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Ultraviolet (UV) networks, owing to their advantages of low eavesdropping rates, low background noise, strong anti-interference abilities, and non–line-of-sight (NLOS) communication, have high prospects for application in the complex electromagnetic environment. In this study, UV network coverage was systematically evaluated based on the NLOS channel. Combined with a designed node model and a specific communication mode, a two-dimensional network coverage model was established and expressions of network coverage area and connectivity were derived mathematically. Subsequently, the effects of the transceiver elevation combination, number of nodes, data rate, and transmission power on the network coverage performance were analyzed, and the communication parameters were optimized to match different network scenarios. The results show that the bigger the transceiver elevation combination, the more stringent the communication parameters required by the UV network coverage. Finally, a coverage evaluation experiment with four-node network was conducted. This study provides theoretical reference information for actual network deployment.

Stem cells have been documented as a new therapeutic method for ovarian injuries such as premature ovarian failure (POF). However, effects of exosomes (Exos) derived from human endometrial stem cells (EnSCs) on diminished ovarian failure remain to be carefully elucidated. Our study aims to investigate the mechanisms of EnSC-Exos in the recovery of the cisplatin-induced granulosa cell injury model in vitro or POF mouses model in vivo and whether the Hippo signaling pathway is involved in the regulation. In this study, we established successful construction of the cisplatin-induced granulosa cell injury model and evaluated Hippo signaling pathway activation in cisplatin-damaged granulosa cells (GCs). Furthermore, laser scanning confocal microscope and immunofluorescence demonstrated that EnSC-Exos can be transferred to cisplatin-damaged GCs to decrease apoptosis. In addition, the enhanced expression of YAP at the protein level as well as YAP/TEAD target genes, such as CTGF, ANKRD1, and the increase of YAP into the nucleus in immunofluorescence staining after the addition of EnSC-Exos to cisplatin-damaged GCs confirmed the suppression of Hippo signaling pathway. While in vivo, EnSC-Exos successfully remedied POF in a mouse model. Collectively, our findings suggest that chemotherapy-induced POF was associated with the activating of Hippo signaling pathway. Human EnSC-Exos significantly elevated the proliferation of ovarian GCs and the ovarian function by regulating Hippo signaling pathway. These findings provide new insights for further understanding of EnSC-Exos in the recovery of ovary function.

This paper investigates the problem of coordinated resource allocation for multiple unmanned aerial vehicles (UAVs) to address the scarcity of communication resources in disaster-affected areas. UAVs carrying modules of free space optical (FSO) and radio frequency (RF) serve as relay nodes and edge offloading nodes, presenting an FSO/RF dual-hop framework. Considering the varying urgency levels of tasks, we assign task priorities and transform the proposed problem into distributed collaborative optimization problem. Based on the K-means algorithm and the multi-agent deep deterministic policy gradient (MADDPG) algorithm, we propose a UAV-coordinated K-means MADDPG (KMADDPG) to maximize the number of completed tasks while prioritizing high-priority tasks. Simulation results show that KMADDPG is 5% to 10% better than the benchmark DRL methods in convergence performance.

The combined application of chemical and organic fertilizers has been recognized to enhance soil fertility and foster the soil microbial ecosystem. However, the optimal ratio of chemical and organic fertilizers in oilseed rape cultivation is still uncertain, and the role of rhizosphere effect is still unclear. Thus, this study aimed to elucidate the impacts of varying ratios of chemical and organic fertilizers on the structure and potential functionalities of rhizosphere and non-rhizosphere soil microbial communities. The interplay of microbial communities with soil properties and oilseed rape root exudates was investigated in controlled pot cultivations receiving varying ratios of chemical and organic fertilizers. Results indicated clear segregation in the soil bacterial community, influenced by both fertilization treatments and rhizosphere effects. The bacterial community structure significantly correlated with nitrate nitrogen, organic acids, and dissolved organic carbon (DOC) content. Rhizosphere effects led to increased bacteria abundance, reduced diversity, and decreased network stability. Notably, F3 treatment receiving 25% chemical and 75% organic fertilizers showed a significantly higher abundance at 1.43 × 1011 copies g−1 dry soil, accompanied by increased species and genetic diversity, and ecological network complexity. This treatment also yielded the highest aboveground biomass of oilseed rape. However, the application of organic fertilizers also increased the risk of plant pathogenicity. This study reveals the impact of fertilizers and rhizosphere effects on soil microbial community structure and function, shedding light on the establishment of more effective fertilization schemes for oilseed rape agriculture.

