Experimental Investigation on Magnetic Abrasive Finishing for Internal Surfaces of Waveguides Produced by Selective Laser Melting.

To enhance the surface quality of metal 3D-printed components, magnetic abrasive finishing (MAF) technology was employed for post-processing polishing. Experimental investigation employing response surface methodology was conducted to explore the impact of processing gap, rotational speed of the magnetic field, auxiliary vibration, and magnetic abrasive particle (MAP) size on the quality enhancement of internal surfaces. A regression model correlating roughness with crucial process parameters was established, followed by parameter optimization. Ultimately, the internal surface finishing of waveguides with blind cavities was achieved, and the finishing quality was comprehensively evaluated. Results indicate that under optimal process conditions, the roughness of the specimens decreased from Ra 2.5 µm to Ra 0.65 µm, reflecting a reduction rate of 74%. Following sequential rough and fine processing, the roughnesses of the cavity bottom, side wall, and convex surface inside the waveguide reduced to 0.59 µm, 0.61 µm, and 1.9 µm, respectively, from the original Ra above 12 µm. The findings of this study provide valuable technical insights into the surface finishing of metal 3D-printed components.

Machine learning-based prediction of vitamin D deficiency: NHANES 2001-2018.

Background: Vitamin D deficiency is strongly associated with the development of several diseases. In the current context of a global pandemic of vitamin D deficiency, it is critical to identify people at high risk of vitamin D deficiency. There are no prediction tools for predicting the risk of vitamin D deficiency in the general community population, and this study aims to use machine learning to predict the risk of vitamin D deficiency using data that can be obtained through simple interviews in the community. Methods: The National Health and Nutrition Examination Survey 2001-2018 dataset is used for the analysis which is randomly divided into training and validation sets in the ratio of 70:30. GBM, LR, NNet, RF, SVM, XGBoost methods are used to construct the models and their performance is evaluated. The best performed model was interpreted using the SHAP value and further development of the online web calculator. Results: There were 62,919 participants enrolled in the study, and all participants included in the study were 2 years old and above, of which 20,204 (32.1%) participants had vitamin D deficiency. The models constructed by each method were evaluated using AUC as the primary evaluation statistic and ACC, PPV, NPV, SEN, SPE, F1 score, MCC, Kappa, and Brier score as secondary evaluation statistics. Finally, the XGBoost-based model has the best and near-perfect performance. The summary plot of SHAP values shows that the top three important features for this model are race, age, and BMI. An online web calculator based on this model can easily and quickly predict the risk of vitamin D deficiency. Conclusion: In this study, the XGBoost-based prediction tool performs flawlessly and is highly accurate in predicting the risk of vitamin D deficiency in community populations.

Targeting and Microenvironment-Activated Nanoreactor for Diabetic Chronic Wound Healing via Multienzyme Cascade Reactions.

The development of cell-like nanoreactors with the ability to initiate biocatalytic cascades under special conditions holds tremendous potential for therapeutic applications. Herein, conformationally gated nanoreactors that respond to the acidic microenvironment of infected diabetic wounds were developed by cucur[8]bituril (CB[8])-based supramolecular assembly. The bioinspired nanoreactors exhibit not only self-regulated permeability and selectivity to control internal enzyme activities by substance exchange but also distinct binding specificities toward Gram-positive and Gram-negative bacteria via noncovalent modification with different ligands. The encapsulation of glucose oxidase (GOx), Fe3O4 nanozyme, and l-arginine (l-Arg) into the nanocarriers enables intelligent activation of multienzyme cascade reactions upon glucose (Glu) uptake to produce gluconic acid (GA) and hydrogen peroxide (H2O2), which is further converted into highly toxic hydroxyl radicals (·OH) for selective antibacterial activity. Moreover, acidic H2O2 promotes the oxidization of l-Arg, leading to the release of nitric oxide (NO). Consequently, this nanoreactor provides a multifunctional and synergistic platform for diabetic chronic wound healing by combining enzyme dynamic therapy with NO gas therapy to combat bacterial infections and inflammation under high blood Glu levels.

