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Compared with electromagnetic compatibility (EMC) testing in anechoic rooms, open-area EMC testing takes advantage of in situ and engine running status measurement but suffers from non-negligible external electromagnetic interference. This paper proposes a novel environmental interference suppression method (named the EMC environmental interference suppression algorithm ( E 2 I S A )) that separates signals from backgrounds via image segmentation and recognizes the near–far site signal via a group of time-varying features based on the difference in the near-site EM radiative characteristic. We find that the proposed E 2 I S A method, which combines the deep learning segmentation network with the classical recognition methods, is able to suppress environmental interference signals accurately. The experiment results show that the accuracy of E 2 I S A reaches up to 95% in the face of VHF (Very High Frequency) EMC testing tasks. [ABSTRACT FROM AUTHOR]

The increasing use of molybdate has raised concerns about its potential toxicity in humans. However, the potential toxicity of molybdate under the current level of human exposure remains largely unknown. Endogenous metabolic alterations that are caused in humans by environmental exposure to pollutants are associated with the occurrence and progression of many diseases. This study exposed eight-week-old male C57 mice to sodium molybdate at doses relevant to humans (0.01 and 1 mg/kg/day) for eight weeks. Inductively coupled plasma mass spectrometry (ICP-MS) and ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS) were utilized to assess changes in urine element levels and serum metabolites in mice, respectively. A total of 838 subjects from the NHANES 2017–2018 population database were also included in our study to verify the associations between molybdenum and cadmium found in mice. Analysis of the metabolome in mice revealed that four metabolites in blood serum exhibited significant changes, including 5-aminolevulinic acid, glycolic acid, l-acetylcarnitine, and 2,3-dihydroxypropyl octanoate. Analysis of the elementome revealed a significant increase in urine levels of cadmium after molybdate exposure in mice. Notably, molybdenum also showed a positive correlation with cadmium in humans from the NHANES database. Further analysis identified a positive correlation between cadmium and 2,3-dihydroxypropyl octanoate in mice. In conclusion, these findings suggest that molybdate exposure disrupted amino acid and lipid metabolism, which may be partially mediated by molybdate-altered cadmium levels. The integration of elementome and metabolome data provides sensitive information on molybdate-induced metabolic disorders and associated toxicities at levels relevant to human exposure. [ABSTRACT FROM AUTHOR]

Simple Summary: The synthesis of natural or non-natural products in microorganisms frequently involves multiple genes or pathways. Enhancing and balancing the expression of these genes is essential to prevent growth inhibition and product synthesis blockage resulting from the accumulation of intermediate products, thereby effectively increasing the yield of target products. However, there is a deficiency in robust tools for balancing gene expression in Bacillus subtilis. Herein, we identified a 5′ UTR sequence that enhances gene expression levels. The library constructed from this sequence enables a broad spectrum of gene variations. Utilizing this library to construct a synthesized rib operon markedly boosts riboflavin production. Therefore, a novel 5′ UTR sequence was screened and characterized, supplementing the synthetic biology toolkit for B. subtilis. The application of synthetic biology tools to modulate gene expression to increase yield has been thoroughly demonstrated as an effective and convenient approach in industrial production. In this study, we employed a high-throughput screening strategy to identify a 5′ UTR sequence from the genome of B. subtilis 168. This sequence resulted in a 5.8-fold increase in the expression level of EGFP. By utilizing the 5′ UTR sequence to overexpress individual genes within the rib operon, it was determined that the genes ribD and ribAB serve as rate-limiting enzymes in the riboflavin synthesis pathway. Constructing a 5′ UTR library to regulate EGFP expression resulted in a variation range in gene expression levels exceeding 100-fold. Employing the same 5′ UTR library to regulate the expression of EGFP and mCherry within the operon led to a change in the expression ratio of these two genes by over 10,000-fold. So, employing a 5′ UTR library to modulate the expression of the rib operon gene and construct a synthetic rib operon resulted in a 2.09-fold increase in riboflavin production. These results indicate that the 5′ UTR sequence identified and characterized in this study can serve as a versatile synthetic biology toolkit for achieving complex metabolic network reconstruction. This toolkit can facilitate the fine-tuning of gene expression to produce target products. [ABSTRACT FROM AUTHOR]

This study introduces a pioneering multimodal fusion framework to enhance near-field 3D Synthetic Aperture Radar (SAR) imaging, crucial for applications like radar cross-section measurement and concealed object detection. Traditional near-field 3D SAR imaging struggles with issues like target–background confusion due to clutter and multipath interference, shape distortion from high sidelobes, and lack of color and texture information, all of which impede effective target recognition and scattering diagnosis. The proposed approach presents the first known application of multimodal fusion in near-field 3D SAR imaging, integrating LiDAR and optical camera data to overcome its inherent limitations. The framework comprises data preprocessing, point cloud registration, and data fusion, where registration between multi-sensor data is the core of effective integration. Recognizing the inadequacy of traditional registration methods in handling varying data formats, noise, and resolution differences, particularly between near-field 3D SAR and other sensors, this work introduces a novel three-stage registration process to effectively address these challenges. First, the approach designs a structure–intensity-constrained centroid distance detector, enabling key point extraction that reduces heterogeneity and accelerates the process. Second, a sample consensus initial alignment algorithm with SHOT features and geometric relationship constraints is proposed for enhanced coarse registration. Finally, the fine registration phase employs adaptive thresholding in the iterative closest point algorithm for precise and efficient data alignment. Both visual and quantitative analyses of measured data demonstrate the effectiveness of our method. The experimental results show significant improvements in registration accuracy and efficiency, laying the groundwork for future multimodal fusion advancements in near-field 3D SAR imaging. [ABSTRACT FROM AUTHOR]

Through an interesting physical perspective and a certain contraction of the Ricci curvature tensor in Finsler geometry, Akbar-Zadeh introduced the concept of scalar curvature for the Finsler metric. In this paper, we show that the Kropina metric is of isotropic scalar curvature if and only if F is an Einstein metric according to the navigation data. Moreover, we obtain the three-dimensional rigidity theorem for an Einstein–Kropina metric. [ABSTRACT FROM AUTHOR]

Wheat stripe rust, triggered by Puccinia striiformis f. sp. tritici, is among the most widespread and damaging wheat (Triticum L.) diseases. The development of cultivars harboring adult plant resistance (APR) to stripe rust is a better approach to control the disease. The current study aimed to map APR to stripe rust via the QTL mapping of 165 F2–6 recombinant inbred lines (RILs), derivatives of Weimai 8/Zhengzhou 5389. The collection of phenotypic data for the stripe-rust resistance of both parents and all 165 RILs were conducted at Baoding, Hebei Province, during the 2016–2017 cropping seasons, and at Mianyang, Sichuan Province, during the 2017–2018 and 2018–2019 sowing seasons. The RIL populations and parents were also genotyped with 860 pairs of simple-sequence-repeat (SSR) primers to map APR QTLs to stripe-rust resistant. Moreover, a 55K SNP chip was used for small group bulk segregant analysis conducted to locate the genetic map location and concentration of the SNP markers on the wheat genome. Inclusive composite interval mapping (IciMapping 3.2) software identified four QTLs of stripe-rust resistance on chromosomes 1B, 2AS, 2BS, and 7DS, named QYr.wmy-1B, QYr.wmy-2AS, QYr.wmy-2BS, and QYr.wmy-7DS, which significantly explained 11.56–16.64%, 9.35–12.70%, 7.27–9.95%, and 11.49–15.07% of the phenotypic variation, respectively. All these QTLs were found from the resistant parent, Weimai 8. Meanwhile, the QTLs located on chromosomes 1B and 2AS were found close to Yr18 and Yr9, respectively. Furthermore, the results indicated that QYr.wmy-1B is possibly Yr9 and QYr.wmy-2AS is similar to Yr18 or might be a new QTL, whilst QYr.wmy-2BS and QYr.wmy-7DS were found to be different from previously reported stripe-rust-resistance QTLs and are possibly new QTLs. Overall, the QTLs and their closely associated molecular markers detected in this study could be a great source of input for marker-assisted selection to adult plant stripe-rust resistance in wheat-breeding programs. [ABSTRACT FROM AUTHOR]