Summary: Amino acid formula (AAF) is increasingly consumed in infants with cow’s milk protein allergy; however, the long-term influences on health are less described. In this study, we established a mouse model by subjecting neonatal mice to an amino acid diet (AAD) to mimic the feeding regimen of infants on AAF. Surprisingly, AAD-fed mice exhibited dysbiotic microbiota and increased neuronal activity in both the intestine and brain, as well as gastrointestinal peristalsis disorders and depressive-like behavior. Furthermore, fecal microbiota transplantation from AAD-fed mice or AAF-fed infants to recipient mice led to elevated neuronal activations and exacerbated depressive-like behaviors compared to that from normal chow-fed mice or cow’s-milk-formula-fed infants, respectively. Our findings highlight the necessity to avoid the excessive use of AAF, which may influence the neuronal development and mental health of children.

Background The C-C motif chemokine receptor 5 (CCR5)/C-C motif chemokine ligand 5 (CCL5) axis plays a major role in colorectal cancer (CRC). We aimed to characterize the molecular features associated with CCR5/CCL5 expression in CRC and to determine whether CCR5/CCL5 levels could impact treatment outcomes.Methods 7604 CRCs tested with NextGen Sequencing on DNA and RNA were analyzed. Molecular features were evaluated according to CCR5 and CCL5 tumor gene expression quartiles. The impact on treatment outcomes was assessed in two cohorts, including 6341 real-world patients and 429 patients from the Cancer and Leukemia Group B (CALGB)/SWOG 80405 trial.Results CCR5/CCL5 expression was higher in right-sided versus left-sided tumors, and positively associated with consensus molecular subtypes 1 and 4. Higher CCR5/CCL5 expression was associated with higher tumor mutational burden, deficiency in mismatch repair and programmed cell death ligand 1 (PD-L1) levels. Additionally, high CCR5/CCL5 were associated with higher immune cell infiltration in the tumor microenvironment (TME) of MMR proficient tumors. Ingenuity pathway analysis revealed upregulation of the programmed cell death protein 1 (PD-1)/PD-L1 cancer immunotherapy pathway, phosphatase and tensin homolog (PTEN) and peroxisome proliferator-activated receptors (PPAR) signaling, and cytotoxic T-lymphocyte antigen 4 (CTLA-4) signaling in cytotoxic T lymphocytes, whereas several inflammation-related pathways were downregulated. Low CCR5/CCL5 expression was associated with increased benefit from cetuximab-FOLFOX treatment in the CALGB/SWOG 80405 trial, where significant treatment interaction was observed with biologic agents and chemotherapy backbone.Conclusions Our data show a strong association between CCR5/CCL5 gene expression and distinct molecular features, gene expression profiles, TME cell infiltration, and treatment benefit in CRC. Targeting the CCR5/CCL5 axis may have clinical applications in selected CRC subgroups and may play a key role in developing and deploying strategies to modulate the immune TME for CRC treatment.

In target detection, tracking, and recognition tasks, high-quality images can achieve better results. However, in actual scenarios, the visual effects and data quality of images are greatly reduced due to the influence of environmental factors, which affect subsequent detection, recognition, and other tasks. Therefore, this paper proposes an image rain removal algorithm based on multi-scale features, which can effectively remove rain streaks. First of all, this paper proposes a deraining algorithm that combines spatial information to improve the network’s generalization ability on real images, aiming at the problem of synthetic datasets used by previous deraining algorithms. Then, by proposing a multi-scale rain removal algorithm, it improves the feature extraction capabilities of existing deraining algorithms. Before extracting deep rain features, a preliminary fusion of multi-scale shallow features can be performed, which can show better performance in images of different sizes. In addition, a spatial attention module and channel are introduced. The attention module increases the ability to extract rain information at each scale; the resulting multi-scale feature image rain removal algorithm is called MFD. Finally, the rain removal algorithm is validated on the rain removal dataset, and the proposed method can effectively remove rain patterns, provide strong performance improvement on several datasets in the image rain removal task, and provide high-quality images for subsequent detection and recognition tasks.

