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Simple shear behavior of sand is representative in geotechnical engineering with potential failure occurred along a thin shear zone. The strength and deformation of sand under simple shear are accompanied with principal stress rotation (PSR). This study proposes a modified SANISAND‐F model to capture the simple shear behavior of sand with PSR. A new plastic flow rule along with a kinematic hardening modulus is proposed to consider the evolution of the PSR and intermediate stress ratio. The developed model, with thirteen parameters, can reasonably the capture the stress‐strain and PSR of sand under simple shear. [ABSTRACT FROM AUTHOR]

Vehicular fog computing (VFC), as the extension of the mobile cloud, has been widely investigated. However, in most existing VFC works, the scenario with point-of-interests (PoIs) is rarely considered, which can lead to underutilization of vehicle computing resources and overloading of the computing load for roadside units (RSUs). Therefore, this paper focuses on improving the quality of services for PoI-based VFC by utilizing the computation capabilities of vehicles to assist the RSU. An NP-hard problem is formulated to maximize the number of tasks completed within the duration of delay constraints. To solve the problem, a delay-aware task offloading algorithm, named Delay-Aware Task Offloading (DTO), is proposed. DTO preferentially operates on tasks with tighter delay constraints. To minimize the completion delay for each offloaded task, DTO selects the set of service vehicles to perform tasks. Moreover, a vehicle-resource-aware task offloading algorithm, named Vehicle-Resources-Aware Task Offloading (VRTO), is proposed to maximize the number of tasks completed within the duration of delay constraints by efficiently utilizing the available resource of the vehicle. Specifically, VRTO allocates the data to each service vehicle according to the service configuration of vehicles and the delay constraints of tasks. Finally, extensive experiments are carried out to evaluate the effectiveness of the proposed algorithms. The performance is successfully improved by more than 30% compared with the baselines in terms of the number of completed tasks. [ABSTRACT FROM AUTHOR]

Background: Sodium-Glucose cotransporter 1 and 2 (SGLT1/2) belong to the family of glucose transporters, encoded by SLC5A1 and SLC5A2, respectively. SGLT2 is almost exclusively expressed in the renal proximal convoluted tubule cells. SGLT1 is expressed in the kidneys but also in other organs throughout the body. Many SGLT inhibitor drugs have been developed based on the mechanism of blocking glucose (re)absorption mediated by SGLT1/2, and several have gained major regulatory agencies' approval for treating diabetes. Intriguingly these drugs are also effective in treating diseases beyond diabetes, for example heart failure and chronic kidney disease. We recently discovered that SGLT1 is upregulated in the airway epithelial cells derived from patients of cystic fibrosis (CF), a devastating genetic disease affecting greater than 70,000 worldwide. Results: In the present work, we show that the SGLT1 upregulation is coupled with elevated endoplasmic reticulum (ER) stress response, indicated by activation of the primary ER stress senor inositol-requiring protein 1α (IRE1α) and the ER stress-induced transcription factor X-box binding protein 1 (XBP1), in CF epithelial cells, and in epithelial cells of other stress conditions. Through biochemistry experiments, we demonstrated that the spliced form of XBP1 (XBP1s) acts as a transcription factor for SLC5A1 by directly binding to its promoter region. Targeting this ER stress → SLC5A1 axis by either the ER stress inhibitor Rapamycin or the SGLT1 inhibitor Sotagliflozin was effective in attenuating the ER stress response and reducing the SGLT1 level in these cellular model systems. Conclusions: The present work establishes a causal relationship between ER stress and SGLT1 upregulation and provides a mechanistic explanation why SGLT inhibitor drugs benefit diseases beyond diabetes. [ABSTRACT FROM AUTHOR]

This article discusses the need for improved reporting and transparency in the neurosurgical literature. The authors highlight the increasing number of publications in the field and the importance of rigorous reporting for clinical decision-making. They mention the existence of reporting guidelines, such as CONSORT, STROBE, and PRISMA, which outline the minimum information needed for adequate assessment of studies. The authors conducted a systematic assessment of reporting quality and transparency in four major North American neurosurgical journals and found a lack of adherence to reporting guidelines and minimal disclosure of data availability. They emphasize the need for stricter adherence to consensus guidelines in order to improve the quality of research in neurosurgery. [Extracted from the article]

Background: Polycystic ovarian syndrome (PCOS) is recognized as the most prevalent endocrine disorder among women of reproductive age. While the utilization of assisted reproductive technology (ART) has resulted in favorable outcomes for infertility treatment in PCOS patients, the inherent pathophysiological features of the condition give rise to complications and consequences during pregnancy and delivery for both the mother and offspring. This study was to assess the correlation between maternal PCOS and various pregnancy complications and neonatal outcomes undergone ART. Methods: A systematic search was conducted on PubMed, EmBase, and the Cochrane Library to identify observational studies that investigated the association between PCOS and the risk of various pregnancy complications and neonatal outcomes, including gestational diabetes mellitus (GDM), hypertension in pregnancy (PIH), preeclampsia (PE), preterm birth, abortion, congenital malformations (CA), small for gestational age (SGA), large for gestational age (LGA), low birth weight (LBW), macrosomia, neonatal intensive care unit (NICU) admission and birth weight. Eligible studies were selected based on predetermined inclusion and exclusion criteria. The meta-analysis was conducted using Review Manager and Stata software, with odds ratios (ORs) or mean difference (MD), confidence intervals (CIs), and heterogeneity (I2) being calculated. The search was conducted up to March 2023. Results: A total of 33 studies with a combined sample size of 92,810 participants were identified. The findings indicate that PCOS is significantly associated with an increased risk of GDM (OR 1.51, 95% CI:1.17–1.94), PIH (OR 1.72, 95% CI:1.25–2.39), PE (OR 2.12, 95% CI:1.49–3.02), preterm birth (OR 1.29, 95% CI:1.21–1.39), and LBW (OR 1.29, 95% CI:1.14–1.47). In subgroup analyses, the risks of GDM (OR 1.80, 95% CI:1.23–2.62) and abortion (OR 1.41, 95% CI:1.08–1.84) were elevated in fresh embryo transferred (ET) subgroup, whereas elevated risk of PE (OR 1.82, 95% CI:1.17–2.83) and preterm birth (OR 1.31, 95% CI:1.21–1.42) was identified in frozen ET subgroup. Whatever with or without hyperandrogenism, patients with PCOS had a higher risk in preterm birth (OR 1.69, 95% CI: 1.31–2.18; OR 1.24, 95% CI:1.02–1.50) and abortion (OR 1.38, 95% CI:1.12–1.71; OR 1.23, 95% CI:1.06–1.43). Conclusion: Our findings suggest that individuals with PCOS undergone ART are at a notably elevated risk for experiencing pregnancy complications and unfavorable neonatal outcomes. Nevertheless, to establish a definitive association between PCOS and pregnancy-related outcomes, it is necessary to conduct extensive prospective, blinded cohort studies and effectively control for confounding variables. [ABSTRACT FROM AUTHOR]

