Efficient Rescue Research Articles

Myc functions downstream of InR and their concurrent upregulation additively restricts pathogenesis of human poly(Q) disorders in Drosophila disease models

Human polyglutamine [poly(Q)] disorders are caused by abnormal expansion of CAG repeats in one gene (disease specific), yet a plethora of cellular pathways are found to be involved in their pathogenesis and progression. Despite the tremendous effort, all pursuits for the development of intervention therapy against these disorders seem futile. Recent reports suggest combination therapy as a potential strategy to combat the complex pathogenesis of such neurodegenerative disorders. The present study attempted to identify a combinatorial intervention strategy against human poly(Q) disorders in Drosophila disease models. Due to its immense potential to be stimulated by drugs, the evolutionarily conserved insulin signalling cascade which is well-established modifier of human poly(Q) pathogenesis was selected for the selected for the study. Genetic screening studies identified Drosophila Myc as a potential partner of insulin receptor (InR) that conferred additive rescue against poly(Q) induced neurodegeneration. Comprehensive analyses demonstrated InR and Myc to confer additive rescue against several events of pathogenesis, including aggregation of expanded poly(Q) containing proteins, transcriptional dysregulation, upsurge of cell death cascades, etc. Also, the synergistic rescue efficiency of InR and Myc was equally efficient in mitigating poly(Q) induced structural and functional deficits. The study also demonstrates that Myc functions downstream of InR signalling cascade to deliver rescue against human poly(Q) mediated toxicity in Drosophila disease models. In conclusion, the present study suggests that InR and Myc have the potential to be developed as a combinatorial therapeutic approach against human poly(Q) diseases.

Enhancing urban emergency response: A Euclidean distance-based framework for optimizing rescue facility layouts

This study presents a Euclidean distance-based framework for optimizing the layout of urban emergency rescue facilities. Traditional precinct-based (Type 1) and dynamic time-based (Type 2) models are compared with the proposed Euclidean distance-based (Type 3) model. The analysis uses geospatial and statistical methods to evaluate accessibility, variability, and fairness across different times of the day. The results indicate that the Euclidean distance-based model enhances rescue response efficiency and maintains a more equitable service distribution relative to traditional models. The study identifies a “threshold effect” in rescue times, emphasizing the critical distance beyond which rescue efficiency declines. By leveraging real-time traffic data and integrating Euclidean distance principles, the proposed framework offers a robust and practical approach for urban planners to improve emergency response capabilities and urban resilience. This research underscores the importance of considering both geometric proximity and dynamic traffic conditions in the strategic placement of rescue facilities, providing valuable insights for future urban emergency management and planning.

Research Note: Establishment of vector system harboring duck RNA polymerase I promoter for avian influenza virus

Reverse genetics (RG) systems are extensively utilized to investigate the characteristics of influenza viruses and develop vaccines, predominantly relying on human RNA polymerase I (pol I). However, the efficiency of RG systems for avian-origin influenza viruses may be compromised due to potential species-specific interactions of RNA pol I. In this study, we reported the polymerase activities of the duck RNA pol I promoter in avian cells and the generation of recombinant avian-derived influenza viruses using a novel vector system containing the duck RNA pol I promoter region to enhance the rescue efficiency of the RG system in avian cells. Initially, we explored a putative duck promoter region and identified the optimal size to improve the existing system. Subsequently, we established an RG system incorporating the duck RNA pol I promoter and compared its rescue efficiency with the human pol I system by generating recombinant influenza viruses in several cell lines. Notably, the 250-bp duck RNA pol I promoter demonstrated effective functionality in avian cells, exhibiting higher polymerase activity in a minigenome assay. The newly constructed RG system was significantly improved, enabling the rescue of influenza viruses in 293T, DF-1, and CCL141 cells. Furthermore, HPAI viruses were successfully rescued in DF-1 cells, a result that had not been achieved in previous experiments. In conclusion, our novel RG system harboring duck RNA pol I offers an additional tool for researching influenza viruses and may facilitate the development of vaccines for poultry.

Multiple Symmetric Multi-link Search and Rescue Robot Based on a New Movable Structure

In the event of disasters such as fires and earthquakes, the efficiency of rescue operations is often limited due to the difficulty for firefighters to enter narrow spaces. To address this issue, our team has innovated on traditional search and rescue robots by incorporating advanced sensing systems, obstacle-crossing capabilities, and autonomous return functions. This enables the robots to quickly reach disaster sites for personnel search and significantly improves rescue efficiency.

