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Semi-parametric transformation models provide a general and flexible class of models for regression analysis of failure time data and many methods have been developed for their estimation. In particular, they include the proportional hazards and proportional odds models as special cases. In this paper, we discuss the situation where one observes left-truncated and interval-censored data, for which it does not seem to exist an established method. For the problem, in contrast to the commonly used conditional approach that may not be efficient, a pairwise pseudo-likelihood method is proposed to recover some missing information in the conditional method. The proposed estimators are proved to be consistent and asymptotically efficient and normal. A simulation study is conducted to assess the empirical performance of the method and suggests that it works well in practical situations. This method is illustrated by using a set of real data arising from an HIV/AIDS cohort study.

Load response characteristics of high thermal power units are critical to a safe and efficient grid-connected utilization of large-scale renewable energy with strong randomness. A condensate throttling regulation was reported to improve the units variable load rate. However, it is not verified by tests in different types of units. In the paper, the influence of the storage capacity of the steam generator’s condensate system on the load response characteristics is studied for four subcritical 330 MW heating units by simulation and an experimental test. The result shows the feasibility of the condensate throttling regulation, which is of great value to the practical engineering application in the future. In addition, this paper obtains the condensate regulation potential of units under different power generation load conditions through simulation and actual tests. These real data will help other units of the same type to carry out similar modifications or tests.

Functional data is a powerful tool for capturing and analyzing complex patterns and relationships in a variety of fields, allowing for more precise modeling, visualization, and decision-making. For example, in healthcare, functional data such as medical images can help doctors make more accurate diagnoses and develop more effective treatment plans. However, understanding the causal relationships between functional predictors and time-to-event outcomes remains a challenge. To address this, we propose a functional causal framework including a functional accelerated failure time (FAFT) model and three causal approaches. The regression adjustment approach is based on conditional FAFT with subsequent confounding marginalization, while the functional-inverse-probability-weighting approach is based on marginal FAFT with well-defined functional propensity scores. The double robust approach combines the strengths of both methods and achieves a balance condition through the weighted residuals between imputed observations and regression adjustment outcomes. Our approach can accurately estimate causality, predict outcomes, and is robust to different censoring rates. We demonstrate the power of our framework with simulations and real-world data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Our findings provide more precise subregions of the hippocampus that align with medical research, highlighting the power of this work for improving healthcare outcomes., Comment: 22 pages with references, 2 figures, and 3 tables in the main paper. Supplementary material is included

Additional file 1.

To characterise the dose-dependent pharmacokinetics of midazolam and evaluate the intestinal and hepatic first-pass effects on midazolam in Sprague-Dawley rats, the concentrations and area under the concentration-time curve (AUC) of midazolam in the portal and systemic plasma were simultaneously determined with a double cannulation method.It was found that about 75% of the dose was left in the portal blood with different oral administration doses, while the bioavailability in the liver was 37.86% at 20 mg/kg, significantly higher than 9.16% at 2 mg/kg.The disproportional increase in AUC of midazolam and significant decrease in exposure of metabolites were observed in systemic plasma after hepatic portal vein administration. And in the in vitro study, the formation rate of the metabolites of midazolam significantly decreased when midazolam was at 300 μM compared with 100 μM.These results indicated that not only the saturation of first-pass metabolism but also the inhibition of hepatic metabolism is responsible for the nonlinear PK of midazolam. Thus, a rational dose should be chosen when midazolam is used as a probe in the drug-drug interaction study, particularly for orally administered drugs that undergo hepatic first-pass metabolism. To characterise the dose-dependent pharmacokinetics of midazolam and evaluate the intestinal and hepatic first-pass effects on midazolam in Sprague-Dawley rats, the concentrations and area under the concentration-time curve (AUC) of midazolam in the portal and systemic plasma were simultaneously determined with a double cannulation method. It was found that about 75% of the dose was left in the portal blood with different oral administration doses, while the bioavailability in the liver was 37.86% at 20 mg/kg, significantly higher than 9.16% at 2 mg/kg. The disproportional increase in AUC of midazolam and significant decrease in exposure of metabolites were observed in systemic plasma after hepatic portal vein administration. And in the in vitro study, the formation rate of the metabolites of midazolam significantly decreased when midazolam was at 300 μM compared with 100 μM. These results indicated that not only the saturation of first-pass metabolism but also the inhibition of hepatic metabolism is responsible for the nonlinear PK of midazolam. Thus, a rational dose should be chosen when midazolam is used as a probe in the drug-drug interaction study, particularly for orally administered drugs that undergo hepatic first-pass metabolism.

