This paper describes a method that allows one to recover both components of a 2-D vector field based on boundary information only, by solving a system of linear equations. The analysis is carried out in the digital domain and takes advantage of the redundancy in the boundary data, since these may be viewed as weighted sums of the local vector field's Cartesian components. Furthermore, a sampling of lines is used in order to combine the available measurements along continuous tracing lines with the digitised 2-D space where the solution is sought. A significant enhancement in the performance of the proposed algorithm is achieved by using, apart from real data, also boundary data obtained at virtual sensors. The potential of the proposed method is demonstrated by presenting an example of vector field reconstruction.
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http://dx.doi.org/10.1109/IEMBS.2008.4649765 | DOI Listing |
Rev Sci Instrum
January 2025
Shenyang Bluewisdom Technology Co., Ltd., Shenyang, Liaoning Province 110623, China.
Existing lower limb exoskeletons (LLEs) have demonstrated a lack of sufficient patient involvement during rehabilitation training. To address this issue and better incorporate the patient's motion intentions, this paper proposes an online brain-computer interface (BCI) system for LLE based motor imagery and stacked ensemble. The establishment of this online BCI system enables a comprehensive closed-loop control process, which includes the collection and decoding of brain signals, robotic control, and real-time feedback mechanisms.
View Article and Find Full Text PDFAntib Ther
January 2025
Research and Development, Promega Corp., Madison, WI 53711, United States.
Background: Chimeric antigen receptor (CAR)-T-cell therapy is a breakthrough in the field of cancer immunotherapy, wherein T cells are genetically modified to recognize and attack cancer cells. Delivery of the CAR gene is a critical step in this therapy and is usually achieved by transducing patient T cells with a lentiviral vector (LV). Because the LV is an essential component of CAR-T manufacturing, there is a need for simple bioassays that reflect the mechanism of action (MOA) of the LV and can measure LV potency with accuracy and specificity.
View Article and Find Full Text PDFZhongguo Xue Xi Chong Bing Fang Zhi Za Zhi
November 2024
Jiangxi Provincial Institute of Parasitic Diseases, Nanchang, Jiangxi 330096, China.
Objective: To investigate the health education needs and available resources in schistosomiasis-endemic areas based on integration of community resources, and to explore the operation pathways for health education and promotion during the stage of schistosomiasis elimination.
Methods: A community was selected from Nanji Township, Xinjian District, Nanchang City, Jiangxi Province around the Poyang Lake areas as the study site, and a questionnaire survey on health education needs for schistosomiasis control was conducted among permanent residents at ages of 20 years in the community during the period between June and July, 2022 using face-to-face interviews. In addition, the resources available for the community-based schistosomiasis control health education were investigated among workers in township-level specialized institutions, members of civil society organizations, villagers, teachers and high and primary school students through field observations, field surveys and thematic interviews.
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi
December 2024
Department of Epidemiology, School of Public Health, Fudan University; Key Laboratory of Public Health Safety, Ministry of Education; Tropical Disease Research Center, Fudan University, Shanghai 200032, China.
The rapid development of artificial intelligence poses a huge impact on health and has become a core driving force for the new generation of the scientific and technological revolution in the field of healthcare. Recently, artificial intelligence has been gradually applied in the field of parasitic diseases and parasitology, including disease diagnosis, prognosis prediction, prediction of transmission risk, intelligent identification of vectors and intermediate hosts, and disease prevention and control, which facilitates the progress towards elimination of parasitic diseases. In addition, artificial intelligence provides highly efficient tools and approaches for healthcare workers and researchers.
View Article and Find Full Text PDFJ Med Entomol
January 2025
Center for Vector Biology & Zoonotic Diseases, Department of Entomology, The Connecticut Agricultural Experiment Station, New Haven, CT, USA.
Filarial nematodes are parasitic roundworms transmitted by mosquitoes that can cause morbidity and mortality for their human and animal hosts. The filariae community, specifically infection prevalence of heartworm, Dirofilaria immitis (Filarioidea: Onchocercidae) (Leidy), and its primary mosquito vector species, has not been described in Connecticut since 1977. In light of the recent invasion and establishment of an important filariasis vector, Aedes albopictus (Diptera: Culicidae) (Skuse), we used molecular-based sequencing methods to identify filarial species infecting field-caught mosquitoes in Connecticut, United States.
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