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Sugar beet is a nitrogen (N)-sensitive crop, and its N regulation and utilization are critical for enhancing productivity. Sugar beet seedlings at the two-true-leaf-pair stage were hydroponically grown in an artificial climate chamber. Leaves and roots from three seedlings per treatment were sampled at 10, 20, 25, and 30 days after exposure to N treatments (N5: 5 mmol/L, N10: 10 mmol/L, N15: 15 mmol/L, and N20: 20 mmol/L) to assess the effects of N supply level on growth, photosynthesis, and carbon and nitrogen metabolism. The results revealed a time-dependent dynamics in beet biomass accumulation, with N20 inducing chlorosis and necrosis symptoms by 10 days post-treatment (DPT), resulting in the lowest biomass. While N15 significantly promoted root biomass by 30 DPT, showing a 23.70% (root dry weight, RDW) increase over N20; chlorophyll content and gas exchange parameters-net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr) exhibited significant N dependence, with N15 maintaining high chlorophyll level (0.78 mg/g) and photosynthetic rate (220.33 μmol/(m2·s). Nitrogen assimilation, as indicated by glutamine synthetase and glutamate synthetase activity (GS and GOGAT), was stronger under N15, promoting amino acid synthesis and root growth, whereas N20 inhibited enzyme activity. Carbon metabolism analysis revealed that N15-driven sucrose synthesis significantly increased root sucrose content, sucrose phosphate synthase and sucrose synthase activity (SPS and SS), optimizing source–sink allocation. Correlation analysis showed a positive relationship between leaf and root biomass (r = 0.91), and root sucrose content was positively correlated with GOGAT activity (r = 0.90), emphasizing the synergistic regulation of C/N metabolism. On the contrary, N20 led to disrupted C/N metabolic homeostasis, inhibited enzyme activity, and C/N distribution. These results indicated that the photosynthetic output, enzyme efficiency, and sucrose distribution were coordinated by nitrogen optimization, and the growth of sugar beet seedlings was optimized.

In the new era of adopting and managing new and robust technologies in banking, the use of blockchain technology has significantly transformed overall banking systems. To add new insights to the body of existing knowledge, the authors conducted a systematic review with bibliographic network mapping to identify and analyse the factors contributing to adopting blockchain in the banking industry. Following the latest protocols of the PRISMA flowchart, this study acknowledged 16 relevant publications from 2590 papers in the databases, namely Scopus, ScienceDirect, Web of Science, and IEEE Xplore. The bibliographic data were grouped and analysed using VOSviewer to create network visualization maps that included citation and co‐citation, bibliographic coupling, co‐authorship, and co‐occurrence of terms. Subsequently, significant terms were identified through the analyses and compared with those found in the 16 relevant papers. The aggregate findings suggest that multiple influencing factors have been recognized and later categorized into three thematic drivers: transparency‐driven security, collaborative interoperability, and organizational infrastructure. The current research provides valuable insights for policymakers, technologists, researchers, consultants, and practitioners of information systems by proposing a technological framework, which will aid in developing tailored strategies to facilitate the sustainable practice of blockchain in the banking industry to a wider extent. The authors conducted a systematic review to explore the factors influencing the adoption of blockchain technology (BCT) in banking and proposed a technological framework. They analysed 16 relevant publications out of 2590 papers from databases like Scopus, ScienceDirect, Web of Science, and IEEE Xplore. By employing methods like bibliographic network mapping and VOSviewer analysis, they identified three main drivers for BCT adoption in banking: transparency‐driven security, collaborative interoperability, and organizational infrastructure. Their findings provide insights for policymakers, technologists, and practitioners, offering a new technological framework to facilitate the sustainable implementation of blockchain in banking.

A composite material based on CuFe-ZIF-derived CuFe.sub.2O.sub.4 nano-microspheres grown in situ and well-ordered on carbon sheets (CS) was prepared and applied for highly effective determination of bisphenol A (BPA). The composite material possessed inherently high redox activity due to the presence of both Cu and Fe ions with various oxidation states (Cu²âº/Cu⺠and Fe³âº/Fe²âº), high specific surface area, uniform distribution of Cu and Fe ions, and a robust framework imparted by its precursor CuFe-ZIF. This led to increased active sites for electrochemical reactions, improved electron transfer efficiency, and structural integrity during electrochemical cycling. Furthermore, combining CS with CuFe.sub.2O.sub.4 not only provided a large surface area to support well-ordered CuFeâOâ nano-microspheres without aggregation, but also enhanced the conductivity and mechanical stability of the CuFeâOâ/CS composite. This results in synergistic effects that enhanced the overall performance of the composite material. In addition, both copper and iron are relatively non-toxic and abundant, making CuFeâOâ/CS safe and cost-effective for large-scale applications. Consequently, the CuFe.sub.2O.sub.4/CS-modified electrode shows highly efficient electrochemical sensing properties with a wider detection range of 0.009-168 µM and lower detection limit of 0.0027 µM (S/N = 3) compared with most reported BPA sensors. It also has an optimized current at pH 7 which is convenient for real world applications. This CuFe.sub.2O.sub.4/CS modified electrode as a highly sensitive electrochemical platform can be applied to monitor BPA concentrations in bottled water with good recovery (97.2-102.2%). Graphical

