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This study aims to improve the accuracy of fire source detection, the efficiency of path planning, and the precision of firefighting operations in drone swarms during fire emergencies. It proposes an intelligent firefighting technology for drone swarms based on multi-sensor integrated path planning. The technology integrates the You Only Look Once version 8 (YOLOv8) algorithm and its optimization strategies to enhance real-time fire source detection capabilities. Additionally, this study employs multi-sensor data fusion and swarm cooperative path-planning techniques to optimize the deployment of firefighting materials and flight paths, thereby improving firefighting efficiency and precision. First, a deformable convolution module is introduced into the backbone network of YOLOv8 to enable the detection network to flexibly adjust its receptive field when processing targets, thereby enhancing fire source detection accuracy. Second, an attention mechanism is incorporated into the neck portion of YOLOv8, which focuses on fire source feature regions, significantly reducing interference from background noise and further improving recognition accuracy in complex environments. Finally, a new High Intersection over Union (HIoU) loss function is proposed to address the challenge of computing localization and classification loss for targets. This function dynamically adjusts the weight of various loss components during training, achieving more precise fire source localization and classification. In terms of path planning, this study integrates data from visual sensors, infrared sensors, and LiDAR sensors and adopts the Information Acquisition Optimizer (IAO) and the Catch Fish Optimization Algorithm (CFOA) to plan paths and optimize coordinated flight for drone swarms. By dynamically adjusting path planning and deployment locations, the drone swarm can reach fire sources in the shortest possible time and carry out precise firefighting operations. Experimental results demonstrate that this study significantly improves fire source detection accuracy and firefighting efficiency by optimizing the YOLOv8 algorithm, path-planning algorithms, and cooperative flight strategies. The optimized YOLOv8 achieved a fire source detection accuracy of 94.6% for small fires, with a false detection rate reduced to 5.4%. The wind speed compensation strategy effectively mitigated the impact of wind on the accuracy of material deployment. This study not only enhances the firefighting efficiency of drone swarms but also enables rapid response in complex fire scenarios, offering broad application prospects, particularly for urban firefighting and forest fire disaster rescue.

Increasing evidence suggests that signaling through the prolactin/prolactin receptor axis is important for stimulation the growth of many cancers including glioblastoma multiforme, breast and ovarian carcinoma. Efficient inhibitors of signaling have previously been developed but their applicability as cancer drugs is limited by the short in vivo half-life. In this study, we show that a fusion protein, consisting of the prolactin receptor antagonist PrlRA and an albumin binding domain for half-life extension can be expressed as inclusion bodies in Escherichia coli and efficiently refolded and purified to homogeneity. The fusion protein was found to have strong affinity for the two intended targets: the prolactin receptor (KD = 2.3±0.2 nM) and mouse serum albumin (KD = 0.38±0.01 nM). Further investigation showed that it could efficiently prevent prolactin mediated phosphorylation of STAT5 at 100 nM concentration and above, similar to the PrlRA itself, suggesting a potential as drug for cancer therapy in the future. Complexion with HSA weakened the affinity for the receptor to 21±3 nM, however the ability to prevent phosphorylation of STAT5 was still prominent. Injection into rats showed a 100-fold higher concentration in blood after 24 h compared to PrlRA itself.

The Insulin-like growth factor-1 receptor (IGF-1R) is a molecular target for several monoclonal antibodies undergoing clinical evaluation as anticancer therapeutics. The non-invasive detection of IGF-1R expression in tumors might enable stratification of patients for specific treatment and improve the outcome of both clinical trials and routine treatment. The affibody molecule ZIGF-1R:4551 binds specifically to IGF-1R with subnanomolar affinity. The goal of this study was to evaluate the 68Ga and 111In-labeled affibody construct NODAGA-(HE)3-ZIGF-1R:4551 for the imaging of IGF-1R expression, using PET and SPECT. The labeling was efficient and provided stable coupling of both radionuclides. The two imaging probes, [68Ga]Ga-NODAGA-(HE)3-ZIGF-1R:4551 and [111In]In-NODAGA-(HE)3-ZIGF-1R:4551, demonstrated specific binding to IGF-1R-expressing human cancer cell lines in vitro and to IGF-1R-expressing xenografts in mice. Preclinical PET and SPECT/CT imaging demonstrated visualization of IGF-1R-expressing xenografts already one hour after injection. The tumor-to-blood ratios at 3 h after injection were 7.8 ± 0.2 and 8.0 ± 0.6 for [68Ga]Ga-NODAGA-(HE)3-ZIGF-1R:4551 and [111In]In-NODAGA-(HE)3-ZIGF-1R:4551, respectively. In conclusion, a molecular design of the ZIGF-1R:4551 affibody molecule, including placement of a (HE)3-tag on the N-terminus and site-specific coupling of a NODAGA chelator on the C-terminus, provides a tracer with improved imaging properties for visualization of IGF-1R in malignant tumors, using PET and SPECT.

