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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.

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.

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.

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.

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.

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.

Abstract Capacitive deionization (CDI) is a green and pollution‐free emerging technology that can be used for ions removal and recovery from water. However, the traditional electro‐adsorption process to achieve ions removal faces the challenges of non‐selectivity, low efficiency and difficult ion recovery. In recent years, the conversion reaction has attracted wide attention due to its high selectivity, reversibility and excellent desalination ability for ions removal and recovery. This concept article will focus on the connotation and representative applications of conversion reactions that can be used for ion‐selective removal and recovery and put forward the perspectives and outlooks of conversion reactions.

Abstract Background Although palliation of psycho-spiritual distress is of great importance in terminally ill cancer patients, there is a little information about screening patients who benefit from palliative care and identifying the cancer care targets. This study explored the relationship of pain management and positive expectations with depression, anxiety and spiritual well-being (SWB) in terminal cancer patients admitted to a palliative care unit. Methods Eighty-four terminal cancer inpatients were recruited from the Hospice Ward, Shengjing Hospital of China Medical University. Optimism and general self-efficacy (GSE) were evaluated at admission. Patients completed self-report questionnaires on SWB, depression, anxiety and pain both on admission and one week later. The repeated designed analysis of variance was used to explore the correlates of depression, anxiety and SWB (meaning, peace, faith). Results In our sample, only cancer pain diminished significantly one week later. For depression (p = 0.041) and faith (p = 0.013), there was a significant pain group (relieved vs. not relieved) × time interaction effect, such that those with satisfied pain control experienced the improved psycho-spiritual outcomes at 1 week. The relationship between positive expectations, peace and faith was also statistically significant, indicating that the improvement of peace or faith was significant in the low group of optimism and GSE. Conclusions Our findings indicated that pain management lied at the center of depression and SWB, meaning that effective pain management may reduce depression, and improve SWB among terminal cancer patients. Moreover, positive expectations, especially for optimism, may be the new target for SWB-related intervention research. Palliative care nurse should require the identification of terminal cancer patients who may more benefit from short-term palliative care, and target them with effective cancer care.

To improve the cushioning performance of soft-landing systems, a novel origami-inspired combined cushion airbag is proposed. The geometry size, initial pressure, and exhaust vent area of the cushion airbags are designed preliminarily using a theoretical model. The finite element models, including the returnable spacecraft and cushion airbags, are established via the control volume method (CVM) to analyze the impact dynamic behavior and cushioning performance during the landing attenuation process. The cushioning performance of the cushion airbags in complex landing environments are studied to investigate the influence of horizontal velocity, lateral velocity and nonhorizontal landing surfaces. Four design parameters of the cushion airbags, including the initial pressure, venting threshold pressure, exhaust vent area and polygon edge number, are employed to study their influence on the cushioning performance. A multi-objective optimization model of the cushion airbags based on the neural network and multi-objective water cycle algorithm is established to realize the rapid optimization design. The Pareto front of the maximum overload and specific energy absorption is obtained. The analysis results show that the maximum overload of the proposed combined cushion airbags is 7.30 g. The system with the anti-rollover design can avoid rollover and achieve outstanding cushioning performance in complex landing environments. The maximum overload of the returning spacecraft is decreased by 16.4% from 7.30 g to 6.10 g after multi-objective optimizations. This study could provide the technical support for the soft-landing system design of returnable spacecrafts.

