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At optical frequencies, the collective excitation of the periodic array of metallic meta-molecules can support surface lattice resonances (SLRs) due to the coupling of localized surface-plasmon (LSP) resonances to Rayleigh anomaly diffraction. However, the LSP effect in metal blocks becomes negligible in the microwave region. Thus, how the SLRs behave in the microwave regime is very interesting. In this paper, a microwave metasurface consisting of periodic metallic cubes on an ultrathin dielectric substrate is investigated. Two high-Q transmission dips are found, which can be attributed, respectively, to electric-dipole SLRs (ED-SLRs) and magnetic-dipole SLRs (MD-SLRs), because of the non-resonant Rayleigh-like scattering of single cubes and diffraction coupling of the periodic array. The frequencies of both ED- and MD-SLRs are sensitive to the refractive index of the substrate, suggesting that the proposed metasurface may be used to measure the refractive index in the microwave range. [ABSTRACT FROM AUTHOR]

Highly cross‐linked thermosets possess superior mechanical properties and have found broad applications in engineering. However, due to the dense cross‐links, it is extremely challenging to recycle highly cross‐linked thermosets, causing serious environmental and sustainable concerns. Herein, a mechanochemical recycling strategy is proposed for highly cross‐linked thermosets via the synergy of mechanics and chemistry, which is economic‐friendly and pollution‐free. To demonstrate the strategy, diverse metal‐complex catalysts are employed to recycle highly cross‐linked epoxy incorporated with switchable covalent bonds at certain pressure, temperature, and time. The effect of each catalyst on the efficiency of recycling is evaluated, which follows a descending order: Zr4+ > Mn3+ > Co3+ > Fe3+ > Co2+ > Zn2+ > OTi4+ > Ni2+. The results show that the mechanical properties, such as Young's modulus, ultimate strength, peak strain, impact strength, flexural modulus and strength, as well as thermal stability of the recycled samples are almost identical to those of as‐synthesized ones. The effectiveness of the method is further confirmed via micromorphology. [ABSTRACT FROM AUTHOR]

Engineering projects necessitate efficient and resilient collaboration. Enhancing collaboration requires designing and optimizing information interactions. Current methodologies lack a processual and relational lens; also, they seldom emphasize the significance of information. This study focused on information collaboration (IC) in engineering projects. We devised an integrated framework with an information perspective and a network perspective. The framework comprises (1) a two-layer information collaboration network model, integrating information demands and flows; (2) network analysis across different project levels, detecting stakeholder roles in collaboration, revealing unevenly distributed collaboration configurations, and presenting overall collaboration patterns; and (3) a novel assessment method for collaboration performance considering the efficiency and resilience of IC. The framework's practicality was validated through a large-scale international airport project. It offers strategies for enhancing collaboration via network interventions and information optimization. Findings promote the recognition of collaboration, clarify the mechanism between information demands and flows, and improve collaboration performance. It also paves the way for project resilience management. [ABSTRACT FROM AUTHOR]

Aims A mechanistic link between depression and risk of arrhythmias could be attributed to altered catecholamine metabolism in the heart. Monoamine oxidase-A (MAO-A), a key enzyme involved in catecholamine metabolism and longstanding antidepressant target, is highly expressed in the myocardium. The present study aimed to elucidate the functional significance and underlying mechanisms of cardiac MAO-A in arrhythmogenesis. Methods and results Analysis of the TriNetX database revealed that depressed patients treated with MAO inhibitors had a lower risk of arrhythmias compared with those treated with selective serotonin reuptake inhibitors. This effect was phenocopied in mice with cardiomyocyte-specific MAO-A deficiency (cMAO-Adef), which showed a significant reduction in both incidence and duration of catecholamine stress-induced ventricular tachycardia compared with wild-type mice. Additionally, cMAO-Adef cardiomyocytes exhibited altered Ca2+ handling under catecholamine stimulation, with increased diastolic Ca2+ reuptake, reduced diastolic Ca2+ leak, and diminished systolic Ca2+ release. Mechanistically, cMAO-Adef hearts had reduced catecholamine levels under sympathetic stress, along with reduced levels of reactive oxygen species and protein carbonylation, leading to decreased oxidation of Type II PKA and CaMKII. These changes potentiated phospholamban (PLB) phosphorylation, thereby enhancing diastolic Ca2+ reuptake, while reducing ryanodine receptor 2 (RyR2) phosphorylation to decrease diastolic Ca2+ leak. Consequently, cMAO-Adef hearts exhibited lower diastolic Ca2+ levels and fewer arrhythmogenic Ca2+ waves during sympathetic overstimulation. Conclusion Cardiac MAO-A inhibition exerts an anti-arrhythmic effect by enhancing diastolic Ca2+ handling under catecholamine stress. [ABSTRACT FROM AUTHOR]

Purpose: Recent research has focused on the impact of communication networks on the performance of construction project teams, attempting empirical exploration from various social network analysis perspectives. However, there is still a significant gap in understanding the variations in performance and the mechanisms for teams using different communication networks. Drawing from organizational learning theory and social network theory, this study, based on the Input-Mediator-Output (IMO) model, explores the effects of the interaction between centralization and tie strength in communication networks on team performance, as well as the mediating mechanisms of knowledge sharing behavior and team resilience performance in engineering project teams. Methods: Drawing on classic group communication experiment, we design an online communication and collaboration platform to simulate the execution of a construction engineering project. Finally, data was collected through the communication experiment with 720 participants, and hypotheses were tested using ANOVA and PROCESS. Results: The results indicate that under conditions of weak tie strength, centralized communication networks yield higher performance. Conversely, under conditions of strong tie strength, decentralized communication networks demonstrate superior performance. Furthermore, this study also verifies the mediating role of knowledge sharing behavior and team resilience performance when tie strength is strong. Conclusion: This study focuses on engineering project team, exploring the evolutionary development of knowledge sharing behavior and team resilience performance from the perspective of the interaction of communication network structural characteristics, as well as the paths to enhancing team performance. Our research results highlight the interactive effects of structural indicators and relational indicators of communication networks, revealing the mechanism by which the structure of communication networks impacts team performance. Additionally, from the perspectives of forming and timely adjusting team communication models, and motivating and supporting employee communication behavior, our study provides practical insights for project managers and relevant administrators. [ABSTRACT FROM AUTHOR]

