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Abstract Background Increased hemoglobin concentrations may increase the risk of varicose veins. However, the underlying relationship between them was not yet understood. Methods Mendelian randomization (MR) analysis was performed to investigate causal effect between mean corpuscular hemoglobin concentration (MCHC, exposure factor) and varicose veins (outcome). Afterward, sensitivity analysis was used to ensure the reliability of MR analysis results. Then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of SNPs were performed. A search tool for recurring instances of neighbouring genes (STRING) database was used to construct a protein-protein interaction (PPI) network. Results Therefore, the inverse-variance weighted (IVW) results showed there existed a causal relationship between MCHC and varicose veins (p = 0.0026), with MCHC serving as a significant risk factor. (odd ratio [OR] = 1.2321). In addition, the validity of the results of the forward MR analysis was verified by sensitivity analysis. Further, a PPI network of 92 single-nucleotide polymorphisms (SNPs) which used for forward MR analysis related genes was constructed. And they were found to be closely associated with the peroxisome proliferator-activated receptor (PPAR) signalling pathway and cellular response to external stimulus by enrichment analysis. In addition, we clarified that the effect of varicose veins on MCHC was minimal by reverse MR analysis, suggesting that the results of forward MR analysis were not disturbed by reverse results. Conclusion This study found a causal relationship between varicose veins and MCHC, which provided strong evidence for the effect of hemoglobin on varicose veins, and a new thought for the diagnosis and prevention of varicose veins in the future.

Abstract Tooth wear and pain are the primary concerns of patients undergoing periodontal scaling. The aims of this study were to compare the effects of a new magnetostrictive ultrasonic scaler and a traditional piezoelectric ultrasonic scaler on tooth surface roughness and calculus removal and to determine their impacts on patient discomfort during supragingival cleaning. This article had two parts: an in vitro study and a clinical study. In the in vitro study, thirty teeth with subgingival calculus were randomly assigned to two scaling treatment groups: magnetostrictive scalers (n = 15) and piezoelectric scalers (n = 15). Surface roughness measurements were taken at baseline and after scaling, and the root samples were visualised by SEM after scaling. Additionally, a single-centre randomised split-mouth clinical trial was conducted. Eighty-five participants diagnosed with chronic gingivitis or periodontitis were randomly assigned to receive supragingival scaling. The magnetostrictive scaler was used in half of the mouths (n = 85), and the piezoelectric scaler was used in the other half of the mouths (n = 85). Data on pain, noise, and vibration were collected using a VAS questionnaire, and the operating time was recorded. In both in vitro and clinical studies, magnetostrictive scalers were reported to be more effective than piezoelectric scalers in removing dental deposits (P

Lithium (Li) metal batteries though with high energy density are still facing issues like Li dendrite growth, dead Li formation, and thick solid electrolyte interphase (SEI) formation, hindering their long-term stability. Recently, Li-Ag alloys have been reported to potentially address these challenges possibly due to their superior conductivity, lithiophilicity, and mechanical stability. In the pursuit of high-energy-density batteries, Li-Ag alloys typically employ a high Li content phase (γ1). In this study, we applied density functional theory (DFT) calculations to compare the thermodynamic stability, Li adsorption, and Li diffusion of Ag-rich Li-Ag alloy within the γ1 phase (AR-γ1), Ag-poor Li-Ag alloy within the γ1 phase (AP-γ1), and pure Li. AR-γ1 showed better thermodynamic stability and improved Li adsorption and diffusion properties compared to AP-γ1 and pure Li. Electrochemical tests further confirmed the advantages of AR-γ1 in terms of electrode kinetics and cell stability compared to AP-γ1 and pure Li. Our study offers guidance for the selection of the most suitable Li-Ag alloys that can be utilized in high-energy-density lithium batteries.

Abstract Stochasticity plays a significant role in the low‐power operation of a biological neural network. In an artificial neural network, stochasticity also contributes to critical functions such as the uncertainty quantification (UQ) for estimating the probability for the correctness of prediction. This UQ is vital for cutting‐edge applications, including medical diagnostics, autopilots, and large language models. Thanks to nonlinear variation of analogous Hall resistance with high computing velocity and low dissipation, a stochastic spin‐orbit‐torque (SOT) device exhibits significant potential for implementing the UQ. However, up until now, the application of UQ for stochastic SOT devices remains unexplored. In this study, based on SOT‐induced stochastic magnetic domain wall (DW) motion with varying velocity, a SOT synapse is fabricated that can emulate stochastic weight update following the Spike‐Timing‐Dependent‐Plasticity (STDP) rule. Furthermore, a stochastic SNN is set up, which, when compared to its deterministic counterpart, demonstrates a clear advantage in quantifying uncertainty for diagnosing the type of breast tumor (benign or malignant).

Composite ceramic phosphor (CCP) is a candidate light-conversion material to obtain the high-quality laser lighting source. Phosphors based on the transmissive configuration model could not simultaneously meet the requirements of angular color uniformity and high thermal stability. In this study, a novel composite structure ceramic was designed, including Al2O3–YAG:Ce/YAG layered ceramic with a size of 1 mm × 1 mm for lighting, and Al2O3 ceramic (φ = 16.0 mm) was used as the wrapping material due to its outstanding thermal stability. The prepared ceramics exhibited excellent thermal performance and no yellow ring phenomenon. Through this design, we achieved the match of the intensity distribution of the blue and yellow lights, resulting in a high angular color uniformity of 0.9 with a view angle of ±80°. All ceramics showed no luminous saturation phenomenon, even the laser power density was increased up to 47.51 W/mm2. A high-brightness white-light source with a luminous flux of 618 lm, a luminous efficiency of 126 lm/W, a CCT of 6615 K, and a CRI of 69.9 was obtained in the transmissive configuration. In particular, the surface temperature of the ceramic was as low as 74.1 ℃ under a high laser radiation (47.51 W/mm2). These results indicate that Al2O3/Al2O3–YAG:Ce/YAG composite structure ceramic is a promising luminescent material in the high-power laser lighting applications.

Abstract Plasma membrane lysis is an effective anticancer strategy, which mostly relying on soluble molecular membranolytic agents. However, nanomaterial-based membranolytic agents has been largely unexplored. Herein, we introduce a mesoporous membranolytic nanoperforators (MLNPs) via a nano- and molecular-scale multi-patterning strategy, featuring a spiky surface topography (nanoscale patterning) and molecular-level periodicity in the spikes with a benzene-bridged organosilica composition (molecular-scale patterning), which cooperatively endow an intrinsic membranolytic activity. Computational modelling reveals a nanospike-mediated multivalent perforation behaviour, i.e., multiple spikes induce nonlinearly enlarged membrane pores compared to a single spike, and that benzene groups aligned parallelly to a phospholipid molecule show considerably higher binding energy than other alignments, underpinning the importance of molecular ordering in phospholipid extraction for membranolysis. Finally, the antitumour activity of MLNPs is demonstrated in female Balb/c mouse models. This work demonstrates assembly of organosilica based bioactive nanostructures, enabling new understandings on nano-/molecular patterns co-governed nano-bio interaction.

