Channel Model in Urban Environment for Unmanned Aerial Vehicle Communications

In order to develop and analyze reliable communications links for unmanned aerial vehicles (UAVs), accurate models for the propagation channel are required. The radio channel properties in the urban scenario are different from those in the suburb scenario and open area due to so many scattering paths from office buildings, especially when the UAV flies in the low altitude. We took some measurement campaigns on the campus of Tsinghua University with crowded apartments and office buildings. Based on the measurement result we extract the main parameters of pathloss model, and propose a simplified Saleh-Valenzuela (SV) model with specific parameters. The typical scene of central lawn is compared with the office buildings in the analysis of K-factor and root-mean-square (RMS) delay spread.Comment: to prepare in European Conference on Antennas and Propagetion (EUCAP), 201

Development of an Aptamer-Conjugated Polyrotaxane-Based Biodegradable Magnetic Resonance Contrast Agent for Tumor-Targeted Imaging

Gadolinium-based magnetic resonance imaging (MRI) contrast agents with biodegradability, biosafety, and high efficiency are highly desirable for tumor diagnosis. Herein, a biodegradable, AS1411-conjugated, α-cyclodextrin polyrotaxane-based MRI contrast agent (AS1411-G2­(DTPA-Gd)-SS-PR) was developed for targeted imaging of cancer. The polyrotaxane-based contrast agent was achieved by the complexation of α-cyclodextrin (α-CD) and a linear poly­(ethylene glycol) (PEG) chain containing disulfide linkages at two terminals. The disulfides enable the dethreading of the polyrotaxane into excretable small units due to cleavage of the disulfide linkages by reducing agents such as intracellular glutathione (GSH). Furthermore, the second-generation lysine dendron conjugated with gadolinium chelates and AS1411, a G-quadruplex oligonucleotide that has high binding affinity to nucleolin generally presenting a high level on the surface of tumor cells, coupled to the α-CD via click chemistry. The longitudinal relaxivity of AS1411-G2­(DTPA-Gd)-SS-PR (11.7 mM–1 s–1) was two times higher than the clinically used Gd-DTPA (4.16 mM–1 s–1) at 0.5 T. The in vitro degradability was confirmed by incubating with 10 mM 1,4-dithiothreitol (DTT). Additionally, the cytotoxicity, histological assessment, and gadolinium retention studies showed that the prepared polyrotaxane-based contrast agent had a superior biocompatibility and was predominantly cleared renally without long-term accumulation toxicity. Importantly, AS1411-G2­(DTPA-Gd)-SS-PR displayed the enhanced performance in MRI of breast cancer cells in vitro as well as a subcutaneous breast tumor in vivo due to the targeting ability of the AS1411 aptamer. The enhanced performance was due to efficient multivalent interactions with tumor cells, producing faster accumulation and longer contrast imaging time at the tumor site. This work clearly confirms that the specially designed and fabricated α-CD-based polyrotaxane is a promising contrast agent with an excellent contrast imaging performance and biosafety for tumor MR imaging

Single-cell mapping of N6-methyladenosine in esophageal squamous cell carcinoma and exploration of the risk model for immune infiltration

BackgroundN6-methyladenosine (m6A) modification is the most common RNA modification, but its potential role in the development of esophageal cancer and its specific mechanisms still need to be further investigated.MethodsBulk RNA-seq of 174 patients with esophageal squamous carcinoma from the TCGA-ESCC cohort, GSE53625, and single-cell sequencing data from patients with esophageal squamous carcinoma from GSE188900 were included in this study. Single-cell analysis of scRNA-seq data from GSE188900 of 4 esophageal squamous carcinoma samples and calculation of PROGENy scores. Demonstrate the scoring of tumor-associated pathways for different cell populations. Cell Chat was calculated for cell populations. thereafter, m6A-related differential genes were sought and risk models were constructed to analyze the relevant biological functions and impact pathways of potential m6A genes and their impact on immune infiltration and tumor treatment sensitivity in ESCC was investigated.ResultsBy umap downscaling analysis, ESCC single-cell data were labelled into clusters of seven immune cell classes. Cellchat analysis showed that the network interactions of four signaling pathways, MIF, AFF, FN1 and CD99, all showed different cell type interactions. The prognostic risk model constructed by screening for m6A-related differential genes was of significant value in the prognostic stratification of ESCC patients and had a significant impact on immune infiltration and chemotherapy sensitivity in ESCC patients.ConclusionIn our study, we explored a blueprint for the distribution of single cells in ESCC based on m6A methylation and constructed a risk model for immune infiltration analysis and tumor efficacy stratification in ESCC on this basis. This may provide important potential guidance for revealing the role of m6A in immune escape and treatment resistance in esophageal cancer

