseline characteristics of LVO patients identified by aLVOd, those LVOs missed by aLVOd, or historical LVO controls undergoing MT. A total of 68 patients were diagnosed with LVO during the AI study period; 45 (66.2%) were identified by aLVOd and confirmed by neuroradiology, and 23 (33.8%) were missed by AI. The majority of LVOs missed by AI were distal M2 or posterior circulation occlusions (60.8%). Of the 100 total aLVOd alerts, 55 were not LVO based on the neuroradiologist interpretation (18 were normal, 36 high grade intracranial atherosclerosis, and one was non-diagnostic). The average NIHSS score of LVO patients detected by AI was 11 (IQR 3, 19). Forty-one patients during the AI study period underwent MT and 36 historical LVO controls underwent mechanical thrombectomy. The sensitivity and specificity of aLVOd was 66.2% and 91% while the positive predictive value and negative predictive values of AI were 45% and 96%, respectively. Median time from CTA completion to treating team notification for all patients was significantly shorter for aLVOd patients (8 minutes (IQR 4.25, 9) v 17 min (IQR 13, 28.5), p Conclusion VizAI aLVOd is a robust screening tool for identifying LVO based on its high negative predictive value. This system is a valuable adjunct to traditional imaging interpretation. The use of AI to detect LVO improves stroke workflow metrics in a hub and spoke stroke system of care. Disclosures D. Dornbos: None. D. Hoit: None. V. Inoa-Acosta: None. C. Nickele: None. A. Arthur: None. L. Elijovich: None.
Examining entrepreneurial experience in relation to pre-launch and post-launch learning activities affecting venture performance

Entrepreneurship research distinguishes between prelaunch learning in the form of customer and technology planning prior to launch and postlaunch learning in the form of product or market pivots af...
A multi-sensor fusion method based on EKF on granary robot

In this paper, a set of algorithms of granary robot is designed. The traditional outdoor localization technology is not suitable for indoor because of the shelter of the wall of the building. Traditional indoor localization technology has a poor effect because of the continuous change of terrain in barn. Compared with various indoor localization technologies, ultra wide band (UWB) localization technology has the advantages of strong penetration, low power consumption performance and high localization accuracy, but the problems of the system is a low updating frequency. Because of a low update frequency of UWB localization system, the paper presents a joint localization system which fuse Inertial-Measurement-Unit (IMU) inertial navigation system and the UWB localization system based on Extended Kalman Filter (EKF). It makes full use of the complementary features of the functions of the inertial localization system and the UWB localization system. The inertial localization system can continuously process the UWB localization information and it compensates the problem of a low refresh frequency of the UWB module, continuously optimizes the position and attitude in the dynamic process. The experiment shows that this joint localization method breaks the limitation of moving speed and improves the localization accuracy. The dynamic localization accuracy error of the system is about 10cm, and the calculation error of the attitude is within 0.3 degree. The localization result meets the needs of the granary robot.
Survey of Edge Computing Based on a Generalized Framework and Some Recommendation

The fast adoption and success of IoT and 5G related technology, accompanied by the ever-increasing critical demand for better QoS, revolutionized the paradigm shift from centralized cloud computing to some combination of distributed edge computing and traditional cloud computing. There are substantial researches and reviews on edge computing, and several industry-specific frameworks were proposed, but general purpose frameworks that could enable speedy utilization of millions of innovated business/IT services worldwide across the entire spectrum of the current computing paradigm is not yet properly addressed. We first proposed a generalized and service-oriented edge computing framework, based on a relatively complete survey of recent publications, then we conducted an in-depth analysis of selected works from both academia and industry aimed to access the maturity, and the gaps in this arena. Finally, we summarize the challenges and opportunities in edge computing, and we hope that this paper can inspire significant future improvements.
Using process tracing to evaluate stakeholder disputes: The case of tender award dispute in Nepal’s forestry:

In donor aid programs, the claims of causal links between inputs and outputs are crucial to establish the effectiveness of aid. As a result, there has been ample research on the degree and directio...
Precision Medicine Care in ADHD: The Case for Neural Excitation and Inhibition

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that has become increasingly prevalent worldwide. Its core symptoms, including difficulties regulating attention, activity level, and impulses, appear in early childhood and can persist throughout the lifespan. Current pharmacological options targeting catecholamine neurotransmissions have effectively alleviated symptoms in some, but not all affected individuals, leaving clinicians to implement trial-and-error approach to treatment. In this review, we discuss recent experimental evidence from both preclinical and human studies that suggest imbalance of excitation/inhibition (E/I) in the fronto-striatal circuitry during early development may lead to enduring neuroanatomical abnormality of the circuitry, causing persistence of ADHD symptoms in adulthood. We propose a model of precision medicine care that includes E/I balance as a candidate biomarker for ADHD, development of GABA-modulating medications, and use of magnetic resonance spectroscopy and scalp electrophysiology methods to monitor the effects of treatments on shifting E/I balance throughout the lifespan.
Ricci flow on Kaehler manifolds