The prediction of rock bursts is of paramount importance in ensuring the safety of coal mine production. In order to enhance the precision of rock burst prediction, this paper utilizes a working face of the Gengcun Coal Mine as a case study. The paper employs a three-year microseismic monitoring data set from the working face and employs a sensitivity analysis to identify three monitoring indicators with a higher correlation with rock bursts: daily total energy, daily maximum energy, and daily frequency. Three subsets are created from the 10-day monitoring data: daily frequency, daily maximum energy, and daily total energy. The impact risk score of the next day is assessed as the sample label by the expert assessment system. Sample input and sample label define the data set. The long short-term memory (LSTM) neural network is employed to extract the features of time series. The Bayesian optimization algorithm is introduced to optimize the model, and the Bayesian optimization–long short-term memory (BO-LSTM) combination model is established. The prediction effect of the BO-LSTM model is compared with that of the gated recurrent unit (GRU) and the convolutional neural network (1DCNN). The results demonstrate that the BO-LSTM combined model has a practical application value because the four evaluation indexes of the model are mean absolute error (MAE), mean absolute percentage error (MAPE), variance accounted for (VAF), and mean squared error (MSE) of 0.026272, 0.226405, 0.870296, and 0.001102, respectively. These values are better than those of the other two single models. The rock explosion prediction model can make use of the research findings as a guide.

Abstract The utilization of novel noncollinear antiferromagnetic materials holds great promise for the development of energy‐efficient spintronic devices. However, only a few studies have reported on the all‐electrical control of perpendicular magnetization switching using noncollinear antiferromagnets as the spin source, and the underlying mechanism behind the unconventional spin‐orbit torque (SOT) is still a topic of debate. In this work, deterministic perpendicular magnetization switching in Mn3Sn/CoTb bilayers is successfully achieved. Compared to the control samples with heavy metal as the spin source, the critical switching current density is over one order of magnitude reduced, indicating an enhanced efficiency of the out‐of‐plane charge‐to‐spin conversion in the textured Mn3Sn films. The influence of film thickness and growth temperature on the efficiency of different spin polarizations suggests potential roles of crystal quality and spin texture in spin diffusion with different spin polarization directions. These findings provide valuable insights into the crystal structure, spin‐orbit torque effects, and charge‐to‐spin conversion in Mn3Sn films, highlighting the importance of understanding interface and bulk contributions in antiferromagnetic spin transport phenomena.

Abstract Cubic materials host high crystal symmetry and hence are not expected to support anisotropy in transport phenomena. In contrast to this common expectation, here we report an anomalous anisotropy of spin current can emerge in the (001) film of Mn3Pt, a noncollinear antiferromagnetic spin source with face-centered cubic structure. Such spin current anisotropy originates from the intertwined time reversal-odd ( $${{{{{\mathscr{T}}}}}}$$ T -odd) and time reversal-even ( $${{{{{\mathscr{T}}}}}}$$ T -even) spin Hall effects. Based on symmetry analyses and experimental characterizations of the current-induced spin torques in Mn3Pt-based heterostructures, we find that the spin current generated by Mn3Pt (001) exhibits exotic dependences on the current direction for all the spin components, deviating from that in conventional cubic systems. We also demonstrate that such an anisotropic spin current can be used to realize low-power spintronic applications such as the efficient field-free switching of the perpendicular magnetizations.

Concrete is one of the most used building materials globally, leading to a large amount of greenhouse gas (GHG) emissions. Using supplementary cementitious materials (SCM) as replacements for cement in concrete provides an effective way to reduce GHG emissions. However, quantifying the construction performance of using SCM concrete is hard because of complex interactions between concrete’s mechanical properties and construction characteristics, like local energy supply, surrounding temperature and construction plans, which leads to only the fragmental performance of using SCM concrete being explored in previous studies. There still lacks an effective way to quantify the comprehensive performance and provide decision support for contractors about how to use SCM concrete. To deal with the gap, this research proposes a Collection–Simulation–Calculation–Decision (CSCD) method to analyze the complex interactions between concrete and construction, and to quantify the performance of the supply chain–construction when using SCM. A case study is also conducted to demonstrate the effectiveness of the proposed method. The results show that the proposed method is effective in quantifying the performance of using SCM concrete in construction and providing decision support for construction decision makers. A scenario analysis is also conducted to demonstrate the effectiveness of the proposed method in different project characteristics, including the global warming potential (GWP) factors for different construction sites, seasonal temperature changes and different construction plans. The proposed method is an effective tool to quantify the construction performance of using SCM concrete considering complex interactions between concrete mechanical properties and construction characteristics. The results of the research can assist construction decision makers to make decisions about using SCM concrete by comprehensively understanding the impacts shifting along the concrete supply chain and construction.

Abstract Resource‐ and energy‐efficient biomass exploitation for green graphite production is one of the most effective strategies for satisfying graphite demand while minimizing energy consumption and carbon emissions. This study investigated green graphite production from biomass waste and its applications to establish a green graphite industry. Biomass pyrolysis and catalytic graphitization of biochar were studied first to produce green graphite. The optimized green graphite exhibited a reversible capacity of 264 mA h/g and 97% capacity retention over 100 cycles in a half‐cell. Green graphite electrodes with a resistivity lower than 5 μΩ m were fabricated by using organic fraction bio‐oil as a green binder. Other green graphite applications, including printing, conductive printing, pencils, and refractories, were also achieved. The overall process of graphite anode and electrode synthesis from biomass waste and short‐rotation energy crops was modeled. Approx. 95 kg of battery graphite or 109 kg of metallurgical graphite electrodes can be produced per ton of biomass with low primary energy consumption and carbon footprint. Prominently, the modeling result and life cycle assessment demonstrated that, for the production of battery graphite from biomass waste, net‐negative‐CO2 emissions (−0.57 kg CO2‐eq/kg graphite powders) with net‐negative‐primary energy consumption (−28.31 MJ/kg graphite powders) was achieved.

Introduction: Optical and SAR image matching is one of the fields within multi-sensor imaging and fusion. It is crucial for various applications such as disaster response, environmental monitoring, and urban planning, as it enables comprehensive and accurate analysis by combining the visual information of optical images with the penetrating capability of SAR images. However, the differences in imaging mechanisms between optical and SAR images result in significant nonlinear radiation distortion. Especially for SAR images, which are affected by speckle noises, resulting in low resolution and blurry edge structures, making optical and SAR image matching difficult and challenging. The key to successful matching lies in reducing modal differences and extracting similarity information from the images.Method: In light of this, we propose a structure similarity virtual map generation network (SVGNet) to address the task of optical and SAR image matching. The core innovation of this paper is that we take inspiration from the concept of image generation, to handle the predicament of image matching between different modalities. Firstly, we introduce the Attention U-Net as a generator to decouple and characterize optical images. And then, SAR images are consistently converted into optical images with similar textures and structures. At the same time, using the structural similarity (SSIM) to constrain structural spatial information to improve the quality of generated images. Secondly, a conditional generative adversarial network is employed to further guide the image generation process. By combining synthesized SAR images and their corresponding optical images in a dual channel, we can enhance prior information. This combined data is then fed into the discriminator to determine whether the images are true or false, guiding the generator to optimize feature learning. Finally, we employ least squares loss (LSGAN) to stabilize the training of the generative adversarial network.Results and Discussion: Experiments have demonstrated that the SVGNet proposed in this paper is capable of effectively reducing modal differences, and it increases the matching success rate. Compared to direct image matching, using image generation ideas results in a matching accuracy improvement of more than twice.