IntroductionThe SARS-CoV-2 Omicron variant has become the dominant SARS-CoV-2 variant and exhibits immune escape to current COVID-19 vaccines, the further boosting strategies are required.MethodsWe have conducted a non-randomized, open-label and parallel-controlled phase 4 trial to evaluate the magnitude and longevity of immune responses to booster vaccination with intramuscular adenovirus vectored vaccine (Ad5-nCoV), aerosolized Ad5-nCoV, a recombinant protein subunit vaccine (ZF2001) or homologous inactivated vaccine (CoronaVac) in those who received two doses of inactivated COVID-19 vaccines. ResultsThe aerosolized Ad5-nCoV induced the most robust and long-lasting neutralizing activity against Omicron variant and IFNg T-cell response among all the boosters, with a distinct mucosal immune response. SARS-CoV-2-specific mucosal IgA response was substantially generated in subjects boosted with the aerosolized Ad5-nCoV at day 14 post-vaccination. At month 6, participants boosted with the aerosolized Ad5-nCoV had remarkably higher median titer and seroconversion of the Omicron BA.4/5-specific neutralizing antibody than those who received other boosters. DiscussionOur findings suggest that aerosolized Ad5-nCoV may provide an efficient alternative in response to the spread of the Omicron BA.4/5 variant.Clinical trial registrationhttps://www.chictr.org.cn/showproj.html?proj=152729, identifier ChiCTR2200057278.

Continuous generation of reactive oxygen species is desirable in the advanced oxidation process. Here, the authors report a self-cycled photoFenton-like with a scalable artificial leaf for production of H2O2 from water with solar-to-H2O2 efficiency of 1.46%.

Aluminum (alum) adjuvant is the most extensively used protein subunit vaccine adjuvant, and its effectiveness and safety have been widely recognized. The surface charge of the antigen determines its electrostatic adsorption to alum adjuvant, which directly affects the immune efficacy of the protein vaccine. In our study, we precisely modified its surface charge by inserting charged amino acids into the flexible region of the SARS-CoV-2 receptor-binding domain (RBD), achieving electrostatic adsorption and a site-specific anchor between the immunogen and alum adjuvant. This innovative strategy extended the bioavailability of the RBD and directionally displayed the neutralizing epitopes, thereby significantly enhancing humoral and cellular immunity. Furthermore, the required dose of antigen and alum adjuvant was greatly reduced, which improved the safety and accessibility of the protein subunit vaccine. On this basis, the wide applicability of this novel strategy to a series of representative pathogen antigens such as SARS-RBD, MERS-RBD, Mpox-M1, MenB-fHbp, and Tularemia-Tul4 was further confirmed. Charge modification of antigens provides a straightforward approach for antigenicity optimization of alum-adjuvanted vaccines, which has great potential to be adopted as a global defense against infectious diseases.

Abstract Background Machine learning has become an alternative approach for pharmaceutical formulation development. However, many machine learning applications in pharmaceutics only focus on model performance rather than model interpretability. Aim This study aims to propose an attention‐based deep neural network (DNN) for pharmaceutical formulation development. Methods An attention‐based DNN, AttPharm, was proposed. AttPharm separately handled feature values and feature physical meaning by representation learning to successfully apply the attention mechanism to the pharmaceutical tabular data. Furthermore, the distributions of the attention weights were computed using AttPharm. Two post hoc methods, local interpretable model‐agnostic explanation (LIME) and TreeSHAP, were utilized to obtain the post hoc model interpretability for lightGBM. Results The results demonstrated that AttPharm significantly improved the model performance of plain neural networks on a pharmaceutical cyclodextrin dataset because the attention mechanism could extract related features and find minute variation. Notably, the attention weights were analyzed, which illustrated global and local feature‐level and sample‐level model interpretability, thus providing insights for formulation design. Comparing with post hoc methods, AttPharm can be used without the concern of the faithfulness of interpretability. Conclusion This is the first step in applying the attention‐based DNN to pharmaceutical formulation development. Considering the importance of model interpretability, the proposed approach may have a wide range of applications in pharmaceutics.

Graphitic carbon nitride (g-C3N4) photocatalysis for water splitting is harvested as a fascinating way for addressing the global energy crisis. At present, numerous research subjects have been achieved to design and develop g-C3N4 photocatalysis, and the photocatalytic system still suffers from low efficiency that is far from practical applications. Here, there is an inspiring review on the latest progress of the doping strategies to modify g-C3N4 for enhancing the efficiency of photocatalytic water splitting, including non-metal doping, metal doping, and molecular doping. Finally, the review concludes a summary and highlights some perspectives on the challenges and future research of g-C3N4 photocatalysts.