Research on Drought Monitoring Based on Deep Learning: A Case Study of the Huang-Huai-Hai Region in China

As climate change intensifies, drought has become a major global engineering and environmental challenge. In critical areas such as agricultural production, accurate drought monitoring is vital for the sustainable development of regional agriculture. Currently, despite extensive use of traditional meteorological stations and remote sensing methods, these approaches have proven to be inadequate in capturing the full extent of drought information and adequately reflecting spatial characteristics. Therefore, to improve the accuracy of drought forecasts and achieve predictions across extensive areas, this paper employs deep learning models, specifically introducing an attention-weighted long short-term memory network model (AW-LSTM), constructs a composite drought monitoring index (CDMI) and validates the model. Results show that: (1) The AW-LSTM model significantly outperforms traditional long short-term memory (LSTM), support vector machine (SVM) and artificial neural network (ANN) models in drought monitoring, offering not only better applicability in meteorological and agricultural drought monitoring but also the ability to accurately predict drought events one month in advance compared to machine learning models, providing a new method for precise and comprehensive regional drought assessment. (2) The Huang-Huai-Hai Plain has shown significant regional variations in drought conditions across different years and months, with the drought situation gradually worsening in the northern part of Hebei Province, Beijing, Tianjin, the southern part of Huai North and the central part of Henan Province from 2001 to 2022, while drought conditions in the northern part of Huai North, southern Shandong Province, western Henan Province and southwestern Hebei Province have been alleviated. (3) During the sowing (June) and harvesting (September) periods for summer maize, the likelihood of drought occurrences is higher, necessitating flexible adjustments to agricultural production strategies to adapt to varying drought conditions

Identification of Aberrantly Expressed miRNAs in Gastric Cancer

The noncoding components of the genome, including miRNA, can contribute to pathogenesis of gastric cancer. Their expression has been profiled in many human cancers, but there are a few published studies in gastric cancer. It is necessary to identify novel aberrantly expressed miRNAs in gastric cancer. In this study, the expression profile of 1891 miRNAs was analyzed using a miRCURY array LNA miRNA chip from three gastric cancer tissues and three normal tissues. The expression levels of 4 miRNAs were compared by real-time PCR between cancerous and normal tissues. We found that 31 miRNAs are upregulated in gastric cancer (P<0.05) and 10 miRNAs have never been reported by other studies; 30 miRNA are downregulated (P<0.05) in gastric cancer tissues. Gene ontology analysis revealed that those dysregulated miRNAs mainly take part in regulating cell proliferation. The levels of has-miR-105, -213*, -514b, and -548n were tested by real-time PCR and have high levels in cancerous tissues. Here, we report a miRNA profile of gastric cancer and provide new perspective to understand this malignant disease. This novel information suggests the potential roles of these miRNAs in the diagnosis, prognosis biomarkers, or therapy targets of gastric cancer

Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data

The Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost algorithm directly to imbalanced data since it is designed mainly for processing misclassified samples rather than samples of minority classes. To better process imbalanced data, this paper introduces the indicator Area Under Curve (AUC) which can reflect the comprehensive performance of the model, and proposes an improved AdaBoost algorithm based on AUC (AdaBoost-A) which improves the error calculation performance of the AdaBoost algorithm by comprehensively considering the effects of misclassification probability and AUC. To prevent redundant or useless weak classifiers the traditional AdaBoost algorithm generated from consuming too much system resources, this paper proposes an ensemble algorithm, PSOPD-AdaBoost-A, which can re-initialize parameters to avoid falling into local optimum, and optimize the coefficients of AdaBoost weak classifiers. Experiment results show that the proposed algorithm is effective for processing imbalanced data, especially the data with relatively high imbalances