Miracle-dz/UniFAR-FTUs huggingface dataset:
SimCLR-Unet: An ECG Feature wave segmentation algorithm based on a self-supervised learning strategy

With the technological progress in the field of remote health monitoring and the wide application of wearable ECG monitoring equipment, it is necessary to develop ECG model to analyze the massive ECG data in real time, and the segmentation of ECG signal feature wave is the primary work of the automatic diagnosis system of heart disease. The traditional machine learning algorithm relies too much on the empirical parameters and the feature engineering to segment the feature waves, and the robustness is poor. The emergence of deep learning automates the segmentation process in medical imaging, but it requires the use of a large amount of labeled data to train the model. The labeling of such a large amount of data requires professional biomedical analysts, which is not only cumbersome and expensive, but also easy to be affected by the subjectivity of ECG experts. To address these problems, this paper proposes a self-supervised learning algorithm (SimCLR-Unet) for ECG feature wave segmentation, and experiments on LUDB show that the proposed algorithm is better than other state-of-the-art supervised segmentation methods in feature wave detection accuracy. In particular, the F1-score with only 25% of the labeled data for model tuning and used to detect initiation of P and T waves and QRS waves was at least 97.78%, 98.97%, and 99.98%, respectively.
InstruMentAR: Auto-Generation of Augmented Reality Tutorials for Operating Digital Instruments Through Recording Embodied Demonstration

Augmented Reality tutorials, which provide necessary context by directly superimposing visual guidance on the physical referent, represent an effective way of scaffolding complex instrument operations. However, current AR tutorial authoring processes are not seamless as they require users to continuously alternate between operating instruments and interacting with virtual elements. We present InstruMentAR, a system that automatically generates AR tutorials through recording user demonstrations. We design a multimodal approach that fuses gestural information and hand-worn pressure sensor data to detect and register the user’s step-by-step manipulations on the control panel. With this information, the system autonomously generates virtual cues with designated scales to respective locations for each step. Voice recognition and background capture are employed to automate the creation of text and images as AR content. For novice users receiving the authored AR tutorials, we facilitate immediate feedback through haptic modules. We compared InstruMentAR with traditional systems in the user study.
Understanding the Online Social Support Dynamics for Postpartum Depression

Postpartum depression (PPD) is one of the most prevalent mental health disorder following childbirth. Mothers utilize social media and online forums throughout postpartum to seek mental health support. In this paper, we aim to gain a comprehensive understanding of the topics of naturally occurring online discussions and the online social support dynamics associated with PPD. We qualitatively analyze posts and comments from three postpartum depression support communities corresponding to well-known online social platforms (Reddit, What to expect, and Babycenter). We discovered that the three communities share common themes of discussion that include the causes, stressors, symptoms, and coping mechanisms for PPD. Seeking both informational and emotional support through venting and storytelling was prevalent, and emotional support seeking was more common in Reddit than the other two communities. We provide recommendations for future postpartum depression research and design considerations based on pervasive help-seeking approaches.
Key Considerations for The Design of Technology for Enrichment in Residential Aged Care: An Ethnographic Study

Technology can be valuable for providing social and emotional enrichment for people living in residential aged care, but its use is difficult to sustain because of the complexity of the aged care environment. This paper aims to advance understanding of care environments to inform the sensitive design of technologies for social benefit in those settings. We conducted an ethnographic study in which the researcher assisted a care home's leisure and lifestyle team over four weeks. This included supporting the use of video-calling for social connectedness. A decrease in the use of video-calling and an absence of other technology for enrichment in the setting were observed. This paper identifies barriers to effective use of technology for enrichment and the interplay of cultural factors. For technology to best support residents and staff, we recommend key design considerations that target cultural, psychological and physical support and that ensure technologies, when used appropriately, better fit within the environment.
Method for Creating a Computer Agent Based on the Jordan-Elman Neural Network for Supply Chains

The paper proposes a method for creating a computer agent for supply chains. The novelty of the research lies in the fact that to increase the efficiency of the computer agent, its functioning is based on the connectionist approach instead of using the classical production and logical approach. To expand the range of tasks solved by agents, the article proposes a reactive agent with feedback, which makes a decision based on perception or a sequence of perceptions and a previous action or a sequence of previous actions, as well as a reactive agent with an internal state and feedback, which is an extension of the reactive agent with an internal state and makes a decision based on perception, previous internal state, and previous action. For a reactive agent with an internal state and feedback, a Jordan-Elman artificial neural network was proposed, which is a combination of Jordan and Elman neural networks, and the structure of its model was determined in the course of a numerical study. The experiments performed showed that when the number of hidden neurons is not less than the number of neurons in the input layer, the value of the root mean square error does not change significantly, and the selected network gives results with a minimum error. Methods for determining the parameters of the proposed Jordan-Elman neural network model were proposed. This made it possible to ensure high speed and accuracy of calculations based on the model. The proposed method for creating an agent based on artificial neural networks can be used in various intelligent computer systems that use multi-agent interaction.
Object Sorting in Chimpanzees

