PICT-DPA: A Quality-Compliance Data Processing Architecture to Improve the Performance of Integrated Emergency Care Clinical Decision Support System

Emergency Care System (ECS) is a critical component of health care systems by providing acute resuscitation and life-saving care. As a time-sensitive care operation system, any delay and mistake in the decision-making of these EC functions can create additional risks of adverse events and clinical incidents. The Emergency Care Clinical Decision Support System (EC-CDSS) has proven to improve the quality of the aforementioned EC functions. However, the literature is scarce on how to implement and evaluate the EC-CDSS with regard to the improvement of PHOs, which is the ultimate goal of ECS. The reasons are twofold: 1) lack of clear connections between the implementation of EC-CDSS and PHOs because of unknown quality attributes; and 2) lack of clear identification of stakeholders and their decision processes. Both lead to the lack of a data processing architecture for an integrated EC-CDSS that can fulfill all quality attributes while satisfying all stakeholders’ information needs with the goal of improving PHOs. This dissertation identified quality attributes (PICT: Performance of the decision support, Interoperability, Cost, and Timeliness) and stakeholders through a systematic literature review and designed a new data processing architecture of EC-CDSS, called PICT-DPA, through design science research. The PICT-DPA was evaluated by a prototype of integrated PICT-DPA EC-CDSS, called PICTEDS, and a semi-structured user interview. The evaluation results demonstrated that the PICT-DPA is able to improve the quality attributes of EC-CDSS while satisfying stakeholders’ information needs. This dissertation made theoretical contributions to the identification of quality attributes (with related metrics) and stakeholders of EC-CDSS and the PICT Quality Attribute model that explains how EC-CDSSs may improve PHOs through the relationships between each quality attribute and PHOs. This dissertation also made practical contributions on how quality attributes with metrics and variable stakeholders could be able to guide the design, implementation, and evaluation of any EC-CDSS and how the data processing architecture is general enough to guide the design of other decision support systems with requirements of the similar quality attributes

Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation

Deep Neural Networks (DNNs) have been widely applied in various recognition tasks. However, recently DNNs have been shown to be vulnerable against adversarial examples, which can mislead DNNs to make arbitrary incorrect predictions. While adversarial examples are well studied in classification tasks, other learning problems may have different properties. For instance, semantic segmentation requires additional components such as dilated convolutions and multiscale processing. In this paper, we aim to characterize adversarial examples based on spatial context information in semantic segmentation. We observe that spatial consistency information can be potentially leveraged to detect adversarial examples robustly even when a strong adaptive attacker has access to the model and detection strategies. We also show that adversarial examples based on attacks considered within the paper barely transfer among models, even though transferability is common in classification. Our observations shed new light on developing adversarial attacks and defenses to better understand the vulnerabilities of DNNs.Comment: Accepted to ECCV 201

Hemodialysis catheter-related infection caused by Pannonibacter phragmitetus: a rare case report in China

Pannonibacter phragmitetus (P. phragmitetus) is rarely related with human disease. We reported a case of catheter-related infection caused by P. phragmitetus in a 68-year-old woman on hemodialysis. The patient developed recurrent fever during hemodialysis and blood cultures were positive for P. phragmitetus. The patient’s body temperature returned to normal after intravenous cefoperazone/sulbactam treatment, and the hemodialysis catheter was locked with gentamicin and urokinase. The potential anti-infective treatment against P. phragmitetus was discussed

Downstream-agnostic Adversarial Examples

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial use. In this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder.Comment: This paper has been accepted by the International Conference on Computer Vision (ICCV '23, October 2--6, 2023, Paris, France

Case Report: Diabetes in Chinese Bloom Syndrome

Bloom syndrome (BS) is a rare autosomal recessive disorder that causes several endocrine abnormalities. So far, only one BS pedigree, without diabetes, has been reported in the Chinese population. We presented the first case of BS with diabetes in the Chinese population and explored the clinical spectrum associated with endocrine. Possible molecular mechanisms were also investigated. Our study indicated that BS may be one rare cause of diabetes in the Chinese population. We also found a new pathogenic sequence variant in BLM (BLM RecQ like helicase gene)(NM_000057.4) c.692T>G, which may expand the spectrum of BLM variants

Dual‐Salt Electrolyte Additives Enabled Stable Lithium Metal Anode/Lithium–Manganese‐Rich Cathode Batteries

