Search

This study investigates the impact of the salinity barrier layer (BL) on the upper ocean response to Super Typhoon Mangkhut (2018) in the western North Pacific. After the passage of Mangkhut, a noticeable increase (∼0.6 psu) in sea surface salinity and a weak decrease (< 1°C) in sea surface temperature (SST) were observed on the right side of the typhoon track. Mangkhut-induced SST change can be divided into the three stages, corresponding to the variations in BL thickness and SST before, during, and after the passage of Mangkhut. During the pre-typhoon stage, SST slightly warmed due to the entrainment of BL warm water, which suppressed the cooling induced by surface heat fluxes and horizontal advection. During the forced stage, SST cooling was controlled by entrainment, and the preexisting BL reduced the total cooling by 0.89°C d−1, thus significantly weakening the overall SST cooling induced by Mangkhut. During the relaxation stage, the SST cooling was primarily caused by the entrainment. Our results indicate that a preexisting BL can limit typhoon-induced SST cooling by suppressing the entrainment of cold thermocline water, which contributed to Mangkhut becoming the strongest typhoon in 2018.

Background: The association of lung function with the risk of developing prodromal and clinical-diagnosed Parkinson’s disease (PD) and with the risk of mortality among individuals with PD remains unknown.Objective: To prospectively examine the associations of lung function with the risk of prodromal, clinical-diagnosed PD, and PD-related mortality in participants of the UK Biobank.Methods: Included were 452,518 participants free of PD at baseline. Baseline lung function, including forced expiratory volume in 1-s (FEV1), forced vital capacity (FVC), peak expiratory flow (PEF), and FEV1/FVC ratio, was assessed. Eight prodromal features were measured using self-reported diagnoses, hospital admission, and primary care data. Incident PD cases were identified using linkages with hospital admission, death register, and self-report. Vital status and date of death were provided by the UK National Health Service (NHS) and the NHS Central Register. We used Cox proportional hazard models to evaluate these associations.Results: Poor lung function was associated with higher risk of PD in a dose-response relationship: the adjusted hazard ratio comparing the lowest vs. the highest lung function quintile was 1.18 (95% CI, 1.02– 1.37) for FEV1, 1.14 (95% CI, 0.99– 1.29) for FVC, and 1.23 (95% CI, 1.08– 1.41) for PEF (p-trend <0.05 for all). Similar results were obtained for risk of prodromal PD and mortality among individuals with PD.Conclusions: The current study showed that individuals with poor lung function had a high future risk of prodromal and clinical PD and a higher rate of PD-related mortality.

With numerous applications coilable masts in high-precision astronomical observations, such as X-ray source observations, it is important to investigate mast stiffness. To date, there have been many studies on the bending stiffness of coilable masts, but few studies on their torsional stiffness, especially regarding the nonlinear characteristics of torsional stiffness of coilable masts under large torsional deformation. In this paper, a nonlinear analysis method is presented to examine the torsional stiffness of coilable masts with triangular sections. Based on the second-order bending buckling hypothesis of battens under large torsion deformation, the nonlinear relationship between torsional torque and torsional angle is obtained by analyzing torsional deformation and force of coilable masts. This method is used to analyze the torsional stiffness nonlinearity of a certain type of coilable mast which will be used in a practical application in the future and the results are verified by simulation and testing. The comparison results show that the error is within the acceptable range, which proves the effectiveness of the proposed method.

Micronuclear batteries harness energy from the radioactive decay of radioisotopes to generate electricity on a small scale, typically in the nanowatt or microwatt range1,2. Contrary to chemical batteries, the longevity of a micronuclear battery is tied to the half-life of the used radioisotope, enabling operational lifetimes that can span several decades3. Furthermore, the radioactive decay remains unaffected by environmental factors such as temperature, pressure and magnetic fields, making the micronuclear battery an enduring and reliable power source in scenarios in which conventional batteries prove impractical or challenging to replace4. Common radioisotopes of americium (241Am and 243Am) are α-decay emitters with half-lives longer than hundreds of years. Severe self-adsorption in traditional architectures of micronuclear batteries impedes high-efficiency α-decay energy conversion, making the development of α-radioisotope micronuclear batteries challenging5,6. Here we propose a micronuclear battery architecture that includes a coalescent energy transducer by incorporating 243Am into a luminescent lanthanide coordination polymer. This couples radioisotopes with energy transducers at the molecular level, resulting in an 8,000-fold enhancement in energy conversion efficiency from α decay energy to sustained autoluminescence compared with that of conventional architectures. When implemented in conjunction with a photovoltaic cell that translates autoluminescence into electricity, a new type of radiophotovoltaic micronuclear battery with a total power conversion efficiency of 0.889% and a power per activity of 139 microwatts per curie (μW Ci−1) is obtained.

Accurate and rapid braking pressure regulation in electric pneumatic braking systems (EPBS) is vital to vehicle safety. Due to the switching behaviors of the on-off solenoid valves, the operation of the EPBS shows a hybrid nature with both continuous variables and discrete events, which raises the hybrid control problem. One of the possible solutions is to employ the hybrid model predictive controller with the mixed logical dynamical (MLD) model based on the linear approximation of the system dynamics. However, the nonlinearity and complexity of the EPBS make the MLD model obtained by linearizing the system equations directly require high storage and computing capacity. To address these issues, this article presents a practical hybrid model predictive controller based on the system dynamics simplified expressions considering the EPBS pressure variations caused by on-off solenoid valve states at the current sampling time and the last sampling time. The relationship between the pressure variations and the on-off solenoid valve states is first studied by the system mathematical model, followed by applying the mixed logic dynamical modeling approach to establish the hybrid model of the pressure continuous dynamics with discrete features of on-off solenoid valves. Based on these, a hybrid model predictive controller is formulated to solve the EPBS pressure control problem. The simulations and bench experiments are carried out to verify the controller. Besides, an existing model predictive control (MPC) controller is compared with the proposed controller. All the results demonstrate the effectiveness of the hybrid model predictive controller.