Research on Personnel Image Segmentation Based on MobileNetV2 H-Swish CBAM PSPNet in Search and Rescue Scenarios

In post-disaster search and rescue scenarios, the accurate image segmentation of individuals is essential for efficient resource allocation and effective rescue operations. However, challenges such as image blur and limited resources complicate personnel segmentation. This paper introduces an enhanced, lightweight version of the Pyramid Scene Parsing Network (MHC-PSPNet). By substituting ResNet50 with the more efficient MobileNetV2 as the model backbone, the computational complexity is significantly reduced. Furthermore, replacing the ReLU6 activation function in MobileNetV2 with H-Swish enhances segmentation accuracy without increasing the parameter count. To further amplify high-level semantic features, global pooled features are fed into an attention mechanism network. The experimental results demonstrate that MHC-PSPNet performs exceptionally well on our custom dataset, achieving 97.15% accuracy, 89.21% precision, an F1 score of 94.53%, and an Intersection over Union (IoU) of 83.82%. Compared to the ResNet50 version, parameters are reduced by approximately 18.6 times, while detection accuracy improves, underscoring the efficiency and practicality of the proposed algorithm.

Abstract PR014: PVT1 fusion on extrachromosomal DNA (ecDNA) increases oncogene RNA stability and cancer cell growth

Abstract Extrachromosomal DNA (ecDNA), highly amplified circular DNA, is widespread in human cancers and serves as a primary location for oncogene amplification. Due to its dynamic structural rearrangement and asymmetric inheritance during cell division, ecDNA increases tumor genome complexity, contributing to drug resistance and shortened patient survival. Here, we show that ecDNA-mediated genomic amplification is highly associated with transcript fusion events, which are prevalent in cancer and often lead to tumor development. We discovered that transcripts with the highest fusion events originate from genes amplified on ecDNA across ecDNA(+) cell lines. Notably, PVT1 (Plasmacytoma Variant Translocation 1) long noncoding RNA, which is known to be a hotspot for chromosomal translocation, is the most frequently fused RNA species in MYC/PVT1-amplified ecDNA(+) cancer cells. Exon 1 of PVT1 is the predominant fusion partner and confers RNA stability, increasing the RNA abundance of the partner oncogene. Using a model cell line with PVT1-MYC fusion on ecDNA, we found that RNA expression of PVT1-MYC increases in vivo in an ecDNA-dependent manner, while canonical MYC does not show a prominent increase. Additionally, ectopic expression of PVT1- MYC in MYC-depleted cancer cells provides higher rescue efficiency than canonical MYC. In contrast, a PVT1-MYC mutant that is unable to enhance RNA stability fails to rescue, highlighting the functional significance of PVT1-mediated RNA stabilization in cancer. This study unveils the link between PVT1 fusion and ecDNA in cancer, providing insights for developing diagnostic and therapeutic approaches tailored to target ecDNA. Citation Format: Hyerim Yi, Shu Zhang, Matthew G Jones, Julia A Belk, Jason Swinderman, Quanming Shi, Ellis J Curtis, Vishnu P Kanakaveti, Paul S Mischel, Howard Y Chang. PVT1 fusion on extrachromosomal DNA (ecDNA) increases oncogene RNA stability and cancer cell growth [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr PR014.

Rapid production of recombinant rotaviruses by overexpression of NSP2 and NSP5 genes with modified nucleotide sequences.

Reverse genetics systems for rotaviruses (RV) facilitate the generation of genetically engineered RVs by transfection of 11 plasmids encoding 11 genomic viral RNA segments. In addition to viral genome expression, overexpression of NSP2 and NSP5 has been used to increase the rescue efficiency of recombinant RVs. Here, we showed that the overexpression of nucleotide sequence-modified NSP2 and NSP5 enabled the rapid and efficient production of recombinant RVs. Using improved reverse genetics, we established a reverse genetics system for human and bovine RV clinical isolates, as well as laboratory strains of bovine RV (NCDV and UK) and porcine RV (Gottfried). In addition, we rescued low-replicating recombinant RVs carrying a mutant NSP4 lacking the double-layered particle-binding domain, which was deficient in the efficient production of mature virions. These advancements in reverse genetics enabled the generation of molecular clones of RV clinical isolates and recombinant RVs harboring critical amino acid mutations, offering a versatile platform for investigating RV biology and pathogenesis.IMPORTANCERecombinant rotavirus (RV) synthesis via reverse genetics relies on both the viral propagation capacity and the efficiency of the experimental system. Since the establishment of our reverse genetics system, several enhancements have been implemented to augment the rescue efficiency. Nevertheless, challenges persist in generating RV clinical strains and recombinant viruses with low replication capacities. Notably, this improved reverse genetics system successfully facilitated the establishment of molecular clones of human and bovine RV clinical isolates. Fecal samples from patients with RV typically harbor quasi-species or, occasionally, multiple genotypes of RV. In the present study, we performed the genetic sequencing of clinical viral strains during the early propagation stages in cultured cells. Subsequently, infectious viruses were synthesized, allowing the characterization of circulating viruses in nature. This approach provides valuable insights into the genetic diversity and dynamics of RV populations and contributes to a more comprehensive understanding of viral pathogenesis and evolution.

