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China’s equipment manufacturing system and Europe’s experience in project complement each other. Firstly, this paper focused on the industrial chain and key technologies of floating offshore such as wind turbines, floating foundations, mooring and anchoring system, dynamic submarine cables, survey and design, construction, transportation and installation, operation and maintenance, etc. An evaluation system for the industrial chain with deep vertical analysis and horizontal expansion and comparison was established. Then, combined with the results of China-Europe comparison in different segments, 18 key cooperation areas were proposed. Finally, the study also explored the 4 stages of cooperation paths: exchange & sharing, cooperative construction, scale application and global promotion. It is expected to solve the problems of sole form of demonstration prototype foundation, high cost and immature industry chain,and provide certain ideas and inspiration between China and Europe floating offshore wind power cooperation.

Eva Futtrup Maksten,1,2 Lasse Hjort Jakobsen,1,3 Kristian Hay Kragholm,4 Joachim Baech,1,2 Mikkel Porsborg Andersen,5 Jakob Madsen,1,2 Judit Mészáros Jørgensen,6 Michael Roost Clausen,7 Robert Schou Pedersen,8 Andriette Dessau-Arp,9 Thomas Stauffer Larsen,10 Christian Bjørn Poulsen,11 Anne Ortved Gang,12 Peter Brown,12 Kirsten Fonager,2,13 Tarec C El-Galaly,1,2 Marianne Tang Severinsen1,2 1Department of Haematology, Clinical Cancer Research Unit, Aalborg University Hospital, Aalborg, Denmark; 2Department of Clinical Medicine, Aalborg University, Aalborg, Denmark; 3Department Mathematical Sciences, Aalborg University, Aalborg, Denmark; 4Department of Cardiology & Unit of Epidemiology and Biostatistics, Aalborg University Hospital, Aalborg, Denmark; 5Department of Cardiology, Nordsjaellands Hospital, Hillerød, Denmark; 6Department of Haematology, Aarhus University Hospital, Aarhus, Denmark; 7Department of Haematology, Vejle Hospital, Vejle, Denmark; 8Department of Medicine, Section of Haematology, Regionshospital Goedstrup, Goedstrup, Denmark; 9Department of Haematology, Hospital South West Jutland, Esbjerg, Denmark; 10Department of Haematology, Odense University Hospital, Odense, Denmark; 11Department of Haematology, Zealand University Hospital, Roskilde, Denmark; 12Department of Haematology, Rigshospitalet, Copenhagen University Hospital, Copenhagen and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; 13Department of Social Medicine, Aalborg University Hospital, Aalborg, DenmarkCorrespondence: Eva Futtrup Maksten, Department of Haematology, Clinical Cancer Research Unit, Aalborg University Hospital, Sdr. Skovvej 15, Aalborg, 9000, Denmark, Tel +45 97663872, Fax + 45 97666323, Email efm@rn.dkPurpose: Many patients diagnosed with lymphoma are of working age. Cancer patients are known to have a higher risk of sick leave and disability pension, but this has only been delineated for certain subtypes of lymphoma. Therefore, this study aimed at investigating the overall risk of disability pension for all lymphoma subtypes and at quantifying return to work for patients with lymphoma in work before diagnosis.Patients and Methods: Patients aged 18– 60 years with lymphoma in complete remission (CR) diagnosed between 2000 and 2019 were included in the study. Using national registers, each patient was matched with five comparators from the general population with same sex, birth year, and level of Charlson Comorbidity Index. Risk of disability pension was calculated from 90 days after CR or end of treatment with competing events (death, retirement pension, early retirement pension, relapse for patients, or lymphoma diagnosis for comparators). Return to work for patients was calculated annually until 5 years after diagnosis for patients employed before diagnosis.Results: In total, 4072 patients and 20,360 comparators were included. There was a significant increased risk of disability pension for patients with all types of lymphoma compared to the general population (5-year risk difference: 5.3 (95% confidence interval (CI): 4.4;6.2)). Patients with non-Hodgkin lymphoma were more likely to get disability pension than patients with Hodgkin lymphoma (sex- and age-adjusted 10-year risk difference: 2.9 (95% CI: 0.3;5.5)). One year after diagnosis, 24.5% of the relapse-free patients were on sick leave. Return to work was highest 2 years after diagnosis (82.1%).Conclusion: Patients with lymphoma across all subtypes have a significantly higher risk of disability pension. Return to work peaks at 2 years after diagnosis.Keywords: lymphoma, disability pension, return to work

