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ABSTRACTBackground States across the country have recognized and prioritized the value of oral health for children by legislating measures such as the California Oral Health Assessment Mandate, which requires children to receive a dental screening when entering public school. Sometimes known as the kindergarten oral health assessment (KOHA), these screenings utilize a public health approach to link children at highest risk of dental disease to dental homes. Though challenges exist in implementing the objectives of these dental screening laws, myriad strategies have risen to meet these challenges. This article describes the goals of mandated school oral health assessments and examines some of the barriers to and strategies for increasing participation in and effectiveness of these assessments.Types of Studies Reviewed A summary of the literature related to the KOHA is presented, along with barriers and innovative approaches to its implementation.Conclusions The data collected through school districts’ KOHA result in collaborative approaches to emphasize and meet a community’s need for early dental care in the context of overall health.Practical Implications The KOHA plays a valuable role in combating the high prevalence of caries in the pediatric population. The barriers to oral health equity and access to care highlighted by KOHA screening data underscores the need for engaging the dental workforce in pursuit of contemporary approaches to ensure children with high caries risk are connected to oral healthcare providers in their communities.

Scientific applications continue to grow and produce extremely large amounts of data, which require efficient compression algorithms for long-term storage. Compression errors in scientific applications can have a deleterious impact on downstream processing. Thus, it is crucial to preserve all the “known” Quantities of Interest (QoI) during compression. To address this issue, most existing approaches guarantee the reconstruction error of the original data or primary data (PD), but cannot directly control the problem of preserving the QoI. In this work, we propose a physics-informed compression technique that is composed of two parts: (i) reduction of the PD with bounded errors and (ii) preservation of the QoI. In the first step, we combine tensor decompositions, autoencoders, product quantizers, and error-bounded lossy compressors to bound the reconstruction error at high levels of compression. In the second step, we use constraint satisfaction post-processing followed by quantization to preserve the QoI. To illustrate the challenges of reducing the reconstruction errors of the PD and QoI, we focus on simulation data generated by a large-scale fusion code, XGC, which can produce tens of petabytes in a single day. The results show that our approach can achieve a high compression amount while accurately preserving the QoI within scientifically acceptable bounds.

We present ADIOS 2, the latest version of the Adaptable Input Output (I/O) System. ADIOS 2 addresses scientific data management needs ranging from scalable I/O in supercomputers, to data analysis in personal computer and cloud systems. Version 2 introduces a unified application programming interface (API) that enables seamless data movement through files, wide-area-networks, and direct memory access, as well as high-level APIs for data analysis. The internal architecture provides a set of reusable and extendable components for managing data presentation and transport mechanisms for new applications. ADIOS 2 bindings are available in C++11, C, Fortran, Python, and Matlab and are currently used across different scientific communities. ADIOS 2 provides a communal framework to tackle data management challenges as we approach the exascale era of supercomputing.

Objective: Opuntia humifusa (O. humifusa) Raf. has been used for the prevention and treatment of rheumatoid arthritis, inflammation, and cancer. Our study was designed to unveil the anti-nociceptive and anti-inflammatory effects of the methanolic extract of O. humifusa Raf stem (OHS). Material and Methods: The anti-nociceptive effect was measured by hot plate, acetic acid-induced writhing, and tail flick assays in mice and rats. Moreover, the anti-inflammatory effect was measured by vascular permeability and carrageenan and serotonin-induced paw edema tests in rats. Furthermore, anti-inflammatory effect was also measured using macrophage-like LPS-induced RAW 264.7 cells. Results: OHS extract inhibited acetic acid-induced writhing (p

In this paper, we propose an extended self-adaptive differential evolution algorithm, called A-jDE. A-jDE algorithm is based on jDE algorithm with the asynchronous method. jDE algorithm is one of the popular DE variants, which shows robust optimization performance on various problems. However, jDE algorithm uses a slow mutation strategy so that its convergence speed is slow compared to several state-of-the-art DE algorithms. The asynchronous method is one of the recently investigated approaches that if it finds a better solution, the solution is included in the current population immediately so it can be served as a donor individual. Therefore, it can improve the convergence speed significantly. We evaluated the optimization performance of A-jDE algorithm in 13 scalable benchmark problems on 30 and 100 dimensions. Our experiments prove that incorporating jDE algorithm with the asynchronous method can improve the optimization performance in not only a unimodal benchmark problem but also multimodal benchmark problem significantly.