Search for: "analysis"

Belonging to a community is essential for wellbeing, but potentially unattainable for those dissimilar from a group. In the present work, we ask whether belongingness is better predicted by acting and thinking like peers or believing you act and think like peers. Students (N = 1181) reported their belonging and how much they, their friends, and an “average student” endorsed local behavioral norms and general values. We calculated difference scores for behaviors and values capturing perceived similarity to the average, actual similarity to the average, and accuracy around the norm. Key results indicate that perceived behavioral similarity to the average, when controlling for other differences, predicts belonging and most robustly mediates between identity and belonging. Using social network analysis, we find behavioral differences from friends are meaningfully linked to network density and racial homophily. Efficient interventions for enhanced belonging could highlight similarities between students and their peers. 

In this study, we conduct a parametric analysis to evaluate the sensitivities of wall-modeled large-eddy simulation (LES) with respect to subgrid-scale (SGS) models, mesh resolution, wall boundary conditions and mesh anisotropy. While such investigations have been conducted for attached/flat-plate flow configurations, systematic studies specifically targeting turbulent flows with separation are notably sparse. To bridge this gap, our study focuses on the flow over a two-dimensional Gaussian-shaped bump at a moderately high Reynolds number, which involves smooth-body separation of a turbulent boundary layer under pressure-gradient and surface- curvature effects. In the simulations, the no-slip condition at the wall is replaced by three different forms of boundary condition based on the thin boundary layer equations and the mean wall-shear stress from high-fidelity numerical simulation to avoid the additional complexity of modeling the wall-shear stress. Various statistics, including the mean separation bubble size, mean velocity profile, and dissipation from SGS model, are compared and analyzed. The results reveal that capturing the separation bubble strongly depends on the choice of SGS model. While simulations approach grid convergence with resolutions nearing those of wall-resolved LES meshes, above this limit, the LES predictions exhibit intricate sensitivities to mesh resolution. Furthermore, both wall boundary conditions and the anisotropy of mesh cells exert discernible impacts on the turbulent flow predictions, yet the magnitudes of these impacts vary based on the specific SGS model chosen for the simulation. 

This study delves into the polarization properties of various hair colors using several techniques, including polarization ray tracing, full Stokes, and Mueller matrix imaging. Our analysis involved studying hair in both indoor and outdoor settings under varying lighting conditions. Our results demonstrate a strong correlation between hair color and the degree of linear polarization. Specifically, light-colored hair, such as white and blond, exhibits high albedo and low DoLP. In contrast, dark hair, like black and brown hair, has low albedo and high DoLP. Our research also revealed that a single hair strand displays high diattenuation near specular reflections but high depolarization in areas with diffuse reflections. Additionally, we investigated the wavelength dependency of the polarization properties by comparing the Mueller matrix under illumination at 450 nm and 589 nm. Our investigation demonstrates the impact of hair shade and color on polarization properties and the Umov effect.

 

Despite increasing efforts across various disciplines, the fate, transport, and impact of synthetic plastics on the environment and public health remain poorly understood. To better elucidate the microbial ecology of plastic waste and its potential for biotransformation, we conducted a large-scale analysis of all publicly available meta-omic studies investigating plastics (n = 27) in the environment. Notably, we observed low prevalence of known plastic degraders throughout most environments, except for substantial enrichment in riverine systems. This indicates rivers may be a highly promising environment for discovery of novel plastic bioremediation products. Ocean samples associated with degrading plastics showed clear differentiation from non-degrading polymers, showing enrichment of novel putative biodegrading taxa in the degraded samples. Regarding plastisphere pathogenicity, we observed significant enrichment of antimicrobial resistance genes on plastics but not of virulence factors. Additionally, we report a co-occurrence network analysis of 10 + million proteins associated with the plastisphere. This analysis revealed a localized sub-region enriched with known and putative plastizymes—these may be useful for deeper investigation of nature’s ability to biodegrade man-made plastics. Finally, the combined data from our meta-analysis was used to construct a publicly available database, the Plastics Meta-omic Database (PMDB)—accessible at plasticmdb.org. These data should aid in the integrated exploration of the microbial plastisphere and facilitate research efforts investigating the fate and bioremediation potential of environmental plastic waste.

 

The vagus nerve (VN) plays an important role in regulating physiological conditions in the gastrointestinal (GI) tract by communicating via the parasympathetic pathway to the enteric nervous system (ENS). However, the lack of knowledge in the neurophysiology of the VN and GI tract limits the development of advanced treatments for autonomic dysfunctions related to the VN. To better understand the complicated underlying mechanisms of the VN-GI tract neurophysiology, it is necessary to use an advanced device enabled by microfabrication technologies. Among several candidates including intraneural probe array and extraneural cuff electrodes, microchannel electrode array devices can be used to interface with smaller numbers of nerve fibers by securing them in the separate channel structures. Previous microchannel electrode array devices to interface teased nerve structures are relatively bulky with thickness around 200 µm. The thick design can potentially harm the delicate tissue structures, including the nerve itself. In this paper, we present a flexible thin film based microchannel electrode array device (thickness: 11.5 µm) that can interface with one of the subdiaphragmatic nerve branches of the VN in a rat. We demonstrated recording evoked compound action potentials (ECAP) from a transected nerve ending that has multiple nerve fibers. Moreover, our analysis confirmed that the signals are from C-fibers that are critical in regulating autonomic neurophysiology in the GI tract.