Lowering the total level of Immunoglobulin G (IgG) in circulation is a promising general treatment option for many autoimmune diseases driven by pathogenic autoantibodies. The half-life of IgG in circulation is unusually long as a consequence of its interaction with the neonatal Fc receptor (FcRn), which protects it from lysosomal degradation by cells in contact with blood. Blocking the IgG/FcRn interaction prevents FcRn-mediated rescue, which may lead to increased catabolism and a lowering of the total IgG level. Here, we find that an engineered alternative scaffold protein, an affibody molecule, interacting specifically with FcRn, is able to block the IgG/FcRn interaction in vitro . The affibody molecule (Z FcRn ) was expressed alone or as a fusion to an albumin binding domain (ABD), to extend its half-life in circulation, in both cases with retained affinity and blocking potential. Repeated i.v. injections in mice of Z FcRn and Z FcRn -ABD were found to result in an up to 40% reduction of the IgG serum-level after 5 days. Potential applications of Z FcRn as a general treatment modality for autoimmune diseases are discussed.

A variable fragment of a heavy chain antibody (VHH) directed against rotavirus, also referred to as anti-rotavirus protein 1 (ARP1), was shown to confer protection against rotavirus induced diarrhea in infant mouse model of rotavirus induced diarrhea. In this study, we have fused the mouse IgG1 Fc to ARP1 to improve the protective capacity of ARP1 by inducing an Fc-mediated effector function. We have shown that the Fc-ARP1 fusion protein confers significantly increased protection against rotavirus in a neonatal mouse model of rotavirus-induced diarrhea by reducing the prevalence, duration and severity of diarrhea and the viral load in the small intestines, suggesting that the Fc part of immunoglobulins may be engaged in Fc-mediated neutralization of rotavirus. Engineered conventional-like antibodies, by fusion of the Fc part of immunoglobulins to antigen-specific heavy-chain only VHH fragments, might be applied to novel antibody-based therapeutic approaches to enhance elimination of pathogens by activation of distinct effector signaling pathways.

The Insulin-like growth factor-1 receptor (IGF-1R) is a molecular target for several monoclonal antibodies undergoing clinical evaluation as anticancer therapeutics. The non-invasive detection of IGF-1R expression in tumors might enable stratification of patients for specific treatment and improve the outcome of both clinical trials and routine treatment. The affibody molecule Z(IGF-1R:4551) binds specifically to IGF-1R with subnanomolar affinity. The goal of this study was to evaluate the Ga-68 and In-111-labeled affibody construct NODAGA-(HE)(3)-Z(IGF-1R:4551) for the imaging of IGF-1R expression, using PET and SPECT. The labeling was efficient and provided stable coupling of both radionuclides. The two imaging probes, [Ga-68]Ga-NODAGA-(HE)(3)-Z(IGF-1R:4551) and [In-111]In-NODAGA-(HE)(3)-Z(IGF-1R:4551), demonstrated specific binding to IGF-1R-expressing human cancer cell lines in vitro and to IGF-1R-expressing xenografts in mice. Preclinical PET and SPECT/CT imaging demonstrated visualization of IGF-1R-expressing xenografts already one hour after injection. The tumor-to-blood ratios at 3 h after injection were 7.8 +/- 0.2 and 8.0 +/- 0.6 for [Ga-68]Ga-NODAGA-(HE)(3)-Z(IGF-1R:4551) and [In-111]In-NODAGA-(HE)(3)-Z(IGF-1R:4551), respectively. In conclusion, a molecular design of the Z(IGF-1R:4551) affibody molecule, including placement of a (HE)(3)-tag on the N-terminus and site-specific coupling of a NODAGA chelator on the C-terminus, provides a tracer with improved imaging properties for visualization of IGF-1R in malignant tumors, using PET and SPECT.

