Dietary pattern analysis is a promising approach to understanding the complex relationship between diet and health. While many statistical methods exist, the literature predominantly focuses on ...classical methods such as dietary quality scores, principal component analysis, factor analysis, clustering analysis, and reduced rank regression. There are some emerging methods that have rarely or never been reviewed or discussed adequately.
This paper presents a landscape review of the existing statistical methods used to derive dietary patterns, especially the finite mixture model, treelet transform, data mining, least absolute shrinkage and selection operator and compositional data analysis, in terms of their underlying concepts, advantages and disadvantages, and available software and packages for implementation.
While all statistical methods for dietary pattern analysis have unique features and serve distinct purposes, emerging methods warrant more attention. However, future research is needed to evaluate these emerging methods' performance in terms of reproducibility, validity, and ability to predict different outcomes.
Selection of the most appropriate method mainly depends on the research questions. As an evolving subject, there is always scope for deriving dietary patterns through new analytic methodologies.
Researchers generally believe that abusive supervision leads to poor employee well‐being (e.g. poor mental health and lower job satisfaction). However, these relationships are not always observed. ...Based on the cognitive appraisal theory, the current research extended the content domain of abusive supervision research by examining the moderating effect of power distance orientation (the extent to which an individual accepts the unequal distribution of power in institutions and organisations), a kind of cultural value, on these relationships. We tested two independent samples (N
1 = 762 and N
2 = 347) using different methods. Results showed that employees' power distance orientation moderated the relationships of abusive supervision with employee psychological health and job satisfaction, such that the negative relationships were weaker for employees with higher power distance orientation. The findings suggest the adaptive function of cultural values employees hold in organisational behavior. The implications for theory and practice are discussed.
Flexible materials with high electromechanical coupling performance are highly demanded for wide applications for electromechanical sensors and transducers, including mechanical energy harvesters. ...Here, outstanding electromechanical performance is obtained in electrospun‐aligned polyvinylidene fluoride (PVDF) fiber film. A theoretical model is developed from systematic theoretical analyses to clarify the underlying constructive piezoelectric‐triboelectric mechanism in the polarized PVDF fiber films that explains the experimental observations well. The electrospinning process induces polarization alignment and thus tunes the electron affinity for PVDF fibers with different polarization terminals, which results in the constructive piezoelectric and triboelectric responses in the obtained PVDF fiber films. Extremely large effective piezoelectric performance properties are achieved in the direct piezoelectric measurements, reaching the maximum effective piezoelectric strain and voltage coefficients of −1065 pm V−1 and −9178 V mm N−1, respectively, at 100 Hz. In the converse piezoelectric measurements without a significant contribution from reversible triboelectric effect, the maximum effective piezoelectric strain and voltage coefficients are −166 pm V−1 and −1499 V mm N−1, respectively. The theoretical analyses and experimental results show the great potential of the electrospun aligned polar PVDF fiber material for various electromechanical device applications, particularly for mechanical energy harvesting.
Outstanding electromechanical conversion performance is achieved through constructive piezoelectric and triboelectric effects in aligned electrospun polyvinylidene fluoride (PVDF) fibers. A theoretical model is developed from systematic theoretical analyses to clarify the constructive piezoelectric‐triboelectric mechanism and explain the giant electric output in response to vibration in the polarized PVDF fiber material. Showing great potential for mechanical energy harvesting applications.
