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Abstract We evaluate the value of oral contrast-enhanced gastric ultrasonography (OCUS) by comparing it with conventional gastroscopy in diagnosing and staging benign peptic ulcer. From July 2018 to December 2020, 44 patients with gastroscopy-confirmed benign peptic ulcers (a total of 45 ulcers were detected), who also received OCUS, were retrospectively reviewed. Each patient’s ultrasound images were compared with gastroscopy and pathology findings. The characteristics of ultrasonic images of different stages of ulcer were analysed. A total of 43 ulcers were detected by OCUS in 44 patients with benign peptic ulcers. There were no false positive results among the OCUS exams, but two ulcers were misdiagnosed. OCUS for benign peptic ulcer staging also shows acceptable clinical practice results. OCUS is useful for detecting and staging benign peptic ulcer, and may be considered an alternative method for conventional gastroscopy. OCUS is especially useful in the follow-up of BPU treatment, but futher study is needed to improve the diagnostic accuracy of benign and malignant ulcers.

Weak target signal processing is the cornerstone and prerequisite for radar to achieve excellent detection performance. In complex practical applications, due to strong clutter interference, weak target signals, unclear image features, and difficult effective feature extraction, weak target detection and recognition have always been challenging in the field of radar processing. Conventional model-based processing methods do not accurately match the actual working background and target characteristics, leading to weak universality. Recently, deep learning has made significant progress in the field of radar intelligent information processing. By building deep neural networks, deep learning algorithms can automatically learn feature representations from a large amount of radar data, improving the performance of target detection and recognition. This article systematically reviews and summarizes recent research progress in the intelligent processing of weak radar targets in terms of signal processing, image processing, feature extraction, target classification, and target recognition. This article discusses noise and clutter suppression, target signal enhancement, low- and high-resolution radar image and feature processing, feature extraction, and fusion. In response to the limited generalization ability, single feature expression, and insufficient interpretability of existing intelligent processing applications for weak targets, this article underscores future developments from the aspects of small sample object detection (based on transfer learning and reinforcement learning), multidimensional and multifeature fusion, network model interpretability, and joint knowledge- and data-driven processing.

Detection of small, slow-moving targets, such as drones using Unmanned Aerial Vehicles (UAVs) poses considerable challenges to radar target detection and recognition technology. There is an urgent need to establish relevant datasets to support the development and application of techniques for detecting small, slow-moving targets. This paper presents a dataset for detecting low-speed and small-size targets using a multiband Frequency Modulated Continuous Wave (FMCW) radar. The dataset utilizes Ku-band and L-band FMCW radar to collect echo data from six UAV types and exhibits diverse temporal and frequency domain resolutions and measurement capabilities by modulating radar cycles and bandwidth, generating an LSS-FMCWR-1.0 dataset (Low Slow Small, LSS). To further enhance the capability for extracting micro-Doppler features from UAVs, this paper proposes a method for UAV micro-Doppler extraction and parameter estimation based on the local maximum synchroextracting transform. Based on the Short Time Fourier Transform (STFT), this method extracts values at the maximum energy point in the time-frequency domain to retain useful signals and refine the time-frequency energy representation. Validation and analysis using the LSS-FMCWR-1.0 dataset demonstrate that this approach reduces entropy on an average by 5.3 dB and decreases estimation errors in rotor blade length by 27.7% compared with traditional time-frequency methods. Moreover, the proposed method provides the foundation for subsequent target recognition efforts because it balances high time-frequency resolution and parameter estimation capabilities.

Background: In recent years, abnormal expression of collagen triple helix repeat containing 1 (CTHRC1) has been found in some tumors, closely related to the poor prognosis of cancer patients. However, the clinical significance of CTHRC1 in gliomas is not completely understood. Methods: We investigated the expression, prognostic value, and potential biological function of CTHRC1 in different types of gliomas through bioinformatics analysis and experimental verification. Results: Bioinformatics analysis revealed several key findings regarding the expression and clinical significance of CTHRC1 in gliomas. First, the analysis demonstrated a positive correlation between CTHRC1 expression and the World Health Organization (WHO) grading of gliomas, a relationship that was validated through immunohistochemistry experiments. In addition, a trend was observed in which CTHRC1 expression increased with the extent of glioma invasion, as supported by Western blot experiments. Subsequent bioinformatics analysis identified the mesenchymal subtype of gliomas as having the highest levels of CTHRC1 expression, a finding reinforced by immunohistochemical staining. Moreover, high CTHRC1 expression was associated with poor prognosis in gliomas and emerged as an independent prognostic factor, with varying impacts on prognosis between low-grade gliomas (LGGs) and glioblastoma (GBM) subgroups. Notably, comparative analysis unveiled distinct patterns of immune infiltration of CTHRC1 in LGG and GBM. Furthermore, alterations in copy number variations and DNA methylation were identified as potential mechanisms underlying elevated CTHRC1 levels in gliomas. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that CTHRC1 and its associated genes mainly function in the extracellular matrix and participate in tumor-related signaling pathways. Conclusions: The CTHRC1 has shown significant clinical utility as a prognostic marker and mesenchymal subtype marker of glioma.