Anchorage failure occurs mostly at the anchorage interface, and the mechanical mechanism of the anchorage interface is the key point and difficulty in the analysis and research of anchorage. In order to study the mechanics and failure mechanism of the anchorage interface, the shear failure characteristics of the anchorage interface and the deformation rule of the anchored rock under different rib spacings were analyzed through the indoor plane anchorage pull-out test. Reasonable rib spacing can significantly improve the shear strength of the anchored solid. Finite element software ABAQUS was used to create an axisymmetric plane model of rock bolt pull-out under different rib conditions to simulate the whole process of rock bolt pull-out and anchorage interface failure. The results show that during the pull-out process of anchorage rock mass, stress concentration zones occur at rib corners, and peak shear stress moves to the depths of the anchorage section. As the load increases, cracks emerge from the top of the rib and the edge of the rib angle of the rock bolt and propagate to the deep anchorage along the edge of the costal angle, resulting in damage at the rock mass interface. Load–displacement curves of different transverse rib shapes, heights, widths, and spacings indicate that rib changes affect the anchor-bearing limit. The anchoring failure mode changes from rib wear and spreading slip, rib shear convex body propagation to interfacial crack propagation, and rib causes varying degrees of damage to the surrounding rock mass. Therefore, reasonable rib parameter improvements can enhance anchoring force performance.

Dendrite formation, contact loss, and continuous formation of the solid electrolyte interphase (SEI) preclude the practical use of the energy-dense lithium (Li) metal. Li–Ag alloys have the potential to address these issues due to their exceptional lithiophilicity, outstanding mechanical stability, and moderate chemical stability. This study evaluates all phases in the Li–Ag phase diagram based on lithiation capacity, Li insertion, mechanical property, and chemical stability. Our findings suggest that Li4Ag is the most promising phase, and the Gibbs free energy of nucleation (∆Gnucle) for Li–Ag alloys is 3 to 5 orders of magnitude smaller compared to pure Li, resulting in uniform nucleation and deposition behavior. We proposed practical applications within the Li4Ag phases or from the Li9Ag4 to the Li4Ag phases, which may provide a usable capacity of 409 to 696 mAh/g, respectively. Experiments indicate that Li4Ag exhibits not only the smallest impedance but also the highest capacity retention compared to Li9Ag4 and pure Li. The study provides valuable guidance for the selection and application of Li-containing alloys in future battery development.

The exclusive-or (XOR) hash combiner is a classical hash function combiner, which is well known as a good PRF and MAC combiner, and is used in practice in TLS versions 1.0 and 1.1. In this work, we analyze the second preimage resistance of the XOR combiner underlying two different narrow-pipe hash functions with weak ideal compression functions. To control simultaneously the behavior of the two different hash functions, we develop a new structure called multicollision-and-double-diamond. Multicollision-and-double-diamond structure is constructed using the idea of meet-in-the-middle technique, combined with Joux’s multicollision and Chen’s inverse-diamond structure. Then based on the multicollision-and-double-diamond structure, we present a second preimage attack on the XOR hash combiner with the time complexity of about O2n+12n/2+n−l2n−l+n−k2n−k+2l+1+2k+1) (n is the size of the XOR hash combiner and l and k are respectively the depths of the two inverse-diamond structures), less than the ideal time complexity O2n, and memory of about O2k+2l.

Recognizing 3D objects from point clouds is a crucial technology for autonomous vehicles. Nevertheless, LiDAR (Light Detection and Ranging) point clouds are generally sparse, and they provide limited contextual information, resulting in unsatisfactory recognition performance for distant or small objects. Consequently, this article proposes an object recognition algorithm named Adaptive Scale and Correlative Attention PointPillars (ASCA-PointPillars) to address this problem. Firstly, an innovative adaptive scale pillars (ASP) encoding method is proposed, which encodes point clouds using pillars of varying sizes. Secondly, ASCA-PointPillars introduces a feature enhancement mechanism called correlative point attention (CPA) to enhance the feature associations within each pillar. Additionally, a data augmentation algorithm called random sampling data augmentation (RS-Aug) is proposed to solve the class imbalance problem. The experimental results on the KITTI 3D object dataset demonstrate that the proposed ASCA-PointPillars algorithm significantly boosts the recognition performance and RS-Aug effectively enhances the training effects on an imbalanced dataset.

As a new type of public transportation, demand responsive transit has gradually attracted attention for its flexibility and efficiency. In order to solve problems such as single-vehicle type and fixed stop, and improve its operation efficiency, a collaborative scheduling method combining multi-occupancy vehicle type with non-fixed stop is proposed. Different from the previous studies on scheduling problems of demand responsive transit, which only focused on stop modes such as fixed or non-fixed stop or vehicle types containing single-occupancy or multi-occupancy, this paper also studies the vehicle scheduling of demand responsive transit from the perspective of combination of non-fixed stop and multi-vehicle type. In addition, carbon emission cost is innovatively added into the scheduling model, and an improved genetic algorithm with multiple crossovers within individuals is designed to accelerate the convergence speed of the algorithm and improve the solution efficiency. Finally, taking Shijiazhuang downtown regional road network as an example, the validity of the proposed scheduling method is verified. The results show that compared with the single-occupancy vehicle scheduling method, the operating costs of the multi-occupancy vehicle scheduling method can be reduced by up to 25.0%, and the average passenger in-vehicle time is decreased by up to 8.8%, which could significantly reduce the system operating costs on the premise of ensuring shorter total passenger travel time. Compared with the mode with fixed stops, the average full load ratio of the mode with non-fixed stops increased by 21.7%. Besides, the convergence speed and solving speed of the proposed improved genetic algorithm are increased by 31.7% and 4.8%, compared with the traditional genetic algorithm.

Abstract Coronavirus disease-19 (Covid-19) pandemic have demonstrated the importantance of vaccines in disease prevention. Self-amplifying mRNA vaccines could be another option for disease prevention if demonstrated to be safe and immunogenic. Phase 1 of this randomized, double-blinded, placebo-controlled trial (N = 42) assessed the safety, tolerability, and immunogenicity in healthy young and older adults of ascending levels of one-dose ARCT-021, a self-amplifying mRNA vaccine against Covid-19. Phase 2 (N = 64) tested two-doses of ARCT-021 given 28 days apart. During phase 1, ARCT-021 was well tolerated up to one 7.5 μg dose and two 5.0 μg doses. Local solicited AEs, namely injection-site pain and tenderness were more common in ARCT-021vaccinated, while systemic solicited AEs, mainly fatigue, headache and myalgia were reported in 62.8% and 46.4% of ARCT-021 and placebo recipients, respectively. Seroconversion rate for anti-S IgG was 100% in all cohorts, except for the 1 μg one-dose in younger adults and the 7.5 μg one-dose in older adults. Anti-S IgG and neutralizing antibody titers showed a general increase with increasing dose, and overlapped with titers in Covid-19 convalescent patients. T-cell responses were also observed in response to stimulation with S-protein peptides. Taken collectively, ARCT-021 is immunogenic and has favorable safety profile for further development.

