Vortex signal detection method with stochastic resonance based on adaptive coupled feedback control

The control of stochastic resonance is the key to its application. A feedback method is proposed to control the generation of stochastic resonance with coupling, and then enhance resonance effect with the optimization of control parameters. The method is applied to detect vortex signal. Artificial fish swarm algorithm is used to adjust the control variables adaptively, thus the optimal control of the coupled bistable stochastic resonance is realized. Numerical simulation and experimental results manifest that by this means the resonance effect can be enhanced effectively, the signal-to-noise ratio (SNR) of vortex signal can be improved, and the vortex shedding frequency can be obtained accurately

Applying Systems Thinking and Machine Learning Techniques to Identify Leverage Points for Intervening in Perioperative Opioid Use and Developing Risk Score Tools to Guide Perioperative Opioid Prescription

Study Background and Objectives:Excessive perioperative opioid prescribing has been detrimental to public health, contributing to the elevated prevalence of opioid use disorder. Since 2016, rigorous regulation of opioid prescribing has reduced over-prescription, but has also led to opioid-phobia. The 2022 CDC guideline promotes person-centered decisions on pain management by relaxing restrictions on opioid prescription. The determination of opioid requirements for surgical pain management is influenced by various factors and stakeholders. Despite extensive research, the mechanisms underlying perioperative pain management and the persistence of opioid use after surgery remain unclear. Clinicians currently lack tools to guide opioid prescription in clinical settings, and patients often face a dearth of information regarding expected pain levels, proper opioid use, and options for surgical pain management. The main objective of my doctoral project is to disentangle the intricate relationships among patients, healthcare providers, and policy changes in perioperative opioid prescription for pain management and to identify key intervention points to balance the beneficial effects of proper opioids use against the risks of addition. Another objective is to develop a risk score algorithm for perioperative opioid requirements to help with decision-making in clinical practice. Materials and Methods:In chapter 1, I undertook a systematic review and meta-analysis, and investigated the percentage of adult patients scheduled for general surgeries who received opioid analgesia for perioperative pain management, the quantities of opioids prescribed to patients, the actual quantities consumed, the percentage of patients without prior opioid exposure experiencing prolonged opioid use, and the evolution of perioperative opioid prescription patterns since the policy changes. A causal loop diagram was used to visualize the complex conceptual framework of perioperative pain management and post-surgical prolonged use of opioids based on insights derived from the systematic review and meta-analysis. In chapter 2 and 3, data from patients aged 18-64 years undergoing one of 12 commonly performed procedures (e.g., laparoscopic cholecystectomy) from 2015 to 2018 at a single institution were analyzed. Perioperative opioid requirements (none/low, medium, high) were determined based on patients’ self-reported pain scores and opioid prescription/administration from 30 days before to 2 weeks after surgery. Patients’ clinical and procedure-related factors were collected as potential predictors. Random forest, the Least Absolute Shrinkage and Selection Operator (LASSO), and multinomial logistic regression were used to develop prediction models. Models’ performance, including discrimination, calibration, classification measures were evaluated. A nomogram based on multinomial logistic regression was generated as a score tool, and decision curve analysis was used to examine the clinical utility of the final prediction model dichotomizing the opioid prescription as none/sparing versus medium/high requirements. Results: My systematic review and meta-analysis revealed that around 85% of surgical patients received opioids perioperatively. The pooled mean total amount of opioids dispensed was 210 MME per patient per surgical procedure. Notably, only approximately 44% of the prescribed opioids were consumed. Among opioid-naïve patients who initiated opioid use perioperatively, 7.1% persisted in opioid use beyond the conventional three-month postoperative recovery timeframe. Intervention programs (such as setting up maximum limits of opioids prescription, providing trainings to health providers, monitoring opioids prescription behaviors, providing health education to patients, etcetera) reduced perioperative opioid prescription by 38% and opioid consumption by 63.2%. The causal loop diagram illustrates a balancing feedback loop between policy and over-prescription, highlighting the pivotal role of a decision tool in reducing the over-prescription of perioperative opioids while ensuring the fulfillment of opioid needs for effective perioperative pain management. To develop a decision-aid tool based on prediction models, I included 2733 patients in the training dataset and 1081 in the testing dataset, all of whom underwent general surgeries. All prediction models demonstrated moderate discrimination in the testing dataset. The null hypothesis of perfect calibration intercepts and calibration slopes was rejected. In analyses restricted to patients undergoing laparoscopic cholecystectomy, model discrimination remained similar while model calibration improved. The revised LASSO model had an accuracy of around 65% in the testing dataset, classifying future cases correctly into opioid requirements groups in laparoscopic cholecystectomy cohort. Features in the final laparoscopic cholecystectomy model included the use of opioid/NSAID/anti-depressant before surgery, emergency surgery, anesthesia type, and surgical indication for cholelithiasis/cholecystitis. A nomogram was created to guide perioperative opioids use among laparoscopic cholecystectomy patients, and the decision curve analysis demonstrated the clinical utility of the prediction model; it generated higher net benefits than the strategy of prescribing no opioids or opioid sparing to surgical patients and the strategy of prescribing medium or high opioids doses to all patients, with a broad threshold probability from 18% to 92%. Conclusions:In summary, this dissertation described the historically high levels of perioperative opioid prescriptions and highlighted their adverse impacts: persistent opioid use and community diversion. Although the implementation of guidance and policies has significantly reduced nationwide over-prescriptions of opioids, it is essential to recognize the potential benefits of appropriate opioid use in perioperative pain management. The incorporation of a machine-learning approach with subject-matter knowledge may achieve more accurate predictions of opioid requirements than employing machine-learning techniques alone and increase the interpretability of the prediction model. Notably, the surgery-specific model demonstrated superior performance than the model for general surgeries. Future studies should further validate the conceptual model of perioperative opioid prescription and misuse in real-world scenarios, enhance model discrimination, extend external validation efforts, and develop electronic applications tailored to contemporary medical practices

