A new and unified method for regression analysis of interval-censored failure time data under semiparametric transformation models with missing covariates.

This paper discusses regression analysis of interval-censored failure time data arising from semiparametric transformation models in the presence of missing covariates. Although some methods have been developed for the problem, they either apply only to limited situations or may have some computational issues. Corresponding to these, we propose a new and unified two-step inference procedure that can be easily implemented using the existing or standard software. The proposed method makes use of a set of working models to extract partial information from incomplete observations and yields a consistent estimator of regression parameters assuming missing at random. An extensive simulation study is conducted and indicates that it performs well in practical situations. Finally, we apply the proposed approach to an Alzheimer's Disease study that motivated this study.

Regression analysis of doubly censored failure time data with ancillary information.

Doubly censored failure time data occur in many areas and for the situation, the failure time of interest usually represents the elapsed time between two related events such as an infection and the resulting disease onset. Although many methods have been proposed for regression analysis of such data, most of them are conditional on the occurrence time of the initial event and ignore the relationship between the two events or the ancillary information contained in the initial event. Corresponding to this, a new sieve maximum likelihood approach is proposed that makes use of the ancillary information, and in the method, the logistic model and Cox proportional hazards model are employed to model the initial event and the failure time of interest, respectively. A simulation study is conducted and suggests that the proposed method works well in practice and is more efficient than the existing methods as expected. The approach is applied to an AIDS study that motivated this investigation.

Postoperative abdominal herpes zoster complicated by intestinal obstruction: A case report.

BACKGROUND: Intestinal obstruction is a common occurrence in clinical practice. However, the occurrence of herpes zoster complicated by intestinal obstruction after abdominal surgery is exceedingly rare. In the diagnostic and treatment process, clinicians consider it crucial to identify the primary causes of its occurrence to ensure effective treatment and avoiding misdiagnosis. CASE SUMMARY: Herein, we present the case of a 40-year-old female patient with intestinal obstruction who underwent laparoscopic appendectomy and developed herpes zoster after surgery. Combining the patient's clinical manifestations and relevant laboratory tests, it was suggested that the varicella zoster virus reactivated during the latent period after abdominal surgery, causing herpes zoster. Subsequently, the herpes virus invaded the visceral nerve fibers, causing gastrointestinal dysfunction and loss of intestinal peristalsis, which eventually led to intestinal obstruction. The patient was successfully treated through conservative treatment and antiviral therapy and subsequently discharged from the hospital. CONCLUSION: Pseudo-intestinal obstruction secondary to herpes zoster infection is difficult to distinguish from mechanical intestinal obstruction owing to various causes. In cases of inexplicable intestinal obstructions, considering the possibility of a viral infection is essential to minimize misdiagnosis and missed diagnoses.

Martingale-residual-based greedy model averaging for high-dimensional current status data.

Current status data are a type of failure time data that arise when the failure time of study subject cannot be determined precisely but is known only to occur before or after a random monitoring time. Variable selection methods for the failure time data have been discussed extensively in the literature. However, the statistical inference of the model selected based on the variable selection method ignores the uncertainty caused by model selection. To enhance the prediction accuracy for risk quantities such as survival probability, we propose two optimal model averaging methods under semiparametric additive hazards models. Specifically, based on martingale residuals processes, a delete-one cross-validation (CV) process is defined, and two new CV functional criteria are derived for choosing model weights. Furthermore, we present a greedy algorithm for the implementation of the techniques, and the asymptotic optimality of the proposed model averaging approaches is established, along with the convergence of the greedy averaging algorithms. A series of simulation experiments demonstrate the effectiveness and superiority of the proposed methods. Finally, a real-data example is provided as an illustration.

Significant contribution of carbonyls to atmospheric oxidation capacity (AOC) during the winter haze pollution over North China Plain.