Improvement of embryo rescue efficiency in haploid melon (Cucumis melo L.) via irradiated pollen pollination

Improvement of embryo rescue efficiency in haploid melon (Cucumis melo L.) via irradiated pollen pollination

Research on Submersible Position Prediction Based on Kalman Filter Algorithm

The global situation of the world's submersible technology is experiencing a boom, with countries competing to develop advanced submersible equipment to explore the deep sea, undersea resources, and uncharted territories. With the wide range of applications of submersibles in various fields, the risks of communication loss and mechanical failures faced during their missions require the establishment of accurate predictive models of submersible positions to ensure safe and efficient rescue. In this study, a model containing equations of motion is developed from the motion characteristics of a submersible on the seafloor. Uncertainties such as sea currents, topography, and seawater density are considered, and a random walk model is used to simulate these effects to ensure that the model is closer to reality. Subsequently, combined with the Kalman filter algorithm, a time-dependent position prediction model is constructed in this study. The model can predict the position of the submersible in real-time, and after error analysis, it shows that the average variance and root mean square error are at a low level, which proves that its prediction is accurate and reliable, and provides strong technical support for the safe rescue of the submersible.

Modeling and optimization of two-stage emergency supply chain network under uncertain environment

In order to formulate an effective emergency supply chain network planning scheme, improve the efficiency of emergency organization rescue and realize the reasonable allocation of resources and materials, considering the uncertainty of supply and demand, a distribution mode combining multiple transportation modes is adopted, and the optimization objectives are to minimize network response time, cost and carbon emissions. A two-stage emergency supply chain mixed integer programming model is constructed. At the same time, an adjustable robust optimization model is constructed based on robust optimization theory to enhance the network's ability to cope with uncertain factors, and the constraints with uncertain parameters are transformed through linear duality theory. In order to improve the solution effect of the model, an optimize cuckoo search (OCS) algorithm is proposed, and a benchmark example is introduced to verify the superiority and applicability of the OCS algorithm in solving multi-objective functions. Finally, the emergency material distribution data during the COVID-19 epidemic in Wuhan is used to study the emergency supply chain network decision-making problem with uncertain parameters, and the effective suppression of the robust control coefficient on uncertain disturbances is proved through sensitivity analysis.

701P AAV.U7.ex44 mediates efficient exon skipping, protein restoration & phenotype rescue – pre-clinical intramuscular and dose escalation study for a mutational hotspot of the Duchenne muscular dystrophy (DMD)

701P AAV.U7.ex44 mediates efficient exon skipping, protein restoration & phenotype rescue – pre-clinical intramuscular and dose escalation study for a mutational hotspot of the Duchenne muscular dystrophy (DMD)

Firefighting Robot Extinguishment Decision‐Making Based on Visual Guidance: A Novel Attention and Scale U‐Net Model and Genetic Algorithm

ABSTRACTAt present, rescue firefighting relies mainly on firefighting robots, and robots with perception and decision‐making functions are the key elements for achieving intelligent firefighting. However, traditional firefighting robots often lack the ability for autonomous perception and decision‐making when extinguishing multiple fire sources, leading to low rescue efficiency and increased risk for rescue personnel, especially when making firefighting decisions in extreme fire scenes, which poses a challenge. To effectively handle firefighting tasks and ensure operational efficiency, a robot firefighting decision‐making method based on drone visual guidance is proposed. First, we introduce a novel Attention and Scale U‐Net (ASUNet) model to accurately capture crucial target information, including fire location and size, in a fire scene. The ASUNet model adopts an effective multiscale fusion strategy and attention mechanism to enhance the model's performance. Subsequently, based on the results of the ASUNet model, through pixel segmentation clustering and a genetic optimization algorithm, we obtain the robot's firefighting decision results, thereby guiding the robot to carry out firefighting operations systematically. Finally, through numerical experiments, it is verified that the proposed ASUNet model is superior and effective, as the model can perceive important information in a fire scene and extract it well. The use of improved genetic optimization can further accelerate algorithm convergence. To our knowledge, this study is the first to use drone‐based monocular vision guidance for firefighting decision‐making, providing significant engineering value.