In three experiments, five chimpanzees were given an object-sorting task to assess their categorizing ability. In preliminary training, chimpanzees were presented a set of two identical objects (AA) and one odd object (B). They were trained to sort the objects in two categories, AA and B; four kinds of objects were used in this training. All the chimpanzees were able to learn this type of sorting easily. In Exp. 1, 2, and 3, test stimulus sets were inserted in random positions among the sets of training. In test trials, the test sets differed for each trial, and the chimpanzees always received food regardless of how they sorted. In Exp. 1, test sets were novel objects for the subjects. In Exp. 2, the objects in each test set were identical either in color or in shape, and could be sorted on the basis of the other dimension. In Exp. 3, the objects in each test set were different one another, and could be sorted on the basis of either color or shape. In test trials of the experiments, chimpanzees sorted categorically and their performance were highly significant. The implications of these results and the utility of the object-sorting task are discussed.
Methods for assessment of the tumour microenvironment and immune interactions in non-small cell lung cancer. A narrative review

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer death worldwide. Immunotherapy with immune checkpoint inhibitors (ICI) has significantly improved outcomes in some patients, however 80-85% of patients receiving immunotherapy develop primary resistance, manifesting as a lack of response to therapy. Of those that do have an initial response, disease progression may occur due to acquired resistance. The make-up of the tumour microenvironment (TME) and the interaction between tumour infiltrating immune cells and cancer cells can have a large impact on the response to immunotherapy. Robust assessment of the TME with accurate and reproducible methods is vital to understanding mechanisms of immunotherapy resistance. In this paper we will review the evidence of several methodologies to assess the TME, including multiplex immunohistochemistry, imaging mass cytometry, flow cytometry, mass cytometry and RNA sequencing.
Editorial: Factors influencing user engagement with digital mental health interventions

COPYRIGHT © 2023 Borghouts, Pretorius, Ayobi, Abdullah and Eikey. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Small molecule glucagon release inhibitors with activity in human islets

Purpose Type 1 diabetes (T1D) accounts for an estimated 5% of all diabetes in the United States, afflicting over 1.25 million individuals. Maintaining long-term blood glucose control is the major goal for individuals with T1D. In T1D, insulin-secreting pancreatic islet β-cells are destroyed by the immune system, but glucagon-secreting islet α-cells survive. These remaining α-cells no longer respond properly to fluctuating blood glucose concentrations. Dysregulated α-cell function contributes to hyper- and hypoglycemia which can lead to macrovascular and microvascular complications. To this end, we sought to discover small molecules that suppress α-cell function for their potential as preclinical candidate compounds. Prior high-throughput screening identified a set of glucagon-suppressing compounds using a rodent α-cell line model, but these compounds were not validated in human systems. Results Here, we dissociated and replated primary human islet cells and exposed them to 24 h treatment with this set of candidate glucagon-suppressing compounds. Glucagon accumulation in the medium was measured and we determined that compounds SW049164 and SW088799 exhibited significant activity. Candidate compounds were also counter-screened in our InsGLuc-MIN6 β-cell insulin secretion reporter assay. SW049164 and SW088799 had minimal impact on insulin release after a 24 h exposure. To further validate these hits, we treated intact human islets with a selection of the top candidates for 24 h. SW049164 and SW088799 significantly inhibited glucagon release into the medium without significantly altering whole islet glucagon or insulin content. In concentration-response curves SW088799 exhibited significant inhibition of glucagon release with an IC50 of 1.26 µM. Conclusion Given the set of tested candidates were all top hits from the primary screen in rodent α-cells, this suggests some conservation of mechanism of action between human and rodents, at least for SW088799. Future structure-activity relationship studies of SW088799 may aid in elucidating its protein target(s) or enable its use as a tool compound to suppress α-cell activity in vitro.
PI3K/AKT/mTOR pathway-derived risk score exhibits correlation with immune infiltration in uveal melanoma patients

