Inferred expression regulator activities suggest genes mediating cardiometabolic genetic signals

Expression QTL (eQTL) analyses have suggested many genes mediating genome-wide association study (GWAS) signals but most GWAS signals still lack compelling explanatory genes. We have leveraged an adipose-specific gene regulatory network to infer expression regulator activities and phenotypic master regulators (MRs), which were used to detect activity QTLs (aQTLs) at cardiometabolic trait GWAS loci. Regulator activities were inferred with the VIPER algorithm that integrates enrichment of expected expression changes among a regulator’s target genes with confidence in their regulator-target network interactions and target overlap between different regulators (i.e., pleiotropy). Phenotypic MRs were identified as those regulators whose activities were most important in predicting their respective phenotypes using random forest modeling. While eQTLs were typically more significant than aQTLs in cis, the opposite was true among candidate MRs in trans. Several GWAS loci colocalized with MR trans-eQTLs/aQTLs in the absence of colocalized cis-QTLs. Intriguingly, at the 1p36.1 BMI GWAS locus the EPHB2 cis-aQTL was stronger than its cis-eQTL and colocalized with the GWAS signal and 35 BMI MR trans-aQTLs, suggesting the GWAS signal may be mediated by effects on EPHB2 activity and its downstream effects on a network of BMI MRs. These MR and aQTL analyses represent systems genetic methods that may be broadly applied to supplement standard eQTL analyses for suggesting molecular effects mediating GWAS signals.

Influence of Digital Media Technology on Forest Scene Animation Design

The animation construction of forest scene is a virtual stand scene visualization framework which uses the related technologies of virtual forest modeling and stand scene visualization, and uses the scene graph technology to manage. This paper studies the influence of digital media technology on the animation design of forest scene. In this paper, the model of virtual stand scene is mainly completed by Creator modeling software of MultiGen company. In order to reduce the number of scene patches and ensure realism, the tree model is designed with OpenFlight tree hierarchy. At the same time, the key technologies of Creator modeling and model optimization are analyzed. The virtual stand scene visualization framework uses the open source graphics rendering engine OpenSceneGraph (OSG) as the scene driver to realize the stand scene visualization. This paper provides a variety of roaming control methods. The experimental results show that the virtual forest scene visualization framework can better simulate the forest scene and has a strong sense of reality.

Different Temporal Stability and Responses to Droughts between Needleleaf Forests and Broadleaf Forests in North China during 2001–2018

Droughts can affect the physiological activity of trees, damage tissues, and even trigger mortality, yet the response of different forest types to drought at the decadal time scale remains uncertain. In this study, we used two remote sensing-based vegetation products, the MODIS enhanced vegetation index (EVI) and MODIS gross primary productivity (GPP), to explore the temporal stability of deciduous needleleaf forests (DNFs) and deciduous broadleaf forests (DBFs) in droughts and their legacy effects in North China from 2001 to 2018. The results of both products showed that the temporal stability of DBFs was consistently much higher than that of DNFs, even though the DBFs experienced extreme droughts and the DNFs did not. The DBFs also exhibited similar patterns in their legacy effects from droughts, with these effects extending up to 4 years after the droughts. These results indicate that DBFs have been better acclimated to drought events in North China. Furthermore, the results suggest that the GPP was more sensitive to water variability than EVI. These findings will be helpful for forest modeling, management, and conservation.

Aggregate Trends of Apolipoprotein E on Cognition in Transgenic Alzheimer’s Disease Mice

Background: Apolipoprotein E (APOE) genotypes typically increase risk of amyloid-β deposition and onset of clinical Alzheimer’s disease (AD). However, cognitive assessments in APOE transgenic AD mice have resulted in discord. Objective: Analysis of 31 peer-reviewed AD APOE mouse publications (n = 3,045 mice) uncovered aggregate trends between age, APOE genotype, gender, modulatory treatments, and cognition. Methods: T-tests with Bonferroni correction (significance = p < 0.002) compared age-normalized Morris water maze (MWM) escape latencies in wild type (WT), APOE2 knock-in (KI2), APOE3 knock-in (KI3), APOE4 knock-in (KI4), and APOE knock-out (KO) mice. Positive treatments (t+) to favorably modulate APOE to improve cognition, negative treatments (t–) to perturb etiology and diminish cognition, and untreated (t0) mice were compared. Machine learning with random forest modeling predicted MWM escape latency performance based on 12 features: mouse genotype (WT, KI2, KI3, KI4, KO), modulatory treatment (t+, t–, t0), mouse age, and mouse gender (male = g_m; female = g_f, mixed gender = g_mi). Results: KI3 mice performed significantly better in MWM, but KI4 and KO performed significantly worse than WT. KI2 performed similarly to WT. KI4 performed significantly worse compared to every other genotype. Positive treatments significantly improved cognition in WT, KI4, and KO compared to untreated. Interestingly, negative treatments in KI4 also significantly improved mean MWM escape latency. Random forest modeling resulted in the following feature importance for predicting superior MWM performance: [KI3, age, g_m, KI4, t0, t+, KO, WT, g_mi, t–, g_f, KI2] = [0.270, 0.094, 0.092, 0.088, 0.077, 0.074, 0.069, 0.061, 0.058, 0.054, 0.038, 0.023]. Conclusion: APOE3, age, and male gender was most important for predicting superior mouse cognitive performance.

