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Abstract To prevent immediate mortality in patients with a tracheostomy tube, it is essential to ensure timely suctioning or replacement of the tube. Breathing sounds at the entrance of tracheostomy tubes were recorded with a microphone and analyzed using a spectrogram to detect airway problems. The sounds were classified into three categories based on the waveform of the spectrogram according to the obstacle status: normal breathing sounds (NS), vibrant breathing sounds (VS) caused by movable obstacles, and sharp breathing sounds (SS) caused by fixed obstacles. A total of 3950 breathing sounds from 23 patients were analyzed. Despite neither the patients nor the medical staff recognizing any airway problems, the number and percentage of NS, VS, and SS were 1449 (36.7%), 1313 (33.2%), and 1188 (30.1%), respectively. Artificial intelligence (AI) was utilized to automatically classify breathing sounds. MobileNet and Inception_v3 exhibited the highest sensitivity and specificity scores of 0.9441 and 0.9414, respectively. When classifying into three categories, ResNet_50 showed the highest accuracy of 0.9027, and AlexNet showed the highest accuracy of 0.9660 in abnormal sounds. Classifying breathing sounds into three categories is very useful in deciding whether to suction or change the tracheostomy tubes, and AI can accomplish this with high accuracy.

Abstract Genomic DNA (gDNA) undergoes structural interconversion between single- and double-stranded states during transcription, DNA repair and replication, which is critical for cellular homeostasis. We describe “CHEX-seq” which identifies the single-stranded DNA (ssDNA) in situ in individual cells. CHEX-seq uses 3’-terminal blocked, light-activatable probes to prime the copying of ssDNA into complementary DNA that is sequenced, thereby reporting the genome-wide single-stranded chromatin landscape. CHEX-seq is benchmarked in human K562 cells, and its utilities are demonstrated in cultures of mouse and human brain cells as well as immunostained spatially localized neurons in brain sections. The amount of ssDNA is dynamically regulated in response to perturbation. CHEX-seq also identifies single-stranded regions of mitochondrial DNA in single cells. Surprisingly, CHEX-seq identifies single-stranded loci in mouse and human gDNA that catalyze porphyrin metalation in vitro, suggesting a catalytic activity for genomic ssDNA. We posit that endogenous DNA enzymatic activity is a function of genomic ssDNA.

It is highly necessary that a barrier of virtual emotion detection for surveillance reinforcement aims to fit with multi districts instead of only single area. This paper proposes a trustworthy clash-free surveillance framework using virtual emotion detection with 6G-enabled multi districts. Then, we formally define a problem with ILP representations. Its goal is to minimize the total moving trajectory of system members including mobile robots and UAVs such that trustworthy clash-free security barrier by low-cost moving strategy is created in the requested 6G-enabled districts. To solve the problem, we devise two different schemes and evaluate their outcomes through extensive simulations.

Abstract Abnormal voice may identify those at risk of post-stroke aspiration. This study was aimed to determine whether machine learning algorithms with voice recorded via a mobile device can accurately classify those with dysphagia at risk of tube feeding and post-stroke aspiration pneumonia and be used as digital biomarkers. Voice samples from patients referred for swallowing disturbance in a university-affiliated hospital were collected prospectively using a mobile device. Subjects that required tube feeding were further classified to high risk of respiratory complication, based on the voluntary cough strength and abnormal chest x-ray images. A total of 449 samples were obtained, with 234 requiring tube feeding and 113 showing high risk of respiratory complications. The eXtreme gradient boosting multimodal models that included abnormal acoustic features and clinical variables showed high sensitivity levels of 88.7% (95% CI 82.6–94.7) and 84.5% (95% CI 76.9–92.1) in the classification of those at risk of tube feeding and at high risk of respiratory complications; respectively. In both cases, voice features proved to be the strongest contributing factors in these models. Voice features may be considered as viable digital biomarkers in those at risk of respiratory complications related to post-stroke dysphagia.

This paper introduces an efficient barrier model for enhancing smart building surveillance in harsh environment with thin walls and structures. After the main research problem of minimizing the total number of wall-recognition surveillance barriers, we propose two distinct algorithms, Centralized Node Deployment and Adaptation Node Deployment, which are designed to address the challenge by strategic placement of surveillance nodes within the smart building. The Centralized Node Deployment aligns nodes along the thin walls, ensuring consistent communication coverage and effectively countering potential disruptions. Conversely, the Adaptation Node Deployment begins with random node placement, which adapts over time to ensure efficient communication across the building. The novelty of this work is in designing a novel barrier system to achieve energy efficiency and reinforced surveillance in a thin-wall environment. Instead of a real environment, we use an ad hoc server for simulations with various scenarios and parameters. Then, two different algorithms are executed through those simulation environments and settings. Also, with detailed discussions, we provide the performance analysis, which shows that both algorithms deliver similar performance metrics over extended periods, indicating their suitability for long-term operation in smart infrastructure.