Small-molecule drugs are essential for maintaining human health. The objective of this study is to identify a molecule that can inhibit the Factor Xa protein and be easily procured. An optimization-based de novodrug design framework, DrugCAMD, that integrates a deep learning model with a mixed-integer nonlinear programming model is used for designing drug candidates. Within this framework, a virtual chemical library is specifically tailored to inhibit Factor Xa. To further filter and narrow down the lead compounds from the designed compounds, comprehensive approaches involving molecular docking, binding pose metadynamics (BPMD), binding free energy calculations, and enzyme activity inhibition analysis are utilized. To maximize efficiency in terms of time and resources, molecules for in vitroactivity testing are initially selected from commercially available portions of customized virtual chemical libraries. In vitrostudies assessing inhibitor activities have confirmed that the compound EN300-331859 shows potential Factor Xa inhibition, with an IC50value of 34.57 μmol·L−1. Through in silicomolecular docking and BPMD, the most plausible binding pose for the EN300-331859-Factor Xa complex are identified. The estimated binding free energy values correlate well with the results obtained from biological assays. Consequently, EN300-331859 is identified as a novel and effective sub-micromolar inhibitor of Factor Xa.

ABSTRACTLong-term mapping of winter wheat is vital for assessing food security and formulating agricultural policies. Landsat data are the only available source for long-term winter wheat mapping in the North China Plain due to the fragmented landscape in this area. Although various methods, such as index-based methods, curve similarity-based methods and machine learning-based methods, have been developed for winter wheat mapping based on remote sensing, the former two often require satellite data with high temporal resolution, which are unsuitable for Landsat data with sparse time-series. Machine learning is an effective method for crop classification using Landsat data. Yet, applying machine learning for winter wheat mapping in the North China Plain encounters two main issues: 1) the lack of adequate and accurate samples for classifier training; and 2) the difficulty of training a single classifier to accomplish the large-scale crop mapping due to the high spatial heterogeneity in this area. To address these two issues, we first designed a sample selection rule to build a large sample set based on several existing crop maps derived from recent Sentinel data, with specific consideration of the confusion error between winter wheat and winter rapeseed in the available crop maps. Then, we developed an optimal zoning method based on the quadtree region splitting algorithm with classification feature consistency criterion, which divided the study area into six subzones with uniform classification features. For each subzone, a specific random forest classifier was trained and used to generate annual winter wheat maps from 2013 to 2022 using Landsat 8 OLI data. Field sample validation confirmed the high accuracy of the produced maps, with an average overall accuracy of 91.1% and an average kappa coefficient of 0.810 across different years. The derived winter wheat area also has a good correlation (R2 = 0.949) with census area at the provincial level. The results underscore the reliability of the produced annual winter wheat maps. Additional experiments demonstrate that our proposed optimal zoning method outperforms other zoning methods, including Köppen climate zoning, wheat planting zoning and non-zoning methods, in enhancing wheat mapping accuracy. It indicates that the proposed zoning is capable of generating more reasonable subzones for large-scale crop mapping.

Glass is a good candidate for substrate material of integrated passive device, due to its high resistivity and low dielectric loss. 3D through glass via (TGV) inductors can be formed with high radio‐frequency performance. This paper analyzes failure mechanisms of TGV inductors and proposes the method of process optimization.

Psoriasis is a chronic, recurrent, inflammatory systemic disease that is not only characterized by skin manifestations but may also be accompanied by various comorbidities, imposing a heavy burden on patients’ physical and mental health and affecting their quality of life. Although the continuous approval of various biological agents for clinical use has provided more effective treatment options to patients with psoriasis, no consensus on the evaluation of comprehensive treatment goals has yet been established. The treat-to-target (T2T) strategy requires consideration of multiple dimensions of treatment outcomes, the development of long-term management goals, and regular assessments of treatment conditions, which are often used in the management of chronic diseases. Therefore, based on the latest consensuses and guidelines, research data, and clinical experience as well as the combination of survey results and expert group discussions, the present consensus focuses on 4 dimensions of short- and long-term integrated management goals for biological agents: alleviating skin lesions, improving quality of life, screening and managing psoriasis comorbidities, and ensuring drug safety. The implementation methods, evaluation time, treatment monitoring, and program adjustments are also herein described to achieve comprehensive management of psoriasis to the maximum extent. This consensus provides a reference for clinical practice.

Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories.

To achieve the precise operation trajectory and posture required for pot seedling transplanting and solve the problem that the partial reversal of the transplanting arm’s output in the double planet carrier transplanting mechanism, which is unconducive to the realization of the seedling pushing action, a motion synthesis method for a double planet carrier gear train mechanism with attitude constraints based on the output angle and four exact task poses is proposed. First, the ideal transplanting trajectory is planned according to the transplanting agronomic requirements, and the position and attitude information of the four key points on the ideal trajectory are extracted as the constraints of the mechanism design. Then, based on the relative displacement equation, a comprehensive design equation of the motion of the plane 3R mechanism (simplified model of the transplanting mechanism) about the output rotation angle and the precise four pose constraints is established, and the mechanism parameters that meet the constraint requirements are obtained by solving the multicellular homotopy method. A mathematical model of the roundness of the non-circular gear was also established, and the roundness of the transmission non-circular gears in the gear-train transplanting mechanism was optimized by using the genetic algorithm. Finally, the structural design, virtual simulation, and experimental analysis of the double-planet carrier gear train transplanting mechanism were completed. The results show that the actual trajectory and attitude are consistent with the theoretical design, and when then the mechanism was under the transplanting efficiency of 120 and 180 plants/min, the success rates of picking seedlings were 91.53% and 87.46%, respectively, meeting the design requirements.