Simple Summary: Gonadal development and gametogenesis are key processes for fish genetic breeding. Particularly, reproductive development-related genes such as zygote arrest-1 (Zar1) and Wilms' tumor 1 (Wt1) play an important role in oogenesis, with the latter also involved in testicular development and gender differentiation. Here, the cDNA fragments of Lczar1 and Lcwt1b were identified in Asian seabass (Lates calcarifer), a valuable hermaphrodite fish for studying sex reverse. Furthermore, their mRNA distributions in the cells of gonadal tissues were detected. Lczar1 mRNA was exclusively expressed in the ovary, while Lcwt1b mRNA was majorly expressed in the gonads in a higher amount in the testis than in the ovary. Specifically, Lczar1 mRNA was mainly concentrated in oogonia and oocytes at early stages in the ovary, but was undetectable in the testis. Lcwt1b mRNA was localized not only in gonadal somatic cells (testis and ovary), but also in female and male germ cells at the early developmental stages. Our study indicated that Lczar1 and Lcwt1b possibly play roles in gonadal development. Therefore, the findings of this study will provide a basis for clarifying the mechanism of Lczar1 and Lcwt1b in regulating germ cell development and the sex reverse of Asian seabass and even other hermaphroditic species. Zygote arrest-1 (Zar1) and Wilms' tumor 1 (Wt1) play an important role in oogenesis, with the latter also involved in testicular development and gender differentiation. Here, Lczar1 and Lcwt1b were identified in Asian seabass (Lates calcarifer), a hermaphrodite fish, as the valuable model for studying sex differentiation. The cloned cDNA fragments of Lczar1 were 1192 bp, encoding 336 amino acids, and contained a zinc-binding domain, while those of Lcwt1b cDNA were 1521 bp, encoding a peptide of 423 amino acids with a Zn finger domain belonging to Wt1b family. RT-qPCR analysis showed that Lczar1 mRNA was exclusively expressed in the ovary, while Lcwt1b mRNA was majorly expressed in the gonads in a higher amount in the testis than in the ovary. In situ hybridization results showed that Lczar1 mRNA was mainly concentrated in oogonia and oocytes at early stages in the ovary, but were undetectable in the testis. Lcwt1b mRNA was localized not only in gonadal somatic cells (the testis and ovary), but also in female and male germ cells in the early developmental stages, such as those of previtellogenic oocytes, spermatogonia, spermatocytes and spermatids. These results indicated that Lczar1 and Lcwt1b possibly play roles in gonadal development. Therefore, the findings of this study will provide a basis for clarifying the mechanism of Lczar1 and Lcwt1b in regulating germ cell development and the sex reversal of Asian seabass and even other hermaphroditic species. [ABSTRACT FROM AUTHOR]

Metabolism underlies the pathogenesis of acute myeloid leukemia (AML) and can be influenced by gut microbiota. However, the specific metabolic changes in different tissues and the role of gut microbiota in AML remain unclear. In this study, we analyzed the metabolome differences in blood samples from patients with AML and healthy controls using UPLC-Q-Exactive. Additionally, we examined the serum, liver, and fecal metabolome of AML model mice and control mice using UPLC-Q-Exactive. The gut microbiota of the mice were analyzed using 16S rRNA sequencing. Our UPLC-MS analysis revealed significant differences in metabolites between the AML and control groups in multiple tissue samples. Through cross-species validation in humans and animals, as well as reverse validation of Celastrol, we discovered that the Carnosine–Histidine metabolic pathway may play a potential role in the occurrence and progression of AML. Furthermore, our analysis of gut microbiota showed no significant diversity changes, but we observed a significant negative correlation between the key metabolite Carnosine and Peptococcaceae and Campylobacteraceae. In conclusion, the Carnosine–Histidine metabolic pathway influences the occurrence and progression of AML, while the gut microbiota might play a role in this process. [ABSTRACT FROM AUTHOR]

Bacterioplankton communities are critical components of varied ecosystems in the oceans. Their occurrences represent a variety of connections between environmental and ecological elements. However, our current knowledge about the shaping factors of surface bacterioplankton communities in the eastern East China Sea (ECS) is still limited. In this study, we reveal the spatial patterns of the taxonomic and functional profiles of the surface bacterioplankton communitiesies in the nearshore and offshore areas in the eastern ECS, based on 16S rRNA gene pyrosequencing and functional annotation analysis. The obtained results show that the surface bacterioplankton communities in the nearshore areas are mainly dominated by the firmicutes (85.9%), actinobacteria (8.1%), and proteobacteria (5.4%), which are mainly involved in organic compound metabolism. Meanwhile, different bacteria predominate the composition of the offshore group, namely proteobacteria (71.1%) and bacteroidetes (22.0%) responsible for nitrogen and sulfur metabolism. Furthermore, their distribution pattern is shown to be spatially determined, along with a modest finding of functional diversity when comparing the bacterial species. The primary two shaping factors of bacterioplankton diversity are found to be the offshore distance and temperature. Overall, these findings add to those previously published on bacterial species and offer up functional information on the surface bacterioplankton communities in the eastern ECS. To extend our research, we propose that, in the future, it may be beneficial to monitor the dynamics of the ecosystem in this sea area. [ABSTRACT FROM AUTHOR]

Phycosphere niches host rich microbial consortia that harbor dynamic algae–bacteria interactions with fundamental significance in varied natural ecosystems. Hence, culturing the uncultured microbial majority of the phycosphere microbiota is vital for deep understanding of the intricate mechanisms governing the dynamic interactions, and also to provide novel and rich microbial resources, and to discover new natural bioactive metabolites. Synechococcus elongatus PCC 7942 is a robust model cyanobacterium widely used in environment, synthesis biology, and biotechnology research. To expand the number of novel phycosphere species that were brought into culture and to discover the natural bioactivities, we presented a new yellow-pigmented bacterium named ABI-127-1, which was recovered from the phycosphere of PCC 7942, using an optimized bacterial isolation procedure. Combined polyphasic taxonomic and phylogenomic characterization was performed to confidently identify the new isolate as a potential novel species belonging to the genus Qipengyuania. The observed bioactivity of strain ABI-127-1 with promoting potential towards the growth and CO2 fixation efficiency of the host microalgae was measured. Additionally, the bacterial production of active bioflocculant exopolysaccharides was evaluated after culture optimization. Thus, these findings revealed the potential environmental and biotechnological implications of this new microalgae growth-promoting bacterium isolated from the phycosphere microenvironment. [ABSTRACT FROM AUTHOR]

