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Against the backdrop of the fourth technological revolution, industrial intelligence (INDI) represented by industrial robots has rapidly developed. This evolution provides favorable opportunities for precise decision-making in pollution control and achieving China's "dual carbon" goals. Previous studies have mainly discussed the economic effects of INDI from the perspective of the labor market. This study shifts its focus to examining the impact of INDI on the land green utilization efficiency (LGUE) in cities. Using the panel data of Chinese cities spanning 2009–2021, this study empirically tests the effect and transmission mechanism of INDI on LGUE. We find that urban INDI significantly enhances LGUE. In terms of its transmission mechanism, INDI drives improvements in urban LGUE through technological progress, energy structure optimization, and industrial structure upgrading. Urban infrastructure construction and financial agglomeration level can further strengthen the positive impact of INDI on LGUE. In addition, the improvement in LGUE due to INDI is more significant in non-resource-based and large-sized cities than resource-based and small and medium-sized cities. Therefore, each region should enhance the integration of intelligent technology with traditional industrial manufacturing. Doing so is essential to establish comprehensive assessment indicators that balance environmental protection and economic growth, strengthen regional information infrastructure construction, ensure steady financial flow, and support green development initiatives across regions. [ABSTRACT FROM AUTHOR]

Investigations and research on the giant African snail (GAS) mainly focus on Achatina fulica. However, in recent years, a more harmful GAS, Achatina immaculata, has been reported. In order to understand the invasive status of A. immaculata in China, we take Shenzhen, a coastal city in Southern China, as an example to carry out an ecological survey on the field populations of the two species of GAS in various districts. We explore the differences in the invasive characteristics of the two species of snails in terms of their dietary intake, cold adaptation and drought resistance. The results indicate that, based on the phylogenetic tree analysis, more than half of the sampled snails exhibit higher similarity to A. immaculata. The number of wild A. immaculata is significantly greater than that of A. fulica, and 70.64% of the 746 GAS are A. immaculata. At the same time, it is also found that the maximum shell length of A. immaculata is 135.83 mm, with an average shell length of 76.00 mm, which is significantly different from the average shell length of A. fulica (56.57 mm, p < 0.01). The food intake assay shows that there is no difference in the food preferences of the two species, but the food demand of A. immaculata is significantly greater than that of A. fulica (2.32 fold, p < 0.01). In the cold adaptation assay, A. immaculata recovers from the cold dormancy state significantly faster than A. fulica (1.92 fold, p < 0.05), and the speed with which A. immaculata enters the dormancy state in the drought environment is significantly slower than that of A. fulica (0.706 fold, p < 0.05). With the characteristics of a large body size, large food intake and strong resistance to cold and drought resistance, A. immaculata has the potential to be dominant in competition with A. fulica in the same ecological niche, and it has become the main invasive species of GAS in Shenzhen. [ABSTRACT FROM AUTHOR]

Localizing the brain regions affected by tasks is crucial to understanding the mechanisms of brain function. However, traditional statistical analysis does not accurately identify the brain regions of interest due to factors such as sample size, task design, and statistical effects. Here, we propose a hypergraph-based multitask feature selection framework, referred to as HMTFS, which we apply to a functional magnetic resonance imaging (fMRI) dataset to extract task-related brain regions. HMTFS is characterized by its ability to construct a hypergraph through correlations between subjects, treating each subject as a node to preserve high-order information of time-varying signals. Additionally, it manages feature selection across different time windows in fMRI data as multiple tasks, facilitating time-constrained group sparse learning with a smoothness constraint. We utilize a large fMRI dataset from the Human Connectome Project (HCP) to validate the performance of HMTFS in feature selection. Experimental results demonstrate that brain regions selected by HMTFS can provide higher accuracy for downstream classification tasks compared to other competing feature selection methods and align with findings from previous neuroscience studies. [ABSTRACT FROM AUTHOR]

Fusion of multimodal medical data provides multifaceted, disease-relevant information for diagnosis or prognosis prediction modeling. Traditional fusion strategies such as feature concatenation often fail to learn hidden complementary and discriminative manifestations from high-dimensional multimodal data. To this end, we proposed a methodology for the integration of multimodality medical data by matching their moments in a latent space, where the hidden, shared information of multimodal data is gradually learned by optimization with multiple feature collinearity and correlation constrains. We first obtained the multimodal hidden representations by learning mappings between the original domain and shared latent space. Within this shared space, we utilized several relational regularizations, including data attribute preservation, feature collinearity and feature-task correlation, to encourage learning of the underlying associations inherent in multimodal data. The fused multimodal latent features were finally fed to a logistic regression classifier for diagnostic prediction. Extensive evaluations on three independent clinical datasets have demonstrated the effectiveness of the proposed method in fusing multimodal data for medical prediction modeling. [ABSTRACT FROM AUTHOR]

Tigecycline, one of the last-resort therapeutic options for complicated infections caused by multidrug-resistant pathogens, especially carbapenem-resistant Enterobacterales and Acinetobacter in recent years. The emergence of antibiotic-resistant bacteria and antibiotic-resistant genes has threatened the effectiveness of antibiotics and public health with the excessive use of antibiotics in clinics. However, the emergence and dissemination of high-level mobile tigecycline-resistance gene tet(X) is challenging for clinical effectiveness of antimicrobial agent. This study aimed to characterize an E. coli strain T43, isolated from an inpatient in a teaching hospital in China. The E. coli T43 was resistant to almost all antimicrobials except colistin and consisted of a 4,774,080 bp chromosome and three plasmids. Plasmids pT43-1 and pT43-2 contained tigecycline-resistance gene tet(X4). Plasmid pT43-1 had a size of 152,423 bp with 51.05% GC content and harbored 151 putative open reading frames. pT43-1 was the largest plasmid in strain T43 and carried numerous resistance genes, especially tigecycline resistance gene tet(X4) and carbapenemase resistance gene blaNDM-5. The tet(X) gene was associated with IS26. Co-occurrence of numerous resistance genes in a single plasmid possibly contributed to the dissemination of these genes under antibiotics stress. It might explain the presence of clinically crucial resistance genes tet(X) and blaNDM-5 in clinics. This study suggested the applicable use of antibiotics and continued surveillance of tet(X) and blaNDM-5 in clinics are imperative. [ABSTRACT FROM AUTHOR]