Background: Cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) are altered many years before the onset of clinical symptoms of mild cognitive impairment (MCI). Incorporating clinical symptom onset time into biomarker modeling may enhance our understanding of changes preceding MCI. Objective: Using a new analytical approach, we examined patterns of biomarker change prior to MCI symptom onset among individuals who progressed from normal cognition to MCI, stratified based on the age of symptom onset. We also analyzed biomarker patterns of change among participants who remained cognitively normal, and examined potential modifiers of biomarker trajectories, including demographics and apolipoprotein E (APOE) status. Methods: Analyses included 93 participants who progressed from normal cognition to MCI and 186 participants who remained cognitively normal, over an average follow-up period of 16.2 years. CSF biomarkers, including Aβ42, Aβ40, total tau (t-tau), and phosphorylated tau181 (p-tau181), were measured using the fully automated Lumipulse assays. Results: Among participants who progressed to MCI, Aβ42/Aβ40 decreased, and t-tau and p-tau181 increased. For participants who did not progress to MCI, CSF biomarkers showed relatively stable patterns. In both progressors and non-progressors, APOE4 carriers showed lower Aβ 42/Aβ40 levels (compared to non-carriers) at each point of the mean curves. Among non-progressors, APOE4 carriers had higher levels of p-tau181, p-tau181/(Aβ 42/Aβ40), and t-tau/(Aβ 42/Aβ 40). Additionally, among those who did not progress, female sex was associated with higher levels of t-tau, p-tau181, t-tau/(Aβ 42/Aβ 40), and p-tau181/(Aβ 42/Aβ 40). Conclusions: These findings suggest that this analytic approach may provide additional insights into biomarker changes during early phases of AD. [ABSTRACT FROM AUTHOR]

Perovskite nickelates belong to a family of strongly correlated materials, which have drawn broad attention due to their thermally induced metal-to-insulator transition. Recent discoveries show that orbital filling mediated by ion intercalation can trigger a colossal non-volatile conductivity change in nickelates. The coupling and interaction between two types of charge carriers (i.e., ions and electrons) enable nickelate as an exotic mixed conductor for electronic, biological, and energy applications. In this Perspective, we first summarize the fundamentals and recent progresses in the manipulation of ground states of perovskite nickelates by controlling orbital filling via ion intercalation. Then, we present a comprehensive overview of perovskite nickelate as a unique platform for vast cutting-edge research fields, including neuromorphic computing, bio-electronic interfaces, as well as electrocatalysis applications by taking advantage of such electron-filling-controlled modulation phenomena. Finally, we provide an overview of future perspectives and remaining challenges toward the exploitation and commercialization of quantum nickelates for future multidisciplinary research. [ABSTRACT FROM AUTHOR]

The semantic segmentation method based on high-resolution RGB images obtained by unmanned aerial vehicle (UAV) provides a cost-effective way to improve the accuracy of detection and classification in forestry. Few studies have explored the impact of sample distribution patterns on deep learning model detection accuracy. The study was carried out using the data from the 4.78 km2 RGB image of a chestnut (Castanea mollissima Blume) plantation obtained by the DJI Phantom 4-RTK, and the model training was conducted with 18,144 samples of manually delineated chestnut tree clusters. The performance of four semantic segmentation models (U-Net, DeepLab V3, PSPNet, and DeepLab V3+) paired with backbones (ResNet-34, ResNet-50) was evaluated. Then, the influence of chestnut data from different planting patterns on the accuracy and generalization performance of deep learning models was examined. The results showed that the combination of DeepLab V3 with ResNet-34 backbone gives the best performance (F1 score = 86.41%), while the combination of DeepLab V3+ with ResNet-50 backbone performed the worst. The influence of different backbone networks on the detection performance of semantic segmentation models did not show a clear pattern. Additionally, different spatial distribution patterns of chestnut planting affected the classification accuracy. The model MIX, trained on comprehensive training data, achieves higher classification accuracies (F1 score = 86.13%) compared to the model trained on single training data (F1 score (DP) = 82.46%; F1 score (SP) = 83.81%). The model performance in complex scenario data training is superior to that of the model in simple scene data training. In conclusion, comprehensive training databases can improve the generalization performance of chestnut classification with different spatial distribution patterns. This study provides an effective method for detecting chestnut cover area based on semantic segmentation, allowing for better quantitative evaluation of its resource utilization and further development of inventories for other tree species. [ABSTRACT FROM AUTHOR]

N6-methyladenosine (m6A), one of the most common RNA methylation modifications, has emerged in recent years as a new layer of the regulatory mechanism controlling gene expression in eukaryotes. As a reversible epigenetic modification, m6A not only occurs on mRNAs but also on Long non-coding RNAs (LncRNAs). As we all known, despite LncRNAs cannot encode proteins, they affect the expression of proteins by interacting with mRNAs or miRNAs, thus playing important roles in the occurrence and development of a variety of tumors. Up to now, it has been widely accepted that m6A modification on LncRNAs affects the fate of the corresponding LncRNAs. Interestingly, levels and functions of m6A modifications are also mediated by LncRNAs through affecting the m6A methyltransferases (METTL3, METTL14, WTAP, METTL16, etc.), demethylases (FTO, ALKBH5) and methyl-binding proteins (YTHDFs, YTHDCs, IGF2BPs, HNRNPs, etc.), which are collectively referred to as "m6A regulators". In this review, we summarized the mutual regulation mechanisms between N6-methyladenosine modification and LncRNAs in cancer progression, metastasis, invasion and drug resistance. In detail, we focus on the specific mechanisms of m6A modification, which is mediated by methyltransferases and demethylases, involves in the regulation of LncRNA levels and functions in the first part. And section two intensively displays the mediation roles of LncRNAs in m6A modification via changing the regulatory proteins. At last part, we described the interaction effects between LncRNAs and methyl-binding proteins of m6A modification during various tumor occurrence and development. [ABSTRACT FROM AUTHOR]

Four Chinese herbs from the Citrus genus, namely Aurantii Fructus Immaturus (Zhishi), Aurantii Fructus (Zhiqiao), Citri Reticulatae Pericarpium Viride (Qingpi) and Citri Reticulatae Pericarpium (Chenpi), are widely used for treating various cardiovascular and gastrointestinal diseases. Many ingredients have already been identified from these herbs, and their various bioactivities provide some interpretations for the pharmacological functions of these herbs. However, the complex functions of these herbs imply undisclosed cholinergic activity. To discover some ingredients with cholinergic activity and further clarify possible reasons for the complex pharmacological functions presented by these herbs, depending on the extended structure–activity relationships of cholinergic and anti-cholinergic agents, a simple method was established here for quickly discovering possible choline analogs using a specific TLC method, and then stachydrine and choline were first identified from these Citrus herb decoctions based on their NMR and HRMS data. After this, two TLC scanning (TLCS) methods were first established for the quantitative analyses of stachydrine and choline, and the contents of the two ingredients and synephrine in 39 samples were determined using the valid TLCS and HPLC methods, respectively. The results showed that the contents of stachydrine (3.04‰) were 2.4 times greater than those of synephrine (1.25‰) in Zhiqiao and about one-third to two-thirds of those of Zhishi, Qingpi and Chenpi. Simultaneously, the contents of stachydrine, choline and synephrine in these herbs present similar decreasing trends with the delay of harvest time; e.g., those of stachydrine decrease from 5.16‰ (Zhishi) to 3.04‰ (Zhike) and from 1.98‰ (Qingpi) to 1.68‰ (Chenpi). Differently, the contents of synephrine decrease the fastest, while those of stachydrine decrease the slowest. Based on these results, compared with the pharmacological activities and pharmacokinetics reported for stachydrine and synephrine, it is indicated that stachydrine can be considered as a bioactive equilibrist for synephrine, especially in the cardio-cerebrovascular protection from these citrus herbs. Additionally, the results confirmed that stachydrine plays an important role in the pharmacological functions of these citrus herbs, especially in dual-directionally regulating the uterus, and in various beneficial effects on the cardio-cerebrovascular system, kidneys and liver. [ABSTRACT FROM AUTHOR]