Second language (L2) listening is a common challenge for language learners. It remains largely unknown how bilinguals process L2 listening. The literature has suggested an interactive model of L2 listening processing. However, few studies have examined the model from an experimental approach. The current study tried to provide empirical evidence for the interactive model of L2 listening processing in bilinguals by exploring the relationships among English spoken word segmentation (SWS), cognitive inhibition, cognitive flexibility, and L2 listening proficiency. The results showed positive associations among SWS, cognitive inhibition, cognitive flexibility, and L2 listening proficiency. Mediation analysis suggested that SWS might have a positive influence on L2 listening proficiency both directly and indirectly through cognitive inhibition and cognitive flexibility, respectively. These results imply that both bottom-up (reflected at SWS) and top-down (reflected at cognitive inhibition and flexibility) processes are engaged in bilinguals’ L2 listening processing.

A vaccine preventing infection and transmission of SARS-CoV-2 is needed. Here, Wu et al. generate an adenovirus-vector vaccine expressing SARS-CoV-2 spike protein and show that a single dose of mucosal vaccination protects mice and ferrets from infection and inhibits virus replication in the upper respiratory tract.

Titanium alloy radiation rods have excellent physical and chemical properties compared to other materials, and are commonly used for ultrasonic casting of 2A14 aluminum alloy. However, titanium alloys are chemically corroded in high-temperature aluminum melts for a long time, making it difficult to precisely regulate the elemental composition during casting. In order to better understand the high-temperature chemical corrosion mechanism of titanium alloy radiation rods, this research looks into the corrosion morphology, weight loss, surface roughness, and reaction layer. The study’s findings suggest that the rate of chemical corrosion of titanium alloy in high-temperature aluminum melt is often inversely correlated with the degree of roughness, with the degree of roughness changing nonlinearly during the corrosion process. Titanium alloy weight loss rates with roughness Ra0.4 μ m, Ra7.2 μ m, Ra9.5 μ m and Ra9.8 μ m are 0.16 mg per min, 0.25 mg per min, 0.37 mg per min and 0.29 mg per min, respectively. The corrosion product of the chemical corrosion process is TiAl _3 , which is granular. Under varying roughness conditions, the solid-liquid interface of Al/Ti emerges reactants after 4 min, and the TiAl _3 reaction layer arises after 12 min. Furthermore, the reaction layer with little roughness is flat and compact, whereas the reaction layer with great roughness is loose and contains many faults. At the same time, the growth rate of the reaction layer decreases slightly. And the greater the surface roughness, the greater the TiAl _3 reaction layer grows at the titanium alloy matrix.

The COVID-19 pandemic has brought severe impact on language learners' emotional states and their performance in creativity. Yet, their ability to regulate emotions is crucial for everyday functioning during times of crisis. The question of how adaptive emotion regulation (ER) strategies, which help an individual maintain appropriate and stable mood states, might affect bilinguals' creativity remains unexplored. The present study investigated this issue by measuring various indicators of the psychological impact of the COVID-19 pandemic, L2 proficiency, adaptive ER strategies, and bilinguals' cognitive creativity (CC) and emotional creativity (EC) during the pandemic. Results from a sample of 235 bilingual participants who completed a battery of survey instruments showed that: (1) bilinguals' negative mood significantly increased during the pandemic compared with their mood state before the pandemic; (2) their negative mood during the pandemic was positively associated with their adaptive ER strategies; (3) L2 proficiency had a direct effect on bilinguals' cognitive flexibility, CC, and EC; (4) L2 proficiency also indirectly influenced bilinguals' CC through cognitive flexibility. These results suggest that cognitive flexibility had a simple mediation effect on the association between L2 proficiency and CC. However, the current study further found that bilinguals had different cognitive patterns in EC. L2 proficiency influenced bilinguals' EC through cognitive flexibility indirectly only when adaptive ER strategies had a moderation effect on the association between cognitive flexibility and EC. However, this moderated mediation effect was not significant in CC. The current study implies that bilinguals' adaptive ER strategies played a distinct role in bilinguals' EC during the COVID-19 pandemic.