Conventional straight fin (SF) radiators have difficulties meeting the cooling requirements of high-power electronic components. Therefore, based on the structure and technology of the detachable fin radiator, this paper proposes a kind of radiator embedded in the heat pipe base and uses the roll-bond flat heat pipe (RBFHP) to replace the traditional fin. The radiator has the advantages of modularity, easy manufacturing, low cost and good heat balance. In this study, the heat pipes (HPs)-RBFHPs radiator was tested in natural convection and forced convection to mimic the actual application scenario and compared with the conventional aluminum radiator. Heating power, angle, wind speed and other aspects were studied. The results showed that the cooling performance of the HPs-RBFHPs radiator was improved by 10.7% to 55% compared with that of the SF radiator under different working conditions. The minimum total thermal resistance in the horizontal state was only 0.37 °C/W. The temperature equalization of the base played a dominant role in the performance of the radiator at a large angle, and the fin group could be ineffective when the angle was greater than 60°. Under the most economical conditions with an inclination of 0° and a wind speed of 2 m/s, the input power was 340 W, the heat source temperature of the HPs-RBFHPs was only 64.2 °C, and the heat dissipation performance was 55.4% higher than that of SFs.

A point cloud is a simple and concise 3D representation, but point cloud generation is a long-term challenging task in 3D vision. However, most existing methods only focus on their effectiveness of generation and auto-encoding separately. Furthermore, both generative adversarial networks (GANs) and auto-encoders (AEs) are the most popular generative models. But there is a lack of related research that investigates the implicit connections between them in the field of point cloud generation. Thus, we propose a new bidirectional network (BI-Net) trained with collaborative learning, introducing more priors through the alternate parameter optimizations of a GAN and AE combination, which is different from the way of combining them at the network structure and loss function level. Specifically, BI-Net acts as a GAN and AE in different data processing directions, where their network structures can be reused. If optimizing only the GAN without the AE, there is no direct constraint of ground truth on the generator’s parameter optimization. This unique approach enables better network optimization and leads to superior generation results. Moreover, we propose a nearest neighbor mutual exclusion (NNME) loss to further homogenize the spatial distribution of generated points during the reverse direction. Extensive experiments were conducted, and the results show that the BI-Net produces competitive and high-quality results on reasonable structure and uniform distributions compared to existing state-of-the-art methods. We believe that our network structure (BI-Net) with collaborative learning could provide a new promising method for future point cloud generation tasks.

Background: Patient-based real-time quality control (PBRTQC) can be a valuable tool in clinical laboratories due to its cost-effectiveness and constant monitoring. More focus is placed on discovering and improving algorithms that compliment conventional internal control techniques. The practical implementation of PBRTQC with a biochemical instrument comparison is lacking. We aim to evaluate PBRTQC’s efficacy and practicality by comparing low-density lipoprotein cholesterol (LDL-C) test results to ensure consistent real-time monitoring across biochemical instrumentations in clinical laboratories. Method: From 1 September 2021 to 30 August 2022, the First Affiliated Hospital of Xi’an Jiaotong University collected data from 158,259 both healthy and diseased patients, including 84,187 male and 74,072 female patients, and examined their LDL-C results. This dataset encompassed a group comprising 50,556 individuals undergoing health examinations, a group comprising 42,472 inpatients (IP), and a group comprising 75,490 outpatients (OP) for the PBRTQC intelligent monitoring platform to conduct daily tests, parameter configuration, program development, real-time execution, and performance validation of the patients’ data. Moreover 40 patients’ LDL-C levels were assessed using two biochemical analyzers, designated as the reference and comparator instruments. A total of 160 LDL-C results were obtained from 40 both healthy and diseased patients, including 14 OP, 16 IP, and 10 health examination attendees, who were selected to represent LDL-C levels broadly. Two biochemical instruments measured LDL-C measurements from the same individuals to investigate consistency and reproducibility across patient statuses and settings. We employed exponentially weighted moving average (EWMA) and moving median (MM) methods to calculate inter-instrument bias and ensure analytical accuracy. Inter-instrument bias for LDL-C measurements was determined by analyzing fresh serum samples, different concentrations of quality control (QC), and commercialized calibrators, employing both EWMA and MM within two assay systems. The assessment of inter-instrumental bias with five different methods adhered to the external quality assessment standards of the Clinical Laboratory Center of the Health Planning Commission, which mandates a bias within ±15.0%. Result: We calculated inter-instrument comparison bias with each of the five methods based on patient big data. The comparison of fresh serum samples, different concentrations of QC, commercialized calibrators, and EWMA were all in the permissive range, except for MM. MM showed that the bias between two biochemical instruments in the concentration ranges of 1.5 mmoL/L–6.2 mmoL/L exceeded the permissible range. This was mainly due to the small number of specimens, affected by variations among individual patients, leading to increased false alarms and reduced effectiveness in monitoring the consistency of the inter-instrumental results. Moreover, the inter-comparison bias derived from EWMA was less than 3.01%, meeting the 15% range assessment criteria. The bias result for MM was lower than 24.66%, which was much higher than EWMA. Thus, EWMA is better than MM for monitoring inter-instrument comparability. PBRTQC can complement the use of inter-comparison bias between biochemical analyzers. EWMA has comparable inter-instrument comparability monitoring efficacy. Conclusions: The utilization of AI-based PBRTQC enables the automated real-time comparison of test results across different biochemical instruments, leading to a reduction in laboratory operating costs, enhanced work efficiency, and improved QC. This advanced technology facilitates seamless data integration and analysis, ultimately contributing to a more streamlined and efficient laboratory workflow in the biomedical field.