In the past 20 years, the global per capita protein demand has been increasing year by year, and thus the search for sustainable and healthier alternative proteins is the focus of current food research. Yeast proteins are of microbial origin with environmentally friendly advantages. They are high-quality proteins with high bioavailability and are expected to be used as novel alternative proteins for food applications. Although reports on yeast extracts continue to increase, studies on the composition, production methods, and functional properties of yeast proteins lack systematic reviews. In addition, the application of yeast proteins in the food industry has not been summarized previously. This paper reviews the composition of yeast proteins and the approaches for their isolation and extraction, and systematically introduces the functional properties and the wide applications in food fields. Future perspectives for yeast proteins to sustainably and healthily meet the world's growing protein demand are also elucidated. Yeast proteins have great application potential as alternative proteins with a reasonable proportion of amino acid composition. Yeast proteins can be hydrolyzed to produce yeast peptides and can also be used as protein supplements, fining agents, meat extenders, etc. Overcoming the limitations of high nucleic acid content and high cost, and improving the functional properties and sensory acceptability of yeast proteins will greatly promote their market applications. This review will provide ideas and guidance for the research and development of yeast proteins in food applications. Yeast proteins have high nutritional value with an ideal amino acid composition and a wealth of functions and properties. The excellent characteristics allow yeast proteins to be used as sustainable alternative proteins in food applications. [Display omitted] • The compositions and extraction approaches of yeast proteins are overviewed. • The functional properties of yeast proteins are summarized. • The applications of yeast proteins as alternative proteins in food are reviewed. • The strategies to improve the utilization of yeast proteins are emphasized. [ABSTRACT FROM AUTHOR]

[Display omitted] • The mesocosm experiment was used to track bacterial community dynamics. • Organic amendments increase the bacterial diversity during drought stress. • Organic amendments increase the compositional resilience during rewetting. • Pathogen inhibition ability in NOF was enhanced after rewetting. • NOF possesses a strategy-balancing community during whole drought event. The soil bacterial microbiome plays a crucial role in ecosystem functioning. The composition and functioning of the microbiome are tightly controlled by the physicochemical surrounding. Therefore, the microbiome is responsive to management, such as fertilization, and to climate change, such as extreme drought. It remains a challenge to retain microbiome functioning under drought. This work aims to reveal if fertilization with organic fertilizer, can enhance resistance and resilience of bacterial communities and their function in extreme drought and subsequent rewetting compared with conventional fertilizers. In soil mesocosms, we induced a long-term drought for 80 days with subsequent rewetting for 170 days to follow bacterial community dynamics in organic (NOF) and chemical (NCF) fertilization regimes. Our results showed that bacterial diversity was higher with NOF than with NCF during drought. In particular, the ecological resilience and recovery of bacterial communities under NOF were higher than in NCF. We found these bacterial community features to enhance pathogen-inhibiting functions in NOF compared to NCF during late recovery. The other soil ecology functional analyses revealed that bacterial biomass recovered in the early stage after rewetting, while soil respiration increased continuously following prolonged time after rewetting. Together, our study indicates that organic fertilization can enhance the stability of the soil microbiome and ensures that specific bacterial-driven ecosystem functions recover after rewetting. This may provide the basis for more sustainable agricultural practices to counterbalance negative climate change-induced effects on soil functioning. [ABSTRACT FROM AUTHOR]

The selective ensemble aims to search the optimal subset balanced accuracy and diversity from the original base classifier set to construct an ensemble classifier with strong generalization performance. A selective ensemble classifier named BRFS-BDA-SENC is proposed in this paper, which realizes the generation and selection of a set of accurate and diverse base classifiers, respectively. In the first step, a multimodal perturbation method is introduced to train distinct base classifiers. The method perturbs the sample space by bootstrap and disturbs the feature space under a newly proposed semi-random feature selection, which is a combination of the core attribute theory and the improved maximum relevance minimum redundancy criterion. Then, to search the optimal classifier subset, the binary dragonfly algorithm is utilized to adaptively select eligible base classifiers. UCI data sets and an actual data set of the blade icing of wind turbine are employed to verify the performance of BRFS-BDA-SENC. The experimental results demonstrate that BRFS-BDA-SENC has significant difference with other selective ensemble methods and improves the classification accuracy. [ABSTRACT FROM AUTHOR]

In two experiments, we used multiple probe designs to first examine the correlation between the presence of transformation of stimulus function (TSF) across math operants and the presence of TSF across saying and writing for spelling, and then tested the effects of the establishment of TSF across saying and writing words on the establishment of TSF across math operants. Eight middle school students with learning disabilities participated in Experiment 1. Four participants demonstrated TSF across saying and writing for spelling. The dependent variables of Experiment 1 were the accuracy and rate of solving word problems after receiving fluency training on math facts, and the number of counting strategies used when solving word problems. Results showed that all participants who demonstrated TSF demonstrated significant increases in accuracy and rate when solving word problems whereas three participants without TSF did not. Three participants subsequently participated in Experiment 2, which tested the effects of establishment of TSF across saying and writing words on the establishment of TSF across math operants. All three participants demonstrated increased accuracy and rate for word problems and extinction of counting strategies. The results of the two experiments suggested the importance of teaching math as a verbal behavior, in particular, as a speaker-as-own-listener behavior. [ABSTRACT FROM AUTHOR]

Fractional calculus plays an increasingly important role in mechanics research. This review investigates the progress of an interdisciplinary approach, fractional plasticity (FP), based on fractional derivative and classic plasticity since FP was proposed as an efficient alternative to modelling state-dependent nonassociativity without an additional plastic potential function. Firstly, the stress length scale (SLS) is defined to conduct fractional differential, which influences the direction and intensity of the nonassociated flow of geomaterials owing to the integral definition of the fractional operator. Based on the role of SLS, two branches of FP, respectively considering the past stress and future reference critical state can be developed. Merits and demerits of these approaches are then discussed, which leads to the definition of the third branch of FP, by considering the influences of both past and future stress states. In addition, some specific cases and potential applications of the third branch can be realised when specific SLS are adopted. [ABSTRACT FROM AUTHOR]

Context • Effectively inhibiting gastric-cancer metastasis and decreasing the recurrence of gastric cancer after surgery are urgent problems. In recent years, abnormal microRNA (miRNA) expression has been found in gastric cancer, and miRNA-17-5p has been found to regulate the occurrence and progression of various cancers. It’s necessary to analyze miRNA-17-5p’s action mechanism in gastric cancer. Objective • The study intended to investigate: (1) miR-17-5p expression in gastric-cancer tissues and adjacent normal tissues in clinical patients, (2) to explore the regulatory effects of miR-17-5p on the biological behavior of gastric cancer at the cellular level, by constructing a gastric-cancer cell model that uses overexpressing miR-17-5p, and (3) to discuss preliminarily its action mechanism. Design • The research team designed a laboratory study. Setting • The study took place at the First Affiliated Hospital of Jinzhou Medical University in Jinzhou, Liaoning Province, China. Participants • Clinical specimens of gastric-cancer tissues and adjacent normal tissues were obtained from 20 patients who had undergone surgical resection for gastric cancer at the hospital. Outcome Measures • Quantitative reverse transcription (qRT)-polymerase chain reaction (PCR) was employed to detect miR-17-5p expression in the gastric-cancer tissues and cells. The si-miR-17-5p was transfected into gastric-cancer cell lines to establish a cell model, with the si-miR-17-5p becoming the intervention group and a negative control group, the si-NC group also being created. The cultured SGC-7901 cells were divided into miR-17-5p mimic group (si-miR-17-5p) and negative control group (si-NC) by transfecting si-miR-17-5p and transfection reagent Lipo2000 for research. Cell proliferation was detected using the cell counting kit-8 (CCK-8) assay; cell invasion and migration were measured using a Transwell assay; and cell migration was also measured using a wound-healing assay. A bioinformatics prediction, luciferase reporter gene assay, and western blot assay were applied to verify the downstream target protein of miR-17-5p. Results • The miR-17-5p levels were significantly higher in gastric-cancer tissues and cell lines than in adjacent normal tissues or gastric epithelial cells. Gastric-cancer cells that were transfected with si-miR-17-5p were found to have significantly inhibited cell proliferation. The si-miR-17-5p could significantly suppress the invasion and metastasis of gastric-cancer cells. A bioinformatics prediction concluded that phosphatase and tensin homolog (PTEN) was the downstream target protein of miR-17-5p, which was confirmed by the Luciferase reporter gene assay and Western blot assay. Conclusions • The miR-17-5p was upregulated in gastric-cancer patients. Simultaneously, si-miR-17-5p dramatically inhibited the proliferation, invasion, and metastasis of gastric-cancer cells, and PTEN protein was its downstream target protein. The study provides a new approach to the treatment of gastric cancer. [ABSTRACT FROM AUTHOR]