Non-contact voltage sensors based on the principle of electric field coupling have the advantages of simple loading and unloading, high construction safety, and the fact that they are not affected by line insulation. They can accurately measure line voltage without the need to connect to the measured object. Starting from the principle of non-contact voltage measurement, this article abstracts a non-contact voltage measurement model into the principle of capacitive voltage sharing and deduces its transfer relationship. Secondly, it is theoretically inferred that the edge effect of the traditional symmetric structure sensor plate will cause the actual capacitance value between the sensor plates to be greater than the theoretically calculated capacitance value, resulting in a certain measurement error. Therefore, the addition of an equipotential ring structure is proposed to eliminate the edge additional capacitance caused by the edge effect in order to design the sensor structure. In addition, due to the influence of sensor volume, material dielectric constant, and other factors, the capacitance value of the sensor itself is only at pF level, resulting in poor low-frequency performance and imbuing the sensor with a low voltage division ratio. In this regard, this article analyzes the measurement principle of non-contact voltage sensors. By paralleling ceramic capacitors between the two electrode plates of the sensor, the capacitance of the sensor itself is effectively increased, improving the low-frequency performance of the sensor while also increasing the sensor’s voltage division ratio. In addition, by introducing a single pole double throw switch to switch parallel capacitors with different capacitance values, the sensor can have different voltage division ratios in different measurement scenarios, giving it a certain degree of adaptability. The final sensor prototype was made, and a high and low voltage experimental platform was built to test the sensor performance. The experimental results showed that the sensor has good linearity and high measurement accuracy, with a ratio error of within ±3%.

According to the requirements of weak current measurement in power grid, a weak current sensor with anti-low frequency interference ability is developed. The sensor adopts the principle of fluxgate detection and adds a magnetic ring on the original basis. The structure of the magnetic ring is simulated using comsol to further improve detection sensitivity. In order to solve the problem that the electromagnetic current sensor is vulnerable to the interference of geomagnetic field and power frequency magnetic field in weak current measurement, a magnetic shielding method with low cost is selected, and the shielding shell structure is designed using a finite element analysis method. The experimental results show that the minimum measurable current is 1 mA, the measurement range is 1 mA–1 A, and the bandwidth is DC-16 kHz. The designed magnetic shielding shell can effectively reduce 97.3% of the DC magnetic field interference and 95.7% of the power frequency magnetic field interference. The sensor can realize accurate measurement of weak current in power grid.

With the accelerated construction of the smart grid, new energy sources such as photovoltaic and wind power are connected to the grid. In addition to power frequency, the current signal of power grid also includes several DC signals, as well as medium-high and high-frequency transient signals. Traditional current sensors for power grids are bulky, have a narrow measurement range, and cannot measure both AC and DC at the same time. Therefore, this paper designs a non-intrusive, AC-DC wide-bandwidth current sensor based on the composite measurement principle. The proposed composite current detection scheme combines two different isolation detection technologies, namely tunneling reluctance and the Rogowski coil. These two current sensing techniques are complementary (tunneling magnetoresistive sensors have good low-frequency characteristics and Rogowski coils have good high-frequency characteristics, allowing for a wide detection bandwidth). Through theoretical and simulation analysis, the feasibility of the composite measurement scheme was verified. The prototype of composite current sensor was developed. The DC and AC transmission characteristics of the sensor prototype were measured, and the sensitivity and linearity were 11.96 mV/A, 1.14%, respectively. Finally, the sweep current method and pulse current method experiments prove that the designed composite current sensor can realize the current measurement from DC to 17 MHz.

Rare actinomycete genera are highly recognized as a promising source of structurally diverse and bioactive natural products. Among these genera, Allokutzneria and Kibdelosporangium are two phylogenetically closely related and have been reported to encode some valuable biosynthetic enzymes and secondary metabolites. However, there is currently no relevant systematic research available to outline the linkage of genomic and metabolomics for specific secondary metabolites in these two promising genera. In this study, we first investigated the genus-specific secondary metabolic potential in Allokutzneria and Kibdelosporangium by comparing the diversity and novelty of their secondary metabolite biosynthetic gene clusters (BGCs). The specific secondary metabolites produced by two representative strains of these genera were comprehensively investigated using untargeted metabolomics techniques. The findings unveiled that the majority (95.4%) of the gene cluster families (GCFs) encoded by Allokutzneria and Kibdelosporangium were genus-specific, including NRPS GCFs encoding siderophores. The untargeted metabolomics analysis revealed that the metabolic profiles of two representative strains exhibit extensive specificity, with the culture medium having a big impact on the metabolic profiles. Besides, an MS-cluster featuring a series of hydroxamate-type siderophores was identified from Allokutzneria albata JCM 9917, with two of them, including a novel one (N-deoxy arthrobactin A), being experimentally validated. The present study offers valuable insights for the targeted discovery of genus-specific natural products from microorganisms. [ABSTRACT FROM AUTHOR]