For practical maritime SAR image classification tasks with special imaging platforms, scenes to be classified are often different from those in the training sets. The quantity and diversity of the available training data can also be extremely limited. This problem of out-of-distribution (OOD) generalization with limited training samples leads to a sharp drop in the performance of conventional deep learning algorithms. In this paper, a knowledge-guided neural network (KGNN) model is proposed to overcome these challenges. By analyzing the saliency features of various maritime SAR scenes, universal knowledge in descriptive sentences is summarized. A feature integration strategy is designed to assign the descriptive knowledge to the ResNet-18 backbone. Both the individual semantic information and the inherent relations of the entities in SAR images are addressed. The experimental results show that our KGNN method outperforms conventional deep learning models in OOD scenarios with varying training sample sizes and achieves higher robustness in handling distributional shifts caused by weather conditions, terrain type, and sensor characteristics. In addition, the KGNN model converges within many fewer epochs during training. The performance improvement indicates that the KGNN model learns representations guided by beneficial properties for ODD generalization with limited training samples. [ABSTRACT FROM AUTHOR]

Black carbon (BC) and CO2 emissions are the two major factors responsible for global climate change and the associated health risks. Quantifying the impact of economic activities in urban agglomerations on BC and CO2 emissions is essential for finding a balance between climate change mitigation and pollution reduction. In this study, we utilized a city-level environmental extended multi-regional input–output model (EE-MRIO), integrated nexus strength (INS), and structural path analysis (SPA) to quantify the BC and CO2 footprints, nexus nodes, and supply chains of 21 cities in the Sichuan urban agglomeration (SUA) from 2012 to 2017. The results revealed that approximately 70% of the BC and CO2 footprints come from inter-city transactions, with Chengdu being the largest importing city, while the supply of other cities was greater than their consumption. The SUA has transitioned from a supply-side city cluster to a consumption-oriented city cluster in its trade with other domestic regions. The SPA analysis highlighted that the construction sector was the largest emitter of downstream BC and CO2, while the electricity supply, metal/nonmetallic manufacture, oil refining and coking, transportation, and extraction industry sectors were the main nexus nodes for BC and CO2 emissions in the SUA. Notably, the reduction in BC emissions was due to decreased indirect emissions from oil refining and coking, while the decrease in CO2 emissions was a result of reduced indirect emissions from electricity supply. This article presents, for the first time, a quantification of the heterogeneous impacts and emission supply chains of BC and CO2 emissions from economic activities in the SUA, providing valuable insights for developing climate mitigation policies tailored to different urban clusters. [ABSTRACT FROM AUTHOR]

Epimedium is a classical Chinese herbal medicine, which has been used extensively to treat various diseases, such as sexual dysfunction, osteoporosis, cancer, rheumatoid arthritis, and brain diseases. Flavonoids, such as icariin, baohuoside I, icaritin, and epimedin C, are the main active ingredients with diverse pharmacological activities. Currently, most Epimedium flavonoids are extracted from Epimedium plants, but this method cannot meet the increasing market demand. Biotransformation strategies promised huge potential for increasing the contents of high-value Epimedium flavonoids, which would promote the full use of the Epimedium herb. Complete biosynthesis of major Epimedium flavonoids by microbial cell factories would enable industrial-scale production of Epimedium flavonoids. This review summarizes the structures, pharmacological activities, and biosynthesis pathways in the Epimedium plant, as well as the extraction methods of major Epimedium flavonoids, and advancements in the biotransformation and complete microbial synthesis of Epimedium flavonoids, which would provide valuable insights for future studies on Epimedium herb usage and the production of Epimedium flavonoids. [ABSTRACT FROM AUTHOR]

Fermented vegetables have a long history and are enjoyed worldwide for their unique flavors and health benefits. The process of fermentation improves the nutritional value, taste, and shelf life of foods. Microorganisms play a crucial role in this process through the production of metabolites. The flavors of fermented vegetables are closely related to the evaluation and succession of microbiota. Lactic acid bacteria (LABs) are typically the dominant bacteria in fermented vegetables, and they help inhibit the growth of spoilage bacteria and maintain a healthy gut microbiota in humans. However, homemade and small-scale artisanal products rely on spontaneous fermentation using bacteria naturally present on fresh vegetables or from aged brine, which may introduce external microorganisms and lead to spoilage and substandard products. Hence, understanding the role of LABs and other probiotics in maintaining the quality and safety of fermented vegetables is essential. Additionally, selecting probiotic fermentation microbiota and isolating beneficial probiotics from fermented vegetables can facilitate the use of safe and healthy starter cultures for large-scale industrial production. This review provides insights into the traditional fermentation process of making fermented vegetables, explains the mechanisms involved, and discusses the use of modern microbiome technologies to regulate fermentation microorganisms and create probiotic fermentation microbiota for the production of highly effective, wholesome, safe, and healthy fermented vegetable foods. [ABSTRACT FROM AUTHOR]

Transcription factors (TFs) have been shown to play a key role in the occurrence and development of tumors, including triple-negative breast cancer (TNBC), with a worse prognosis. Machine learning is widely used for establishing prediction models and screening key tumor drivers. Current studies lack TF integration in TNBC, so targeted research on TF prognostic models and targeted drugs is beneficial to improve clinical translational application. The purpose of this study was to use the Least Absolute Shrinkage and Selection Operator to build a prognostic TFs model after cohort normalization based on housekeeping gene expression levels. Potential targeted drugs were then screened on the basis of molecular docking, and a multi-drug combination strategy was used for both in vivo and in vitro experimental studies. The machine learning model of TFs built by E2F8, FOXM1, and MYBL2 has broad applicability, with an AUC value of up to 0.877 at one year. As a high-risk clinical factor, its abnormal disorder may lead to upregulation of the activity of pathways related to cell proliferation. This model can also be used to predict the adverse effects of immunotherapy in patients with TNBC. Molecular docking was used to screen three drugs that target TFs: Trichostatin A (TSA), Doxorubicin (DOX), and Calcitriol. In vitro and in vivo experiments showed that TSA + DOX was able to effectively reduce DOX dosage, and TSA + DOX + Calcitriol may be able to effectively reduce the toxic side effects of DOX on the heart. In conclusion, the machine learning model based on three TFs provides new biomarkers for clinical and prognostic diagnosis of TNBC, and the combination targeted drug strategy offers a novel research perspective for TNBC treatment. [ABSTRACT FROM AUTHOR]

