Burnout syndrome in a single cohort of chiropractic students: A longitudinal study.

OBJECTIVE: Physician well-being impacts all areas of health care. There is, however, a paucity of evidence regarding burnout among chiropractic students. The purpose of this project is to evaluate burnout among a single cohort of chiropractic students as they progress through their clinical rotation. METHODS: The Professional Fulfillment Index (PFI) was administered to chiropractic students (n = 108) at the start of their internship in January of 2021. The PFI was also distributed to students at the end of each of their 3 internship terms. The Maslach Burnout Inventory-Human Services Survey (MBI-HSS) was administered at the end of interns' eighth term of study to assess validity with the PFI. The MBI and PFI were analyzed via pairwise correlation. Demographic and relevant term questions were asked at baseline and respective terms and analyzed with descriptive statistics. RESULTS: Of the study's 78 respondents (72.2% response rate), 55.8% were male with an average age of 28.6 years. Pairwise comparisons revealed statistically significant burnout differences between the baseline timepoint and the end of the eighth term, and between the baseline and the end of the ninth term. PFI burnout was correlated to emotional exhaustion in the MBI. CONCLUSION: Our study found that approximately half of the participating chiropractic students suffered from burnout. This percentage is similar to burnout rates seen in other health care providers. Moreover, there is no widely accepted burnout threshold used in the literature. As such, studies examining standardization of burnout calculations may be warranted.

Point Cloud Completion: A Survey.

Point cloud completion is the task of producing a complete 3D shape given an input of a partial point cloud. It has become a vital process in 3D computer graphics, vision and applications such as autonomous driving, robotics, and augmented reality. These applications often rely on the presence of a complete 3D representation of the environment. Over the past few years, many completion algorithms have been proposed and a substantial amount of research has been carried out. However, there are not many in-depth surveys that summarise the research progress in such a way that allows users to make an informed choice of what algorithms to employ given the type of data they have, the end result they want, the challenges they may face and the possible strategies they could use. In this study, we present a comprehensive survey and classification of papers on point cloud completion untill August 2023 based on the strategies, techniques, inputs, outputs, and network architectures. We will also cover datasets, evaluation methods, and application areas in point cloud completion. Finally, we discuss challenges faced by the research community and future research directions.

An Analysis of Machine- and Human-Analytics in Classification.

In this work, we present a study that traces the technical and cognitive processes in two visual analytics applications to a common theoretic model of soft knowledge that may be added into a visual analytics process for constructing a decision-tree model. Both case studies involved the development of classification models based on the "bag of features" approach. Both compared a visual analytics approach using parallel coordinates with a machine-learning approach using information theory. Both found that the visual analytics approach had some advantages over the machine learning approach, especially when sparse datasets were used as the ground truth. We examine various possible factors that may have contributed to such advantages, and collect empirical evidence for supporting the observation and reasoning of these factors. We propose an information-theoretic model as a common theoretic basis to explain the phenomena exhibited in these two case studies. Together we provide interconnected empirical and theoretical evidence to support the usefulness of visual analytics.

Finding complete 3D vertex correspondence for statistical shape modeling.

A statistical shape model that accurately generalizes a family of 3D shapes requires establishing correspondences across the set of shapes. However in 3D anatomical meshes, finding a sufficient number of landmarks to accurately describe the shape can be a challenge, and often only a few points are easily identifiable due to the smooth nature of the object surface. Using a sparse set of landmarks, this paper finds a dense set of vertex correspondences across a set of 3D aortic root meshes. This is achieved by non-rigidly transforming a source mesh to a target mesh. Then, for every vertex on the target, a corresponding vertex on the deformed source is found, resulting in complete correspondence. A more accurate transformation results in better correspondence establishment, and our mesh registration experiments show an average Hausdorff distance of 3.65mm, and an average point-to-mesh distance of 0.41mm, i. e. within one voxel.

Gene regulatory network discovery using pairwise Granger causality.

Discovery of gene regulatory network from gene expression data can yield a useful insight to drug development. Among the methods applied to time-series data, Granger causality (GC) has emerged as a powerful tool with several merits. Since gene expression data usually have a much larger number of genes than time points therefore a full model cannot be applied in a straightforward manner, GC is often applied to genes pair wisely. In this study, the authors first investigate with synthetic data how spurious causalities (false discoveries) may arise because of the use of pairwise rather than full-model GC detection. Furthermore, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. As a remedy, the authors demonstrate that model validation techniques can effectively reduce the number of false discoveries. Then, they apply pairwise GC with model validation to the real human HeLa cell-cycle dataset. They find that Akaike information criterion is generally most suitable for determining model order, but precaution should be taken for extremely short time series. With the authors proposed implementation, degree distributions and network hubs are obtained and compared with existing results, giving a new observation that the hubs tend to act as sources rather than receivers of interactions.

Registration of 3D point clouds and meshes: a survey from rigid to nonrigid.