AutoMolDesigner for Antibiotic Discovery: An AI-Based Open-Source Software for Automated Design of Small-Molecule Antibiotics.

Discovery of small-molecule antibiotics with novel chemotypes serves as one of the essential strategies to address antibiotic resistance. Although a considerable number of computational tools committed to molecular design have been reported, there is a deficit in holistic and efficient tools specifically developed for small-molecule antibiotic discovery. To address this issue, we report AutoMolDesigner, a computational modeling software dedicated to small-molecule antibiotic design. It is a generalized framework comprising two functional modules, i.e., generative-deep-learning-enabled molecular generation and automated machine-learning-based antibacterial activity/property prediction, wherein individually trained models and curated datasets are out-of-the-box for whole-cell-based antibiotic screening and design. It is open-source, thus allowing for the incorporation of new features for flexible use. Unlike most software programs based on Linux and command lines, this application equipped with a Qt-based graphical user interface can be run on personal computers with multiple operating systems, making it much easier to use for experimental scientists. The software and related materials are freely available at GitHub (https://github.com/taoshen99/AutoMolDesigner) and Zenodo (https://zenodo.org/record/10097899).

Revisiting the Environmental Kuznets Curve (EKC) Hypothesis of Carbon Emissions: Exploring the Impact of Geopolitical Risks, Natural Resource Rents, Corrupt Governance, and Energy Intensity.

The global imperative to mitigate carbon emissions for sustainable development has spurred extensive research into economic, social, and energy-related factors. However, prior studies present a complex landscape, yielding mixed conclusions regarding the influence of geopolitical risk, natural resource rents, corrupt governance, and energy intensity. To untangle this ambiguity, we construct a research model grounded in the Environmental Kuznets Curve, employing panel data from 38 countries spanning 2002 to 2020. Employing panel quantile regression models, we directly assess the impact of identified factors. Our findings affirm the alignment between economic growth and carbon emissions, supporting the Environmental Kuznets Curve hypothesis. Notably, increased geopolitical risk and energy intensity correlate with heightened carbon emissions over time, while corruption governance and natural resource rents exhibit a mitigating effect. Additionally, our study explores the indirect impact of these factors using a panel threshold regression model. Results indicate a diminishing influence of economic growth on carbon emissions. Intriguingly, natural resource rents initially curtail, then amplify the connection between economic growth and carbon emissions. Conversely, rising energy intensity magnifies the relationship between economic expansion and carbon emissions.

A Giant Exchange Bias Effect Due to Enhanced Ferromagnetism Using a Mixed Martensitic Phase in Ni50Mn37Ga13 Spun Ribbons.

The structure of a material is an important factor in determining its physical properties. Here, we adjust the structure of the Ni50Mn37Ga13 spun ribbons by changing the wheel speed to regulate the exchange bias effect of the material. The characterization results of micromorphology and structure show that as the wheel speed increases, the martensite lath decreases from 200 nm to 50 nm, the structure changed from the NM to a NM and 10M mixed martensitic structure containing mainly NM, then changed to NM and 10M where 10M and NM are approaching. Meanwhile, HE first increased and then decreased as the wheel speed increased. The optimum exchange bias effect (HE = 7.2 kOe) occurs when the wheel speed is 25 m∙s-1, mainly attributed to the enhanced ferromagnetism caused by part of 10M in NM martensite, which enhanced the exchange coupling of ferromagnetism and antiferromagnetism. This work reveals the structural dependence of exchange bias and provides a way to tune the magnitude of the exchange bias of Heusler alloys.

Prevalence and genetic characterization of Campylobacter from clinical poultry cases in China.

IMPORTANCE: Campylobacter is a major cause of campylobacteriosis worldwide, and poultry is the main reservoir for its transmission. Campylobacter was generally considered to be a harmless commensal organism in poultry without pathogenic properties. However, it was proposed that a Campylobacter-like organism may be the cause of vibrionic hepatitis, which poses a significant public health risk. The occurrence and epidemiology of Campylobacter in healthy poultry have been studied systematically, but little is known about the epidemiology of Campylobacter isolates from diseased poultry in China. Therefore, this study determined the prevalence and molecular characterization of Campylobacter from diseased chickens, ducks, and geese in Yangzhou Veterinary Hospital between December 2016 and September 2017, which was critical for improving the diagnosis and prevention of Campylobacter infections.