Background: General surgeons play an important role in the provision of trauma care in Canada and the current extent of their trauma experience during training is unknown. We sought to quantify the operative and nonoperative educational experiences among Canadian general surgery trainees. Methods: We conducted a multicentre retrospective study of major operative exposures experienced by general surgery residents, as identified using institutional trauma registries and subsequent chart-level review, for 2008–2018. We also conducted a site survey on trauma education and structure. Results: We collected data on operative exposure for general surgery residents from 7 programs and survey data from 10 programs. Operations predominantly occurred after hours (73% after 1700 or on weekends) and general surgery residents were absent from a substantial proportion (25%) of relevant trauma operations. The structure of trauma education was heterogeneous among programs, with considerable site-specific variability in the involvement of surgical specialties in trauma care. During their training, graduating general surgery residents each experienced around 4 index trauma laparotomies, 1 splenectomy, 1 thoracotomy, and 0 neck explorations for trauma. Conclusion: General surgery residents who train in Canada receive variable and limited exposure to operative and nonoperative trauma care. These data can be used as a baseline to inform the application of competency-based medical education in trauma care for general surgery training in Canada. [ABSTRACT FROM AUTHOR]

Sea ice motion (SIM) plays a crucial role in setting the distribution of the ice cover in the Arctic. Limited by images' spatial resolution and tracking algorithms, challenges exist in obtaining coastal sea ice motion (SIM) based on passive microwave satellite sensors. In this study, we developed a method based on random forest (RF) models to obtain Arctic SIM in winter by incorporating wind field and coastal geographic location information. These random forest models were trained using Synthetic Aperture Radar (SAR) SIM data. Our results show good consistency with SIM data retrieved from satellite imagery and buoy observations. With respect to the SAR data, compared with SIM estimated with RF model training using reanalysis surface wind, the results by additional coastal information input had a lower root mean square error (RMSE) and a higher correlation coefficient by 31% and 14% relative improvement, respectively. The latter SIM result also showed a better performance for magnitude, especially within 100 km of the coastline in the north of the Canadian Arctic Archipelago. In addition, the influence of coastline on SIM is quantified through variable importance calculation, at 22% and 28% importance of all RF variables for east and north SIM components, respectively. These results indicate the great potential of RF models for estimating SIM over the whole Arctic Ocean in winter. [ABSTRACT FROM AUTHOR]

Elymus dahucirus is an essential plant for ecological restoration in fragile ecological areas and mining area restoration. As lawn grass, it can quickly cover soil and prevent soil erosion, so it is commonly used as a pioneer grass for lawn greening and slope protection. In recent years, with the development of mineral resources, Qinghai–Tibet Plateau soil is facing the threat of heavy metal cadmium (Cd) pollution. E. dahuricus can host the filamentous fungus Epichloë bromicola. To make better use of the advantages that Epichloë bring to host plants to alleviate heavy metal pollution in soil, plant growth and antioxidant capacity effects on E. bromicola infected (E+) and uninfected (E−) E. dahuricus were determined under Cd stress. During Cd treatment, plant growth was decreased by Cd stress, while E+ plants exhibited equal or better growth compared to E− plants. Cd treatment induces a proline and antioxidant enzyme burst in infected plants, while malondialdehyde (MDA) increases. E. bromicola improved plant growth and antioxidant capacity. E. dahuricus breeding strategies could use the information here in efforts to improve the performance of E. dahuricus in both environmental protection and agronomic contexts. [ABSTRACT FROM AUTHOR]

Purpose: To explore the relationship between long-term glycemic variability and anxiety and depression in patients with type 2 diabetes.Participants and Methods: A cohort comprising 214 individuals diagnosed with type 2 diabetes participated in this study. Comprehensive demographic and laboratory information was gathered for them. The evaluation of anxiety relied on the 7-item Generalized Anxiety Disorder Scale (GAD-7), while depression was assessed utilizing the 9-item Health Questionnaire (PHQ-9). Based on the presence or absence of anxiety and depression, participants were categorized into either the mood disorder or control groups. Subsequently, univariate and stepwise multiple binary logistic regression analyses were conducted to investigate the potential correlations between factors and the presence of anxiety and depression.Results: The prevalence of anxiety disorders is 23%, and depression is 32%. The prevalence of smoking, diabetic autonomic neuropathy, stroke, and osteoporosis in the mood disorder group was significantly higher than that in the control group (P < 0.05), the glycated hemoglobin A1c variability score (HVS), mean hemoglobin A1c value, total cholesterol, urinary albumin/creatinine and systemic immune-inflammatory index (SII) were significantly higher in the control group (P < 0.05). The level of high-density lipoprotein in the mood disorder group was significantly lower than the control group (P < 0.05). In stepwise multiple binary logistic regression analyses, the main factors associated with anxiety were depression (P < 0.001, OR=117.581) and gender (P < 0.001, OR=9.466), and the main factors related to depression included anxiety (P < 0.001, OR=49.424), smoking (P=0.042, OR=2.728), HVS (P=0.004, OR=8.664), and SII (P=0.014, OR=1.002).Conclusion: Persistent fluctuations in blood glucose levels have been linked to anxiety and depression. Consequently, maintaining an optimal level of glycemic control and minimizing fluctuations becomes imperative in the comprehensive management of diabetes. [ABSTRACT FROM AUTHOR]