In this paper, we announce the following results: Let M be a Kaehler-Einstein manifold with positive scalar curvature. If the initial metric has nonnegative bisectional curvature and positive at least at one point, then the Kahler-Ricci flow converges exponentially fast to a Kaehler-Einstein metric with constant bisectional curvature.
Investigating Associations Between Screen Time and Symptomatology in Individuals With Serious Mental Illness: Longitudinal Observational Study.

Background: Increasing screen time exposure from digital devices like smartphones has shown a variety of mixed associations with cognition, behavior, and well-being in adults and children but little is known about its associations with symptomatology in individuals with serious mental illness. Objective: To determine the range of associations between screen time and symptoms of individuals with mental illness, we utilized a method called specification curve analysis. Methods: In this observational study, we recruited smartphone-owning adults (≥18 years old) with schizophrenia and healthy controls. We installed 2 research-source smartphone apps, mindLAMP and Beiwe, to collect survey results, cognitive test results, and screen time metrics over a period of 3 months. Surveys were scheduled for twice a week, but participants were instructed to take the surveys naturally as much or as little as they wanted. Screen time was collected continuously in the background. A total of 140 participants was recruited from the outpatient clinic population as well as through general public advertising. Age-matched, smartphone-owning healthy controls were also part of the recruitment pool. A specification curve analysis was a priori designed to explore the relationship between every combination of independent variable and dependent variable in order to demonstrate to what degree screen time relates to symptoms in individuals with serious mental illness. Results: The sample consisted of 88 participants (54 with schizophrenia and 34 healthy controls) who completed both the initial and follow-up visits, completed at least one self-reported survey, and had a minimum passive data cutoff of 5 consecutive days. While we found an association between smartphone screen time metrics and cognition (adjusted R2=0.107, P<.001), specification curve analysis revealed a wide range of heterogenous associations with screen time from very negative to very positive. The effects differed based on diagnostic group, age bracket, type of regression model used, and the specific independent and dependent variables selected for analysis. Conclusions: The associations between screen time and mental health in patients with schizophrenia are heterogenous when examined with methods that reduce analytical bias. The heterogeneity in associations suggests that complex and personalized potential effects must be understood in the greater context of an individual. This analysis of longitudinally collected screen time data shows potential for future research that could benefit from high resolution metrics on smartphone use.
Impact of a pharmacy technician and pharmacist on time to inhaled tobramycin therapy in a pediatric cystic fibrosis clinic.

BACKGROUND Cystic fibrosis (CF) patients who grow Pseudomonas aeruginosa on respiratory culture are commonly prescribed inhaled tobramycin (TIS) to eradicate the organism. The objective of this study was to determine the impact of a pharmacy technician/pharmacist team, in conjunction with an integrated health-system specialty pharmacy (IHSSP), on the time from positive culture to prescribing and access to TIS in a pediatric CF clinic. METHODS A retrospective study of CF patients positive for P. aeruginosa who were prescribed TIS for eradication. RESULTS The study included 20 patients in the pregroup and 42 patients in the postgroup. Total median (interquartile range) days from positive culture to TIS being shipped to the patient from the pharmacy was significantly different: 15 (10.25-21) days in the pregroup and 9 (7-14) days in the post groups (p = .005). The time from positive culture to TIS prescribing was significantly different: 6 (5-12.75) days in the pregroup and 5 (3.75-6) days in the postgroup (p = .01). In the postgroup median time from prescription to the patient receiving the TIS was significantly different between the two groups 2 (2-5) days IHSSP group versus 6 (3-9) external specialty pharmacy group (p = .003). Time from prescription to prior authorization approval was the same in both groups. CONCLUSIONS The addition of the pharmacy team reduced time from culture to TIS being received by the patient. Patients able to fill at the IHSSP received their medication sooner than an external specialty pharmacy. The study shows the benefit of an integrated pharmacy model in conjunction with an IHSSP.
Operationalizing the Use of Biofabricated Tissue Models as Preclinical Screening Platforms for Drug Discovery and Development.

