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Mo2Ti2C3Tx MXene materials, bare or loaded with strongly anchored single Pt atoms, were investigated using various methods, including STEM, XPS, XAS and DFT calculations. Upon Pt impregnation, the delaminated Mo-rich MXene surface undergoes partial oxidation, which is reversed by an H2 thermal treatment at 400 °C. The optimized MXene shows high catalytic activity for CO2 hydrogenation to CO and smaller amounts of methane and methanol. Around and above the pretreatment temperature of 400 °C, the MXene is gradually defunctionalized from O- and F-containing groups and depleted in carbidic carbon, leading to deactivation. Single Pt atoms are cationic after impregnation, and reduce upon H2 treatment, filling surface Mo vacancies. Pt addition increases the MXene activity, in particular by facilitating H2 dissociation, but has little effect on the single-atom catalyst selectivity and on the rate dependence upon reactant partial pressures. The lowest Pt loading leads to the highest turnover frequency, indicating that the MXene surface sites are key to CO2 activation.

ObjectiveTo understand the chronic disease comorbidities among middle-aged and elderly Chinese population with different depression levels and provide a scientific basis for optimizing depression screening and chronic disease prevention and treatment. MethodsData from 14 222 middle-aged and elderly individuals aged ≥ 45 years old were collected from the 2018 China Health and Retirement Longitudinal Study (CHARLS). A complex network model was used to analyze the patterns of chronic disease comorbidities in middle-aged and elderly population with different depression levels, and Gephi 0.10.1 software was used to visualize the chronic disease comorbidity network. Results Among the 14 222 middle-aged and elderly Chinese individuals, 7 020 had chronic disease comorbidities, with a comorbidity rate of 49.36%. There were 9 066 (63.8%) individuals without depression, 4 141 (29.1%) with mild depression, and 1 015 (7.1%) with severe depression. The number of chronic disease comorbidities was 3 841, 2 463, and 716 among those without depression, with mild depression, and with severe depression, respectively, and the comorbidity rates were 42.37%, 59.48%, and 70.54%, respectively. The comorbidity rate increased with the severity of depression (χ2trend = 537.35, P < 0.001). The proportions of individuals with 2, 3, and ≥ 4 chronic diseases were 43.7% (1 679/3 841), 26.5% (1 016/3 841), and 29.8% (1 146/3 841) among those without depression; 34.2% (843/2 463), 26.1% (644/2 463), and 39.6% (976/2 463) among those with mild depression; and 28.2% (202/716), 22.2% (159/716), and 49.6% (355/716) among those with severe depression, respectively. Gamma correlation analysis showed that the number of chronic disease comorbidities increased with the severity of depression (Gamma = 0.209, P < 0.001). The comorbidity pattern of hypertension & dyslipidemia had the highest prevalence (9.22%) among individuals without depression, while the comorbidity pattern of gastrointestinal or digestive system diseases & arthritis or rheumatism had the highest prevalence among those with mild (16.95%) and severe (24.33%) depression. Network analysis results showed that the absolute network densities of the chronic disease comorbidity networks were 11.46, 16.24, and 20.30 for individuals without depression, with mild depression, and with severe depression, respectively, and the absolute network density increased with the severity of depression. ConclusionThe chronic disease comorbidity rate was high among middle-aged and elderly Chinese population, and the patterns of chronic disease comorbidities differed among middle-aged and elderly population with different depression levels. The chronic disease comorbidity rate increased with the severity of depression.

Transition metal carbides are known as efficient catalysts or catalyst supports and two-dimensional carbides (MXenes) offer renewed possibilities to anchor metal atoms and promote catalytic performances. This paper first presents an in-depth study of the elaboration of Pt or Pd-loaded Ti3C2Tx MXenes and their unstacking for gas-phase catalysis investigations, along with step-by-step characterization by XRD, XPS, SEM and STEM. In particular, the influence of the MXene preparation method (HF vs. LiF-HCl etchants) on surface structure/composition and metal dispersion/oxidation state is disclosed. Second, the catalytic hydrogenation performances of these materials are reported, and reveal the interest of low-loaded Pt/MXene single-atom catalysts in terms of activity, selectivity and resistance to sintering. They present an unusually high selectivity to 2-butene – without butane formation – in butadiene hydrogenation, a model reaction of applied interest for the petrochemical industry. Moreover, in CO2 reduction to CO (reverse water-gas shift reaction, relevant to greenhouse-gas valorization), these catalysts exhibit up to 99 % selectivity and a superior Pt-molar activity with respect to oxide-supported references. This work may stimulate the elaboration and investigation of other MXene-based systems for thermal heterogeneous catalysis, which remains rarely addressed on these materials.