2-phenylethanol (2-PE) has been widely utilized as an aromatic additive in various industries, including cosmetics, beer, olive oil, tea, and coffee, due to its rose-honey-like aroma. However, no reports have investigated the production of 2-PE by Starmerella bacillaris. Here, S. bacillaris (syn., Candida zemplinina, and named strain R5) was identified by analysis of morphology, physiology and biochemistry, and 26S rRNA and ITS gene sequence. Then, based on the analysis of whole-genome sequencing and comparison with the KEGG database, it was inferred that strain R5 could synthesize 2-PE from L-phe or glucose through the Ehrlich pathway or shikimate pathway. For further verification of the 2-PE synthesis pathway, strain R5 was cultured in M3 (NH4+), M3 (NH4+ + Phe), and M3 (Phe) medium. In M3 (Phe) medium, the maximum concentration of 2-PE reached 1.28 g/L, which was 16-fold and 2.29-fold higher than that in M3 (NH4+) and M3 (Phe + NH4+) media, respectively. These results indicated that 2-PE could be synthesized by strain R5 through the shikimate pathway or Ehrlich pathway, and the biotransformation from L-phe to 2-PE was more efficient than that from glucose. The qRT-PCR results suggested that compared to M3 (Phe + NH4+) medium, the mRNA expression levels of YAT were 124-fold and 86-fold higher in M3 (Phe) and M3 (NH4+) media, respectively, indicating that the transport of L-phe was inhibited when both NH4+ and Phe were present in the medium. In the M3 (Phe) and M3 (Phe + NH4+) media, the mRNA expression level of ADH5 was higher than PDC, hisC, GOT1, and YAT, and it was 2.6 times higher and 2.48 times higher, respectively, compared to the M3 (NH4+) medium, revealing that the key gene catalyzing the dehydrogenation of benzaldehyde to 2-PE is ADH5. Furthermore, strain R5 exhibits tolerance to high concentrations of 2-PE, reaching 3 g/L, which conferred an ideal tolerance to 2-PE. In summary, the synthesis pathway of 2-PE, mainly for the Ehrlich pathway, was proved for the first time in S. bacillaris, which had not been previously explored and provided a basis for non-Saccharomyces yeast-producing 2-PE and its applications. [ABSTRACT FROM AUTHOR]

The class of warped product metrics can often be interpreted as key space models for the general theory of relativity and theory of space–time. In this paper, we study several non-Riemannian quantities in Finsler geometry. These non-Riemannian quantities play an important role in understanding the geometric properties of Finsler metrics. In particular, we find differential equations of Finsler warped product metrics with vanishing χ -curvature or vanishing H-curvature. Furthermore, we show that, for Finsler warped product metrics, the χ -curvature vanishes if and only if the H-curvature vanishes. [ABSTRACT FROM AUTHOR]

The class of warped product metrics can often be interpreted as key space models for the general theory of relativity and theory of space-time. In this paper, we first obtain the PDE characterization of Finsler warped product metrics with a vanishing Riemannian curvature. Moreover, we obtain equivalent conditions for locally Minkowski Finsler warped product spaces. Finally, we explicitly construct two types of non-Riemannian examples. [ABSTRACT FROM AUTHOR]

In this paper, we study conformal transformations between two almost regular general (α , β) -metrics. By using the method of special coordinate system, the necessary and sufficient conditions for conformal transformations preserving the mean Landsberg curvature are obtained. Further, a rigidity theorem for regular general (α , β) -metrics is proved. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) ship detection is of significant importance in military and commercial applications. However, a high similarity in intensity and spatial distribution of scattering characteristics between the ship target and harbor facilities, along with a fuzzy sea-land boundary due to the strong speckle noise, result in a low detection accuracy and high false alarm rate for SAR ship detection with complex inshore scenes. In this paper, a new inshore ship detection method based on multi-modality saliency is proposed to overcome these challenges. Four saliency maps are established from different perspectives: an ocean-buffer saliency map (OBSM) outlining more accurate coastline under speckle noises; a local stability saliency map (LSSM) addressing pixel spatial distribution; a super-pixel saliency map (SPSM) extracting critical region-based features for inshore ship detection; and an intensity saliency map (ISM) to highlight target pixels with intensity distribution. By combining these saliency maps, ship targets in complex inshore scenes can be successfully detected. The method provides a novel interdisciplinary perspective (surface metrology) for SAR image segmentation, discovers the difference in spatial characteristics of SAR image elements, and proposes a novel robust CFAR procedure for background clutter fitting. Experiments on a public SAR ship detection dataset (SSDD) shows that our method achieves excellent detection performance, with a low false alarm rate, in offshore scenes, inshore scenes, inshore scenes with confusing metallic port facilities, and large-scale scenes. The results outperform several widely used methods, such as CFAR-based methods and super-pixel methods. [ABSTRACT FROM AUTHOR]

In this paper, we study Kropina metrics with isotropic scalar curvature. First, we obtain the expressions of Ricci curvature tensor and scalar curvature. Then, we characterize the Kropina metrics with isotropic scalar curvature on by tensor analysis. [ABSTRACT FROM AUTHOR]

Glutamic acid is an important amino acid that is used widely in the fields of food, medicine, and agriculture. One of the methods of glutamic acid production is direct microbial fermentation, so the genetic stability and glutamic-acid-producing capacity of the producing strain are the keys to improving glutamic acid concentration. Experiments were carried out using Corynebacterium glutamicum GL−6 as the parental strain, with two iterations of mutagenesis by atmospheric and room temperature plasma (ARTP) and screening with agar plates tolerant to high sugar and malonic acid, and the best strains with stable phenotypes were verified by fermentation in 20 L tanks. The results show that the optimal mutagenesis time of ARTP was 140 s, with lethality and positive mutation rates of 93.0% and 15.6%, respectively. The concentrations of the high-sugar and malonic acid agar plates were 240 g/L and 35 g/L, respectively. A mutant strain, P−45, with improved glutamic acid production capacity and genetic stability, was obtained through two rounds of iterative mutagenesis screening. The concentration of this strain in the Erlenmeyer flasks was 17.7 g/L, which was 18.8% higher than that of the parental strain, GL−6, and could be inherited stably for 10 generations. In the glutamic acid synthesis pathway, the upregulation of the gene encoding citrate synthase (cs), gene encoding isocitrate dehydrogenase (icdh), and gene encoding glutamate dehydrogenase (gdh), and the downregulation of the gene encoding oxoglutarate dehydrogenase complex (odhc) increased the carbon flows of the TCA cycle and its branch metabolic flow to glutamic acid synthesis. P−45 showed a glutamic acid concentration of 147.0 g/L under fed-batch fermentation conditions in 20 L tanks, which was 81.5% higher than the starting strain, GL−6. This study provides a new technical solution for improving microbial metabolites and genetic stability. [ABSTRACT FROM AUTHOR]

China's economic development has made world-renowned achievements which have subsequently brought serious environmental pollution problems. As a large manufacturing country, the industrial value added in China accounts for nearly 30% of the global share. However, the manufacturing industry lacks competence. Therefore, considering environmental protection issues while promoting manufacturing industry upgrading (MIU) is a critical goal for China at present. By applying panel data from China's Yangtze River Economic Belt (YREB) from 2010 to 2019, this paper uses a spatial econometric model to study the effect of green finance (GF) on upgrading (hereafter, upgrading refers to the improvement of the manufacturing structure and the transition toward green manufacturing) the manufacturing industry. This study found that the impact of GF on MIU in the YREB is significantly positive. GF has a good positive spatial spillover effect. Moreover, the high level of urbanization and high freight volume have positive impacts, the impacts of foreign investment and government fiscal expenditure are significantly negative, and the effect of the level of human capital on upgrading is not obvious. Therefore, vigorously developing green finance, optimizing the environment for manufacturing upgrades, and cultivating green and high-quality talents should be the future direction of government policy formulation. [ABSTRACT FROM AUTHOR]

