Efficient and Direct Inference of Heart Rate Variability using Both Signal Processing and Machine Learning

Heart Rate Variability (HRV) measures the variation of the time between consecutive heartbeats and is a major indicator of physical and mental health. Recent research has demonstrated that photoplethysmography (PPG) sensors can be used to infer HRV. However, many prior studies had high errors because they only employed signal processing or machine learning (ML), or because they indirectly inferred HRV, or because there lacks large training datasets. Many prior studies may also require large ML models. The low accuracy and large model sizes limit their applications to small embedded devices and potential future use in healthcare. To address the above issues, we first collected a large dataset of PPG signals and HRV ground truth. With this dataset, we developed HRV models that combine signal processing and ML to directly infer HRV. Evaluation results show that our method had errors between 3.5% to 25.7% and outperformed signal-processing-only and ML-only methods. We also explored different ML models, which showed that Decision Trees and Multi-level Perceptrons have 13.0% and 9.1% errors on average with models at most hundreds of KB and inference time less than 1ms. Hence, they are more suitable for small embedded devices and potentially enable the future use of PPG-based HRV monitoring in healthcare

PPG-based Heart Rate Estimation with Efficient Sensor Sampling and Learning Models

Recent studies showed that Photoplethysmography (PPG) sensors embedded in wearable devices can estimate heart rate (HR) with high accuracy. However, despite of prior research efforts, applying PPG sensor based HR estimation to embedded devices still faces challenges due to the energy-intensive high-frequency PPG sampling and the resource-intensive machine-learning models. In this work, we aim to explore HR estimation techniques that are more suitable for lower-power and resource-constrained embedded devices. More specifically, we seek to design techniques that could provide high-accuracy HR estimation with low-frequency PPG sampling, small model size, and fast inference time. First, we show that by combining signal processing and ML, it is possible to reduce the PPG sampling frequency from 125 Hz to only 25 Hz while providing higher HR estimation accuracy. This combination also helps to reduce the ML model feature size, leading to smaller models. Additionally, we present a comprehensive analysis on different ML models and feature sizes to compare their accuracy, model size, and inference time. The models explored include Decision Tree (DT), Random Forest (RF), K-nearest neighbor (KNN), Support vector machines (SVM), and Multi-layer perceptron (MLP). Experiments were conducted using both a widely-utilized dataset and our self-collected dataset. The experimental results show that our method by combining signal processing and ML had only 5% error for HR estimation using low-frequency PPG data. Moreover, our analysis showed that DT models with 10 to 20 input features usually have good accuracy, while are several magnitude smaller in model sizes and faster in inference time

A stilbene synthase allele from a Chinese wild grapevine confers resistance to powdery mildew by recruiting salicylic acid signalling for efficient defence

Stilbenes are central phytoalexins in Vitis, and induction of the key enzyme stilbene synthase (STS) is pivotal for disease resistance. Here, we address the potential for breeding resistance using an STS allele isolated from Chinese wild grapevine Vitis pseudoreticulata (VpSTS) by comparison with its homologue from Vitis vinifera cv. ‘Carigane’ (VvSTS). Although the coding regions of both alleles are very similar (>99% identity on the amino acid level), the promoter regions are significantly different. By expression in Arabidopsis as a heterologous system, we show that the allele from the wild Chinese grapevine can confer accumulation of stilbenes and resistance against the powdery mildew Golovinomyces cichoracearum, whereas the allele from the vinifera cultivar cannot. To dissect the upstream signalling driving the activation of this promoter, we used a dual-luciferase reporter system in a grapevine cell culture. We show elevated responsiveness of the promoter from the wild grape to salicylic acid (SA) and to the pathogen-associated molecular pattern (PAMP) flg22, equal induction of both alleles by jasmonic acid (JA), and a lack of response to the cell death-inducing elicitor Harpin. This elevated SA response of the VpSTS promoter depends on calcium influx, oxidative burst by RboH, mitogen-activated protein kinase (MAPK) signalling, and JA synthesis. We integrate the data in the context of a model where the resistance of V. pseudoreticulata is linked to a more efficient recruitment of SA signalling for phytoalexin synthesis

Data Distribution Dynamics in Real-World WiFi-Based Patient Activity Monitoring for Home Healthcare

This paper examines the application of WiFi signals for real-world monitoring of daily activities in home healthcare scenarios. While the state-of-the-art of WiFi-based activity recognition is promising in lab environments, challenges arise in real-world settings due to environmental, subject, and system configuration variables, affecting accuracy and adaptability. The research involved deploying systems in various settings and analyzing data shifts. It aims to guide realistic development of robust, context-aware WiFi sensing systems for elderly care. The findings suggest a shift in WiFi-based activity sensing, bridging the gap between academic research and practical applications, enhancing life quality through technology

