Comorbidity between neurodevelopmental disorders and childhood-onset type 1 diabetes

Childhood-onset type 1 diabetes and neurodevelopmental disorders, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, and intellectual disability, globally represent substantial health challenges. Each condition places a substantial challenge on the individuals, their families, and healthcare systems. The comorbidity between these two types of disorders has been a research focus, with findings suggesting a higher prevalence of neurodevelopmental disorders among individuals with childhood-onset type 1 diabetes. However, the underlying mechanism of this comorbidity remains largely unknown, and the potential alteration in the health and socio-economic outcomes due to this comorbidity remains unexplored. This thesis aimed to elucidate the potential mechanisms behind the comorbidity between childhood-onset type 1 diabetes and neurodevelopmental disorders and explore its impact on health and education outcomes, with the goal of improving early detection, prevention, and management strategies to enhance the quality of life for the affected children and adolescents. In Study I, we examined the impact of childhood-onset type 1 diabetes and the role of glycemic control on the risk of subsequent neurodevelopmental disorders. We found that individuals with childhood-onset type 1 diabetes had a higher risk of developing neurodevelopmental disorders than the general population. Notably, this risk was highest among those with less optimal glycemic control. These findings provided insight into the role of glycemic control, a crucial diabetes-related factor, in the occurrence of comorbidity between childhood-onset type 1 diabetes and neurodevelopmental disorders. In Study II, we investigated the potential contribution from shared familial liability to the comorbidity between childhood-onset type 1 diabetes and neurodevelopmental disorders. We found that the elevated risk of neurodevelopmental disorders did not only appear in individuals with childhood-onset type 1 diabetes but also in their full-siblings. The general family co-aggregation pattern and the results of the quantitative genetic modeling, however, did not conclusively show that familial liability contributes to the comorbidity. This ambiguity underscores the need for subsequent research to further elucidate the underlying causes of this comorbidity. In Study III, we explored the impacts of neurodevelopmental disorders on long-term glycemic control and the risk of diabetic complications in individuals with childhoodonset type 1 diabetes. We found that neurodevelopmental disorders, particularly ADHD and intellectual disability, were associated with increased risk of poor glycemic control and diabetic complications such as nephropathy and retinopathy. These observations highlight that taking neurodevelopmental aspects into account can be crucial when designing interventions and follow-ups for individuals with childhood-onset type 1 diabetes. In Study IV, evaluated the interplay between childhood-onset type 1 diabetes, ADHD, and academic outcomes, spanning from compulsory education to university levels. We found that children and adolescents with both type 1 diabetes and ADHD were significantly less likely to achieve educational milestones, crossing different education levels, and had less favorable compulsory school performances compared to their peers without these conditions. These results underline the importance of providing targeted support to minimize the educational gap between the affected children and adolescents and their peers

Device-free Localization using Received Signal Strength Measurements in Radio Frequency Network

Device-free localization (DFL) based on the received signal strength (RSS) measurements of radio frequency (RF)links is the method using RSS variation due to the presence of the target to localize the target without attaching any device. The majority of DFL methods utilize the fact the link will experience great attenuation when obstructed. Thus that localization accuracy depends on the model which describes the relationship between RSS loss caused by obstruction and the position of the target. The existing models is too rough to explain some phenomenon observed in the experiment measurements. In this paper, we propose a new model based on diffraction theory in which the target is modeled as a cylinder instead of a point mass. The proposed model can will greatly fits the experiment measurements and well explain the cases like link crossing and walking along the link line. Because the measurement model is nonlinear, particle filtering tracing is used to recursively give the approximate Bayesian estimation of the position. The posterior Cramer-Rao lower bound (PCRLB) of proposed tracking method is also derived. The results of field experiments with 8 radio sensors and a monitored area of 3.5m 3.5m show that the tracking error of proposed model is improved by at least 36 percent in the single target case and 25 percent in the two targets case compared to other models.Comment: This paper has been withdrawn by the author due to some mistake

Fair Division of Mixed Divisible and Indivisible Goods

We study the problem of fair division when the resources contain both divisible and indivisible goods. Classic fairness notions such as envy-freeness (EF) and envy-freeness up to one good (EF1) cannot be directly applied to the mixed goods setting. In this work, we propose a new fairness notion envy-freeness for mixed goods (EFM), which is a direct generalization of both EF and EF1 to the mixed goods setting. We prove that an EFM allocation always exists for any number of agents. We also propose efficient algorithms to compute an EFM allocation for two agents and for $n$ agents with piecewise linear valuations over the divisible goods. Finally, we relax the envy-free requirement, instead asking for $\epsilon$-envy-freeness for mixed goods ($\epsilon$-EFM), and present an algorithm that finds an $\epsilon$-EFM allocation in time polynomial in the number of agents, the number of indivisible goods, and $1/\epsilon$.Comment: Appears in the 34th AAAI Conference on Artificial Intelligence (AAAI), 202

Does Small-Scale Organic Farming Contribute to the Local Environment—A Case Study in Suburban Shanghai, China

Small-scale organic farming is developing rapidly in China, especially in suburbs of megacities, and enriches the connotation of urban agriculture. Much attention has been paid to the socio-economic aspects of small-scale organic farming and takes for granted that it contributes to the local environment and the sustainable agriculture while little has been explored regarding its actual environmental contributions and associated influencing factors, especially in those rapid developing suburb areas. Based on the case study of three small-scale organic farms in the suburbs of Shanghai, we examined uncertificated organic farming practices, focusing on the farm diversity, fertilization and pest control without chemical inputs, and the restoration of biosystems. Potential of environmental contributions were evaluated from the production perspective of input reductions. It was found that such uncertificated small-scale organic farming does contribute to the local water environment, helping improve soil quality, and gradual recovery of farm biodiversity. However, all the environmental benefits are fragile and highly dependent on the profit availability and professional knowledge of the farm as well as the availability of policy supports.</p