TSRNet: Simple Framework for Real-time ECG Anomaly Detection with Multimodal Time and Spectrogram Restoration Network

The electrocardiogram (ECG) is a valuable signal used to assess various aspects of heart health, such as heart rate and rhythm. It plays a crucial role in identifying cardiac conditions and detecting anomalies in ECG data. However, distinguishing between normal and abnormal ECG signals can be a challenging task. In this paper, we propose an approach that leverages anomaly detection to identify unhealthy conditions using solely normal ECG data for training. Furthermore, to enhance the information available and build a robust system, we suggest considering both the time series and time-frequency domain aspects of the ECG signal. As a result, we introduce a specialized network called the Multimodal Time and Spectrogram Restoration Network (TSRNet) designed specifically for detecting anomalies in ECG signals. TSRNet falls into the category of restoration-based anomaly detection and draws inspiration from both the time series and spectrogram domains. By extracting representations from both domains, TSRNet effectively captures the comprehensive characteristics of the ECG signal. This approach enables the network to learn robust representations with superior discrimination abilities, allowing it to distinguish between normal and abnormal ECG patterns more effectively. Furthermore, we introduce a novel inference method, termed Peak-based Error, that specifically focuses on ECG peaks, a critical component in detecting abnormalities. The experimental result on the large-scale dataset PTB-XL has demonstrated the effectiveness of our approach in ECG anomaly detection, while also prioritizing efficiency by minimizing the number of trainable parameters. Our code is available at https://github.com/UARK-AICV/TSRNet.Comment: Accepted at ISBI 202

SAM3D: Segment Anything Model in Volumetric Medical Images

Image segmentation remains a pivotal component in medical image analysis, aiding in the extraction of critical information for precise diagnostic practices. With the advent of deep learning, automated image segmentation methods have risen to prominence, showcasing exceptional proficiency in processing medical imagery. Motivated by the Segment Anything Model (SAM)-a foundational model renowned for its remarkable precision and robust generalization capabilities in segmenting 2D natural images-we introduce SAM3D, an innovative adaptation tailored for 3D volumetric medical image analysis. Unlike current SAM-based methods that segment volumetric data by converting the volume into separate 2D slices for individual analysis, our SAM3D model processes the entire 3D volume image in a unified approach. Extensive experiments are conducted on multiple medical image datasets to demonstrate that our network attains competitive results compared with other state-of-the-art methods in 3D medical segmentation tasks while being significantly efficient in terms of parameters. Code and checkpoints are available at https://github.com/UARK-AICV/SAM3D.Comment: Accepted at ISBI 202

M^2UNet: MetaFormer Multi-scale Upsampling Network for Polyp Segmentation

Polyp segmentation has recently garnered significant attention, and multiple methods have been formulated to achieve commendable outcomes. However, these techniques often confront difficulty when working with the complex polyp foreground and their surrounding regions because of the nature of convolution operation. Besides, most existing methods forget to exploit the potential information from multiple decoder stages. To address this challenge, we suggest combining MetaFormer, introduced as a baseline for integrating CNN and Transformer, with UNet framework and incorporating our Multi-scale Upsampling block (MU). This simple module makes it possible to combine multi-level information by exploring multiple receptive field paths of the shallow decoder stage and then adding with the higher stage to aggregate better feature representation, which is essential in medical image segmentation. Taken all together, we propose MetaFormer Multi-scale Upsampling Network (M$^2$UNet) for the polyp segmentation task. Extensive experiments on five benchmark datasets demonstrate that our method achieved competitive performance compared with several previous methods

Urinary catecholamine excretion, cardiovascular variability, and outcomes in tetanus

Severe tetanus is characterized by muscle spasm and cardiovascular system disturbance. The pathophysiology of muscle spasm is relatively well understood and involves inhibition of central inhibitory synapses by tetanus toxin. That of cardiovascular disturbance is less clear, but is believed to relate to disinhibition of the autonomic nervous system. The clinical syndrome of autonomic nervous system dysfunction (ANSD) seen in severe tetanus is characterized principally by changes in heart rate and blood pressure which have been linked to increased circulating catecholamines. Previous studies have described varying relationships between catecholamines and signs of ANSD in tetanus, but are limited by confounders and assays used. In this study, we aimed to perform detailed characterization of the relationship between catecholamines (adrenaline and noradrenaline), cardiovascular parameters (heart rate and blood pressure) and clinical outcomes (ANSD, mechanical ventilation required, and length of intensive care unit stay) in adults with tetanus, as well as examine whether intrathecal antitoxin administration affected subsequent catecholamine excretion. Noradrenaline and adrenaline were measured by ELISA from 24-h urine collections taken on day 5 of hospitalization in 272 patients enrolled in a 2 × 2 factorial-blinded randomized controlled trial in a Vietnamese hospital. Catecholamine results measured from 263 patients were available for analysis. After adjustment for potential confounders (i.e., age, sex, intervention treatment, and medications), there were indications of non-linear relationships between urinary catecholamines and heart rate. Adrenaline and noradrenaline were associated with subsequent development of ANSD, and length of ICU stay

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

A cross-linguistic approach to analysing cohesive devices in expository writing by Asian EFL teachers

