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Immunosuppressive tumor microenvironments challenge the effectiveness of protein-based biopharmaceuticals in cancer immunotherapy. Reestablishing tumor cell immunogenicity by enhancing calreticulin (CRT) exposure is expected to improve tumor immunotherapy. Given that CRT translocation is inherently modulated by phosphorylated eIF2α, the selective inhibition of protein phosphatase 1 (PP1) emerges as an effective strategy to augment tumor immunogenicity. To harness the PP1-disrupting potential of GADD34-derived motifs and address their limited intracellular delivery, we integrated these sequences into an enzyme-triggered, cell-penetrating peptide-mediated chimeric protein scaffold. This design not only facilitates efficient cytoplasmic delivery of these immunostimulatory motifs to induce “eat-me” signaling, but also provides a versatile platform for combination immunotherapy. Fabrication of biomodulators with cytotoxic BLF1 provides additional “eat-me” signaling through phosphatidylserine exposure or that with an immunomodulatory designed ankyrin repeat protein disables “don't-find-me” signaling by antagonizing PD-L1. Notably, these bifunctional biomodulators exhibit remarkable ability to induce macrophage phagocytosis, dendritic cell maturation, and CD8+T activation, ultimately substantially inhibiting tumor growth. This study presents a modular genetic coding strategy for PP1-centered therapies that enables seamless integration of immunostimulatory sequences into protein-based anti-tumor cocktail therapies, thereby offering novel alternatives for improving antitumor efficacy.

The multicategory support vector machine (MSVM) has been widely used for multicategory classification. Despite its widespread popularity, regular MSVM cannot provide direct probabilistic results and suffers from excessive computational cost, as it is formulated on the hinge loss function and it solves a sum-to-zero constrained quadratic programming problem. In this study, we propose a general refinement of regular MSVM, termed as the simplex-based proximal MSVM (SPMSVM). Our SPMSVM uses a novel family of squared error loss functions in place of the hinge loss and it removes the explicit sum-to-zero constraint by the simplex structure. Consequently, the SPMSVM only requires solving an unconstrained linear system, leading to closed-form solutions. In addition, the SPMSVM can be cast into a weighted regression problem so that it is scalable for large-scale applications. Moreover, the SPMSVM naturally yields an estimate of the conditional category probability, which is more informative than regular MSVM. Theoretically, the SPMSVM is shown to include many existing MSVMs as its special cases, and its asymptotic and finite-sample statistical properties are well established. Simulations and real examples show that the proposed SPMSVM is a stable, scalable and competitive classifier.

AbstractThe gamma distribution is one of the most important parametric models in probability theory and statistics. Although a multitude of studies have theoretically investigated the properties of the gamma distribution in the literature, there is still a serious lack of tailored statistical tools to facilitate its practical applications. To fill the gap, this paper develops a comprehensive R package for the gamma distribution. In specific, the R package focuses on the following three important tasks: generate the gamma random variables, estimate the model parameters, and construct statistical limits, including confidence limits, prediction limits, and tolerance limits based on the gamma random variables. The proposed package encompasses the state-of-the-art methods of the gamma distribution in the literature and its usage is illustrated by a real application.

V‐doped WO3microspheres composed of nanorods radially grown from the center were fabricated via a simple hydrothermal method. The as‐obtained products were characterized by several techniques such as X‐ray powder diffraction (XRD), energy dispersive spectrometer (EDS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), Raman spectrum, and Brunauer–Emmett–Teller (BET). It was found that V atoms were incorporated inside the hexagonal WO3lattice. On the basis of time‐dependent experiments, the formation mechanism for the as‐prepared V‐doped WO3micro‐nanostructures was discussed. In addition, the catalytic properties of the synthesized material on the thermal decomposition of ammonium perchlorate (AP) were evaluated by thermal gravimetric and differential thermal analysis (TG/DTA), which showed the thermal decomposition temperatures of AP in the presence of 1, 2, and 4 wt% of V‐doped WO3micro‐nanostructures were reduced to 399, 393, and 380°C, respectively. Nanorods assembled V‐doped WO3 microspheres were fabricated via a one‐pot hydrothermal method. The formation mechanism was discussed and the catalytic properties on the thermal decomposition of ammonium perchlorate were evaluated.

AbstractThis study proposes an efficient approach to detect one or more change points for gamma distribution. We plug a closed-form estimator into the gamma log-likelihood function to obtain a sharp approximation to the maximum of log-likelihood. We further derive a closed form calibration of approximate likelihood which is asymptotically equivalent to the exact log-likelihood. This circumvents iterative optimization procedures to find maximum likelihood estimates which can be a burden in detecting multiple change points. The simulation study shows that the approximation is accurate and the change points can be detected much faster. Two case studies on the time between events arising from industrial accidents are presented and extensively investigated.