Uveal melanoma (UVM) is a rare but highly aggressive intraocular tumor with a poor prognosis and limited therapeutic options. Recent studies have implicated the PI3K/AKT/mTOR pathway in the pathogenesis and progression of UVM. Here, we aimed to explore the potential mechanism of PI3K/AKT/mTOR pathway-related genes (PRGs) in UVM and develop a novel prognostic-related risk model. Using unsupervised clustering on 14 PRGs profiles, we identified three distinct subtypes with varying immune characteristics. Subtype A demonstrated the worst overall survival and showed higher expression of human leukocyte antigen, immune checkpoints, and immune cell infiltration. Further enrichment analysis revealed that subtype A mainly functioned in inflammatory response, apoptosis, angiogenesis, and the PI3K/AKT/mTOR signaling pathway. Differential analysis between different subtypes identified 56 differentially expressed genes (DEGs), with the major enrichment pathway of these DEGs associated with PI3K/AKT/mTOR. Based on these DEGs, we developed a consensus machine learning-derived signature (RSF model) that exhibited the best power for predicting prognosis among 76 algorithm combinations. The novel signature demonstrated excellent robustness and predictive ability for the overall survival of patients. Moreover, we observed that patients classified by risk scores had distinguishable immune status and mutation. In conclusion, our study identified a consensus machine learning-derived signature as a potential biomarker for prognostic prediction in UVM patients. Our findings suggest that this signature is correlated with tumor immune infiltration and may serve as a valuable tool for personalized therapy in the clinical setting.
Analgesic effects and metabolome analyses of laser- and electro-acupuncture combined therapies in paclitaxel-induced neuropathic pain model

Introduction Allodynia, which can be induced by paclitaxel administration, is the presence of pain as a result of a stimulus that does not usually provoke pain. Many studies have investigated the analgesic efficacy of acupuncture, including laser acupuncture (LA) and electroacupuncture (EA). Although pain-related diseases are relatively common, few studies have analyzed the analgesic effects and mechanisms of LA combined with EA. The purpose of this study was to investigate the therapeutic effect and mechanism of manual acupuncture (MA), EA, LA, and combined therapy (LA + EA) in a paclitaxel-induced allodynia rat model. Methods A total of 56 rats were classified into eight groups: a normal (Nor, n = 7), a control (Con, n = 7), an MA (n = 7), an EA (n = 7), a 650-nm LA (650LA, n = 7), an 830-nm LA (830LA, n = 7), a 650-nm LA combined with EA (650LA + EA, n = 7), and an 830-nm LA combined with EA group (830LA + EA, n = 7). Allodynia was induced by intraperitoneal injection of 2 mg/kg of paclitaxel every other day for a total of four times except the Nor group. Acupuncture treatments were conducted at the points of Jungwan (CV12) and Joksamni (ST36) once every other day for 6 min, for a total of nine times. Withdrawal response reaction times and force intensity of the foot were measured before the start of the experiment, after the 4th paclitaxel administration (day 8), and after the 9th and last treatment (day 15). On the 16th day, mRNA and protein expression in the spinal nerves was assessed, and a metabolome analysis of the animals’ feces was performed. Results and discussion Our analyses show that 650LA + EA treatment resulted in an upregulation of protein expression related to pain relief and nerve regeneration, whereas 830LA + EA treatment led to significant changes in metabolomes. This study demonstrates that a combination treatment of EA and LA can suppress allodynia and promote upregulation of protein expression related to nerve regeneration and is effective in changing the intestinal microbiome. Further large-scale research is required to assess the exact mechanism underlying the therapeutic effect of this combination treatment in pain-related diseases.
Editorial: Mobile and wearable systems for health monitoring

COPYRIGHT © 2023 Elgendi, Fletcher, Abbott, Zheng, Kyriacou and Menon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Youth pre-pandemic executive function relates to year one COVID-19 difficulties

Introduction The first year of the COVID-19 pandemic presented a series of stressors that could relate to psychological difficulties in children and adolescents. Executive functioning (EF) supports goal achievement and is associated with life success, and better outcomes following early life adversity. EF is also strongly related to processing speed, another predictor of life outcomes. Methods This longitudinal study examined 149 youths’ pre-pandemic EF and processing speed abilities as predictors of self-reported emotional, cognitive, and social experiences during the first year of the COVID-19 pandemic. EF and processing speed were measured with a total of 11 behavioral tasks. The COVID-era data was collected during two timepoints, during early (May-July 2020) and mid- (January-March of 2021) pandemic. Results Better pre-pandemic EF skills and processing speed abilities predicted more mid-COVID-19 pandemic emotional and cognitive difficulties. On the other hand, better switching (a subcomponent of EF) and processing speed abilities predicted more mid-pandemic social interactions. EF and processing speed abilities did not relate to the well-being reports from the initial months of the pandemic. Our EF - but not processing speed - results were largely maintained when controlling for pre-pandemic mental health burden, socioeconomic status (SES), and gender. Discussion Better cognitive abilities may have contributed to worse mid-pandemic functioning by supporting the meta-cognition needed for attending to the chaotic and ever-changing pandemic news and advice, leading to higher stress-induced worry and rumination. Our study highlights a potential downside of higher EF – often largely viewed as a protective factor - in youth.
The molecular mechanisms in prenatal drug exposure-induced fetal programmed adult cardiovascular disease

