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Background: Although prior research has estimated the overarching cost burden of heart failure (HF), a thorough analysis examining medical expense differences and trends, specifically among commercially insured patients with heart failure, is still lacking. Thus, the study aims to examine historical trends and differences in medical costs for commercially insured heart failure patients in the United States from 2006 to 2021. Methods: A population-based, cross-sectional analysis of medical and pharmacy claims data (IQVIA PharMetrics® Plus for Academic) from 2006 to 2021 was conducted. The cohort included adult patients (age > = 18) who were enrolled in commercial insurance plans and had healthcare encounters with a primary diagnosis of HF. The primary outcome measures were the average total annual payment per patient and per cost categories encompassing hospitalization, surgery, emergency department (ED) visits, outpatient care, post-discharge care, and medications. The sub-group measures included systolic, diastolic, and systolic combined with diastolic, age, gender, comorbidity, regions, states, insurance payment, and self-payment. Results: The study included 422,289 commercially insured heart failure (HF) patients in the U.S. evaluated from 2006 to 2021. The average total annual cost per patient decreased overall from $9,636.99 to $8,201.89, with an average annual percentage change (AAPC) of -1.11% (95% CI: -2% to -0.26%). Hospitalization and medication costs decreased with an AAPC of -1.99% (95% CI: -3.25% to -0.8%) and − 3.1% (95% CI: -6.86–0.69%). On the other hand, post-discharge, outpatient, ED visit, and surgery costs increased by an AAPC of 0.84% (95% CI: 0.12–1.49%), 4.31% (95% CI: 1.03–7.63%), 7.21% (95% CI: 6.44–8.12%), and 9.36% (95% CI: 8.61–10.19%). Conclusions: The study's findings reveal a rising trend in average total annual payments per patient from 2006 to 2015, followed by a subsequent decrease from 2016 to 2021. This decrease was attributed to the decline in average patient costs within the Medicare Cost insurance category after 2016, coinciding with the implementation of the Medicare Access and CHIP Reauthorization Act (MACRA) of 2015. Additionally, expenses related to surgical procedures, emergency department (ED) visits, and outpatient care have shown substantial growth over time. Moreover, significant differences across various variables have been identified. [ABSTRACT FROM AUTHOR]

Primary care physicians (PCPs) play an indispensable role in providing comprehensive care and referring patients for specialty care and other medical services. As the COVID-19 outbreak disrupts patient access to care, understanding the quality of primary care is critical at this unprecedented moment to support patients with complex medical needs in the primary care setting and inform policymakers to redesign our primary care system. The traditional way of collecting information from patient surveys is time-consuming and costly, and novel data collection and analysis methods are needed. In this review paper, we describe the existing algorithms and metrics that use the real-world data to qualify and quantify primary care, including the identification of an individual's likely PCP (identification of plurality provider and major provider), assessment of process quality (for example, appropriate-care-model composite measures), and continuity and regularity of care index (including the interval index, variance index and relative variance index), and highlight the strength and limitation of real world data from electronic health records (EHRs) and claims data in determining the quality of PCP care. The EHR audits facilitate assessing the quality of the workflow process and clinical appropriateness of primary care practices. With extensive and diverse records, administrative claims data can provide reliable information as it assesses primary care quality through coded information from different providers or networks. The use of EHRs and administrative claims data may be a cost-effective analytic strategy for evaluating the quality of primary care. [ABSTRACT FROM AUTHOR]

In a recent work (Wang et al. 2020), a partial order ⪯, a join operation ⊔ and a meet operation ⊓ of probabilistic linguistic term sets (PLTSs) were introduced and it was proved that L1 ⊓ L2 ⪯ L1 ⪯ L1 ⊔ L2 and L1 ⊓ L2 ⪯ L2 ⪯ L1 ⊔ L2. In this paper, we demonstrate that its join and meet operations are not satisfy the above requirement. To satisfy this requirement, we modify its join and meet operations. Moreover, we define a negation operation of PLTSs based on the partial order ⪯. The combinations of the proposed negation, the modified join and meet operations yield a bounded, distributive lattice over PLTSs. Meanwhile, we also define a new join operation and a new meet operation which, together with the negation operation, yield a bounded De Morgan over PLTSs. [ABSTRACT FROM AUTHOR]

This article explores English readers' reception of Fortress Besieged as a translated literary work in the digital space, based on descriptive statistics, sentiment analysis, and content analysis of their comments at categorized dimensions. This study discovers that English readership online tends to devalue the translators' craftsmanship, especially the interlingual transfer of cultural contents, and the sluggish pace of this novel's storyline. However, the generally successful preservation of the humor and satire of the source text is appreciated. Through bringing cyberspace readers' voices to the academic frontier, this study highlights the necessity of a fluent plot, cultural transfer challenges, and the adornment of figurative language when translating novels for English readers. [ABSTRACT FROM AUTHOR]

In this paper, we propose a partial ordering ⪯ on the set of ordered weighted averaging (OWA) operators. Based on this relation ⪯, we introduce the negation, conjunction and disjunction operations, and establish a bounded De Morgan lattice equipped with an involutive negation for OWA operators. Finally, we develop a similarity measure between OWA operators based on the ordering ⪯. [ABSTRACT FROM AUTHOR]

Parallelism and data reuse are the most critical issues for the design of hardware acceleration in a deep learning processor. Besides, abundant on-chip memories and precise data management are intrinsic design requirements because most of deep learning algorithms are data-driven and memory-bound. In this paper, we propose a compiler-architecture co-design scheme targeting a reconfigurable and algorithm-oriented array processor, named ReAAP. Given specific deep neural networks, the proposed co-design scheme is effective to perform parallelism and data reuse optimization on compute-intensive layers for guiding reconfigurable computing in hardware. Especially, the systemic optimization is performed in our proposed domain-specific compiler to deal with the intrinsic tensions between parallelism and data locality, for the purpose of automatically mapping diverse layer-level workloads onto our proposed reconfigurable array architecture. In this architecture, abundant on-chip memories are software-controlled and its massive data access is precisely handled by compiler-generated instructions. In our experiments, the ReAAP is implemented on an embedded FPGA platform. Experimental results demonstrate that our proposed co-design scheme is effective to integrate software flexibility with hardware parallelism for accelerating diverse deep learning workloads. As a whole system, ReAAP achieves a consistently high utilization of hardware resource for accelerating all the diverse compute-intensive layers in ResNet, MobileNet, and BERT. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Deciphering the virulent Vibrio harveyi causing spoilage in muscle of aquatic crustacean Litopenaeus vannamei" published in Scientific Reports. The correction addresses an error in the affiliation of the authors. The correct affiliation is the Guangdong-Hong Kong-Macau University Joint Laboratory of Marine Bioresource Conservation and Exploitation, College of Marine Sciences, South China Agricultural University, Guangzhou, China. The original article has been corrected. The authors of the article are Lian Gan, Jianwei Zheng, Wei-Hua Xu, Jianhao Lin, Jingshu Liu, Yu Zhang, Zizhan Wu, Zhaolin Lv, Youming Jia, Qingqi Guo, Shijun Chen, Chuanhe Liu, Tom Defoirdt, Qiwei Qin, and Yiying Liu. [Extracted from the article]