As an important part of information extraction, relationship extraction aims to extract the relationships between given entities from natural language text. Based on the pretraining model R-BERT, this paper proposes an entity relationship extraction method that integrates an entity dependency path and pretraining model, which generates a dependency parse tree by dependency parsing, obtains the dependency path of an entity pair via a given entity, and uses an entity dependency path to exclude information such as modifier chunks and useless entities in sentences. This model has achieved good F1 value performance on the SemEval2010 Task 8 dataset. Experiments on datasets show that dependency parsing can provide context information for models and improve performance. [ABSTRACT FROM AUTHOR]

Engineering structures in the field are exposed to three-dimensional (3D) stress-displacement conditions with fabric evolution occurring within the soil–structure interface. Upon 3D shearing, the fabric of the interface would change with shear displacement, which influences the dilatancy or plastic flow response of the interface. To consider the 3D stress state and fabric effect, an enhanced fractional model for the interface under monotonic and cyclic loads is developed by using a multiaxial loading vector, a fabric-dependent plastic flow vector, and a kinematic hardening rule. The developed model has three key features: (1) 3D stress-displacement response with shear coupling in different directions is considered by the 3D elastoplastic relation; (2) fabric-dependent dilatancy state line of the interface is captured via a fabric scalar; and (3) dependence of particle breakage on the critical state line of the interface is also considered by introducing a shift stress. To validate the proposed model, a series of test results obtained from the interface direct shear test and simple shear test are simulated. Discussions on model performances with and without considering the fabric effect are then carried out. It is found that the developed model can capture the key features, e.g., hardening/softening, dilatancy/contraction, and strength degradation, of different soil–structure interfaces. The evolution of the fabric scalar mobilized the dilatancy state of the interface under cyclic loads, through which the remarkable normal contraction response under cyclic loading is successfully captured when compared to those without considering fabric effect. [ABSTRACT FROM AUTHOR]

In disease settings where study participants are at risk for death and a serious nonfatal event, composite endpoints defined as the time until the earliest of death or the nonfatal event are often used as the primary endpoint in clinical trials. In practice, if the nonfatal event can only be detected at clinic visits and the death time is known exactly, the resulting composite endpoint exhibits "component‐wise censoring." The standard method used to estimate event‐free survival in this setting fails to account for component‐wise censoring. We apply a kernel smoothing method previously proposed for a marker process in a novel way to produce a nonparametric estimator for event‐free survival that accounts for component‐wise censoring. The key insight that allows us to apply this kernel method is thinking of nonfatal event status as an intermittently observed binary time‐dependent variable rather than thinking of time to the nonfatal event as interval‐censored. We also propose estimators for the probability in state and restricted mean time in state for reversible or irreversible illness‐death models, under component‐wise censoring, and derive their large‐sample properties. We perform a simulation study to compare our method to existing multistate survival methods and apply the methods on data from a large randomized trial studying a multifactor intervention for reducing morbidity and mortality among men at above average risk of coronary heart disease. [ABSTRACT FROM AUTHOR]

• Using Sentinel-2 imagery and deep learning, rapeseed fields' spatial distribution can be detected during initial flowering. • PSPNet accurately identified rapeseed fields with high effectiveness (F1 = 88.17 %). • The detection of initial flowering provides valuable data for the management of pollinators. • The PSPNet rapeseed field detection model in the initial flowering period has been validated. In high-intensity farming, swiftly and accurately adjusting the proportion of artificially reared pollinators according to the flowering phenology of crops is vital. This adjustment is essential for sustaining crop yields without disrupting the ecological niche of native pollinators. Although advancements in remote sensing provide practical means to gather data on crop coverage and extent, identifying insect-pollinated crops during their initial flowering to guide the deployment of managed pollinators is an underexamined research area. Here, we tested the capability of utilizing Sentinel-2 satellite images combined with a deep learning model to map crop fields during the initial flowering stage of insect-pollinated crops in Zhaosu County, Xinjiang, China. Specifically, we examined rapeseed fields by employing 12 neural network designs to identify images of the initial flowering stage. Different network combinations of three convolutional neural network (CNN) models (U-Net, PSPNet, and DeepLab V3) and four different backbone networks (ResNet-18, ResNet-34, ResNet-50, and ResNet-101) were explored to determine the most effective model for detecting rapeseed fields at the initial flowering stage. A comparison was conducted with Sentinel-2 images obtained at the peak stage of rapeseed flowering. Our results suggest that the use of a deep learning model in combination with Sentinel-2 image data can successfully identify rapeseed fields at the initial flowering stage, thereby offering preliminary insights for the strategic introduction of managed pollinators. The PSPNet model emerged as the superior choice for the identification of rapeseed fields, exhibiting high accuracy in both detection and boundary recognition, with F1 scores of 88.17 % and an intersection over union (IoU) of 53.11 % during initial flowering and F1 scores of 93.33 % with an IoU of 53.49 % during peak flowering. The planting area of rapeseed fields detected by the model in Zhaosu County was 122.16 km2, distributed within an area of 1068 km2. These findings can provide foundational data for informed decisions regarding artificial pollinator supplementation, helping to sustain the health and stability of agricultural ecosystems. [ABSTRACT FROM AUTHOR]

The emerging massive noisy and incomplete data is transforming the conventional graph to the uncertain graph. In this paper, we study rough maximal cliques enumeration (RMCE) in incomplete graphs, which we define as the novel problem of enumerating all maximal cliques where some of edges are unknown to users. The hardness of RMCE is proved to NP-complete. To tackle this problem, an efficient framework for obtaining the rough maximal cliques based on P artially- K nown C oncept L earning (PKCL). With this framework, a given incomplete graph is initially represented as an incomplete formal context. Then, the partially-known SE-ISI concept lattice is generated through the constructed incomplete formal context. Based on the constructed SE-ISI concept lattice, an equivalence theorem between SE-ISI equiconcepts and rough maximal cliques is presented. The detailed topological structural analysis from the point of views of roughness and SE-ISI concept stability of rough maximal cliques are separately discussed. The evaluation results demonstrate that our proposed PKCL algorithm can better identify the rough maximal cliques under different probability distribution models of links compared to the existing baseline algorithms. [ABSTRACT FROM AUTHOR]

