Due to the dangers of hypoglycemia, HI requires timely diagnosis.ĬHI, a 501(c)3, is a lifeline to those born with congenital hyperinsulinism (HI) and their families. Prolonged or severe hypoglycemia can cause seizures, permanent brain damage or death, if left untreated. For those with HI the beta cells of the pancreas secrete too much insulin in an unregulated manner. In most countries the estimated incidence of HI is approximately 1/25,000 to 1/50,000 births. HI is a life-threatening genetic disorder that causes severe low blood sugar. The project is sponsored by Congenital Hyperinsulinism International (CHI) and governed by a group of internationally recognized HI patient advocates and experts, known as the HI Global Registry Steering Committee. The IAMRARE™ Platform was created with input from patient, caregiver, and government stakeholders to ensure a safe and user – friendly system for study participation. HIGR data is stored on the secure cloud-based IAMRARE™ Platform which was developed and is hosted by the National Organization for Rare Disorders (NORD). The investigators intend to initiate an annual reporting process with more complete study data beginning in early 2020. As participation grows, the pool of HI data will become increasingly more significant. The foundation for a HI natural history study has been established with the launch of HIGR. The early data appears to align with some known features of the disease and its community: (1) the incidence of HI is global, occurring on every continent (2) HI is not only a disease of the young (3) there are many types of HI including those from known and unknown genetic causes and (4) HI occurs together with a number of syndromes. This early glimpse into the high level data collected by HIGR between the date of its launch in October 2018 and mid-February 2019 lays the groundwork for an HI natural history study reported by those who live with the disease. The report includes an introduction to the research project and an early glimpse of data from it. Many years in the making, HIGR launched on October 8, 2018. ![]() People with HI or their parents or caregivers can participate from anywhere in the world. It consists of a series of surveys with questions about health, treatment, development, and quality of life. The approach could also be employed for mapping and monitoring other forest disturbance issues.HIGR is the first global patient-powered congenital hyperinsulinism (HI) patient-reported registry developed for the patient community to share their experiences of living with HI and advancing knowledge and research. Therefore, we proposed a new approach combining the merits of HI and LiDAR data to precisely predict PWD infection stages at the tree level, allowing better PWD monitoring and control. ![]() We obtained the following results: (1) The classification accuracies of HI (OA: 66.86%, Kappa: 0.57) were higher than those of LiDAR (OA: 45.56%, Kappa: 0.27) for predicting PWD infection stages, and their combination had the best accuracies (OA: 73.96%, Kappa: 0.66) (2) LiDAR data had higher ability for dead tree identification than HI data and (3) The combined use of HI and LiDAR data for estimation of PWD infection stages showed that LiDAR metrics (e.g., crown volume) were essential in the classification model, although the variables derived from HI data contributed more than those extracted from LiDAR. We estimated the power of the hyperspectral method (HI data only), LiDAR (LiDAR data only), and their combination (HI plus LiDAR data) to predict the infection stages of PWD using the random forest (RF) algorithm. In this paper, PWD infection was divided into five stages (green, early, middle, heavy, and grey), and HI and LiDAR data were integrated to detect PWD. However, few previous studies have used airborne HI and LiDAR to detect PWD and compared the capability for predicting PWD infection stage at the tree level. Unmanned aerial vehicle (UAV)-based hyperspectral imaging (HI) and light detection and ranging (LiDAR) technique is an effective approach for forest health monitoring. Therefore, the establishment of an effective method to accurately monitor and map the infection stage by PWD is imperative. Pine wilt disease (PWD) is a global destructive threat to forests, having caused extreme damage in China.
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