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Infection is one of the primary causes of death from immune thrombocytopenia (ITP), and the lungs are the most common site of infection. We identified the factors associated with hospitalization for community-acquired pneumonia (CAP) in nonsplenectomized adults with ITP and established the ACPA prediction model to predict the incidence of hospitalization for CAP. This was a retrospective study of nonsplenectomized adult patients with ITP from 10 large medical centers in China. The derivation cohort included 145 ITP inpatients with CAP and 1360 inpatients without CAP from 5 medical centers, and the validation cohort included the remaining 63 ITP inpatients with CAP and 526 inpatients without CAP from the other 5 centers. The 4-item ACPA model, which included age, Charlson Comorbidity Index score, initial platelet count, and initial absolute lymphocyte count, was established by multivariable analysis of the derivation cohort. Internal and external validation were conducted to assess the performance of the model. The ACPA model had an area under the curve of 0.853 (95% confidence interval [CI], 0.818-0.889) in the derivation cohort and 0.862 (95% CI, 0.807-0.916) in the validation cohort, which indicated the good discrimination power of the model. Calibration plots showed high agreement between the estimated and observed probabilities. Decision curve analysis indicated that ITP patients could benefit from the clinical application of the ACPA model. To summarize, the ACPA model was developed and validated to predict the occurrence of hospitalization for CAP, which might help identify ITP patients with a high risk of hospitalization for CAP.

Background: Due to the lack of a reliable means to evaluate the immune status, we proposed a novel method, namely Mingdao immune cells analysis and Mingdao immune score system, to quantify the immunity by scores. Methods: Data from healthy controls in addition to liver transplant recipients, who underwent a single liver transplant or were followed up at Beijing Chaoyang Hospital, were retrospectively collected and analyzed between January 2017 and June 2020. Furthermore, peripheral blood was taken for flow cytometric measurement. Results: A total of 376 healthy controls with 376 tests and 148 liver transplant recipients with 284 tests were included in this study. Evaluated by Mingdao immune cells analysis and Mingdao immune score system, the mean values of healthy controls were near zero inspite of different age groups and gender in contrast to negative mean values in liver transplant recipients before and after liver transplantation. The preoperative scores were used to decide the first dose of the immunosuppressants as patients with lower scores (≤ -5) were suggested to give a lower dose in an attempt to reach a balance between over- and under- immunosuppression. Moreover, we found positive scores or increased scores (≥ 5) could reflect immune activation. Finally, the score was defined to be the only significant independent risk factor for clinical acute rejection (odds ratio, 0.913; confidence interval, 0.845-0.987; P = 0.021). Conclusion: The novel method could be used as an indicator to reflect the immunity and to regulate the immunosuppressants.

Purpose: Based on the theorized concept “Curriculum Safety Zone (CSZ),” this study was conducted to identify the factors for breaking CSZ by contrasting the experiential accounts of two pre-service...

In this review, we have summarized the research progress on stability of solid acid catalysts. Three reasons for the deactivation of solid acids and corresponding catalytic activity improvement are discussed by taking several examples, giving a good inspiration for the study and development of stable solid acid catalysts in the future.

We aimed to investigate the frequency, risk factors, and outcome of active tuberculosis (TB) after allogeneic hematopoietic stem cell transplantation (allo-HSCT). This retrospective, nested, case-control study reviewed data from 6236 patients who received allo-HSCT from January 2008 to December 2018 at a single center; thirty-three patients (0.5%) with active TB and 99 controls without active TB after allo-HSCT were identified. We performed propensity score matching by randomly selecting 3 controls for each identified active TB patient according to the time of transplantation and follow-up period. History of pretransplant active TB previously treated and inactive at time of transplantation (P.001) was an independent risk factor. No significant differences in overall survival (P= .342), nonrelapse mortality (P= .497), or incidence of relapse (P= .807) were found. Thirty (90.9%) patients were treated with 4-drug (isoniazid, rifampicin/three rifapentine, pyrazinamide, and ethambutol) or 3-drug combination first-line therapy, with a response rate of 76.7%. Twenty-six (78.8%) patients were treated with first-line and second-line combined therapy, and the response rate was 76.9%. Five (15.2%) patients developed hepatotoxicity. In conclusion, history of pretransplant active TB previously treated and inactive at time of transplantation was an independent risk factor of active TB after allo-HSCT. No significant differences in prognosis between the TB and control groups were found. More studies are needed to help develop standardized therapeutic strategies for patients with post-transplant TB.