We investigate false vacuum decay catalysed by black holes under the influence of the higher order Gauss-Bonnet term. We study both bubble nucleation and Hawking-Moss types of phase transition in arbitrary dimension. The equations of motion of "bounce" solutions in which bubbles nucleate around arbitrary dimensional black holes are found in the thin wall approximation, and the instanton action is computed. The headline result that the tunnelling action for static instantons is the difference in entropy of the seed and remnant black holes is shown to hold for arbitrary dimension. We also study the Hawking-Moss transition and find a picture similar to the Einstein case, with one curious five-dimensional exception (due to a mass gap). In four dimensions, we find as expected that the Gauss-Bonnet term only impacts topology changing transitions, i.e. when vacuum decay removes the seed black hole altogether, or in a (Hawking-Moss) transition where a black hole is created. In the former case, topology changing transitions are suppressed (for positive GB coupling α), whereas the latter case results in an enhanced transition. [ABSTRACT FROM AUTHOR]

Linear chains in thermoplastics make them relatively weak in performance but inherently weldable and recyclable. By contrast, thermosets with permanently crosslinked networks possess outstanding mechanical performance, thermal, and chemical stability, but are unweldable and unrecyclable. In the last decade, a kind of thermoplastics‐like thermosets termed as vitrimer has been developed with extensive applications, in which welding of vitrimers plays a central and fundamental role. Herein, we present the current state of the art of the welding of vitrimers and discuss the welding of vitrimers from a broad picture of chemistry, physics, and mechanics: i) chemistry and mechanics of the welding of vitrimers; ii) applicability of the mechanical assessment methods for the welding of vitrimers; iii) design principles and implement strategies to the welding of vitrimers; iv) effects of welding conditions on the welding strength and toughness; and v) applications to the adhesion of chemically inert materials. Finally, advantages, challenges, and open questions to the welding of vitrimers are highlighted, and future opportunities in chemistry, mechanics, design of tough welding, artificial intelligence aided programming of welding technology, mechanical assessment standard, and so on are discussed. The development of vitrimer welding would fuse disciplines and make transformative impact in polymer industry. [ABSTRACT FROM AUTHOR]

As a narrow‐bandgap semiconductor, InSb is widely used in infrared (IR) detection due to its excellent performance and other characteristics such as ultrahigh electron mobility, extremely high quantum efficiency, and robust chemical properties. Herein, an ultralow dark current room‐temperature IR photodetector based on InSb nanosheets (NSs)/MoS2 flakes van der Waals (vdW) heterostructure is presented. Benefiting from a large surface‐to‐volume ratio and phonon scattering suppressed on the nanostructure, InSb NSs devices have high photosensitivity and low dark current density (16.67 A cm−2). To further suppress the dark current, a vdW heterojunction composed of InSb NSs and MoS2 is fabricated. When the InSb NSs/MoS2 vdW heterostructures' photodetector work on a photovoltaic model (zero‐bias operation), the device shows a dark current density as low as 0.12 A cm−2 at room temperature, exhibiting a high external quantum efficiency (EQE) of 3.6 × 102%, the responsivity of 3.8 A W−1, and detectivity of 1.2 × 109 cm Hz1/2 W−1 under 1310 nm laser illumination. These results demonstrate that InSb NSs vdW heterostructure is a feasible scheme to realize InSb room‐temperature IR detection. [ABSTRACT FROM AUTHOR]

Background: RP11-296E3.2 is a novel long noncoding RNA (lncRNA) associated with colorectal cancer (CRC) metastasis, that was reported in our previous clinical studies. However, the mechanisms of RP11-296E3.2 in colorectal tumorigenesis remain elusive. Methods: RNA sequencing (RNA-seq), Fluorescence in situ hybridization (FISH), Transwell assays and others, were performed to evaluate the function of RP11-296E3.2 for proliferation and metastasis in vitro. In situ and metastatic tumor models were performed to evaluate the function of RP11-296E3.2 for proliferation and metastasis in vivo. RNA-pulldown, RNA-interacting protein immunoprecipitation (RIP), tissue microarray (TMA) assay, a luciferase reporter assay, chromatin immunoprecipitation (ChIP) and others were performed to explore the mechanisms by which RP11-296E3.2 regulates CRC tumorigenesis. Results: RP11-296E3.2 was confirmed to be associated with CRC cell proliferation and metastasis in vitro and in vivo. Mechanistically, RP11-296E3.2 directly bound to recombinant Y-Box Binding Protein 1 (YBX1) and enhanced signal transducer and activator of transcription 3 (STAT3) transcription and phosphorylation. YBX1 promoted the CRC cell proliferation and migration, while knockdown of RP11-296E3.2 attenuated the effects of YBX1 on CRC cell proliferation, and metastasis and the expression of several related downstream genes. We are the first to discover and confirm the existence of the YBX1/STAT3 pathway, a pathway dependent on RP11-296E3.2. Conclusion: Together, these novel findings show that the RP11-296E3.2/YBX1 pathway promotes colorectal tumorigenesis and progression by activating STAT3 transcription and phosphorylation, and suggest that RP11-296E3.2 is a potential diagnostic biomarker and therapeutic target in CRC. [ABSTRACT FROM AUTHOR]