Efficient Rescue of Retinal Degeneration in Pde6a Mice by Engineered Base Editing and Prime Editing.

Retinitis pigmentosa (RP) is a complex spectrum of inherited retinal diseases marked by the gradual loss of photoreceptor cells, ultimately leading to blindness. Among these, mutations in PDE6A, responsible for encoding a cGMP-specific phosphodiesterase, stand out as pivotal in autosomal recessive RP (RP43). Unfortunately, no effective therapy currently exists for this specific form of RP. However, recent advancements in genome editing, such as base editing (BE) and prime editing (PE), offer a promising avenue for precise and efficient gene therapy. Here, it is illustrated that the engineered BE and PE systems, particularly PE, exhibit high efficiency in rescuing a target point mutation with minimal bystander effects in an RP mouse model carrying the Pde6a (c.2009A > G, p.D670G) mutation. The optimized BE and PE systems are first screened in N2a cells and subsequently assessed in electroporated mouse retinas. Notably, the optimal PE system, delivered via dual adeno-associated virus (AAV), precisely corrects the pathogenic mutation with average 9.4% efficiency, with no detectable bystander editing. This correction restores PDE6A protein expression, preserved photoreceptors, and rescued retinal function in Pde6a mice. Therefore, this study offers a proof-of-concept demonstration for the treatment of Pde6a-related retinal degeneration using BE and PE systems.

Fire Safety Disparities in Sweden: Sociodemographic Influences and the Impact of Societal Protection on Personal Fire Prevention Measures

AbstractPrevious research has identified sociodemographic inequalities in fire prevention measures. This study examined whether sociodemographic differences persist in the Swedish population concerning fire prevention measures and particularly whether there remains an inverted u-curve related to age in protection habits. Additionally, it investigated whether fire protection practices are influenced by the level of societal protection. The research utilised survey data and register data from The Swedish Civil Contingencies Agency and Statistics Sweden. A latent class analysis was conducted, dividing respondents into four latent classes, followed by two binomial regression analyses. The study revealed three key findings regarding fire protection measures. First, certain demographic groups, namely the young, women, single and childfree households, low-income and low-education individuals, immigrants, and urban residents, are disproportionately lacking optimal fire safety measures. Second, although a safety maturity curve is still observed, older adults in Sweden today are considerably more protected compared to 15–20 years ago, indicating that safety practices employed during middle age continue into old age. Third, a trend is observed where individuals living in areas with more efficient professional rescue services tend to have lower levels of personal fire protection, suggesting a rational choice based on the perceived level of societal protection.

Disaster Rescue Drone Based on YOLOv4 Algorithm

Abstract With the rapid development of artificial intelligence technology, the application of unmanned aerial vehicles (UAV) in disaster relief is becoming more widespread. This article presents a disaster relief UAV based on the YOLOv4 algorithm, aimed at improving the speed and efficiency of emergency response and rescue. The article designs and implements a UAV integrated with the YOLOv4 object detection algorithm, used for real-time identification and location of people within disaster areas and for deploying rescue materials using a mechanical claw. Through experiments and comparative verification, the system has demonstrated high-efficiency in target detection and tracking in various disaster environments.

Optimization of embryo rescue technique for development of hybrid plants in stenospermic grapes

The development of seedless cultivars is a primary goal in grapevine breeding. Since grapes are stenospermic and tend to abort their embryos before development, traditional breeding methods often yield seedless cultivars at a low frequency. Therefore, embryo rescue has emerged as a promising approach for creating seedless grape cultivars. This study aimed to optimize the ideal sampling time and protocol for efficient embryo rescue in grapes. Ovules from immature berries collected at various days after pollination (20DAP, 30DAP, 40DAP, and 50DAP) were cultured on Nitsch and Nitsch (NN) medium with different concentrations of Benzyl aminopurine (BAP) (0.1, 0.2, and 0.5 mg/L) and Activated Charcoal (AC) (1.5, 2, and 2.5 g/L). Several parameters were assessed, including maximum ovule growth, the percentage of enlarged ovules, percentage of collapsed ovules, callus formation percentage, and embryo germination percentage. The study’s results indicated that berries collected at 40 DAP yielded the best outcomes across all parameters. Regarding treatments, the most favorable results were achieved when ovules were cultured on NN medium supplemented with 0.5 mg/L BAP and 2 g/L AC. In conclusion, the study underscores the significance of choosing the right sampling time and treatments to ensure efficient embryo rescue in grapes. The protocol standardized from this research is recommended for effectively rescuing embryos and developing seedless hybrids.

