Patterns, Influences and Genetic Underpinnings of the Development of ADHD

Background Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterised by age-inappropriate, disruptive and pervasive manifestations of inattention and/or hyperactivity/impulsivity. ADHD symptoms typically emerge in childhood and persist into later stages of life. ADHD also frequently co-occurs with a number of psychiatric disorders and medical conditions, thereby bringing a tremendous burden to affected individuals as well as society. In addition to symptom severity and chronicity, the development of ADHD also plays a determinant role in disease outcomes. However, few studies have systematically investigated different predictive factors and underlying aetiologies associated with the development of ADHD. Aims This thesis aims to examine patterns, influences and genetic underpinnings of the development of ADHD from childhood to adolescence. The first study investigates childhood factors that differentiate late-onset ADHD from childhood-onset ADHD and differences in adolescent outcomes. The second study examines genetic and environmental contributions underlying the effects of the development of inattention on academic performance. The third and the fourth studies investigate the developmental relationships between ADHD and BMI through triangulation of evidence from longitudinal statistical analyses and genetically informed causal inference approaches. Methods All of the studies adopt a development-sensitive design using data from the “Twin Early Development Study” (TEDS), a longitudinal cohort in the UK. A pluralistic statistical approach is employed for different study objectives. To strengthen causal inference, this thesis compares and contrasts findings from longitudinal statistical approaches and different genetically informed methods under a triangulation framework. Results Findings of this thesis suggest that 1) late-onset ADHD is more likely to be found in males and children who exhibit increased conduct problems and experience more childhood family adversity. Moreover, low socioeconomic status specifically predicts de novo late-onset ADHD, while additional factors predict subthreshold late-onset ADHD; 2) both the baseline level and the developmental course of inattention influence academic performance. Genetic contributions to the development of inattention also affect academic performance; 3) longitudinal statistical analyses identify unidirectional effects from ADHD symptoms to subsequent BMI, while genetic methods suggest a bidirectional causal relationship. Triangulation of evidence shows that multiple sources of confounding are involved in the relationships between ADHD and BMI, including unmeasured confounding and dynastic effects. Conclusions This thesis identifies specific childhood risk factors and genetic underpinnings associated with different developmental patterns of ADHD. Influences of the developmental course of ADHD on psychological and functional outcomes can be attributable to direct causal relationships, genetic and environmental confounding, or a combination of both. Altogether, these findings contribute to a more complete and systematic understanding of different developmental aspects of ADHD. To disentangle aetiological pathways between the development of ADHD and associated conditions, a pluralistic statistical approach to triangulate evidence regarding causal mechanisms is necessary

Inverse Evolution Layers: Physics-informed Regularizers for Deep Neural Networks

This paper proposes a novel approach to integrating partial differential equation (PDE)-based evolution models into neural networks through a new type of regularization. Specifically, we propose inverse evolution layers (IELs) based on evolution equations. These layers can achieve specific regularization objectives and endow neural networks' outputs with corresponding properties of the evolution models. Moreover, IELs are straightforward to construct and implement, and can be easily designed for various physical evolutions and neural networks. Additionally, the design process for these layers can provide neural networks with intuitive and mathematical interpretability, thus enhancing the transparency and explainability of the approach. To demonstrate the effectiveness, efficiency, and simplicity of our approach, we present an example of endowing semantic segmentation models with the smoothness property based on the heat diffusion model. To achieve this goal, we design heat-diffusion IELs and apply them to address the challenge of semantic segmentation with noisy labels. The experimental results demonstrate that the heat-diffusion IELs can effectively mitigate the overfitting problem caused by noisy labels

Identification of discharge regimes of cyclone dipleg-trickle valve system based on pressure fluctuation profiles

An experiment was conducted on the Φ150mm×5000mmcyclone dipleg-trickle valve setup, which was focused on analyzing the discharge characteristics of trickle valve of cyclone dipleg by means of the dynamic pressure measurement. The effects of two operating parameters, negative pressure drop (0~11kPa) and solids flux rate (0~50 kg/m2.s), on the discharge patterns were investigated. The experimental results show that there are two kinds of discharge patterns in the trickle valve. One is continuous trickling discharge at low negative pressure drop and high solids flux rate, which is characterized by valve plate opening continuously, and the measured pressure with high frequency and low amplitude. The other is intermittent periodic dumping discharge at high negative pressure drop and low solids flux rate, which has the properties of valve plate opening interval, and the measured pressure with low frequency and high amplitude. The two discharge patterns could transform each other as varying the negative pressure drop or solids flux rate. The discharge regime map was proposed based on the experimental data, which is related to the negative. Please click Additional Files below to see the full abstract

Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising

Significant progress has been made in self-supervised image denoising (SSID) in the recent few years. However, most methods focus on dealing with spatially independent noise, and they have little practicality on real-world sRGB images with spatially correlated noise. Although pixel-shuffle downsampling has been suggested for breaking the noise correlation, it breaks the original information of images, which limits the denoising performance. In this paper, we propose a novel perspective to solve this problem, i.e., seeking for spatially adaptive supervision for real-world sRGB image denoising. Specifically, we take into account the respective characteristics of flat and textured regions in noisy images, and construct supervisions for them separately. For flat areas, the supervision can be safely derived from non-adjacent pixels, which are much far from the current pixel for excluding the influence of the noise-correlated ones. And we extend the blind-spot network to a blind-neighborhood network (BNN) for providing supervision on flat areas. For textured regions, the supervision has to be closely related to the content of adjacent pixels. And we present a locally aware network (LAN) to meet the requirement, while LAN itself is selectively supervised with the output of BNN. Combining these two supervisions, a denoising network (e.g., U-Net) can be well-trained. Extensive experiments show that our method performs favorably against state-of-the-art SSID methods on real-world sRGB photographs. The code is available at https://github.com/nagejacob/SpatiallyAdaptiveSSID.Comment: CVPR 2023 Camera Read

Taxonomic reconsideration of Prunus veitchii (Rosaceae)

Prunus veitchii was published in 1912 and was treated as a synonym of P. serrulata var. pubescens. The information about this taxon is relatively scarce. When consulting specimens of Prunus L., type materials of Prunus veitchii were found to belong to three taxa and P. veitchii, P. concinna, P. japonica var. zhejiangensis, C. jingningensis and C. xueluoensis were found to be conspecific. The taxonomic status of P. veitchii is reconsidered in the present paper. Morphometric analyses were performed to evaluate the significance of differences between P. veitchii and P. serrulata var. pubescens. The results show that the leaves of P. veitchii are significantly smaller and narrower than the leaves of P. serrulata var. pubescens and the peduncle and pedicels are shorter. According to the results of morphometric analyses, P. veitchii should be treated as a separate species. To address these results, a lectotype of P. veitchii is designated here and P. concinna, Cerasus jingningensis and C. xueluoensis are here designated as synonyms of P. veitchii