Layered compounds characterized by van der Waals gaps are often associated with relatively weak interlayer particle interactions. However, in specific scenarios, these seemingly feeble forces can exert an impact on interlayer interactions through subtle energy fluctuations, which can give rise to a diverse range of physical and chemical properties, particularly intriguing in the context of thermal transport. In this study, taking a natural superlattice composed of alternately stacked PbS and SnS2sublayers as a model, we proposed that in a superlattice, there is strong hybridization between acoustic phonons of heavy sublayers and optical phonons of light sublayers. We identified newly generated vibration modes in the superlattice, such as interlayer shear and breathing, which exhibit lower sound velocity and contribute less to heat transport compared to their parent materials, which significantly alters the thermal behaviors of the superlattice compared to its bulk counterparts. Our findings on the behavior of interlayer phonons in superlattices not only can shed light on developing functional materials with enhanced thermal dissipation capabilities but also contribute to the broader field of condensed matter physics, offering insights into various fields, including thermoelectrics and phononic devices, and may pave the way for technological advancements in these areas.

Transcription factors (TFs) play vital roles in regulating gene expressions. Dysregulated or mutated TFs are implicated in a wide array of diseases, highlighting their potential as drug targets. However, TFs are considered undruggable by conventional inhibitor-based modalities. The recently emerged proteolysis targeting chimeras (PROTACs) have exhibited great potential in tackling TFs. In particular, DNA-ligand chimeras that arm E3 ligase-recruiting ligands to TF-binding double-strand DNA represent a promising strategy for the targeted proteolysis of TFs. Here, we report a DNAzyme-inducible PROTAC (DzTAC) that selectively degrades NF-κB in a zinc ion-dependent manner. We further applied zinc-imidazolate metal-organic framework-90 (ZIF-90) to encapsulate DzTAC (ZIF-90@DzTAC), facilitating its delivery into cancer cells while self-supplying Zn2+via adenosine triphosphate (ATP)-triggered ZIF-90 dissociation. Moreover, ZIF-90@DzTAC can enhance the treatment efficacy of doxorubicin-based chemotherapy in tumor-bearing mice. The developed DzTAC provides specific modulation over TF activity and opens an avenue for more functional nucleic acid-based control over targeted protein degradation.

The development of digital twins and smart bearings in bearing industry demands efficient computational methods for factors, such as strain analysis. Symbolic regression (SR) offers a promising solution by efficiently deriving analytical expressions from limited data, offering low computational cost, rapid speed, high accuracy, and strong interpretability. In this article, SR is applied to the strain calculation problem of radially loaded hollow cylindrical rollers (HCRs). The resulting expressions are evaluated and selected based on physical insights obtained from theoretical analysis and physical experiments. Compared to other methods, the selected expression retains interpretability and generalization capabilities while achieving superior computational accuracy.

Household load forecasting is increasingly essential since it enables various demand-side management applications. The federated learning approach is becoming popular for its advantages in fully using different households’ load data with privacy preservation. However, due to the non-independent and identically distributed (non-IID) characteristic of each household’s local data, the knowledge acquired by local training may have a strong bias. It can introduce contamination and make the global model vulnerable if locally trained models are simply aggregated as traditional FL methods do. To this end, we develop a novel framework that integrates federated domain separation to alleviate the negative effects caused by non-IID data. Specifically, we divide the acquired knowledge into the useful part and potentially contaminating part. By acquiring the former and removing the latter through a well-designed algorithm, a more anti-contamination and more personalized FL model can be expected. Compared to current post-processing personalization methods, the proposed framework can avoid global knowledge forgetting, thus achieving more comprehensive knowledge utilization to give more accurate results. Extensive comparison experiments with benchmarking methods are conducted on a publicly available dataset to validate the superiority of the proposed framework, while a variety of ablation experiments prove the effectiveness of all inner components.

An algorithm for powered, fixed-wing unmanned aircraft systems to seek and navigate thermal updrafts is proposed. The algorithm consists of an outer and an inner loop. The outer loop selects between a powered cruise, gliding or soaring flight mode, while the inner loop models the low-level three-axis and motor control provided by an autopilot during flight. The soaring strategy is based on a tree architecture similar to the decision-making process of glider pilots. The algorithm does not require complex and resource-intensive computations and can be implemented in popular flight controllers, such as ArduPilot. The algorithm is tested using a simulation environment based on a three-degrees-of-freedom point-mass aircraft model and a thermal model.

Status epilepticus (SE), a serious and often life-threatening medical emergency, is characterized by abnormally prolonged seizures. It is not effectively managed by present first-line anti-seizure medications and could readily develop into drug resistance without timely treatment. In this study, we highlight the therapeutic potential of CZL80, a small molecule that inhibits caspase-1, in SE termination and its related mechanisms. We found that delayed treatment of diazepam (0.5 h) easily induces resistance in kainic acid (KA)-induced SE. CZL80 dose-dependently terminated diazepam-resistant SE, extending the therapeutic time window to 3 h following SE, and also protected against neuronal damage. Interestingly, the effect of CZL80 on SE termination was model-dependent, as evidenced by ineffectiveness in the pilocarpine-induced SE. Further, we found that CZL80 did not terminate KA-induced SE in Caspase-1−/−mice but partially terminated SE in IL1R1−/−mice, suggesting the SE termination effect of CZL80 was dependent on the caspase-1, but not entirely through the downstream IL-1β pathway. Furthermore, in vivo calcium fiber photometry revealed that CZL80 completely reversed the neuroinflammation-augmented glutamatergic transmission in SE. Together, our results demonstrate that caspase-1 inhibitor CZL80 terminates diazepam-resistant SE by blocking glutamatergic transmission. This may be of great therapeutic significance for the clinical treatment of refractory SE.

