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In this paper, a fast method to generate a constant-modulus (CM) waveform for a multiple-input, multiple-output, (MIMO) radar is proposed. To simplify the optimization process, the design of the transmit waveform is decoupled from the design of transmit beamformers (TBs) and subpulses. To further improve the computational efficiency, the TBs' optimization is conducted in parallel, and a linear programming model is proposed to match the desired beampattern. Additionally, we incorporate the perturbation vectors into the TBs' optimization so that the TBs can be adjusted to satisfy the CM constraint. To quickly generate the CM subpulses with the desired range-compression (RC) performance, the classical linear frequency modulation (LFM) signal and non-LFM (NLFM) are adopted as subpulses. Meanwhile, to guarantee the RC performance of the final angular waveform, the selection of LFM signal parameters is analyzed to achieve a low cross-correlation between subpulses. Numerical simulations verify the transmit beampattern performance, RC performance, and computational efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Brucella spp. are facultative intracellular pathogens that cause zoonosis- brucellosis worldwide. There has been a trend of the re-emergence of brucellosis worldwide in recent years. The epidemic situation of brucellosis is serious in Xinjiang. To analyze the epidemic situation of Brucella spp. in Xinjiang among humans and animals, this study identified 144 Brucella isolates from Xinjiang using classical identification and 16 S rRNA sequencing. MLVA, drug resistance testing, and wgSNP detection were also performed. At the same time, analysis was conducted based on the published data of Brucella isolates worldwide. The results showed that the dominant species was B. melitensis biovar 3, which belonged to GT42 (MLVA-8 typing) and the East Mediterranean lineage. The correlation among isolates was high both in humans or animals. The isolates in Xinjiang exhibited higher polymorphism compared to other locations in China, with polymorphism increasing each year since 2010. No amikacin/kanamycin-resistant strains were detected, but six rifampicin-intermediate isolates were identified without rpoB gene variation. The NJ tree of the wgSNP results indicated that there were three main complexes of the B. melitensis epidemic in Xinjiang. Based on the results of this study, the prevention and control of brucellosis in Xinjiang should focus on B. melitensis, particularly strains belonging to B. melitensis bv.3 GT42 (MLVA-8 typing) and East Mediterranean lineage. Additionally, the rifampicin- and trimethoprim-sulfamethoxazole- resistance of isolates in Xinjiang should be closely monitored to avoid compromising the therapeutic efficacy and causing greater losses. These results provide essential data for the prevention and control of brucellosis in Xinjiang and China. Although the isolates from Xinjiang have significant characteristics among Chinese isolates and can reflect the epidemiological situation of brucellosis in China to some extent, this study cannot represent the characteristics of isolates from other regions. [ABSTRACT FROM AUTHOR]

The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes. [ABSTRACT FROM AUTHOR]

Purpose Mucosal inflammation is a key feature of ulcerative colitis (UC), a chronic relapsing and remitting form of inflammatory bowel disease. Omentin-1, a newly discovered adipokine, is reported to have anti-inflammatory effects and has been found to be decreased in patients with inflammatory bowel disease. The aim of our study was to investigate the association between serum omentin-1 levels and mucosal disease activity in patients with UC. Study design A total of 126 patients with UC and 77 healthy volunteers were enrolled in the study. Serum omentin-1 expression levels were measured using enzyme-linked immunosorbent assay to evaluate its potential for monitoring disease activity, including clinical and endoscopic activity. Results Serum omentin-1 levels were significantly lower in patients with UC compared to healthy controls (HC) (UC, 61.7 interquartile range: 51.5–72.6 versus healthy controls, 103.5 interquartile range: 48.3–156.2 ng/ml; P  < .001). Furthermore, serum omentin-1 levels were associated with both clinical and endoscopic activity in patients with UC. Notably, omentin-1 levels were significantly lower in patients who achieved mucosal healing. Receiver operating characteristic curves indicated that serum omentin-1 levels could potentially serve as an activity index for evaluating UC. Conclusions These findings provide further insight into the association between omentin-1 and UC, suggesting that omentin-1 may be a useful biomarker for monitoring mucosal disease activity in patients with UC. Key message What is already known on this topic?   UC is a chronic inflammatory bowel disease characterized by mucosal inflammation. MH is an important treatment goal, as failure to achieve MH predicts clinical relapse. Serum omentin-1, an adipokine with anti-inflammatory properties, has shown reduced levels in patients with IBD. What this study adds?   Our study revealed significantly lower serum omentin-1 levels in UC patients compared to healthy controls. Serum omentin-1 levels were associated with clinical and endoscopic activity and might indicate MH. How this study might affect research, practice, or policy?   Our findings suggest that measuring serum omentin-1 levels could serve as a potential biomarker for monitoring mucosal disease activity in UC. Incorporating omentin-1 assessment into clinical practice may aid in early detection of disease activity and monitoring treatment response. [ABSTRACT FROM AUTHOR]

Co-clustering is the simultaneous clustering of the samples and attributes of a data matrix that provides deeper insight into data than traditional clustering. However, there is a lack of representation learning algorithms that serve this mechanism of co-clustering, and the current representation learning algorithms are limited to the sample perspective and lack the use of information in the attribute perspective. To solve this problem, in this article, ctSNE, a co-representation learning model based on t-distributed stochastic neighbor embedding, is proposed for unsupervised co-clustering, where ctSNE makes the dataset representation outputted more discriminative of row and column clusters (i.e. co-discrimination). On the basis of t-distributed stochastic neighbor embedding retaining the sample data distribution and local data structure, the philosophy of collaboration is introduced (i.e., row and column hidden relationship information) so that the ctSNE model is equipped with co-representation learning capability, which can effectively improve the performance of co-clustering. To prove the effectiveness of the ctSNE model, several classic co-clustering algorithms are used to check the co-representation performance of ctSNE, and a novel internal index based on an internal clustering index, known as total inertia, is proposed to demonstrate the effect of co-clustering. The numerous experimental results show that ctSNE has tremendous co-representation capability and can significantly improve the performance of co-clustering algorithms. [ABSTRACT FROM AUTHOR]