Abstract Background Spontaneous intracerebral hemorrhage (SICH) has high morbidity and mortality, with no clear standard of treatment available. Compared with the craniotomy approach, neuroendoscopy is a relatively minimally invasive treatment method, and may be an efficient alternative. Therefore, this meta-analysis aimed to assess the clinical efficacy of neuroendoscopy and craniotomy in SICH patients. Methods The electronic databases Web of Science, PubMed, EmBase, MEDLINE, and the Cochrane Library were systematically searched. According to the PRISMA template, we finally selected and analyzed 14 eligible studies that evaluated neuroendoscopy versus craniotomy. Primary outcomes included operation time, intraoperative blood loss volume, evacuation rate, residual hematoma, complications, hospital stay duration, clinical outcomes, and other parameters. Results A total of 4 randomized controlled trials (RCTs) and 10 retrospective studies (non-RCTs) involving 1652 patients were included in the final analysis. In the neuroendoscopy (NE) group, operation time (p < 0.00001), intraoperative blood loss volume (p < 0.0001), hematoma evacuation rate (p = 0.0002), complications (p < 0.00001), hospitalization days (p = 0.004), and mortality (p < 0.0001) were significantly different from those of the craniotomy (C) group, with a higher rate of good recovery compared with the craniotomy group (P < 0.00001). Conclusions These findings suggest that patients with SICH and physicians may benefit more from neuroendoscopic surgery than craniotomy.

This paper proposes a joint design method to optimize the transmit waveforms and receive filter bank in Multiple-Input Multiple-Output (MIMO) structure ensuring spectral compatibility with the surrounding communication service network. Considering the signal-dependent clutter interference, under the constraints of transmission energy, waveform similarity and spectrum compatibility, the formulated optimization problem of the output Signal-to-Interference-plus-Noise Ratio (SINR) maximization is NP-hard. Toward this end, an auxiliary variable is first introduced to modify the original problem, and then a primal-dual algorithm based on the Alternating Block Successive Upper-bound Minimization (ABSUM) method is developed to deal with the resulting problem. Furthermore, an interior point method is used to handle the quadratic programming problem involved in each update procedure of the devised ABSUM method. Finally, numerical simulations are performed to demonstrate the superiority of the proposed method over state-of-the-art methods in terms of the optimized SINR, beampattern, computational complexity and ambiguity properties.

Craniopharyngioma (CP) is a rare benign tumor that develops from the residual epithelial cells of the craniopharynx, accounting for < 5% of intracranial tumors. It is common for CPs to grow in the sellar/parasellar region and extend suprasellar. The pathology classifies CPs into adamantinomatous craniopharyngiomas (ACP) and papillary craniopharyngiomas (PCP). The PCP is mainly solid and occurs only in adults. ACP is predominantly cystic and more common in childhood and adolescent. Multilocular cystic ACP involving the anterior, middle, and posterior cranial fossa is rare in adults. Here, we report a case of a 46-year-old adult male patient who presented with recurrent headaches for 1 year with choking and hoarseness. Computed tomography (CT) and magnetic resonance imaging (MRI) revealed multiple cystic masses in the anterior, middle, and posterior cranial fossa. Initial hypotheses included the following: CP, colloid cyst, enterogenous cyst, epidermoid cyst, and dermoid cyst. Subsequently, the patient underwent surgery and postoperative histopathology diagnosed ACP. Adults with ACP involving the anterior, middle, and posterior cranial fossae are uncommon. This is a rare condition that radiologists should be aware of.

Wheat stripe rust is an important foliar disease that affects the wheat yield globally. Breeding for resistant wheat varieties is one of the most economically and environmentally effective ways to control this disease. The common wheat (Triticum aestivum L.) cultivar “Pascal” exhibited susceptibility to stripe rust at the seedling stage but it showed high resistance to stripe rust at the adult plant stage over 20 years in Gansu, a hotspot of the disease in northwestern China. To understand the genetic mechanism of stripe rust resistance in this cultivar, a 55K SNP array was used to analyze the two parents and the 220 recombinant inbred lines (RILs) derived from the cross of “Huixianhong” × “Pascal.” We detected three new stripe rust adult plant resistance (APR) quantitative trait locus (QTL) contributed by Pascal, viz. QYr.gaas-1AL, QYr.gaas-3DL, and QYr.gaas-5AS, using the inclusive composite interval mapping method. They were flanked by SNP markers AX-111218361—AX-110577861, AX-111460455—AX-108798599, and AX-111523523—AX-110028503, respectively, and explained the phenotypic variation ranging from 11.0 to 23.1%. Bulked segregant exome capture sequencing (BSE-Seq) was used for fine mapping of QYr.gaas-1AL and selection of candidate genes, TraesCS1A02G313700, TraesCS1A02G313800, and TraesCS1A02G314900 for QYr.gaas-1AL. KASP markers BSE-1A-12 and HXPA-3D for QYr.gaas-1AL and QYr.gaas-3DL were developed for breeders to develop durable stripe rust-resistant wheat varieties.