With the remarkable success of digital histopathology and the deep learning technology, many whole-slide pathological images (WSIs) based deep learning models are designed to help pathologists diagnose human cancers. Recently, rather than predicting categorical variables as in cancer diagnosis, several deep learning studies are also proposed to estimate the continuous variables such as the patients’ survival or their transcriptional profile. However, most of the existing studies focus on conducting these predicting tasks separately, which overlooks the useful intrinsic correlation among them that can boost the prediction performance of each individual task. In addition, it is sill challenge to design the WSI-based deep learning models, since a WSI is with huge size but annotated with coarse label. In this study, we propose a general multi-instance multi-task learning framework (HistMIMT) for multi-purpose prediction from WSIs. Specifically, we firstly propose a novel multi-instance learning module (TMICS) considering both common and specific task information across different tasks to generate bag representation for each individual task. Then, a soft-mask based fusion module with channel attention (SFCA) is developed to leverage useful information from the related tasks to help improve the prediction performance on target task. We evaluate our method on three cancer cohorts derived from the Cancer Genome Atlas (TCGA). For each cohort, our multi-purpose prediction tasks range from cancer diagnosis, survival prediction and estimating the transcriptional profile of gene TP53. The experimental results demonstrated that HistMIMT can yield better outcome on all clinical prediction tasks than its competitors.

In this article, we focus on human-to-cobot dual-arm handover operations for large box-type objects. The efficiency of handover operations should be ensured and the naturalness as if the handover is going on between two humans. First of all, we study the human–human dual-arm large box-type object natural handover process to guide this research. Then, for efficiency, we combine the probabilistic approach with the online learning algorithm to predict the beginning of the handover task and handover positions. The online updating probabilistic models can deal with not only human givers’ regular motion patterns but also their irregular motion patterns. Then, to guarantee that human givers can perform handover operations naturally, we apply the probabilistic robot skill learning method kernelized movement primitives (KMPs) to adapt the learned receiving skills and fulfill some constraints for safety based on online predicted results. Furthermore, we give special attention to the dual-arm grasp strategy and control design to guarantee a stable grasp. In addition, we equip this handover system on a Baxter cobot and extend its grippers to make it more suitable for dual-arm handover operations. The experimental results show that the proposed handover system can solve human-to-cobot dual-arm handover operations for large box-type objects naturally and efficiently.

The idler roller is the bearing device of the belt conveyor, whose health status is crucial for the efficient transportation of materials in coal mines and other industries. In this article, we propose a new embedded belt conveyor idler-monitoring node with the capacities for self-powering, multiparameter sensing, and wireless communication for the real-time health monitoring of the idler roller. First, the architecture of the belt conveyor idler-monitoring network was proposed, and three functional modules were designed for the node: an energy-harvesting module based on the Halbach permanent magnet array, an energy-management module that adapts the energy harvester to the node’s power consumption, and a control/sensing module that employs wireless communication. Subsequently, the performance of the node was tested and analyzed. The power of the energy harvester designed here was 171.33 mW, which sufficiently provides for the node’s power consumption. The energy management circuit can provide the network with a remaining operating time of 74 min. The communication test showed the influence of narrow space on the reliable communication distance of the nodes. Concluding the research, an overall test of the node was conducted, demonstrating that the node can accurately collect and transmit temperature and vibration data pertaining to the idler, meeting the monitoring requirements. The proposed node provides a technical solution for the health monitoring of belt conveyor idlers and the overall system.

In this article, we present a novel approach, namely distributed matching-by-clone hungarian-based algorithm (DMCHBA), to multiagent task-allocation problems, in which the number of agents is smaller than the number of tasks. The proposed DMCHBA assumes that agents employ an implicit coordination mechanism and consists of two iterative phases, i.e., the communication phase and the assignment phase. In the communication phase, agents communicate with their connected neighbors and exchange their local knowledge base until they converge on the global knowledge base. In the assignment phase, each agent builds a squared cost matrix by cloning agents and adding pseudotasks when necessary, and applying the Hungarian method for task allocation. A local planning algorithm is then applied to identify the order of task execution for an agent. The proposed DMCHBA is proven to produce conflict-free assignments among agents in finite time. We compare the performance of DMCHBA with the consensus-based bundle algorithm, the distributed recursive Hungarian-based algorithms, and the cluster-based Hungarian algorithm (CBHA) in Monte-Carlo simulations with different numbers of agents and tasks. The numerical results reveal the superior convergence and optimality of DMCHBA over all other selected algorithms.

Bearings are vital components of a rotary machine. Real-time fault diagnosis of bearings has great significance in the maintenance of equipment. Current vibration-based fault diagnosis methods rely on the usage of long time series data to reduce the influence of noise but often suffer from imbalances in datasets, significantly affecting diagnosis accuracy. In this article, a fault diagnosis method based on intertemporal return plot (IRP) and data augmentation is proposed. The IRP is employed to transform 1-D time series data into 2-D images. The Wasserstein generative adversarial network (WGAN) is employed to generate synthetic images for data argumentation. This approach helps reduce the influence of data imbalance, thereby improving the accuracy and convergence speed of the fault diagnosis. Comparisons with existing well-known methods are performed to demonstrate the effectiveness of the proposed method. The results show that the proposed method achieves a high accuracy of fault diagnosis for short time series data. An inverse transformation is also proposed to convert the images to time series data.