Atmospheric carbonyl compounds play significant roles in the cycling of radicals and have exhibited surprisingly high levels in winter that were well correlated to particulate matter, for which the reason have not been clearly elucidated. Here we measured carbonyl compounds and other trace gasses together with PM2.5 over urban Jinan in North China Plain during the winter. Markedly higher carbonyl concentrations (average: 14.63 ± 4.21 ppbv) were found during wintertime haze pollution, about one to three-times relative to those on non-haze days, with slight difference in chemical composition except formaldehyde (HCHO). HCHO (3.68 ppbv), acetone (3.17 ppbv), and acetaldehyde (CH3CHO) (2.83 ppbv) were the three most abundant species, accounting for ∼75% of the total carbonylson both haze and non-haze days. Results from observational-based model (OBM) with atmospheric oxidation capacity (AOC) indicated that AOC significantly increased with the increasing carbonyls during the winter haze events. Carbonyl photolysis have supplied key oxidants such as RO2 and HO2, and thereby enhancing the formation of fine particles and secondary organic aerosols, elucidating the observed haze-carbonyls inter-correlation. Diurnal variation with carbonyls exhibiting peak values at early-noon and night highlighted the combined contribution of both secondary formation and primary diesel-fuel sources. 1-butene was further confirmed to be the major precursor for HCHO. This study confirms the great contribution of carbonyls to AOC, and also suggests that reducing the emissions of carbonyls would be an effective way to mitigate haze pollution in urban area of the NCP region.

Does green credit really increase green technology innovation?

Considering China's green credit policy (GCP) as a quasi-natural experiment, this study discusses the effect of GCP on enterprise green innovation (GI) using a difference-in-difference method based on data from Chinese listed companies from 2009 to 2020. The results indicate that green credit enhances the strategic GI of heavy polluters while significantly inhibiting essential GI, thus suggesting the nonexistence of the Porter effect. In addition, the inhibition effect is attributed to an increase in financing constraints and a reduction in government subsidies, firm research and development investment, and employment scale. This disincentive effect is particularly pronounced in privately owned firms, small cities, and capital-intensive low-profitability firms. Resource misallocation caused by the GCP fails to stimulate the green transformation of heavily polluting industries through the Porter effect. Hence, governments should establish a diversified green financial system, integrate green venture capital and GI elements, and guide the flow of social capital toward green industries.

Does postcholecystectomy increase the risk of colorectal cancer?

With the increasing number of cholecystectomy and the high proportion of colorectal cancer in malignant tumors, the question of whether cholecystectomy is a risk factor for colorectal disease has been widely concerned. After reviewing the literature at home and abroad, the authors will summarize the research progress of the correlation between the occurrence of colorectal tumors after cholecystectomy, in order to provide help for the prevention and treatment of colorectal tumors.

Assessing the impact of economic growth target constraints on environmental pollution: Does environmental decentralization matter?

In the context of the goal of "carbon neutrality", an economic development model that achieves emission reduction goals and ensures stable economic growth is currently being advocated by China. Based on provincial panel data in China from 2005 to 2016, we analyse the impact of economic growth target (EGT) constraints on environmental pollution using a spatial econometric method. The results indicate that EGT constraints significantly exacerbate environmental pollution in local regions and adjacent areas. Local governments are motivated to achieve economic growth goals at the expense of the ecological environment. The positive effects are attributed to a reduction in environmental regulation (ER), industrial structure upgrading and technological innovation and an increase in foreign direct investment (FDI). Moreover, environmental decentralization (ED) plays a positive regulatory role and can weaken the adverse influences of EGT constraints on environmental pollution. Interestingly, the nonlinear impact of EGT constraints on environmental pollution relies on different types of ED. Environmental administration decentralization (EDA) and environmental supervision decentralization (EDS) can reduce the positive effect of EGT constraints on environmental pollution, while an improvement in environmental monitoring decentralization (EDM) can increase the promoting influences of the constraints of economic growth goals on environmental pollution. The above conclusions still hold under a series of robustness tests. Based on the above findings, we suggest that local governments set scientific growth targets, establish scientific assessment indicators for officials, and optimize the ED management institution.

Deep convolutional neural networks using an active learning strategy for cervical cancer screening and diagnosis.