The actinomycete genus Rhodococcus is known for its diverse biosynthetic enzymes, with potential in pollutant degradation, chemical biocatalysis, and natural product exploration. Comparative genomics have analyzed the distribution patterns of non-ribosomal peptide synthetases (NRPSs) in Rhodococcus. The diversity and specificity of its secondary metabolism offer valuable insights for exploring natural products, yet remain understudied. In the present study, we analyzed the distribution patterns of biosynthetic gene clusters (BGCs) in the most comprehensive Rhodococcus genome data to date. The results show that 86.5% of the gene cluster families (GCFs) are only distributed in a specific phylogenomic-clade of Rhodococcus, with the most predominant types of gene clusters being NRPS and ribosomally synthesized and post-translationally modified peptides (RiPPs). In-depth mining of RiPP gene clusters revealed that Rhodococcus encodes many clade-specific novel RiPPs, with thirteen core peptides showing antibacterial potential. High-throughput elicitor screening (HiTES) and non-targeted metabolomics revealed that a marine-derived Rhodococcus strain produces a large number of new aurachin-like compounds when exposed to specific elicitors. The present study highlights the diversity and specificity of secondary biosynthetic potential in Rhodococcus, and provides valuable information for the targeted exploration of novel natural products from Rhodococcus, especially for phylogenomic-clade-specific metabolites. [ABSTRACT FROM AUTHOR]

Inflammation is an innate immune response to infection, and it is the main factor causing bodily injury and other complications in the pathological process. Ginsenoside Rh4 (G-Rh4), a minor ginsenoside of Panax ginseng C. A. Meyer and Panax notoginseng, has excellent pharmacological properties. However, many of its major pharmacological mechanisms, including anti-inflammatory actions, remain unrevealed. In this study, network pharmacology and an experimental approach were employed to elucidate the drug target and pathways of G-Rh4 in treating inflammation. The potential targets of G-Rh4 were selected from the multi-source databases, and 58 overlapping gene symbols related to G-Rh4 and inflammation were obtained for generating a protein–protein interaction (PPI) network. Molecular docking revealed the high affinities between key proteins and G-Rh4. Gene ontology (GO) and pathway enrichment analyses were used to analyze the screened core targets and explore the target–pathway networks. It was found that the JAK-STAT signaling pathway, TNF signaling pathway, NF-κB signaling pathway, and PI3K-Akt signaling pathway may be the key and main pathways of G-Rh4 to treat inflammation. Additionally, the potential molecular mechanisms of G-Rh4 predicted from network pharmacology analysis were validated in RAW264.7 cells. RT-PCR, Western blot, and ELISA analysis indicated that G-Rh4 significantly inhibited the production of pro-inflammatory cytokines such as TNF-α, IL-6, and IL-1β, as well as inflammation-related enzymes in lipopolysaccharide (LPS)-stimulated RAW264.7 cells. Moreover, in vitro experiments evaluated that Ginsenoside Rh4 exerts anti-inflammatory effects via the NF-κB and STAT3 signaling pathways. It is believed that our study will provide the basic scientific evidence that G-Rh4 has potential anti-inflammatory effects for further clinical studies.

Silica composites with low dielectric constants have broad application prospects in the fields of low dielectric and thermal insulation materials. However, silica composites still face challenges such as strong water absorption and strict storage conditions in practical applications. Modification of SiO2@quartz fiber composites was conducted with hydrophobic modifiers (hexamethyldisilazane HMDS, stearic acid, sodium dodecyl sulfate) to study the effects of type and mass fraction of hydrophobic modifiers on the hydrophobic and dielectric properties of the composites. The hydrophobic-modified composites were made into coaxial cables to study the effect of heat treatment on the performance of the cables. The results show that the 3% HMDS-modified composite have the best hydrophobic modification with contact angle of 151.6°, the lowest contact angle is 132.6° after 240 days of storage, and the dielectric constant is as low as 1.87 after 200 °C calcination. The hydrophobic-modified composites were made into coaxial communication cables, and the cables had a voltage standing wave ratio of 1.48, an attenuation value of 1.56 dB, and a characteristic impedance of 48 Ω. The hydrophobic-modified composite materials exhibit excellent cable performance, demonstrating potential application value in communication cables and other fields. [ABSTRACT FROM AUTHOR]