Motivation Modern methods for computation-intensive tasks in sequence analysis (e.g. read mapping, sequence alignment, genome assembly, etc.) often first transform each sequence into a list of short, regular-length seeds so that compact data structures and efficient algorithms can be employed to handle the ever-growing large-scale data. Seeding methods using k mers (substrings of length k) have gained tremendous success in processing sequencing data with low mutation/error rates. However, they are much less effective for sequencing data with high error rates as k mers cannot tolerate errors. Results We propose SubseqHash, a strategy that uses subsequences, rather than substrings, as seeds. Formally, SubseqHash maps a string of length n to its smallest subsequence of length k , k  <  n , according to a given order overall length- k strings. Finding the smallest subsequence of a string by enumeration is impractical as the number of subsequences grows exponentially. To overcome this barrier, we propose a novel algorithmic framework that consists of a specifically designed order (termed ABC order) and an algorithm that computes the minimized subsequence under an ABC order in polynomial time. We first show that the ABC order exhibits the desired property and the probability of hash collision using the ABC order is close to the Jaccard index. We then show that SubseqHash overwhelmingly outperforms the substring-based seeding methods in producing high-quality seed-matches for three critical applications: read mapping, sequence alignment, and overlap detection. SubseqHash presents a major algorithmic breakthrough for tackling the high error rates and we expect it to be widely adapted for long-reads analysis. Availability and implementation SubseqHash is freely available at https://github.com/Shao-Group/subseqhash. [ABSTRACT FROM AUTHOR]

Sugarcane leaf blight (SLB), a major fungal leaf disease of sugarcane (Saccharum spp.), has been attributed to Stagonospora tainanensis. In December 2020 and May 2021, signs of leaf blight were observed on sugarcane in the fields of Chongzuo City, in the Guangxi Province of China. Lesions on the leaves were characterized by yellow or dark red spots in the center. Fungal species were isolated, purified and subjected to pathogenicity evaluation on the sugarcane plants. An isolate that caused symptoms the same as those observed in the field was initially identified as S. tainanensis (Leptosphaeria taiwanensis, perfect state) based on its morphological characteristics both of asexual and sexual stages. Dark brown and nearly spherical pycnidia with conidia of long ellipsoidal, hyaline, one to four cells and 29.27 to 54.39 μm long and 9.03 to 16.12 μm wide were found on corn meal agar medium. Ascomata with asci of cylindrical to clavate, a short stipe and eight spores slightly constricted at the septum, with the size of the spore ranging from 36 to 44 μm long and 8.5 to 12 μm wide, were formed on the sugarcane-leaf-decoction saccharose agar medium. The identity of the species was further confirmed by rDNA ITS and TEF-1α sequencing. The optimal temperature for mycelial growth was 25 °C and the optimal pH was 6.0. The pathogen grew well in a medium with oats as the carbon source and yeast extract as the nitrogen source, but poorly in a medium with urea as the nitrogen source. This study is the first to identify the sugarcane leaf blight pathogen in Guangxi, and the first publication describing the biological characterization of S. tainanensis. The occurrence of sugarcane leaf blight should alert sugarcane breeders and plant pathologists to consider integrating control of this potentially important disease into the agenda of their breeding and disease control programs. [ABSTRACT FROM AUTHOR]

Urban green space (UGS) is an important component in the urban ecosystem and has great significance to the urban ecological environment. Although the development of remote sensing platforms and deep learning technologies have provided opportunities for UGS mapping from high-resolution images (HRIs), challenges still exist in its large-scale and fine-grained application due to insufficient annotated datasets and specially designed methods for UGS. Moreover, the domain shift between images from different regions is also a problem that must be solved. To address these issues, a general deep learning (DL) framework is proposed for UGS mapping in the large scale, and fine-grained UGS maps of 31 major cities in mainland China are generated (UGS-1m). The DL framework consists of a generator and a discriminator. The generator is a fully convolutional network designed for UGS extraction (UGSNet), which integrates attention mechanisms to improve the discrimination to UGS, and employs a point-rending strategy for edge recovery. The discriminator is a fully connected network aiming to deal with the domain shift between images. To support the model training, an urban green space dataset (UGSet) with a total number of 4544 samples of 512×512 in size is provided. The main steps to obtain UGS-1m can be summarized as follows: (a) first, the UGSNet will be pre-trained on the UGSet in order to obtain a good starting training point for the generator. (b) After pre-training on the UGSet, the discriminator is responsible for adapting the pre-trained UGSNet to different cities through adversarial training. (c) Finally, the UGS results of 31 major cities in China (UGS-1m) are obtained using 2179 Google Earth images with a data frame of 7 ′ 30 ′′ in longitude and 5 ′ 00 ′′ in latitude and a spatial resolution of nearly 1.1 m. An evaluation of the performance of the proposed framework by samples from five different cities shows the validity of the UGS-1m products, with an average overall accuracy (OA) of 87.56 % and an F1 score of 74.86 %. Comparative experiments on UGSet with the existing state-of-the-art (SOTA) DL models proves the effectiveness of UGSNet as the generator, with the highest F1 score of 77.30 %. Furthermore, an ablation study on the discriminator fully reveals the necessity and effectiveness of introducing the discriminator into adversarial learning for domain adaptation. Finally, a comparison with existing products further shows the feasibility of the UGS-1m and the great potential of the proposed DL framework. The UGS-1m can be downloaded from 10.57760/sciencedb.07049. [ABSTRACT FROM AUTHOR]

The relationship patterns of organizations participating in disaster response projects have a significant impact on their performance. However, research on appropriate relationship patterns of disaster response organizations is lacking both in theory and practice. To fill this gap, we conducted a comparative study on three interorganizational relationship patterns: government-centric, decentralized participatory, and partially collaborative. Drawing on the complex network modeling method, the mechanism of the interorganizational network composed of participating organizations in disaster response projects are conceptualized to depict different relationship patterns. Then, we conducted numerical simulation to further explore the optimal relationship pattern from the perspective of disaster response project effectiveness, efficiency, and fairness. The results reveal that the optimal organizational relationship pattern varies with changes in disaster response conditions. In the initial situation of an emergency with insufficient resources, government agencies should coordinate nongovernmental organizations (NGOs) to participate in disaster response in an orderly manner. When the demand for emergency resources drops, or the supply of emergency resources gradually becomes sufficient as disaster response projects progress, the government-centric pattern becomes the optimal relationship pattern. Moreover, the relationship between the response network structure and disaster response performance turns out to be inconsistent. At the early stage, response performance can be improved by reducing the number of steps to complete disaster relief and by appropriately increasing the connections between government agencies and NGOs. When a disaster situation is mitigated, the centralization of response power and unified coordination should be emphasized to improve emergency response performance. This study contributes to improving disaster response performance from the organizational relationship perspective and enriching disaster management theories. [ABSTRACT FROM AUTHOR]