Consumption plays an important role in economic growth and sustainable economic development. The Chinese government emphasizes the theme of promoting high-quality development. This aim has led to the implementation of strategies to expand domestic demand through a deep structural reform in the supply chain, enhance the endogenous power and reliability of the large domestic cycle, improve the quality and level of the international cycle, and accelerate the construction of a modern economic system. Based on the clarification of the consumption effect of housing support expenditure, this paper uses a combination of theoretical and empirical analysis to clarify the relationship between housing support expenditure and urban residents' consumption. This is carried out to seek a breakthrough point for raising the total level of urban residents' consumption expenditure in China from the level of housing support. This study revealed that government housing support has a certain degree of influence on the total level of urban residents' consumption expenditure and that the government's support of housing support is conducive to improving the total level of urban residents' consumption expenditure. Also, if the housing price is not controlled and adjusted, the effect of government housing support on urban residents' consumption expenditure will be greatly reduced, which will affect the lives of urban residents in the long run. Finally, suggestions and countermeasures for improving the overall level of urban residents' consumption and the sustainable development of the social economy in China are provided. [ABSTRACT FROM AUTHOR]

The conformal transformation, which preserves Einstein metrics on Finsler warped product manifolds, is studied in this paper. We obtain sufficient and necessary conditions of a conformal transformation preserving Einstein metrics. In addition, we provide nontrivial examples of conformal transformations. Furthermore, we completely classify Einstein Riemannian warped product metrics and obtain the existence of a nontrivial conformal transformation that preserves Einstein metrics. [ABSTRACT FROM AUTHOR]

Carbon reduction is an important aspect of achieving high-quality environmental development; environmental regulation and industrial agglomeration are important ways to affect carbon emissions. Therefore, studying the relationship between industrial agglomeration, environmental regulation, and carbon emissions has important theoretical and practical significance. Firstly, this article adopts the method of location entropy to measure the indicators of industrial agglomeration. Secondly, it proposes an environmental regulation indicator system based on the experience of previous scholars and measures the environmental regulation indicators using the entropy method. Next, eight types of energy consumption are used to measure carbon emissions based on the carbon emission coefficient method provided by the IPCC. Finally, based on the Moran index, the spatial correlation of carbon emission levels in various regions of the Yangtze River Economic Belt (YREB) is tested. A spatial econometric model was introduced to explore the relationship between industrial agglomeration, environmental regulation, and carbon emissions at a deeper level, and the following conclusions were drawn: (1) The regression coefficient of the spatial term of industrial agglomeration on carbon emissions is 0.848, which is significantly positive at the 10% level, indicating that under the influence of spatial effects, industrial agglomeration has a significant promoting effect on carbon emissions. (2) The regression coefficient of the spatial term of environmental regulation on carbon emissions is −0.011, which is significantly negative at the 10% level, indicating that environmental regulation has an inhibitory effect on carbon emissions under the influence of spatial effects. Based on the above conclusions, useful suggestions have been provided for optimizing industrial structure, improving environmental regulation levels, and alleviating carbon emission issues. [ABSTRACT FROM AUTHOR]

Loop-mediated isothermal amplification (LAMP), as the rank one alternative to a polymerase chain reaction (PCR), has been widely applied in point-of-care testing (POCT) due to its rapid, simple, and cost-effective characteristics. However, it is difficult to achieve real-time monitoring and multiplex detection with the traditional LAMP method. In addition, these approaches that use turbidimetry, sequence-independent intercalating dyes, or pH-sensitive indicators to indirectly reflect amplification can result in false-positive results if non-specific amplification occurs. To fulfill the needs of specific target detection and one-pot multiplex detection, a variety of probe-based LAMP assays have been developed. This review focuses on the principles of these assays, summarizes their applications in pathogen detection, and discusses their features and advantages over the traditional LAMP methods. [ABSTRACT FROM AUTHOR]

In this paper, we identify the sufficient and necessary conditions for conformally related Randers metrics to have the same χ -curvature. Further, if s 0 = 0 holds, we conclude that the conformal transformation must be a homothety. Using the divergence theorem, we prove that, on the compact manifold, there is no nontrivial conformal transformation preserving the χ -curvature of the Randers metrics invariant. [ABSTRACT FROM AUTHOR]

Ship instance segmentation in synthetic aperture radar (SAR) images can provide more detailed location information and shape information, which is of great significance for port ship scheduling and traffic management. However, there is little research work on SAR ship instance segmentation, and the general accuracy is low because the characteristics of target SAR ship task, such as multi-scale, ship aspect ratio, and noise interference, are not considered. In order to solve these problems, we propose an idea of scale in scale (SIS) for SAR ship instance segmentation. Its essence is to establish multi-scale modes in a single scale. In consideration of the characteristic of the targeted SAR ship instance segmentation task, SIS is equipped with four tentative modes in this paper, i.e., an input mode, a backbone mode, an RPN mode (region proposal network), and an ROI mode (region of interest). The input mode establishes multi-scale inputs in a single scale. The backbone mode enhances the ability to extract multi-scale features. The RPN mode makes bounding boxes better accord with ship aspect ratios. The ROI mode expands the receptive field. Combined with them, a SIS network (SISNet) is reported, dedicated to high-quality SAR ship instance segmentation on the basis of the prevailing Mask R-CNN framework. For Mask R-CNN, we also redesign (1) its feature pyramid network (FPN) for better small ship detection and (2) its detection head (DH) for a more refined box regression. We conduct extensive experiments to verify the effectiveness of SISNet on the open SSDD and HRSID datasets. The experimental results reveal that SISNet surpasses the other nine competitive models. Specifically, the segmentation average precision (AP) index is superior to the suboptimal model by 4.4% on SSDD and 2.5% on HRSID. [ABSTRACT FROM AUTHOR]

Background: The thalamus has been reported to be associated with pain modulation and processing. However, the functional changes that occur in the thalamus of vestibular migraine (VM) patients remain unknown. Methods: In total, 28 VM patients and 28 healthy controls who were matched for age and sex underwent resting-state functional magnetic resonance imaging. They also responded to standardized questionnaires aimed at assessing the clinical features associated with migraine and vertigo. Differences in the amplitude of low-frequency fluctuation (ALFF) were analyzed and brain regions with altered ALFF in the two groups were used for further analysis of whole-brain functional connectivity (FC). The relationship between clusters and clinical features was investigated by correlation analyses. Results: The ALFF in the thalamus was significantly decreased in the VM group versus the control group. In the VM group, the ALFF in the left thalamus negatively correlated with VM episode frequency. Furthermore, the left thalamus showed significantly weaker FC than both regions of the medial prefrontal cortex, both regions of the anterior cingulum cortex, the left superior/middle temporal gyrus, and the left temporal pole in the VM group. Conclusions: The thalamus plays an important role in VM patients and it is suggested that connectivity abnormalities of the thalamocortical region contribute to abnormal pain information processing and modulation, transmission, and multisensory integration in patients with VM. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is an important active microwave imaging sensor. There is no doubt that applying DL to more SAR fields (such as terrain classification, SAR agriculture monitoring, SAR imaging algorithm updating, SAR forest applications, marine pollution, etc.) is of great significance for earth remote sensing. Conclusions Recently, as many SAR systems have been put into use, massive SAR data are available, providing important support for exploring how to apply DL to SAR fields. [Extracted from the article]