The muscle of aquatic crustaceans is perishable and susceptible to environmental contamination. Vibrio harveyi is a widely occurring pathogen in aquatic animals. Here, bath treatment with a virulent V. harveyi strain (which was added directly in the rearing water to imitate environmental contamination) isolated from the muscle of the whiteleg shrimp, Litopenaeus vannamei, caused the muscle of Li. vannamei to display a whitish-opaque appearance due to microscopic changes including muscle lysis, muscle fiber damage and microbial colonization. When administered orally by incorporating this isolate in feed (which is an imitation of infection via natural route), rather than direct invasion followed by colonization in the muscle, this isolate indirectly stimulated severe muscle necrosis in Li. vannamei via steering the enrichment of two important (human) pathogens, V. cholerae and V. vulnificus, and one environmental bacterium Pseudomonas oleovorans, based on the meta-taxonomic analyses. In addition to the scientifically proven viral diseases, our research proved that bacterial agents are also capable of causing muscle spoilage in crustaceans via changing the microbial composition, and that the crustaceans might be exploited as the wide-spectrum sensitive bio-detector to indicate the extent of microbial contamination. [ABSTRACT FROM AUTHOR]

Artificial support system takes advantages of self-bearing capacity of surrounding rock mass in roadway support, which is one of the important supporting ideas in modern times. The implementation way of an artificial support system is worthy of deep investigation. In this study, an HLBR hypothesis was proposed with comprehensive consideration of mechanical properties of surrounding rock mass based on the reinforced arch theory and axial variation theory. A model of bearing structure in surrounding rock mass was constructed, and the stress state on the bearing structure was analyzed. It was believed that when the axial ratio (k) of this bearing structure is equal to the lateral stress factor (λ) (which is the optimal axial ratio), the tangential stress on the bearing boundary reaches the minimum and is in uniform distribution. At this moment, the roadway and the supporting structure can make good use of the self-carrying capacity of surrounding rock mass, which is conducive to lowering the stress distribution level of surrounding rock mass. According to prestress diffusion characteristics of bolt, some ways to achieve HLBR were proposed, which were the optimal axial ratio design, difference support and local grouting modification, and finally performance of the original support design in a belt roadway of the mining district IV of a mine in Shanxi Province. A new supporting scheme was provided according to the HLBR hypothesis. In addition, the FLAC3D strain-softening model was modified by C++, which was used to interpret the reasonability of the suggested supporting scheme. The proposed HLBR hypothesis discloses ways to use self-capacity of surrounding rock mass. This study provides a new idea for roadway sectional design and roadway support design, which are beneficial to maintaining the stability of surrounding rock mass in roadways. [ABSTRACT FROM AUTHOR]

Low-rank modeling has achieved great success in visual data completion. However, the low-rank assumption of original visual data may be in approximate mode, which leads to suboptimality for the recovery of underlying details, especially when the missing rate is extremely high. In this paper, we go further by providing a detailed analysis about the rank distributions in Hankel structured and clustered cases, and figure out both non-local similarity and patch-based structuralization play a positive role. This motivates us to develop a new Hankel low-rank tensor recovery method that is competent to truthfully capture the underlying details with sacrifice of slightly more computational burden. First, benefiting from the correlation of different spectral bands and the smoothness of local spatial neighborhood, we divide the visual data into overlapping 3D patches and group the similar ones into individual clusters exploring the non-local similarity. Second, the 3D patches are individually mapped to the structured Hankel tensors for better revealing low-rank property of the image. Finally, we solve the tensor completion model via the well-known alternating direction method of multiplier (ADMM) optimization algorithm. Due to the fact that size expansion happens inevitably in Hankelization operation, we further propose a fast randomized skinny tensor singular value decomposition (rst-SVD) to accelerate the per-iteration running efficiency. Extensive experimental results on real world datasets verify the superiority of our method compared to the state-of-the-art visual inpainting approaches. [ABSTRACT FROM AUTHOR]

This article explores the differences of translation processes for expressive and informative texts by professional translators primarily by triangulating pausing and revision data retrieved by Keystroke logging experiment and rational report thereof via questionnaire survey and interview. The article discovers that in terms of temporal distribution, the drafting of informative text accounts for a larger proportion of time, while orientation and checking accounts for a smaller proportion compared with those of expressive texts. At orientation interval, translators come to fully understand the source expressive text at the very beginning, while generally conducting syntactic analysis of and re-sequencing the semantic component of source text in target language in translating informative text. At drafting interval, structural modification frequency word rate (MWR) in translating the expressive text is larger than that in translating the informative texts, while spelling and lexical MWRs of both texts are similar. At checking interval, both texts hardly see any structural modification, but lexical MWR for the former is much bigger than that for the latter. These data, along with pausing and revision rational report, suggest the priority of familiarity with standardized grammar and expertise in specialized field for translating informative text, and the importance of embedded reading and writing training for translating expressive text. [ABSTRACT FROM AUTHOR]

Core–shell nanocomposites with a catalytic metal-organic framework (MOF) shell are more effective and stable than bare MOF. We have successfully designed an effective heterogeneous catalyst for the synthesis of benzimidazole by integrating acidic catalytic activity, and promoted the aerobic oxidation and magnetic recyclability of core–shell nanocomposite Fe3O4@SiO2@UiO-66. The Fe3O4@SiO2 core is encapsulated by the in situ-grown UiO-66 shell, and the UiO-66 shell retains the porous structure and crystallinity of UiO-66 with abundant exposed Lewis acid sites. It shows high catalytic ability for the synthesis of various benzimidazoles through the acid-catalyzed condensation and aerobic oxidation with in situ oxygen. The Fe3O4@SiO2 core provides magnetic recyclability of Fe3O4@SiO2@UiO-66, and maintains high catalytic ability and stability over six cycles. [ABSTRACT FROM AUTHOR]