A specific functional electric component is connected to any branch of the neural circuit, which can enhance the corresponding biological function as a smart sensor, whereas the temperature has a significant effect on the firing rhythm of neurons by regulating channel conductance and excitability. So for this paper, a controllable thermosensitive neuron model is established by paralleling two thermistors in the FitzHugh–Nagumo neural circuit. Especially, the thermistors are controlled by two switches, where three working modes can be selected, for which bifurcation diagrams, time response diagrams and spectral entropy (SE) complexity are used to analyze the dynamic behaviors in different operating modes. It is found that any modulation of thermistor can regulate the branch current and output voltage completely under the three working modes. More importantly, the neuronal activities show different mode transitions from periodic to bursts or chaos. The superior dynamic characteristics and rich firing behavior make the new model more suitable for images encryption. For this purpose, we consider switching the three thermosensitive neuron models continuously to obtain more complex chaotic sequences. Next, based on the feature of high pixel correlation among color image components, a 3D projection scrambling algorithm is designed, and then employed in combination with the universal gravitational diffusion method to propose a color image encryption scheme. In addition, the designed algorithm is executed by encrypting and decrypting some color images. The experimental results show that the algorithm cannot only encrypt the color image effectively, but also has no limitation on the size of the test image. It is worth mentioning that the designed encrypted algorithm has satisfactory security performance and considerable robustness against noise attacks and shearing attacks. The exploration of this paper about the thermosensitive neuron model and its application may provide some theoretical guidance and experimental basis in the field of secure communication. [ABSTRACT FROM AUTHOR]

A series of new chiral liquid crystal compounds named Mt4BOPnAB containing (-)-menthyl group has been prepared and characterised by FT-IR and 1H NMR spectra. The series contains nine membersthat vary in the terminal length of the alkanoyloxy chain (Cn−1H2n−1COO-, where n = 2, 4, 6, 8, 10, 12, 14, 16, 18). The thermal properties and optical properties were investigated by differential scanning calorimetry (DSC), polarising optical microscopy (POM), X-ray diffraction (XRD) and ultraviolet/visible sepctroscopy (UV/Vis). High chiral menthyl group in Mt4BOPnAB molecules triggers the formation of a cubic structure of a blue phase (BPI/II). Increasing the length of the terminal alkanoyloxy group leads to the reduction in Tc, narrowing the N* phase, and contributing to the development of the Sc* phase. In addition, only CLCs with short terminal chains (n ≤ 8) show selective reflection of visible light and themochromic behaviors. [ABSTRACT FROM AUTHOR]

Different-resolution change detection (DRCD) is now becoming an urgent problem to be solved, which is of great potential in rapid monitoring, such as disaster assessment and urban expansion. In DRCD tasks, bitemporal inputs are given in the form of different resolutions, and thus, conventional change detection (CD) methods cannot be applied directly. Previous studies have attempted to deal with this problem by reconstructing the low-resolution (LR) image into a high-resolution (HR) one, including interpolation and super-resolution (SR). However, these solutions are limited by the availability of training data, making it hard to meet different kinds of needs. Besides, these image-level strategies have also ignored the interaction and alignment of high-level features. Therefore, we propose a new approach based on multimodel Transformers (MM-Trans), which solves the resolution gaps of bitemporal inputs in DRCD tasks from the perspective of feature alignment. In the MM-Trans, a weight-unshared feature extractor is first utilized to precisely capture the features of the different-resolution inputs. Then, a spatial-aligned Transformer (sp-Trans) is introduced to align the LR-image features to the same size of the HR-image ones, which can be optimized in a learnable way by an auxiliary token loss. After that, a semantic-aligned Transformer (se-Trans) is adopted, in which the bitemporal features can be further interacted and aligned semantically. Finally, a prediction head is employed to obtain fine-grained change results. Experiments conducted on three common CD datasets, CDD, S2Looking, and HTCD dataset, have shown the advancement of the MM-Trans and fully demonstrated its potential in DSCD tasks. [ABSTRACT FROM AUTHOR]