Instead of only considering the radar estimation error in the traditional radar system (TRS), for the integrated radar and communication system (IRCS), we investigate the Cramer-Rao bound (CRB) of the localization estimation, which is influenced by both radar estimation error and communication transmission error. The functions of radar and communication are operated simultaneously by embedding the communication symbols into the multicarrier radar waveforms. Firstly, we derive the CRB of time/direction of arrival (TOA/DOA) estimation. To minimize the estimation error, we maximize the signal-to-interference-plus-noise ratio (SINR) of radar by iteratively optimizing the radar transmit and receive beamformers (with the constraint of available transmit power). Then, the CRB of localization estimation is derived using hybrid TOA/DOA measurement. The local CRBs from different IRCSs are fused according to the linear fusion rule at the fusion center (FC). Finally, numerical results demonstrate that the additional estimation errors for the IRCS are mainly determined by the channel conditions of communication and available transmit power; the estimation accuracy for both IRCS and TRS can be improved through the iterative transmit and receive beamforming (ITRB) technique.

Local Outlier Factor (LOF) outlier detecting algorithm has good accuracy in detecting global and local outliers. However, the algorithm needs to traverse the entire dataset when calculating the local outlier factor of each data point, which adds extra time overhead and makes the algorithm execution inefficient. In addition, if the K-distance neighborhood of an outlier point P contains some outliers that are incorrectly judged by the algorithm as normal points, then P may be misidentified as normal point. To solve the above problems, this paper proposes a Neighbor Entropy Local Outlier Factor (NELOF) outlier detecting algorithm. Firstly, we improve the Self-Organizing Feature Map (SOFM) algorithm and use the optimized SOFM clustering algorithm to cluster the dataset. Therefore, the calculation of each data point's local outlier factor only needs to be performed inside the small cluster. Secondly, this paper replaces the K-distance neighborhood with relative K-distance neighborhood and utilizes the entropy of relative K neighborhood to redefine the local outlier factor, which improves the accuracy of outlier detection. Experiments results confirm that our optimized SOFM algorithm can avoid the random selection of neurons, and improve clustering effect of traditional SOFM algorithm. In addition, the proposed NELOF algorithm outperforms LOF algorithm in both accuracy and execution time of outlier detection.

Coal-to-gas/electricity conversion (hereafter referred to as CTGC/CTEC) as the core project of a clean heating campaign has been widely adopted to replace and reduce the combustion of residential coal in Northern China since 2017. In this study, simulations based on the WRF-Chem model were carried out to quantitatively assess the impacts of the CTGC/CTEC project on air quality in the Beijing–Tianjin–Hebei (BTH) region. It was found that the CTGC/CTEC projects exert a remarkable effect on improving the air quality in the BTH region, especially in the plain area. The maximum decrease in the concentrations of PM2.5 and PM10 averaged during January can reach 30 and 40 μg/m3, respectively. In addition, the spillover effects due to CTGC/CTEC projects are rather small; that is, the local reduced emissions tend to provide more benefit to the local air quality but less for its surrounding regions. It is also noteworthy that the effects due to meteorological condition changes are comparable with, or even larger, than those due to CTGC/CTEC projects, which are not spatially uniform for the BTH region among various cities. Overall, these results not only demonstrate the effectiveness of CTGC/CTEC projects on air-quality improvement in the BTH region, but also indicate the importance of meteorological conditions in modulating the local air quality. To sustain better air quality in the future, residential coal replacement, all over China, can be further promoted. In addition, continued policy refinement can be essential for the nationwide implementation of clean heating projects.

Presently, many users are involved in multiple social networks. Identifying the same user in different networks, also known as anchor link prediction, becomes an important problem, which can serve numerous applications, e.g., cross-network recommendation, user profiling, etc. Previous studies mainly use hand-crafted structure features, which, if not carefully designed, may fail to reflect the intrinsic structure regularities. Moreover, most of the methods neglect the attribute information of social networks. In this paper, we propose a novel semi-supervised network-embedding model to address the problem. In the model, each node of the multiple networks is represented by a vector for anchor link prediction, which is learnt with awareness of observed anchor links as semi-supervised information, and topology structure and attributes as input. Experimental results on the real-world data sets demonstrate the superiority of the proposed model compared to state-of-the-art techniques.