Although lithium (Li) metal anode/lithium–manganese-rich (LMR) cathode batteries have an ultrahigh energy density, the highly active Li metal and structural deterioration of LMR can make the usage of these batteries difficult. Herein, a multifunctional electrolyte containing LiBF4 and LiFSI dual-salt additives is designed, which enables the superior cyclability of Li/LMR cells with capacity retentions of ≈83.4%, 80.4%, and 76.6% after 400 cycles at 0.5, 1, and 2 C, respectively. The dual-salt electrolyte can form a thin, uniform, and inorganic species-rich solid electrolyte interphase (SEI) and cathode electrolyte interphase (CEI). In addition, it alleviates the bulk Li corrosion and enhances the structural sustainability of LMR cathode. Moreover, the electrolyte design strategy provides insights to develop other high-voltage lithium metal batteries (HVLMBs) to enhance the cycle stability

Revealing the Various Electrochemical Behaviors of Sn4P3 Binary Alloy Anodes in Alkali Metal Ion Batteries

Sn4P3 binary alloy anode has attracted much attention, not only because of the synergistic effect of P and Sn, but also its universal popularity in alkali metal ion batteries (AIBs), including lithium-ion batteries (LIBs), sodium-ion batteries (SIBs), and potassium-ion batteries (PIBs). However, the alkali metal ion (A+) storage and capacity attenuation mechanism of Sn4P3 anodes in AIBs are not well understood. Herein, a combination of ex situ X-ray diffraction, transmission electron microscopy, and density functional theory calculations reveals that the Sn4P3 anode undergoes segregation of Sn and P, followed by the intercalation of A+ in P and then in Sn. In addition, differential electrochemical curves and ex situ XPS results demonstrate that the deep insertion of A+ in P and Sn, especially in P, contributes to the reduction in capacity of AIBs. Serious sodium metal dendrite growth causes further reduction in the capacity of SIBs, while in PIBs it is the unstable solid electrolyte interphase and sluggish dynamics that lead to capacity decay. Not only the failure mechanism, including structural deterioration, unstable SEI, dendrite growth, and sluggish kinetics, but also the modification strategy and systematic analysis method provide theoretical guidance for the development of other alloy-based anode materials. © 2021 The Authors. Advanced Functional Materials published by Wiley-VCH Gmb

Epidemiological and laboratory characteristics of Omicron infection in a general hospital in Guangzhou: a retrospective study

The COVID-19 pandemic caused by SARS-CoV-2 has emerged as a major global public health concern. In November 2022, Guangzhou experienced a significant outbreak of Omicron. This study presents detailed epidemiological and laboratory data on Omicron infection in a general hospital in Guangzhou between December 1, 2022, and January 31, 2023. Out of the 55,296 individuals tested, 12,346 were found to be positive for Omicron. The highest prevalence of positive cases was observed in the 20 to 39 age group (24.6%), while the lowest was in children aged 0 to 9 years (1.42%). Females had a higher incidence of infection than males, accounting for 56.6% of cases. The peak time of Omicron infection varied across different populations. The viral load was higher in older adults and children infected with Omicron, indicating age-related differences. Spearman’s rank correlation analysis revealed positive correlations between Ct values and laboratory parameters in hospitalized patients with Omicron infection. These parameters included CRP (rs = 0.059, p = 0.009), PT (rs = 0.057, p = 0.009), INR (rs = 0.055, p = 0.013), AST (rs = 0.067, p = 0.002), LDH (rs = 0.078, p = 0.001), and BNP (rs = 0.063, p = 0.014). However, EO (Eosinophil, rs = −0.118, p < 0.001), BASO (basophil, rs = −0.093, p < 0.001), and LY (lymphocyte, rs = −0.069, p = 0.001) counts showed negative correlations with Ct values. Although statistically significant, the correlation coefficients between Ct values and these laboratory indices were very low. These findings provide valuable insights into the epidemiology of Omicron infection, including variations in Ct values across gender and age groups. However, caution should be exercised when utilizing Ct values in clinical settings for evaluating Omicron infection

Accelerating O-redox kinetics with carbon nanotubes for stable lithium-rich cathodes

Lithium-rich cathodes (LRCs) show great potential to improve the energy density of commercial lithium-ion batteries owing to their cationic and anionic redox characteristics. Herein, a complete conductive network using carbon nanotubes (CNTs) additives to improve the poor kinetics of LRCs is fabricated. Ex situ X-ray photoelectron spectroscopy first demonstrates that the slope at a low potential and the following long platform can be assigned to the transition metal and oxygen redox, respectively. The combination of galvanostatic intermittent titration technique and electrochemical impedance spectroscopy further reveal that a battery with CNTs exhibited accelerated kinetics, especially for the O-redox process. Consequently, LRCs with CNTs exhibit a much better rate and cycling performance (approximate to 89% capacity retention at 2 C for over 200 cycles) than the Super P case. Eventually, TEM results imply that the improved electrochemical performance of the CNTs case also benefits from its more stable bulk and surface structures. Such a facile conductive additive modification strategy also provides a universal approach for the enhancement of the electron diffusion properties of other electrode materials.Web of Science67art. no. 220044