Interval-censored failure time data frequently occur in many areas and a great deal of literature on their analyses has been established. In this article, we discuss the situation where one faces bivariate interval-censored data arising from case-cohort studies, which are commonly used as a tool to save costs when disease incidence is low and covariates are difficult to obtain. For this problem, a class of copula-based semi-parametric models is presented and for estimation, a sieve weighted maximum likelihood estimation procedure is developed. The resulting estimators of regression parameters are shown to be strongly consistent and asymptotically normal. Furthermore, the proposed method is generalized to the situation of non rare diseases. A simulation study is conducted to assess the finite sample performance of the proposed method and suggests that it performs well in practice.

Supplementary Material 1: The role of fibronectin on cell migration and invasion in HCC827, H1975 and A549 cells.

Background: The intervention timing of immune checkpoint inhibitors (ICIs) and radiotherapy fractionations are critical factors in clinical efficacy. This study aims to explore dynamic changes of the tumor immune microenvironment (TIME) after hypofractionated radiotherapy (HFRT) at different timepoints and fractionation doses in non–small-cell lung cancer (NSCLC).Methods: In the implanted mouse model, the experimental groups received HFRT 3.7 Gy × 4 F, 4.6 Gy × 3 F, 6.2 Gy × 2 F, and 10 Gy × 1 F, respectively, with the same biological equivalent dose (BED) of 20Gy. Tumor volume and survival time were compared with those of the control group. Flow cytometry was performed to detect immune cells and their PD-1/PD-L1 expressions using tail-tip blood at different timepoints and tumor tissues at 48 h after radiotherapy. In NSCLC patients, immune cells, PD-1/PD-L1, and cytokines were detected in peripheral blood for 4 consecutive days after different fractionation radiotherapy with the same BED of 40Gy.Results: Tumor volumes were significantly reduced in all experimental groups compared with the control group, and the survival time in 6.2 Gy × 2 F (p < 0.05) was significantly prolonged. In tail-tip blood of mice, CD8+ T counts increased from 48 h to 3 weeks in 4.6 Gy × 3 F and 6.2 Gy × 2 F, and CD8+ PD-1 shortly increased from 48 h to 2 weeks in 6.2 Gy × 2 F and 10 Gy × 1 F (p < 0.05). Dentritic cells (DCs) were recruited from 2 to 3 weeks (p < 0.01). As for NSCLC patients, CD8+ T counts and PD-1 expression increased from 24 h in 6.2 Gy × 4 F, and CD8+ T counts increased at 96 h in 10 Gy × 2 F (p < 0.05) in peripheral blood. DC cells were tentatively recruited at 48 h and enhanced PD-L1 expression from 24 h in both 6.2 Gy × 4 F and 10 Gy × 2 F (p < 0.05). Besides, serum IL-10 increased from 24 h in 6.2 Gy × 4 F (p < 0.05). Conversely, serum IL-4 decreased at 24 and 96 h in 10 Gy × 2 F (p < 0.05).Conclusion: HFRT induces the increase in CD8+ T cells and positive immune cytokine response in specific periods and fractionation doses. It was the optimal time window from 48 h to 2 weeks for the immune response, especially in 6.2 Gy fractionation. The best immune response was 96 h later in 10 Gy fractionation, delivering twice instead of a single dose. During this time window, the intervention of immunotherapy may achieve a better effect.