The Unified Modeling Language (UML) is the de facto standard for requirements modeling and analysis in the software industry. However, it lacks the ability of formal analysis and verification. In this paper, we propose a synthetic approach UML-SR that integrates UML with service refinement (SR) to support the formal requirements modeling and analysis as well as formal verification. The UML-SR requirements model contains a use case diagram, the system sequence diagrams of use cases, a conceptual class diagram, and the formal contracts of system interfaces. To make this integration viable, we extend service refinement with the concepts of visibility in UML. With the visibility extension, we are able to formally specify and verify both internal and external interactions of the system. To demonstrate the effectiveness of our proposed approach, we investigate a case study of an Online Shopping System. The results show that a consistent requirements model can be eventually derived through formal refinement and verification. The proposed approach is useful and can be further applied for the requirements modeling and formal verification in the software industry.

Model checking as a computer-assisted verification method is widely used in many fields to verify whether a design model satisfies the requirements specifications of the target system. In practice, it is difficult to design a system without the sophisticated requirements analysis. Unlike other model checking tools, the labeled transition system analyzer (LTSA) not only can specify the property specifications of the target system but also provides a structure diagram to specify the system architecture of the requirements model, which can be further used to design the target system. In this paper, we demonstrate the abilities of LTSA shipped with the classic case study of the steam boiler system. In the requirements analysis, the LTSA can specify the cyber and physical components of the target system and interactions between the components and the safety properties of the target system. In system design, the LTSA can automatically generate a start-up design model as the finite state process from the requirements model, and then a design model can be further accomplished by system architects and developers. Finally, the LTSA can automatically verify whether the design model meets the requirements specifications. Our work demonstrates the potential power of model checking tools can be applied and useful in software engineering for requirements analysis, system design, and verification.

Background: Macrophage polarization has been reported to induce podocyte injury, which is a typical characteristic of diabetic nephropathy (DN). Trichosanthes kirilowii is an herb showing renal protective effect as well as immune-regulating effect. Therefore, it was hypothesized that the renal protective effect of Trichosanthes kirilowii was associated with its modulation on macrophage polarization. In the current study, we tested the hypothesis by subjecting DN rats to treatment of Trichosanthes kirilowii lectin (TKL), an active component of Trichosanthes kirilowii. Method: DN was induced using streptozocin (STZ) method, and after 3 days, treatments were performed with different doses of TKL for eight weeks. The effect of TKL on the renal function, structure, and inflammation was assessed. To explain the pathway mediating the effect of TKL on renal tissues, the expressions of markers involved in macrophage polarization, podocyte proliferation, and Notch signaling were determined. Moreover, the DN rats were further administrated with Notch signaling inhibitor, Dibenzazepine (DIB), to verify the key role of Notch signaling in the renal protective effect of TKL. Results: STZ induced damages in renal function and structure, which was attenuated by TKL of different doses. Moreover, STZ also increased the production of TNF-α and iNOS while suppressed the production of IL-10 and arginase-1 (Arg-1). The induced inflammation by STZ was inhibited by TKL. The polarization of macrophage into M1 type during the development of DN was blocked by TKL, contributing to the increased proliferation potential of podocytes. Regarding Notch signaling, TKL administration inhibited the activation of the pathway by suppressing the expression of Notch1, NICD1, and Hes1. The administration of DIB had similar effect to that of TKL administration on renal function and structure. Conclusions: The study for the first time showed that TKL attenuated deterioration in renal structure and function by increasing M2 macrophage proportion via inhibition of Notch signaling.

Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of the present research is to apply deep learning methods to predict pharmaceutical formulations. In this paper, two types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assess the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. Results showed that the accuracies of both two deep neural networks were above 80% and higher than other machine learning models; the latter showed good prediction of pharmaceutical formulations. In summary, deep learning employing an automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was developed for the prediction of pharmaceutical formulations for the first time. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical research from experience-dependent studies to data-driven methodologies. KEY WORDS: Pharmaceutical formulation, Deep learning, Small data, Automatic dataset selection algorithm, Oral fast disintegrating films, Oral sustained release matrix tablets

The COVID-19 epidemic is associated with negative mood, which has the potential to be a powerful driver of creativity. However, the influence of negative mood on cognitive creativity and emotional creativity remains elusive. Previous research has indicated that self-focused attention is likely to be related to both negative mood and creativity. The current study introduced two self-focused attention variables (i.e., rumination, reflection) to explore how negative mood might contribute to cognitive creativity and emotional creativity. Based on a sample of 351 participants, our study found that (1) negative mood during the outbreak of COVID-19 was associated with cognitive creativity and emotional creativity. Meanwhile, there were significant serial mediation effects of rumination and reflection in the relationship between negative mood and creativity and (2) the psychological impact after exposure to the COVID-19 epidemic was positively correlated with emotional creativity but not with cognitive creativity. These results suggested that individuals, in real life and work, could achieve better creative performance through moderate self-focus. Moreover, individuals with different mood states can be induced to enhance their creativity in times of crisis through intervention training to promote reflection.

Oral disintegrating tablets (ODTs) are a novel dosage form that can Peer review under responsibility of Shenyang Pharmaceutical University. be dissolved on the tongue within 3 min or less especially for geriatric and pediatric patients. Current ODT formulation studies usually rely on the personal experience of pharmaceutical experts and trial-and-error in the laboratory, which is inefficient and time-consuming. The aim of current research was to establish the prediction model of ODT formulations with direct compression process by artificial neural network (ANN) and deep neural network (DNN) techniques. 145 formulation data were extracted from Web of Science. All datasets were divided into three parts: training set (105 data), validation set (20) and testing set (20). ANN and DNN were compared for the prediction of the disintegrating time. The accuracy of the ANN model have reached 85.60%, 80.00% and 75.00% on the training set, validation set and testing set respectively, whereas that of the DNN model were 85.60%, 85.00% and 80.00%, respectively. Compared with the ANN, DNN showed the better prediction for ODT formulations. It is the first time that deep neural network with the improved dataset selection algorithm is applied to formulation prediction on small data. The proposed predictive approach could evaluate the critical parameters about quality control of formulation, and guide research and process development. The implementation of this prediction model could effectively reduce drug product development timeline and material usage, and proactively facilitate the development of a robust drug product. Keywords: Oral disintegrating tablets, Formulation prediction, Artificial neural network, Deep neural network, Deep-learning

Legacy electronic health record systems were not developed with the level of connectivity expected from them nowadays. Therefore, interoperability weakness inherent in the legacy systems can result in poor patient care and waste of financial resources. Simultaneously, healthcare providers are not yet ready to dispose of them. Large hospitals are also less likely to share their data with external care providers due to economic and political reasons. To overcome the barriers in the effective medical data exchange process, we present a novel hybrid cloud called MedShare, dealing with interoperability issues among disconnected but autonomously functioning healthcare providers. The proposed system architecture and its implementation is based upon: 1) custom data extractors to extract legacy medical data from the three hemodialysis centers under consideration; 2) negotiated and converted to a common data model in each of the private cloud of a provider; 3) indexed patient information using the HashMap technique into the public cloud that operates on private clouds, called a hybrid cloud; and 4) a set of services and tools installed as a coherent environment to exchange information smoothly. This paper enables healthcare professionals to appropriately access and securely share a patient's medical information. MedShare allows the healthcare providers and administrators to maintain the control of their patient data, which is always the primary concern in building a trustworthy environment for exchanging patient information. Medshare effectively addresses primary security and privacy concerns surrounding the deployment of data exchange process by including patient consent and a two-way authorization process.

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.

