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Abstract Motivated by the very recent observation of the $$B^+_c\rightarrow J/\psi +\pi ^+ +\pi ^0$$ B c + → J / ψ + π + + π 0 decay using proton–proton collision data by the LHCb collaboration, we study the four-body angular distributions and the quantum entanglement effects in the $$B^+_c\rightarrow J/\psi +\pi ^+ +\pi ^0$$ B c + → J / ψ + π + + π 0 associated with $$J/\psi \rightarrow \mu ^++\mu ^-.$$ J / ψ → μ + + μ - . The helicity angular distributions are given in the QCD effective theory and the von Neumann entropy is obtained in $$B^\pm _c\rightarrow J/\psi (\rightarrow \mu ^+\mu ^-)+\rho ^\pm (\rightarrow \pi ^\pm \pi ^0)$$ B c ± → J / ψ ( → μ + μ - ) + ρ ± ( → π ± π 0 ) decay process.

Background: Cervical cancer ranks the fourth most prevalent type of cancer worldwide, characterized by a notably low survival rate, particularly in its metastatic stage. Despite 5-aminolevulinic acid photodynamic therapy (ALA-PDT) demonstrating potential anti-tumor effects against cervical cancer, the intricate mechanisms underlying its efficacy necessitate further investigation. Here, the study aims to elucidate the impact of ALA-PDT on the cancer cell viability, invasion and migration, alongside delineating the underlying molecular mechanisms. Methods: Cervical cancer SiHa cells were subjected to ALA and red light irradiation, and we then measured the ALA-PDT's effects on cell functions using various assays. The potential interaction between miR-152–3p and JAK1 was explored through bioinformatics analyses and validated by dual-luciferase reporter assays. Post-transfection with miR-152–3p and JAK1 vectors, cellular functions were re-evaluated. The efficacy of ALA-PDT in tumor suppression was further investigated through tumor transplantation experiment in vivo. Results: ALA-PDT markedly suppressed SiHa cell viability, invasion and migration, impacting critical markers of proliferation, apoptosis, and epithelial-mesenchymal transition(EMT). And these effects were echoed by the inhibition of miR-152–3p. JAK1 was identified as a direct target of miR-152–3p, and ALA-PDT was found to regulate the expression levels of miR-152–3p, consequently influencing the JAK1/STAT1 signaling pathway. Augmentation of miR-152–3p expression and inhibition of the JAK1/STAT1 pathway mitigated the anti-cancer effects of ALA-PDT, whereas JAK1 overexpression diminished these effects. In vivo analyses demonstrated that ALA-PDT suppressed tumor growth and modulated the miR-152–3p/JAK1/STAT1 pathway expression. Conclusions: ALA-PDT inhibits the viability, invasion, and migration of cervical cancer SiHa cells by modulating the miR-152–3p/JAK1/STAT1 axis, offering a promising therapeutic avenue for combating invasive cervical cancer.

Zebrafish is a highly advantageous model animal for drug screening and toxicity evaluation thanks to its amenability to optical imaging (i.e., transparency), possession of organ structures similar to humans, and the ease with which disease models can be established. However, current zebrafish drug screening technologies and devices suffer from limitations such as low level of automation and throughput, and low accuracy caused by the heterogeneity among individual zebrafish specimens. To address these issues, we herein develop a high-throughput zebrafish drug screening system. This system is capable of maintaining optimal culturing conditions and simultaneously monitoring and analyzing the movement of 288 zebrafish larvae under various external conditions, such as drug combinations. Moreover, to eliminate the effect of heterogeneity, locomotion of participating zebrafish is assessed and grouped before experiments. It is demonstrated that in contrast to the experimental results without pre-selection, which shows ∼20 % damaged motor function (i.e., degree of attenuation), the drug-induced variations among zebrafish with equivalent mobility reaches ∼80 %. Overall, our high-throughput zebrafish drug screening system overcomes current limitations by improving automation, throughput, and accuracy, resulting in enhanced detection of drug-induced variations in zebrafish motor function.

Background Gamma delta (γδ) T cells play dual roles in human tumors, with both antitumor and tumor-promoting functions. However, the role of γδT cells in HPV-infected cervical cancer is still undetermined. Therefore, we aimed to identify γδT cell-related prognostic signatures in the cervical tumor microenvironment. Methods Single-cell RNA-sequencing (scRNA-seq) data, bulk RNA-seq data, and corresponding clinical information of cervical cancer patients were obtained from the TCGA and GEO databases. The Seurat R package was used for single-cell analysis, and machine learning algorithms were used to screen and construct a γδT cell-related prognostic signature. Real-time quantitative PCR (RT-qPCR) was performed to detect the expression of prognostic signature genes. Results Single-cell analysis indicated distinct populations of γδT cells between HPV-positive (HPV+) and HPV-negative (HPV-) cervical cancers. A trajectory analysis indicated γδT cells clustered into differential clusters with the pseudotime. High-dimensional Weighted Gene Co-expression Network Analysis (hdWGCNA) identified the key γδT cell-related gene modules. Bulk RNA-seq analysis also demonstrated the heterogeneity of immune cells, and the γδT-score was positively associated with inflammatory response and negatively associated with MYC stemness. Eight γδT cell-related hub genes (GTRGs), including ITGAE, IKZF3, LSP1, NEDD9, CLEC2D, RBPJ, TRBC2, and OXNAD1, were selected and validated as a prognostic signature for cervical cancer. Conclusion We identified γδT cell-related prognostic signatures that can be considered independent factors for survival prediction in cervical cancer.