Functional coatings, including organic and inorganic coatings, play a vital role in various industries by providing a protective layer and introducing unique functionalities. However, its design often involves time-consuming experimentation with multiple materials and processing parameters. To overcome these limitations, data-driven approaches are gaining traction in materials science. In this paper, recent advances in data-driven materials research and development (R&D) for functional coatings, highlighting the importance, data sources, working processes, and applications of this paradigm are summarized. It is begun by discussing the challenges of traditional methods, then introduce typical data-driven processes. It is demonstrated how data-driven approaches enable the identification of correlations between input parameters and coating performance, thus allowing for efficient prediction and design. Furthermore, carefully selected case studies are presented across diverse industries that exemplify the effectiveness of data-driven methods in accelerating the discovery of new functional coatings with tailored properties. Finally, the emerging research directions, involving integrating advanced techniques and data from different sources, are addressed. Overall, this review provides an overview of data-driven materials R&D for functional coatings, shedding light on its potential and future developments., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Background: Previous studies have confirmed that malignant transformation of dysplastic nodule (DN) into hepatocellular carcinoma (HCC) is accompanied by reduction of iron content in nodules. This pathological abnormality can serve as the basis for magnetic resonance imaging (MRI). This study was designed to identify the feasibility of iterative decomposition of water and fat with echo asymmetry and least squares estimation-iron quantitative (IDEAL-IQ) measurement to distinguish early hepatocellular carcinoma (eHCC) from DN., Methods: We reviewed MRI studies of 35 eHCC and 23 DN lesions (46 participants with 58 lesions total, 37 males, 9 females, 31-80 years old). The exams include IDEAL-IQ sequence and 3.0T MR conventional scan [including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and Gadopentic acid (Gd-GDPA)-enhanced]. Then, 3 readers independently diagnosed eHCC, DN, or were unable to distinguish eHCC from DN using conventional MRI (CMRI), and then assessed R2* value of nodules [R2* value represents the nodule iron content (NIC)] and R2* value of liver background [R2* value represents the liver background iron content (LBIC)] with IDEAL-IQ. Statistical analysis was conducted using the t -test for comparison of means, the Mann-Whitney test for comparison of medians, the chi-square test for comparison of frequencies, and diagnostic efficacy was evaluated by using receiver operating characteristic (ROC) curve., Results: This study evaluated 35 eHCC participants (17 males, 6 females, 34-81 years old, nodule size: 10.5-27.6 mm, median 18.0 mm) and 23 DN participants (20 males, 3 females, 31-76 years old, nodule size: 16.30±4.095 mm). The NIC and ratio of NIC to LIBC (NIC/LBIC) of the eHCC group (35.926±12.806 sec -1 , 0.327±0.107) was lower than that of the DN group (176.635±87.686 sec -1 , 1.799±0.629) (P<0.001). Using NIC and NIC/LBIC to distinguish eHCC from DN, the true positive/false positive rates were 91.3%/94.3% and 87.0%/97.1%, respectively. The rates of CMRI, NIC and NIC/LBIC in diagnosis of eHCC were 77.1%, and 94.3%, 97.1%, respectively, and those of DN were 65.2%, 91.3%, and 87.0%, respectively. The diagnosis rate of eHCC and DN by CMRI was lower than that of NIC and NIC/LBIC (eHCC: P=0.03, 0.04, DN: P=0.02, 0.04)., Conclusions: Using IDEAL-IQ measurement can distinguish DN from eHCC., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-1593/coif). All authors report that this study received funding from Shenzhen High-level Hospital Construction Fund (No. G2022108). The authors have no other conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Tigecycline, one of the last-resort therapeutic options for complicated infections caused by multidrug-resistant pathogens, especially carbapenem-resistant Enterobacterales and Acinetobacter in recent years. The emergence of antibiotic-resistant bacteria and antibiotic-resistant genes has threatened the effectiveness of antibiotics and public health with the excessive use of antibiotics in clinics. However, the emergence and dissemination of high-level mobile tigecycline-resistance gene tet(X) is challenging for clinical effectiveness of antimicrobial agent. This study aimed to characterize an E. coli strain T43, isolated from an inpatient in a teaching hospital in China. The E. coli T43 was resistant to almost all antimicrobials except colistin and consisted of a 4,774,080 bp chromosome and three plasmids. Plasmids pT43-1 and pT43-2 contained tigecycline-resistance gene tet(X4) . Plasmid pT43-1 had a size of 152,423 bp with 51.05% GC content and harbored 151 putative open reading frames. pT43-1 was the largest plasmid in strain T43 and carried numerous resistance genes, especially tigecycline resistance gene tet(X4) and carbapenemase resistance gene bla NDM-5 . The tet(X) gene was associated with IS26. Co-occurrence of numerous resistance genes in a single plasmid possibly contributed to the dissemination of these genes under antibiotics stress. It might explain the presence of clinically crucial resistance genes tet(X) and bla NDM-5 in clinics. This study suggested the applicable use of antibiotics and continued surveillance of tet(X) and bla NDM-5 in clinics are imperative.