The “developmental origins of health and disease” (DOHaD) hypothesis posits that early-life environmental exposures have a lasting impact on individual’s health and permanently shape growth, structure, and metabolism. This reprogramming, which results from fetal stress, is believed to contribute to the development of adulthood cardiovascular diseases such as hypertension, coronary artery disease, heart failure, and increased susceptibility to ischemic injuries. Recent studies have shown that prenatal exposure to drugs, such as glucocorticoids, antibiotics, antidepressants, antiepileptics, and other toxins, increases the risk of adult-onset cardiovascular diseases. In addition, observational and animal experimental studies have demonstrated the association between prenatal drug exposure and the programming of cardiovascular disease in the offspring. The molecular mechanisms underlying these effects are still being explored but are thought to involve metabolism dysregulation. This review summarizes the current evidence on the relationship between prenatal drug exposure and the risk of adult cardiovascular disorders. Additionally, we present the latest insights into the molecular mechanisms that lead to programmed cardiovascular phenotypes after prenatal drug exposure.
Metformin acts as a dual glucose regulator in mouse brain

Aims: Metformin improves glucose regulation through various mechanisms in the periphery. Our previous study revealed that oral intake of metformin activates several brain regions, including the hypothalamus, and directly activates hypothalamic S6 kinase in mice. In this study, we aimed to identify the direct effects of metformin on glucose regulation in the brain. Materials and methods: We investigated the role of metformin in peripheral glucose regulation by directly administering metformin intracerebroventricularly in mice. The effect of centrally administered metformin (central metformin) on peripheral glucose regulation was evaluated by oral or intraperitoneal glucose, insulin, and pyruvate tolerance tests. Hepatic gluconeogenesis and gastric emptying were assessed to elucidate the underlying mechanisms. Liver-specific and systemic sympathetic denervation were performed. Results: Central metformin improved the glycemic response to oral glucose load in mice compared to that in the control group, and worsened the response to intraperitoneal glucose load, indicating its dual role in peripheral glucose regulation. It lowered the ability of insulin to decrease serum glucose levels and worsened the glycemic response to pyruvate load relative to the control group. Furthermore, it increased the expression of hepatic G6pc and decreased the phosphorylation of STAT3, suggesting that central metformin increased hepatic glucose production. The effect was mediated by sympathetic nervous system activation. In contrast, it induced a significant delay in gastric emptying in mice, suggesting its potent role in suppressing intestinal glucose absorption. Conclusion: Central metformin improves glucose tolerance by delaying gastric emptying through the brain-gut axis, but at the same time worsens it by increasing hepatic glucose production via the brain-liver axis. However, with its ordinary intake, central metformin may effectively enhance its glucose-lowering effect through the brain-gut axis, which could surpass its effect on glucose regulation via the brain-liver axis.
Static-Voltage Gauge

1 Problem Show that it is also possible to (re)define the scalar potential V of electrodynamics to have no time dependence, such that the time-varying part of the electric field is entirely due to the vector potential A. In electrostatics the electric field E can be related to a (static) scalar potential V according to E = −∇V 0 , (1) and inversely, V a − V b = − a b E · dl (2) expresses the fact that a unique voltage difference V a − V b can be defined for any pair of points a and b independent of the path of integration between them. The static electric field is said to be conservative, and eqs. (1)-(2) are equivalent to the vector calculus relation ∇ × E = 0. (3) In electrodynamics Faraday discovered (as later interpreted by Maxwell) that eq. (3) must be generalized to ∇ × E = − ∂B ∂t , (4) in SI units, which implies that time-dependent magnetic fields B lead to additional electric fields beyond those associated with the scalar potential V. The nonexistence (so far as we know) of isolated magnetic charges (monopoles) implies that ∇ · B = 0, (5) and hence that the magnetic field can be related to a vector potential A according to