Although disturbed functional connectivity is known to be a factor influencing cognitive impairment, the neuropathological mechanisms underlying the cognitive impairment caused by type 2 diabetes mellitus (T2DM) remain unclear. To characterize the neural mechanisms underlying T2DM-related brain damage, we explored the altered functional architecture patterns in different cognitive states in T2DM patients. Thirty-seven T2DM patients with normal cognitive function (DMCN), 40 T2DM patients with mild cognitive impairment (MCI) (DMCI), and 40 healthy controls underwent neuropsychological assessments and resting-state functional MRI examinations. Functional connectivity density (FCD) analysis was performed, and the relationship between abnormal FCD and clinical/cognitive variables was assessed. The regions showing abnormal FCD in T2DM patients were mainly located in the temporal lobe and cerebellum, but the abnormal functional architecture was more extensive in DMCI patients. Moreover, in comparison with the DMCN group, DMCI patients showed reduced long-range FCD in the left superior temporal gyrus (STG), which was correlated with the Rey auditory verbal learning test score in all T2DM patients. Thus, DMCI patients show functional architecture abnormalities in more brain regions involved in higher-level cognitive function (executive function and auditory memory function), and the left STG may be involved in the neuropathology of auditory memory in T2DM patients. These findings provide some new insights into understanding the neural mechanisms underlying T2DM-related cognitive impairment. [ABSTRACT FROM AUTHOR]

Streptococcus suis (S. suis) is an important zoonotic pathogen. It mainly uses quorum sensing (QS) to adapt to complex and changeable environments. QS is a universal cell-to-cell communication system that has been widely studied for its physiological functions, including the regulation of bacterial adhesion, virulence, and biofilm formation. Quorum sensing inhibitors (QSIs) are highly effective at interfering with the QS system and bacteria have trouble developing resistance to them. We review the current research status of the S. suis LuxS/AI-2 QS system and QSIs. Studies showed that by inhibiting the formation of AI-2, targeting the LuxS protein, inhibiting the expression of luxs gene can control the LuxS/AI-2 QS system of S. suis. Other potential QSIs targets are summarized, which may be preventing and treating S. suis infections, including AI-2 production, transmission, LuxS protein, blockage of AI-2 binding to receptors, AI-2-mediated QS. Since antibiotics are becoming increasingly ineffective due to the emergence of resistant bacteria, including S. suis, it is thus critical to find new antibacterial drugs with different mechanisms of action. QSIs provide hope for the development of such drugs. [ABSTRACT FROM AUTHOR]

Simple Summary: Yaks are one of the sources of greenhouse gas emissions from livestock farming on the Qinghai–Tibet Plateau region, and regulating greenhouse gas emissions from yaks has important ecological significance. In this study, we evaluated potential links between basal diet, rumen microbiota composition, and CH4 and CO2 emissions of yaks under different feeding regimes. We found the CO2 and CH4 emissions of yaks were lower in traditional grazing than in warm-grazing and cold-indoor feeding regimes. The rumen microbiota of the yaks changed because of differences in basal diet. The CO2 and CH4 emissions of yaks were related to complementarity among members of the rumen functional genera. We believe that shifts in feeding regimes are effective measures reducing greenhouse gas emissions from yaks and rumen microbiome characterization could be useful screening tools for selecting yaks with low gas emissions. Shifts in feeding regimes are important factors affecting greenhouse gas (GHG) emissions from livestock farming. However, the quantitative values and associated drivers of GHG emissions from yaks (Bos grunniens) following shifts in feeding regimes have yet to be fully described. In this study, we aimed to investigate CH4 and CO2 emissions differences of yaks under different feeding regimes and their potential microbial mechanisms. Using static breathing chamber and Picarro G2508 gas concentration analyzer, we measured the CO2 and CH4 emissions from yaks under traditional grazing (TG) and warm-grazing and cold-indoor feeding (WGCF) regimes. Microbial inventories from the ruminal fluid of the yaks were determined via Illumina 16S rRNA and ITS sequencing. Results showed that implementing the TG regime in yaks decreased their CO2 and CH4 emissions compared to the WGCF regime. The alpha diversity of ruminal archaeal community was higher in the TG regime than in the WGCF regime. The beta diversity showed that significant differences in the rumen microbial composition of the TG regime and the WGCF regime. Changes in the rumen microbiota of the yaks were driven by differences in dietary nutritional parameters. The relative abundances of the phyla Neocallimastigomycota and Euryarchaeota and the functional genera Prevotella, Ruminococcus, Orpinomyces, and Methanobrevibacter were significantly higher in the WGCF regime than in the TG regime. CO2 and CH4 emissions from yaks differed mainly because of the enrichment relationship of functional H2- and CO2-producing microorganisms, hydrogen-consuming microbiota, and hydrogenotrophic methanogenic microbiota. Our results provided a view that it is ecologically important to develop GHG emissions reduction strategies for yaks on the Qinghai–Tibet Plateau based on traditional grazing regime. [ABSTRACT FROM AUTHOR]

Ship detection in synthetic aperture radar (SAR) images is a significant and challenging task. However, most existing deep learning-based SAR ship detection approaches are confined to single-polarization SAR images and fail to leverage dual-polarization characteristics, which increases the difficulty of further improving the detection performance. One problem that requires a solution is how to make full use of the dual-polarization characteristics and how to excavate polarization features using the ship detection network. To tackle the problem, we propose a group-wise feature enhancement-and-fusion network with dual-polarization feature enrichment (GWFEF-Net) for better dual-polarization SAR ship detection. GWFEF-Net offers four contributions: (1) dual-polarization feature enrichment (DFE) for enriching the feature library and suppressing clutter interferences to facilitate feature extraction; (2) group-wise feature enhancement (GFE) for enhancing each polarization semantic feature to highlight each polarization feature region; (3) group-wise feature fusion (GFF) for fusing multi-scale polarization features to realize polarization features' group-wise information interaction; (4) hybrid pooling channel attention (HPCA) for channel modeling to balance each polarization feature's contribution. We conduct sufficient ablation studies to verify the effectiveness of each contribution. Extensive experiments on the Sentinel-1 dual-polarization SAR ship dataset demonstrate the superior performance of GWFEF-Net, with 94.18% in average precision (AP), compared with the other ten competitive methods. Specifically, GWFEF-Net can yield a 2.51% AP improvement compared with the second-best method. [ABSTRACT FROM AUTHOR]

Diverse pectinolytic enzymes are widely applied in the food, papermaking, and other industries, and they account for more than 25% of the global industrial enzyme demands. Efficient lignocellulose degradation microbiota are reservoirs of pectinolytic enzymes and other lignocellulose-degrading genes. Metagenomics has been widely used to discover new pectinolytic enzymes. Here, we used a metagenomic strategy to characterize pectinolytic genes from one efficient lignocellulose-degrading microbiota derived from pulp and paper wastewater treatment microbiota. A total of 23 predicted full-length GH28 and PL1 family pectinolytic genes were selectively cloned and expressed in Escherichia coli, and 5 of the expressed proteins had pectinolytic activities. Among them, the characterization of one pectinolytic enzyme, PW-pGH28-3, which has a 58.4% identity with an exo-polygalacturonase gene of Aquipluma nitroreducens, was further investigated. The optimal pH and optimal temperature of PW-pGH28-3 were 8.0 and 40 °C, respectively, and its pectinolytic activity at the optimal condition was 13.5 ± 1.1 U/mg protein. Bioinformatics analyses and structural modeling suggest that PW-pGH28-3 is a novel secretory exo-polygalacturonase, which is confirmed by its hydrolysates of polygalacturonic acid. The detection of PW-pGH28-3 and other pectinolytic genes showed that efficient lignocellulose degradation microbiota could provide potential efficient pectinolytic enzymes for industrial application. In the future, improving metagenomic screening efficiency would discover efficient lignocellulose-degrading enzymes and lead to the sustainable and green utilization of lignocellulose. [ABSTRACT FROM AUTHOR]