UMBRELLA (A Living Laboratory: https://www.umbrellaiot.com/) is an open, large-scale IoT ecosystem deployed across South Gloucestershire, UK. It is intended to accelerate innovation across multiple technology domains. UMBRELLA is built to bridge the gap between existing specialised testbeds and address holistically real-world technological challenges in a System-of-Systems (SoS) fashion. UMBRELLA provides open access to real-world devices and infrastructure, enabling researchers and the industry to evaluate solutions for Smart Cities, Robotics, Wireless Communications, Edge Intelligence, and more. Key features include over 200 multi-sensor nodes installed on public infrastructure, a robotics arena with 20 mobile robots, a 5G network-in-a-box solution, and a unified backend platform for management, control and secure user access. The heterogeneity of hardware components, including diverse sensors, communication interfaces, and GPU-enabled edge devices, coupled with tools like digital twins, allows for comprehensive experimentation and benchmarking of innovative solutions unviable in lab environments. This paper provides a comprehensive overview of UMBRELLA’s multi-domain architecture and capabilities, making it an ideal playground for Internet of Things (IoT) and Industrial IoT (IIoT) innovation. It discusses the challenges in designing, developing and operating UMBRELLA as an open, sustainable testbed and shares lessons learned to guide similar future initiatives. With its unique openness, heterogeneity, realism and tools, UMBRELLA aims to continue accelerating cutting-edge technology research, development and translation into real-world progress.

Abstract Recently, zebrafish has received more and more attention due to its wide range of applications such as regeneration promoting therapeutics and drug discovery. Therefore, vision‐based trackers are utilized to record the swimming trajectory of zebrafish. In this paper, a re‐association method is introduced in the 3D reconstruction process to generate missed targets caused by occlusion. Since the variation of the overall tracking targets has the property of continuity and stability, a topology association model (TAM) is proposed by point group similarity into the tracking framework. TAM describes the movement of zebrafish from the macroscopic level and utilizes the changes of the point group structure for tracking. Experimental results show that the tracking framework enhances the overall performance and promotes the trajectory integrity. On the latest 3D‐ZeF20 benchmark, state‐of‐the‐art results are achieved. In addition, TAM tracking framework is applied to 2D general tracking to prove that the method is useful and have great advantage in other scenarios with relatively stable amount of targets as well.

Although traditional Chinese medicine (TCM) and modern medicine (MM) have considerably different treatment philosophies, they both make important contributions to human health care. TCM physicians usually treat diseases using TCM formula (TCMF), which is a combination of specific herbs, based on the holistic philosophy of TCM, whereas MM physicians treat diseases using chemical drugs that interact with specific biological molecules. The difference between the holistic view of TCM and the atomistic view of MM hinders their combination. Tools that are able to bridge together TCM and MM are essential for promoting the combination of these disciplines. In this paper, we present TCMFVis, a visual analytics system that would help domain experts explore the potential use of TCMFs in MM at the molecular level. TCMFVis deals with two significant challenges, namely, (i) intuitively obtaining valuable insights from heterogeneous data involved in TCMFs and (ii) efficiently identifying the common features among a cluster of TCMFs. In this study, a four-level (herb-ingredient-target-disease) visual analytics framework was designed to facilitate the analysis of heterogeneous data in a proper workflow. Several set visualization techniques were first introduced into the system to facilitate the identification of common features among TCMFs. Case studies on two groups of TCMFs clustered by function were conducted by domain experts to evaluate TCMFVis. The results of these case studies demonstrate the usability and scalability of the system.

LncRNA PVT1 has been implicated in numerous pathophysiological processes and diseases, especially cancers. However, the role and mechanism of PVT1 in the tumorigenesis of glioblastoma remain unclear. We investigated the alteration of PVT1 and its key functions in glioblastoma. PVT1 was upregulated and associated with poor prognosis in glioblastoma. We demonstrated that PVT1 silencing suppressed cell proliferation, colony formation, and orthotopic xenograft tumor growth. Mechanistic investigations found that PVT1 interacted with TRIM24 directly and increased its protein stability. PVT1 recruited COPS5 to deubiquitinate TRIM24; reciprocally, PVT1 depletion impaired the interaction between COPS5 and TRIM24, resulting in decreased expression of TRIM24. PVT1, TRIM24, and COPS5 coordinately contributed to the activation of STAT3 signaling and malignant phenotype of glioblastoma. Collectively, this study elucidates the essential role of PVT1 in the tumorigenesis of glioblastoma, which provides candidacy therapeutic target for glioblastoma treatment.