To investigate the plastic yielding behaviour of soil, a new plasticity framework using stress Hausdorff derivative was developed in this study. Two hypotheses concerning the effect of scale transform on physical behaviours of soil were proposed, based on which the stress–strain relations in the scaled stress space were provided, and then mapped back to the ordinary stress space for describing the nonlinear strength characteristics of soil. As an alternative approach to the classic plasticity, two case studies on simulating a series of experimental results of sand and clay were made, where it was found that the proposed approach can capture the plastic yielding behaviour of soils. [ABSTRACT FROM AUTHOR]

With the increasing availability of data in the public domain, there has been a growing interest in exploiting information from external sources to improve the analysis of smaller scale studies. An emerging challenge in the era of big data is that the subject‐level data are high dimensional, but the external information is at an aggregate level and of a lower dimension. Moreover, heterogeneity and uncertainty in the auxiliary information are often not accounted for in information synthesis. In this paper, we propose a unified framework to summarize various forms of aggregated information via estimating equations and develop a penalized empirical likelihood approach to incorporate such information in logistic regression. When the homogeneity assumption is violated, we extend the method to account for population heterogeneity among different sources of information. When the uncertainty in the external information is not negligible, we propose a variance estimator adjusting for the uncertainty. The proposed estimators are asymptotically more efficient than the conventional penalized maximum likelihood estimator and enjoy the oracle property even with a diverging number of predictors. Simulation studies show that the proposed approaches yield higher accuracy in variable selection compared with competitors. We illustrate the proposed methodologies with a pediatric kidney transplant study. [ABSTRACT FROM AUTHOR]

Single-index models have gained increased popularity in time-to-event analysis owing to their model flexibility and advantage in dimension reduction. We propose a semiparametric framework for the rate function of a recurrent event counting process by modelling its size and shape components with single-index models. With additional monotone constraints on the two link functions for the size and shape components, the proposed model possesses the desired directional interpretability of covariate effects and encompasses many commonly used models as special cases. To tackle the analytical challenges arising from leaving the two link functions unspecified, we develop a two-step rank-based estimation procedure to estimate the regression parameters with or without informative censoring. The proposed estimators are asymptotically normal, with a root- |$n$| convergence rate. To guide model selection, we develop hypothesis testing procedures for checking shape and size independence. Simulation studies and a data example on a hematopoietic stem cell transplantation study are presented to illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]

The pathogenesis of acute kidney injury (AKI) is associated with the activation of multiple signaling pathways, including Wnt/β-catenin signaling. However, the mechanism of Wnt/β-catenin pathway activation in renal interstitial fibroblasts during AKI is unclear. S100 calcium-binding protein A16 (S100A16), a new member of calcium-binding protein S100 family, is a multi-functional signaling factor involved in various pathogenies, including tumors, glycolipid metabolism disorder, and chronic kidney disease (CKD). We investigated the potential participation of S100A16 in Wnt/β-catenin pathway activation during AKI by subjecting wild-type (WT) and S100A16 knockout (S100A16+/−) mice to the ischemia–reperfusion injury (IRI), and revealed S100A16 upregulation in this model, in which knockout of S100A16 impeded the Wnt/β-catenin signaling pathway activation and recovered the expression of downstream hepatocyte growth factor (HGF). We also found that S100A16 was highly expressed in Platelet-derived growth factor receptor beta (PDGFRβ) positive renal fibroblasts in vivo. Consistently, in rat renal interstitial fibroblasts (NRK-49F cells), both hypoxia/reoxygenation and S100A16 overexpression exacerbated fibroblasts apoptosis and inhibited HGF secretion; whereas S100A16 knockdown or Wnt/β-catenin pathway inhibitor ICG-001 reversed these changes. Mechanistically, we showed that S100A16 promoted Wnt/β-catenin signaling activation via the ubiquitylation and degradation of β-catenin complex members, glycogen synthase kinase 3β (GSK3β) and casein kinase 1α (CK1α), mediated by E3 ubiquitin ligase, the HMG-CoA reductase degradation protein 1 (HRD1). Our study identified the S100A16 as a key regulator in the activation of Wnt/β-catenin signaling pathway in AKI. [ABSTRACT FROM AUTHOR]

To capture the state-dependent behaviour of granular soil, an interdisciplinary approach, fractional plasticity, was proposed. However, the permissible value of the fractional-derivative order has not been evaluated. This technical note aims to analyse the permissible range of fractional order from the perspective of modelling flow liquefaction of sand. A state-dependent fractional elastoplastic model without using predefined state parameters is proposed. Then, a series of test results of different sands under flow liquefactions are simulated. Permissible range of the fractional order is analysed by evaluating the second-order work for predicting flow liquefaction instability of sand, where it is suggested that the fractional order should be less than two. [ABSTRACT FROM AUTHOR]

Geomaterials, such as clay, sand, rockfill and ballast, etc., in the field are usually exposed to complex physical or mechanical conditions, where anomalous behaviors, e.g., state-dependent non-associated flow, non-Fickian diffusion, or non-Darcy seepage, usually take place. References 1 He S.-H., Ding Z., Hu H.-B., Gao M. Effect of Grain Size on Microscopic Pore Structure and Fractal Characteristics of Carbonate-Based Sand and Silicate-Based Sand. The authors develop a fractal-entropy model to predict the hydraulic conductivity of granular soils [[16]], where the pore size distribution (PSD) is discretized based on fractal entropy, while the effective diameter of soil is computed using the grading entropy theory. [Extracted from the article]

The decomposition-based algorithm, for example, multiobjective evolutionary algorithm based on decomposition (MOEA/D), has been proved effective and useful in a variety of multiobjective optimization problems (MOPs). On the basis of MOEA/D, the MOEA/D-DE replaces the simulated binary crossover (SBX) operator with differential evolution (DE) operator, which is used to enhance the diversity of the solutions more effectively. However, the amplification factor and the crossover probability are fixed in MOEA/D-DE, which would lead to a low convergence rate and be more likely to fall into local optimum. To overcome such a prematurity problem, this paper proposes three different adaptive operators in DE with crossover probability and amplification factors to adjust the parameter settings adaptively. We incorporate these three adaptive operators in MOEA/D-DE and MOEA/D-PaS to solve MOPs and many-objective optimization problems (MaOPs), respectively. This paper also designs a sensitive experiment for the changeable parameter η in the proposed adaptive operators to explore how η would affect the convergence of the proposed algorithms. These adaptive algorithms are tested on many benchmark problems, including ZDT, DTLZ, WFG, and MaF test suites. The experimental results illustrate that the three proposed adaptive algorithms have better performance on most benchmark problems. [ABSTRACT FROM AUTHOR]

Synthesizing external aggregated information has been proven useful in improving estimation efficiency when conducting statistical analysis using a limited amount of data. In this paper, we develop a unified framework for combining information from high‐dimensional individual‐level data and potentially low‐dimensional external aggregate data under the Cox model. We summarize various forms of external aggregated information by population estimating equations and propose a penalized empirical likelihood approach to borrow information from these estimating equations. The proposed methods possess the flexibility to handle the case where individual‐level data and external aggregate data are from heterogeneous populations. Specifically, a penalized empirical likelihood ratio test is developed to check for the potential heterogeneity, and a semiparametric density ratio model is postulated to account for the heterogeneity. Moreover, we study the impact of uncertainty in the auxiliary information on the efficiency gain and propose a modified variance estimator to adjust for the uncertainty. The proposed estimators enjoy the oracle property and are asymptotically more efficient than the penalized partial likelihood estimator that does not exploit the external aggregated information. Simulation studies show improvement in both estimation efficiency and variable selection over the competitors. The proposed approaches are applied to the analysis of a pediatric kidney transplant study for illustration. [ABSTRACT FROM AUTHOR]