High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is a promising technique for applications such as maritime surveillance. In the maritime environment, normally only a few targets such as ships are interested. However, before detecting them, considerable receiving resources and computation time are required to receive the echoes of the whole scene and process them to obtain imaging results. This is a heavy burden for online monitoring on platforms with limited resources. To address these issues, different from the concept of whole-scene-oriented imaging, we propose a target-oriented imaging concept, which is implemented by an adaptive receiving–processing–decision feedback framework with feedback connection. (1) To reduce receiving resource consumption, we propose a two-dimensional adaptive receiving module. It receives sub-band echoes of targets only through dechirping and subaperture decomposition in the range and azimuth directions, respectively. (2) To reduce computation time, we propose a target-oriented processing module. It processes subarea echoes of targets only through parallelly conducting inverse fast Fourier transform (IFFT) and back projection (BP) in the range and azimuth directions, respectively. (3) To allocate resources reasonably, we propose a decision module. It decides the necessary receiving window, bandwidth, and image resolution through constant false alarm rate (CFAR) detection. (4) To allocate resources adaptively, we connect three modules with a closed loop to enable feedback. This enables progressive target imaging and detection from rough to fine. Experimental results verify the feasibility of the proposed framework. Compared with the current one, for a typical scenario, at least 30% of the system's resources and 99% of computation time are saved. [ABSTRACT FROM AUTHOR]

The traditional quantitative analysis methods of ascorbic acid (AA), which require expensive equipment, a large amount of samples and professional technicians, are usually complex and time-consuming. A low-cost and high-efficiency AA detection device is reported in this work. It integrates a three-electrode sensor module prepared by screen printing technology, and a microfluidic chip with a finger-actuated micropump peeled from the liquid-crystal display (LCD) 3D printing resin molds. The AA detection process on this device is easy to operate. On-chip detection has been demonstrated to be 2.48 times more sensitive than off-chip detection and requires only a microliter-scale sample volume, which is much smaller than that required in traditional electrochemical methods. Experiments show that the sample and buffer can be fully mixed in the microchannel, which is consistent with the numerical simulation results wherein the mixing efficiency is greater than 90%. Commercially available tablets and beverages are also tested, and the result shows the reliability and accuracy of the device, demonstrating its broad application prospects in the field of point-of-care testing (POCT). [ABSTRACT FROM AUTHOR]

In this paper, the super-resolution structural point cloud matching (S2-PCM) framework is proposed for video synthetic aperture radar (SAR) inter-frame registration, which consists of a feature recurrence super-resolution network (FRSR-Net), structural point cloud extraction network (SPCE-Net) and robust point matching network (RPM-Net). FRSR-Net is implemented by integrating the feature recurrence structure and residual dense block (RDB) for super-resolution enhancement, SPCE-Net is implemented by training a U-Net with data augmentation, and RPM-Net is applied for robust point cloud matching. Experimental results show that compared with the classical SIFT-like algorithms, S2-PCM achieves higher registration accuracy for video-SAR images under diverse evaluation metrics, such as mutual information (MI), normalized mutual information (NMI), entropy correlation coefficient (ECC), structural similarity (SSIM), etc. The proposed FRSR-Net can significantly improve the quality of video-SAR images and point cloud extraction accuracy. Combining FRSR-Net with S2-PCM, we can obtain higher inter-frame registration accuracy, which is crucial for moving target detection and shadow tracking. [ABSTRACT FROM AUTHOR]

The ability of immature embryos to induce embryogenic callus (EC) is crucial for genetic transformation in maize, which is highly genotype-dependent. To dissect the genetic basis of maize EC induction, we conducted QTL mapping for four EC induction-related traits, the rate of embryogenic callus induction (REC), rate of shoot formation (RSF), length of shoot (LS), and diameter of callus (DC) under three environments by using an IBM Syn10 DH population derived from a cross of B73 and Mo17. These EC induction traits showed high broad-sense heritability (>80%), and significantly negative correlations were observed between REC and each of the other traits across multiple environments. A total of 41 QTLs for EC induction were identified, among which 13, 12, 10, and 6 QTLs were responsible for DC, RSF, LS, and REC, respectively. Among them, three major QTLs accounted for >10% of the phenotypic variation, including qLS1-1 (11.54%), qLS1-3 (10.68%), and qREC4-1 (11.45%). Based on the expression data of the 215 candidate genes located in these QTL intervals, we performed a weighted gene co-expression network analysis (WGCNA). A combined use of KEGG pathway enrichment and eigengene-based connectivity (KME) values identified the EC induction-associated module and four hub genes (Zm00001d028477, Zm00001d047896, Zm00001d034388, and Zm00001d022542). Gene-based association analyses validated that the variations in Zm00001d028477 and Zm00001d034388, which were involved in tryptophan biosynthesis and metabolism, respectively, significantly affected EC induction ability among different inbred lines. Our study brings novel insights into the genetic and molecular mechanisms of EC induction and helps to promote marker-assisted selection of high-REC varieties in maize. [ABSTRACT FROM AUTHOR]

Hantaviruses are zoonotic pathogens that are widely distributed worldwide. Hantaan virus (HTNV) and Seoul virus (SEOV) are two most common hantaviruses that infect humans and cause hemorrhagic fever with renal syndrome (HFRS). Rapid and sensitive detection of HTNV and SEOV are crucial for surveillance, clinical treatment and management of HFRS. This study aimed to develop a rapid HFman probe-based mulstiplex reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay to simultaneously detect HTNV and SEOV. A novel multiplex RT-LAMP assay was developed, and 46 serum samples obtained from clinically suspected patients were used for evaluation. The novel RT-LAMP assay can detect as low as 3 copies/reaction of hantaviruses with a detection limit of 41 and 73 copies per reaction for HTNV and SEOV, respectively. A clinical evaluation showed that the consistencies of the multiplex RT-LAMP with RT-qPCR assay were 100% and 97.8% for HTNV and SEOV, respectively. In view of the high prevalence of HTNV and SEOV in rural areas with high rodent density, a colorimetric visual determination method was also developed for point-of-care testing (POCT) for the diagnosis of the two viruses. The novel multiplex RT-LAMP assay is a sensitive, specific, and efficient method for simultaneously detecting HTNV and SEOV. [ABSTRACT FROM AUTHOR]