Fidelity mechanics is formalized as a framework for investigating critical phenomena in quantum many-body systems. Fidelity temperature is introduced for quantifying quantum fluctuations, which, together with fidelity entropy and fidelity internal energy, constitute three basic state functions in fidelity mechanics, thus enabling us to formulate analogues of the four thermodynamic laws and Landauer's principle at zero temperature. Fidelity flows, which are irreversible, are defined and may be interpreted as an alternative form of renormalization group flows. Thus, fidelity mechanics offers a means to characterize both stable and unstable fixed points: divergent fidelity temperature for unstable fixed points and zero-fidelity temperature and (locally) maximal fidelity entropy for stable fixed points. In addition, fidelity entropy behaves differently at an unstable fixed point for topological phase transitions and at a stable fixed point for topological quantum states of matter. A detailed analysis of fidelity mechanical-state functions is presented for six fundamental models—the quantum spin- 1 / 2 XY model, the transverse-field quantum Ising model in a longitudinal field, the quantum spin- 1 / 2 XYZ model, the quantum spin- 1 / 2 XXZ model in a magnetic field, the quantum spin-1 XYZ model, and the spin- 1 / 2 Kitaev model on a honeycomb lattice for illustrative purposes. We also present an argument to justify why the thermodynamic, psychological/computational, and cosmological arrows of time should align with each other, with the psychological/computational arrow of time being singled out as a master arrow of time. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the value of preoperative CT energy spectrum imaging in detecting lymph node metastasis of colorectal cancer. Methods: From September 2019 to November 2021, a retrospective study was performed for the eighty-two patients with colorectal cancer through preoperative colonoscopy or surgical pathology confirmed in our hospital. Based on the lymph node metastasis status, these cases were divided into the metastasis and non-metastasis groups. GE Revolution CT scanner was used to scan the patients with energy spectrum imaging, it measured and recorded the single-energy CT values from 40 to 140 keV and various energy spectrum parameters of lymph nodes around the lesions in the arterial and venous phases, and statistically analyze the above indices. Results: In the arterial and venous phases: the single-energy CT values of 40–140 keV in the non-metastatic group were higher than those in the metastatic group (all P < 0.05); the parameter values of IC (iodine concentration), NIC (normalized iodine concentration), λ (the slope of the energy spectrum curve), and Eff-Z (effective-Z) in the non-metastatic group were higher than those in the metastatic group (all P < 0.05). Further evaluation of ROC curve showed that the higher AUC (area under curve) of the single-energy CT value of 50 keV in the arterial phase was 0.889, among the energy spectrum parameters of IC, NIC, λ, and Eff-Z, the NIC had the better diagnostic efficiency and the AUC of the NIC was 0.873, the highest AUC of the combination of NIC and λ was 0.885 when the energy spectrum parameters were combined. The higher AUC of the single-energy CT value of 60 keV in the venous phase was 0.853, among the energy spectrum parameters of IC, NIC, λ, and Eff-Z, the λ had the better diagnostic efficiency and the AUC of the λ was 0.822, the higher AUC of the combination of NIC, λ, and Eff-Z was 0.840 when the energy spectra were combined. Conclusions: Parameters of energy spectrum CT imaging can effectively evaluate whether lymph nodes have metastases, and provide favorable imaging diagnosis basis for the range and the number of lymph nodes to be cleaned during clinical operation and can evaluate the prognosis of patients. It is worthy of clinical recommendation. [ABSTRACT FROM AUTHOR]

PURPOSE: The recommended duration of adjuvant fluoropyrimidine and oxaliplatin chemotherapy for patients with stage III colon cancer is based on tumor classification into clinically low-risk (T1-3 N1) and high-risk (T4 or N2) groups. We determined whether Immunoscore can enhance prognostication within these risk groups. MATERIALS AND METHODS: Patients with stage III colon carcinomas (N = 600) were randomly selected from the infusional fluorouracil, leucovorin, and oxaliplatin arm of adjuvant trial NCCTG N0147 (Alliance for Clinical Trials in Oncology). Tumors were evaluated for Immunoscore that quantifies CD3+ and CD8+ T-cell densities in the tumor center and invasive margin by digital image analysis. Disease-free survival (DFS) by Immunoscore was analyzed using a multivariable Cox regression model in each risk group with adjustment for covariates including KRAS , BRAF V600E , and mismatch repair status. RESULTS: Of 559 cancers with Immunoscore data, 299 (53.5%) were classified as clinically low-risk (T1-3 N1) and 260 (46.5%) as clinically high-risk (T4 and/or N2). Among patients with low-risk tumors, those with Immunoscore-Low versus Immunoscore-High tumors had significantly worse 5-year DFS rates (77.5% v 91.8%; hazard ratio, 1.70; 95% CI, 1.03 to 2.79; P =.037). Among patients with high-risk tumors, those with Immunoscore-Low versus Immunoscore-High tumors also had significantly worse DFS (55.3% v 70.3%; hazard ratio, 1.65; 95% CI, 1.11 to 2.47; P =.013). Tumors that were low-risk/Immunoscore-Low had similar outcomes as did tumors that were high-risk/Immunoscore-High (P =.174). Prognostication was significantly improved in multivariable models where Immunoscore was added to clinical risk parameters and limited biomarkers (likelihood ratio test P =.0003). CONCLUSION: Immunoscore can refine patient prognosis beyond clinical risk group classification, suggesting its potential utility for adjuvant decision making. Immunoscore can refine prognosis beyond clinical risk group classification in stage III colon cancer. [ABSTRACT FROM AUTHOR]

Microglia are a specialized type of neuroimmune cells that undergo morphological and molecular changes through multiple signaling pathways in response to pathological protein aggregates, neuronal death, tissue injury, or infections. Microglia express Trem2, which serves as a receptor for a multitude of ligands enhancing their phagocytic activity. Trem2 has emerged as a critical modulator of microglial activity, especially in many neurodegenerative disorders. Human TREM2 mutations are associated with an increased risk of developing Alzheimer disease (AD) and other neurodegenerative diseases. Trem2 plays dual roles in neuroinflammation and more specifically in disease-associated microglia. Most recent developments on the molecular mechanisms of Trem2, emphasizing its role in uptake and clearance of amyloid β (Aβ) aggregates and other tissue debris to help protect and preserve the brain, are encouraging. Although Trem2 normally stimulates defense mechanisms, its dysregulation can intensify inflammation, which poses major therapeutic challenges. Recent therapeutic approaches targeting Trem2 via agonistic antibodies and gene therapy methodologies present possible avenues for reducing the burden of neurodegenerative diseases. This review highlights the promise of Trem2 as a therapeutic target, especially for Aβ-associated AD, and calls for more mechanistic investigations to understand the context-specific role of microglial Trem2 in developing effective therapies against neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Aiming at the problem of unsteady power output caused by the position misalignment of the coupler in the wireless charging process, a hybrid inductive power transfer (IPT) system with misalignment tolerance and constant voltage output based on the bipolar (BP) coupler optimization design is proposed in this paper. Firstly, the mathematical model of the hybrid topology circuit with intermediate coils is established, and the necessary conditions for the mutual inductance parameters configuration are summarized. Secondly, a pad parameter design method based on the BP coil structure is proposed to realize the maximum mutual inductance offset range. Meanwhile, the sensitivity of the compensation parameters in the proposed topology and the implementation of zero voltage switch (ZVS) are analyzed. Finally, a prototype is built to verify the theoretical analysis. The experimental results show that within 50% x-axis misalignment and −10 to 50% z-axis misalignment, the output voltage fluctuation range can be controlled within 5% when the load varies from 20 to 80 Ω, which indicates that the proposed system can achieve a well misalignment tolerance and constant voltage output. And the highest output efficiency of the system can reach 93.6%. [ABSTRACT FROM AUTHOR]