The stress–dilatancy relation is of critical importance for constitutive modelling of geomaterial. A novel fractional-order stress–dilatancy equation had been developed for granular soil, where a nonlinear stress–dilatancy response was always predicted. However, it was experimentally observed that after a certain extent of shearing, an almost linear response between the stress ratio and the dilatancy ratio, rather than the nonlinear response, usually existed. To capture such stress–dilatancy behaviour, a new fractional stress–dilatancy model is developed in this study, where an apparent linear response of the stress–dilatancy behaviour of soil after sufficient shearing is obtained via analytical solution. As the fractional order varies, the derived stress–dilatancy curve and the associated phase transformation state stress ratio keep changing. But, unlike existing researches, no other specific parameters, except the parameter related to fractional order, concerning such shift are required. Then, the developed stress–dilatancy model is applied to constitutive modelling of granular soil and soil–structure interface, for further validation. A series of test results of different granular soils and soil–structure interfaces under different loading conditions are simulated and compared, where a good model performance is observed. [ABSTRACT FROM AUTHOR]

[Display omitted] Sludge derived carbon (SC) has been widely used in advanced oxidation processes as an effective and economic catalyst. In this study, we applied surface modified SC for the first time to catalyze the heterogeneous photo-Fenton process with ciprofloxacin, a highly concerned emerging contaminant, as a model substance. H 2 SO 4 was used to acidify the SCs under varying acid dosages, temperatures, and reaction time lengths. The surface acidity of SCs was quantitatively characterized with NH 3 -TPD. A strong correlation between the surface acidity and the catalytic activity was clearly demonstrated. The highest catalytic activity was obtained with SC whose acidity was 0.149 mmol·g−1 after being modified with 6 mol·L−1 H 2 SO 4 at −20 ℃ for 24 h. In addition, XRD, XRF, BET, XPS, and HRTEM were also used to characterize the obtained SC. ·OH radicals were found to be the main reactive species by EPR. Ten transformation products were identified by GC–MS, based on which three possible reaction pathways were proposed. [ABSTRACT FROM AUTHOR]

As one of the main filled materials of the Lianghekou (LHK) rockfill dam, the strength and deformation characteristics of LHK rockfill is of great interest for hydraulic and geotechnical engineers. Correct interpretation of the state-dependent behavior of LHK rockfill is necessary for the design, construction, and operation of the dam. Therefore, a series of large-scale triaxial tests on LHK rockfill with different initial states were carried out in this study, where the state-dependent strength and deformation behaviors of LHK rockfill were investigated. It was found that both the phase transformation state and peak state of LHK rockfill are dependent on the material state. To capture such state-dependent behavior, two elastoplastic models (i.e., α1-model and α2-model) based on different stress–fractional concepts were developed and compared. It was found that the fractional order (α1) in α1-model exhibits insignificant dependence on the material state of LHK rockfill, whereas the fractional order (α2) in α2-model is dependent on the material state. As the fractional order determines the plastic flow direction of rockfill, it should be identified carefully, otherwise, an overestimation or underestimation of the critical-state stress may occur when using α2-model. Further validation against the large-scale triaxial data of LHK rockfill shows that without using additional plastic potentials, the two models can reasonably reproduce the state-dependent behavior of LHK rockfill. A slightly better performance on simulating the stress–strain response is observed when using α1-model, whereas α2-model works better when simulating the stress–dilatancy response. [ABSTRACT FROM AUTHOR]

An algorithm with different parameter settings often performs differently on the same problem. The parameter settings are difficult to determine before the optimization process. The variants of particle swarm optimization (PSO) algorithms are studied as exemplars of swarm intelligence algorithms. Based on the concept of building block thesis, a PSO algorithm with multiple phases was proposed to analyze the relation between search strategies and the solved problems. Two variants of the PSO algorithm, which were termed as the PSO with fixed phase (PSOFP) algorithm and PSO with dynamic phase (PSODP) algorithm, were compared with six variants of the standard PSO algorithm in the experimental study. The benchmark functions for single-objective numerical optimization, which includes 12 functions in 50 and 100 dimensions, are used in the experimental study, respectively. The experimental results have verified the generalization ability of the proposed PSO variants. [ABSTRACT FROM AUTHOR]

Although increasingly used as a data resource for assembling cohorts, electronic health records (EHRs) pose many analytic challenges. In particular, a patient's health status influences when and what data are recorded, generating sampling bias in the collected data. In this article, we consider recurrent event analysis using EHR data. Conventional regression methods for event risk analysis usually require the values of covariates to be observed throughout the follow-up period. In EHR databases, time-dependent covariates are intermittently measured during clinical visits, and the timing of these visits is informative in the sense that it depends on the disease course. Simple methods, such as the last-observation-carried-forward approach, can lead to biased estimation. On the other hand, complex joint models require additional assumptions on the covariate process and cannot be easily extended to handle multiple longitudinal predictors. By incorporating sampling weights derived from estimating the observation time process, we develop a novel estimation procedure based on inverse-rate-weighting and kernel-smoothing for the semiparametric proportional rate model of recurrent events. The proposed methods do not require model specifications for the covariate processes and can easily handle multiple time-dependent covariates. Our methods are applied to a kidney transplant study for illustration. for this article are available online. [ABSTRACT FROM AUTHOR]

Aim: Asian forests are becoming increasingly degraded and fragmented by the extensive intensification of anthropogenic activities; these activities threaten wildlife and ecosystem sustainability. Facing a defaunation crisis, managers need more information on species assemblages to guide conservation efforts. We tested the relative influence of natural and anthropogenic factors on mammalian species richness and occupancy in temperate forests in Northeast Asia. Location: Northeast China. Methods: Camera‐trapping data and multispecies occupancy models were used to estimate the species richness of a terrestrial mammal community in a working landscape and assess community‐, group‐, and species‐specific responses to natural and anthropogenic features while accounting for imperfect detection. Species were grouped based on body size, diet and activity pattern. Results: We deployed 138 cameras and photographed 21 mammalian species over 22,976 trap days across the China–Russia border. Both natural and anthropogenic correlates varied in their importance in predicting the presence of different animals. Vegetation cover and cattle were found to have significantly positive and negative influences on community‐level mammalian occupancy, respectively. The positive relationship with vegetation cover was most evident for large or diurnal species; the negative relationship with cattle was most evident for diurnal and wild ungulate species. Large species occupancy was also negatively associated with human settlements. The predicted richness across each station varied from 5 to 14 unique species, and species had a mean occupancy probability of 0.45 (95% credible interval = 0.09–0.86). Species richness was generally the lowest in livestock grazing areas and close to human settlements. Human influence is more important than the influences of vegetation and environmental variables. Main conclusions: Our results highlight that livestock grazing was the primary human disturbance that had a negative impact on species occupancy and richness. Multispecies occupancy models helped to identify drivers of biodiversity declines and will inform conservation strategies in human‐dominated landscapes in Northeast Asian forests. We suggest that landscape conservation planning seeks to maximize forest protection and ecosystem services such as biodiversity and carbon storage. [ABSTRACT FROM AUTHOR]