Inflammatory pain results from the heightened sensitivity and reduced threshold of nociceptor sensory neurons due to exposure to inflammatory mediators. However, the cellular and transcriptional diversity of immune cell and sensory neuron types makes it challenging to decipher the immune mechanisms underlying pain. Here we used single-cell transcriptomics to determine the immune gene signatures associated with pain development in three skin inflammatory pain models in mice: zymosan injection, skin incision and ultraviolet burn. We found that macrophage and neutrophil recruitment closely mirrored the kinetics of pain development and identified cell-type-specific transcriptional programs associated with pain and its resolution. Using a comprehensive list of potential interactions mediated by receptors, ligands, ion channels and metabolites to generate injury-specific neuroimmune interactomes, we also uncovered that thrombospondin-1 upregulated by immune cells upon injury inhibited nociceptor sensitization. This study lays the groundwork for identifying the neuroimmune axes that modulate pain in diverse disease contexts.

Little is known about the links of disturbed lipid metabolism with hyperuricemia (HUA). We aimed to investigate the associations of lipidomic profiles with uric acid (UA)/HUA and their modifying factors in middle-aged and elderly Chinese. A total of 350 lipids were quantified in 2247 community-based Chinese aged 50–70 years by high-coverage targeted lipidomics. HUA was defined by plasma UA > 420 μmol/L in men or > 360 μmol/L in women. The prevalence of HUA in this population was 10.4%. After multivariable adjustment including BMI and lifestyle, 123 lipids were significantly associated with UA, predominantly glycerolipids (GLs) and glycerophospholipids (GPs). Specifically, diacylglycerol [DAG (16:0/22:5), DAG (16:0/22:6), DAG (18:1/20:5), DAG (18:1/22:6)], phosphatidylcholine [PC (16:0/20:5)), and triacylglycerol (TAG (53:0)] were the most significant lipid signatures positively associated with HUA risk, while lysophosphatidylcholine (LPC (20:2)) was inversely associated with HUA risk (p< 0.05). Network analysis also showed a positive association between TAGs/PCs/DAGs contained module and HUA risk (p< 0.01). Notably, HUA-related lipids were associated with de novo lipogenesis fatty acids, especially 16:1n-7 (Spearman correlation coefficients = 0.32–0.41, p< 0.001). Reduced rank regression showed that increased aquatic products intake was correlated to elevated HUA risk and HUA-associated lipids; while high dairy consumption was correlated with low level of HUA-associated lipids (|factor loadings| ≥ 0.2). Moreover, mediation analyses suggested that the lipid-HUA associations were partially mediated by retinol-binding protein 4 (RBP4, mediation proportion 5–14%), an adipokine linked with dyslipidemia and insulin resistance. In conclusion, disturbed specific metabolisms of GLs and GPs were associated with high prevalent HUA, partially mediated by RBP4 and/or influenced by certain dietary factors.

Sensors are essential for the prognosis and health management of equipment. The fusion of multiple sensors data could improve the accuracy of fault diagnosis. Current data fusion methods focus on high-quality sensor data. However, in practical applications, constraints, such as cost or space, may limit the use of multiple high-quality sensors. Therefore, it is necessary to explore how to effectively use all available sensor data, even those containing some useful information but less effective when used individually. This article presents a novel method for sensor data fusion, utilizing the highest performing sensor data as a backbone and complementing it with preliminary data-level fusion data through multistage adaptive feature complementation. Comparative results validate the effectiveness and superiority of this method. The proposed method also demonstrates strong predictive capability by using data from a single period as training samples to forecast future data states.

Ageing is a critical factor in spinal-cord-associated disorders1, yet the ageing-specific mechanisms underlying this relationship remain poorly understood. Here, to address this knowledge gap, we combined single-nucleus RNA-sequencing analysis with behavioural and neurophysiological analysis in non-human primates (NHPs). We identified motor neuron senescence and neuroinflammation with microglial hyperactivation as intertwined hallmarks of spinal cord ageing. As an underlying mechanism, we identified a neurotoxic microglial state demarcated by elevated expression of CHIT1 (a secreted mammalian chitinase) specific to the aged spinal cords in NHP and human biopsies. In the aged spinal cord, CHIT1-positive microglia preferentially localize around motor neurons, and they have the ability to trigger senescence, partly by activating SMAD signalling. We further validated the driving role of secreted CHIT1 on MN senescence using multimodal experiments both in vivo, using the NHP spinal cord as a model, and in vitro, using a sophisticated system modelling the human motor-neuron–microenvironment interplay. Moreover, we demonstrated that ascorbic acid, a geroprotective compound, counteracted the pro-senescent effect of CHIT1 and mitigated motor neuron senescence in aged monkeys. Our findings provide the single-cell resolution cellular and molecular landscape of the aged primate spinal cord and identify a new biomarker and intervention target for spinal cord degeneration.

Energy forecasting is crucial in scheduling and planning future electric load, so as to improve the reliability and safeness of the power grid. Despite recent developments of forecasting algorithms in the machine learning community, there is a lack of general and advanced algorithms specifically considering requirements from the power industry perspective. In this paper, we present eForecaster, a unified AI platform including robust, flexible, and explainable machine learning algorithms for diversified energy forecasting applications. Since October 2021, multiple commercial bus load, system load, and renewable energy forecasting systems built upon eForecaster have been deployed in seven provinces of China. The deployed systems consistently reduce the average Mean Absolute Error (MAE) by 39.8% to 77.0%, with reduced manual work and explainable guidance. In particular, eForecaster also integrates multiple interpretation methods to uncover the working mechanism of the predictive models, which significantly improves forecasts adoption and user satisfaction.