Machine learning algorithms to predict risk of postoperative pneumonia in elderly with hip fracture.

BACKGROUND: Hip fracture (HF) is one of the most common fractures in the elderly and is significantly associated with high mortality and unfavorable prognosis. Postoperative pneumonia (POP), the most common postoperative complication of HF, can seriously affect patient prognosis and increase the burden on the healthcare system. The aim of this study was to develop machine learning models for identifying elderly patients at high risk of pneumonia after hip fracture surgery. METHODS: From May 2016 to November 2022, patients admitted to a single central hospital for HF served as the study population. We extracted data that could be collected within 24 h of patient admission. The dataset was divided into training and validation sets according to 70:30. Based on the screened risk factors, prediction models were developed using seven machine learning algorithms, namely CART, GBM, KNN, LR, NNet, RF, and XGBoost, and their performance was evaluated. RESULTS: Eight hundred five patients were finally included in the analysis and 75 (9.3%) patients suffered from POP. Age, CI, COPD, WBC, HB, GLU, STB, GLOB, Ka+ which are used as features to build machine learning models. By evaluating the model's AUC value, accuracy, sensitivity, specificity, Kappa value, MCC value, Brier score value, calibration curve, and DCA curve, the model constructed by XGBoost algorithm has the best and near-perfect performance. CONCLUSION: The machine learning model we created is ideal for detecting elderly patients at high risk of POP after HF at an early stage.

[Characteristics of the chloroplast genome of Dracaena marginata and phylogenetic analysis].

Dracaena marginata is a widely cultivated horticultural plant in the world, which has high ornamental and medicinal value. In this study, the whole genome of leaves from D. marginata was sequenced by Illumina HiSeq 4000 platform. The chloroplast genome were assembled for functional annotation, sequence characteristics and phylogenetic analysis. The results showed that the chloroplast genome of D. marginata composed of four regions with a size of 154 926 bp, which was the smallest chloroplast genome reported for Dracaena species to date. A total of 132 genes were identified, including 86 coding genes, 38 tRNA genes and 8 rRNA genes. Codon bias analysis found that the codon usage bias was weak and there was a bias for using A/U base endings. 46 simple sequence repeat and 54 repeats loci were detected in the chloroplast genome, with the maximum detection rate in the large single copy region and inverted repeat region, respectively. The inverted repeats boundaries of D. marginata and Dracaena were highly conserved, whereas gene location differences occurred. Phylogenetic analysis revealed that D. serrulata and D. cinnabari form a monophyletic clade, which was the closest relationship and conformed to the morphological classification characteristics. The analysis of the chloroplast genome of D. marginata provides important data basis for species identification, genetic diversity and chloroplast genome engineering of Dracaena.

Recent advances in ß-cyclodextrin-based materials for chiral recognition.

In nature, enantiomers are pairs of chiral compounds, and have semblable chemical and physical properties but mostly show opposite biological effects when they enter an organism. Therefore, chiral recognition has a crucial research value in the fields of medicine, food, biochemistry, etc. Cyclodextrins (CDs) are produced by cyclodextrin glucosyltransferase in some species of bacillus on starch and include three main members α-, ß-, and γ-CD with six, seven and eight units of glucose, respectively. With a hydrophilic external cavity and a hydrophobic internal cavity, ß-CD can also combine with a variety of materials (e.g., graphene, nanoparticles, COFs, and OFETs) to enhance the chiral recognition of guest molecules in a chiral sensor. This review presents the progress of ß-CD modification with different materials for chiral recognition and describes in detail how different materials assist ß-CD in chiral recognition and improve the effect of ß-CD chiral discrimination.

Current physical techniques for the degradation of aflatoxins in food and feed: Safety evaluation methods, degradation mechanisms and products.