Promoting rural sustainable development requires improving rural systems' self-organization to reduce dependence on external resources, which is inherently difficult in peasant economies due to low ...rural household income. Bottom-up collective action can help address these issues. However, few studies have examined how networks of elite and non-elite actors influence collective action and system transitions toward sustainability. This study scrutinizes the changing structures of collaborative networks in three Chinese villages through analysis of elite and non-elite actor groups and their relationships. We also examine the key elements that influence system transitions at every phase of rural sustainable development. The three case studies demonstrate that (1) elites play a vital role in the formation of collaborative networks and facilitate actor awareness; (2) spatial relationships are as essential as institutional design for successful collective action in response to sustainable development problems; (3) highly centralized collaborative networks help to improve the efficiency of the reorganization, renewal, and innovation of the village system, but the collective action outcome depends on the leadership and spatial relationships of the central actors; and (4) social memory and human capital are the most important system elements needed to exploit technology-driven windows of opportunity and achieve strong sustainability. These results provide important insights for enhancing rural systems' capacity to self-organize and capturing windows of opportunity to achieve sustainable development.
Purpose
To develop a CT-based radiomics signature and assess its ability for preoperatively predicting the early recurrence (≤1 year) of hepatocellular carcinoma (HCC).
Methods
A total of 215 HCC ...patients who underwent partial hepatectomy were enrolled in this retrospective study, and all the patients were followed up at least within 1 year. Radiomics features were extracted from arterial- and portal venous-phase CT images, and a radiomics signature was built by the least absolute shrinkage and selection operator (LASSO) logistic regression model. Preoperative clinical factors associated with early recurrence were evaluated. A radiomics signature, a clinical model, and a combined model were built, and the area under the curve (AUC) of operating characteristics (ROC) was used to explore their performance to discriminate early recurrence.
Results
Twenty-one radiomics features were chosen from 300 candidate features to build a radiomics signature that was significantly associated with early recurrence (
P
< 0.001), and they presented good performance in the discrimination of early recurrence alone with an AUC of 0.817 (95% CI: 0.758–0.866), sensitivity of 0.794, and specificity of 0.699. The AUCs of the clinical and combined models were 0.781 (95% CI: 0.719–0.834) and 0.836 (95% CI: 0.779–0.883), respectively, with the sensitivity being 0.784 and 0.824, and the specificity being 0.619 and 0.708, respectively. Adding a radiomics signature into conventional clinical variables can significantly improve the accuracy of the preoperative model in predicting early recurrence (
P
= 0.01).
Conclusions
The radiomics signature was a significant predictor for early recurrence in HCC. Incorporating radiomics signature into conventional clinical factors performed better for preoperative estimation of early recurrence than with clinical variables alone.
The ATP-dependent caseinolytic protease (Clp) system has been reported to play an important role in plant growth, development, and defense against pathogens. However, whether the Clp system is ...involved in plant defense against herbivores remains largely unclear. We explore the role of the Clp system in rice defenses against brown planthopper (BPH)
by combining chemical analysis, transcriptome, and molecular analyses, as well as insect bioassays. We found the expression of a rice Clp proteolytic subunit gene,
, was suppressed by infestation of BPH gravid females and mechanical wounding. Silencing
enhanced the level of BPH-induced jasmonic acid (JA), JA-isoleucine (JA-Ile), and ABA, which in turn promoted the production of BPH-elicited rice volatiles and increased the resistance of rice to BPH. Field trials showed that silencing
decreased the population densities of BPH and WBPH. We also observed that silencing
decreased chlorophyll content in rice leaves at early developmental stages and impaired rice root growth and seed setting rate. These findings demonstrate that an OsClpP6-mediated Clp system in rice was involved in plant growth-defense trade-offs by affecting the biosynthesis of defense-related signaling molecules in chloroplasts. Moreover, rice plants, after recognizing BPH infestation, can enhance rice resistance to BPH by decreasing the Clp system activity. The work might provide a new way to breed rice varieties that are resistant to herbivores.
•Nano-micelles were formed from food-derived hydroxyethyl starch and curcumin.•The micelles significantly improved the solubility and stability of curcumin.•The micelles released curcumin in an ...acid-responsive manner.•The micelles showed stronger antioxidant and anticancer activity than curcumin.