Accurate ultrasound (US) image segmentation is crucial for the screening and diagnosis of diseases. However, it faces two significant challenges: 1) pixel-level annotation is a time-consuming and laborious process; 2) the presence of shadow artifacts leads to missing anatomy and ambiguous boundaries, which negatively impact reliable segmentation results. To address these challenges, we propose a novel semi-supervised shadow aware network with boundary refinement (SABR-Net). Specifically, we add shadow imitation regions to the original US, and design shadow-masked transformer blocks to perceive missing anatomy of shadow regions. Shadow-masked transformer block contains an adaptive shadow attention mechanism that introduces an adaptive mask, which is updated automatically to promote the network training. Additionally, we utilize unlabeled US images to train a missing structure inpainting path with shadow-masked transformer, which further facilitates semi-supervised segmentation. Experiments on two public US datasets demonstrate the superior performance of the SABR-Net over other state-of-the-art semi-supervised segmentation methods. In addition, experiments on a private breast US dataset prove that our method has a good generalization to clinical small-scale US datasets.

Building-level load forecasting is becoming increasingly crucial since it forms the foundation for better building energy management, which will lower energy consumption and reduce CO2 emissions. However, building-level load forecasting faces the challenges of high load volatility and heterogeneous consumption behaviors. Simple regression models may fail to fit the complex load curves, whereas sophisticated models are prone to overfitting due to the limited data of an individual building. To this end, we develop a novel forecasting model that integrates federated learning (FL), the differentiable architecture search (DARTS) technique, and a two-stage personalization approach. Specifically, buildings are first grouped according to the model architectures, and for each building cluster, a global model is designed and trained in a federated manner. Then, a local fine-tuning approach is used to adapt the cluster global model to each individual building. In this way, data resources from multiple buildings can be utilized to construct high-performance forecasting models while protecting each building’s data privacy. Furthermore, personalized models with specific architectures can be trained for heterogeneous buildings. Extensive experiments on a publicly available dataset are conducted to validate the superiority of the proposed method.

The tumor-infiltrating lymphocytes (TILs) and its correlation with tumors have shown significant values in the development of cancers. Many observations indicated that the combination of the whole-slide pathological images (WSIs) and genomic data can better characterize the immunological mechanisms of TILs. However, the existing image-genomic studies evaluated the TILs by the combination of pathological image and single-type of omics data (e.g., mRNA), which is difficulty in assessing the underlying molecular processes of TILs holistically. Additionally, it is still very challenging to characterize the intersections between TILs and tumor regions in WSIs and the high dimensional genomic data also brings difficulty for the integrative analysis with WSIs. Based on the above considerations, we proposed an end-to-end deep learning framework i.e., IMO-TILs that can integrate pathological image with multi-omics data (i.e., mRNA and miRNA) to analyze TILs and explore the survival-associated interactions between TILs and tumors. Specifically, we firstly apply the graph attention network to describe the spatial interactions between TILs and tumor regions in WSIs. As to genomic data, the Concrete AutoEncoder (i.e., CAE) is adopted to select survival-associated Eigengenes from the high-dimensional multi-omics data. Finally, the deep generalized canonical correlation analysis (DGCCA) accompanied with the attention layer is implemented to fuse the image and multi-omics data for prognosis prediction of human cancers. The experimental results on three cancer cohorts derived from the Cancer Genome Atlas (TCGA) indicated that our method can both achieve higher prognosis results and identify consistent imaging and multi-omics bio-markers correlated strongly with the prognosis of human cancers.

This article presents a novel approach for extending the upper stopband of lowpass filters. The proposed technique involves dividing the hairpin unit cells' coupled lines into multiple smaller components, which shifts the harmonics to higher frequencies while maintaining the cut-off frequency. In addition, the stopband is further extended by modifying the tapping position to suppress the second harmonic. For demonstration, an improved dual-hairpin unit cell is devised, which is able to extend the stopband up to 11.3 times the cut-off frequency. Furthermore, a cascaded lowpass filter utilizing multiple improved dual-hairpin unit cells is also designed, which is divided into two subfilters to suppress enclosure resonance. The cascaded filter is fabricated on an high-temperature superconducting thin film deposited on a MgO substrate, demonstrating a cut-off frequency of 483 MHz, extending the stopband to 25.1 GHz (51.9fc), affording a rejection level exceeding 60 dB and insertion loss below 0.04 dB. Notably, the roll-off rate exceeds 569 dB/GHz, and the size measures only 0.144λg × 0.030λg.

In this study, Al–7Si–Cu alloys with different contents of Cu were prepared by ultrasonic agitation casting method. The microstructures were characterized by scanning electron microscopy (SEM) and energy dispersion spectroscopy (EDS). Dry sliding friction and wear properties with different Cu contents and loads were investigated. The results indicated that the alloy with 3.0 wt% Cu content revealed the optimal microstructure, comprehensive mechanical properties, and tribological properties. The hardness, tensile strength, and yield strength were enhanced by 83%, 57%, and 59%, respectively. The friction coefficient and wear rate reduced by 33% and 24% at 40 N load, respectively. The wear mechanism of Al–7Si–Cu alloy was mainly abrasive wear and delamination wear accompanied by oxidation wear. The improvement of the tribological properties of the alloy was attributed to the combined improvement of the microstructure and mechanical properties.

Video action recognition is a fundamental task for video understanding. Action recognition in complex spatio-temporal contexts generally requires fusing of different multi-granularity action information. However, existing works do not consider spatio-temporal information modeling and fusion from the perspective of action granularity. To address this problem, this paper proposes an Action Granularity Pyramid Network (AGPN) for action recognition, which can be flexibly integrated into 2D backbone networks. The core module is the Action Granularity Pyramid Module (AGPM), a hierarchical pyramid structure with residual connections, which is established to fuse multi-granularity action spatio-temporal information. From top to bottom level in the designed pyramid structure, the receptive field decreases and action granularity becomes more refined. To enrich temporal information of the inputs, a Multiple Frame Rate Module (MFM) is proposed to mix different frame rates at a fine-grained pixel-wise level. Moreover, a Spatio-temporal Anchor Module (SAM) is employed to fix spatio-temporal feature anchors to promote the effectiveness of feature extraction. We conduct extensive experiments on three large-scale action recognition datasets, Something-Something V1 & V2 and Kinetics-400. The results demonstrate that our proposed AGPN outperforms the state-of-the-art methods for the tasks of video action recognition.