Railway ballast undergoes rearrangement, abrasion, and even breakage, when subjected to high-speed train loads. To reproduce the deformation and degradation behavior of ballast under realistic boundaries used in laboratory triaxial tests, bonded particle clusters and clumps sampled within flexible and rigid boundaries were established, using the discrete element method and finite difference method. The models were then calibrated and validated against a series of experimental results. It is found that boundary condition has a considerable effect on the contact force chains and coordination number. The flexible boundary induces more uniform stress distribution between particle contacts, and consequently higher strength, lower dilation, and impartial breakage. A unimodal frequency distribution of the coordination number is observed when using flexible boundary, while rigid boundary can result in multi-modal distribution in breakable specimens. The flexible boundary also induces more particle breakage with high fragmentation. The rigid boundary specimens exhibit a bimodal distribution of particle breakage along the specimen height after test, with fewer fragments existing in the middle part; however, a unimodal distribution of particle breakage is found in the flexible boundary ones, which agrees more with the laboratory observation. [ABSTRACT FROM AUTHOR]

Data science is a newly-formed and, as yet, loosely—defined discipline that has nonetheless emerged as a critical component of successful scientific research. We seek to provide an understanding of the term "data science," particularly as it relates to public health; to identify ways that data science methods can strengthen public health research; to propose ways to strengthen education for public health data science; and to discuss issues in data science that may benefit from a public health perspective. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ternary ZnO/Ag/g-C 3 N 4 heterojunction was synthesized by a polymer-network gel method. • ZnO/Ag/g-C 3 N 4 possesses high surface area, strong sunlight absorption and enhanced charge separation. • ZnO/Ag/g-C 3 N 4 exhibits excellent photocatalytic activity to Rhodamine B. • ZnO/Ag/g-C 3 N 4 shows superior photoactivated gas-sensing performance for NO 2 detection. It is important to seek effective strategies to fabricate nanomaterials with multi-functionalities in noxious gas detection and poisonous waste treatment. Herein, Ag nanoparticles and g-C 3 N 4 nanosheets co-modified ZnO heterostructure was synthesized via a one-step polymer-network gel method, which is used as both photocatalyst and sensing material. Being a visible-light-driven photocatalyst, the ternary ZnO/Ag/g-C 3 N 4 composite produced 100 % Rhodamine B degradation within 30 min with good cycling stability. Besides, the gas sensor based on ZnO/Ag/g-C 3 N 4 exhibited superb room-temperature sensing performance under neutral light excitation, with a response value of 15.3–5 ppm NO 2 and a lower detection limitation. The excellent performance was mainly attributed to the significantly enhanced visible-light absorption as well as effectively suppressed charge recombination because of the synergism between the individual components. Moreover, the formation of g-C 3 N 4 nanosheets during thermal oxidation etching plays a critical role in improving the surface area and enriching the surface oxygen vacancies. Thus, this work provides novel insights for the synthesis and multi-applications of ZnO/Ag/g-C 3 N 4 heterostructure with high performance. [ABSTRACT FROM AUTHOR]

Tree‐based methods are popular nonparametric tools in studying time‐to‐event outcomes. In this article, we introduce a novel framework for survival trees and ensembles, where the trees partition the dynamic survivor population and can handle time‐dependent covariates. Using the idea of randomized tests, we develop generalized time‐dependent receiver operating characteristic (ROC) curves for evaluating the performance of survival trees. The tree‐building algorithm is guided by decision‐theoretic criteria based on ROC, targeting specifically for prediction accuracy. To address the instability issue of a single tree, we propose a novel ensemble procedure based on averaging martingale estimating equations, which is different from existing methods that average the predicted survival or cumulative hazard functions from individual trees. Extensive simulation studies are conducted to examine the performance of the proposed methods. We apply the methods to a study on AIDS for illustration. [ABSTRACT FROM AUTHOR]

Bounding surface plasticity has been widely used for capturing the stress–strain behaviour of geomaterials. However, it may require multiple sets of model parameters for constitutive modelling of sands with a wide range of initial states, because of the distinct critical state characteristics under low and high densities or pressures in the e - ln p ′ plane. In this study, an enhanced bounding surface plasticity approach for sand with a wide range of initial material states is developed. A fractional plastic flow rule and a modified critical state line are suggested, which ensures that without using any predefined state indices, the developed model can consider the state-dependent dilatancy and hardening behaviours of sand subjected to low and high pressures/densities. The approach is validated by simulating the well-documented test results of Toyoura sand and Sacramento River sand. For comparison, the original state-dependent dilatancy approach in Li and Dafalias (Géotechnique 50(4):449–460, 2000. 10.1680/geot.2000.50.4.449) is also adopted and implemented. It is found that the two approaches can reasonably capture the typical stress–strain behaviour, e.g. hardening/contraction, softening/dilation, liquefaction, quasi-steady state flow, and non-flow, of sands with different initial material states, by using a single set of model parameters. However, compared to the current work, Li and Dafalias (2000) model relied on a predefined state parameter, for capturing the state-dependent behaviour of sand under a wide range of initial states [ABSTRACT FROM AUTHOR]

Introduction: The aim of our study was to investigate the effect of a 'virtual experience' on reducing people's anxiety levels and improving image quality.Methods: This study included 200 people who underwent 18 F-FDG PET/CT scan for the first time. Healthy people (n = 100) and patients (n = 100) were randomly divided into a control group and an intervention group. In the intervention group, we used a 'virtual experience' as an intervention before the scan. We used the Spielberger State-Trait Anxiety Inventory (STAI) and satisfaction questionnaires for evaluation. Additionally, the image quality was analysed.Results: In the control group, more patients presented anxiety than healthy people (26(52%) versus 15(30%)) (P = 0.041). However, when the 'virtual experience' was provided, the number of cases of anxiety in the patient group decreased to 19(38%). Furthermore, patients in the intervention group had lower STAI-related scores than those in the control group (STAI-S: 37.08 ± 9.42 versus 43.34 ± 10.49, P = 0.109; STAI-T: 36.24 ± 9.55 versus 40.72 ± 9.00, P = 0.019). With respect to image quality, people who had higher STAI-related scores were more likely to have unqualified images.Conclusion: A 'virtual experience' provided by an audio-visual installation can ease patients' anxiety and improve image quality. [ABSTRACT FROM AUTHOR]

There has been a rising interest in better exploiting auxiliary summary information from large databases in the analysis of smaller‐scale studies that collect more comprehensive patient‐level information. The purpose of this paper is twofold: first, we propose a novel approach to synthesize information from both the aggregate summary statistics and the individual‐level data in censored linear regression. We show that the auxiliary information amounts to a system of nonsmooth estimating equations and thus can be combined with the conventional weighted log‐rank estimating equations by using the generalized method of moments (GMM) approach. The proposed methodology can be further extended to account for the potential inconsistency in information from different sources. Second, in the absence of auxiliary information, we propose to improve estimation efficiency by combining the overidentified weighted log‐rank estimating equations with different weight functions via the GMM framework. To deal with the nonsmooth GMM‐type objective functions, we develop an asymptotics‐guided algorithm for parameter and variance estimation. We establish the asymptotic normality of the proposed GMM‐type estimators. Simulation studies show that the proposed estimators can yield substantial efficiency gain over the conventional weighted log‐rank estimators. The proposed methods are applied to a pancreatic cancer study for illustration. [ABSTRACT FROM AUTHOR]