Megaproject management has continuously faced new challenges that require new theories and methods. The network perspective has been an increasing trend in this context. However, there is a lack of consensus concerning the applicability of network thinking to megaprojects and the status of network analytic methods with respect to the facilitation of megaproject management. This paper aims to present the full status of the network perspective in the context of megaproject management, integrate network thinking, clarify the application of network analytic methods, and accelerate the network paradigm in the context of megaproject management. This research included a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analyses framework. A total of 152 relevant studies were retrieved from three major databases and reviewed. The findings of this study were based on a novel integrated framework that included the constitutive and the analytic perspectives of the network paradigm. The constitutive perspective sheds light on the megaproject network, its definition, characteristics pertaining to network thinking, and megaproject governance through network logic. The analytic perspective illuminates the applicability of network methods to megaproject issues in detailed subknowledge areas, including stakeholder management, risk management, communication, coordination and collaboration, knowledge management, and interface management. Furthermore, these two perspectives were generalized and converged into pure categories of network research, including network mechanisms and antecedents, network patterns and dynamics, network functions and outcomes, and network controls and optimization. Based on meticulous discussions concerning the integrated framework, limitations and future directions for progress in this area were proposed to facilitate a deeper exchange of ideas in the context of any future research efforts. [ABSTRACT FROM AUTHOR]

A three-dimensional crosslinked CFs@CNT/CoSx nanocomposite was successfully synthesized by in situ growing carbon nanotubes on carbon nanofibers and a facile sulfurization process. The carbon nanotubes synthesized by sintering melamine under the catalysis of cobalt can increase the specific surface areas and provide abundant sodium ion diffusion channels for the composite. Meanwhile, the formed cobalt sulfide nanoparticles will increase the active sites on the surface of CFs@CNT/CoSx. Due to the rational design of the composite structure, such an anode can deliver a specific capacity of 423.7 mA h g−1 after 100 cycles at 100 mA g−1 and exhibit superior rate performance of retaining 324.1 mA h g−1 at 2 A g−1 for sodium storage. [ABSTRACT FROM AUTHOR]

State estimation is the core function of distribution management system, and its estimation accuracy directly affects the operation of other advanced application functions of distribution management system. However, the access of a large number of distributed generation brings non‐Gaussian noise and bad data problems, which affect the accuracy of state estimation. Therefore, based on the conventional cubature particle filter (CPF), two new state estimation operators, robust cubature filter 1 (RCPF1) and robust cubature filter 2 (RCPF2), are proposed by introducing the Huber's M‐estimation theory into the CPF algorithm and integrating a time‐varying, multi‐dimensional scale factor with the CPF, respectively. The simulation results based on IEEE33 bus system showed that: (1) RCPF has better estimation accuracy and robustness than conventional cubature particle filter and cubature Kalman filter; (2) RCPF2 has better estimation performance than RCPF1 in the case of non‐Gaussian noise and bad data. [ABSTRACT FROM AUTHOR]

The structure design and switching regulation of pulse groups in multi‐function radars (MFRs) are closely related to the work mode. The sequential extraction and recognition of MFR pulse group structure is a fundamental task to analysing and interpreting the work modes and behaviour intentions of an MFR. In this study, the temporal structure of MFR pulse group is expressed hierarchically, which is intensively modelled based on regular grammar. Besides, a corresponding hierarchical automaton is established to sequentially extract and recognise the MFR pulse group structure in pulse train. The hierarchical automaton used for pulse group recognition has a two‐layer structure. The bottom layer of the hierarchical automaton realises the sequential input of pulses and recognition of pulse subgroups, and the sequential input of pulse subgroups and recognition of pulse groups are realised at the top layer. The simulation results demonstrate that the proposed method performs satisfyingly in recognising the pulse group structure and is robust to not only pulse noises but also to the emitter number and pulse group scale. [ABSTRACT FROM AUTHOR]

Change detection (CD) aims to distinguish surface changes based on bitemporal images. Since high-resolution (HR) images cannot be typically acquired continuously over time, bitemporal images with different resolutions are often adopted for CD in practical applications. Traditional subpixel-based methods for CD using images with different resolutions may lead to substantial error accumulation when the HR images are employed, which is because of intraclass heterogeneity and interclass similarity. Therefore, it is necessary to develop a novel method for CD using images with different resolutions that are more suitable for the HR images. To this end, we propose a super-resolution-based change detection network (SRCDNet) with a stacked attention module (SAM). The SRCDNet employs a super-resolution (SR) module containing a generator and a discriminator to directly learn the SR images through adversarial learning and overcome the resolution difference between the bitemporal images. To enhance the useful information in multiscale features, a SAM consisting of five convolutional block attention modules (CBAMs) is integrated to the feature extractor. The final change map is obtained through a metric learning-based change decision module, wherein a distance map between bitemporal features is calculated. Ablation study and comparative experiments on two large datasets, building change detection dataset (BCDD) and season-varying change detection dataset (CDD), and a real-image experiment on the Google dataset fully demonstrate the superiority of the proposed method. The source code of SRCDNet is available at https://github.com/liumency/SRCDNet. [ABSTRACT FROM AUTHOR]

