Low Growth Hormone Levels in Short-Stature Children with Pituitary Hyperplasia Secondary to Primary Hypothyroidism

Objective. The follow-up of GH levels in short-stature children with pituitary hyperplasia secondary to primary hypothyroidism (PPH) is reported in a few cases. We aimed to observe changes in GH secretion in short-stature children with PPH. Methods. A total of 11 short-stature children with PPH accompanied by low GH levels were included. They received levothyroxine therapy after diagnosis. Their thyroid hormones, IGF-1, PRL, and pituitary height were measured at baseline and 3 months after therapy. GH stimulation tests were performed at baseline and after regression of thyroid hormones and pituitary. Results. At baseline, they had decreased GH peak and FT3 and FT4 levels and elevated TSH levels. Decreased IGF-1 levels were found in seven children. Elevated PRL levels and positive thyroid antibodies were found in 10 children. The mean pituitary height was 14.3±3.8 mm. After 3 months, FT3, FT4, and IGF-1 levels were significantly increased (all p<0.01), and values of TSH, PRL, and pituitary height were significantly decreased (all p<0.001). After 6 months, pituitary hyperplasia completely regressed. GH levels returned to normal in nine children and were still low in two children. Conclusion. GH secretion can be resolved in most short-stature children with PPH

Orthodontic mini-implants: A systematic review.

&nbsp;AbstractPurposeTo compile and analyze the literature regarding orthodontic mini-implants (MIs) placement, clinical applications, success rate, adverse effects and patients&rsquo; pain experience in clinical practice.MethodologyPublications about orthodontic MIs variables were systematically searched from PubMed, Science Direct, and Google Scholar Beta electronic data bases using &ldquo;orthodontic in conjunction with implant, microimplant, screw, miniscrew, screw implant, mini-implant, and temporary anchorage&rdquo; as keywords. Data from selected articles were extracted and compiled to produce a summarized report. ResultsSeveral areas are suitable for MI placement. However; the region between second premolar and first molar is the safest. The MI success rate ranges from 77.7% to 93.43%. The pain associated with MIs is far less than tooth extraction and significantly lower than patients&rsquo; expectation. Root resorption is among the adverse effects and gonial angle pattern influences the MI success rate. ConclusionMIs offer a wide range of clinical anchorage application due to their minimal anatomical location limitation. The success rate of MI is reliably high. The pain caused by orthodontics MI is significantly lower than patients&rsquo; expectation.&nbsp;&nbsp

Research on Some Phenomenon of E-Government Service Capacity Distribution in Mainland China Based on Multi-channel Perspective

In the context of the government\u27s increasing emphasis on e-government services, this is an urgent need for empirical research of large sample and multi-channels. Therefore, based on the government website, WeChat, Micro-blog, app, by using the existing mature evaluation index system, this paper analyzes e-government service capacity of the city above prefecture- level and provincial. Then, this paper selects the administrative level, economic level, regional balance as the differentiation attribute. It is found that both administrative level and economic level are positively correlated with government service capacity in all the channels. The channel capacity distribution varies related to attribute of administrative and economic, government type of city and province, but it is not restricted by level and region. It provides direction and intensity management to balance and promote channel service capacity for China government

Rethinking skip connection model as a learnable Markov chain

Over past few years afterward the birth of ResNet, skip connection has become the defacto standard for the design of modern architectures due to its widespread adoption, easy optimization and proven performance. Prior work has explained the effectiveness of the skip connection mechanism from different perspectives. In this work, we deep dive into the model's behaviors with skip connections which can be formulated as a learnable Markov chain. An efficient Markov chain is preferred as it always maps the input data to the target domain in a better way. However, while a model is explained as a Markov chain, it is not guaranteed to be optimized following an efficient Markov chain by existing SGD-based optimizers which are prone to get trapped in local optimal points. In order to towards a more efficient Markov chain, we propose a simple routine of penal connection to make any residual-like model become a learnable Markov chain. Aside from that, the penal connection can also be viewed as a particular model regularization and can be easily implemented with one line of code in the most popular deep learning frameworks~\footnote{Source code: \url{https://github.com/densechen/penal-connection}}. The encouraging experimental results in multi-modal translation and image recognition empirically confirm our conjecture of the learnable Markov chain view and demonstrate the superiority of the proposed penal connection.Comment: 12 pages, 4 figure

The Influencing Path of Public Engaging Intention in the Value Co-Creation of E-Gov Services：An Empirical Investigation

The wide acceptability of ICTs and social media enriches the delivery platform of e-gov services (EGS). EGS is an important interaction and collaboration channel between the government and the public. The public can conveniently and timely explore problems, provide ideas, and design solutions to improve EGS. The roles of the public changed to active, informed partners or co- creators of EGS innovation and problem solving. This study builds the influence factor model on public engaging intention of value co-creation for EGS based on technology acceptance theory, trust theory, and motivation theory to explore impact factors and impact paths. Path analysis interpreted how the public would accept and adopt value co-creation behavior for EGS. This study also introduced a comprehensive picture of the new paradigm of public service value creation in an era of increasing user dominance, that is, the public

Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation

Generative adversarial networks (GANs) have achieved remarkable progress in the natural image field. However, when applying GANs in the remote sensing (RS) image generation task, an extraordinary phenomenon is observed: the GAN model is more sensitive to the size of training data for RS image generation than for natural image generation. In other words, the generation quality of RS images will change significantly with the number of training categories or samples per category. In this paper, we first analyze this phenomenon from two kinds of toy experiments and conclude that the amount of feature information contained in the GAN model decreases with reduced training data. Then we establish a structural causal model (SCM) of the data generation process and interpret the generated data as the counterfactuals. Based on this SCM, we theoretically prove that the quality of generated images is positively correlated with the amount of feature information. This provides insights for enriching the feature information learned by the GAN model during training. Consequently, we propose two innovative adjustment schemes, namely Uniformity Regularization (UR) and Entropy Regularization (ER), to increase the information learned by the GAN model at the distributional and sample levels, respectively. We theoretically and empirically demonstrate the effectiveness and versatility of our methods. Extensive experiments on three RS datasets and two natural datasets show that our methods outperform the well-established models on RS image generation tasks. The source code is available at https://github.com/rootSue/Causal-RSGAN