Concept Drift Detection by Tracking Weighted Prediction Confidence of Incremental Learning

Data stream mining is great significant in many real-world scenarios, especially in the big data area. However, conventional machine learning algorithms are incapable to process because of its two characteristics (1) potential unlimited number of data is generated in real-time way, it is impossible to store all the data (2) evolving over time, namely, concept drift, will influence the performance of predictor trained on previous data. Concept drift detection method could detect and locate the concept drift in data stream. However, existing methods only utilize the prediction result as indicator. In this article, we propose a weighted concept drift indicator based on incremental ensemble learning to detect the concept. The indicator not only considers the prediction result, but the change of prediction stability of predictor with occurs of concept drift. Also, an incremental ensemble learning based on vote mechanism is especially used to get constantly updated value of indicator. Based on the experiment result on both benchmark and real-world dataset, our method could effectively detect concept drift and outperform other existing methods

A Pilot Study on the Impacts of Lung-Strengthening Qigong on Wellbeing

Background Qigong embraces a range of self-care exercises originating from China. Lung-Strengthening Qigong (LSQ) is a specific technique for maintaining and improving physical and mental wellbeing. Methods We recruited 170 practitioners and 42 non-practitioner/control samples to investigate the impacts of LSQ practice on body, mind, thoughts, and feelings. This is a pilot study pursued to plan for an adequately powered, non-clinical randomized controlled trials (RCT) on overall wellbeing and health and to evaluate the adequacy of delivering the physical activity intervention with fidelity. Self-evaluation-based data collection schemes were developed by regularly requesting completion of a questionnaire from both practitioner and control group, and an online diary and end of study survey (EOS) completion only from the practitioners. Diverse types of analyses were conducted, including statistical tests, machine learning, and qualitative thematic models. Results We evaluated all different data resources together and observed that (a)the impacts are diverse, including improvements in physical (e.g., elevated sleep quality, physical energy, reduced fatigue), mental (e.g., increased positivity, reduced stress), and relational (e.g., enhanced connections to self and nature) wellbeing, which were not observed in control group; (b)measured by the level-of-effectiveness, four distinct clusters were identified, from no-effect to a high-level of effect; (c)a majority (84 %) of the LSQ practitioners experienced an improvement in wellbeing; (d)qualitative and quantitative analyses of the diary entries, questionnaires, and EOS were all found to be consistent, (e)majority of the positively impacted practitioners had no or some little prior experience with LSQ. Conclusions Novel features of this study include (i)an increased sample size vis-à-vis other related studies; (ii)provision of weekly live-streamed LSQ sessions; (iii)integration of quantitative and qualitative type of analyses. The pilot study indicated that the proportion of practitioners who continued to engage in completing the regular-interval questionnaires over time was higher for practitioners compared to the control group. The engagement of practitioners may have been sustained by participation in the regular live LSQ sessions. To fully understand the impacts of LSQ on clinical/physiological outcomes, especially for specific patient groups, more objective biomarkers (e.g. respiratory rate, heart rate variation) could be tracked in future studies

Efficient internalization of TAT peptide in zwitterionic DOPC phospholipid membrane revealed by neutron diffraction

The aim of this study is to investigate the interactions between TAT peptides and a neutral DOPC bilayer by using neutron lamellar diffraction. The distribution of TAT peptides and the perturbation of water distribution across the DOPC bilayer were revealed. When compared to our previous study on an anionic DOPC/DOPS bilayer (X. Chen et al., Biochim Biophys Acta. 2013. 1828 (8), 1982\u20131988), a much deeper insertion of TAT peptides was found in the hydrophobic core of DOPC bilayer at a depth of 6.0 \uc5 from the center of the bilayer, a position close to the double bond of fatty acyl chain. We conclude that the electrostatic attractions between the positively charged TAT peptides and the negatively charged headgroups of phospholipid are not essential for the direct translocation. Furthermore, the interactions of TAT peptides with the DOPC bilayer were found to vary in a concentration-dependent manner. A limited number of peptides first associate with the phosphate moieties on the lipid headgroups by using the guanidinium ions pairing. Then the energetically favorable water defect structures are adopted to maintain the arginine residues hydrated by drawing water molecules and lipid headgroups into the bilayer core. Such bilayer deformations consequently lead to the deep intercalation of TAT peptides into the bilayer core. Once a threshold concentration of TAT peptide in the bilayer is reached, a significant rearrangement of bilayer will happen and steady-state water pores will form.Peer reviewed: YesNRC publication: Ye

The ratio of systolic and diastolic pressure is associated with carotid and femoral atherosclerosis