A dataset of street light images is presented. Our dataset consists of ∼350 k images, taken from 140 UMBRELLA nodes installed in the South Gloucestershire region in the UK. Each UMBRELLA node is installed on the pole of a lamppost and is equipped with a Raspberry Pi Camera Module v1 facing upwards towards the sky and lamppost light bulb. Each node collects an image at hourly intervals for 24 h every day. The data collection spans for a period of six months.Each image taken is logged as a single entry in the dataset along with the Global Positioning System (GPS) coordinates of the lamppost. All entries in the dataset have been post-processed and labelled based on the operation of the lamppost, i.e., whether the lamppost is switched ON or OFF. The dataset can be used to train deep neural networks and generate pre-trained models providing feature representations for smart city CCTV applications, smart weather detection algorithms, or street infrastructure monitoring. The dataset can be found at 10.5281/zenodo.6046758.

Steerable directional antennas are increasingly utilised to improve the overall performance of the traditional wireless sensor networks. Steerable directional antenna based networking solutions increase the network capacity by providing a longer range of transmission and reduced interference as compared to networking solutions with omni-directional antennas. However, the use of smart antennas requires complex algorithms and such algorithms may not be easily leveraged in low power Internet of Things (IoT) networks. This study presents mechanisms for integrating low complexity smart antenna solutions into IETF 6TiSCH protocol with the aim of creating scalable and reliable industrial IoT networks. The solution defines extensions to MAC layer and scheduling mechanisms of IETF 6TiSCH protocol to enable its seamless integration with low complexity steerable smart antennas. The results of this study show that smart antenna enabled 6TiSCH protocol stack outperforms the legacy 6TiSCH stack in terms of data delivery performance especially in high density scenarios.

Abstract Background Complex mechanisms participate in microglial activation after a traumatic brain injury (TBI). TBI can induce autophagy and apoptosis in neurons and glial cells, and moderate hypothermia plays a protective role in the acute phase of TBI. In the present study, we evaluated the effect of TBI and moderate hypothermia on microglial activation and investigated the possible roles of autophagy/apoptosis and toll-like receptor 4 (TLR4). Methods The TBI model was induced with a fluid percussion TBI device. Moderate hypothermia was achieved under general anesthesia by partial immersion in a water bath for 4 h. All rats were killed 24 h after the TBI. Results Our results showed downregulation of the microglial activation and autophagy, but upregulation of microglial apoptosis, upon post-TBI hypothermia treatment. The expression of TLR4 and downstream myeloid differentiation primary response 88 (MyD88) was attenuated. Moderate hypothermia reduced neural cell death post-TBI. Conclusions Moderate hypothermia can reduce the number of activated microglia by inhibiting autophagy and promoting apoptosis, probably through a negative modulation between autophagy and apoptosis. Moderate hypothermia may attenuate the pro-inflammatory function of microglia by inhibiting the MyD88-dependent TLR4 signaling pathway.

The latest 6TiSCH standard enables highly reliable industrial monitoring and control applications through deterministic wireless communications and efficient allocation of radio resources among wireless nodes. However, much of these benefits are tied to the ability of the scheduler in orchestrating communication over individual links in an efficient and a non-conflicting manner. The state-of-the-art 6TiSCH scheduling algorithms only tackle the scheduling problem at the MAC layer with an assumption of having an optimal routing layer. Moreover, to the best of our knowledge, no solution so far has taken contentspecific scheduling into account. This paper proposes CONCISE, which is a content-centric cross-layer scheduling solution. CONCISE creates content-independent routing topologies and schedulers, resulting in an overlaid routing structure and multiple content-based schedules. Further, CONCISE addresses in-network processing and data aggregation with the objective of reducing network traffic. Performance evaluation demonstrates that CONCISE can effectively reduce inner layer network traffic by up to 65%. As a result, it can provide up to 50% delay reduction and achieve significantly higher packet delivery ratio, compared with existing scheduling solutions. Initial proof of concept is implemented and evaluated in Contiki OS.

This paper presents a task allocation-oriented framework to enable efficient in-network processing and cost-effective multi-hop resource sharing for dynamic multi-hop multimedia wireless sensor networks with low node mobility, e.g., pedestrian speeds. The proposed system incorporates a fast task reallocation algorithm to quickly recover from possible network service disruptions, such as node or link failures. An evolutional self-learning mechanism based on a genetic algorithm continuously adapts the system parameters in order to meet the desired application delay requirements, while also achieving a sufficiently long network lifetime. Since the algorithm runtime incurs considerable time delay while updating task assignments, we introduce an adaptive window size to limit the delay periods and ensure an up-to-date solution based on node mobility patterns and device processing capabilities. To the best of our knowledge, this is the first study that yields multi-objective task allocation in a mobile multi-hop wireless environment under dynamic conditions. Simulations are performed in various settings, and the results show considerable performance improvement in extending network lifetime compared to heuristic mechanisms. Furthermore, the proposed framework provides noticeable reduction in the frequency of missing application deadlines.