ObjectivesTo investigate the potential of deep learning in assessing pneumoconiosis depicted on digital chest radiographs and to compare its performance with certified radiologists.MethodsWe retrospectively collected a dataset consisting of 1881 chest X-ray images in the form of digital radiography. These images were acquired in a screening setting on subjects who had a history of working in an environment that exposed them to harmful dust. Among these subjects, 923 were diagnosed with pneumoconiosis, and 958 were normal. To identify the subjects with pneumoconiosis, we applied a classical deep convolutional neural network (CNN) called Inception-V3 to these image sets and validated the classification performance of the trained models using the area under the receiver operating characteristic curve (AUC). In addition, we asked two certified radiologists to independently interpret the images in the testing dataset and compared their performance with the computerised scheme.ResultsThe Inception-V3 CNN architecture, which was trained on the combination of the three image sets, achieved an AUC of 0.878 (95% CI 0.811 to 0.946). The performance of the two radiologists in terms of AUC was 0.668 (95% CI 0.555 to 0.782) and 0.772 (95% CI 0.677 to 0.866), respectively. The agreement between the two readers was moderate (kappa: 0.423, pConclusionOur experimental results demonstrated that the deep leaning solution could achieve a relatively better performance in classification as compared with other models and the certified radiologists, suggesting the feasibility of deep learning techniques in screening pneumoconiosis.

Fe3O4 nanoparticles (NPs) have previously been employed in various fields owing to their unique physical and chemical properties. In this paper, Fe3O4 NPs are prepared by co-precipitation method in a helical microreactor under different reaction conditions which affect the size of Fe3O4 NPs. The product is characterized by FT-IR and XPS. Also, VSM characterization shows that Fe3O4 exhibits typical superparamagnetic behavior and the saturation magnetization of NPs is 53 emu/g.

El enoturismo es un producto turístico emergente. En China, cuna del vino en Oriente, el enoturismo es nuevo; en el mundo occidental, España es uno de los principales países productores de vino, pero en comparación con otros países productores de su entorno, el enoturismo en España ha tenido un desarrollo más lento.En este trabajo se seleccionan dos Denominaciones de Origen Protegido (D.O.P.) del vino; Yantai y Ningxia, para analizarlas y compararlas con las de la Comunidad Autónoma de Aragón. Se analiza el desarrollo actual del enoturismo en ambos países; su sistema territorial, los puntos fuertes, los puntos débiles, las oportunidades y las amen1azas, y finalmente se identifican las características y las similitudes del enoturismo en los dos países de D.O.P. y se proponen recomendaciones para su desarrollo posterior.

Thrombocytopenia is associated with life-threatening bleeding and is common in myelodysplastic syndromes (MDS). Robust molecular prognostic biomarkers need to be developed to improve clinical decision making for patients with MDS with thrombocytopenia. Wilms tumor 1 (WT1) and preferentially expressed antigen in melanoma (PRAME) are promising immunogenic antigen candidates for immunotherapy, and their clinical effects on patients with MDS with thrombocytopenia are still not well understood. We performed a multicenter observational study of adult patients with MDS with thrombocytopenia from 7 different tertiary medical centers in China. We examined bone marrow samples collected at diagnosis for WT1 and PRAME transcript levels and then analyzed their prognostic effect for patients with MDS with thrombocytopenia. In total, we enrolled 1110 patients diagnosed with MDS with thrombocytopenia. Overexpression of WT1 and PRAME was associated with elevated blast percentage, worse cytogenetics, and higher Revised International Prognostic Scoring System (IPSS-R) risk. Further, both WT1 and PRAME overexpression were independent poor prognostic factors for acute myeloid leukemia evolution, overall survival, and progression-free survival. Together, the 2 genes overexpression identified a population of patients with MDS with substantially worse survival. On the basis of WT1 and PRAME transcript levels, patients with MDS with IPSS-R low risk were classified into 2 significantly divergent prognostic risk groups: a low-favorable group and a low-adverse group. The low-adverse group had survival similar to that of patients in the intermediate-risk group. Our study demonstrates that the evaluation of WT1/PRAME transcript analysis may improve the prognostication precision and better risk-stratify the patients.

