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Abstract Background Extended depth of focus (EDOF) and multifocal (Multi) intraocular lenses (IOL) can provide a fixed distance of near vision, which may result in some discomfort for patients who prefer different near distances. The aim of this study was to compare the vision, comfortable near distance (CND) and visual comfort in patients who underwent implantation of EDOF, Multi, and monofocal (Mono) IOLs. Methods A total of 100 eyes were implanted with Tecnis ZXR00, ZMB00 or ZCB00 IOLs. Uncorrected distance, intermediate, and near visual acuity (UDVA, UIVA, and UNVA, respectively), corrected distance visual acuity (CDVA), the fluctuations of CND, the ability to see at comfortable or standard near distance and visual comfort were evaluated at 3-month postoperative. Results At 3 months postoperative, the EDOF and Multi groups showed non-inferiority compared to the Mono group in the UDVA (P > 0.05) and CDVA (P > 0.05) but superiority in the UNVA (P

Abstract Background Conducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exploring drug-disease associations has far-reaching implications for identifying disease pathogenesis and treatment. However, reliable detection of drug-disease relationships via traditional methods is costly and slow. Therefore, investigations into computational methods for predicting drug-disease associations are currently needed. Results This paper presents a novel drug-disease association prediction method, RAFGAE. First, RAFGAE integrates known associations between diseases and drugs into a bipartite network. Second, RAFGAE designs the Re_GAT framework, which includes multilayer graph attention networks (GATs) and two residual networks. The multilayer GATs are utilized for learning the node embeddings, which is achieved by aggregating information from multihop neighbors. The two residual networks are used to alleviate the deep network oversmoothing problem, and an attention mechanism is introduced to combine the node embeddings from different attention layers. Third, two graph autoencoders (GAEs) with collaborative training are constructed to simulate label propagation to predict potential associations. On this basis, free multiscale adversarial training (FMAT) is introduced. FMAT enhances node feature quality through small gradient adversarial perturbation iterations, improving the prediction performance. Finally, tenfold cross-validations on two benchmark datasets show that RAFGAE outperforms current methods. In addition, case studies have confirmed that RAFGAE can detect novel drug-disease associations. Conclusions The comprehensive experimental results validate the utility and accuracy of RAFGAE. We believe that this method may serve as an excellent predictor for identifying unobserved disease-drug associations.

Highlights A series of layered oxide cathode materials were synthesized by high-temperature shock strategy for the first time. The approaching ultimate solid reaction rate of the layered nickel-rich layered oxide LiNixCoyMnzO2 was investigated for the first time. Ultrafast average reaction rate of phase transition from Ni0.6Co0.2Mn0.2(OH)2 to Li-containing oxides is 66.7 (% s-1), that is, taking only 1.5 s.

The creep behavior of crushed sugarcane end-leaf (SEL) was studied, with consideration of different loading force, moisture content, and feeding amount. Statistical analysis software was used to develop and fit the regression data to a strain change law as a function of time. The four-element Burgers model was used. A further goal was to analyze the effect of different test conditions on the fitted creep characteristic parameters. The instantaneous elasticity coefficient E0 was found to increase when the loading force increased; the value of delayed elasticity coefficient E1 increased and the value of cohesion coefficient η0 decreased when the feeding amount increased; the value of delayed elasticity coefficient E1 and cohesion coefficient η0 decreased when the moisture content was increased. Therefore, the loading force, moisture content, and feeding amount of crushed SEL all affected the creep capacity of crushed SEL in compression. The results can provide substantial theoretical reference for the silage production of crushed SEL.

Gentiana szechenyii Kanitz. and Gentiana veitchiorum Hemsl. are two wild medicinal plants widely used in Tibetan medicine. In recent decades, their wild populations have declined rapidly due to persistent over-harvesting of their flowers. Because their flowers are small and dense, it is difficult to rely on manual counting under wild conditions. The combination of deep learning and unmanned aerial vehicle remote sensing (UAVRS) is a new method for plant surveys. We trained 12 of the most advanced or widely used YOLO models on a custom dataset to achieve quantitative detection of both plant flowers in UAVRS images. The accuracy, precision and recall of G. szechenyii Flower (GSF) detection can reach 97.00%, 90.40% and 95.40%, respectively (based on YOLOv7). Similarly, those of G. veitchiorum Flower (GVF) detection can reach 93.40%, 91.30% and 90.60%, respectively (based on YOLOv5n), and the highest mAP can reach 94.90% (based on YOLOv5m). Based on the detection results, it is calculated that the total yield of dried GSF and GVF that can be harvested as medicinal materials in study area ranges from 1.88 to 2.10 g·m−2 and 4.42–4.62 g·m−2, respectively. The results show that deep learning and UAVRS can be used for quantitative detection of GSF and GVF, which is helpful for further research and protection of these two plants.