The application of surface-functionalized nanoparticles (NPs) in enhanced oil recovery (EOR) has been rapidly increasing. Adsorption of NPs on the rock surface is the key step. In this study, we systematically investigated the adsorption and desorption mechanisms of various brush-like zwitterionic oligomers functionalized silica NPs on the hydrophilic rock surface in an aqueous solution using molecular dynamics simulations. Our simulations show that the adsorption is primarily influenced by the oligomer chains, while the neutral silica NP core contributes very weakly only when it has direct contact with the rock surface at the grafting density of 5 or below. The interactions from SO 3 ⁻ groups located at the tail of zwitterionic side chains account for more than 72% of the non-bonded interaction energy E nb between the NPs and the rock. The adsorption becomes stronger for NPs with shorter (4 monomer units) oligomer chains but slightly weaker for NPs with longer (9 monomer units) oligomer chains when increasing the grafting density, which is well explained by the change in the number of adsorbed SO 3 ⁻ groups. Desorption simulations with the umbrella sampling method indicate that the free energy of desorption decreases with increasing the grafting density. The silica NPs grafted with longer oligomer chains (9 monomer units) require higher free energies of desorption than the ones with shorter oligomer chains. The adsorption of NPs on the rock surface enhances surface hydrophilicity, and the effect of longer oligomer chains is more profound than that of short oligomer chains. This research provides valuable insights into the adsorption and desorption mechanisms of surface-modified silica NPs on the rock surface at the molecular level and thus gives hints to the design of nanofluids. [Display omitted] • Silica nanoparticles are functionalized with brush-like zwitterionic oligomers. • Adsorption is primarily influenced by the oligomer chains with charged groups • Longer oligomer chains result in stronger adsorption and higher desorption barriers • Adsorption of hydrophilic NPs enhances the hydrophilicity of the rock surface [ABSTRACT FROM AUTHOR]

Recently, convolution neural network (CNN)-based hyperspectral image (HSI) classification has enjoyed high popularity due to its appealing performance. However, using 2-D or 3-D convolution in a standalone mode may be suboptimal in real applications. On the one hand, the 2-D convolution overlooks the spectral information in extracting feature maps. On the other hand, the 3-D convolution suffers from heavy computation in practice and seems to perform poorly in scenarios having analogous textures along with consecutive spectral bands. To solve these problems, we propose a mixed CNN with covariance pooling for HSI classification. Specifically, our network architecture starts with spectral–spatial 3-D convolutions that followed by a spatial 2-D convolution. Through this mixture operation, we fuse the feature maps generated by 3-D convolutions along the spectral bands for providing complementary information and reducing the dimension of channels. In addition, the covariance pooling technique is adopted to fully extract the second-order information from spectral–spatial feature maps. Motivated by the channel-wise attention mechanism, we further propose two principal component analysis (PCA)-involved strategies, channel-wise shift and channel-wise weighting, to highlight the importance of different spectral bands and recalibrate channel-wise feature response, which can effectively improve the classification accuracy and stability, especially in the case of limited sample size. To verify the effectiveness of the proposed model, we conduct classification experiments on three well-known HSI data sets, Indian Pines, University of Pavia, and Salinas Scene. The experimental results show that our proposal, although with less parameters, achieves better accuracy than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Recently, low-rank (LR) and total variation (TV) constrained tensor completion algorithms have been broadly studied for image restoration. These algorithms, however, ignore the difference of the intrinsic properties along spatial structure, spectral correlation, and unfolded mode. In this paper, we go further by providing a detailed comparison of the LR and TV properties in matrix and tensor cases, and figure out the LRTV constraints for pixel matrices are more evident and accordant than for others. This inspires us to develop a simple yet effective multichannel LRTV model that is capable of genuinely discovering the intrinsic properties with reduced computational cost. Moreover, due to the suboptimality of nuclear norm and $l_{1}$ norm in approximating the essential low rank and low gradient properties, we employ two enhanced constraints, i.e., truncated nuclear norm (TNN) and total $p$ variation ${\text{T}}_{p}\text{V}$ , for a better performance. This results in a challenging problem since that both TNN and ${\text{T}}_{p}\text{V}$ are nonsmooth and nonconvex. Observing that the Moreau approximation of ${\text{T}}_{p}\text{V}$ constraint is a continuous difference-of-convex function, we then develop a first-order method by repeatedly computing two simple proximal operators. Under mild assumption, we further prove that the sequence generated by our method clusters at a stationary point. Extensive experimental results on color image completion show the efficacy and efficiency of our method over state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

• A novel hyperspectral image denoising method is proposed by using l0 gradient. • A global l0 gradient constraint is utilized to recover the global smoothness of the HSI. • An efficient algorithm is designed to solve the constrained nonconvex optimization problem. • It is revealed that the developed algorithm is more effective than the low rank based methods in matrix recovery. Hyperspectral image (HSI) acquisition often suffers from the mixed noise, which greatly limits its subsequent applications. This paper proposes a novel HSI denoising method by using local low-rank matrix recovery and ℓ 0 gradient, which can simultaneously identify the low-rank structures of the clean HSI and the sparse components of the mixed noise. Specifically, the HSI is modeled locally and a scheme of rank-fixed low-rank matrix recovery is employed to separate the latent clean HSI patches from the noisy counterpart. Meanwhile, the ℓ 0 gradient constraint mechanism is utilized to characterize the piecewise smooth structure along both the spectral and spatial dimensions of the reconstructed image from the patches. Moreover, the ℓ 1 norm regularization is adopted to suppress the sparse noise, such as stripes, deadlines, impulse noise, and so on. Also, an efficient iterative schema is developed based on an augmented Lagrange algorithm. The corresponding closed-form solutions of the subproblems are derived for calculating an approximate result of the nonconvex optimization problem. Extensive experiments on both simulated and real HSIs prove that the ℓ 0 gradient constraint can preserve the edge details well and the proposed method can yield better performance than the state-of-the-art methods for HSI denoising. [ABSTRACT FROM AUTHOR]