The susceptibility of lysosomal membranes in tumor cells to cationic amphiphilic drugs (CADs) enables CADs to induce lysosomal membrane permeabilization (LMP) and trigger lysosome-dependent cell death (LDCD), suggesting a potential antitumor therapeutic approach. However, the existence of intrinsic lysosomal damage response mechanisms limits the display of the pharmacological activity of CADs. In this study, we report that low concentrations of QS-21, a saponin with cationic amphiphilicity extracted from Quillaja Saponariatree, can induce LMP but has nontoxicity to tumor cells. QS-21 and MAP30, a type I ribosome-inactivating protein, synergistically induce apoptosis in tumor cells at low concentrations of both. Mechanistically, QS-21-induced LMP helps MAP30 escape from endosomes or lysosomes and subsequently enter the endoplasmic reticulum, where MAP30 downregulates the expression of autophagy-associated LC3 proteins, thereby inhibiting lysophagy. The inhibition of lysophagy results in the impaired clearance of damaged lysosomes, leading to the leakage of massive lysosomal contents such as cathepsins into the cytoplasm, ultimately triggering LDCD. In summary, our study showed that coadministration of QS-21 and MAP30 amplified the lysosomal disruption and can be a new synergistic LDCD-based antitumor therapy.

AbstractProcess capability indices (PCIs) are widely used to assess whether an in-control process meets manufacturing specifications. In most applications of classical PCIs, the process characteristic is assumed normally distributed. However, the normal distribution has been found inappropriate in various applications. In the literature, the percentile-based PCIs are widely used to deal with the nonnormal process. One problem associated with the percentile-based PCIs is that they do not provide a quantitative interpretation to the process capability. In this study, new PCIs that have a consistent quantification to the process capability for both normal and nonnormal processes are proposed. The proposed PCIs are generalizations of the classical normal PCIs in the sense that they are the same as the classical PCIs when the process characteristic follows a normal distribution, and they offer the same interpretation to the process capability as the classical PCIs when the process characteristic is nonnormal. We then discuss nonparametric and parametric estimation of the proposed PCIs. The nonparametric estimator is based on the kernel density estimation and confidence limits are obtained by the nonparametric bootstrap, while the parametric estimator is based on the maximum likelihood estimation and confidence limits are constructed by the method of generalized pivots. The proposed methodologies are demonstrated using a real example from a manufacturing factory.

ABSTRACTDegradation data are an important source of product reliability information. Two popular stochastic models for degradation data are the Gamma process and the inverse Gaussian (IG) process, both of which possess monotone degradation paths. Although these two models have been used in numerous applications, the existing interval estimation methods are either inaccurate given a moderate sample size of the degradation data or require a significant computation time when the size of the degradation data is large. To bridge this gap, this article develops a general framework of interval estimation for the Gamma and IG processes based on the method of generalized pivotal quantities. Extensive simulations are conducted to compare the proposed methods with existing methods under moderate and large sample sizes. Degradation data from capacitors are used to illustrate the proposed methods.

This study develops methods for constructing some important statistical limits of a gamma distribution. First, we construct upper prediction limits and tolerance limits for a gamma distribution. In addition, upper prediction limits for at least pof mmeasurements from a gamma distribution at each of rlocations are constructed. This problem often arises in the field of environmental monitoring, as well as quality control. For each problem discussed in this study, the inferential procedure based on the generalized fiducial method is outlined and a simulation is conducted to assess its performance. Real data are used to demonstrate the proposed methods. In addition to the prediction and tolerance limits, our proposed methods can also be applied to the stress-strength problem involving two independent gamma random variables. Our investigation shows that the proposed methods perform uniformly well and they outperform existing methods.

Objectives This article assessed the balance between industry and drug policy objectives in the pharmaceutical sector in China.Methods The articles were mainly identified through databases such as Elsevier, Google Scholar, and SpringerLink, among others. Related articles were mainly separated into three categories: studies on drug policies, studies related to China’s new health-care reform policy, and studies concerning patent policies.Results A relatively healthy environment for continuous innovation and drug patent protection in the pharmaceutical industry has been created in China, and the public’s drug benefits have also significantly improved. However, the balance between industrial and drug policy objectives in the pharmaceutical sector in China requires additional attention.Discussion and conclusions The results suggest that the government should pay more attention to incentivizing enterprises’ innovation, but the current Essential Medicines System in China has limited innovation. Hence, the mechanism for selecting essential medicines should be reformed, and certain appropriate and reasonably innovative medicines should be included. Additionally, medicine coverage, especially the coverage of essential drugs for primary care should be expanded to improve public health benefits. Furthermore, the pharmaceutical industry should be incorporated into the prospective National Drug Policy to achieve a balance between public benefits and pharmaceutical industry development in the future.

Reservoirs have been widely constructed all over the world in recent years to alleviate the shortage of water resources. Nevertheless, the assessment method of water quality and its trophic status needs to be developed. In this study, water quality parameters of a newly constructed shallow reservoir were regularly monitored for a period of two years, which is used as a major drinking water source of Shanghai, China. Furthermore, the trophic state indices of the reservoir were investigated based on literature and evaluated with the two-year field data. The results indicated that the studied reservoir is reaching the mesotrophic–eutrophic boundary, where further nutrient enrichment could cause water quality degradation; total phosphorus is the limiting nutrient for algal growth in the studied reservoir. Results of the study can help operators understand change and status of water quality of the reservoir and provide valuable data demonstrating variation of water quality indicators and trophic characteristics in shallow reservoirs or lakes.