BackgroundAlthough the impact of hypertension on carotid intima-media thickness (IMT) and plaques has been well established, its association with femoral IMT and plaques has not been extensively examined. In addition, the role of the ratio of systolic and diastolic pressure (SDR) in the subclinical atherosclerosis (AS) risk remains unknown. We assessed the relationship between SDR and carotid and femoral AS in a general population.MethodsA total of 7,263 participants aged 35–74 years enrolled from January 2019 to June 2021 in a southeast region of China were included in a cross-sectional study. Systolic and diastolic blood pressure (SBP and DBP) were used to define SDR. Ultrasonography was applied to assess the AS, including thickened IMT (TIMT) and plaque in the carotid and femoral arteries. Logistic regression and restricted cubic spline (RCS) models were the main approaches.ResultsThe prevalence of TIMT, plaque, and AS were 17.3%, 12.4%, and 22.7% in the carotid artery; 15.2%, 10.7%, and 19.5% in the femoral artery; and 23.8%, 17.9% and 30.0% in either the carotid or femoral artery, respectively. Multivariable logistic regression analysis found a significant positive association between high-tertile SDR and the higher risk of overall TIMT (OR = 1.28, 95% CI = 1.10–1.49), plaques (OR = 1.36, 95%CI = 1.16–1.61), or AS (OR = 1.36, 95% CI = 1.17–1.57), especially in the carotid artery. RCS analysis further revealed the observed positive associations were linear. Further analyses showed that as compared to the low-tertile SDR and non-hypertension group, high-tertile SDR was associated with increased risks of overall and carotid TIMT, plaques, or AS in both groups with or without hypertension.ConclusionsSDR is related to a higher risk of subclinical AS, regardless of hypertension or not, suggesting that as a readily obtainable index, SDR can contribute to providing additional predictive value for AS

The distribution, fate, and environmental impacts of food additive nanomaterials in soil and aquatic ecosystems.

Nanomaterials in the food industry are used as food additives, and the main function of these food additives is to improve food qualities including texture, flavor, color, consistency, preservation, and nutrient bioavailability. This review aims to provide an overview of the distribution, fate, and environmental and health impacts of food additive nanomaterials in soil and aquatic ecosystems. Some of the major nanomaterials in food additives include titanium dioxide, silver, gold, silicon dioxide, iron oxide, and zinc oxide. Ingestion of food products containing food additive nanomaterials via dietary intake is considered to be one of the major pathways of human exposure to nanomaterials. Food additive nanomaterials reach the terrestrial and aquatic environments directly through the disposal of food wastes in landfills and the application of food waste-derived soil amendments. A significant amount of ingested food additive nanomaterials (> 90 %) is excreted, and these nanomaterials are not efficiently removed in the wastewater system, thereby reaching the environment indirectly through the disposal of recycled water and sewage sludge in agricultural land. Food additive nanomaterials undergo various transformation and reaction processes, such as adsorption, aggregation-sedimentation, desorption, degradation, dissolution, and bio-mediated reactions in the environment. These processes significantly impact the transport and bioavailability of nanomaterials as well as their behaviour and fate in the environment. These nanomaterials are toxic to soil and aquatic organisms, and reach the food chain through plant uptake and animal transfer. The environmental and health risks of food additive nanomaterials can be overcome by eliminating their emission through recycled water and sewage sludge. [Abstract copyright: Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.

Concept drift detection using machine learning in data stream

Machine learning applications in streaming data often grapple with dynamic changes in data distribution, particularly concept drift, where shifts in classification boundaries undermine the model’s performance. This challenge is further complicated by the inherent complexity of data streams, the underutilization of deep neural networks in addressing the issue, and a lack of comprehensive understanding of the concept drift. Data streams are non-stationary and complex by nature, which poses significant challenges to concept drift detection. Deep neural networks, despite their immense predictive power, are rarely employed in this context due to their high computational costs. Current detection methods typically concentrate on pinpointing when a concept drift occurs, neglecting to explore the detailed information about the concept drift. This dearth of information, such as the concept drift’s onset, duration, severity, or endpoint, could be invaluable for a more nuanced understanding of the phenomenon. To mitigate these issues, this thesis introduces several innovative methods: Drift Detection Method with False Positive rate for Multi-label classification (DDM-FP-M): This novel approach extends the existing Drift Detection Method (DDM) to multi-label classification data streams. It incorporates a unique mechanism to adjust for false positives, enhancing the adaptability and accuracy of drift detection in complex data stream scenarios. Noise Tolerant Drift Detection Method (NTDDM): NTDDM introduces a two-step process to discern true drifts from noise-induced false positives. It refines drift detection by filtering out misleading signals through subsampling and statistical detection methods, improving the reliability of drift detection in noisy data environments. The efficacy of this method is further validated through three newly proposed performance metrics specifically designed for concept drift detection. Incremental Weighted Performance Drift Detection Method (IWPDDM): This method employs prediction confidence derived from the incremental learning of ensemble models to detect concept drift. It represents a shift in focus towards the model’s own response to concept drift, rather than solely relying on the model’s output. It creates an indicator using weighted prediction confidence from these models, ensuring stable and accurate drift detection, a significant improvement over traditional methods. Model-centric Transfer Learning (MCDD) Framework: Recognizing the limited use of deep neural networks in concept drift detection due to computational constraints, this thesis proposes the MCDD framework. This approach relies solely on the model’s intrinsic changes to detect concept drift, making it a model-centric method. Our experiments demonstrate that mere changes in the model itself can accurately reflect concept drift. This framework strategically utilizes transfer learning to freeze parts of the network, significantly reducing computational needs while enhancing drift detection performance. Quadruple-based Approach for Understanding Concept Drift in Data Streams (QuadCDD): The QuadCDD framework aims to provide a holistic understanding of concept drift. Most existing methods focus only on detecting the start point of concept drift, yet there is much information about concept drift that remains unexplored, such as its endpoint, severity, and type. This lack of information greatly reduces the specificity of adaptation strategies. The QuadCDD framework goes beyond merely identifying the onset of drift, equipping models with comprehensive information for more effective adjustments and a deeper understanding of the drift dynamics. In conclusion, this thesis addresses a critical issue in machine learning on data streams. It provides practical and innovative concept drift detection algorithms, contributing significantly to both scientific research and practical applications in the field