There is an exciting possibility to decentralize ammonia synthesis for fertilizer production or energy storage without carbon emission from H2 obtained from renewables at small units operated at lower pressure. However, no suitable catalyst has yet been developed. Ru catalysts are known to be promoted by heavier alkali dopants. Instead of using heavy alkali metals, Li is herein shown to give the highest rate through surface polarisation despite its poorest electron donating ability. This exceptional promotion rate makes Ru–Li catalysts suitable for ammonia synthesis, which outclasses industrial Fe counterparts by at least 195 fold. Akin to enzyme catalysis, it is for the first time shown that Ru–Li catalysts hydrogenate end‐on adsorbed N2 stabilized by Li+ on Ru terrace sites to ammonia in a stepwise manner, in contrast to typical N2 dissociation on stepped sites adopted by Ru–Cs counterparts, giving new insights in activating N2 by metallic catalysts. [ABSTRACT FROM AUTHOR]

There is an exciting possibility to decentralize ammonia synthesis for fertilizer production or energy storage without carbon emission from H2 obtained from renewables at small units operated at lower pressure. However, no suitable catalyst has yet been developed. Ru catalysts are known to be promoted by heavier alkali dopants. Instead of using heavy alkali metals, Li is herein shown to give the highest rate through surface polarisation despite its poorest electron donating ability. This exceptional promotion rate makes Ru–Li catalysts suitable for ammonia synthesis, which outclasses industrial Fe counterparts by at least 195 fold. Akin to enzyme catalysis, it is for the first time shown that Ru–Li catalysts hydrogenate end‐on adsorbed N2 stabilized by Li+ on Ru terrace sites to ammonia in a stepwise manner, in contrast to typical N2 dissociation on stepped sites adopted by Ru–Cs counterparts, giving new insights in activating N2 by metallic catalysts. [ABSTRACT FROM AUTHOR]

Under artificially simulated conditions, the water purification effects of Lythrum salicaria, Typha minima, Scirpus validus, Iris wilsonii and their configurations were studied. The results showed that various aquatic plants and their configurations showed purification effects for total nitrogen (TN), nitrate nitrogen (NO3 -), total phosphorus (TP), orthophosphate (PO4 3-) and chemical oxygen demand (COD) in water body, and the purification effects of aquatic plant configurations were better than those of single ones. Regression analysis was conducted for dynamics of various water quality indicators. The removal rates of TN and COD within 50 d by the combination of L. salicaria and I. wilsonii were 73.83% and 77.4%, respectively, with the best purification effect; the removal rate of NO3 - within 20 d by the combination of S. validus and I. wilsonii was 89.41%; and the combination of S. validus and I. wilsonii showed the best removal effect for TP and PO4 3-, of which the 50-d removal rates were 88.98% and 92.39%, respectively. Reasonable choice of local aquatic plants and their optimal combinations can be applied in the improvement of water quality of rivers. [ABSTRACT FROM AUTHOR]

Based on the extensive analysis of natural and social conditions, regional development, and characteristics of tree species in the Beijing-Tianjin corridor, a comprehensive evaluation index system that consists of A, B, C, D layers was established for introduced tree species. Nine local representative introduced tree species were evaluated using the analytic hierarchy process (AHP). The results showed that Platanus orientalis, Quercus mongolicus, and Zizyphusjujube performed better comprehensively and were recommended as the dominant introduced tree species in the Beijing-Tianjin corridor. Other representative introduced tree species should be appropriately developed in the region. Main factors that should be taken into account for introduced tree species including drought tolerance, wintering habit, growth rate, leanness tolerance, pollution tolerance, saline tolerance, and disease resistance. [ABSTRACT FROM AUTHOR]

In the present study, the effects of microRNA-29a (miRNA-29a) on colon cancer cell viability and the molecular mechanisms underlying the effects were investigated. The expression of miRNA-29a in colon cancer serum samples was notably downregulated, compared with in the normal group. First, miRNA-29a mimic was used to increase the expression of miRNA-29a in HCT-116 cells. Furthermore, upregulation of miRNA-29a suppressed cell viability, increased lactate dehydrogenase levels and apoptosis, and promoted caspase-3/9 activities and B-cell lymphoma 2-associated X protein and phosphatase and tensin homolog (PTEN) protein expression in colon cancer cells. Furthermore, upregulation of miRNA-29a decreased phosphoinositide 3-kinase, phosphorylated (p)-protein kinase B (Akt) and p-glycogen synthase kinase 3β (GSK3β) protein expression and suppressed the Wnt/β-catenin signaling pathway in colon cancer cells. The results of the present study verified that the protective effects of miRNA-29a suppress the PTEN/Akt/GSK3β and Wnt/β-catenin signaling pathways in colon cancer. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new classifier named kernel group sparse representation via structural and non-convex constraints (KGSRSN) for image recognition. The new approach integrates both group sparsity and structure locality in the kernel feature space and then penalties a non-convex function to the representation coefficients. On the one hand, by mapping the training samples into the kernel space, the so-called norm normalization problem will be naturally alleviated. On the other hand, an interval for the parameter of penalty function is provided to promote more sparsity without sacrificing the uniqueness of the solution and robustness of convex optimization. Our method is computationally efficient due to the utilization of the Alternating Direction Method of Multipliers (ADMM) and Majorization-Minimization (MM). Experimental results on three real-world benchmark datasets, i.e., AR face database, PIE face database and MNIST handwritten digits database, demonstrate that KGSRSN can achieve more discriminative sparse coefficients, and it outperforms many state-of-the-art approaches for classification with respect to both recognition rates and running time. [ABSTRACT FROM AUTHOR]

In this paper, we consider the robust face recognition problem via iterative re-constrained group sparse classifier (IRGSC) with adaptive weights learning. Specifically, we propose a group sparse representation classification (GSRC) approach in which weighted features and groups are collaboratively adopted to encode more structure information and discriminative information than other regression based methods. In addition, we derive an efficient algorithm to optimize the proposed objective function, and theoretically prove the convergence. There are several appealing aspects associated with IRGSC. First, adaptively learned weights can be seamlessly incorporated into the GSRC framework. This integrates the locality structure of the data and validity information of the features into l2,p -norm regularization to form a unified formulation. Second, IRGSC is very flexible to different size of training set as well as feature dimension thanks to the l2,p -norm regularization. Third, the derived solution is proved to be a stationary point (globally optimal if $p \geq 1$ ). Comprehensive experiments on representative data sets demonstrate that IRGSC is a robust discriminative classifier which significantly improves the performance and efficiency compared with the state-of-the-art methods in dealing with face occlusion, corruption, and illumination changes, and so on. [ABSTRACT FROM PUBLISHER]