 

The tools and techniques such as imaging and machine learning used in the measurement of many material and microstructural properties are rapidly evolving. In metals, the grain size is routinely measured to estimate the yield strength. This paper describes some of the algorithms used in processing the microstructures to conduct quantitative measurements. The image processing methods provide the possibility to go beyond calculating the ASTM grain size number and calculate the actual surface area of each grain, grain boundary length, and the shape of the grains. The image analysis methods can be very helpful in conducting detailed quantitative analysis with greater accuracy than many labour-intensive manual methods currently in use. The work describes the complexities in applying the imaging methods and approaches in the metallurgical and materials fields. Successful application of such methods can reduce the time and effort required to characterise microstructures and can provide more precise information. 

Research shows that certain external factors can affect the mental health of many people in a community. Moreover, the importance of mental health has significantly increased in recent years due to the COVID-19 pandemic. Many people communicate and express their emotions through social media platforms, which provide researchers with opportunities to examine insights into their opinions and mental state. While social sensing studies using social media data have flourished in the last decade, many studies using social media data to detect and predict mental health status have focused on the individual level. In this study, we aim to generate a social sensing index for mental health to monitor emotional well-being, which is closely related to mental health, and to identify daily trends in negative emotions at the city level. We conduct sentiment analysis on Twitter data and compute entropy of the degree of sentiment change to develop the index. We observe sentiment trends fluctuate significantly in response to unusual events. It is found that the social sensing index for mental health reflects both city-wide and local events that trigger negative emotions, as well as areas where negative emotions persist. The study contributes to the growing body of research that uses social media data to examine mental health at a city-level. We focus on mental health at the city-level rather than individual, which provides a broader perspective on the mental health of a population. Social sensing index for mental health allows public health professionals to monitor and identify persistent negative sentiments and potential areas where mental health issues may emerge.

 

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.

 

Configurational and conformational analysis of the biologically relevant natural product artemisinin was conducted using carbon–carbon residual dipolar couplings (¹D_CCRDCs) at natural abundance. These RDCs were measured through the 2D‐INADEQUATE NMR experiment using a sample aligned in a compressed poly (methyl methacrylate) (PMMA) gel swollen in CDCl₃. Singular value decomposition (SVD) fitting analysis of all carbon–carbon bonds,¹D_CCRDCs, in relation to the full configuration/conformational space (32 diastereoisomers) of artemisinin, unambiguously identified the correct configuration of artemisinin.

 

Ever since the first image of a coral reef was captured in 1885, people worldwide have been accumulating images of coral reefscapes that document the historic conditions of reefs. However, these innumerable reefscape images suffer from perspective distortion, which reduces the apparent size of distant taxa, rendering the images unusable for quantitative analysis of reef conditions. Here we solve this century-long distortion problem by developing a novel computer-vision algorithm,ReScape, which removes the perspective distortion from reefscape images by transforming them into top-down views, making them usable for quantitative analysis of reef conditions. In doing so, we demonstrate the first-ever ecological application and extension of inverse-perspective mapping—a foundational technique used in the autonomous-driving industry. TheReScapealgorithm is composed of seven functions that (1) calibrate the camera lens, (2) remove the inherent lens-induced image distortions, (3) detect the scene’s horizon line, (4) remove the camera-roll angle, (5) detect the transformable reef area, (6) detect the scene’s perspective geometry, and (7) apply brute-force inverse-perspective mapping. The performance of theReScapealgorithm was evaluated by transforming the perspective of 125 reefscape images. Eighty-five percent of the images had no processing errors and of those, 95% were successfully transformed into top-down views.ReScapewas validated by demonstrating that same-length transects, placed increasingly further from the camera, became the same length after transformation. The mission of theReScapealgorithm is to (i) unlock historical information about coral-reef conditions from previously unquantified periods and localities, (ii) enable citizen scientists and recreational photographers to contribute reefscape images to the scientific process, and (iii) provide a new survey technique that can rigorously assess relatively large areas of coral reefs, and other marine and even terrestrial ecosystems, worldwide. To facilitate this mission, we compiled theReScapealgorithm into a free, user-friendly App that does not require any coding experience. Equipped with theReScapeApp, scientists can improve the management and prediction of the future of coral reefs by uncovering historical information from reefscape-image archives and by using reefscape images as a new, rapid survey method, opening a new era of coral-reef monitoring.