Factors to Effective Telemedicine Visits During the COVID-19 Pandemic: Cohort Study (Preprint)

BACKGROUND With COVID-19 there was a rapid and abrupt rise in telemedicine implementation often without sufficient time for providers or patients to adapt. As telemedicine visits are likely to continue to play an important role in health care, it is crucial to strive for a better understanding of how to ensure completed telemedicine visits in our health system. Awareness of these barriers to effective telemedicine visits is necessary for a proactive approach to addressing issues. OBJECTIVE The objective of this study was to identify variables that may affect telemedicine visit completion in order to determine actions that can be enacted across the entire health system to benefit all patients. METHODS Data were collected from scheduled telemedicine visits (n=362,764) at the University of Miami Health System (UHealth) between March 1, 2020 and October 31, 2020. Descriptive statistics, mixed effects logistic regression, and random forest modeling were used to identify the most important patient-agnostic predictors of telemedicine completion. RESULTS Using descriptive statistics, struggling telemedicine specialties, providers, and clinic locations were identified. Through mixed effects logistic regression (adjusting for clustering at the clinic site level), the most important predictors of completion included previsit phone call/SMS text message reminder status (confirmed vs not answered) (odds ratio [OR] 6.599, 95% CI 6.483-6.717), MyUHealthChart patient portal status (not activated vs activated) (OR 0.315, 95% CI 0.305-0.325), provider’s specialty (primary care vs medical specialty) (OR 1.514, 95% CI 1.472-1.558), new to the UHealth system (yes vs no) (OR 1.285, 95% CI 1.201-1.374), and new to provider (yes vs no) (OR 0.875, 95% CI 0.859-0.891). Random forest modeling results mirrored those from logistic regression. CONCLUSIONS The highest association with a completed telemedicine visit was the previsit appointment confirmation by the patient via phone call/SMS text message. An active patient portal account was the second strongest variable associated with completion, which underscored the importance of patients having set up their portal account before the telemedicine visit. Provider’s specialty was the third strongest patient-agnostic characteristic associated with telemedicine completion rate. Telemedicine will likely continue to have an integral role in health care, and these results should be used as an important guide to improvement efforts. As a first step toward increasing completion rates, health care systems should focus on improvement of patient portal usage and use of previsit reminders. Optimization and intervention are necessary for those that are struggling with implementing telemedicine. We advise setting up a standardized workflow for staff.

A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data

Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m−2 as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management.

Who Is to Blame and What Is to Be Done: Analysis of Comments to Films about the Beslan School Siege

Objective. Analysis of YouTube comments to documentaries about the fifteenth anniversary of the Beslan school siege. Background. Amid reactivation of the Beslan discourse, demand for social reflection of the tragedy is increasing. It seems relevant to address nonreactive data to evaluate framing perception. Study design. Using random forest modeling we examine the content of YouTube comments to evaluate their specific characteristics. Data. Array of comments to Yuri Dud, Ksenia Sobchak and Novaya Gazeta films about Beslan (N=141,966, upload date: 02/29/2020, parsing loss&lt;1% of the total). Measurements. Random forest modeling for textual data. Results. In the comments to all three films, blame is mainly reattributed to the state. The moral performatives are different: in the comments to Y. Dud and K. Sobchak’s films there are appeals for helping victims of the terrorist attack; in the case of Novaya Gazeta — to the punishment of the guilty. Conclusions. The Beslan tragedy turns out to be a serious point of social dissociation, there is neither rallying around the state nor a consensus on how to heal the trauma.