Cervical cancer (CC) is the fourth most common malignant tumor among women worldwide. Constructing a high-accuracy deep convolutional neural network (DCNN) for cervical cancer screening and diagnosis is important for the successful prevention of cervical cancer. In this work, we proposed a robust DCNN for cervical cancer screening using whole-slide images (WSI) of ThinPrep cytologic test (TCT) slides from 211 cervical cancer and 189 normal patients. We used an active learning strategy to improve the efficiency and accuracy of image labeling. The sensitivity, specificity, and accuracy of the best model were 96.21%, 98.95%, and 97.5% for CC patient identification respectively. Our results also demonstrated that the active learning strategy was superior to the traditional supervised learning strategy in cost reduction and improvement of image labeling quality. The related data and source code are freely available at https://github.com/hqyone/cancer_rcnn.

Variable selection for bivariate interval-censored failure time data under linear transformation models.

Variable selection is needed and performed in almost every field and a large literature on it has been established, especially under the context of linear models or for complete data. Many authors have also investigated the variable selection problem for incomplete data such as right-censored failure time data. In this paper, we discuss variable selection when one faces bivariate interval-censored failure time data arising from a linear transformation model, for which it does not seem to exist an established procedure. For the problem, a penalized maximum likelihood approach is proposed and in particular, a novel Poisson-based EM algorithm is developed for the implementation. The oracle property of the proposed method is established, and the numerical studies suggest that the method works well for practical situations.

Towards sustainable development in China: How do green technology innovation and resource misallocation affect carbon emission performance?

Green technology innovation is an effective way through which to achieve carbon neutrality and sustainable development. Based on provincial panel data of 30 provinces in China from 2005 to 2018, this work examines the tripartite relationship among green technology innovation, resource misallocation, and carbon emission performance by constructing panel regression models and a dynamic threshold panel model. The research results show that green technology innovation significantly improves carbon emission performance. Further analysis shows that both capital and labour misallocation have a negative impact on carbon emission performance and hinder the contribution of green technology innovation to the improvement of carbon emission performance. The regression results show that there is a threshold effect of green technology innovation on carbon emission performance: as the degree of resource misallocation increases, the positive impact of green technology innovation on carbon emission performance gradually decreases. This study provides an important reference for policy-makers in implementing policies to improve carbon emission performance. Policy-makers should continue to promote the level of green technology innovation and improve the efficiency of labour and capital allocation.

Going green in China: how does digital finance affect environmental pollution? Mechanism discussion and empirical test.

With the continuous advancement of the technological revolution and industrial transformation, environmental governance supported by digital finance has become an important engine for achieving carbon neutrality. Based on panel data from 30 provinces in China, this study discusses the spatial spillover effect and transmission mechanism between digital finance and environmental pollution. Our research results confirm that the inhibitory effect of digital finance on local environmental pollution gradually increases with the improvement of digital finance. Interestingly, digital finance has a significant positive spatial spillover effect on environmental pollution in surrounding areas. The mediating effect shows that digital finance can alleviate environmental pollution by improving technological innovation, industrial upgrading and industrial structure rationalization. A higher degree of marketization and governmental support can increase the positive influences of digital finance on pollution reduction. This research proves the effectiveness of digital finance in improving environmental governance, and it encourages policy-makers around the world to rely on digital finance to promote ecological governance and achieve high-quality economic development.

[Ozone Formation and Key VOCs of a Continuous Summertime O3 Pollution Event in Ji'nan].

In the summer of 2019, field measurements of ozone (O3) and its precursors[volatile organic compounds (VOCs) and nitrogen oxides (NOx)] were carried out at an urban site in Ji'nan. We found that the daily maximum 8-hour averages φ(O3) were (103.0±14.5)×10-9. The average φ(NOx) and φ(VOCs), which are ozone precursors, were (16.7±11.3)×10-9and (22.4±9.4)×10-9, respectively. The ·OH reactivity of VOCs was determined (9.6±3.8) s-1. Ji'nan suffered from serious O3 pollution. An observation-constrained chemical box model was deployed to evaluate in situ photochemical O3 production, which indicated that chemical reactions made positive contributions to O3 production rates between 07:00 and 19:00 LT, with the average hourly O3 production rate of 35.6×10-9 h-1. To evaluate the effectiveness of various ozone precursor control strategies in reducing ozone pollution, we combined the observation-based model (OBM) with the relative incremental reactivity (RIR) method. The key indicators that affect the local ozone production rate were identified. Ji'nan was under VOC-limited conditions and the key VOC precursors were alkenes. The O3 formation mechanism changed from the VOC-limited regime in the morning to the transitional regime in the afternoon. Correspondingly, the simulated local O3 production rate was increased from 18.3×10-9 h-1 to 29.6×10-9 h-1. To further explore the role of anthropogenic emissions in ozone pollution, we used the positive matrix factorization (PMF) model to identify the major sources contributing to VOCs. The major sources in Ji'nan were vehicular exhaust and gasoline evaporation, accounting for more than 50% of the observed VOCs. Therefore, constraints on vehicular emissions is the most effective strategy to control O3 pollution in Ji'nan.