Ship detection with rotated bounding boxes in synthetic aperture radar (SAR) images is now a hot spot. However, there are still some obstacles, such as multi-scale ships, misalignment between rotated anchors and features, and the opposite requirements for spatial sensitivity of regression tasks and classification tasks. In order to solve these problems, we propose a rotated balanced feature-aligned network (RBFA-Net) where three targeted networks are designed. They are, respectively, a balanced attention feature pyramid network (BAFPN), an anchor-guided feature alignment network (AFAN) and a rotational detection network (RDN). BAFPN is an improved FPN, with attention module for fusing and enhancing multi-level features, by which we can decrease the negative impact of multi-scale ship feature differences. In AFAN, we adopt an alignment convolution layer to adaptively align the convolution features according to rotated anchor boxes for solving the misalignment problem. In RDN, we propose a task decoupling module (TDM) to adjust the feature maps, respectively, for solving the conflict between the regression task and classification task. In addition, we adopt a balanced L1 loss to balance the classification loss and regression loss. Based on the SAR rotation ship detection dataset, we conduct extensive ablation experiments and compare our RBFA-Net with eight other state-of-the-art rotated detection networks. The experiment results show that among the eight state-of-the-art rotated detection networks, RBFA-Net makes a 7.19% improvement with mean average precision compared to the second-best network. [ABSTRACT FROM AUTHOR]

Highly convection-related short-duration heavy rainfall (SDHR), defined as rainfall greater than 20 mm h−1 of a whole hour, causes severe damage every year in China. An objective forecasting method is developed to provide guidance products for the short-term probability of SDHR. Representative parameters of environmental moisture content, instability, and dynamical forcing are selected as predictors based on the ingredients-based methodology. The predictors are selected by comparing their ability to discriminate between SDHR and both no rainfall and ordinary rainfall with hourly rainfall records and the NCEP reanalysis dataset during the warm seasons of 2002 and 2009. A fuzzy logic approach is obtained for the calculation of SDHR probability. Intervals of intensities are obtained based on specific percentiles and various weight settings examined. The probabilistic SDHR forecasts during the 2015 warm seasons with the NCEP GFS dataset are obtained, and forecasts are evaluated by using an operational used spatial verification method. Results show that the reference operational SDHR forecasts are surpassed by the 00–12 h period objective SDHR forecasts measured with the maximum critical success index (CSI), and even the average CSI (CSIave) for the top groups is better than the reference. The guidance SDHR products are skillful within 60 h. Although the weights vary significantly, the short-term patterns of the SDHR probability are mainly determined by the environmental conditions. The objective forecasting method is ingredients-based but is combined with fuzzy logic algorithms. The new approach provides a feasible exploration of the convective weather phenomenon. [ABSTRACT FROM AUTHOR]

Abnormally elevated circulating bile acids (BA) during pregnancy endanger fetal survival and offspring health; however, the pathology and underlying mechanisms are poorly understood. A total of nineteen pregnant sows were randomly assigned to day 60 of gestation, day 90 of gestation (G60, G90), and the farrowing day (L0), to investigate the intercorrelation of reproductive hormone, including estradiol, progesterone and sulfated progesterone metabolites (PMSs), and BA in the peripheral blood of mother and fetuses during pregnancy. All data were analyzed by Student's t-test or one-way ANOVA of GraphPad Prism and further compared by using the Student–Newman–Keuls test. Correlation analysis was also carried out using the CORR procedure of SAS to study the relationship between PMSs and BA levels in both maternal and fetal serum at G60, G90, and L0. Allopregnanolone sulphate (PM4S) and epiallopregnanolone sulphate (PM5S) were firstly identified in the maternal and fetal peripheral blood of pregnant sows by using newly developed ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) methods. Correlation analysis showed that pregnancy-associated maternal BA homeostasis was correlated with maternal serum PM4S levels, whereas fetal BA homeostasis was correlated with fetal serum PM5S levels. The antagonist activity role of PM5S on farnesoid X receptor (FXR)-mediated BA homeostasis and fibroblast growth factor 19 (FGF19) were confirmed in the PM5S and FXR activator co-treated pig primary hepatocytes model, and the antagonist role of PM4S on FXR-mediated BA homeostasis and FGF19 were also identified in the PM4S-treated pig primary hepatocytes model. Together with the high relative expression of FGF19 in pig hepatocytes, the pregnant sow is a promising animal model to investigate the pathogenesis of cholestasis during pregnancy. [ABSTRACT FROM AUTHOR]

Phase filtering is a vital step for interferometric synthetic aperture radar (InSAR) terrain elevation measurements. Existing phase filtering methods can be divided into two categories: traditional model-based and deep learning (DL)-based. Previous studies have shown that DL-based methods are frequently superior to traditional ones. However, most of the existing DL-based methods are purely data-driven and neglect the filtering model, so that they often need to use a large-scale complex architecture to fit the huge training sets. The issue brings a challenge to improve the accuracy of interferometric phase filtering without sacrificing speed. Therefore, we propose a sparse-model-driven network (SMD-Net) for efficient and high-accuracy InSAR phase filtering by unrolling the sparse regularization (SR) algorithm to solve the filtering model into a network. Unlike the existing DL-based filtering methods, the SMD-Net models the physical process of filtering in the network and contains fewer layers and parameters. It is thus expected to ensure the accuracy of the filtering without sacrificing speed. In addition, unlike the traditional SR algorithm setting the spare transform by handcrafting, a convolutional neural network (CNN) module was established to adaptively learn such a transform, which significantly improved the filtering performance. Extensive experimental results on the simulated and measured data demonstrated that the proposed method outperformed several advanced InSAR phase filtering methods in both accuracy and speed. In addition, to verify the filtering performance of the proposed method under small training samples, the training samples were reduced to 10%. The results show that the performance of the proposed method was comparable on the simulated data and superior on the real data compared with another DL-based method, which demonstrates that our method is not constrained by the requirement of a huge number of training samples. [ABSTRACT FROM AUTHOR]

Existing instance segmentation models mostly pay less attention to the targeted characteristics of ships in synthetic aperture radar (SAR) images, which hinders further accuracy improvements, leading to poor segmentation performance in more complex SAR image scenes. To solve this problem, we propose a hybrid task cascade plus (HTC+) for better SAR ship instance segmentation. Aiming at the specific SAR ship task, seven techniques are proposed to ensure the excellent performance of HTC+ in more complex SAR image scenes, i.e., a multi-resolution feature extraction network (MRFEN), an enhanced feature pyramid net-work (EFPN), a semantic-guided anchor adaptive learning network (SGAALN), a context ROI extractor (CROIE), an enhanced mask interaction network (EMIN), a post-processing technique (PPT), and a hard sample mining training strategy (HSMTS). Results show that each of them offers an observable accuracy gain, and the instance segmentation performance in more complex SAR image scenes becomes better. On two public datasets SSDD and HRSID, HTC+ surpasses the other nine competitive models. It achieves 6.7% higher box AP and 5.0% higher mask AP than HTC on SSDD. These are 4.9% and 3.9% on HRSID. [ABSTRACT FROM AUTHOR]

The wind turbine system in the offshore area will be subjected to the combined action of complex marine environment dynamic loads. At present, there is a certain advantage to using traditional mechanical devices to simulate marine environmental loads. In this study, the self-developed complex dynamic load test loading system is adopted to carry out a series of model tests for pile foundations. The theoretical calculation results of wind, wave and current load are applied to the wind turbine system, and the responses of the pile and soil are analyzed according to the actual model test. The main conclusions are as follows: the development of pile displacement is basically linear with the logarithm of cycle times, and pile–soil interaction is directly affected by the form of load, the results of dynamic response obtained in this test are closer to that under the actual marine environmental load. [ABSTRACT FROM AUTHOR]