In this study, castings of TiC nanoparticle reinforced 2219 aluminum matrix composites with different TiC nanoparticle contents (0, 0.5, 0.9, 1.3, and 1.7 wt.%) prepared using an ultrasound-assisted stirring technology were deformed by multidirectional forging at 510 °C followed by T6 aging treatment. The microstructural evolution and mechanical properties of the 2219 alloy and its composites were investigated and compared. Optical microscopy and scanning electron microscopy revealed that the composite with 0.9 wt.% TiC nanoparticle content possessed finer grains and the lowest amount of Al2Cu phases. The electron backscattered diffraction (EBSD) was used to characterize the sub-grains. The precipitation microstructures of the 2219 alloy and composites with different nanoparticle contents were characterized by differential scanning calorimetry and transmission electron microscopy. It was found that 0.9 wt.% TiC/2219 nanocomposites contained the highest amount of θ″ and θ′ phases with shorter lengths. This might imply that the nanoparticles uniformly dispersed in the matrix could facilitate the precipitation of θ″ and θ′ phases during aging. Thus, the 0.9 wt.% TiC/2219 nanocomposite showed the best mechanical properties. The ultimate tensile strength, yield strength, and elongation of the 0.9 wt.% TiC/2219 nanocomposite increased by 24.2, 46.1, and 37.2%, respectively, compared to those of the 2219 alloy.

Healthcare information exchange is an important research topic, which can benefit both healthcare providers and patients. In healthcare data sharing, many cloud-based solutions have been proposed, but the trustworthiness of a third-party cloud service is questionable. Recently, blockchain has been introduced in healthcare record sharing, which does not rely on trusting a third party. However, existing approaches only focus on the records collected from medical examination. They are not efficient in sharing data streams continuously generated from sensors and other monitoring devices. Today, IoT devices have been widely deployed and sensors and mobile applications can monitor patients’ body conditions. The collected data are shared to laboratories and institutions for diagnosis and further study. Moreover, existing approaches are too rigid to efficiently support metadata change. In this paper, an efficient data-sharing scheme is proposed, called MedChain, which combines blockchain, digest chain, and structured P2P network techniques to overcome the above efficiency issues in the existing approaches for sharing both types of healthcare data. Based on MedChain, a session-based healthcare data-sharing scheme is devised, which brings flexibility in data sharing. The evaluation results show that MedChain can achieve higher efficiency and satisfy the security requirements in data sharing.

Rice blast, caused by Magnaporthe oryzae, is a major disease of rice almost worldwide. The Chinese indica cultivar 93-11 is resistant to numerous isolates of the blast fungus in China, and can be used as broad-spectrum resistance resource, particularly in japonica rice breeding programs. In this study, we identified and mapped two blast resistance genes, Pi60(t) and Pi61(t), in cv. 93-11 using F2 and F3 populations derived from a cross between the susceptible cv. Lijiangxintuanheigu (LTH) and resistant cv. 93-11 and inoculated with M. oryzae isolates from different geographic origins. Pi60(t) was delimited to a 274 kb region on the short arm of chromosome 11, flanked by InDel markers K1-4 and E12 and cosegregated with InDel markers B1 and Y10. Pi61(t) was mapped to a 200 kb region on the short arm (near the centromere) of chromosome 12, flanked by InDel markers M2 and S29 and cosegregating with InDel marker M9. In the 274 kb region of Pi60(t), 93-11 contains six NBS-LRR genes including the two Pia/PiCO39 alleles (BGIOSGA034263 and BGIOSGA035032) which are quite close to the two Pia/PiCO39 alleles (SasRGA4 and SasRGA5) in Sasanishiki and CO39, with only nine amino acids differing in the protein sequences of BGIOSGA035032 and SasRGA5. In the 200 kb region of Pi61(t), 93-11 contains four NBS-LRR genes, all of which show high identities in protein sequence with their corresponding NBS-LRR alleles in susceptible cv. Nipponbare. Comparison of the response spectra and physical positions between the target genes and other R genes in the same chromosome regions indicated that Pi60(t) could be Pia/PiCO39 or its allele, whereas Pi61(t) appears to be different from Pita, Pita-2, Pi19(t), Pi39(t) and Pi42(t) in the same R gene cluster. DNA markers tightly linked to Pi60(t) and Pi61(t) will enable marker-assisted breeding and map-based cloning.