Aflatoxins are the most toxic natural mycotoxins discovered so far, posing a serious menace to the food safety and trading economy of the world, especially developing countries. How to effectively detoxify has persistently occupied a place on the list of "global hot-point" concerns. Among the developed detoxification methods, physical methods, as the authoritative techniques for aflatoxins degradation, could rapidly induce irreversible denaturation of aflatoxins. This review presents a brief overview of aflatoxins detection and degradation product structure identification methods. Four main safety evaluation methods for aflatoxins and degradation product toxicity assessment are highlighted combined with an update on research of aflatoxins decontamination in the last decade. Furthermore, the latest applications, degradation mechanisms and products of physical aflatoxin decontamination techniques including microwave heating, irradiation, pulsed light, cold plasma and ultrasound are discussed in detail. Regulatory issues related to "detoxification" are also explained. Finally, we put forward the challenges and future work in studying aflatoxin degradation based on the existing research. The purpose of supplying this information is to help researchers have a deeper understanding on the degradation of aflatoxins, break through the existing bottleneck, and further improve and innovate the detoxification methods of aflatoxins.

Light-controlled artificial transmembrane signal transduction for 'ON/OFF'-switchable transphosphorylation of an RNA model substrate.

Inspired by nature, it is of significant importance to design and construct biomimetic signaling systems to mimic natural signal transduction. Herein, we report an azobenzene/α-cyclodextrin (α-CD)-based signal transduction system with three functional modules: a light-responsive headgroup, lipid-anchored group, pro-catalyst tailgroup. The transducer can be inserted into the vesicular membrane to trigger the transmembrane translocation of molecules under the activation of light, forming a ribonuclease-like effector site and leading to the transphosphorylation of the RNA model substrate inside the vesicles. Moreover, the transphosphorylation process can be reversibly turned 'ON/OFF' over multiple cycles by the activation and deactivation of the pro-catalyst. This artificial photo-controlled signal transduction successfully constructs a signal responsive catalysis system across the membrane to utilize light to reversibly control the internal transphosphorylation process of an RNA model substrate, which might provide a new strategy for future design to utilize exogenous signals for implementing endogenous enzyme manipulation and gene regulation.

Type 2 diabetes and the risk of synovitis-tenosynovitis: a two-sample Mendelian randomization study.

Introduction: It has been shown that people with type 2 diabetes have a higher risk of synovitis and tenosynovitis, but previous studies were mainly observational, which may be biased and does not allow for a cause-and-effect relationship. Therefore, we conducted a two-sample Mendelian randomization (MR) study to investigate the causal relationship. Method: We obtained data on "type 2 diabetes" and "synovitis, tenosynovitis" from published large-scale genome-wide association studies (GWAS). The data were obtained from the FinnGen consortium and UK Biobank, both from European population samples. We used three methods to perform a two-sample MR analysis and also performed sensitivity analysis. Results: The results of all three MR methods we used for the analysis illustrated that T2DM increases the risk factor for the development of synovitis and tenosynovitis. Specifically, for the IVW method as the primary analysis outcome, OR = 1.0015 (95% CI, 1.0005 to 1.0026), P = 0.0047; for the MR Egger method as the supplementary analysis outcome, OR = 1.0032 (95% CI, 1.0007 to 1.0056), P = 0.0161; for the weighted median method, OR = 1.0022 (95% CI, 1.0008 to 1.0037), p = 0.0018. In addition, the results of our sensitivity analysis suggest the absence of heterogeneity and pleiotropy in our MR analysis. Conclusion: In conclusion, the results of our MR analysis suggest that T2DM is an independent risk factor for increased synovitis and tenosynovitis.

Exploring the role of nuclear energy in the energy transition: A comparative perspective of the effects of coal, oil, natural gas, renewable energy, and nuclear power on economic growth and carbon emissions.