Synergistic rescue of temperature-sensitive p53 mutants by hypothermia and arsenic trioxide.

The p53 tumor suppressor is inactivated by mutations in about 50% of tumors. Rescuing the transcriptional function of mutant p53 has potential therapeutic benefits. Approximately 15% of p53 mutants are temperature sensitive (TS) and regain maximal activity at 32°C. Proof of concept study showed that induction of 32°C hypothermia in mice restored TS mutant p53 activity and inhibited tumor growth. However, 32°C is the lower limit of therapeutic hypothermia procedures for humans. Higher temperatures are preferable but result in suboptimal TS p53 activation. Recently, arsenic trioxide (ATO) was shown to rescue the conformation of p53 structural mutants by stabilizing the DNA binding domain. We examined the responses of 17 frequently observed p53 TS mutants to functional rescue by temperature shift and ATO. The results showed that ATO only rescued mild p53 TS mutants with high basal activity at 37°C. Mild TS mutants showed a common feature of regaining significant activity at the semi-permissive temperature of 35°C and could be further stimulated by ATO at 35°C. TS p53 rescue by ATO was antagonized by the cellular redox mechanism and was rapidly reversible. Inhibition of glutathione (GSH) biosynthesis enhanced ATO rescue efficiency and sustained p53 activity after ATO washout. The results suggest that mild TS p53 mutants are uniquely responsive to functional rescue by ATO due to small thermostability deficits and inherent potential to regain active conformation. Combining mild hypothermia and ATO may provide an effective and safe procedure for targeting tumors with p53 TS mutations.

Design & Development of Rescue Assistance Rover for Land-Based Operations

In the face of natural disasters like earthquakes and landslides, timely and efficient rescue operations are paramount to saving lives. However, these operations are often hindered by hazardous conditions and inaccessible terrain. This research focuses on the design and development of a Rescue Assistance Rover (RAR) aimed at enhancing the capabilities of rescuers in challenging environments. The RAR integrates advanced robotics, sensory systems, and communication technologies to navigate through debris, assess hazards, and provide crucial assistance to rescue teams. By leveraging autonomy and remote operation capabilities, the RAR aims to augment human efforts, improving overall response effectiveness and minimizing risks to both rescuers and survivors. This paper outlines the conceptualization, design considerations, development stages, and potential applications of the RAR, highlighting its potential to revolutionize disaster response efforts and save lives in critical situations. Key Words: Additive Manufacturing, 3D Printing, Rocker Bogie Mechanism, Arduino Programming, Rescue Operations, Robotics

Mediatized governance in environmental disasters: self-organizing community rescue through collaborative online documents

The frequency of risks and disasters in contemporary society has created new challenges for rescue governance. Through content analysis and participant observation, this study examined the development of an online collaborative and self-organizing community during the 2021 rainstorm rescue effort in Zhengzhou, China. The study shows that this community provided and optimized major public goods and services, adapted rescue resources to victim needs, facilitated streamlined information transfer, protected privacy, and connected a multitude of actors. The findings illustrate the significance of a new mode of mediatized governance that emphasizes the continuous process of reconciling conflicting interests and encouraging joint action among multiple actors, thereby realizing a flexible and efficient rescue that could not have been achieved by a top-down government-led administration.

Firefighting Vehicle Dispatch and Route Planning Model Based on Priority Passage Rights

<div>Efficient fire rescue operations in urban environments are critical for saving lives and reducing property damage. By utilizing connected vehicle systems (CVS) for firefighting vehicles planning, we can reduce the response time to fires while lowering the operational costs of fire stations. This research presents an innovative nonlinear mixed-integer programming model to enhance fire rescue operations in urban settings. The model focuses on expediting the movement of firefighting vehicles within intricate traffic networks, effectively tackling the complexities associated with collaborative dispatch decisions and optimal path planning for multiple response units. This method is validated using a small-scale traffic network, providing foundational insights into parameter impacts. A case study in Sioux Falls shows its superiority over traditional “nearest dispatch” methods, optimizing both cost and response time significantly. Sensitivity analyses involving clearance speed, clearance time, minimum rescue force, and fire loss parameters contribute to the enhancement of urban fire rescue operations and the refinement of practical decision support systems.</div>