The Hydrodynamic Interaction in Polymer Solutions Simulated with Dissipative Particle Dynamics

We analyzed extensively the dynamics of polymer chains in solutions simulated with dissipative particle dynamics (DPD), with a special focus on the potential influence of a low Schmidt number of a typical DPD fluid on the simulated polymer dynamics. It has been argued that a low Schmidt number in a DPD fluid can lead to underdevelopment of the hydrodynamic interaction in polymer solutions. Our analyses reveal that equilibrium polymer dynamics in dilute solution, under a typical DPD simulation conditions, obey the Zimm model very well. With a further reduction in the Schmidt number, a deviation from the Zimm model to the Rouse model is observed. This implies that the hydrodynamic interaction between monomers is reasonably developed under typical conditions of a DPD simulation. Only when the Schmidt number is further reduced, the hydrodynamic interaction within the chains becomes underdeveloped. The screening of the hydrodynamic interaction and the excluded volume interaction as the polymer volume fraction is increased are well reproduced by the DPD simulations. The use of soft interaction between polymer beads and a low Schmidt number do not produce noticeable problems for the simulated dynamics at high concentrations, except that the entanglement effect which is not captured in the simulations.Comment: 27 pages, 13 page

TRAC: A Textual Benchmark for Reasoning about Actions and Change

Reasoning about actions and change (RAC) is essential to understand and interact with the ever-changing environment. Previous AI research has shown the importance of fundamental and indispensable knowledge of actions, i.e., preconditions and effects. However, traditional methods rely on logical formalization which hinders practical applications. With recent transformer-based language models (LMs), reasoning over text is desirable and seemingly feasible, leading to the question of whether LMs can effectively and efficiently learn to solve RAC problems. We propose four essential RAC tasks as a comprehensive textual benchmark and generate problems in a way that minimizes the influence of other linguistic requirements (e.g., grounding) to focus on RAC. The resulting benchmark, TRAC, encompassing problems of various complexities, facilitates a more granular evaluation of LMs, precisely targeting the structural generalization ability much needed for RAC. Experiments with three high-performing transformers indicates that additional efforts are needed to tackle challenges raised by TRAC

Exploiting Image-Related Inductive Biases in Single-Branch Visual Tracking

Despite achieving state-of-the-art performance in visual tracking, recent single-branch trackers tend to overlook the weak prior assumptions associated with the Vision Transformer (ViT) encoder and inference pipeline. Moreover, the effectiveness of discriminative trackers remains constrained due to the adoption of the dual-branch pipeline. To tackle the inferior effectiveness of the vanilla ViT, we propose an Adaptive ViT Model Prediction tracker (AViTMP) to bridge the gap between single-branch network and discriminative models. Specifically, in the proposed encoder AViT-Enc, we introduce an adaptor module and joint target state embedding to enrich the dense embedding paradigm based on ViT. Then, we combine AViT-Enc with a dense-fusion decoder and a discriminative target model to predict accurate location. Further, to mitigate the limitations of conventional inference practice, we present a novel inference pipeline called CycleTrack, which bolsters the tracking robustness in the presence of distractors via bidirectional cycle tracking verification. Lastly, we propose a dual-frame update inference strategy that adeptively handles significant challenges in long-term scenarios. In the experiments, we evaluate AViTMP on ten tracking benchmarks for a comprehensive assessment, including LaSOT, LaSOTExtSub, AVisT, etc. The experimental results unequivocally establish that AViTMP attains state-of-the-art performance, especially on long-time tracking and robustness.Comment: 13 pages, 8 figures, under revie