Abstract Remarkable potency has been demonstrated for mRNA vaccines in reducing the global burden of the ongoing COVID-19 pandemic. An alternative form of the mRNA vaccine is the self-amplifying mRNA (sa-mRNA) vaccine, which encodes an alphavirus replicase that self-amplifies the full-length mRNA and SARS-CoV-2 spike (S) transgene. However, early-phase clinical trials of sa-mRNA COVID-19 vaccine candidates have questioned the potential of this platform to develop potent vaccines. We examined the immune gene response to a candidate sa-mRNA vaccine against COVID-19, ARCT-021, and compared our findings to the host response to other forms of vaccines. In blood samples from healthy volunteers that participated in a phase I/II clinical trial, greater induction of transcripts involved in Toll-like receptor (TLR) signalling, antigen presentation and complement activation at 1 day post-vaccination was associated with higher anti-S antibody titers. Conversely, transcripts involved in T-cell maturation at day 7 post-vaccination informed the magnitude of eventual S-specific T-cell responses. The transcriptomic signature for ARCT-021 vaccination strongly correlated with live viral vector vaccines, adjuvanted vaccines and BNT162b2 1 day post-vaccination. Moreover, the ARCT-021 signature correlated with day 7 YF17D live-attenuated vaccine transcriptomic responses. Altogether, our findings show that sa-mRNA vaccination induces innate immune responses that are associated with the development of adaptive immunity from other forms of vaccines, supporting further development of this vaccine platform for clinical application.

Abstract Background China is rapidly expanding its general practitioner (GP) workforce as part of recent healthcare reform, with an extra 400,000 GPs by 2030. This scoping review identifies the published strategies for GP recruitment that are being implemented and the challenges encountered. Methods We searched six English and three Chinese databases from 2015 to April 2022, following Arksey and O’Malley’s framework and the PRISMA ScR reporting guidelines. Results A total of 40 Chinese-language and 5 English-language records were included. We identified multiple policies, pathways and programmes focused on expanding GP recruitment. Twenty-two evaluations of these initiatives show varying degrees of effectiveness. Selecting general practice as a career is affected by many factors, including individual’s background, remuneration and benefits, career prospects, working environment, self-fulfilment, and current national developments and reorganisations of primary care. The challenge most frequently reported was the adequate provision of qualified GP in rural regions. The targeting of students from rural areas and provision of free education in return for an obligatory six-years’ working in their hometown upon graduation appears to be effective. Extracted records mostly studied views of trainees in a defined locality, and we identified a paucity of studies which explored the perspectives of organisations and institutions, similarly there were areas of China not contributing to the literature and there were no records taking a national perspective. Conclusions Long-term monitoring is required to assess policy changes and to systematically evaluate the effectiveness of the interventions nationally. The monitoring of the challenges influencing GP recruitment can be used to inform the design of future initiatives. Development of a minimum agreed standardised set of outcomes used to measure and report evaluations will help assess the relative contributions and cost effectiveness of different approaches being used to boost GP numbers. We provide suggestions for improving the benefits and rewards for GPs and how to promote recruitment to the more rural or less attractive areas.

Background: sST2 has emerged as a potential disease biomarker of cardiac and inflammatory diseases in pediatrics. This study aimed to evaluate the performance of the new Pylon sST2 assay and establish the reference intervals of sST2 in children and adolescence in China. Methods: The experiments on precision, linearity, effects of interferents and sample stability were carried out to evaluate the analytical performances. A total of 240 healthy participants, aged from 2 to 17 years were enrolled. The nonparametric method was used to calculate the age- and sex-specified reference intervals. sST2 levels were measured in children with different diseases to evaluate the assay's diagnostic performance. Results: The repeatability and within-laboratory imprecision CVs of the assay were 6.0% and 7.6% at 19.5 ng/ml, and 3.1% and 5.9% at 289.8 ng/ml, respectively. The method showed linearity between 2.5 and 918.5 ng/ml. It was also noteworthy that the sST2 level was not affected in the presence of hemoglobin (2 mg/ml), triglyceride (30 mg/ml), bilirubin (0.3 mg/ml) and cholesterol (5 mg/ml). sST2 was found stable for 5 days at 4 °C in serum sample. The reference interval was determined as 2.1–21.0 ng/ml in general. No significant variation was observed by sex. However, sST2 increased constantly with age, especially in male. Increased sST2 was found in patients of systemic lupus erythematosus, sepsis, Crohn's diseases, respiratory failure and post cardiac surgery. Conclusions: The Pylon sST2 assay showed good analytical performances. The reference intervals were established in children and adolescence and sST2 showed potential clinical values in several diseases in pediatrics.

In order to understand the mechanism of uracil on the growth by Aureobasidium pullulans and synthesis of pullulan, the optimal dosage and time of uracil in pullulan polysaccharide fermentation were studied. The label-free quantitative method and liquid chromatography-tandem mass spectrometry were used to compare the protein components in the late fermentation stage (88 h) by Aureobasidium pullulans and the differential proteins were analyzed by bioinformatics. The results showed that adding 0.5g/L uracil at 48 h had the most significant increasing in pullulan polysaccharide yield, which was verified on 5 L fermenter from 70.13 to 86.27 g/L, increasing by 23%. Protein component analysis identified 80 different proteins, including 40 increases protein and 40 lower protein (difference>2, P

Abstract Background In addition to Mycoplasma haemocanis and Candidatus Mycoplasma haematoparvum, a few hemoplasma species that mainly infect other livestock have been detected in dogs. ‘Candidatus Mycoplasma haemobos’ (Ca. M. haemobos) has been found in a variety of animals in China. The present study was aimed to investigate the occurrence of ‘Ca. M. haemobos’ infections in dogs and ticks collected from the Henan province, China. Results Overall, 55 dog blood samples and 378 ticks on skins were collected from anemic and healthy dogs, and these samples were subjected to PCR, sequence analysis, and identification. The results showed that Haemaphysalis longicornis (266) and Rhipicephalus (Boophilus) microplus (112) were the only two parasitic ticks on dogs. Molecular detection revealed that 163 M. haemocanis, 88 ‘Ca. M. haemobos’ and 32 Anaplasma platys positive amplicons could be amplified from dogs, H. longicornis and R. (B.) microplus. In addition, co-infections (M. haemocanis + A. platys and ‘Ca. M. haemobos’+ A. platys) could be also detected. Conclusions To the best of our knowledge, this is the first molecular evidence of ‘Ca. M. haemobos’ natural infection in dogs and tick species identified as H. longicornis and R. (B.) microplus from China.