In computer-assisted systems, real-time instrument segmentation is an essential module, and real-time segmentation conducted against streaming data reaches into treasure data in real time. The major benefit of real-time segmentation is that it can be used to build categories based on new movements made by users on a website, such as those who are searching or coming for the first time. Furthermore, real-time segmentation and identification algorithms for wires in mobile terminals are extensively used and be useful and efficient in this process. In this study, we employed the Fuzzy Analytical Hierarchy Process (FAHP) to evaluate real-time segmentation and identification methods. FAHP is a simple and intuitive method for calculating the weights of alternatives, criteria, and ranking them to identify the most efficient and effective solution. In this research, we have used the FAHP approach with fuzzy geometric mean values to rank six criteria and three alternatives. The FAHP approach is generally used in multi-decision-making situations where ambiguity and uncertainty are involved. It was tried to increase the context for the creative growth of real-time segmentation and identification algorithms for wires by evaluating these possibilities.

A stable and innovative composite film-modified electrode based on Dawson polyoxometalates H 8 P 2 Mo 16 V 2 O 62 (P 2 Mo 16 V 2 ) and ionic liquid (BMIMBr)-decorated carbon nanotubes, annotated as PEI/(P 2 Mo 16 V 2 /BMIMBr-CNTs) 8 , has been constructed by using the layer-by-layer self-assembly (LBL) method for the determination of L-tyrosine. The combination of three active components not only offers higher conductivity to facilitate rapid electron transfer, but also avoids the accumulation of P 2 Mo 16 V 2 to expand the contact area and increase the reactive active sites. The modified electrode exhibits outstanding sensing performance for determination of Tyr with wide linear determination range of 5.8×10 −7 M ~ 1.2×10 −4 M, low determination limit of 1.7×10 −7 M (S/N=3), high selectivity for common interferences, and excellent stability at the potential of +0.78 V (vs. Ag/AgCl (3 M KCl)). The relative standard deviation (RSD) of 4.3% for five groups of parallel experiments shows the satisfactory repeatability of PEI/(P 2 Mo 16 V 2 /BMIMBr-CNTs) 8 . In addition, for determination of Tyr, the PEI/(P 2 Mo 16 V 2 /BMIMBr-CNTs) 8 shows good recoveries of 98.8–99.8% in meat floss, which can be feasible in practical application. Graphical abstract

A composite material based on CuFe-ZIF-derived CuFe 2 O 4 nano-microspheres grown in situ and well-ordered on carbon sheets (CS) was prepared and applied for highly effective determination of bisphenol A (BPA). The composite material possessed inherently high redox activity due to the presence of both Cu and Fe ions with various oxidation states (Cu²⁺/Cu⁺ and Fe³⁺/Fe²⁺), high specific surface area, uniform distribution of Cu and Fe ions, and a robust framework imparted by its precursor CuFe-ZIF. This led to increased active sites for electrochemical reactions, improved electron transfer efficiency, and structural integrity during electrochemical cycling. Furthermore, combining CS with CuFe 2 O 4 not only provided a large surface area to support well-ordered CuFe₂O₄ nano-microspheres without aggregation, but also enhanced the conductivity and mechanical stability of the CuFe₂O₄/CS composite. This results in synergistic effects that enhanced the overall performance of the composite material. In addition, both copper and iron are relatively non-toxic and abundant, making CuFe₂O₄/CS safe and cost-effective for large-scale applications. Consequently, the CuFe 2 O 4 /CS-modified electrode shows highly efficient electrochemical sensing properties with a wider detection range of 0.009-168 µM and lower detection limit of 0.0027 µM (S/ N  = 3) compared with most reported BPA sensors. It also has an optimized current at pH 7 which is convenient for real world applications. This CuFe 2 O 4 /CS modified electrode as a highly sensitive electrochemical platform can be applied to monitor BPA concentrations in bottled water with good recovery (97.2-102.2%). Graphical Abstract

The relationship between corporate social responsibility (CSR) and innovation has received considerable attention in the last two decades. While several studies have explored the impact of CSR on innovation. While several studies have explored the impact of CSR on innovation, few studies have attempted to use bibliometric methods to analyze and visualize the evolution and trends in the CSR and innovation fields. In this research, 1279 Web of Science (WoS) published papers on CSR and innovation were collected and analyzed using VOSviwer, CiteSpace, and Bibliometrix R-package and the MK trend test. The analysis was conducted in terms of the number of articles published per year, most productive journals, authors, and countries, as well as collaboration between countries and authors, keyword analysis, co-citation clustering analysis, and research frontiers. The results showed that: (a) The MK trend test shows that the amount of CSR and innovation research is increasing. The top three journals in terms of productivity are Sustainability, Journal of Cleaner Production, and Corporate Social Responsibility and Environmental Management. The collaboration between authors forms a loose network and Ahmad, N has the most extensive network of international collaborations. There is close cooperation between countries, with a predominance of Asian, European, and North American collaborations, and the MK trend test shows that each country’s publications on the relationship between corporate social responsibility and innovation in the past 20 years have an obvious upward trend. (b) Through the analysis of keywords, it is necessary to research “corporate social responsibility”, “sustainability”, “innovation”, “financial performance “, and other topics associated with these themes. (c) The intellectual structure of CSR and innovation establishes five core clusters, including social innovation, CSR practice, sustainable global value chain, sustainable business model, and buyer–supplier collaboration. (d) Two forward-looking directions for future CSR and innovation research are proposed, and the limitations of the research are discussed.