To improve the path tracking accuracy and yaw stability of distributed drive autonomous vehicles (DDAVs) under extreme working conditions, a cooperative lateral motion control method based on the dynamic boundary is proposed to prevent different road adhesion conditions from affecting the motion stability of DDAVs. Based on the analysis of the DDAV lateral dynamics system coordination mechanism, a dynamic boundary considering the pavement adhesion coefficient is proposed, and the Lateral Motion Synergistic Control System (LMSCS) is designed. The LMSCS is divided into the coordination, control, and executive layers. The coordination layer divides the control domain into the stable, quasi-stable, and unstable domains by the dynamic boundary, and coordinates the control strength of the path following control and yaw stability control. In the control layer, the path following control and yaw stability control laws are designed based on the global fast terminal sliding mode. The executive layer estimates the expected steering wheel angle and expected additional wheel torque. Joint simulations under double line shifting conditions confirmed that LMSCS reflects the impact of the road attachment conditions and improves the path tracking accuracy and vehicle yaw stability. The LMSCS has better overall performance than existing lateral motion control methods.

Human beings perform well in uncertain environments, matching the performance of complex probabilistic models in complex tasks such as language or physical system prediction. Yet people’s judgments about probabilities also display well-known biases. How can this be? Recently cognitive scientists have explored the possibility that the same sampling algorithms that are used in computer science to approximate complex probabilistic models are also used in the mind and the brain. We the review experimental evidence that characterises the human sampling algorithm, and discuss how such an algorithm could potentially explain apects of the movement of asset prices in financial markets. We also discuss how many of the biases that people display may be the direct result of using only a small number of samples, but using them efficiently. As human beings make successful real-time decisions using only rough estimates of uncertainty, this suggests that machine intelligence could do the same.

To investigate the correlation of clinicopathologic factors and immunophenotypic features with betel nut chewing in oral squamous cell carcinoma(OSCC).The data of 88 patients with OSCC were collected. According to the habit of betel nut chewing, the clinicopathologic factors and immunohistochemical parameters were analyzed. The relationship between clinicopathologic factors of OSCC and betel nut chewing was analyzed with univariate analysis and multivariate analysis using SPSS 17.0 software package.46.6% of patients had the habit of betel nut chewing and 67.0% of patients had tongue cancer and buccal cancer. The pathological stages were mainly T2 (40.9%). From univariate analysis of the results, differentiation degree, ki-67, p53 was significantly correlated with the habit of betel nut chewing(P0.05); while gender, age, location, pathological T stage and cervical lymph node metastasis were not significantly correlated with habit of betel nut chewing (P0.05). From multivariate analysis of the results, location and differentiation degree were significantly correlated with the habit of betel nut chewing (P0.05).ki-67 and p53 protein are lowly expressed in OSCC patients with the habit of betel nut chewing, suggesting that clinicopathologic factors such as the proliferation activity, malignancy, differentiation and prognosis of tumor are much better. Differentiation degree are relatively good in OSCC patients with the habit of betel nut chewing. Cheek and tongue are the most common site of OSCC.

This paper deals with the state of charge (SOC) estimation of lithium-ion battery (LIB) in electric vehicles (EVs). In order to accurately describe the dynamic behavior of the battery, a fractional 2nd-order RC model of the battery pack is established. The factional-order battery state equations are characterized by the continuous frequency distributed model. Then, in order to ensure the effective function of nonlinear function, Lipschitz condition and unilateral Lipschitz condition are proposed to solve the problem of nonlinear output equation in the process of observer design. Next, the linear matrix (LMIS) inequality based on Lyapunov’s stability theory and H∞ method is presented as a description of the design criteria for non-fragile observer. Compared with the existing literature that adopts observers, the proposed method takes the advantages of fractional-order systems in modeling accuracy, the robustness of H∞ method in restricting the unknown variables, and the non-fragile property for tolerating slow drifts on observer gain. Finally, The LiCoO2 LIB module is utilized to verify the effectiveness of the proposed observer method in different operation conditions. Experimental results show that the maximum estimation accuracy of the proposed non-fragile observer under three different dynamic conditions is less than 2%.

The study aimed to compare the efficacy and safety of all-trans retinoic acid (ATRA) plus low-dose rituximab (LD-RTX) with LD-RTX monotherapy in corticosteroid-resistant or relapsed immune thrombocytopenia (ITP) patients. Recruited patients were randomized at a ratio of 2:1 into 2 groups: 112 patients received LD-RTX plus ATRA, and 56 patients received LD-RTX monotherapy. Overall response (OR), defined as achieving a platelet count of ≥30 × 109/L confirmed on ≥2 separate occasions (≥7 days apart), at least a doubling of the baseline platelet count without any other ITP-specific treatment, and the absence of bleeding within 1 year after enrollment, was observed in more patients in the LD-RTX plus ATRA group (80%) than in the LD-RTX monotherapy group (59%) (between-group difference, 0.22; 95% CI, 0.07-0.36). Sustained response (SR), defined as maintenance of a platelet count >30 × 109/L, an absence of bleeding, and no requirement for any other ITP-specific treatment for 6 consecutive months after achievement of OR during 1 year following enrollment, was achieved by 68 (61%) patients in the combination group and 23 (41%) patients in the monotherapy group (between-group difference, 0.20; 95% CI, 0.04-0.35). The 2 most common adverse events (AEs) for the combination group were dry skin and headache or dizziness. Our findings demonstrated that ATRA plus LD-RTX significantly increased the overall and sustained response, indicating a promising treatment option for corticosteroid-resistant or relapsed adult ITP. This study is registered at www.clinicaltrials.gov as #NCT03304288.