Epidemic methicillin-resistant Staphylococcus aureus (MRSA) imposes an increasing impact on public health. Due to multi-antibiotics resistance in MRSA strains, there is an urgent need to develop novel therapeutics such as effective monoclonal antibodies (mAbs) against MRSA infections. Staphylococcus aureus surface protein A (SasA), a large surface-located protein (~240 kDa), is one of MSCRAMMs (microbial surface components recognizing adhesive matrix molecules) and a potential target for immunotherapeutic approaches against S. aureus infections. In the present study, we analyzed the sequence of SasA with bioinformatics tools and generated a protective monoclonal antibody (2H7) targeting the conserved domain of SasA. 2H7 was shown to recognize wild-type S. aureus and promote opsonophagocytic killing of S. aureus. In both sepsis and peritoneal infection models, prophylactic administration of 2H7 improved the survival of BALB/c mice challenged by S. aureus strain USA300 and ST239 (prevalent MRSA clones in North America and Asian countries, respectively) and enhanced bacterial clearance in kidneys. Additionally, 2H7 prophylaxis prevented the formation of intraperitoneal abscess in a murine model of peritoneal infection and therapeutic administration of 2H7 showed protective efficacy in a murine sepsis model. Our results presented here provide supporting evidences that an anti-SasA mAb might be a potential component in an antibody-based immunotherapeutic treatment of MRSA infections.

Oxide films or inclusions can reduce the continuity and integrity of materials and they always lead to a significant reduction in the mechanical properties of an aluminum alloy. They can greatly reduce the plastic flow behavior of materials, thus affecting the subsequent processing performance. Therefore, an effective ultrasonic assisted preparation technology has been applied to industrial manufacturing of large-scale aluminum alloy ingots (with diameter: Φ = 1250 mm and height: h = 3750 mm). However, the mechanisms of ultrasonic purification on the large-scale ingots are not clear. Therefore, a number of aluminum alloy casting experiments were carried out to produce a conventional hot top semi-continuous ingot (CHTI) and an ultrasonic hot top semi-continuous ingot (UHTI) in this work. The microstructures of CHTI and UHTI were analyzed by optical microscopy (OM) and scanning electron microscopy (SEM). The results indicated that there were some oxide film defects in the CHTI but some finely dispersed inclusion particles were discovered in the UHTI. The X-ray diffraction (XRD) data showed that the component of inclusion was Al2O3. According to the different cavitation effects of the different areas of the molten aluminum, the process of ultrasonic purification was divided into three periods and the mechanisms in each period were separately studied.

Burnout is a syndrome of emotional exhaustion, cynicism and reduced professional efficacy, which can result from long-term work stress. Although the burnout level is high among iron and steel workers, little is known concerning burnout among iron and steel worker. This study aimed to evaluate the burnout and to explore its associated internal and external factors in iron and steel workers.A cross-sectional survey was conducted in iron and steel workers at the Anshan iron-steel complex in Anshan, northeast China. Self-administered questionnaires were distributed to 1,600 workers, and finally 1,300 questionnaires were returned. Burnout was measured using the Chinese version of the Maslach Burnout Inventory-General Survey (MBI-GS). Effort-reward imbalance (ERI), perceived organizational support (POS), and psychological capital (PsyCap) were measured anonymously. A hierarchical regression model was applied to explore the internal and external factors associated with burnout.Mean MBI-GS scores were 13.11±8.06 for emotional exhaustion, 6.64±6.44 for cynicism, and 28.96±10.39 for professional efficacy. Hierarchical linear regression analysis showed that ERI and POS were the most powerful predictors for emotional exhaustion and cynicism, and PsyCap was the most robust predictor for high professional efficacy.Chinese iron and steel workers have a high level of burnout. Burnout might be associated with internal and external factors, including ERI, POS, and PsyCap. Further studies are recommended to develop an integrated model including both internal and external factors, to reduce the level of ERI, and improve POS and workers' PsyCap, thereby alleviating the level of burnout among iron and steel workers.