Behavior of medial gastrocnemius muscle beneath kinesio taping during isometric contraction and badminton lunge performance after fatigue induction.

Kinesio taping (KT) is widely used in sports for performance improvement and injury prevention. However, little is known of the behavior of the muscle region beneath the KT with movement, particularly when the muscle is fatigued. Accordingly, this study investigated the changes in the medial gastrocnemius muscle architecture and fascia thickness when using KT during maximum isometric plantar flexion (MVIC) and badminton lunges following heel rise exercises performed to exhaustion. Eleven healthy collegiate badminton players (4 males and 7 females) were recruited. All of the participants performed two tasks (MVIC and badminton lunge) with a randomized sequence of no taping, KT and sham taping and repeated following exhaustive repetitive heel rise exercise. In the MVIC task, the fascia thickness with the medial gastrocnemius muscle at rest significantly decreased following fatigue induction both without taping and with KT and sham taping (p = 0.036, p = 0.028 and p = 0.025, respectively). In the lunge task, the fascia thickness reduced after fatigue induction in the no taping and sham taping trials; however, no significant change in the fascia thickness occurred in the KT trials. Overall, the results indicate that KT provides a better effect during dynamic movement than in isometric contraction.

The COVID-19 High-Performance Computing Consortium.

In March of 2020, recognizing the potential of High Performance Computing (HPC) to accelerate understanding and the pace of scientific discovery in the fight to stop COVID-19, the HPC community assembled the largest collection of worldwide HPC resources to enable COVID-19 researchers worldwide to advance their critical efforts. Amazingly, the COVID-19 HPC Consortium was formed within one week through the joint effort of the Office of Science and Technology Policy (OSTP), the U.S. Department of Energy (DOE), the National Science Foundation (NSF), and IBM to create a unique public-private partnership between government, industry, and academic leaders. This article is the Consortium's story-how the Consortium was created, its founding members, what it provides, how it works, and its accomplishments. We will reflect on the lessons learned from the creation and operation of the Consortium and describe how the features of the Consortium could be sustained as a National Strategic Computing Reserve to ensure the nation is prepared for future crises.

Predictive Radiation Oncology - A New NCI-DOE Scientific Space and Community.

With a widely attended virtual kickoff event on January 29, 2021, the National Cancer Institute (NCI) and the Department of Energy (DOE) launched a series of 4 interactive, interdisciplinary workshops-and a final concluding "World Café" on March 29, 2021-focused on advancing computational approaches for predictive oncology in the clinical and research domains of radiation oncology. These events reflect 3,870 human hours of virtual engagement with representation from 8 DOE national laboratories and the Frederick National Laboratory for Cancer Research (FNL), 4 research institutes, 5 cancer centers, 17 medical schools and teaching hospitals, 5 companies, 5 federal agencies, 3 research centers, and 27 universities. Here we summarize the workshops by first describing the background for the workshops. Participants identified twelve key questions-and collaborative parallel ideas-as the focus of work going forward to advance the field. These were then used to define short-term and longer-term "Blue Sky" goals. In addition, the group determined key success factors for predictive oncology in the context of radiation oncology, if not the future of all of medicine. These are: cross-discipline collaboration, targeted talent development, development of mechanistic mathematical and computational models and tools, and access to high-quality multiscale data that bridges mechanisms to phenotype. The workshop participants reported feeling energized and highly motivated to pursue next steps together to address the unmet needs in radiation oncology specifically and in cancer research generally and that NCI and DOE project goals align at the convergence of radiation therapy and advanced computing.

Development of a low-cost colorimeter and its application for determination of environmental pollutants.

Herein, a novel colorimeter based on the Beer-Lambert law was designed for detection of environmental pollutants in water with a high precision, simple, and miniaturized device using a tetracycline-Eu3+ complex, cadmium reduction, diazotization, 1,10-phenanthroline, and periodate oxidation. The newly developed colorimeter could detect many environmental pollutants including tetracycline, nitrate, nitrite, Fe, and Mn, which were used to evaluate its performance. Simultaneously, a modified algorithm was applied to extend the linear response range. The colorimeter was comprised of an Red Green Blue Light Emitting Diode (RGB LED) light, focusing len, 3D printed stand for the cuvette, and light-sensitive photodiode detector. Microcontroller Arduino Uno processing technology was used to form a stable integrated structure. With the use of a novel algorithm, the device exhibited a wide linear response, ranging from 0-20, 0-17, 0-0.3, 0-1.75, and 0-15 mg/L for tetracycline, N-NO3-, N-NO2-, Fe, and Mn, respectively, and low limits of detection (0.88, 0.34, 0.031, 0.08, and 0.47 mg/L for tetracycline, N-NO3-, N-NO2-, Fe, and Mn, respectively). The advantages of high precision and low cost allow the novel design to be used for the detection of environmental pollutants.

AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing.

The application of data science in cancer research has been boosted by major advances in three primary areas: (1) Data: diversity, amount, and availability of biomedical data; (2) Advances in Artificial Intelligence (AI) and Machine Learning (ML) algorithms that enable learning from complex, large-scale data; and (3) Advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build in silico ML models that can provide transformative insights from data including: molecular dynamics simulations, next-generation sequencing, omics, imaging, and unstructured clinical text documents. Unique challenges persist, however, in building ML models related to cancer, including: (1) access, sharing, labeling, and integration of multimodal and multi-institutional data across different cancer types; (2) developing AI models for cancer research capable of scaling on next generation high performance computers; and (3) assessing robustness and reliability in the AI models. In this paper, we review the National Cancer Institute (NCI) -Department of Energy (DOE) collaboration, Joint Design of Advanced Computing Solutions for Cancer (JDACS4C), a multi-institution collaborative effort focused on advancing computing and data technologies to accelerate cancer research on three levels: molecular, cellular, and population. This collaboration integrates various types of generated data, pre-exascale compute resources, and advances in ML models to increase understanding of basic cancer biology, identify promising new treatment options, predict outcomes, and eventually prescribe specialized treatments for patients with cancer.