Additional file 1: Table S1. Confusion matrixes of train and test datasets in the decision tree model

Background Cumulatively, evidences revealed that fenofibrate used in the therapy of hyperlipidemia and hypercholesterolemia has anti-cancer effect in multiple cancer types. However, its function and underlying mechanism of chemosensitization in breast cancer remain poorly understood. Materials and methods The cytotoxicity of fenofibrate and anti-cancer drugs in breast cancer cells was determined by MTT. Apoptosis and mitochondrial membrane potential were measured using flow cytometry. Caspases and PARP cleavage, the Bcl-2 family members’ protein expression, as well as the activation of AKT and NF-κB signaling pathways were evaluated using Western blot assay. Real-time PCR was used to determine the mRNA expression of Bcl-2 family members. Results Our data indicated that fenofibrate suppressed SKBR3 and MDA-MB-231 cell growth in a dose-dependent manner, in the same way as paclitaxel, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), ABT-737, and doxorubicin. Subtoxic levels of fenofibrate significantly augmented paclitaxel, TRAIL, ABT-737, and doxorubicin-induced apoptosis in both these two cell lines. Fenofibrate-promoted chemosensitivity is predominantly mediated by caspase-9 and caspase-3 activation and mitochondrial outer membrane permeabilization. Meanwhile, chemosensitivity promoted by fenofibrate also increased the expression of Bax and Bok and decreased the expression of Mcl-1 and Bcl-xl. Mechanistically, fenofibrate effectively reduced the phosphorylation levels of AKT and NF-κB. In addition, imiquimod, an NF-κB activator, could reverse fenofibrate-induced susceptibility to ABT-737-triggered apoptosis. Conclusion The present study provided the evidence of the underlying mechanisms on chemosensitization of fenofibrate by inducing the apoptosis of breast cancer in an AKT/NF-κB-dependent manner and implicated the potential application of fenofibrate in potentiating chemosensitivity in breast cancer therapy.

Clustered interval-censored failure time data can occur when the failure time of interest is collected from several clusters and known only within certain time intervals. Regression analysis of clustered interval-censored failure time data is discussed assuming that the data arise from the semiparametric additive hazards model. A multiple imputation approach is proposed for inference. A major advantage of the approach is its simplicity because it avoids estimating the correlation within clusters by implementing a resampling-based method. The presented approach can be easily implemented by using the existing software packages for right-censored failure time data. Extensive simulation studies are conducted, indicating that the proposed imputation approach performs well for practical situations. The proposed approach also performs well compared to the existing methods and can be more conveniently applied to various types of data representation. The proposed methodology is further demonstrated by applying it to a lymphatic filariasis study.

Despeckling is a key and indispensable step in SAR image preprocessing, existing deep learning-based methods achieve SAR despeckling by learning some mappings between speckled (different looks) and clean images. However, there exist no clean SAR image in the real world. To this end, in this paper, we propose a self-supervised dense dilated convolutional neural network (BDSS) for blind SAR image despeckling. Proposed BDSS can still learn to suppress speckle noise without clean ground truth by optimized for L2 loss. Besides, three enhanced dense blocks with dilated convolution are employed to improve network performance. The synthetic and real-data experiments demonstrate that proposed BDSS can achieve despeckling effectively while maintaining well features such as edges, point targets, and radiometric. At last, we demonstrate that our proposed BDSS can achieve blind despeckling excellently, i.e., do not need to care about the number of looks.

Immune checkpoint inhibitor (ICI) therapy had achieved significant clinical benefit in multiple malignant solid tumors, such as non-small cell lung cancer, melanoma and urothelial cancer. ICI therapy not only revolutionarily altered the treatment strategy of malignant solid tumors, but also dramatically prolonged overall survival. However, the objective response rate (ORR) of ICI therapy in second line treatment remains 20% or less. How to find patients eligible for ICI therapy by effective biomarkers became hot nowadays. High expression of PD-L1 protein in tumor cells or tumor microenvironment (TME) had been identified to be a logical biomarker for predicting efficacy of ICI therapy and approved by the U.S. Food and Drug Administration to be an indicator of initiating treatment for some solid tumors. Controversially, patients with low PD-L1 protein expression might also show clinical benefit. In this sense, tissue PD-L1 protein expression might not be a precise biomarker. Multimodal detection of PD-L1, such as PD-L1 protein, PD-L1 mRNA, and circulating PD-L1, might provide a comprehensive tumor profile and could find the patients who are more suitable for ICI therapy. Besides, dynamic monitoring of PD-L1 expression could shed light on efficacy assessment and drug resistance. ICI-based combination strategy had demonstrated better outcome than ICI alone. Single biomarker might not be efficient to precisely find advantage patients. Combined biomarkers could better instruct the consideration of therapeutic regimen. In addition, nomogram and artificial intelligence platform could integrate multiparameter information of biomarkers which might shed light on tumor profile and give a hint to treatment decision.