Pericytes are mesenchymal-derived mural cells that wrap around capillaries and directly contact endothelial cells. Present throughout the body, including the cardiovascular system, pericytes are proposed to have multipotent cell-like properties and are involved in numerous biological processes, including regulation of vascular development, maturation, permeability, and homeostasis. Despite their physiological importance, the functional heterogeneity, differentiation process, and pathological roles of pericytes are not yet clearly understood, in part due to the inability to reliably distinguish them from other mural cell populations. Our study focused on identifying pericyte-specific markers by analyzing single-cell RNA sequencing data from tissue-specific mouse pericyte populations generated by the Tabula Muris Senis. We identified the mural cell cluster in murine lung, heart, kidney, and bladder that expressed either of two known pericyte markers, Cspg4 or Pdgfrb. We further defined pericytes as those cells that co-expressed both markers within this cluster. Single-cell differential expression gene analysis compared this subset with other clusters that identified potential pericyte marker candidates, including Kcnk3 (in the lung); Rgs4 (in the heart); Myh11 and Kcna5 (in the kidney); Pcp4l1 (in the bladder); and Higd1b (in lung and heart). In addition, we identified novel markers of tissue-specific pericytes and signaling pathways that may be involved in maintaining their identity. Moreover, the identified markers were further validated in Human Lung Cell Atlas and human heart single-cell RNAseq databases. Intriguingly, we found that markers of heart and lung pericytes in mice were conserved in human heart and lung pericytes. In this study, we, for the first time, identified specific pericyte markers among lung, heart, kidney, and bladder and reveal differentially expressed genes and functional relationships between mural cells.

Barriers are utilized for various tasks in security, environmental monitoring, penetration detection and reconnaissance. It is highly necessary to consider how to support pedestrian tracking and target detection in theme park areas having multiple obstacles. In this paper, we create security barriers through cooperation between mobile robots and UAVs for use in theme park areas where multiple obstacles of undetermined forms are placed. We formally define the problem and the goals. The goals are the following: to maximize the number of convex hull obstacle-aware tracking barriers using mobile robots and UAVs, to satisfy given detection accuracy, and to ensure that all environments are protected by convex hull obstacle-aware tracking barriers without disturbance from irregular obstacles. To address the problem, we propose two different algorithms, to improve security barriers and avoid various forms of obstacles, in a bid to work towards a 6G-enabled virtual emotion environment. Then, the proposed schemes are executed through simulations with various settings, and the numerical results evaluated with detailed discussions and demonstrations.

In this article, we introduce a cooperative obstacle-aware surveillance system for virtual emotion intelligence which is supported by low energy configuration with the minimal wasted communication cost in self-sustainable network with 6G components. We make a formal definition of the main research problem whose goal is to minimize the wasted communication range of system members on condition that the required detection accuracy with the given number of obstacles is satisfied when the requested number of obstacle-aware surveillance low energy barriers are built in self-sustainable network. To solve the problem, we have originally designed and implemented two different approaches, and then thoroughly evaluated them through extensive simulations. Then, their performances based on numerical outcomes are demonstrated with detailed discussions.

In this paper, we aim to envision 6G convergent terrestrial and non-terrestrial infrastructure of virtual emotion and epidemic prevention with two differential perspectives: Green AI and Red AI, where Green AI focuses on efficiency and reduction, and Red AI additionally pursues accuracy. By fitting with each perspective, we introduce promising key applications using smart devices, autonomous UAVs, mobile robots and subsequently suggest critical future research directions and opportunities toward new frontiers in intelligent terrestrial and non-terrestrial vehicular networks.

The substantial progress made in the field of stem cell-based therapy has shown its significant potential applications for the regeneration of defective tissues and organs. Although previous studies have yielded promising results, several limitations remain and should be overcome for translating stem cell-based therapies to clinics. As a possible solution to current bottlenecks, cell sheet engineering (CSE) is an efficient scaffold-free method for harvesting intact cell sheets without the use of proteolytic enzymes, and may be able to accelerate the adoption of stem cell-based treatments for damaged tissues and organs regeneration. CSE uses a temperature-responsive polymer-immobilized surface to form unique, scaffold-free cell sheets composed of one or more cell layers maintained with important intercellular junctions, cell-secreted extracellular matrices, and other important cell surface proteins, which can be achieved by changing the surrounding temperature. These three-dimensional cell sheet-based tissues can be designed for use in clinical applications to target-specific tissue regeneration. This review will highlight the principles, progress, and clinical relevance of current approaches in the cell sheet-based technology, focusing on stem cell-based therapies for bone, periodontal, skin, and vascularized muscles.