A wide range of complex in vitro models (CIVMs) are being developed for scientific research and preclinical drug efficacy and safety testing. The hope is that these CIVMs will mimic human physiolog...
An Integrative Genomic Strategy Identifies sRAGE as a Causal and Protective Biomarker of Lung Function.

Background There are few clinically useful circulating biomarkers of lung function and lung disease. We hypothesized that genome-wide association studies (GWAS) of circulating proteins in conjunction with GWAS of pulmonary traits represents a clinically relevant approach to identifying causal proteins and therapeutically useful insights into mechanisms related to lung function and disease. Study Question Can an integrative genomic strategy using GWAS of plasma soluble receptor for advanced glycation end-products (sRAGE) levels in conjunction with GWAS of lung function traits identify putatively causal relations of sRAGE to lung function? Study Design and Methods Plasma sRAGE levels were measured in 6,861 Framingham Heart Study participants and GWAS of sRAGE was conducted to identify protein quantitative trait loci (pQTL), including cis-pQTL variants at the sRAGE protein-coding gene locus (AGER). We integrated sRAGE pQTL variants with variants from GWAS of lung traits. Colocalization of sRAGE pQTL variants with lung trait GWAS variants was conducted, and Mendelian randomization was performed using sRAGE cis-pQTL variants to infer causality of sRAGE for pulmonary traits. Cross-sectional and longitudinal protein-trait association analyses were conducted for sRAGE in relation to lung traits. Results Colocalization identified shared genetic signals for sRAGE with lung traits. Mendelian randomization analyses suggested protective causal relations of sRAGE to several pulmonary traits. Protein-trait association analyses demonstrated higher sRAGE levels to be cross-sectionally and longitudinally associated with preserved lung function. Interpretation sRAGE is produced by type I alveolar cells, and it acts as a decoy receptor to block the inflammatory cascade. Our integrative genomics approach provides evidence for sRAGE as a causal and protective biomarker of lung function, and the pattern of associations is suggestive of a protective role of sRAGE against restrictive lung physiology. We speculate that targeting the AGER/sRAGE axis may be therapeutically beneficial for the treatment and prevention of inflammation-related lung disease.
A New Analysis Tool for Continuous Glucose Monitor Data

Background:With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable....
E-003 Safety and efficacy of balloon guide catheters in thrombectomy for anterior circulation stroke: a systematic review and meta-analysis

Objective The benefit of balloon guide catheters (BGCs) in mechanical thrombectomy (MT) remains unclear. We examined the effect of BGC on procedural, radiographic, and clinical outcomes. Methods A systematic review was conducted using PubMed, Embase, and Scopus to identify studies comparing MT for acute ischemic stroke with and without the use of BGCs. Patient demographics, procedural metrics, and outcomes were abstracted. Three-month functional outcomes were based on the modified Rankin Score (mRS). Results Of the 2,181 resultant articles identified by the systematic review, 9 met inclusion criteria, comprising 2292 BGC and 1868 non-BGC patients. At baseline, the BGC group had a higher incidence of atrial fibrillation (43.3% versus 36.2%, p Conclusion BGC use during MT for acute ischemic stroke is associated faster to groin-to-recanalization times, improved TICI ≥2b reperfusion, lower incidence of sICH, and lower mortality. These data demonstrate the promise of BGC use for MT and warrant further study. Disclosures N. Shlobin: None. B. Jahromi: None. S. Ansari: None. M. Potts: None.
Versatile microparticle propulsion system by light-guided dielectrophoresis: Proposed method and theoretical calculation

Developing materials for active matter that can efficiently respond to external stimuli with designed multifold mechanical motions remains a major challenge, and overcoming this will greatly propel the advancement of micromachines and microrobots toward unprecedented biomedical, electronic, and particle-separation applications. Here, we propose an innovative working mechanism that allows multifold-translational-motion control of semiconductor microentities by AC dielectrophoresis with simple visible-light stimulation. We study the dielectrophoresis forces on semiconducting particles of various geometries in aqueous suspension by modeling with the consideration of both the Maxwell–Wagner relaxation and the electrical-double-layer-charging effect. With the obtained understanding, we rationally design a manipulation system that can versatilely transport semiconductors and orient them toward desired directions simultaneously by tuning the light intensity in an electric field. This research could provide insights toward developing a new class of micromachines with rarely found control flexibility and precision and offer a new route toward separation and purification of optoelectric microparticles of different geometries.
Neural circuit dynamics of drug-context associative learning in the hippocampus