Bimetallic Ni–Fe nanoparticles supported on carbon nanotubes (CNTs) are prepared and evaluated for the catalytic hydrodeoxygenation (HDO) of a lignin-derived model compound guaiacol. Appropriate combination of Ni and Fe affords high activity and significantly enhances selectivity to cyclohexane or phenol, whereas monometallic Ni and Fe catalysts display poor activities or selectivities. The product tunable behavior of guaiacol HDO is found to be dependent on Ni/Fe atomic ratios. Cyclohexane and phenol are the major products over Ni 5 –Fe 1 /CNT with Ni/Fe atomic ratio at 5/1 and Ni 1 –Fe 5 /CNT with Ni/Fe atomic ratio at 1/5, respectively. Characterization results confirm that Ni–Fe alloys are formed and elicit synergistic effects on the HDO performance. The selectivity-switchable performance of Ni–Fe/CNT can be assigned to the synergism between Ni domains, where H 2 can be easily activated, and Fe domains, which exhibited strong oxophilicity. The bimetallic catalysts give an enhanced stability without significant sintering of metal nanoparticles, while the monometallic catalysts show obvious deactivation due to the agglomeration of metal nanoparticles. Further results reveal that the conversion of guaiacol depends on not only the chemical state but also the size of the metallic nanoparticles. The catalysts with appropriate Ni/Fe atomic ratio and smaller particle perform better hydrogenolysis of C–O bonds, resulting in high selectivity to cyclohexane or phenol. [ABSTRACT FROM AUTHOR]

• We propose cross-attention and cross-scale fusion network (CASF-Net), which interacts the global and local features to maximize the joint advantages of CNN and Transformer for feature sublimation. • The dual branch cross-fusion module (DCF) is proposed to efficiently aggregate the complementary information in different views of the same image, where the cross-attention mechanism aims to better integrate the information and meanwhile reduce the computational complexity. • We utilize a cross-scale feature fusion module in the dual-branch decoder to enhance the contextual information of downstream tasks, by which the efficiency can also be enhanced. • Extensive experiments on three typical tasks in medical image segmentation indicate that CASF-Net consistently outperforms other state-of-the-art methods, especially in the polyp segmentation task, validating the effectiveness of our model. The source code has been uploaded at https://github.com/ZhengJianwei2/CASF-Net. Background: Automatic segmentation of medical images has progressed greatly owing to the development of convolutional neural networks (CNNs). However, there are two uncertainties with current approaches based on convolutional operations: (1) how to eliminate the general limitations that CNNs lack the ability of modeling long-range dependencies and global contextual interactions, and (2) how to efficiently discover and integrate global and local features that are implied in the image. Notably, these two problems are interconnected, yet previous approaches mainly focus on the first problem and ignore the importance of information integration. Methods: In this paper, we propose a novel cross-attention and cross-scale fusion network (CASF-Net), which aims to explicitly tap the potential of dual-branch networks and fully integrate the coarse and fine-grained feature representations. Specifically, the well-designed dual-branch encoder hammers at modeling non-local dependencies and multi-scale contexts, significantly improving the quality of semantic segmentation. Moreover, the proposed cross-attention and cross-scale module efficiently perform multi-scale information fusion, being capable of further exploring the long-range contextual information. Results: Extensive experiments conducted on three different types of medical image segmentation tasks demonstrate the state-of-the-art performance of our proposed method both visually and numerically. Conclusions: This paper assembles the feature representation capabilities of CNN and transformer and proposes cross-attention and cross-scale fusion algorithms. The promising results show new possibilities of using cross-fusion mechanisms in more downstream medical image tasks. [ABSTRACT FROM AUTHOR]

Paclobutrazol is a plant growth regulator and inhibitor of endogenous gibberellin synthesis. It is a powerful inhibitor of vegetative growth by changing the photosynthetic rate and plant hormone levels, thereby affecting plant growth and development. In this study, the effects of paclobutrazol on the model diatom Phaeodactylum tricornutum were investigated. Results show that 2.5-mg/L and 10-mg/L paclobutrazol significantly inhibited the algal growth by inhibiting chlorophyll synthesis, which affects photosynthesis. The antioxidant system, including catalase (CAT) and glutathione peroxidase (GPx) was severely damaged. Chrysolaminarin content was significantly elevated and doubled up to 127 mg/g dry cell weight (DCW) by 10-mg/L paclobutrazol treatment. In combination with transcriptomic analysis, paclobutrazol was demonstrated to play a regulatory role in the accumulation of chrysolaminarin and neutral lipids. [ABSTRACT FROM AUTHOR]

Coupling propidium monoazide (PMA) with quantitative PCR (PMA-qPCR) has been successfully applied to specific detection and quantification of viable cells in various samples. The optimal PMA treatment condition is usually determined through qPCR. However, it is a tedious, time consuming and costly process including DNA extraction and qPCR. To overcome this problem, a flow cytometry-based (FCM-based) method was first proposed in this study to replace qPCR for screening of the optimal PMA treatment condition for Helicobacter pylori , since the pure culture treated with PMA was actually a single cell suspension with fluorescent dye. Results showed that the optimal PMA treatment condition (30 μM of PMA and 8 min of exposure time) determined by the novel method was the same as that determined by the qPCR-based method, which demonstrate the feasibility of this approach. In addition, with the comparison of the qPCR-based method, the FCM-based method allows screening of the optimal PMA treatment condition become much more simple, rapid and economical. [ABSTRACT FROM AUTHOR]

Bimetallic AuAg/SBA-15 catalysts were disclosed to efficiently catalyze vapor-phase chemoselective hydrogenation of crotonaldehyde (CRAL) with improved hydrogenation rate and selectivity to crotonyl alcohol (CROL). The CRAL hydrogenation rate and product distribution were remarkably influenced by reaction parameters such as the reaction pressure and CRAL/hydrogen ratio. Ag decoration on Au/SBA-15 effectively stabilized bimetallic AuAg nano-alloy particles in small size and promoted the catalytic stability. In situ FT-IR investigation revealed that unusual C 2 M 2 di-σ bonded CRAL surface species with reinforced symmetric δ(CH 3 ) IR band under the “metal-surface selection rule” (MSSR) were formed on bimetallic AuAg/SBA-15. The stronger CRAL chemisorption on bimetallic AuAg/SBA-15 surfaces than on monometallic Au/SBA-15 was possibly due to the relatively strong ligating of C O bond on Ag active sites and the enhancement of the co-adsorption of the conjugated double bonds of CRAL. Synergistic interaction between Au and Ag active sites for the activation of H H, C C and C O bond may be the origin of positive effect of Ag decoration on Au/SBA-15. [ABSTRACT FROM AUTHOR]