In this study, amphiphilic conjugates were synthesized by conjugating curcumin (CUR) to a food-derived hydrophilic hydroxyethyl starch (HES) via an acid-labile ester linker. The self-assembly of the conjugates formed uniform micellar nanoparticles (HES-CUR NPs) with a desirable drug loading efficiency, excellent colloidal and storage stability, as well as acid-responsive release manner. Besides, the formation of the nanoparticles increased the solubility of CUR to thousands times higher than free CUR, and effectively protected the loaded CUR from degradation upon exposure to UV light and high temperature. In vitro cytotoxicity assay and radical scavenging experiments demonstrated that the HES-CUR NPs significantly improved the cytocompatibility, anticancer and antioxidant activity of CUR due to the enhanced solubility, stability, and bioavailability. The HES-CUR NPs reported herein have a great potential in developing functional food or pharmaceutical formulations for preventing or treating various diseases such as inflammatory diseases and cancer.
Artificial neural networks (ANNs) are the important approaches for researching human cognition process. However, current ANNs-based cognition methods cannot address the problems of complex ...information understanding and fault-tolerant learning. Here we present a modeling method for cognition mechanism based on a simulated annealing–artificial neural network (SA-ANN). Firstly, the relationship between SA processing procedure and cognition knowledge evolution is analyzed, and a SA-ANN-based inference model is set up. Then, based on the inference model, a Powell SA with combinatorial optimization (PSACO) algorithm is proposed to improve the clustering efficiency and recognition accuracy for the cognition process. Finally, three groups of numerical instances for knowledge clustering are provided, and three comparative experiments are performed by self-developed cognition software. The simulated results show that the proposed method can increase the convergence rate by more than 20%, compared with the back-propagation (BP), SA, and restricted Boltzmann machines based extreme learning machine (RBM-ELM) algorithms. The comparative cognition experiments prove that the method can obtain better performances of information understanding and fault-tolerant learning, and the cognition accuracies for original sample, damaged sample, and transformed sample can reach 99.6%, 99.2%, and 97.1%, respectively.
Relapse is the leading cause of mortality in children with acute lymphoblastic leukemia (ALL). Among chemotherapeutics, thiopurines are key drugs in ALL combination therapy. Using whole-exome ...sequencing, we identified relapse-specific mutations in the phosphoribosyl pyrophosphate synthetase 1 gene (PRPS1), which encodes a rate-limiting purine biosynthesis enzyme, in 24/358 (6.7%) relapsed childhood B cell ALL (B-ALL) cases. All individuals who harbored PRPS1 mutations relapsed early during treatment, and mutated ALL clones expanded exponentially before clinical relapse. Our functional analyses of PRPS1 mutants uncovered a new chemotherapy-resistance mechanism involving reduced feedback inhibition of de novo purine biosynthesis and competitive inhibition of thiopurine activation. Notably, the de novo purine synthesis inhibitor lometrexol effectively abrogated PRPS1 mutant-driven drug resistance. These results highlight the importance of constitutive activation of the de novo purine synthesis pathway in thiopurine resistance, and they offer therapeutic strategies for the treatment of relapsed and thiopurine-resistant ALL.
Accurately diagnosing apple leaf diseases can reduce the use of pesticides and improve the quality of fruits, which is of significance to smart agriculture. Convolutional neural network as a deep ...learning model is widely used in the field of intelligent diagnosis of apple leaf diseases. Deploying a deep neural network for apple disease diagnosis to mobile devices allows for smarter, more efficient, and more accurate disease identification. However, classical convolutional neural networks have some limitations on agricultural disease diagnosis, such as a huge number of parameters, heavy computation, and long inference time. Thus, such a complex deep learning model is not easily deployed to mobile devices. To address the above problems, we propose the ECA-KDNet, an improved lightweight model based on the ECA attention mechanism and knowledge distillation, which shows superiority in accuracy, robustness, and lightweight. The experimental results show that compared with the classical convolutional neural network models, ECA-KDNet improves accuracy (98.28%) while ensuring lightweight (3.38 M).
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