Three-dimensional surface registration transforms multiple three-dimensional data sets into the same coordinate system so as to align overlapping components of these sets. Recent surveys have covered different aspects of either rigid or nonrigid registration, but seldom discuss them as a whole. Our study serves two purposes: 1) To give a comprehensive survey of both types of registration, focusing on three-dimensional point clouds and meshes and 2) to provide a better understanding of registration from the perspective of data fitting. Registration is closely related to data fitting in which it comprises three core interwoven components: model selection, correspondences and constraints, and optimization. Study of these components 1) provides a basis for comparison of the novelties of different techniques, 2) reveals the similarity of rigid and nonrigid registration in terms of problem representations, and 3) shows how overfitting arises in nonrigid registration and the reasons for increasing interest in intrinsic techniques. We further summarize some practical issues of registration which include initializations and evaluations, and discuss some of our own observations, insights and foreseeable research trends.

Visualizing natural image statistics.

Natural image statistics is an important area of research in cognitive sciences and computer vision. Visualization of statistical results can help identify clusters and anomalies as well as analyze deviation, distribution, and correlation. Furthermore, they can provide visual abstractions and symbolism for categorized data. In this paper, we begin our study of visualization of image statistics by considering visual representations of power spectra, which are commonly used to visualize different categories of images. We show that they convey a limited amount of statistical information about image categories and their support for analytical tasks is ineffective. We then introduce several new visual representations, which convey different or more information about image statistics. We apply ANOVA to the image statistics to help select statistically more meaningful measurements in our design process. A task-based user evaluation was carried out to compare the new visual representations with the conventional power spectra plots. Based on the results of the evaluation, we made further improvement of visualizations by introducing composite visual representations of image statistics.

Embedding retrieval of articulated geometry models.

Due to the popularity of computer games and animation, research on 3D articulated geometry model retrieval has attracted a lot of attention in recent years. However, most existing works extract high-dimensional features to represent models and suffer from practical limitations. First, misalignment in high-dimensional features may produce unreliable euclidean distances and affect retrieval accuracy. Second, the curse of dimensionality also degrades efficiency. In this paper, we propose an embedding retrieval framework to improve the practicability of these methods. It is based on a manifold learning technique, the Diffusion Map (DM). We project all pairwise distances onto a low-dimensional space. This improves retrieval accuracy because intercluster distances are exaggerated. Then we adapt the Density-Weighted Nyström extension and further propose a novel step to locally align the Nyström embedding to the eigensolver embedding so as to reduce extension error and preserve retrieval accuracy. Finally, we propose a heuristic to handle disconnected manifolds by augmenting the kernel matrix with multiple similarity measures and shortcut edges, and further discuss the choice of DM parameters. We have incorporated two existing matching algorithms for testing. Our experimental results show improvement in precision at high recalls and in speed. Our work provides a robust retrieval framework for the matching of multimedia data that lie on manifolds.

Deformable model retrieval based on topological and geometric signatures.

With the increasing popularity of 3D applications such as computer games, a lot of 3D geometry models are being created. To encourage sharing and reuse, techniques that support matching and retrieval of these models are emerging. However, only a few of them can handle deformable models, that is, models of different poses, and these methods are generally very slow. In this paper, we present a novel method for efficient matching and retrieval of 3D deformable models. Our research idea stresses using both topological and geometric features at the same time. First, we propose Topological Point Ring (TPR) analysis to locate reliable topological points and rings. Second, we capture both local and global geometric information to characterize each of these topological features. To compare the similarity of two models, we adapt the Earth Mover Distance (EMD) as the distance function and construct an indexing tree to accelerate the retrieval process. We demonstrate the performance of the new method, both in terms of accuracy and speed, through a large number of experiments.

Synthetic AIDS vaccine by targeting HIV receptor.

A class of synthetic peptide immunogens for the cell surface HIV receptor complex has been developed to elicit antibodies that block viral entry by inhibiting gp120-CD4 interaction. These peptides extend our HIV receptor-directed approach from passive immunotherapy with mAb B4 (Proc. Natl. Acad. Sci. U.S.A. 96 (1999) 10367) to active immunization by a synthetic peptide-based vaccine. A peptide site from CD4 was identified as a B cell epitope capable of mimicking a susceptible site on the HIV receptor complex, and then rendered immunogenic. An effective target antigenic site (B cell epitope) for the cell surface HIV receptor complex was selected by epitope mapping from among diverse CD4 and chemokine receptor peptides. It is a cyclized sequence modified from the CDR2-like domain of CD4 (AA 39-66), that was predicted to produce steric hindrance of the discontinuous recognition site of mAb B4. The immunogenicity of the targeted epitope was augmented by tandem combination with promiscuous T helper cell epitopes (Th). The antibody response to this class of immunogens attained sufficient concentrations and affinities of the correct specificity to block the interactions of HIV env glycoprotein with the cellular receptor, and prevent infection. The polyclonal antibodies generated against these fusion constructs in multiple animal species neutralized a broad array of HIV-1 primary isolates from clades A to E. Despite eliciting antibodies to the key CD4 immunomodulatory molecule, the site-specific and chemically defined immunogens displayed no overt immunotoxicity in baboons and have potential for the immunotherapy and immunoprophylaxis of HIV infection.