Kernel discriminant analysis (KDA) which operates in the reproducing kernel Hilbert space (RKHS) is a very popular approach to dimensionality reduction. Kernel discriminative common vectors (KDCV) shares the same modified Fisher linear discriminant criterion with KDA and guarantees a 100 % recognition rate for the training set samples as well as favorable generalization performance. However, KDCV has the disadvantage of high computational complexity in both the training and the testing stage. This paper attempts to improve the computation efficiency of KDCV by two strategies. First, the Cholesky decomposition is introduced to obtain the projection matrix instead of eigen-decomposition. Second, we replace the matrix operation with vector operation in the testing process which reduces the computational complexity. Extensive experiments on COIL images dataset, ORL faces dataset, PIE faces dataset, and USPS handwritten digits dataset demonstrate that the proposed algorithm is more efficient than the traditional KDCV algorithm without loss of accuracy. [ABSTRACT FROM AUTHOR]

Dicycloplatin is a new generation supramolecular platinum-containing anti-cancer drug. Due to its structure, it is difficult to differentiate dicycloplatin from physical mixtures of carboplatin and cyclobutane dicarboxylate, and confounding results may arise during drug characterization. To solve this problem, this study aims to provide a reliable and reproducible standard for the determination of dicycloplatin. A simple method for dicycloplatin quality control has been developed using X-ray powder diffraction (XRPD) and high performance liquid chromatography (HPLC). XRPD allowed the control of impurities and dissociation of the dicycloplatin active ingredient to less than 1%, and HPLC allowed the monitoring and control of the relative molar ratio of carboplatin and cyclobutane dicarboxylate within the purity range. The study proved for the first time that the dicycloplatin supramolecule is substantially different from a physical mixture of carboplatin and cyclobutane dicarboxylate. [ABSTRACT FROM AUTHOR]

Au/ZrO 2 nanoparticles have been widely used as photocatalysts in various organic syntheses because of their localized surface plasmon resonance (LSPR) effects. In our work, Au/ZrO 2 has been synthesized by a solution method and it was used as a heterogeneous catalyst in the synthesis of imines from alcohols and aniline with irradiation by visible light. The reaction occurred in two steps: step 1 was the aerobic oxidation of the alcohols and step 2 was the nucleophilic addition of aniline. Dimethyl sulfoxide (DMSO) was used as a solvent in the reaction. The selectivity in the synthesis of imines over 3 wt% Au/ZrO 2 (with mean particle size of 5 nm) was high (over 90%) with irradiation by visible light at room temperature, and an obvious difference in the conversion was observed between the reactions with light irradiation and those without light. The intensity and wavelength of the light strongly affected the reaction. The Au/ZrO 2 could be used at least 5 times. A reaction mechanism was proposed based on the experimental results. The results indicate that the reaction of alcohols and aniline using Au/ZrO 2 as the photocatalyst can proceed under mild conditions. Furthermore, this process is environmentally friendly and green. [ABSTRACT FROM AUTHOR]

In this study, multi-walled carbon nanotubes supported Pt and Pt-based bimetallic catalysts were prepared and their catalytic activities were investigated to screen effective and economical catalyst for H 2 production in catalytic aqueous phase reforming (CAPR) of glycerol. Nickel promoted Pt catalyst with optimized Ni:Pt molar ratio afforded highest glycerol conversion rate (81.21%) and carbon conversion to gas (15.3%) although hydrogen gasification ratio (7.2%) was poorer than that of noble metals promoted Pt-based bimetallic catalysts. Adding CaO significantly enhanced the fraction and selectivity of H 2 over Pt-Ni catalyst and those of CH 4 were reduced to a negligible level, which was possibly attributed to the facilitated water-gas shift reaction and inhibited methanation through in-situ CO 2 sorption via carbonation. Results suggested that Pt-Ni bimetallic catalysts improved dehydrogenation–decarboxylation and dehydration–hydrogenation reactions, leading to high glycerol conversions. Introducing CaO further favored C–C bond cleavage towards high H 2 yield. The catalytic performance can be completely recovered after regenerating the catalyst and adding sacrificial CaO. In terms of reduced consumption of precious metal catalyst, excellent catalyst performance and hydrothermal stability, combination of Pt-Ni bimetallic catalyst and CaO additive was identified as an effective catalytic system for H 2 production in CAPR of glycerol. [ABSTRACT FROM AUTHOR]

Abstract: For data classification, the standard implementation of projection algorithms do not scale well with large dataset size. It makes the computation of large samples infeasible. In this paper, we utilize a block optimization strategy to propose a new locally discriminant projection algorithm termed min–max projection analysis (MMPA). The algorithm takes into account both intra-class and interclass geometries and also possesses the orthogonality property. Furthermore, an incremental MMPA is proposed to learn the local discriminant subspace with newly inserted data by employing the idea of singular value decomposition updating algorithm. Moreover, we extend MMPA to the semi-supervised case and nonlinear case, namely, semi-supervised MMPA and kernel MMPA. The experimental results on image database, hand written digit database, and face database demonstrate the effectiveness of those proposed algorithms. [Copyright &y& Elsevier]

Abstract: Ag/SBA-15 catalyst is found to exhibit excellent catalytic activity and long-term stability for the chemoselective hydrogenation of dimethyl oxalate to methyl glycolate. The size of Ag crystallites, which is markedly affected by the Ag loading levels and catalyst pretreatment conditions, is a key factor determining the reaction rate of the structure-sensitive hydrogenation but hardly influenced the product distribution. The best catalytic hydrogenation activity is obtained over the Ag/SBA-15 catalyst with an average Ag crystallite size of around 3.9nm. [Copyright &y& Elsevier]

Abstract: Gold (Au)–silver (Ag) bimetallic catalyst supported on ordered mesoporous silica SBA-15 exhibits unprecedentedly high activity and superior stability for the selective hydrogenation of dimethyl oxalate to methyl glycolate at a low temperature of 418K. By contrast, monometallic Au/SBA-15 and Ag/SBA-15 catalysts are almost inactive under identical conditions. A combined tuning of particle dispersion and its surface electronic structure is shown as a consequence of the changes in the size and valence band structure of Au and Ag, which leads to significantly enhanced synergy. Considerably reduced apparent activation energies indicate that special active sites with the ability to activate substrate molecules more efficiently are generated in Au–Ag alloy nanoparticles. The Au–Ag bimetallic catalyst also displays excellent activity for the selective hydrogenation of some other unsaturated or saturated esters and acetic acid. [Copyright &y& Elsevier]