Natural killer cells in the pathogenesis of preeclampsia: a double-edged sword.

OBJECTIVE: To investigate the relationship between natural killer (NK) cells, extravillous trophoblast cells (EVTs) and vessel remodeling in early human pregnancy, and the association between NK cells and preeclampsia (PE) in late human pregnancy. METHODS: Human decidual tissues from women with normal pregnancies were collected and examined for the relationship of NK cells with uterine vessel remodeling using immunohistochemistry. Percentages of peripheral blood NK (pNK) and decidual NK (dNK) cells and the levels of intracellular interferon (IFN)-γ, perforin and granzyme B in normal pregnancies, late-onset and early-onset PE were analyzed using flow cytometry. Cytolytic functions of dNK cells from normal and PE pregnancies were examined. Effects of conditioned medium (CM) of dNK cells from normal and PE pregnancies on first trimester trophoblast invasion and migration were tested. RESULTS: In early pregnancy samples (9-13 weeks of gestation), we noted moderate vessel remodeling with abundant perivascular NK cells but a limited number of surrounding EVTs. The numbers of both human pNK cells and dNK cells and intracellular interferon (IFN)-γ, perforin and granzyme B production were significantly higher in PE compared with normal pregnancies at the time of delivery for both early- and late-onset disease. dNK cells from PE pregnancies not only killed first trimester trophoblasts but also inhibited their invasion and migration when compared to normal controls. CONCLUSION: Our results suggest that NK cells, in conjunction with EVTs, may play an important role in controlling uterine SA remodeling at the early stages of vessel remodeling, but they contribute to the pathogenesis of PE in late pregnancy.

Ultrasound, CT, and MR Imaging for Evaluation of Cystic Renal Masses.

Cystic renal masses are often encountered during abdominal imaging. Although most of them are benign simple cysts, some cystic masses have malignant characteristics. The Bosniak classification system provides a useful way to classify cystic masses. The Bosniak classification is based on the results of a well-established computed tomography protocol. Over the past 30 years, the classification system has been refined and improved. This paper reviews the literature on this topic and compares the advantages and disadvantages of different screening and classification methods. Patients will benefit from multimodal diagnosis for lesions that are difficult to classify after a single examination.

Semiparametric regression analysis of clustered interval-censored failure time data with a cured subgroup.

This article discusses regression analysis of clustered interval-censored failure time data in the presence of a cured fraction or subgroup. Such data often occur in many areas, including epidemiological studies, medical studies, and social sciences. For the problem, a class of semiparametric transformation nonmixture cure models is presented and for estimation, the maximum likelihood estimation procedure is derived. For the implementation of the proposed method, we develop a novel EM algorithm based on a Poisson variable-based augmentation. An extensive simulation study is conducted and suggests that the proposed approach works well in practical situations. Finally the method is applied to an example that motivated this study.

A unified approach to variable selection for Cox's proportional hazards model with interval-censored failure time data.

Cox's proportional hazards model is the most commonly used model for regression analysis of failure time data and some methods have been developed for its variable selection under different situations. In this paper, we consider a general type of failure time data, case K interval-censored data, that include all of other types discussed as special cases, and propose a unified penalized variable selection procedure. In addition to its generality, another significant feature of the proposed approach is that unlike all of the existing variable selection methods for failure time data, the proposed approach allows dependent censoring, which can occur quite often and could lead to biased or misleading conclusions if not taken into account. For the implementation, a coordinate descent algorithm is developed and the oracle property of the proposed method is established. The numerical studies indicate that the proposed approach works well for practical situations and it is applied to a set of real data arising from Alzheimer's Disease Neuroimaging Initiative study that motivated this study.