BackgroundGlucagon-like peptide-1 receptor agonists (GLP-1RAs) have been widely used in treating type 2 diabetes mellitus (T2DM) and obesity in adults, but scientific research about the indication in children and adolescents is scarce. The current study aims to explore the prescriptions of GLP-1RAs in children and adolescents in China and to evaluate its rationality.MethodsGLP-1RA prescriptions of children and adolescents were retrospectively obtained from the Hospital Prescription Analysis Cooperative Project. The study extracted information on patient’s demographic characteristics, monotherapy and combination therapy of GLP-1RAs, and trends in GLP-1RA usage from 2016 to 2021. The rationality of GLP-1RA prescriptions was comprehensively assessed based on the indications approved by China National Medical Products Administration (NMPA), the U.S. Food and Drug Administration (FDA), European Medicines Agency (EMA), Pharmaceuticals and Medical Devices Agency (PMDA), and published randomized controlled trials (RCTs).ResultsA total of 234 prescriptions from 46 hospitals were included, with a median age of 17 years old. The majority of patients were diagnosed with overweight/obesity or prediabetes/diabetes, accounting for 43.59% and 46.15%, respectively. There were 88 patients on GLP-1RA monotherapy. GLP-1RAs plus metformin was the most common combination therapy (38.89%). 12.39% of patients were found a co-administration with orlistat. The share of overweight/obesity prescriptions increased from 27% in 2016 to 54% in 2021, whereas prediabetes/diabetes prescriptions declined from 55% to 42%. The prescriptions were divided into appropriate and questionable groups according to the diagnosis, and the potentially questionable prescription was related to age (p = 0.017), department visited (p = 0.002), and any hospitalization (p < 0.001).ConclusionsThis study described the prescribing of GLP-1RAs in children and adolescents. Our findings indicated that the utilization of GLP-1RAs has increased from 2016 to 2021. There was a strong basis for administering GLP-1RAs in overweight/obesity and prediabetes/diabetes, whereas the evidence was insufficient in other conditions. It is crucial to demand robust and sustained efforts to enhance the awareness of the safety of utilization of GLP-1RAs in children and adolescents.

BackgroundsRobust evidence have demonstrated the beneficial effect of Sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) in T2D patients with cardiovascular diseases and chronic kidney disease. Multiple studies analyzed patterns and predictors of SGLT2i and GLP-1RA in the US, Europe and worldwide. However, there is no study about the utilization of these two classes of drugs in real-world in China.MethodA total of 181743 prescriptions of SGLT2i and 59720 GLP-1RA were retrospectively pooled from Hospital Prescription Analysis Cooperation Project from 2018 to 2021. The social-economic characteristics of patients and prescribers, including age, gender, residency, hospital level, insurance type, department visited, and payment amount, were collected and analyzed to study trends and risk factors associated with preference among two antidiabetics.ResultsAnnual number of prescriptions of SGLT2i significantly increased to approximately 140 folds, while GLP-1RA increased to about 6.5 folds. After adjustment for socio-economic information, several patients or physician characteristics were positively associated with the preference of GLP-1RA, including female gender (OR 1.581, 95% CI 1.528-1.635), residents in second-tier cities (OR 1.194, 95% CI 1.148-1.142), visiting primary or secondary hospital level (OR 2.387, 95% CI 2.268-2.512); while other factors were associated with the preference of SGLT2i, including older adults (OR 0.713, 95% CI 0.688-0.739), uncovered by insurance (OR 0.310, 95% CI 0.293-0.329), visiting other departments compared with endocrinology. In addition, the share of SGLT2i and GLP-1RA was low but in an increasing tendency.ConclusionsSGLT2i and GLP-1RA prescription significantly increased from 2018 to 2021. The socio-economic risk factors in choosing SGLT2i or GLP-1RA highlight an effort required to reduce disparities and improve health outcomes.