Objective To examine the current prevalence and cost of paediatric off-label drug prescriptions in Gansu, China, and the potential influencing factors.Design The prevalence of off-label prescriptions in paediatrics was evaluated according to the National Medical Products Administration drug instructions in the China Pharmaceutical Reference (China Pharmaceutical Reference, MCDEX) database. The evidence of the prescription was determined by existing clinical practice guidelines and the Thomson Grade in the Micromedex 2021 compendium. We used logistic regression to investigate the characteristics that influence paediatric off-label drug use after single-factor regression analysis.Setting A multicentre cross-sectional study of outpatient paediatric prescriptions in 196 secondary and tertiary hospitals in Gansu Province, China, in March and September 2020.Results We retrieved 104 029 paediatric prescriptions, of which 39 480 (38.0%) contained off-label use. The most common diseases treated by off-label drugs were respiratory system diseases (n=15 831, 40.1%). A quarter of off-label prescriptions had adequate evidence basis (n=10 130, 25.6%). Unapproved indications were the most common type of off-label drug use (n=25 891, 65.6%). A total of 1177 different drugs were prescribed off-label, with multienzyme tablets being the most common drug (n=1790, 3.5%). The total cost of the prescribed off-label drugs was ¥106 116/day. Off-label prescriptions were less frequent in tertiary than in secondary hospitals. Topical preparations were more commonly prescribed off-label than other types of drugs. Senior-level clinicians prescribed drugs off-label more often than intermediate and junior clinicians.Conclusion Off-label drug use is widespread in paediatric practice in China. Three-quarters of the prescriptions may potentially include inappropriate medication use, resulting in a daily economic burden of about ¥81 000 in 2020 in Gansu Province with 25 million inhabitants. The management of off-label drug use in paediatrics in China needs improvement.

With the development of my country's industrial level, the prefabricated building (PB) model has gradually become the mainstream. In order to more scientifically study the development policy of the prefabricated building industry, a system dynamics model with the boundary of resource input, technology research and development, production capacity and market demand system is constructed from the perspective of industry chain development, based on the influence of external subjects of the industry chain on the development of the industry chain. Afterwards, dynamic simulations of the development trend of prefabricated buildings under different policy scenarios are carried out. The results show that the increase in capital investment in the industrial chain and the improvement of the prefabricated assembly rate of government-invested construction projects are strong short-term incentive policies, while corporate tax incentives and prefabricated building purchase loan incentives are long-term incentive policies. Combined with the simulation results, the policy formulation recommendations based on the development trend of the industry chain are put forward.

Leakage current is one of the important parameters reflecting the operation status of distribution network-type surge arresters. At this stage, the polymagnetic current sensor has the advantages of miniaturization and high accuracy for leakage current measurement, but the complexity of electromagnetic interference in the field easily introduces more noise interference signals, which limits the performance of the polymagnetic current sensor for field application. To this end, this paper proposes a leakage current measurement method for distribution network-type surge arresters based on EEMD-SVD and low-rank RBF neural network methods. Firstly, the measured leakage current is decomposed by Ensemble Empirical Mode Decomposition (EEMD) to decompose the modal components containing eigenfrequencies, and then Singular Value Decomposition (SVD) is used to extract the non-eigenfrequency signals. Finally, a low-rank Radical Basis Function (RBF) neural network is used to approximate the leakage current signal after de-interference and combined with the Gaussian window function to remove the white noise interference. The experimental results show that the signal-to-noise ratio of the polymagnetic current sensor is improved by about 20dB and the maximum average absolute error is only 2.62%, which can truly reflect the leakage current of the network-type lightning arrester.

Abstract Background Long noncoding RNAs (lncRNAs) are integral to a plethora of critical cellular biological processes, including the regulation of gene expression, cell differentiation, and the development of tumors and cancers. Predicting the relationships between lncRNAs and diseases can contribute to a better understanding of the pathogenic mechanisms of disease and provide strong support for the development of advanced treatment methods. Results Therefore, we present an innovative Node-Adaptive Graph Transformer model for predicting unknown LncRNA-Disease Associations, named NAGTLDA. First, we utilize the node-adaptive feature smoothing (NAFS) method to learn the local feature information of nodes and encode the structural information of the fusion similarity network of diseases and lncRNAs using Structural Deep Network Embedding (SDNE). Next, the Transformer module is used to capture potential association information between the network nodes. Finally, we employ a Transformer module with two multi-headed attention layers for learning global-level embedding fusion. Network structure coding is added as the structural inductive bias of the network to compensate for the missing message-passing mechanism in Transformer. NAGTLDA achieved an average AUC of 0.9531 and AUPR of 0.9537 significantly higher than state-of-the-art methods in 5-fold cross validation. We perform case studies on 4 diseases; 55 out of 60 associations between lncRNAs and diseases have been validated in the literatures. The results demonstrate the enormous potential of the graph Transformer structure to incorporate graph structural information for uncovering lncRNA-disease unknown correlations. Conclusions Our proposed NAGTLDA model can serve as a highly efficient computational method for predicting biological information associations.