Abstract: Visible light active Bismuth doped NaTaO3 powders were synthesized by the conventional solid state route for different Bi concentrations (2.5%, 5.0%, and 7.5% by moles). The optical properties of the doped samples were tuned by changing the molar ratio of Na and Ta in the initial reactants. The doped samples prepared with Na/Ta ratio close to unity (1.01–1.03) resulted in the highest band gap narrowing compared to the other synthesis conditions. It was shown that the photocatalytic hydrogen evolution occurred from these samples under the visible light irradiation (λ > 390 nm) after loading of appropriate amount of platinum co-catalyst. The other synthesis conditions (Na/Ta = 1/1−x; x = 0.025, 0.05, 0.075 and Ta/Na = 1/1−x; x = 0.025, 0.05, 0.075; x is bismuth content) were not useful for the photocatalytic hydrogen evolution. The structural characterization suggested that the samples prepared with Na/Ta ratio close to unity, contain Bi ions located at both Na and Ta sites in the lattice. The Mott–Schottky plots revealed that the flat band potential of the pristine NaTaO3 is highly negative to the H2/H2O reduction potential (−1.19 eV vs. SCE, pH = 7) and for all Bi doped NaTaO3 samples, the flat band potential was sufficient for the hydrogen generation. [Copyright &y& Elsevier]

Abstract: CdTe quantum dots capped by short-chain thioglycolic acid (TGA) are prepared in aqueous solution. In this study, we report the effect of adenosine 5′ triphosphate (ATP), folic acid and l-cysteine on the photoluminescence intensity of CdTe nanocrystals in pH 7.4 Tris-HCl buffer solutions. The luminescence of CdTe nanocrystals is quenched in the presence of ATP or folic acid, which is related to their concentrations. The relationship between the luminescence intensity of CdTe nanocrystals and the concentration of ATP or folic acid is described by the Stern–Volmer equation. The emission intensity of CdTe nanocrystals is enhanced in the presence of l-cysteine. The relationship between the emission intensity of CdTe nanocrystals and the concentration of l-cysteine agrees with Langmuir binding isotherm equation in the range of 5×10−6 to 2×10−4 mol/L. These results show that CdTe nanocrystals can be potentially used as luminescence probes for detecting ATP, folic acid and l-cysteine. [Copyright &y& Elsevier]

C[sub 60] was incorporated onto poly(ethyl methacrylate), poly(methyl acrylate) and poly(ethyl acrylate). The miscibility of poly(vinylidene fluoride) (PVDF) with the C[sub 60]-containing polymers was examined. PVDF is miscible with all the six C[sub 60]-containing polymer samples as shown by the optical clarity of the melt and the existence of a single glass transition temperature in each blend. The polymer-polymer interaction parameters of various blend systems were evaluated by the melting point depression method. [ABSTRACT FROM AUTHOR]

Owing to the outstanding feature extraction capability, convolutional neural networks (CNNs) have been widely applied in hyperspectral image (HSI) classification problems and have achieved an impressive performance. However, it is well known that 2D convolution suffers from the absent consideration of spectral information, while 3D convolution requires a huge amount of computational cost. In addition, the cost of labeling and the limitation of computing resources make it urgent to improve the generalization performance of the model with scarcely labeled samples. To relieve these issues, we design an end-to-end 3D octave and 2D vanilla mixed CNN, namely Oct-MCNN-HS, based on the typical 3D-2D mixed CNN (MCNN). It is worth mentioning that two feature fusion operations are deliberately constructed to climb the top of the discriminative features and practical performance. That is, 2D vanilla convolution merges the feature maps generated by 3D octave convolutions along the channel direction, and homology shifting aggregates the information of the pixels locating at the same spatial position. Extensive experiments are conducted on four publicly available HSI datasets to evaluate the effectiveness and robustness of our model, and the results verify the superiority of Oct-MCNN-HS both in efficacy and efficiency. [ABSTRACT FROM AUTHOR]

Recently, tensor completion (TC), namely high-order extension of matrix completion, has aroused widespread attention. Many priors have been investigated turning the ill-posed TC problem into a well-posed one. The typical methods, however, either perform suboptimally due to missing consideration of appropriate priors, or suffer from overly complicated models by combining too many constraints. In this study, we go deeper by providing a detailed analysis on the rank properties in patch-wise and Hankel cases, figuring out both these two ingredients ensure stricter low-rank prior. This prompts us to design a resultful yet simple TC method using patch-wise and multi-way tensor extension. Specifically, strict low-rank property can be guaranteed in the formulated tensors and this is achieved without requiring any neighborhood setting. Moreover, to approximate the intrinsic rank of the Hankelized tensor, tensor ring decomposition is introduced after deeply comparing its superiority over other high-order decomposition techniques. The proposed method can be efficiently solved using alternating least squares (ALS) approach. Furthermore, a randomized tensor ring approximation is presented for fast initialization. Experimental results on color image inpainting demonstrate that the proposed method can generate better recovery performance compared with state-of-the-art low-rank related competitors, especially in cases of quite limited known pixels, e.g., 10%. [ABSTRACT FROM AUTHOR]

Herein, we synthesized new manganese(I) complexes coordinated with the tetradentate ligand PNNP. The complexes show higher activity and excellent substituent tolerance in contrast to their manganese counterparts and are applicable in the hydrogenation of a wide range of aromatic, aliphatic and heterocyclic ketones to their corresponding alcohols. [ABSTRACT FROM AUTHOR]