Orthoebolavirus is a genus of hazardous pathogens that has caused over 30 outbreaks. However, currently approved therapies are limited in scope, as they are only effective against the Ebola virus and lack cross-protection against other orthoebolaviruses. Here, we demonstrate that a previously isolated human-derived antibody, 2G1, can recognize the glycoprotein (GP) of every orthoebolavirus species. The cryo-electron microscopy structure of 2G1 Fab in complex with the GPΔMucin trimer reveals that 2G1 binds a quaternary pocket formed by three subunits from two GP protomers. 2G1 recognizes highly conserved epitopes among filoviruses and achieves neutralization by blocking GP proteolysis. We designed an efficient mRNA module capable of producing test antibodies at expression levels exceeding 1500 ng/mL in vitro. The lipid nanoparticle (LNP)-encapsulated mRNA-2G1 exhibited potent neutralizing activities against the HIV-pseudotyped Ebola and Sudan viruses that were 19.8 and 12.5 times that of IgG format, respectively. In mice, the antibodies encoded by the mRNA-2G1-LNP peaked within 24 h, effectively blocking the invasion of pseudoviruses with no apparent liver toxicity. This study suggests that the 2G1 antibody and its mRNA formulation represent promising candidate interventions for orthoebolavirus disease, and it provides an efficient mRNA framework applicable to antibody-based therapies.

Abstract The application of nanoscale scaffolds has become a promising strategy in vaccine design, with protein-based nanoparticles offering desirable avenues for the biocompatible and efficient delivery of antigens. Here, we presented a novel endogenous capsid-forming protein, activated-regulated cytoskeleton-associated protein (ARC), which could be engineered through the plug-and-play strategy (SpyCatcher3/SpyTag3) for multivalent display of antigens. Combined with the self-assembly capacity and flexible modularity of ARC, ARC-based vaccines elicited robust immune responses against Mpox or SARS-CoV-2, comparable to those induced by ferritin-based vaccines. Additionally, ARC-based nanoparticles functioned as immunostimulants, efficiently stimulating dendritic cells and facilitating germinal center responses. Even without adjuvants, ARC-based vaccines generated protective immune responses in a lethal challenge model. Hence, this study showed the feasibility of ARC as a novel protein-based nanocarrier for multivalent surface display of pathogenic antigens and demonstrated the potential of exploiting recombinant mammalian retrovirus-like protein as a delivery vehicle for bioactive molecules. Graphic Abstract

Abstract Take-home exam (THE) use has been reported in various disciplines, but research on THE use in language modules in higher education appears to be scarce. The current study employed surveys and interviews to examine how the shift to written THE, in place of the traditional in-class exam (ICE) during the pandemic, impacted language learning and teaching on the China campus of a British university. Additionally, correlation analyses were conducted with ranking data of students from the same cohort under THE and ICE to explore patterns in student performance under these exam conditions. In surveys and interviews, teachers reported that their teaching foci did not change under THE, while many students reported that their learning practices were different under THE and ICE. Students also exhibited a tendency to spend more time practicing skills that they expected to be assessed in the exam. Overall, both teachers and students expressed preference for ICE, with many raising concerns about fairness issues in THE. Furthermore, correlation analyses showed that, overall, for a given group of students, written ICE rankings exhibited strong correlations with each other but written THE rankings did not, suggesting relative instability of THE results. However, when written THE and oral ICE results from the same module are combined, the resultant rankings strongly correlated with pure ICE rankings. This indicates that combining ICE and THE components for assessment could help mitigate some perceived shortcomings, including the instability issue, of THE used alone.

Abstract Robot calligraphy visually reflects the motion capability of robotic manipulators. While traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters, this article presents a generative adversarial network (GAN)‐based motion learning method for robotic calligraphy synthesis (Gan2CS) that can enhance the efficiency in writing complex calligraphy words and reproducing classic calligraphy works. The key technologies in the proposed approach include: (1) adopting the GAN to learn the motion parameters from the robot writing operation; (2) converting the learnt motion data into the style font and realising the transition from static calligraphy images to dynamic writing demonstration; (3) reproducing high‐precision calligraphy works by synthesising the writing motion data hierarchically. In this study, the motion trajectories of sample calligraphy images are firstly extracted and converted into the robot module. The robot performs the writing with motion planning, and the writing motion parameters of calligraphy strokes are learnt with GANs. Then the motion data of basic strokes is synthesised based on the hierarchical process of ‘stroke‐radical‐part‐character’. And the robot re‐writes the synthesised characters whose similarity with the original calligraphy characters is evaluated. Regular calligraphy characters have been tested in the experiments for method validation and the results validated that the robot can actualise the robotic calligraphy synthesis of writing motion data with GAN.