In response to the challenges of accurate identification and localization of garbage in intricate urban street environments, this paper proposes EcoDetect-YOLO, a garbage exposure detection algorithm based on the YOLOv5s framework, utilizing an intricate environment waste exposure detection dataset constructed in this study. Initially, a convolutional block attention module (CBAM) is integrated between the second level of the feature pyramid etwork (P2) and the third level of the feature pyramid network (P3) layers to optimize the extraction of relevant garbage features while mitigating background noise. Subsequently, a P2 small-target detection head enhances the model’s efficacy in identifying small garbage targets. Lastly, a bidirectional feature pyramid network (BiFPN) is introduced to strengthen the model’s capability for deep feature fusion. Experimental results demonstrate EcoDetect-YOLO’s adaptability to urban environments and its superior small-target detection capabilities, effectively recognizing nine types of garbage, such as paper and plastic trash. Compared to the baseline YOLOv5s model, EcoDetect-YOLO achieved a 4.7% increase in mAP0.5, reaching 58.1%, with a compact model size of 15.7 MB and an FPS of 39.36. Notably, even in the presence of strong noise, the model maintained a mAP0.5 exceeding 50%, underscoring its robustness. In summary, EcoDetect-YOLO, as proposed in this paper, boasts high precision, efficiency, and compactness, rendering it suitable for deployment on mobile devices for real-time detection and management of urban garbage exposure, thereby advancing urban automation governance and digital economic development.

In our previous study, a new fermented food (PWF) created by utilizing pineapple by-products and whey proteins as a matrix via co-fermentation with lactic acid bacteria and yeast was developed, and, in the current study, we examined the impact of a pineapple–whey protein fermentation product on a cefixime-induced dysbiosis model in mice using 16S sequencing and untargeted metabolomics techniques. The results indicated that the pineapple–whey protein fermentation product played a positive role in restoring the intestinal flora. In this study, cefixime reduced the overall abundance of intestinal flora and decreased the relative abundance of probiotics in the gut, while also inhibiting amino acid metabolism. The addition of PWF normalized the intestinal flora to a steady state, significantly increasing the populations of Weissella, Lactococcus, Faecalibaculum, and Bacteroides acidophilus, while decreasing the numbers of Akkermansia and Escherichia-Shigella. Additionally, PWF modulated microbial metabolites, such as L-glutamate and L-threonine, and upregulated amino-acid-related metabolic pathways, including those involving glycine, serine, and threonine. In conclusion, PWF can alleviate intestinal flora dysbiosis and metabolic disturbances induced by antibiotic interventions. It is suggested that PWF could be a potential dietary strategy for patients with antibiotic-associated diarrhea.

Li-rich cathode materials are one of the most potential candidates for next-generation Li-ion batteries. However, they suffer from severe capacity degradation because of cracks and continuous structural transformation during cycling. Defect engineering can effectively tune the electronic and crystal structures of nanomaterials and improve the electrochemical performance of cathode materials. To date, it is challenging to introduce dense defects into the materials synthesized via conventional tube furnace calcination due to the thermodynamic and kinetic equilibrium. In this work, a non-equilibrium high-temperature shock (HTS) strategy with ultra-fast cooling, accompanied with the processes of rapid heating and cooling, is reported to introduce twin boundaries (TBs) into Li1.2Ni0.13Co0.13Mn0.54O2. The rock-salt phase along the TBs acts as a rigid framework that mitigates the inherent phase transformation of Li-rich layered-oxide materials during cycling. Benefiting from the unique structure, Li1.2Ni0.13Co0.13Mn0.54O2 prepared via the HTS method exhibits a superior reversible capacity of 278 mA h g−1 at 0.1C, a high initial coulombic efficiency of 81%, and a capacity retention of 89.4% after 100 cycles at 0.5C. This work exerts profound implications on understanding defect engineering for modulating the structure and electrochemical performance of materials. [ABSTRACT FROM AUTHOR]

Green technology innovation is a key tool for fostering green technology innovation, which is a fundamental element of superior economic development. This paper establishes a threshold model and a robustness test model based on balanced data from 31 Chinese provinces from 2017 to 2021 to analyze the "cost of compliance effect", "Porter's hypothesis effect," and the existence of non-linear relationships between environmental regulations at different intensities on green technology innovation in the construction industry in each province of China. The goal is to increase the efficiency of green technological innovation in the construction industry and help it thrive in the sector. The results of the study show that there is a double threshold and a significant "V" shape of environmental regulation on green technology innovation in the construction industry in each province of China. Labour force and industry size have a facilitating effect on green technology innovation in the construction industry, capital investment has a positive but insignificant effect on green technology innovation in the construction industry. The degree of foreign trade development has an inhibiting effect on green technology innovation in the construction industry. Therefore, the government should flexibly adjust the threshold value of environmental regulation according to the actual situation of each province and region, so that it can be located in the optimal range to promote green technology innovation in the construction industry, and ultimately achieve high-quality economic development. [ABSTRACT FROM AUTHOR]