Wireless sensor network (WSN) technology is a hot topic in current information field. It has more and more important application value in power system. On the demand side of the smart grid system, the interaction between user behavior and electricity power grid, the contradiction between supply and demand of the user and the power grid, and the competitive relationship between the user, through the establishment of a mechanism to achieve the optimal allocation of resources to improve the stability of the power grid and the rationality of the demand side. Due to the complexity of requirements, we introduce wireless communication network technology, which is suitable for intelligent network communication of interactive communication system. We introduced the cross-layer optimization routing protocol MM - CTP and put forward a kind of MAC layer and the routing layer of wireless sensor network based on cross layer optimization method, the demand for smart grid side network and cross-layer optimization framework, the classification and concrete method of wireless sensor network were studied.

Due to the fragility of sensor nodes, namely: vulnerability to damage, calibration, unstable communication links, limited carrying energy, and being vulnerable to environmental influences, sensor nodes in wireless sensor networks are prone to fault. In this paper, we mainly focus on noise fault, short-term fault, and fixed fault caused by low battery and calibration. In recent years, a large number of machine learning-based algorithms have been proposed for the anomaly detection of wireless sensor networks. In this paper, we compare and analyze the performance of Support Vector Machine (SVM), Naive Bayes, and Gradient Lifting Decision Tree (GBDT) in identifying and classifying fault. We introduce a comparative study of the above methods on experimental data sets. Experiments show that GBDT algorithm obtains a better fault detection rate.

Gas turbine combustion system (GTCS) works in the highly adverse environmental conditions of high temperature and high pressure. Because GTCS initial temperature is too high to be directly measured, exhaust gas temperature (EGT) is an alternative to detect performance of GTCS. However, various interferences, such as working and ambient conditions as well as compressor and combustion efficiencies and so on, have a comprehensive effect on EGT, which causes low detection accuracy to GTCS. So, it's necessary to build a model that can eliminate the interferences on EGT so as to monitor performance of GTCS effectively. Feature extraction and model structure are keys to modeling. In general, it's based on data-driven or mechanism analysis approaches to build the model. However, the former containing a low information density leads to too many input variables and the overcomplicated model structure. In addition, although the latter has a high information density, modeling of a gas turbine is a difficult task in practice, which still limits the application of model-based approaches. For deficiencies of the existing methods, this paper proposes a method based on data and prior knowledge fusion to build a model called Frequent Pattern model for detecting performance of GTCS. Firstly, it's based on prior knowledge to tentatively select the model feature. And it's found that Average Exhaust Gas Temperature (AET) contains all the influences of various factors on EGT. Secondly, it's determined by data analysis that there is a linear relationship between AET and EGT. Therefore, we conclude that a simple linear model can eliminate the interferences on EGT and characterize the structural differences of different flame tubes, by which performance of the GTCS can be monitored accurately and effectively. Thirdly, a contrast test is done to verify the conclusion. A linear model is built based on the proposed method and a nonlinear model is built only based on data-driven method. By comparison, it's concluded that the proposed method can not only simplify the model but also improve the model accuracy.

Interval-censored failure time data occur in many medical investigations as well as other studies such as demographical and sociological studies. They include the usual right-censored failure time data as a special case but provide much more complex structure and less relevant information than the right-censored data. This article reviews some basic concepts, issues and the corresponding statistical approaches related to the analysis of interval-censored data as well as recent advances. In particular, we discuss estimation of a survival function, comparison of several treatments and regression analysis as well as competing risks analysis and truncation in the presence of interval censoring. A well-known example of interval-censored data is described and analysed to illustrate some of the statistical procedures discussed.