A general method which was developed to prepare hemoprotein models bearing an alkyne functionality to allow their immobilization by click chemistry is reported. The model was immobilized onto a gold electrode surface via copper-(I)-catalyzed azide-alkyne cycloaddition.

Starting from a manifestly Lorentz-invariant chiral Lagrangian, we present a model-free prediction for the transition amplitude of the process nn→ppe−e− induced by light Majorana neutrinos, which is a key process of the neutrinoless double beta decay (0νββ) in heavy nuclei employed in large-scale searches. Contrary to the nonrelativistic case, we show that the transition amplitude can be renormalized at leading order without any uncertain contact operators. The predicted amplitude defines a stringent benchmark for the previous estimation with model-dependent inputs, and greatly reduces the uncertainty of 0νββ transition operator in the calculations of nuclear matrix elements. Generalizations of the present framework could also help to address the uncertainties in 0νββ decay induced by other mechanisms. In addition, the present work motivates a relativistic ab initio calculation of 0νββ decay in light and medium-mass nuclei.

SARS-CoV-2 spike protein (SARS-2-S) induced cell-cell fusion in uninfected cells may occur in long COVID-19 syndrome, as circulating SARS-2-S or extracellular vesicles containing SARS-2-S (S-EVs) were found to be prevalent in post-acute sequelae of COVID-19 (PASC) for up to 12 months after diagnosis. Although isolated recombinant SARS-2-S protein has been shown to increase the SASP in senescent ACE2-expressing cells, the direct linkage of SARS-2-S syncytia with senescence in the absence of virus infection and the degree to which SARS-2-S syncytia affect pathology in the setting of cardiac dysfunction are unknown. Here, we found that the senescent outcome of SARS-2-S induced syncytia exacerbated heart failure progression. We first demonstrated that syncytium formation in cells expressing SARS-2-S delivered by DNA plasmid or LNP-mRNA exhibits a senescence-like phenotype. Extracellular vesicles containing SARS-2-S (S-EVs) also confer a potent ability to form senescent syncytia without de novo synthesis of SARS-2-S. However, it is important to note that currently approved COVID-19 mRNA vaccines do not induce syncytium formation or cellular senescence. Mechanistically, SARS-2-S syncytia provoke the formation of functional MAVS aggregates, which regulate the senescence fate of SARS-2-S syncytia by TNFα. We further demonstrate that senescent SARS-2-S syncytia exhibit shrinked morphology, leading to the activation of WNK1 and impaired cardiac metabolism. In pre-existing heart failure mice, the WNK1 inhibitor WNK463, anti-syncytial drug niclosamide, and senolytic dasatinib protect the heart from exacerbated heart failure triggered by SARS-2-S. Our findings thus suggest a potential mechanism for COVID-19-mediated cardiac pathology and recommend the application of WNK1 inhibitor for therapy especially in individuals with post-acute sequelae of COVID-19.

Abstract Object Constraint Language (OCL) is one kind of lightweight formal specification, which is widely used for software verification and validation in NASA and Object Management Group projects. Although OCL provides a simple expressive syntax, it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first‐order logic, which is approximately half accurate at the first stage of development. A deep neural network named DeepOCL is proposed, which takes the unrestricted natural language as inputs and automatically outputs the best‐scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule‐based generation approaches. To demonstrate the validity of our proposed approach, ablation experiments were conducted on a new sentence‐aligned dataset named OCLPairs. The experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation, scored 74.30 on BLEU, and greatly outperformed experienced developers by 35.19%. The proposed approach is the first deep learning approach to generate the OCL expression from the natural language. It can be further developed as a CASE tool for the software industry.

Abstract Rift Valley fever virus (RVFV) is listed as a priority pathogen by the World Health Organization (WHO) because it causes serious and fatal disease in humans, and there are currently no effective countermeasures. Therefore, it is urgent to develop a safe and efficacious vaccine. Here, we developed six nucleotide-modified mRNA vaccines encoding different regions of the Gn and Gc proteins of RVFV encapsulated in lipid nanoparticles, compared their ability to induce immune responses in mice and found that mRNA vaccine encoding the full-length Gn and Gc proteins had the strongest ability to induce cellular and humoral immune responses. IFNAR(−/−) mice vaccinated with mRNA-GnGc were protected from lethal RVFV challenge. In addition, mRNA-GnGc induced high levels of neutralizing antibodies and cellular responses in rhesus macaques, as well as antigen-specific memory B cells. These data demonstrated that mRNA-GnGc is a potent and promising vaccine candidate for RVFV.