Human beings perform well in uncertain environments, matching the performance of complex probabilistic models in complex tasks such as language or physical system prediction. Yet people’s judgments about probabilities also display well-known biases. How can this be? Recently cognitive scientists have explored the possibility that the same sampling algorithms that are used in computer science to approximate complex probabilistic models are also used in the mind and the brain. We the review experimental evidence that characterises the human sampling algorithm, and discuss how such an algorithm could potentially explain apects of the movement of asset prices in financial markets. We also discuss how many of the biases that people display may be the direct result of using only a small number of samples, but using them efficiently. As human beings make successful real-time decisions using only rough estimates of uncertainty, this suggests that machine intelligence could do the same.

Human probability judgments are both variable and subject to systematic biases. Most probability judgment models treat variability and bias separately: a deterministic model explains the origin of bias, to which a noise process is added to generate variability. But these accounts do not explain the characteristic inverse U-shaped signature linking mean and variance in probability judgments. By contrast, models based on sampling generate the mean and variance of judgments in a unified way: the variability in the response is an inevitable consequence of basing probability judgments on a small sample of remembered or simulated instances of events. We consider two recent sampling models, in which biases are explained either by the sample accumulation being further corrupted by retrieval noise (the Probability Theory + Noise account), or as a Bayesian adjustment to the uncertainty implicit in small samples (the Bayesian sampler). While the mean predictions of these accounts closely mimic one another, they differ regarding the predicted relationship between mean and variance. We show that these models can be distinguished by a novel linear regression method that analyses this crucial mean-variance signature. First, the efficacy of the method is established using model recovery, demonstrating that it more accurately recovers parameters than complex approaches. Second, the method is applied to the mean and variance of both existing and new probability judgment data, confirming that judgments are based on a small number of samples that are adjusted by a prior, as predicted by the Bayesian sampler.

Human cognition is fundamentally noisy. While routinely regarded as a nuisance in experimental investigation, the few studies investigating properties of cognitive noise have found surprising structure. A first line of research has shown that inter-response-time distributions are heavy-tailed. That is, response times between subsequent trials usually change only a small amount, but with occasional large changes. A second, separate, line of research has found that participants’ estimates and response times both exhibit long-range autocorrelations (i.e., 1/f noise). Thus, each judgment and response time not only depends on its immediate predecessor but also on many previous responses. These two lines of research use different tasks and have distinct theoretical explanations: models that account for heavy-tailed response times do not predict 1/f autocorrelations and vice versa. Here, we find that 1/f noise and heavy-tailed response distributions co-occur in both types of tasks. We also show that a statistical sampling algorithm, developed to deal with patchy environments, generates both heavy-tailed distributions and 1/f noise, suggesting that cognitive noise may be a functional adaptation to dealing with a complex world.

Price series in speculative markets show a common set of statistical properties, termed ‘stylised facts’. While some facts support simple efficient markets composed of homogenous rational agents (e.g., the absence of autocorrelation in price increments), others do not (e.g., heavy-tailed distributions of price changes and volatility clustering) (Campbell et al., 1997; Fama, 1970; Mandelbrot, 1966; Mandelbrot, 1963; Cont, 2001). Collectively, these facts have been explained by either more complex markets or markets of heterogeneous agents (Cont 2007; Giardina & Bouchaud, 2003; Hommes, 2006; Barberis & Thaler, 2005), with asset-market experiments validating the latter approach (Hommes 2011; Kirchler & Huber, 2009). However, it is unknown whether markets are necessary to produce these features. Here we show that within-individual variability alone is sufficient to produce many of the stylised facts. In a series of experiments, we increasingly simplified a price prediction task by first removing external information, then removing any interaction between participants. Finally, we removed any resemblance to an asset market by asking participants to simply reproduce temporal intervals. All three experiments produced the main stylised facts. The robustness of the results across tasks suggests a common cognitive-level mechanism underlies these patterns, and we identify a candidate that is a general-purpose approximation to rational behavior. We recommend a stronger focus on individual psychology in macroeconomic theory, and particularly within-individual variability. Combining these insights with existing economic mechanisms could help explain price changes in speculative markets.