This study is primarily intended to present a damage constitutive equation under the combined action of confining pressures and freeze–thaw cycles by subjecting deep expansive clay to the consolidated undrained triaxial tests. We study the influence of the numbers of freeze–thaw cycles on various mechanical indexes of soil by using the TSZ-2 instrument (fully automatic triaxial instrument). As the number of freeze–thaw cycles increases, the ultimate peak stress of the soil decreases, and then, the effect of the freeze–thaw effect on the shear strength gradually weakened. By combining the expression method of the damage variable under the action of loading alone with the expression method under the action of freeze–thaw cycles alone, we brought in the damage evolution equation to obtain the damage constitutive equation under the combined action of confining pressures and freeze–thaw cycles. The stress values under three confining pressures (100 kPa, 200 kPa, and 300 kPa) can be determined by using the final damage constitutive model. The measured data with a water content of 17% and six freeze–thaw cycles were compared with the theoretical data. The actual strength values were 118.4 kPa, 152.3 kPa, and 184.1 kPa, and the theoretical strength values were 120 kPa, 150 kPa, and 186 kPa. The fitting degree of the strength value was as high as 99%, which verifies the feasibility of this model. This study can serve as an available reference for well wall construction and disaster prediction in deep coal mining.

Abstract Background Studies report that medical graduates are not prepared for practice as expected, and interventions have been developed to prepare them for practice. One such intervention is the assistantship, which provides hands-on opportunities to hone clinical skills and undertake responsibilities under supervision. The Lee Kong Chian School of Medicine (LKCMed) is Singapore’s newest medical school, and students undergo a Student Assistantship Programme (SAP) to prepare for practice as junior doctors (PGY1). This study evaluated the SAP from the students’ and clinical supervisors’ perspectives. Methods Students completed online questionnaires to assess readiness for practice before and after SAP, and a subsample were interviewed about their experiences of SAP and its impact on their preparedness for PGY1. In addition, after our graduates had begun work as PGY1 doctors, their clinical supervisors completed an online questionnaire and were interviewed about the perceived benefits of SAP and the attributes of our graduates as junior doctors. Results Fifty (96%) students completed the pre-SAP questionnaire and 46 (92%) completed the post-SAP questionnaire. Levels of preparedness increased post-SAP (mean scores range pre-SAP: 2.38 to 4.32 vs post-SAP: 3.08 to 4.48); so did opportunities to undertake PGY1 duties (pre-SAP: 56% vs post-SAP: 96%), and hands-on experience in medical emergencies (pre-SAP: 76% vs post-SAP: 89%).Experience of acute care situations increased except “paracetamol overdose”. Readiness to be first respondents in ten acute situations improved (statistically significant for asthma, chronic obstructive pulmonary disease exacerbation, gastrointestinal bleed, sepsis, and adverse drug reactions). Three themes emerged from twenty-five student interviews: learning about the work environment, opportunities to learn in a safe environment, and enhancing SAP for future students. Thirty-three supervisors completed the questionnaire, and 70% rated SAP positively in preparing students for PGY1. Eight supervisors interviewed shared positively about the content, timing, and duration of SAP; and suggested future SAPs help students to develop coping and reflective skills. Conclusions The SAP improved students’ preparedness and experience across clinical areas, and students felt the SAP helped bridge undergraduate curriculum and work, provided opportunities to hone their skills and learn from junior doctors. Most clinical supervisors rated the SAP effective in preparing students for PGY1. This is the first formal evaluation of an assistantship in Singapore, and the findings are encouraging from the perspective of students and PGY1 supervisors.

In addition to magnetism, superconductivity, quantum transport, and ferroelectricity, the tunable Rashba spin–orbit coupling from spatial inversion symmetry broken of 2-dimensional electron gas (2DEG) at oxide interfaces has been exploited to induce rich spin-dependent physical effects and has recently become the focus of intense interest. Here, we review the recent advances in this field, including spin–charge interconversion, spin–magnetization interaction, and spin texture. These properties are intriguing due to their potential to advance spintronics devices. All these intriguing properties not only hold great promise for 2DEG at oxide interfaces in spintronic devices but also further deepen our understanding of this frontier field.

A two-loop acceleration autopilot is designed using the twin-delayed deep deterministic policy gradient (TD3) strategy to avoid the tedious design process of conventional tactical missile acceleration autopilots and the difficulty of meeting the performance requirements of the full flight envelope. First, a deep reinforcement learning model for the two-loop autopilot is developed. The flight state information serves as the state, the to-be-designed autopilot control parameters serve as the action, and a reward mechanism based on the stability margin index is designed. The TD3 strategy is subsequently used to offline learn the control parameters for the entire flight envelope. An autopilot control parameter fitting model that can be directly applied to the guidance loop is obtained. Finally, the obtained fitting model is combined with the impact angle constraint in the guidance system and verified online. The simulation results demonstrate that the autopilot based on the TD3 strategy can self-adjust the control parameters online based on the real-time flight state, ensuring system stability and achieving accurate acceleration command tracking.

The practical energy density of solid-state batteries remains limited, partly because of the lack of a general method to fabricate thin membranes for solid-state electrolytes with high ionic conductivity and low area-specific resistance (ASR). Herein, we use an ultrahigh concentration of a ceramic ion conductor (Na3SbS4) to build an ion-conduction “highway”, and a polymer (polyethylene oxide, 2 ​wt%) as a flexible host to prepare a polymer-in-ceramic ion-conducting membrane of approximately 40 ​μm. Without the use of any salt (e.g., NaPF6), the resulting membrane exhibits a threefold increase in electronic ASR and a twofold decrease in ionic ASR compared with a pure ceramic counterpart. The activation energy for sodium-ion transport is only 190 ​meV in the membrane, similar to that in pure ceramic, suggesting ion transport predominantly occurs through a percolated network of ion-conducting ceramic particles. The salt-free design also provides an opportunity to suppress dendritic metal electrodeposits, according to a recently refined chemomechanical model of metal deposition. Our work suggests that salt is not always necessary in composite solid-state electrolytes, which broadens the choice of polymers to allow the optimization of other desired attributes, such as mechanical strength, chemical/electrochemical stability, and cost.

Background The influencing factors of diabetic kidney disease (DKD) in Chinese patients with type 2 diabetes mellitus (T2DM) were explored to develop and validate a DKD diagnostic tool based on nomogram approach for patients with T2DM. Methods A total of 2,163 in-hospital patients with diabetes diagnosed from March 2015 to March 2017 were enrolled. Specified logistic regression models were used to screen the factors and establish four different diagnostic tools based on nomogram according to the final included variables. Discrimination and calibration were used to assess the performance of screening tools. Results Among the 2,163 participants with diabetes (1,227 men and 949 women), 313 patients (194 men and 120 women) were diagnosed with DKD. Four different screening equations (full model, laboratory-based model 1 [LBM1], laboratory-based model 2 [LBM2], and simplified model) showed good discriminations and calibrations. The C-indexes were 0.8450 (95% confidence interval [CI], 0.8202 to 0.8690) for full model, 0.8149 (95% CI, 0.7892 to 0.8405) for LBM1, 0.8171 (95% CI, 0.7912 to 0.8430) for LBM2, and 0.8083 (95% CI, 0.7824 to 0.8342) for simplified model. According to Hosmer-Lemeshow goodness-of-fit test, good agreement between the predicted and observed DKD events in patients with diabetes was observed for full model (χ2=3.2756, P=0.9159), LBM1 (χ2=7.749, P=0.4584), LBM2 (χ2=10.023, P=0.2634), and simplified model (χ2=12.294, P=0.1387). Conclusion LBM1, LBM2, and simplified model exhibited excellent predictive performance and availability and could be recommended for screening DKD cases among Chinese patients with diabetes.