Design and synthesis of active heparan sulfate-based probes

A chemoenzymatic approach for synthesizing heparan sulfate oligosaccharides with a reactive diazoacetyl saccharide residue is reported. The resultant oligosaccharides were demonstrated to serve as specific inhibitors for heparan sulfate sulfotransferases, offering a new set of tools to probe the structural selectivity for heparan sulfate-binding proteins

The serum matrix metalloproteinase-9 level is an independent predictor of recurrence after ablation of persistent atrial fibrillation

OBJECTIVES: This study investigated whether the serum matrix metalloproteinase-9 level is an independent predictor of recurrence after catheter ablation for persistent atrial fibrillation. METHODS: Fifty-eight consecutive patients with persistent atrial fibrillation were enrolled and underwent catheter ablation. The serum matrix metalloproteinase-9 level was detected before ablation and its relationship with recurrent arrhythmia was analyzed at the end of the follow-up. RESULTS: After a mean follow-up of 12.1±7.2 months, 21 (36.2%) patients had a recurrence of their arrhythmia after catheter ablation. At baseline, the matrix metalloproteinase-9 level was higher in the patients with recurrence than in the non-recurrent group (305.77±88.90 vs 234.41±93.36 ng/ml, respectively, p=0.006). A multivariate analysis showed that the matrix metalloproteinase-9 level was an independent predictor of arrhythmia recurrence, as was a history of atrial fibrillation and the diameter of the left atrium. CONCLUSION: The serum matrix metalloproteinase-9 level is an independent predictor of recurrent arrhythmia after catheter ablation in patients with persistent atrial fibrillation

Evaluation of anti-fatigue property of Porphyridium cruentum in mice

Purpose: To evaluate the potential effects of Porphyridium cruentum (PC) on fatigue induced by forced swimming test in mice. Methods: Mice were randomly divided into normal control group (NC, i.e., untreated non-swimming); model control group (MC, untreated swimming); Spirulina treated group (SP, 800 mg/kg); PC-treated groups (50, 100, and 200 mg/kg), respectively. After intragastric administration for 14 consecutive days, a weight-bearing swimming experiment was conducted for the mice, and the biochemical indicators related to fatigue were examined, including exhaustive swimming time, glucose levels (Glu), hepatic glycogen contents (HG), muscle glycogen contents (MG), glutathione peroxidase activities (GSH-Px), creatine kinase (CK), malondialdehyde (MDA), urea nitrogen levels (SUN), lactate dehydrogenase activities (LDH), lactic acid (LA) as well as superoxide dismutase (SOD). Results: PC significantly prolonged the swimming endurance time compared to MC. After PC treatment, Glu, HG and MG were effectively increased dose-dependently, SUN, LA, LDH and CK levels in serum were significantly reduced. Moreover, PC treatment elevated the bioactivities of two antioxidant enzymes, namely, GSH-Px and SOD, while MDA content decreased when compared to MC group. Conclusion: These results indicate that PC exhibits strong anti-fatigue effect. Thus, PC may be suitable for incorporation in functional food to counter fatigue

Uncovering Biphasic Catalytic Mode of C 5 -epimerase in Heparan Sulfate Biosynthesis

Heparan sulfate (HS), a highly sulfated polysaccharide, is biosynthesized through a pathway involving several enzymes. C5-epimerase (C5-epi) is a key enzyme in this pathway. C5-epi is known for being a two-way catalytic enzyme, displaying a “reversible” catalytic mode by converting a glucuronic acid to an iduronic acid residue, and vice versa. Here, we discovered that C5-epi can also serve as a one-way catalyst to convert a glucuronic acid to an iduronic acid residue, displaying an “irreversible” catalytic mode. Our data indicated that the reversible or irreversible catalytic mode strictly depends on the saccharide substrate structures. The biphasic mode of C5-epi offers a novel mechanism to regulate the biosynthesis of HS with the desired biological functions