Minor clone of del(17p) provides a reservoir for relapse in multiple myeloma

The deletion of chromosome 17p (del(17p)) is considered a crucial prognostic factor at the time of diagnosis in patients with multiple myeloma (MM). However, the impact of del(17p) on survival at different clonal sizes at relapse, as well as the patterns of clonal evolution between diagnosis and relapse and their prognostic value, has not been well described. To address these issues, we analyzed the interphase fluorescence in situ hybridization (iFISH) results of 995 newly diagnosed MM (NDMM) patients and 293 patients with MM at their first relapse. Among these patients, 197 had paired iFISH data at diagnosis and first relapse. Our analysis of paired iFISH revealed that a minor clone of del(17p) at relapse but not at diagnosis was associated with poor prognosis in MM (hazard ratio for median overall survival 1.64 vs. 1.44). Fifty-six and 12 patients developed one or more new cytogenetic abnormalities at relapse, mainly del(17p) and gain/amp(1q), respectively. We classified the patients into six groups based on the change patterns in the clonal size of del(17p) between the two time points. Patients who did not have del(17p) during follow-up showed the best outcomes, whereas those who acquired del(17p) during their disease course, experienced compromised survival (median overall survival: 61.3 vs. 49.4 months; hazard ratio =1.64; 95% confidence interval: 1.06-2.56; P<0.05). In conclusion, our data confirmed the adverse impact of a minor clone of del(17p) at relapse and highlighted the importance of designing optimal therapeutic strategies to eliminate high-risk cytogenetic abnormalities (clinicaltrials gov. identifier: NCT04645199)

Chaotic motion behaviors of liquid crystal elastomer pendulum under periodic illumination

The active material of liquid crystal elastomer (LCE) is capable of harvesting energy directly from the surroundings and sustaining continuous motion in the presence of light and heat. It is extensively employed in active machinery, soft robotics, biomedicine, and other fields. To date, there is barely any research on the sustained chaotic motion system of LCE pendulum. The main objective of this paper is to put forward a sustained motion system of a simple pendulum comprising photosensitive LCE. In accordance with the LCE dynamic model, a nonlinear dynamic model of the LCE simple pendulum is established, and its motion behavior characteristics under periodic illumination are examined. The numerical outcomes demonstrate that apart from the in-situ vibration mode, the LCE pendulum experiences two types of displacement motion modes as well, namely periodic oscillation mode and chaotic motion mode. The mechanism underlying the sustained periodic oscillation and chaotic motion is revealed by compensating for the damping dissipation with work done by the LCE contraction. Moreover, the discussion also covers how the system parameters affect the motion modes of the LCE simple pendulum. Through altering the parameters such as illumination period, contraction coefficient, light intensity, damping coefficient and gravitational acceleration, it is possible to realize the distinct motion modes of the LCE simple pendulum. This research may deepen the comprehension on the motion behavior of the simple pendulum, and provide scientific guidance for the design and exploration of chaotic systems based on active materials

Exosomal LncRNAs in Gastrointestinal Cancer: Biological Functions and Emerging Clinical Applications

Due to the lack of specific and effective biomarkers and therapeutic targets, the early diagnosis and treatment of gastrointestinal cancer remain unsatisfactory. As a type of nanosized vesicles derived from living cells, exosomes mediate cell-to-cell communication by transporting bioactive molecules, thus participating in the regulation of many pathophysiological processes. Recent evidence has revealed that several long non-coding RNAs (lncRNAs) are enriched in exosomes. Exosomes-mediated lncRNAs delivery is critically involved in various aspects of gastrointestinal cancer progression, such as tumor proliferation, metastasis, angiogenesis, stemness, immune microenvironment, and drug resistance. Exosomal lncRNAs represent promising candidates to act as the diagnosis biomarkers and anti-tumor targets. This review introduces the major characteristics of exosomes and lncRNAs and describes the biological functions of exosomal lncRNAs in gastrointestinal cancer development. The preclinical studies on using exosomal lncRNAs to monitor and treat gastrointestinal cancer are also discussed, and the opportunities and challenges for translating them into clinical practice are evaluated

Exosomal LncRNAs in Gastrointestinal Cancer: Biological Functions and Emerging Clinical Applications

Due to the lack of specific and effective biomarkers and therapeutic targets, the early diagnosis and treatment of gastrointestinal cancer remain unsatisfactory. As a type of nanosized vesicles derived from living cells, exosomes mediate cell-to-cell communication by transporting bioactive molecules, thus participating in the regulation of many pathophysiological processes. Recent evidence has revealed that several long non-coding RNAs (lncRNAs) are enriched in exosomes. Exosomes-mediated lncRNAs delivery is critically involved in various aspects of gastrointestinal cancer progression, such as tumor proliferation, metastasis, angiogenesis, stemness, immune microenvironment, and drug resistance. Exosomal lncRNAs represent promising candidates to act as the diagnosis biomarkers and anti-tumor targets. This review introduces the major characteristics of exosomes and lncRNAs and describes the biological functions of exosomal lncRNAs in gastrointestinal cancer development. The preclinical studies on using exosomal lncRNAs to monitor and treat gastrointestinal cancer are also discussed, and the opportunities and challenges for translating them into clinical practice are evaluated