Vitamin C (VC) can be used to increase disease resistance in practice in intensive aquaculture. But it is still unconfirmed whether VC could alleviate inflammation and what dosage is suitable. This study investigated the effects of dietary VC on the immunity and enteritis of juvenile blunt snout bream (Megalobrama amblycephala) challenged with Aeromonas hydrophila. The fish were fed with VC levels ranging from 25.35 to 2231.98 mg/kg for 60 days. After that, fish fed with 150.65 mg/kg, 573.79 mg/kg, and 2231.98 mg/kg VC were challenged with A. hydrophila orally to simulate enteritis. The results showed that 285.39 and 573.79 mg/kg dietary VC significant improved growth performance and feed utilization. Dietary VC (573.79 and 1133.79 mg/kg) significantly enhanced the serum immune parameters, antioxidant enzymes’ activities, and relative gene expressions of ikbα and cat in the liver in the 60-day feeding trail. The survival rate was significantly higher in 573.79 mg/kg dietary VC group than the other two treatments. Cytokines were activated after the bacteria challenge. Dietary 573.79 mg/kg VC caused significantly higher TNF-α level at 12 hpi and maintained a high level of IL-8 at 48 to 72 h compared to other treatments. In conclusion, more than 500 mg/kg VC was needed to maintain the health of blunt snout bream juveniles. Suitable VC would activate cytokines to increase disease resistance along with antioxidant enzymes.

Ultraviolet (UV) networks are widely applied in complex electromagnetic environments. Designing an efficient multi-node medium access control (MAC) protocol for these networks is important. In this study, we proposed an enhanced clustering time division multiple access (TDMA) MAC protocol based on clustering and learning automata (LA). Subsequently, the effects of the network topology, class of service, and number of cluster nodes on the network performance under the proposed protocol were analyzed. Then, the protocol was compared with the TDMA protocol and clustering system. Results revealed that it obtained a better network performance, proving its suitability for multi-node UV networking.

The generation of test vectors is a key technique that affects the efficiency and fault detection rate of the boundary scan test. Aiming at the local optimal solution problem of the current common test vectors generation algorithm, this paper proposes a test vectors generation algorithm based on improved GA-AO* model, through which the test vectors are generated by using the idea of heuristic search and backtracking correction. In order to speed up the heuristic search, this paper designed a heuristic function with both prior and posterior parameters to describe the influence of typical faults on the failure probability index of the test vectors. At the same time, this paper used a genetic algorithm (GA) to determine the specific values of the posterior parameters iteratively. Finally, through theoretical analysis and physical verification, compared with the test vector generated by the traditional method, the test vector generated by this method is optimized on the prior failure probability index and performs better in the physical experiment.