Cross-platform recommendation aims to improve recommendation accuracy through associating information from different platforms. Existing cross-platform recommendation approaches assume all cross-platform information to be consistent with each other and can be aligned. However, there remain two unsolved challenges: i) there exist inconsistencies in cross-platform association due to platform-specific disparity, and ii) data from distinct platforms may have different semantic granularities. In this paper, we propose a cross-platform association model for cross-platform video recommendation, i.e., Disparity-preserved Deep Cross-platform Association (DCA), taking platform-specific disparity and granularity difference into consideration. The proposed DCA model employs a partially-connected multi-modal autoencoder, which is capable of explicitly capturing platform-specific information, as well as utilizing nonlinear mapping functions to handle granularity differences. We then present a cross-platform video recommendation approach based on the proposed DCA model. Extensive experiments for our cross-platform recommendation framework on real-world dataset demonstrate that the proposed DCA model significantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics.

Background Much still remains unknown about the pathogenesis of brain arteriovenous malformations (AVMs). Previous studies have revealed the abnormal expression of various angiogenesis-related genes in AVMs. To further understand this disease, we sought to identify genes differently expressed in AVMs by means of the gene microarray technique. Methods Nine AVMs specimen and nine samples of normal vessels are collected. Total RNA isolated from these specimen is hybridized with Oligonucleotide array and gene analysis was conducted. Analyzing data with the help of significance analysis of microarrays (SAM) and a free web-based molecular annotation system 3.0 (MAS 3.0). Results The SAM method identify 37 gene significantly up-regulated and 10 genes down-regulated in AVMs. Conclusions Among those genes, VACN, SPARK and ARHGAP18 seem to play a facilitating role during the genesis of AVMs. Multiple pathways, as MAPK pathway, may also be involved.

Mitophagy is a vital process that controls mitochondria quality, dysregulation of which can promote cancer. Oncoprotein mucin 1 (MUC1) targets mitochondria to attenuate drug-induced apoptosis. However, little is known about whether and how MUC1 contributes to mitochondrial homeostasis in cancer cells. We identified a novel role of MUC1 in promoting mitophagy. Increased mitophagy is coupled with the translocation of MUC1 to mitochondria, where MUC1 interacts with and induces degradation of ATPase family AAA domain-containing 3A (ATAD3A), resulting in protection of PTEN-induced kinase 1 (Pink1) from ATAD3A-mediated cleavage. Interestingly, MUC1-induced mitophagy is associated with increased oncogenicity of cancer cells. Similarly, inhibition of mitophagy significantly suppresses MUC1-induced cancer cell activity in vitro and in vivo. Consistently, MUC1 and ATAD3A protein levels present an inverse relationship in tumor tissues of breast cancer patients. Our data validate that MUC1/ATAD3A/Pink1 axis-mediated mitophagy constitutes a novel mechanism for maintaining the malignancy of cancer cells, providing a novel therapeutic approach for MUC1-positive cancers.

Shale reservoirs provide critical storage space for unconventional oil and gas, yet their frequent vertical facies alternations and complex spatial architectures make it difficult for conventional two-point geostatistical methods to reproduce thin interbedding and reservoir-scale continuity. Multiple-point geostatistics can incorporate structural information through training images (TIs), but practical 3D shale modeling is often hindered by the limited availability of representative 3D TIs. Using the F2 Member in the Qintong Sag, Subei Basin, eastern China, as a case study, we propose a hierarchical 2D-to-3D geological modeling workflow that combines mixed-point geostatistical simulation (MIXSIM) for generating vertical 2D facies sections and a sequential 2D simulation strategy with conditioning data (s2Dcd) for propagating section-based patterns into 3D space under hard well constraints. In the workflow, vertical sections serve as TI carriers to explicitly capture bedding-scale alternations, while well data are imposed as hard conditioning information during 3D simulation. Quantitative evaluation is performed in terms of (i) conditioning-data consistency, (ii) vertical facies-transition statistics quantified by transition counts and Markov transition probability matrices, (iii) global facies proportions summarized as the mean of 10 realizations, and (iv) connectivity characterized by connected geobody analysis. The realizations honor the conditioning data exactly, reproduce vertical transition behavior with a transition-matrix discrepancy of DMAE=0.0396, and maintain global facies proportions close to well-based estimates with a maximum deviation of 2.36%. These results demonstrate that the proposed MIXSIM–s2Dcd workflow provides a practical solution for well-data-driven, high-resolution 3D shale facies modeling when 3D training images are unavailable.