Recent research has shown that cohesive devices contribute substantially to writing quality and reveal L2 writers’ information, such as first or native language. This quantitative study adopted a cross-linguistic approach to investigating the use of cohesive devices in expository writing by Asian EFL teachers compared to native English teachers. The study involved 80 participants from three different L1 backgrounds, including 28 Vietnamese, 26 Filipino, and 26 native English teachers. The Vietnamese and Filipino teachers were at a comparable level of English proficiency. Around 800 words were randomly selected from each participant’s reports for analysis. Results from One-Way ANOVA showed that the Vietnamese and Filipino teachers used a similar pattern of cohesion with lexical cohesion being used most frequently, followed by reference and conjunction. In contrast, the cohesive devices used by the native English teachers dispersed more widely across five main categories. The results further showed that the native English teachers used more cohesive devices in writing than the Vietnamese and Filipino teachers separately. Results from the Independent-Samples T-test showed omission and redundancy to be the two most common error types by both the Vietnamese and Filipino teachers. The study has implications for L2 writing and pedagogy

Post-harvest preservation of green grapes utilizing 405 nm light emitting diode

The public is increasingly concerned about the safety of fruit, so the production and preservation of high-quality fresh fruit is becoming more and more important. This work evaluated the use of a 405 nm light emitting diode (LED) for postharvest preservation of fresh green grapes under tropical temperatures of 29 ± 1 °C. LED exposure to green grapes was done for various lengths of time (0, 24, and 48 hours), corresponding to different dosages of 0, 0.7, and 1.4 kJ/cm2. After 7 days of testing, the results found that 95%, 75%, and 5% of the grapes had been invaded by microorganisms, which matched the pretreatment with LED light at 0 h, 24h, and 48 h. According to the first-order kinetic model, the constant rate of weight loss (k) was 0.039 ± 0.005 (control, LED-0h), 0.037 ± 0.009 (LED-24h), 0.062 ± 0.005 (day−1) (LED-48h), with all p-value < 0.001. Compared to the control condition, weight loss was mostly unaffected by LED-24h. No appreciable color changes were seen for LED-24h and 48h, indicating that the fruit has retained its original color as well as its freshness. In control samples, however, one-third of L* and a* values were reduced from baseline. The control samples are susceptible to fungus infection like Guignardia bidwellii, however, color development and morphology showed that LED-48h with an irradiation dose of 1.4 kJ/cm2 had outstanding anti-fungal effectiveness. These LEDs are a possible replacement for fluorescent lights in fruit market showcase and storage rooms, serving as both a source of illumination and protection against microbial contamination

Uplink and downlink of energy harvesting NOMA system: performance analysis

ABSTRACTIn this paper, we consider an uplink non-orthogonal multiple access (NOMA) for energy harvesting at the base station or access point in the wireless system. By exploiting energy enough, a base station or access point can serve many users at the downlink. The fixed power allocation factors are adopted, and the power splitting energy harvesting protocol brings many benefits to both the uplink and downlink of a wireless system. The closed-form expressions of outage probability are investigated and examined in a group of two users. Moreover, the optimal outage probability for two users is shown numerically. Finally, Monte Carlo simulations are presented to further support the validity of our framework and findings

Path planning for reconfigurable hTetro robot combining heat conduction-based and discrete optimization

Self-reconfigurable robots present advanced solutions for various automation applications in domains, e.g., planetary exploration, rescue missions, cleaning, and maintenance. These robots have the ability to change their morphology according to given requirements or adapt to new circumstances, which, for example, can overcome constraints while navigating within a working environment. However, the autonomous navigation of self-reconfigurable robots is more complex than that of robots with fixed shape because of the intrinsic complexity of robot motions, especially in complicated obstacle environments. To address this challenge, we present a novel path planning method for reconfigurable robots in this study. The technique is inspired by the similarity between a robot motion path and a heat conduction path at the steady-state. In the heat transfer analysis domain, feasible moving locations are modeled as materials with high conductivity, while obstacles are considered thermal insulators, and the initial and destination positions are assigned as heat sink and heat source, respectively. The temperature profile and gradient calculated by finite element analysis are used to indicate the possible moving directions from the heat sink to the heat source. Based on the temperature gradient ascent, a step-wise conductivity reaching algorithm is developed to optimize robot paths using customized multi-objective functions that take the costs of morphology changes, path smoothness, and safety into account. The proposed path planning method is successfully applied to the hinged-tetro self-reconfigurable robot and demonstrated on several virtual environments and a real-world testbed environment

Path planning for reconfigurable hTetro robot combining heat conduction-based and discrete optimization

Self-reconfigurable robots present advanced solutions for various automation applications in domains, e.g., planetary exploration, rescue missions, cleaning, and maintenance. These robots have the ability to change their morphology according to given requirements or adapt to new circumstances, which, for example, can overcome constraints while navigating within a working environment. However, the autonomous navigation of self-reconfigurable robots is more complex than that of robots with fixed shape because of the intrinsic complexity of robot motions, especially in complicated obstacle environments. To address this challenge, we present a novel path planning method for reconfigurable robots in this study. The technique is inspired by the similarity between a robot motion path and a heat conduction path at the steady-state. In the heat transfer analysis domain, feasible moving locations are modeled as materials with high conductivity, while obstacles are considered thermal insulators, and the initial and destination positions are assigned as heat sink and heat source, respectively. The temperature profile and gradient calculated by finite element analysis are used to indicate the possible moving directions from the heat sink to the heat source. Based on the temperature gradient ascent, a step-wise conductivity reaching algorithm is developed to optimize robot paths using customized multi-objective functions that take the costs of morphology changes, path smoothness, and safety into account. The proposed path planning method is successfully applied to the hinged-tetro self-reconfigurable robot and demonstrated on several virtual environments and a real-world testbed environment