BACKGROUND Hydroxychloroquine has been widely administered to patients with Covid-19 without robust evidence supporting its use. METHODS We examined the association between hydroxychloroquine use and intubation or death at a large medical center in New York City. Data were obtained regarding consecutive patients hospitalized with Covid-19, excluding those who were intubated, died, or discharged within 24 hours after presentation to the emergency department (study baseline). The primary end point was a composite of intubation or death in a time-to-event analysis. We compared outcomes in patients who received hydroxychloroquine with those in patients who did not, using a multivariable Cox model with inverse probability weighting according to the propensity score. RESULTS Of 1446 consecutive patients, 70 patients were intubated, died, or discharged within 24 hours after presentation and were excluded from the analysis. Of the remaining 1376 patients, during a median follow-up of 22.5 days, 811 (58.9%) received hydroxychloroquine (600 mg twice on day 1, then 400 mg daily for a median of 5 days); 45.8% of the patients were treated within 24 hours after presentation to the emergency department, and 85.9% within 48 hours. Hydroxychloroquine-treated patients were more severely ill at baseline than those who did not receive hydroxychloroquine (median ratio of partial pressure of arterial oxygen to the fraction of inspired oxygen, 223 vs. 360). Overall, 346 patients (25.1%) had a primary end-point event (180 patients were intubated, of whom 66 subsequently died, and 166 died without intubation). In the main analysis, there was no significant association between hydroxychloroquine use and intubation or death (hazard ratio, 1.04, 95% confidence interval, 0.82 to 1.32). Results were similar in multiple sensitivity analyses. CONCLUSIONS In this observational study involving patients with Covid-19 who had been admitted to the hospital, hydroxychloroquine administration was not associated with either a greatly lowered or an increased risk of the composite end point of intubation or death. Randomized, controlled trials of hydroxychloroquine in patients with Covid-19 are needed. [ABSTRACT FROM AUTHOR]

The stress–strain response of sand was observed to depend on its material state, i.e., pressure and density. Successful modelling of such state-dependent response of sand relied on the correct representation of its state-dependent stress–dilatancy behaviour. In this study, an improved fractional-order α plasticity model for sands with a wide range of initial void ratios and pressures is proposed, based on a state-dependent fractional-order plastic flow rule and a modified yielding surface. Potential positive performances and negative limitations of the proposed approach in terms of the critical state of sand are discussed, based on the simulations of a series of drained and undrained triaxial tests of different sands. It can be found that unlike previous fractional models, the developed model can reasonably simulate the key features, e.g., strain softening/hardening, volumetric dilation/contraction, liquefaction, quasi-steady-state flow as well as steady-state flow, of sand for a wide range of initial states. However, due to typical forms of the critical-state lines being used, negative performances of the fractional approach could occur when simulating the undrained behaviour of very loose sand and the drained behaviour of very dense sand. [ABSTRACT FROM AUTHOR]

Background Cryptococcosis is increasingly recognized in people without human immunodeficiency virus (HIV). Methods A multicenter, prospective cohort study was performed in 25 US centers. Consenting patients were prospectively followed for ≤2 years. Neurological morbidities were assessed with longitudinal event depiction and functional scores (Montreal Cognitive Assessment [MoCA]). Risks of death were analyzed using Cox regression. Results One hundred forty-five subjects were enrolled. Most were male (95; 65.5%) and had immunosuppression (120; 82.8%), including solid organ transplant (SOT; 33.8%), autoimmunity (15.9%), and hematologic malignancies (11.7%). Disease involved the central nervous system (CNS) in 71 subjects (49%). Fever was uncommon, documented in 40 (27.8%) subjects, and absence was associated with diagnostic delay (mean: 48.2 vs 16.5 days; P =.007). Abnormal MoCA scores (<26) were predictive of CNS disease; low scores (<22) were associated with poor long-term cognition. Longitudinal event depiction demonstrated frequent complications in people with CNS disease; 25 subjects (35.2%) required >1 lumbar puncture and 8 (11.3%) required ventriculostomies. In multivariable models, older age (>60 years) was associated with higher risks of death (hazard ratio [HR], 2.14; 95% confidence interval [CI], 1.05–4.38; P =.036), and lower risks were noted with underlying hematologic malignancy (HR, 0.29; 95% CI, 0.09–0.98; P =.05) and prior SOT (HR, 0.153; 95% CI, 0.05–0.44; P =.001). Conclusions Despite aggressive antifungal therapies, outcomes of CNS cryptococcosis in people without HIV are characterized by substantial long-term neurological sequelae. Studies are needed to understand mechanism(s) of cognitive decline and to enable better treatment algorithms. [ABSTRACT FROM AUTHOR]

Microwave-induced thermoacoustic imaging (MITAI) has found diversified applications in biomedical related disciplines and holds the potential to serve as an auxiliary measure for diagnosis and treatment in clinics. Conventional imaging algorithms for MITAI, for example, back-projection (BP), require exceedingly intensive sampling of thermoacoustic signals stemming from a sample under test, and thus, suffer from deficiencies such as low time efficiency, high system cost, and more microwave radiation received by patients. Combining compressive sensing (CS) and MITAI, referred to as CS-MITAI, is promising to address the problem. Reported experiments in two dimensions and simulations in three dimensions (3D) have proved that the CS-MITAI approach can reliably reconstruct images with much fewer measurements than the BP algorithm. However, there is a pressing need for the experimental validation of the CS-MITAI technique in a real three-dimensional (3-D) environment, which has profound implications for its practical applications and further improvement. This article experimentally investigates the 3-D CS-MITAI technique by imaging a sample with 3-D features. Establishment of a dictionary is crucial for the successful implementation of the 3-D CS-MITAI approach. An analytical method is proposed to build the dictionary with much higher efficiency than other reported measures, and its detailed derivation is provided. In addition, modeling work and parametric studies are performed to explore the robustness of the 3-D CS-MITAI mechanism in dealing with different cases. Both simulation and experimental results show that the 3-D CS-MITAI modality can offer comparable imaging quality as the BP method but requires 200 and 68 times fewer measurements, respectively. [ABSTRACT FROM AUTHOR]

The traditional model predictive current control (MPCC) strategy has the advantages of a fast dynamic response and flexible constraint conditions. However, this strategy only determines the optimal voltage vector in a period rather than in multiple periods, which may result in a large current ripple. To solve the above problem, this paper proposes a multi-step MPCC strategy of permanent-magnet synchronous motor. In the proposed multi-step MPCC strategy, the optimal voltage vector and the sub-optimal voltage vector are considered simultaneously. The current response in the next period is predicted on the basis of the optimal and the sub-optimal voltage vectors, respectively. To ensure the optimality of the selected voltage vector in two control periods, the current responses of the two control periods are contrasted. Compared with the traditional MPCC strategy, simulation and experimental results show that the proposed multi-step MPCC strategy can effectively reduce the current ripple and improve the steady-state performance without increasing the switching frequency. [ABSTRACT FROM AUTHOR]

Soils in the field are often subjected to time-dependent compression, where the induced shear strength and deformation could be different upon different loading rates. To capture the rate-dependent stress–strain behaviour of soils under compression, a stress-fractional viscoplastic model is developed in this study, based on Perzyna's overstress theory. The viscoplastic flow direction of soil is modelled with a unit tensor obtained by performing fractional derivatives on the yielding surface. With the increase in the fractional order, the predicted compressive shear strength varies, and a transition from pure strain hardening to strain hardening and softening is observed. Additionally, as the strain rate increases, the predicted shear strength increases, which agrees well with the corresponding experimental observations. Further validation against a series of oedometer and triaxial test results under different strain-rate loads indicates that the fractional-order viscoplastic model can efficiently capture the rate-dependent behaviour of soils. [ABSTRACT FROM AUTHOR]