Abstract Objectives Advanced cancer patients face various symptoms, which can cause physical and psychological distress. As a multidimensional construct, spiritual well-being (SWB) may be an inner resource for dealing with these problems. Our study explored the impact of different dimensions of SWB on physical and psychological symptoms in advanced cancer patients admitted to a palliative care unit. Methods This cross-sectional study was conducted among 108 advanced cancer patients in the Hospice Ward, Shengjing Hospital of China Medical University. Patients completed questionnaires on SWB and cancer-related symptoms (insomnia, fatigue, pain, depression and anxiety) at the time of admission. Linear regression analysis was applied to determine the relationship between SWB (meaning, peace and faith) and symptom distress. Results SWB accounted for an additional variance of cancer-related symptoms (17.8% to 44.4%). Meaning was negatively associated with insomnia (β = -0.516, p < 0.001) and fatigue (β = -0.563, p < 0.001). Peace and faith were related to lower psychological symptoms, while meaning represented a positive effect on anxiety (β = 0.275, p = 0.036). Higher peace was associated with lower cancer pain (β = -0.422, p < 0.001). Conclusions Our findings suggested that achieving peace and faith appeared to function consistently as a positive resource for advanced cancer patients on depression, anxiety and pain, while meaning may serve to facilitate or hinder positive adjustment. Future studies should focus on the potential clinical implications by identifying the distinct dimension of SWB as symptom management targets in the palliative care practice.

Recombinant adenovirus serotype 5 (Ad5) vector has been widely applied in vaccine development targeting infectious diseases, such as Ebola virus disease and coronavirus disease 2019 (COVID-19). However, the high prevalence of preexisting anti-vector immunity compromises the immunogenicity of Ad5-based vaccines. Thus, there is a substantial unmet need to minimize preexisting immunity while improving the insert-induced immunity of Ad5 vectors. Herein, we address this need by utilizing biocompatible nanoparticles to modulate Ad5–host interactions. We show that positively charged human serum albumin nanoparticles ((+)HSAnp), which are capable of forming a complex with Ad5, significantly increase the transgene expression of Ad5 in both coxsackievirus–adenovirus receptor-positive and -negative cells. Furthermore, in charge- and dose-dependent manners, Ad5/(+)HSAnp complexes achieve robust (up to 227-fold higher) and long-term (up to 60 days) transgene expression in the lungs of mice following intranasal instillation. Importantly, in the presence of preexisting anti-Ad5 immunity, complexed Ad5-based Ebola and COVID-19 vaccines significantly enhance antigen-specific humoral response and mucosal immunity. These findings suggest that viral aggregation and charge modification could be leveraged to engineer enhanced viral vectors for vaccines and gene therapies.

Aiming at the communication bottleneck problem when the current federated learning security aggregation algorithm was applied in a complex network environment, an adaptive federated learning secure aggregation scheme based on threshold homomorphic encryption was proposed.While protecting gradient privacy, users adaptively compress gradients based on the current available bandwidth, greatly reduced communication overhead for federated users.Furthermore, the new dynamic decryption task distribution algorithm and gradient combination algorithm were designed in the phase of aggregation gradient decryption, which relieved the user’s uplink communication pressure.The experimental results show that the proposed scheme can sharply reduce the amount of communication to 4% compared with the existing federated learning scheme with a trivial model accuracy loss of 1%.

Abstract Combined sewer overflows represent significant risks to human health as untreated water is discharged to the environment. Municipalities, such as the Metropolitan Sewer District of Greater Cincinnati (MSDGC), recently began collecting large amounts of water‐related data and considering the adoption of deep learning (DL) solutions like recurrent neural network (RNN) for predicting overflow events. Clearly, assessing the DL's fitness for the purpose requires a systematic understanding of the problem context. In this study, we propose a requirements engineering framework that uses the problem frames to identify and structure the stakeholder concerns, analyses the physical situations in which the high‐quality data assumptions may not hold, and derives the software testing criteria in the form of metamorphic relations that incorporate both input transformations and output comparisons. Applying our framework to MSDGC's overflow prediction problem enables a principled way to evaluate different RNN solutions in meeting the requirements.