Objectives: Wilson disease (WD) is a rare autosomal recessive disorder caused by a mutation in the ATP7B gene. Neurological symptoms are one of the most common symptoms of WD. This study aims to construct a model that can predict the occurrence of neurological symptoms by combining clinical multidimensional indicators with machine learning methods. Methods: The study population consisted of WD patients who received treatment at the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from July 2021 to September 2023 and had a Leipzig score ≤ 4 points. Indicators such as general clinical information, imaging, blood and urine tests, and clinical scale measurements were collected from patients, and machine learning methods were employed to construct a prediction model for neurological symptoms. Additionally, the SHAP method was utilized to analyze clinical information to determine which indicators are associated with neurological symptoms. Results: In this study, 185 patients with WD (of whom 163 had neurological symptoms) were analyzed. It was found that using the eXtreme Gradient Boosting (XGB) to predict achieved good performance, with an MCC value of 0.556, ACC value of 0.929, AUROC value of 0.835, and AUPRC value of 0.975. Brainstem damage, blood creatinine (Cr), age, indirect bilirubin (IBIL), and ceruloplasmin (CP) were the top five important predictors. Meanwhile, the presence of brainstemdamage and the higher the values of Cr, Age, and IBIL, the more likely neurological symptoms were to occur, while the lower the CP value, the more likely neurological symptoms were to occur. Conclusions: To sum up, the prediction model constructed using machine learningmethods to predictWD cirrhosis has high accuracy. Themost important indicators in the prediction model were brainstem damage, Cr, age, IBIL, and CP. It provides assistance for clinical decision-making. [ABSTRACT FROM AUTHOR]

Introduction: Although Bacillus species have produced a wide variety of structurally diverse and biologically active natural products, the secondary biosynthetic potential of Bacillus species is widely underestimated due to the limited number of biosynthetic gene clusters (BGCs) in this genus. The significant variation in the diversity and novelty of BGCs across different species within the Bacillus genus presents a major obstacle to the efficient discovery of novel natural products from Bacillus. Methods: In this study, the number of each class of BGCs in all 6,378 high-quality Bacillus genomes was predicted using antiSMASH, the species-specificity of BGC distribution in Bacillus was investigated by Principal component analysis. Then the structural diversity and novelty of the predicted secondary metabolites in Bacillus species with specific BGC distributions were analyzed using molecular networking. Results: Our results revealed a certain degree of species-specificity in the distribution of BGCs in Bacillus, which was mainly contributed by siderophore, type III polyketide synthase (T3PKS), and transAT-PKS BGCs. B. wiedmannii, B. thuringiensis, and B. cereus are rich in RiPP-like and siderophore BGCs, but lack T3PKS BGCs, while B. amyloliquefaciens and B. velezensis are abundant in transAT-PKS BGCs. These Bacillus species collectively encode 77,541 BGCs, with NRPS and RiPPs being the two most dominant types, which are further categorized into 4,291 GCFs. Remarkably, approximately 54.5% of GCFs and 93.8% of the predicted metabolite scaffolds are found exclusively in a single Bacillus species. Notably, B. cereus, B. thuringiensis, and B. velezensis exhibit the highest potential for producing species-specific NRPS and PKS bioinformatic natural products. Taking two species-specific NRPS gene clusters as examples, the potential of Bacillus to synthesize novel species-specific natural products is illustrated. Conclusion: This study highlights the species-specificity of the secondary biosynthetic potential in Bacillus and provides valuable insights for the targeted discovery of novel natural products from this genus. [ABSTRACT FROM AUTHOR]

To ensure the stable operation of the distribution network, it is necessary to timely predict and diagnose its faults. In this paper, distribution network faults were briefly analyzed. To address the problem of redundant and incomplete fault data, they were reduced by the rough set (RS), and then the reduced data were predicted based on the back-propagation neural network (BPNN) model. To further improve the prediction performance, an improved particle swarm optimization (IPSO) algorithm was designed to optimize the parameters of the BPNN model and establish the IPSO-BPNN model. Through example analysis, it was found that RS reduction retained the discriminative ability of the data and improved the prediction accuracy. The accuracy of the IPSO-BPNN algorithm was 79.36% when using non-reduced data; the accuracy of the BPNN algorithm and the IPSO-BPNN algorithm was 87.64% and 95.77%, respectively, when using the reduced data. The experimental results demonstrate the reliability of the IPSO-BPNN algorithm for fault data prediction and its applicability in the actual distribution network. [ABSTRACT FROM AUTHOR]