Chinese Chronic Mucocutaneous Candidiasis: A Case Report Series.

Chronic Mucocutaneous Candidiasis (CMC) is a rare immunodeficiency disease characterized by chronic or recurrent superficial Candida infections on the skin, nail, and mucous membranes. Here, we present four Chinese patients with CMC who manifested oral mucosal leukoplakia and nail thickening during early childhood, all displaying fissured tongue lines. The causative pathogens isolated from their oral mucosa and nails were identified as C. albicans and C. parapsilosis through morphology and molecular sequencing. Notably, among the four patients, one presented with vitiligo, while another had hypothyroidism. We have also conducted a review of reported cases of CMC in China and worldwide over the last five years, highlighting potential approaches for diagnosis and treatment. The current molecular evidence in the literature suggests potential for the development of early diagnosis methods, such as screening genetic variables on STAT1 and STAT3. Additionally, potential treatment avenues, including gene-targeted analogues and GM-CSF analogues, could be explored in conjunction with traditional antifungal therapy.

DeeperGCN: Training Deeper GCNs With Generalized Aggregation Functions.

Graph Neural Networks (GNNs) have been drawing significant attention to representation learning on graphs. Recent works developed frameworks to train very deep GNNs and showed impressive results in tasks like point cloud learning and protein interaction prediction. In this work, we study the performance of such deep models in large-scale graphs. In particular, we look at the effect of adequately choosing an aggregation function on deep models. We find that GNNs are very sensitive to the choice of aggregation functions (e.g. mean, max, and sum) when applied to different datasets. We systematically study and propose to alleviate this issue by introducing a novel class of aggregation functions named Generalized Aggregation Functions. The proposed functions extend beyond commonly used aggregation functions to a wide range of new permutation-invariant functions. Generalized Aggregation Functions are fully differentiable, where their parameters can be learned in an end-to-end fashion to yield a suitable aggregation function for each task. We show that equipped with the proposed aggregation functions, deep residual GNNs outperform state-of-the-art in several benchmarks from Open Graph Benchmark (OGB) across tasks and domains.

Virulence capacity of different Aspergillus species from invasive pulmonary aspergillosis.

Introduction: The opportunistic filamentous fungus Aspergillus causes invasive pulmonary aspergillosis (IPA) that often turns into a fatal infection in immunocompromised hosts. However, the virulence capacity of different Aspergillus species and host inflammation induced by different species in IPA are not well understood. Methods: In the present study, host inflammation, antimicrobial susceptibilities and virulence were compared among clinical Aspergillus strains isolated from IPA patients. Results: A total of 46 strains were isolated from 45 patients with the invasive infection, of which 35 patients were diagnosed as IPA. Aspergillus flavus was the dominant etiological agent appearing in 25 cases (54.3%). We found that the CRP level and leukocyte counts (elevated neutrophilic granulocytes and monocytes, and reduced lymphocytes) were significantly different in IPA patients when compared with healthy individuals (P < 0.05). Antifungal susceptibilities of these Aspergillus isolates from IPA showed that 91%, 31%, 14%, and 14% were resistant to Fluconazole, Micafungin, Amphotericin B and Terbinafine, respectively. The survival rate of larvae infected by A. flavus was lower than larvae infected by A. niger or A. fumigatus (P < 0.05). Discussion: Aspergillus flavus was the dominant clinical etiological agent. Given the prevalence of A. flavus in our local clinical settings, we may face greater challenges when treating IPA patients.

DeepGCNs: Making GCNs Go as Deep as CNNs.

Convolutional neural networks (CNNs) have been very successful at solving a variety of computer vision tasks such as object classification and detection, semantic segmentation, activity understanding, to name just a few. One key enabling factor for their great performance has been the ability to train very deep networks. Despite their huge success in many tasks, CNNs do not work well with non-euclidean data, which is prevalent in many real-world applications. Graph Convolutional Networks (GCNs) offer an alternative that allows for non-Eucledian data input to a neural network. While GCNs already achieve encouraging results, they are currently limited to architectures with a relatively small number of layers, primarily due to vanishing gradients during training. This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs. We show the benefit of using deep GCNs (with as many as 112 layers) experimentally across various datasets and tasks. Specifically, we achieve very promising performance in part segmentation and semantic segmentation on point clouds and in node classification of protein functions across biological protein-protein interaction (PPI) graphs. We believe that the insights in this work will open avenues for future research on GCNs and their application to further tasks not explored in this paper. The source code for this work is available at https://github.com/lightaime/deep_gcns_torch and https://github.com/lightaime/deep_gcns for PyTorch and TensorFlow implementations respectively.

Quantitative and Real-Time Evaluation of Human Respiration Signals with a Shape-Conformal Wireless Sensing System.

Respiration signals reflect many underlying health conditions, including cardiopulmonary functions, autonomic disorders and respiratory distress, therefore continuous measurement of respiration is needed in various cases. Unfortunately, there is still a lack of effective portable electronic devices that meet the demands for medical and daily respiration monitoring. This work showcases a soft, wireless, and non-invasive device for quantitative and real-time evaluation of human respiration. This device simultaneously captures respiration and temperature signatures using customized capacitive and resistive sensors, encapsulated by a breathable layer, and does not limit the user's daily life. Further a machine learning-based respiration classification algorithm with a set of carefully studied features as inputs is proposed and it is deployed into mobile clients. The body status of users, such as being quiet, active and coughing, can be accurately recognized by the algorithm and displayed on clients. Moreover, multiple devices can be linked to a server network to monitor a group of users and provide each user with the statistical duration of physiological activities, coughing alerts, and body health advice. With these devices, individual and group respiratory health status can be quantitatively collected, analyzed, and stored for daily physiological signal detections as well as medical assistance.