In order to evaluate the pharmacokinetic (PK) profile of rabeprazole (RA) sterile powder for injection, a rapid, sensitive and specific assay for quantitative determination of RA in dog plasma was developed and validated. After a liquid-liquid extraction procedure, samples were analyzed by liquid chromatography-electrospray ionization mass spectrometry (LC-ESI-MS) using omepazole as the internal standard (IS). The analyte and IS was chromatographed on a ZORBAX Extend-C(18) analytical column (50 x 2 mm i.d, 5 microm, Agilent Technologies, USA). The assay was linear in the range 1-2000 ng/mL. The lower limit of quantification of RA was 1 ng/mL. The recovery of RA was greater than 70%. The within- and between-batch accuracy was 102.7-107.4% and 103.5-105.7%, respectively. The plasma samples for the PK study were collected at defined time points during and after an intravenous injection (1 mg/kg) to beagle dogs and analyzed by LC-ESI-MS method. The PK parameters, such as half-life, volume of distribution, total clearance and elimination rate constant, were determined. The PK profile of RA gave insights into the application in the clinics.

As an important active constituent of Ginkgo biloba extract, ginkoglide B is a highly selective and competitive PAF receptor antagonist which has been widely used in clinical applications. A novel high-performance liquid chromatography-electrospray ionization mass spectromentry (LC-ESI-MS) method was developed for the determination of ginkgolide B in dog plasma. After liquid/liquid extraction with ether and high-performance liquid chromatography (HPLC) gradient separation with 0.01% of ammonia water (v/v)-methanol as the mobile phase, the deprotonized anions [M-H](-1) at m/z 423 of ginkoglide B, and [M-H](-1) at m/z 492 of internal standard (IS) glibenclamide were analyzed by LC-ESI-MS in selected ion monitoring (SIM) mode. Chromatographic separation was achieved in less than 9 min and calibration curve was linear over a concentration range of 0.1-20 ng/ml. The described assay method was successfully applied to the pre-clinical pharmacokinetic study of ginkoglide B. After intragastric administration of ginkgolide B to beagle dogs, C(max) and T(max) of ginkgolide B were 43.8 +/- 6.24 ng/ml and 0.5 h, respectively, and the elimination half-life (t(1/2)) was 2.85 +/- 0.54 h.

First, the image Jacobian matrix (IJM) for GRB-400 robot was established. Then an augmented design plant model was formed using the GRB-400 plant model and the inverse IJM. And then a control algorithm was applied to the augmented design plant to form a steady visual servo motion feedback control system. The main effort was made to develop a new nonlinear strategy of direct path planning for the robot, which is a new hybrid-coding genetic algorithm. Using this strategy, a least-energy cost path was designed in a certain timeout, and simulation and experiment results were given.

The effect of high modulation rate on pattern reversal visual evoked potentials (VEP) has not been well explored and no general agreement has been reached. Regan (1978) found that patterned VEPs occurred only at low temporal frequencies up to approximately 30 reversals per second (r/s) i.e 15 Hz. After that, the VEPs fall off sharply as frequency increases. By contrast, Tyler et al. (1980) found VEP responses, with signal-to-noise ratios of about 2:1, up to 90 r/s i.e 45 Hz. Moreover, it is recognized that unpatterned flicker stimuli over 60 Hz still elicited VEPs.

The independent yield of88Y from thermal neutron-induced fission of235U was determined relatively to the well-known yield of91Y. An upper limit obtained was ≤1.53×10−8%.

alpha-Formamidinium lead iodide (alpha-FAPbI(3)) is one of the most promising candidate materials for high-efficiency and thermally stable perovskite solar cells (PSCs) owing to its outstanding optoelectrical properties and high thermal stability. However, achieving a stable form of alpha-FAPbI(3) where both the composition and the phase are pure is very challenging. Herein, we report on a combined strategy of precursor engineering and grain anchoring to successfully prepare methylammonium (MA)-free and phase-pure stable alpha-FAPbI(3) films. The incorporation of volatile FA-based additives in the precursor solutions completely suppresses the formation of non-perovskite delta-FAPbI(3) during film crystallization. Grains of the desired alpha-phase are anchored together and stabilized when 4-tert-butylbenzylammonium iodide is permeated into the alpha-FAPbI(3) film interior via grain boundaries. This cooperative scheme leads to a significantly increased efficiency close to 21 % for FAPbI(3) perovskite solar cells. Moreover, the stabilized PSCs exhibit improved thermal stability and maintained approximate to 90 % of their initial efficiency after storage at 50 degrees C for over 1600 hours.