BACKGROUND: The weightings of iterative reconstruction algorithm can affect CT radiomic quantification. But, the effect of ASiR-V levels on the reproducibility of CT radiomic features between ultra-low-dose computed tomography (ULDCT) and low-dose computed tomography (LDCT) is still unknown. The purpose of study is to investigate whether adaptive statistical iterative reconstruction-V (ASiR-V) levels affect radiomic feature quantification using ULDCT and to assess the reproducibility of radiomic features between ULDCT and LDCT. METHODS: Sixty-three patients with pulmonary nodules underwent LDCT (0.70±0.16 mSv) and ULDCT (0.15±0.02 mSv). LDCT was reconstructed with ASiR-V 50%, and ULDCT with ASiR-V 50%, 70%, and 90%. Radiomics analysis was applied, and 107 features were extracted. The concordance correlation coefficient (CCC) was calculated to describe agreement among ULDCTs and between ULDCT and LDCT for each feature. The proportion of features with CCC >0.9 among ULDCTs and between ULDCT and LDCT, and the mean CCC for all features between ULDCT and LDCT were also compared. RESULTS: Sixty-three solid nodules (SNs) and 48 pure ground-glass nodules (pGGNs) were analyzed. There was no difference for the proportion of features in SNs among ULDCTs and between ULDCT and LDCT (P>0.05). The proportion of features in pGGNs were highest for ULDCT(70%) (vs.) (90%) (78.5%) and ULDCT(90%) vs. LDCT(50%) (50.5%). In SNs, the mean CCC for ULDCT(90%) vs. LDCT(50%) was 0.67±0.26, not different with that for ULDCT(50%) vs. LDCT(50%) (0.68±0.24) and ULDCT(70%) vs. LDCT(50%) (0.64±0.21) (P>0.05). In pGGNs, the mean CCC for ULDCT(90%) vs. LDCT(50%) was 0.79±0.19, higher than that for ULDCT(50%) vs. LDCT(50%) (0.61±0.28) and ULDCT(70%) vs. LDCT(50%) (0.76±0.24) (P

A new iterative learning control (ILC) is proposed, which embeds the reference model rather than the plant model to achieve a good transient performance and a fast convergence. First, the uncertain discrete-time linear system and the desired reference with an known internal model (IM) are introduced. Then, based on the IM principle, a new ILC scheme, named as IM-based ILC, is proposed by incorporating the reference IM. The IM-based ILC is independent of the plant model. Moreover, the IM-based ILC is carefully constructed as a state-feedback controller of a two-dimensional (2-D) ILC system, which is the coupling combination of an IM controller (IMC) and an ILC. The proposed coupling architecture combines the ability of an IMC (tracking the reference along the time axis) and that of an ILC (tracking the reference along the iteration axis), ensuring the synchronous convergence along the time and iteration axes. This is not trivial but the key to achieve the good transient performance and the fast convergence. In addition, two criteria are established to design a 2-D $H_{\infty }$ IM-based ILC and monotonically convergent one. Finally, the IM-based ILC is applied to the precision control of a wafer stage, where the experimental setup, the mathematical model, and the desired reference are discussed. And, the efficiency of the IM-based ILC is illustrated in the experiments.

Nonlinear systems are more attractive for the wide bandwidth in contrast to linear vibration energy harvesters. Most available generators belong to a certain type of nonlinear systems and the Duffing oscillator is a most popular kind. Once the system type is determined with a specific equation form, the performance optimization is limited in a certain range, generally related to the parameter study of the system within a given frame. In this paper, a novel generator is proposed by merging the stiffness curves of two different source generators through utilization of the nonlinear cantilever with a curve fixture. The special merged stiffness characteristics break the response limitation of the specific stiffness profile such as the Duffing equation and bring new benefits with the generator bandwidth further broadened by integrating the advantages of the source generator. In order to verify the stiffness-merging concept, two Duffing generators and a proposed generator with stiffness fitted from the former two are fabricated using the nonlinear cantilever with specially designed fixture curves. Experimental and numerical investigations reveal that the proposed generator with the merged stiffness has better performance than the generators with the typical stiffness characteristics of Duffing systems. The effectiveness of the stiffness-merging method is well demonstrated.