Nuclear power has received renewed attention during the energy transition in recent years. This study is aimed to explore whether nuclear energy can promote economic growth without increasing carbon emissions. In order to have a more comprehensive understanding of the relationship between nuclear energy, economic growth, and carbon emissions, this study also discusses the impact of coal, oil, natural gas, and renewable energy on economic growth and carbon emissions. The second-generation panel unit root test, panel cointegration test, panel fully modified ordinary least squares, and Heterogeneous Dumitrescu and Hurlin causality test were used to estimate the long-term elasticity and causality among variables. Results based on panel data from 24 countries with nuclear energy from 2001 to 2020 show that both nuclear energy and renewable energy can curb carbon emissions. Especially in Canada, Finland, Russia, Slovenia, South Korea, and The United Kingdom, nuclear energy reduces carbon emissions more significantly than renewable energy. Meanwhile, there is a positive relationship between increased nuclear energy, increased renewable energy, and economic growth, which means that nuclear energy and renewable energy could increase economic growth as well. There is a positive relationship between increased oil, increased natural gas, and economic growth, while there is a negative relationship between the increase in coal and economic growth. Meanwhile, there is a positive relationship between increased oil, increased coal, and increased carbon emissions, while the positive relationship between increased natural gas and increased carbon emissions is not significant. Thus, in the 22 countries with nuclear power, increased coal consumption does not drive economic growth but increases carbon emissions. Increased oil consumption increases economic growth, but it increases carbon emissions. Increased natural gas consumption boosts economic growth but adds little to carbon emissions. In the authors' view, nuclear power and renewable energy are all options for these nuclear-power countries to pursue economic growth without increasing carbon emissions. Moreover, nuclear power has a better effect on curbing carbon emissions in some countries than renewable energy. Therefore, under the premise of safety, nuclear power should be seriously considered and re-developed.

Traditional Chinese Medicine in Treatment of COVID-19 and Viral Disease: Efficacies and Clinical Evidence.

Coronavirus disease 2019 (COVID-19) remains an uncontained, worldwide pandemic. While battling the disease in China, the Chinese government has actively promoted the use of traditional Chinese medicine, and many studies have been conducted to determine the efficacy of traditional Chinese medicine for treating COVID-19. The present review discusses the effectiveness and safety of traditional Chinese medicine in curing COVID-19 and provides clinical evidence from all confirmed cases in China. Applications of traditional Chinese medicine and specific recipes for treating other viral infections, such as those caused by severe acute respiratory syndrome coronavirus and influenza A viruses (including H1N1), are also discussed. Studies have reported that traditional Chinese medicine treatment plays a significant role in improving clinical symptoms. Therefore, further investigation may be of high translational value in revealing novel targeted therapies for COVID-19.

Effect of Wu Qin Xi exercises on pain and function in people with knee osteoarthritis: A systematic review and meta-analysis.

Background: As a chronic disease that affects the whole world, there is no definite treatment for knee osteoarthritis (KOA). Wu Qin Xi (WQX) is still in preliminary exploration as a traditional Chinese exercise in the treatment of osteoarthritis of the knee. The purpose of this study was to conduct a meta-analysis of previous studies and to investigate the efficacy of the WQX exercises on pain and function in patients with KOA. Methods: We searched six databases (Pubmed, Embase, Cochrane Library, Wanfang, CQVIP, and CNKI) for articles on WQX for KOA up to May 10, 2022. Literature search, study selection, data extraction, and quality evaluation were performed by two independent authors. In terms of statistical results, we presented mean differences (MD), 95% CI, and I 2 to show heterogeneity, and, based on that, we chose either a random effects model or a fixed effects model. Results: Seven studies were selected for inclusion in this meta-analysis. The WQX intervention group showed statistical differences for both the total Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score and its various bylaws, the Visual Analogue Score (VAS), and the presence of general functional exercise in the control group. We also demonstrated the clinically meaningful efficacy of WQX treatment by calculating minimum clinical importance difference (MCID) values that met the MCID values on the WOMAC score. A sensitivity analysis was also performed in this study by subgroup analysis for greater heterogeneity, and it was inferred that the difference in follow-up time was a likely source of heterogeneity. Conclusion: Despite some limitations, the current study showed a definite effect of WQX in improving pain symptoms and joint function in patients with KOA. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier: CRD42022332209.

Different treatments for 3- or 4-part proximal humeral fractures in the elderly patients: A Bayesian network meta-analysis of randomized controlled trials.