A fixed-time trajectory tracking control method for uncertain robotic manipulators with input saturation based on reinforcement learning (RL) is studied. The designed RL control algorithm is implemented by a radial basis function (RBF) neural network (NN), in which the actor NN is used to generate the control strategy and the critic NN is used to evaluate the execution cost. A new nonsingular fast terminal sliding mode technique is used to ensure the convergence of tracking error in fixed time, and the upper bound of convergence time is estimated. To solve the saturation problem of an actuator, a nonlinear antiwindup compensator is designed to compensate for the saturation effect of the joint torque actuator in real time. Finally, the stability of the closed-loop system based on the Lyapunov candidate is analyzed, and the timing convergence of the closed-loop system is proven. Simulation and experimental results show the effectiveness and superiority of the proposed control law.

Bacillusstrains are widely used as biological control agents to protect plants from fungal pathogens. However, whether Bacilluscan exploit fungal pathogens to increase its biocontrol efficacy remains largely unexplored. Here, Bacillus atrophaeusNX-12 showed a high inhibition efficacy against Fusarium oxysporumf. sp. cucumerinum (FOC). The primary extracellular antifungal component of B. atrophaeusNX-12 was identified as fengycin by matrix-assisted laser desorption/ionization–time-of-flight-mass spectrometry (MALDI-TOF-MS) analysis. NX-12-secreted fengycin not only inhibited the germination of FOC spores but also induced the production of reactive oxygen species (ROS) in FOC cells, leading to oxidative stress and the accumulation of glycerol. Additionally, NX-12-secreted fengycin increased FOC cell wall hydrolase activity, leading to cell splitting and the exosmose of accumulated glycerol. The increased exosmose of glycerol further promoted the production of fengycin. Our results showed that in addition to the direct inhibition of FOC, NX-12 can indirectly strengthen its antagonistic efficacy against the pathogen by exploiting the exosmotic glycerol from FOC.

Alcohol consumption remains inconsistently correlated with fracture risk, and a dose–response meta-analysis for specific outcomes is lacking. The objective of this study was to quantitatively integrate the data on the relationship between alcohol consumption and fracture risk. Pertinent articles were identified in PubMed, Web of Science, and Embase databases up to 20 February 2022. Combined RRs and 95% CIs were estimated by random- or fixed-effects models. Restricted cubic splines were used to model linear or nonlinear relationships. Forty-four articles covering 6,069,770 participants and 205,284 cases of fracture were included. The combined RRs and 95% CIs for highest compared with lowest alcohol consumption were 1.26 (1.17–1.37), 1.24 (1.13–1.35), and 1.20 (1.03–1.40) for total, osteoporotic, and hip fractures, respectively. A linear positive relationship between alcohol consumption and total fracture risk was detected (Pnonlinearity= 0.057); the risk was correlated with a 6% increase (RR, 1.06; 95% CI: 1.02, 1.10) per 14 g/d increment of alcohol consumption. J-shaped relationships of alcohol consumption with risk of osteoporotic fractures (Pnonlinearity< 0.001) and hip fractures (Pnonlinearity< 0.001) were found. Alcohol consumption of 0 to 22 g/d was linked to a reduced risk of osteoporotic fractures and hip fractures. Our findings show that any level of alcohol consumption is a risk factor for total fractures. Moreover, this dose–response meta-analysis shows that an alcohol consumption level of 0 to 22 g/d is related to a reduction in the risk of osteoporotic and hip fractures.

AbstractFor the sake of economic design and safety requirement, the prediction of active earth pressure on laggings between stabilising piles is of great significance. However, the nonlinear distribution of active earth pressure on laggings between piles cannot be accurately predicted by using the classical theory. In this study, a novel method of calculating active earth pressure on laggings between piles is proposed based on the differential flat element method. For validation, calculations of the active earth pressure by using the novel method as well as other methods have been compared against the experimental results, indicating that the proposed method considering soil arching effect can well predict the active earth pressure on laggings. Furthermore, parametric analyses are conducted to examine of the most effective means of the piles and lagging system. It is concluded that the decrease rate of total active force flattens out when internal friction angle is larger than a certain degree and the lagging clear span suggests to be 1.5 to 1.7 times of pile diameter to meet economic requirements. This method can provide a reasonable estimation of the active earth pressure on laggings in the design of supporting structures.

It is highly desirable to develop smart and green pesticide nanoformulations for improving pesticide targeting and reducing their inherent toxicity. Herein, we demonstrate a continuous nanoprecipitation method to construct a novel type of enzyme-responsive fluorescent nanopesticides (denoted as ABM@BSA-FITC/GA NPs) based on abamectin, fluorescein isothiocyanate isomer (FITC)-modified protein, and food-grade gum arabic. The as-prepared ABM@BSA-FITC/GA NPs exhibit good water dispersibility, excellent storage stability, and enhanced wettability compared to commercial formulations. The controlled release of pesticides can be achieved through protein degradation caused by trypsin. Most importantly, the deposition, distribution, and transport of the ABM@BSA-FITC/GA NPs are precisely tracked on target plants (cabbage and cucumber) by fluorescence. Furthermore, the ABM@BSA-FITC/GA NPs show the high control efficacy against Plutella xylostella L., which is comparable with commercial emulsifiable concentrate formulation. In consideration of its eco-friendly composition and absence of organic solvent, this pesticide nanoformulation has promising potential in sustainable plant protection.