Inspired by the temporal subspace clustering (TSC) method and low-rank matrix approximation constraint, a new model is proposed termed as temporal plus low-rank subspace clustering (TLRSC) by utilizing both the local and global structural information. On one hand, to solve the drawback that the nuclear norm-based constraint usually results in a suboptimal solution, we incorporate certain nonconvex surrogates into our model, which approximates the low-rank constraint closely and holds the potential for the convexity of the whole cost function. On the other hand, to ensure fast convergence, we propose an efficient iteratively reweighted singular value minimization (IRSVD) algorithm under the majorization-minimization framework. Moreover, we show that for the weighted low-rank constraint, a cutoff can be derived to automatically threshold the singular values computed from the proximal operator. This guarantees the thresholding operation can be reduced to that of two smaller matrices. Accordingly, an efficient singular value thresholding scheme is proposed for acceleration. Comprehensive experiments are conducted on several public available datasets for quantitative evaluation. Results demonstrate the efficacy and efficiency of TLRSC compared with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Endocrine therapies (e.g. tamoxifen and aromatase inhibitors) targeting estrogen action are effective in decreasing mortality of breast cancer. However, their efficacy is limited by intrinsic and acquired resistance. Our previous study demonstrated that overexpression of a histone methyltransferase NSD2 drives tamoxifen resistance in breast cancer cells and that NSD2 is a potential biomarker of tamoxifen resistant breast cancer. Here, we found that DZNep, an indirect inhibitor of histone methyltransferases, potently induces the degradation of NSD2 protein and inhibits the expression of NSD2 target genes (HK2, G6PD, GLUT1 and TIGAR) involved in the pentose phosphate pathway (PPP). DZNep treatment of tamoxifen-resistant breast cancer cells and xenograft tumors also strongly inhibits tumor growth and the cancer cell survival through decreasing cell production of NADPH and glutathione (GSH) and invoking elevated ROS to cause apoptosis. These findings suggest that DZNep-like agents can be developed to target NSD2 histone methyltransferase for effective treatment of tamoxifen-resistant breast cancer. • DZNep potently induces the degradation of NSD2 protein. • DZNep inhibits the expression of NSD2 target genes (HK2, G6PD, GLUT1 and TIGAR) involved in the pentose phosphate pathway. • DZNep decreases cell production of NADPH and glutathione (GSH), and elevated ROS. • DZNep inhibits TamR breast cancer cell growth in vitro and in vivo. [ABSTRACT FROM AUTHOR]

Peritoneal recurrence (PR) in gastric cancer after curative resection has poor prognosis. Therefore, we aimed to construct a nomogram to predict PR, and establish PR score for risk stratification to guide adjuvant chemotherapy. A total of 315 patients with gastric cancer after radical surgery were included, and randomly stratified into training group (n = 221) and validation group (n = 94). Univariate and multivariate analyses were used to determine predictive factors of PR. The nomogram was constructed to predict the risk of PR. We utilized the time-dependent area under the receiver operating characteristic (ROC) curves (AUCs), calibration curves, and decision curve analysis (DCA) to evaluate the performance of the nomogram. Multivariate analysis showed that tumor site, N stage, preoperative CEA, and postoperative CA199 were independent predictors of PR. A nomogram was constructed to predict PR based on these factors. The AUC value was 0.755 in the training group and 0.715 in the validation group. The calibration curves showed good agreement between prediction and observation in the training and validation groups. The decision curve analysis displayed a good net benefit of the nomogram. The novel PR score was developed and patients were stratified into the low-, medium-, and high -risk groups. For the high-risk group, postoperative adjuvant chemotherapy significantly improved patients' overall survival (OS) and disease-free survival (DFS). The establishment of nomogram facilitates the prediction of PR after radical gastrectomy, and a novel PR score may help guide adjuvant chemotherapy for gastric cancer. [ABSTRACT FROM AUTHOR]

Peritoneal recurrence (PR) in gastric cancer after curative resection has poor prognosis. Therefore, we aimed to construct a nomogram to predict PR, and establish PR score for risk stratification to guide adjuvant chemotherapy. A total of 315 patients with gastric cancer after radical surgery were included, and randomly stratified into training group (n = 221) and validation group (n = 94). Univariate and multivariate analyses were used to determine predictive factors of PR. The nomogram was constructed to predict the risk of PR. We utilized the time-dependent area under the receiver operating characteristic (ROC) curves (AUCs), calibration curves, and decision curve analysis (DCA) to evaluate the performance of the nomogram. Multivariate analysis showed that tumor site, N stage, preoperative CEA, and postoperative CA199 were independent predictors of PR. A nomogram was constructed to predict PR based on these factors. The AUC value was 0.755 in the training group and 0.715 in the validation group. The calibration curves showed good agreement between prediction and observation in the training and validation groups. The decision curve analysis displayed a good net benefit of the nomogram. The novel PR score was developed and patients were stratified into the low-, medium-, and high -risk groups. For the high-risk group, postoperative adjuvant chemotherapy significantly improved patients' overall survival (OS) and disease-free survival (DFS). The establishment of nomogram facilitates the prediction of PR after radical gastrectomy, and a novel PR score may help guide adjuvant chemotherapy for gastric cancer. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to examine the mediating role of readiness for hospital discharge (RHD) and stoma self-efficacy (SSE) in the relationship between quality of discharge teaching (QDT) and health-related quality of life (HRQOL) in colorectal cancer patients with temporary enterostomy, and the gender difference of mediating effect. Background: It is not clear how RHD, QDT, SSE and HRQOL interact in colorectal cancer patients with temporary enterostomy. Methods: This was a prospective follow-up survey. 221 colorectal cancer patients with temporary enterostomy were conveniently recruited from a general hospital in Southeast China. The Quality of Discharge Teaching Scale, Readiness for Hospital Discharge Scale, Stoma Self-Efficacy Scale, and Stoma Quality of Life Scale were used to collect data. Pearson's correlation and structural equation models were used to analyze the data. SPSS 26.0 and Amos 28.0 software were used for analysis the collected data. Results: Regarding the relationship of QDT and HRQOL, only QDT-T had a direct effect among colorectal cancer patients with stomas (b = 0.233, P<0.001, percentile 95% CI = [0.145, 0.314]). However, both QDT-T and QDT-R can predict HRQOL indirectly through three paths: (1) the mediating role of SSE (b = 0.050, P = 0.009, percentile 95% CI = [0.013, 0.098]; b = 0.077, P = 0.008, percentile 95% CI = [0.021, 0.164]), (2) the mediating role of RHD (b = 0.044, P = 0.004, percentile 95% CI = [0.014, 0.085]; b = 0.044, P = 0.005, percentile 95% CI = [0.010, 0.102]), and (3) the chain mediating role of SSE and RHD (b = 0.030, P = 0.003, percentile 95% CI = [0.011, 0.059]; b = 0.047, P = 0.003, percentile 95% CI = [0.015, 0.103]). The similar chain mediating effect in male stoma patients was also found (b = 0.041, P = 0.002, percentile 95% CI = [0.016, 0.080]; b = 0.046, P = 0.004, percentile 95% CI = [0.011, 0.114]). Conclusions: Stoma self-efficacy and readiness for hospital discharge played important intermediary roles in the relationship between quality of discharge teaching and health-related quality of life in colorectal cancer patients with stomas. Health care providers can design SSE-enhancing and RHD-enhancing discharge planning for colorectal cancer patients with temporary enterostomies. [ABSTRACT FROM AUTHOR]