Background: Proximal humeral fractures are the third most common fracture in the body, and their incidence is rising year by year as the population ages. However, the treatment of the proximal humerus in parts 3 and 4 is still debatable, necessitating a network meta-analysis to determine the best treatment for each treatment modality. Methods: We searched PubMed, Embase, Cochrane Library for randomized controlled trials on proximal humeral fractures up to June 21, 2022. We performed data extraction and literature quality assessment by two independent authors and extracted constant score and reoperation rate as indicators for evaluation. Stata software, Revman software, JAGS software and the R-based BlandAltmanLeh package, gemtc package and riags package were used to perform this Bayesian network meta-analysis. Results: Following screening, 11 papers with a total of 648 participants were included in the analysis. The SUCRA values for the constant score were in the following order: RSA, IMN, Conservative, HA, and LP, and the SUCRA values for the reoperation rate were LP, HA, IMN, Conservative, and RSA. Conclusion: The elderly with 3- or 4-part proximal humeral fractures should consider RSA because it received the best evaluation ranking in terms of constant score and reoperation rate. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022341209, identifier: CRD42022341209.

Quantum tomography of the photon-plasmon conversion process in a metal hole array.

In the past decades, quantum plasmonics has become an active area due to its potential applications in on-chip plasmonic devices for quantum information processing. However, the fundamental physical process, i.e., how a quantum state of light evolves in the photon-plasmon conversion process, has not been described by a detailed microscopic quantum model. Here, we report a complete characterization of the plasmon-assisted extraordinary optical transmission process through quantum process tomography. By inputting various coherent states to interact with the plasmonic structure and detecting the output states with a homodyne detector, we reconstruct the process tensor of the photon-plasmon conversion process. Both the amplitude and phase information of the process are extracted, which explain the evolution of the quantum-optical state after the coupling with plasmons. Our experimental demonstration constitutes a fundamental block for future on-chip applications of quantum plasmonic circuits.

Theoretical and experimental studies on hydrogen migration in dissociative ionization of the methanol monocation to molecular ions H3 + and H2O.

The dissociative ionization processes of the methanol monocation CH3OH+ to H3 + + CHO and H2O+ + CH2 are studied by ab initio method, and hydrogen migration processes are confirmed in these two dissociation processes. Due to the positive charge assignment in dissociation processes, the fragmentation pathways of CH3OH+ to H3 + CHO+ and CH3OH+ to H2O + CH2 + are also calculated. The calculation results show that a neutral H2 moiety in the methanol monocation CH3OH+ is the origin of the formation of H3 +, and the ejection of fragment ions H3 + and H2O+ is more difficult than CHO+ and CH2 + respectively. Experimentally, by using a dc-slice imaging technique under an 800 nm femtosecond laser field, the velocity distributions of fragment ions H3 +, CHO+, CH2 +, and H2O+ are calculated from their corresponding sliced images. The presence of low-velocity components of these four fragment ions confirms that the formation of these ions is not from the Coulomb explosion of the methanol dication. Hence, the four hydrogen migration pathways from the methanol monocation CH3OH+ to H3 + + CHO, CHO+ + H3, H2O+ + CH2, and CH2 + + H2O are securely confirmed. It can be observed in the time-of-flight mass spectrum of ionization and dissociation of methanol that the ion yields of fragment ions H3 + and H2O+ are lower than CHO+ and CH2 + respectively, which is consistent with the theoretical results according to which dissociation from the methanol monocation to H3 + and H2O+ is more difficult than CHO+ and CH2 + respectively.

Multiple generations of high-order orbital angular momentum modes through cascaded third-harmonic generation in a 2D nonlinear photonic crystal.

We experimentally demonstrate multiple generations of high-order orbital angular momentum (OAM) modes through third-harmonic generation in a 2D nonlinear photonic crystal. Such third-harmonic generation process is achieved by cascading second-harmonic generation and sum-frequency generation using the non-collinear quasi-phase-matching technique. This technique allows multiple OAM modes with different colors to be simultaneously generated. Moreover, the OAM conservation law guarantees that the topological charge is tripled in the cascaded third-harmonic generation process. Our method is effective for obtaining multiple high-order OAM modes for optical imaging, manipulation, and communications.