Regression Analysis of Case-cohort Studies in the Presence of Dependent Interval Censoring.

The case-cohort design is widely used as a means of reducing the cost in large cohort studies, especially when the disease rate is low and covariate measurements may be expensive, and has been discussed by many authors. In this paper, we discuss regression analysis of case-cohort studies that produce interval-censored failure time with dependent censoring, a situation for which there does not seem to exist an established approach. For inference, a sieve inverse probability weighting estimation procedure is developed with the use of Bernstein polynomials to approximate the unknown baseline cumulative hazard functions. The proposed estimators are shown to be consistent and the asymptotic normality of the resulting regression parameter estimators are established. A simulation study is conducted to assess the finite sample properties of the proposed approach and indicates that it works well in practical situations. The proposed method is applied to an HIV/AIDS case-cohort study that motivated this investigation.

MgAl LDH nanosheets loaded with Ni nanoparticles: a multifunctional filler for improving the energy storage performance of PVDF-based nanocomposites.

Polymer-based dielectric nanocomposites as raw materials of dielectric capacitors used in advanced electronics and electrical systems have a great application prospect but remain a huge challenge to energy storage performance in high electric fields. In this work, MgAl layered double hydroxide (MgAl LDH) nanosheets loaded with Ni nanoparticles were designed and synthesized, and incorporated into polyvinylidene fluoride (PVDF) to fabricate Ni-MgAl LDH/PVDF nanocomposites with high energy density. The effect of Ni-MgAl LDH nanosheet content (0.2 to 0.8 wt%) on the energy storage performance of MgAl LDH/PVDF nanocomposites was studied. As a result, after adding 0.6 wt% Ni-MgAl LDH nanosheets, the nanocomposites obtained the highest energy density 23.87 J cm-3 (at 640 kV mm-1) and the charge-discharge efficiency reached 65%, which was 76% and 18% higher than that of pure PVDF, respectively. This improvement could be attributed to the multiple functions of Ni-MgAl LDH nanosheets under an applied electric field. On the one hand, Ni nanoparticles on the surface of the MgAl LDH nanosheets could enhance the interfacial conductivity, form plenty of parallel micro-capacitors and produce Coulomb blockade effect, which resulted in high dielectric constant and high breakdown strength. On the other hand, two functions contributed by MgAl LDH nanosheets, homogenizing the electric field and inhibiting the growth of the electric tree coming from its medium dielectric constant and sheet structure, were beneficial to increase the breakdown strength. Furthermore, finite element simulations were employed to explain the mechanism of improved dielectric properties of the Ni-MgAl LDH/PVDF nanocomposites.

3D Lamellar Structure of Biomass-Based Porous Carbon Derived from Towel Gourd toward Phase Change Composites with Thermal Management and Protection.

The practical application of shape-stable phase change composites (PCCs) is beneficial to thermal energy management and energy conservation due to their superior properties. A shape-stable PCC was fabricated by incorporating poly(ethylene glycol) (PEG) with biomass-based porous carbon that was produced via freeze-drying and carbonization using a low-cost and environmentally friendly fresh towel gourd. The towel gourd derived porous carbon with the characteristics of porosity, unique three-dimensional (3D) lamellar structure, and high specific surface area allowed a high encapsulation capacity (up to 94.5 wt %) for PEG. Structural morphologies, as well as the properties of latent heat storage, thermal reliability, thermal energy management, and thermal protection ability of the fabricated shape-stable PCC, were investigated. The micromorphologies revealed that PEG molecular chains were arranged in a 3D lamellar tissue structure. The shape-stable PCC demonstrated excellent thermal reliability and a high melting latent heat of ∼164.3 J/g. The analysis of infrared thermal images indicated that the shape-stable PCC exhibited remarkable strengths in thermal energy management. The result of the thermal insulation simulation experiment proved that the shape-stable PCC had superior thermal protection ability. This study provided an innovative strategy for the design and development of shape-stable PCCs for great potential in heat-insulating protective textiles, solar thermal energy storage, energy-saving buildings, and infrared stealth of military targets.