A composite material based on CuFe-ZIF-derived CuFe O nano-microspheres grown in situ and well-ordered on carbon sheets (CS) was prepared and applied for highly effective determination of bisphenol A (BPA). The composite material possessed inherently high redox activity due to the presence of both Cu and Fe ions with various oxidation states (Cu²⁺/Cu⁺ and Fe³⁺/Fe²⁺), high specific surface area, uniform distribution of Cu and Fe ions, and a robust framework imparted by its precursor CuFe-ZIF. This led to increased active sites for electrochemical reactions, improved electron transfer efficiency, and structural integrity during electrochemical cycling. Furthermore, combining CS with CuFe O not only provided a large surface area to support well-ordered CuFe₂O₄ nano-microspheres without aggregation, but also enhanced the conductivity and mechanical stability of the CuFe₂O₄/CS composite. This results in synergistic effects that enhanced the overall performance of the composite material. In addition, both copper and iron are relatively non-toxic and abundant, making CuFe₂O₄/CS safe and cost-effective for large-scale applications. Consequently, the CuFe O /CS-modified electrode shows highly efficient electrochemical sensing properties with a wider detection range of 0.009-168 µM and lower detection limit of 0.0027 µM (S/N = 3) compared with most reported BPA sensors. It also has an optimized current at pH 7 which is convenient for real world applications. This CuFe O /CS modified electrode as a highly sensitive electrochemical platform can be applied to monitor BPA concentrations in bottled water with good recovery (97.2-102.2%).

As the most abundant organic substances in nature, carbohydrates are essential for life. Understanding how carbohydrates regulate proteins in the physiological and pathological processes presents opportunities to address crucial biological problems and develop new therapeutics. However, the diversity and complexity of carbohydrates pose a challenge in experimentally identifying the sites where carbohydrates bind to and act on proteins. Here, we introduce a deep learning model, DeepGlycanSite, capable of accurately predicting carbohydrate-binding sites on a given protein structure. Incorporating geometric and evolutionary features of proteins into a deep equivariant graph neural network with the transformer architecture, DeepGlycanSite remarkably outperforms previous state-of-the-art methods and effectively predicts binding sites for diverse carbohydrates. Integrating with a mutagenesis study, DeepGlycanSite reveals the guanosine-5’-diphosphate-sugar-recognition site of an important G-protein coupled receptor. These findings demonstrate DeepGlycanSite is invaluable for carbohydrate-binding site prediction and could provide insights into molecular mechanisms underlying carbohydrate-regulation of therapeutically important proteins. Carbohydrates are essential for regulating various biological processes. Here, the authors developed DeepGlycanSite, a deep learning model that accurately predicts carbohydrate-binding sites on proteins, offering insights into carbohydrate regulation of therapeutically important proteins.

ObjectiveElucidate the core clinical and genetic characteristics and identify the phenotypic variation between different regions and genotypes of fatal familial insomnia (FFI).MethodsA worldwide large sample of FFI patients from our case series and literature review diagnosed by genetic testing were collected. The prevalence of clinical symptoms and genetic profile were obtained, and then the phenotypic comparison between Asians versus non-Asians and 129Met/Met versus 129Met/Val were conducted.ResultsIn total, 131 cases were identified. The age of onset was 47.51±12.53 (range 17–76) years, 106 patients died and disease duration was 13.20±9.04 (range 2–48) months. Insomnia (87.0%) and rapidly progressive dementia (RPD; 83.2%) occurred with the highest frequency. Hypertension (33.6%) was considered to be an objective indicator of autonomic dysfunction. Genotype frequency at codon 129 was Met/Met (84.7%) and Met/Val (15.3%), and allele frequency was Met (92.4%) and Val (7.6%).129 Met was a risk factor (OR: 3.728, 95% CI: 2.194 to 6.333, p=0.000) for FFI in the non-Asian population. Comparison of Asians and non-Asians revealed clinical symptoms and genetic background to show some differences (p<0.05). In the comparison of 129 polymorphisms, a longer disease duration was found in the 129 MV group, with alleviation of some clinical symptoms (p<0.05). After considering survival probability, significant differences in survival time between genotypes remained (p<0.0001).ConclusionsInsomnia, RPD and hypertension are representative key clinical presentations of FFI. Phenotypic variations in genotypes and geographic regions were documented. Prion protein gene 129 Met was considered to be a risk factor for FFI in the non-Asian population, and 129 polymorphisms could modify survival duration.