Background: The Gas Pixel Detector (GPD) is a key part of the Polarimetry Focusing Array (PFA) sub-system onboard the enhance X-ray Timing and Polarimetry mission (eXTP). The GPD can accurately provide the polarized angle and degree for incoming soft X-rays (requirements of minimum detectable polarization &lt;1$\%$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}(10${}^6$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{6}$$\end{document}s, 1 mCrab)) from celestial objects under extreme conditions of density, gravity and magnetism. Purpose: Paste the GPD chips on a matched ceramic package is one of the most important procedures for the GPD assembly, and the parallelism between the top surface of an ASIC chip and the bottom surface of a Gas Electron Multiplier (GEM) foil can have impact on the distribution of electric field within transfer region and reconstruction of photoelectric trajectories, resulting in deterioration of the GPD sensitivity. This study quantifies the influence and gives a reasonable expectation of the parallelism. Methods: A simulation framework of the GPD is constructed to quantify the influence on the GPD performance and values of the parallelism are measured by a gauge with a position resolution of 0.1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu \mathrm m$$\end{document}. Results:: The difference of degree of modulation is less than 1$\%$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}comparing the parallelism with a value of 20 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu \mathrm m$$\end{document}to a value of 0 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu \mathrm m$$\end{document}in response to both polarized and unpolarized X-rays. Meanwhile, the value of parallelism can reach \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$6.9 \pm 3.4 \upmu \mathrm m$$\end{document}($0.{0176}^{\circ }\pm 0.{0087}^{\circ }$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.0176^{\circ } \pm 0.0087^{\circ }$$\end{document}) by testing and verifying. Conclusions: The impact on the GPD performance can be less than 1$\%$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}as the parallelism with an order of 20 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu \mathrm m$$\end{document}, and we also give a reasonable measuring method and verify the controllability and feasibility of the parallelism with an order of 20 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu \mathrm m$$\end{document}for pasting the ASIC chip on the surface of a ceramic package.

Perovskite light-emitting diodes (PeLEDs) have recently demonstrated great potential for next-generation displays. Fabricating PeLEDs by scalable and well-established thermal evaporation—the technique used for industrial manufacturing of organic light-emitting diodes—should accelerate the development of perovskite displays; however, thermal evaporation of perovskites produces films with a high density of defects due to fast and uncontrollable crystal growth during evaporation. As a result, the performance of evaporated PeLEDs is lagging behind their solution-processed counterparts. Here we develop a tri-source co-evaporation strategy to introduce a multifunctional Lewis-base additive that reduces the perovskite grain size while confining charge carriers and passivating surface defects. The process enables the in situ formation of high-quality perovskite nanocrystal films with improved crystallinity, enhanced photoluminescence quantum yield and suppressed defects. A peak external quantum efficiency of 16.4% is achieved for green PeLEDs with an all-thermally evaporated device architecture. We further fabricate active-matrix PeLED displays by integrating top-emitting PeLEDs on a 6.67-inch thin-film transistor backplane. The displays show high-definition images and videos with a resolution of 1,080 × 2,400 and continuous greyscale information. We anticipate that this work will stimulate the exploration of efficient vapour-deposited PeLEDs for industrial display applications.

Although many dielectric polymers exhibit high energy storage density (Ue) with enhanced dipolar polarization at room temperature, the substantially increased electric conduction loss at high applied electric fields and high temperatures remains a great challenge. Here, we report a strategy that high contents of medium-polar ester group and end-group (St) modification are introduced into a biodegradable polymer polylactic acid (PLA) to synergistically reduce the loss and enhance Ueand charge-discharge efficiency (η). The resultant St-modified PLA polymer (PLA-St) exhibits an Ueof 6.5 J/cm3with an ultra-high η(95.4%), far outperforming the best reported dielectric polymers. It is worth noting that the modified molecular structures can generate deep trap centers and restrict the local dipole motions in the polymer, which are responsible for the reduction of conduction loss and improvements in high-temperature capacitive performance. In addition, the PLA-St polymer shows intrinsically excellent self-healing ability and cyclic stability surviving over 500 000 charge-discharge cycles. This work offers an efficient route to next-generation eco-friendly dielectric polymers with high energy density, low loss, and long-term stability.

Introduction: The medium-energy X-ray telescope (ME) is a collimated X-ray telescope onboard the Insighthard X-ray modulation telescope (Insight-HXMT) satellite. It has 1728 Si-PIN pixels readout using 54 low noise application-specific integrated circuits (ASICs). ME covers the energy range of 5–30 keV and has a total detection area of 952 cm${}^2$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^2$$\end{document}. The typical energy resolution of ME at the beginning of the mission is 3 keV at 17.8 keV (full width at half maximum, FWHM), and the time resolution is 255 $\mu$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu $$\end{document}s. In this study, we present the in-orbit performance of ME in its first 5 years of operation. Methods: The performance of ME was monitored using onboard radioactive sources and astronomical X-ray objects. ME carries six ${}^{241}$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{241}$$\end{document}Am radioactive sources for onboard calibration, which can continuously illuminate the calibration pixels. The long-term performance evolution of ME can be quantified using the properties of the accumulated spectra of the calibration pixels. In addition, observations of the Crab Nebula and the pulsar were used to check the long-term evolution of the detection efficiency as a function of energy. Conclusion: After 5 years of operation, 742 cm${}^2$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^2$$\end{document}of the Si-PIN pixels were still working normally. The peak positions of ${}^{241}$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{241}$$\end{document}Am emission lines gradually shifted to the high-energy region, implying a slow increase in ME gain of $1.43\%$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.43\%$$\end{document}. A comparison of the ME spectra of the Crab Nebula and the pulsar shows that the E–C relations and the redistribution matrix file are still acceptable for most data analysis works, and there is no detectable variation in the detection efficiency.

OBJECTIVE To prospectively investigate associations of dairy consumption with risk of type 2 diabetes and changes of cardiometabolic traits. RESEARCH DESIGN AND METHODS In 2005, 2,091 middle-aged and older Chinese [...]