Objectives To date, most research on medical graduates’ preparedness for practice has conceptualised preparedness as something possessed by the individual. However, new doctors work within social settings with other people and, given this, we argue that preparedness has a social and comparative dimension. The aim of this paper is to explore medical students’/graduates’ self-assessments of their preparedness for practice using the lens of social comparison theory.Setting We invited medical students from one of Singapore’s three medical schools who were in their final-year Student Assistantship Programme to participate in semi-structured interviews, and follow-up interviews 6 months later when they were working as junior doctors. Data was collected from two cohorts, in 2018 and 2019. Initial analysis of interview transcripts was inductive and thematic. Social comparison theory was used for subsequent theory-driven analysis.Participants 31 participants took part, of whom 21 also engaged in follow-up interviews.Results We identified three uses of social comparison: as coping strategy to manage uncertainties in transitions where there was no formal, objective testing of their performance; as a means to confirm their self-perceived preparedness (upwards or downwards, eg, being better or worse prepared than comparator others); and as the basis for decision-making (eg, changing career choices).Conclusions Senior medical students and newly-graduated doctors compare themselves with peers and near-peers in terms of prior learning and current performance to evaluate and understand their own performance at work. Future studies need to examine further how the feeling of preparedness or unpreparedness generated from social comparisons may affect subsequent clinical performance and professional development.

Heavy precipitation tends to cause mountain torrents, urban waterlogging and other disasters. It poses a serious threat to people’s life and property safety. Therefore, real-time quantitative precipitation estimation is especially important to keep track of precipitation changes and reduce negative impacts. However, high-resolution and high-accuracy quantitative precipitation estimation is a challenging task due to the complex spatial and temporal variability of microphysics in precipitation processes. Previous studies have focused only on small-scale radar reflectivity factors above rain gauges and did not pay enough attention to the contribution of covariates to model performance. Meteorological and geographical factors play an important role in rain process, so these factors are taken into account during our research. In this study, a quantitative precipitation estimation model that can employ multi-scale radar reflectivity factors and fuse meteorological and geographical factors is proposed to further improve precipitation accuracy. In addition, we propose the muti-scale self-attention (MS-SA) module that can further utilize information at multiple spatial scales to improve the accurate precipitation estimation. The proposed model reduced the root mean square error of precipitation estimation by 83.8% compared to the conventional Z-R relationship that correlates the rainfall and radar reflectivity factors, i.e., Z=aRb, and by 43.7, 24.6, and 22.7% compared to the back propagation neural network (BPNN), convolutional neural network (CNN), and convolutional neural network with the addition of meteorological factors and geographical factors as covariates in the proposed model, respectively. Therefore, we can conclude that multi-scale radar reflectivity factors fused with meteorological and geographical factors can produce more accurate precipitation estimation.

1,5-Anhydroglucitol (1,5-AG) is a sensitive biomarker for real-time detection of diabetes mellitus. In this study, an electrochemical biosensor to specifically detect 1,5-AG levels based on persimmon-tannin-reduced graphene oxide-PtPd nanocomposites (PT-rGO-PtPd NCs), which were modified onto the surface of a screen-printed carbon electrode (SPCE), was designed. The PT-rGO-PtPd NCs were prepared by using PT as the film-forming material and ascorbic acid as the reducing agent. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), ultraviolet–visible spectroscopy (UV–vis), and X-ray diffraction (XRD) spectroscopy analysis were used to characterise the newly synthesised materials. PT-rGO-PtPd NCs present a synergistic effect not only to increase the active surface area to bio-capture more targets, but also to exhibit electrocatalytic efficiency to catalyze the decomposition of hydrogen peroxide (H2O2). A sensitive layer is formed by pyranose oxidase (PROD) attached to the surface of PT-rGO-PtPd NC/SPCE. In the presence of 1,5-AG, PROD catalyzes the oxidization of 1,5-AG to generate 1,5-anhydrofuctose (1,5-AF) and H2O2 which can be decomposed into H2O under the synergistic catalysis of PT-rGO-PtPd NCs. The redox reaction between PT and its oxidative product (quinones, PTox) can be enhanced simultaneously by PT-rGO-PtPd NCs, and the current signal was recorded by the differential pulse voltammetry (DPV) method. Under optimal conditions, our biosensor shows a wide range (0.1–2.0 mg/mL) for 1,5-AG detection with a detection limit of 30 μg/mL (S/N = 3). Moreover, our electrochemical biosensor exhibits acceptable applicability with recoveries from 99.80 to 106.80%. In summary, our study provides an electrochemical method for the determination of 1,5-AG with simple procedures, lower costs, good reproducibility, and acceptable stability.

Direct molecular methods such as real-time polymerase chain reaction (qPCR) and propidium monoazide (PMA)-qPCR have been successfully used for quantifying viable microorganisms in the food industry. This study attempted to use qPCR and PMA-qPCR for quantifying Lactobacillus delbrueckii spp. bulgaricus sp1.1 physiological states. The qPCR standards of the 16S rRNA gene were employed to calibrate the qPCR assay, which contributed to an amplification efficiency of 98.42%. The number of copies of the 16S rRNA gene was linearly related to cell density, and this linear relationship was used to construct a quantitative curve (R2=0.9981) with a detection limit of 15.1 colony-forming units mL−1·reaction−1. qPCR in combination with an optimal PMA concentration (60 μM) helped in discriminating and quantifying the viable cells, without any interference by heat-killed cells. Compared with the conventional methods, the population heterogeneity of viable, culturable, dormant-like and membrane-permeabilized cells were well identified and quantified using qPCR during L. delbrueckii spp. bulgaricus sp1.1 batch culture. Despite the restriction in the enumeration of lysed cells, qPCR-based methods facilitated reliable identification and quantification of bacterial physiological states and provided additional knowledge on the dynamics of L. delbrueckii spp. bulgaricus sp1.1 physiological states.

With the rapid development of social media, the number of online comments has exploded, and more and more people are willing to express their attitudes and feelings on the Internet. Under the influence of a series of major events all over the world, production order is facing a serious challenge, which severely impact on energy market. In 2020, a large number of investors' and consumers' comments related to social events began to appear on the Internet from China. However, the style and quality of online comments vary greatly, making it difficult to accurately extract users' views and tendencies. Based on the investors' and consumers' statements published on Chinese Internet, in this paper, we use statistical methods to classify the sentiment orientation of the Netizens firstly, and then use Bidirectional Encoder Representations from Transformer-Bidirectional Long Short Term Memory (BERT-BiLSTM) which is the combination forecasting method of Bidirectional Encoder Representations from Transformer (BERT) and Bidirectional Long Short Term Memory (BiLSTM), to model and forecast the sentiment orientation of users' statements, as well as being compared with its based models, BERT and BiLSTM. Among them, the accuracy and recall, which represent the predictive abilities on the overall samples and on the focus(the samples of Label 1) respectively, of BERT-BiLSTM model are 0.8620 and 0.7078 respectively, which are superior to those of BERT model (0.8559 and 0.5576) and LSTM model (0.7775 and 0.0747). The research results can accurately predict the sentiment orientation of Internet users during the social events so as to provide technical support for grasping the energy market trend.