The environmental context associated with previous drug consumption serves as a potent trigger for relapse to drug use. The mechanism by which existing neural representations of context are modified to incorporate information associated with a given drug however, remains unknown. Using longitudinal calcium imaging in freely behaving mice, we reveal that drug-context associations for psychostimulants and opioids are encoded in a subset of hippocampal neurons. In these neurons, drug context pairing in a conditioned place preference task weakened their spatial coding for the nondrug-paired context, with drug-induced changes to spatial coding predictive of drug-seeking behavior. Furthermore, the dissociative drug ketamine blocked both the drug-induced changes to hippocampal coding and corresponding drug-seeking behavior. Together, this work reveals how drugs of abuse can alter the hippocampal circuit to encode drug-context associations and points to the hippocampus as a key node in the cognitive process of drug addiction and context-induced drug relapse.
Recruitment and inhibitory action of hippocampal axo-axonic cells during behavior.

The axon initial segment of hippocampal pyramidal cells is a key subcellular compartment for action potential generation, under GABAergic control by the "chandelier" or axo-axonic cells (AACs). Although AACs are the only cellular source of GABA targeting the initial segment, their in vivo activity patterns and influence over pyramidal cell dynamics are not well understood. We achieved cell-type-specific genetic access to AACs in mice and show that AACs in the hippocampal area CA1 are synchronously activated by episodes of locomotion or whisking during rest. Bidirectional intervention experiments in head-restrained mice performing a random foraging task revealed that AACs inhibit CA1 pyramidal cells, indicating that the effect of GABA on the initial segments in the hippocampus is inhibitory in vivo. Finally, optogenetic inhibition of AACs at specific track locations induced remapping of pyramidal cell place fields. These results demonstrate brain-state-specific dynamics of a critical inhibitory controller of cortical circuits.
Abstract Answer Set Solvers

Nieuwenhuis, Oliveras, and Tinelli showed how to describe enhancements of the Davis-Putnam-Logemann-Loveland algorithm using transition systems, instead of pseudocode. We design a similar framework for three algorithms that generate answer sets for logic programs: smodels , asp-sat with Backtracking, and a newly designed and implemented algorithm sup . This approach to describing answer set solvers makes it easier to prove their correctness, to compare them, and to design new systems.
Reconstruction from Randomized Graph via Low Rank Approximation.

The privacy concerns associated with data analysis over social networks have spurred recent research on privacypreserving social network analysis, particularly on privacypreserving publishing of social network data. In this paper, we focus on whether we can reconstruct a graph from the edge randomized graph such that accurate feature values can be recovered. In particular, we present a low rank approximation based reconstruction algorithm. We exploit spectral properties of the graph data and show why noise could be separated from the perturbed graph using low rank approximation. We also show key differences from previous findings of point-wise reconstruction methods on numerical data through empirical evaluations and theoretical justifications.
Steroid receptor coactivator-1 is a histone acetyltransferase

Steroid receptors and coactivator proteins are thought to stimulate gene expression by facilitating the assembly of basal transcription factors into a stable preinitiation complex. What is not clear, however, is how these transcription factors gain access to transcriptionally repressed chromatin to modulate the transactivation of specific gene networks in vivo. The available evidence indicates that acetylation of chromatin in vivo is coupled to transcription and that specific histone acetyltransferases (HATs) target histones bound to DNA and overcome the inhibitory effect of chromatin on gene expression. The steroid-receptor coactivator SRC-1 is a coactivator for many members of the steroid-hormone receptor superfamily of ligand-inducible transcription factors. Here we show that SRC-1 possesses intrinsic histone acetyltransferase activity and that it also interacts with another HAT, p300/CBP-associated factor (PCAF). The HAT activity of SRC-1 maps to its carboxy-terminal region and is primarily specific for histones H3 and H4. Acetylation by SRC-1 and PCAF of histones bound at specific promoters may result from ligand binding to steroid receptors and could be a mechanism by which the activation functions of steroid receptors and associated coactivators enhance formation of a stable preinitiation complex, thereby increasing transcription of specific genes from transcriptionally repressed chromatin templates.
Dataless Data Mining: Association Rules-Based Distributed Privacy-Preserving Data Mining

Today, the desire to mine data from varied sources to discover behaviors and patterns of entities such as customers, diseases, and environmental conditions is on the rise. At the same time, the resistance to share data is also on the raise due to the increase in governmental regulations and individuals desire to preserve privacy. In this paper, we employ association rule mining to preserve individual data privacy without overly compromising on the accuracy of the global data mining task. Here, we describe the proposed methodology and show that the proposed scheme is privacy preserving. The methodology is tested using three commonly available data sets. The results validate our claims regarding the accuracy of synthetic data in its ability to represent original data without compromising privacy.