Although numerous studies have been carried out on chloroplast development and biogenesis, the underlying regulatory mechanisms are still largely elusive. Here, we characterized a chloroplast stromal protein Chloroplast Development and Biogenesis1 (CDB1). The knockout cdb1 mutant exhibits a seedling-lethal and ivory leaf phenotype. Immunoblot and RNA blot analyses show that accumulation of chloroplast ribosomes is compromised in cdb1, resulting in an almost complete loss of plastid-encoded proteins including the core subunits of the plastid-encoded RNA polymerase (PEP) RpoB and RpoC2, and therefore in impaired PEP activity. Orthologs of CDB1 are found in green algae and land plants. Moreover, a protein shows high similarity with CDB1, designated as CDB1-Like (CDB1L), is present in angiosperms. Absence of CDB1L results in impaired embryo development. While CDB1 is specifically located in the chloroplast stroma, CDB1L is localized in both chloroplasts and mitochondria in Arabidopsis. Thus, our results demonstrate that CDB1 is indispensable for chloroplast development and biogenesis through its involvement in chloroplast ribosome assembly whereas CDB1L may fulfill a similar function in both mitochondria and chloroplasts.

Thermal shock is one of the main causes for the fracture of ceramic materials due to their inherent brittleness. Aiming to explore the mechanism of thermal shock cracking behavior of ceramics under different thermal shock conditions, a novel temperature-dependent failure criterion of thermal shock fracture of the ceramic materials was deduced based on the force-heat equivalence energy density principle. Combining this failure criterion and the finite element method, the thermal shock cracking behaviors of the thin circular and rectangular ceramic slabs under different thermal shock initial temperature were simulated. Results show that the morphology, periodicity, hierarchy, and number of thermal shock cracks obtained by numerical simulation are in good agreement with the experimental results. These essential characteristics verify the validity of the temperature-dependent failure criterion for thermal shock fracture. Furthermore, the strain energy of tension produced by thermal shock is proved to be the dominated mechanism for thermal shock-induced fracture.

As one of the new intelligent materials, controllable bionic adhesive materials have great application prospects in many fields, such as wearable electronic devices, wall climbing robot systems, and biomedical engineering. Inspired by the microstructure of the newt pad’s surface, this paper reports a bionic adhesive surface material with controllable adhesion on dry, wet acrylic, and iron sheet surfaces. The material is prepared by mixing the PDMS matrix with micron carbonyl iron powders (CIPs) and then pouring the mixture into a female mold prepared by Photo-curing 3D Printing for curing. As the mold interior is designed with a two-level microstructure array, the material’s surface not only coated a regular hexagonal column array with a side length of 250 μm and a height of 100 μm but also covered seven dome structures with a diameter of 70 μm on each column. In what follows, the adhesion force of the proposed materials contacted three different surfaces are tested with/without magnetic fields. The experimental results show that the MAEs covered with two-level bionic structures(2L-MAE) reported in this paper exhibit a stronger initial adhesion in the three types of surfaces compared to the normal one. Besides, we also found that the magnetic field will noticeably affect their adhesion performance. Generally, the 2L-MAE’s adhesion will increase with the external magnetic field. When the contact surface is an iron sheet, the material adhesion will be reduced by the magnetic field.

ß-poly (L-malic acid) (PMLA) is a polyester ligated by malate subunits. It has a wide prospective application as an anti-cancer drug carrier, and its malate subunits have a great application in the food industry. The strain Aureoabsidium melanogenum could produce a high amount of PMLA during fermentation, and different substrates addition could influence the production. In this study, we directly added potassium acetate, corn steep liquor, MgSO4, MnSO4, vitamin B1, vitamin B2, and nicotinamide as the fermentation substrate to the basic fermentation medium based on a generated random matrix that represented the added value. The PMLA production and four secondary indexes, pH, biomass, osmotic pressure, and viscosity were measured after 144 h fermentation. Finally, a total of 212 samples were collected as the dataset, by which the machine learning methods were deployed to predict the PMLA production by different substrates’ concentrations and the secondary indexes. The results indicated that PMLA production was negatively correlated with corn steep liquor and betaine and positively correlated with potassium acetate. The PMLA production could be predicted using all different substrates’ concentrations with a Mean Absolute Error (MAE) of 4.164 g/L and with an MAE of 6.556 g/L by different secondary indexes. Finally, the convolutional neural network (CNN) was applied to predict the PMLA production by fermentation medium images, in which the collected images were categorized into three groups, 0–20 g/L, 21–40 g/L, and >41 g/L, based on the PMLA production. The CNN model could predict the production with high accuracy. The methods and results presented in this study provided new insight into evaluating different substrates concentration on PMLA production and demonstrating the possibility of using the convolutional neural network model in the PMLA fermentation industry.

Abstract The issue of security and privacy plays an important role in the Internet of Things (IoT) and directly affects its wide applications. In order to meet the security requirements in the IoT environment, a series of lightweight encryption schemes have been proposed. Meanwhile, cryptanalysis against these schemes is critical to the security of IoT. In this paper, a practical distinguishing attack on an IoT‐friendly lightweight cipher ALLPC is presented. Specifically, it is found that there exists a fixed point in the differential of ALLPC's S‐box, then a differential trail with a period of 14‐round has been proposed. By connecting to the periodic differential trail, a differential trail is constructed for the full‐round ALLPC with a probability of 2−24. Applying such property, one can distinguish the full‐round ALLPC block cipher from a random permutation in about 10 min on a laptop.

Abstract The meteorological factors are important determinants of the surface rainfall. However, in studies of quantitative precipitation estimation (QPE) based on Doppler radar data, meteorological elements are usually used as the weighting factors to correct precipitation, and the active role of meteorological factors in determining rainfall is neglected, which limits the improvement of radar QPE accuracy. In this study, the effectiveness of applying one‐dimensional convolutional neural network together with radar data and meteorological factor data to estimate precipitation is explored. Various combinations of meteorological factors were tested for the set of input variables. The proposed model performance was evaluated over the Shijiazhuang area at the spatial resolution of 0.01° and at the 6‐min time scale. The results indicates that the proposed model (RM‐1DCNN) provides more accurate precipitation estimation compared to the Ordinary Kriging interpolation, two Z‐R relationships, and Back Propagation Neural Network. The root mean square error of the RM‐1DCNN with temperature was 0.642 mm per 6 min and the average Threat Score exceed 55%, which was the best among all schemes.

Rational design of thermotropic liquid crystal (LC)-based sensors utilizing different mesophases holds great promise to open up novel detection modalities for various chemical and biological applications. In this context, we present a dissipative particle dynamics study to explore the unique anchoring behavior of nematic and smectic LCs at amphiphile-laden aqueous-LC interface. By increasing the surface coverage of amphiphiles, two distinct anchoring sequences, a continuous planar-tilted-homeotropic transition and a discontinuous planar-to-homeotropic transition, can be observed for the nematic and smectic LCs, respectively. More importantly, the latter occurs at a much lower surface coverage of amphiphiles, demonstrating an outstanding sensitivity for the smectic-based sensors. The dynamics of reorientation further reveals that the formation of homeotropic smectic anchoring is mainly governed by the synchronous growth of smectic layers through the LCs, which is significantly different from the mechanism of interface-to-bulk ordering propagation in nematic anchoring. Furthermore, the smectic LCs have also been proven to possess a potential selectivity in response to a subtle change in the chain rigidity of amphiphiles. These simulation findings are promising and would be valuable for the development of novel smectic-based sensors.

BackgroundThe infant’s intestine contains diverse microbiota, which play an important role in an infant’s health.ObjectiveThis study aimed to analyze the different intestinal microbiota and their function in two delivery modes [vaginal delivery and cesarean section (C-section)] and to investigate the proprieties of bacteria associated with vaginal delivery on the development of intestinal epithelial cells in rat pups.Materials and MethodsWe evaluated the intestinal microbial diversity of the stool samples of 51 infants of subjects who underwent vaginal delivery and C-section by sequencing the V4 regions of the 16S rRNA gene and predicted the function of the microbiotas. The infant stool microbiota in the vaginal delivery group was associated with the digestive system and cell growth and death, whereas that of the C-section group was associated with membrane transport. Then, we isolated the strains based on function prediction.ResultsA total of 95 strains were isolated in the vaginal delivery group. Bifidobacterium bifidum FL-228.1 (FL-228.1) was screened and selected owing to its good surface hydrophobicity, bacterial survivability in the simulated gastrointestinal condition and adhesion ability to the IEC-6 cell line as well as owing to the development of intestinal epithelial cells. Furthermore, in vivo experiments revealed that FL-228.1 exhibited favorable effects on the development of intestinal epithelial cells in rat pups.ConclusionThe results of this study indicate an apparent difference in the bacterial composition of the stool samples collected from infants of the two delivery modes. By analyzing and screening the bacteria in infant stool samples, we found that one strain, i.e., B bifidum FL-228.1, exhibited favorable effects on the development of intestinal epithelial cells.

This paper presents generic structural cryptanalysis against type-I generalized Feistel networks (GFN), in which all the inner transformations are unknown. The target of our attack is to retrieve all the unknown round functions. We provide an improved yoyo game distinguisher, in which one rejects a large group of start guesses by a single wrong guess, hence is quite advantageous for reducing the complexity. Next, we exploit this distinguisher to develop a recovery attack of such structure and find the look-up tables of the first, eighth, and ninth round functions. Then by the encryption and decryption similarity, we recover the LUTs of the second, third, and tenth round functions from the decrypt direction. Finally, we retrieve the rest rounds by using the analytic relationships between the plaintexts and their four-round encryption results. Our complete recovery requires time complexity O(23.36n) and memory O(2n), where n is the branch size. For 64-bit block cipher, our result will approximate a real-life attack. This paper is the first recovery attack against ten-round type-I GFN.

Citrus cultivars are widely spread worldwide, and some of them only differ by specific mutations along the genome. It is difficult to distinguish them by traditional morphological identification. To accurately identify such similar cultivars, the subtle differences between them must be detected. In this study, UPLC-ESI-MS/MS-based widely targeted metabolomics analysis was conducted to study the chemical differences between two closely related citrus cultivars, Citrus reticulata 'DHP' and C. reticulata 'BZH'. Totally 352 metabolites including 11 terpenoids, 35 alkaloids, 80 phenolic acids, 25 coumarins, 7 lignans, 184 flavonoids and 10 other compounds were detected and identified; Among them, 15 metabolites are unique to DHP and 16 metabolites are unique to BZH. Hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal signal correction and partial least squares-discriminant analysis (OPLS-DA) can be used to clearly discriminate between DHP and BZH. 93 metabolites including 36 down-regulated and 57 up-regulated are significantly different in DHP and BZH. They are mainly involved in the biosynthesis of flavonoids, flavones, flavonols, and isoflavonoids. In addition, the relative content levels of flavonoids, alkaloids, and terpenoids are much higher in the peel of DHP than that of BZH, the presence of which may correlate with the quality difference of the peels. The results reported herein indicate that metabolite analysis based on UPLC-ESI-MS/MS is an effective means of identifying cultivars with different genotypes, especially those that cannot be distinguished based on traditional identification methods.

Quantitative precipitation estimation (QPE) based on Doppler radar plays an important role in severe weather monitoring, industrial and agricultural production, and natural disaster prediction and prevention. However, the temporal and spatial variability of precipitation leads to large errors in radar estimates of mixed precipitation. To improve the accuracy of radar QPE, we propose an offline spatiotemporal deep fusion model that uses the reflectivity data of the Shijiazhuang Doppler radar Z9311 and the precipitation data from 17 national weather stations (NWSs) and 260 automatic weather stations (AWSs). Considering the abrupt spatial changes in precipitation, a three-dimensional radar data structure is proposed, and the spatial features of multielevation and multiscale radar data are extracted and merged using the feature fusion network (FFNet). Finally, the time dependence of the precipitation is captured using the long short-term memory (LSTM) network, and the precipitation estimation is obtained. Based on a comparison of the results of the proposed model (FFNet-LSTM) with those of the ordinary kriging (OK) interpolation, two Z-R relationship, the multilayer perceptron (MLP), the LSTM, and the FFNet, the proposed method is superior to these models, has a promising performance, and is a general-purpose rainfall algorithm.

The pandemic spread of a novel coronavirus – SARS coronavirus-2 (SARS-CoV-2) as a cause of acute respiratory illness, named Covid-19, is placing the healthcare systems of many countries under unprecedented stress. Global economies are also spiraling towards a recession in fear of this new life-threatening disease. Vaccines that prevent SARS-CoV-2 infection and therapeutics that reduces the risk of severe Covid-19 are thus urgently needed. A rapid method to derive antiviral treatment for Covid-19 is the use of convalescent plasma derived hyperimmune globulin. However, both hyperimmune globulin and vaccine development face a common hurdle – the risk of antibody-mediated disease enhancement. The goal of this review is to examine the body of evidence supporting the hypothesis of immune enhancement that could be pertinent to Covid-19. We also discuss how this risk could be mitigated so that both hyperimmune globulin and vaccines could be rapidly translated to overcome the current global health crisis.

Bats are reservoirs of a large number of viruses of global public health significance, including the ancestral virus for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the causative agent of coronavirus disease 2019 (COVID-19). Although bats are natural carriers of multiple pathogenic viruses, they rarely display signs of disease. Recent insights suggest that bats have a more balanced host defense and tolerance system to viral infections that may be linked to the evolutionary adaptation to powered flight. Therefore, a deeper understanding of bat immune system may provide intervention strategies to prevent zoonotic disease transmission and to identify new therapeutic targets. Similar to other eutherian mammals, bats have both innate and adaptive immune systems that have evolved to detect and respond to invading pathogens. Bridging these two systems are innate lymphocytes, which are highly abundant within circulation and barrier tissues. These cells share the characteristics of both innate and adaptive immune cells and are poised to mount rapid effector responses. They are ideally suited as the first line of defense against early stages of viral infections. Here, we will focus on the current knowledge of innate lymphocytes in bats, their function, and their potential role in host–pathogen interactions. Moreover, given that studies into bat immune systems are often hindered by a lack of bat-specific research tools, we will discuss strategies that may aid future research in bat immunity, including the potential use of organoid models to delineate the interplay between innate lymphocytes, bat viruses, and host tolerance.