Change detection (CD) aims to identify surface changes from bitemporal images. In recent years, deep learning (DL)-based methods have made substantial breakthroughs in the field of CD. However, CD results can be easily affected by external factors, including illumination, noise, and scale, which leads to pseudo-changes and noise in the detection map. To deal with these problems and achieve more accurate results, a deeply supervised (DS) attention metric-based network (DSAMNet) is proposed in this article. A metric module is employed in DSAMNet to learn change maps by means of deep metric learning, in which convolutional block attention modules (CBAM) are integrated to provide more discriminative features. As an auxiliary, a DS module is introduced to enhance the feature extractor’s learning ability and generate more useful features. Moreover, another challenge encountered by data-driven DL algorithms is posed by the limitations in change detection datasets (CDDs). Therefore, we create a CD dataset, Sun Yat-Sen University (SYSU)-CD, for bitemporal image CD, which contains a total of 20 000 aerial image pairs of size $256\times256$. Experiments are conducted on both the CDD and the SYSU-CD dataset. Compared to other state-of-the-art methods, our network achieves the highest accuracy on both datasets, with an F1 of 93.69% on the CDD dataset and 78.18% on the SYSU-CD dataset. [ABSTRACT FROM AUTHOR]

Long non-coding RNA actin filament-associated protein 1-antisense RNA 1 (AFAP1-AS1) shows crucial regulatory function in tumor progression. Nonetheless, the biological function and underlying mechanism of AFAP1-AS1 in the progression of thyroid cancer is still unclear. Expressions of AFAP1-AS1, miR-204-3p and DUSP4 were quantified utilizing quantitative real-time polymerase chain reaction and/or western blot. In loss-of-function and gain-of-function assays, cell proliferation, migration and invasion were appraised by CCK-8 assay, wound healing assay, Transwell migration and invasion assays, respectively. Luciferase reporter assay was employed for validating the interaction between miR-204-3p and AFAP1-AS1 or the 3'UTR of dual specificity phosphatase 4 (DUSP4). AFAP1-AS1 was highly expressed in thyroid cancer tissues and cell lines. Highly expressed AFAP1-AS1 was in association with advanced TNM stage and positive lymph node metastasis. Knockdown of AFAP1-AS1 suppressed the proliferation, migration and invasion of thyroid cancer cells, and overexpression of AFAP1-AS1 induced a reversed effect. MiR-204-3p was targetedly repressed by AFAP1-AS1, and miR-204-3p could negatively regulate DUSP4 expression. AFAP1-AS1 augmented the expression of DUSP4 via repressing miR-204-3p, and the effects of AFAP1-AS1 overexpression on thyroid cancer cells were also partly abolished by miR-204-3p restoration. In summary, AFAP1-AS1 facilitates thyroid cancer cell proliferation, migration and invasion by regulating miR-204-3p/DUSP4 axis. [ABSTRACT FROM AUTHOR]

The development of multifunctional 3D printing materials from sustainable natural resources is a high priority in additive manufacturing. Using an eco‐friendly method to transform hard pollen grains into stimulus‐responsive microgel particles, we engineered a pollen‐derived microgel suspension that can serve as a functional reinforcement for composite hydrogel inks and as a supporting matrix for versatile freeform 3D printing systems. The pollen microgel particles enabled the printing of composite inks and improved the mechanical and physiological stabilities of alginate and hyaluronic acid hydrogel scaffolds for 3D cell culture applications. Moreover, the particles endowed the inks with stimulus‐responsive controlled release properties. The suitability of the pollen microgel suspension as a supporting matrix for freeform 3D printing of alginate and silicone rubber inks was demonstrated and optimized by tuning the rheological properties of the microgel. Compared with other classes of natural materials, pollen grains have several compelling features, including natural abundance, renewability, affordability, processing ease, monodispersity, and tunable rheological features, which make them attractive candidates to engineer advanced materials for 3D printing applications. [ABSTRACT FROM AUTHOR]

Mixed pixel problem is omnipresent in remote sensing images for urban land use interpretation due to the hardware limitations. Subpixel mapping (SPM) is a usual way to solve this problem by improving the observation scale and realizing a finer spatial resolution land cover mapping. Recently, deep learning-based subpixel mapping network (DLSMNet) was proposed, benefited from its strong representation and learning ability, to restore a visually pleasing finer mapping. However, the spatial context features of artifacts are usually aggregated and progressively lost during the forward pass of the network without sufficient representation, which make it difficult to be learned and restored. In this article, a semantic information modulated (SIM) deep subpixel mapping network (SIMNet) is proposed, which uses low-resolution semantic images as prior, to reinforce the representation of spatial context features. In SIMNet, SIM module is proposed to parametrically incorporate the semantic prior into the state-of-the-art (SOTA) feed forward network architecture in an end-to-end training fashion. Furthermore, stacked SIM module with residual blocks (SIM_ResBlock) is adopted to pass the representation of spatial context feature to the deep layers, to get it fully learned during backpropagation. Experiments have been implemented on three public urban scenario data sets, and the SIMNet generates a clearer outline of artificial facilities with sufficient spatial context, and is distinctive for even individual building, which is challenging for other SOTA DLSMNet. The results demonstrate that the proposed SIMNet is a promising way for high-resolution urban land use mapping from easily available lower resolution remote sensing images.Mixed pixel problem is omnipresent in remote sensing images for urban land use interpretation due to the hardware limitations. Subpixel mapping (SPM) is a usual way to solve this problem by improving the observation scale and realizing a finer spatial resolution land cover mapping. Recently, deep learning-based subpixel mapping network (DLSMNet) was proposed, benefited from its strong representation and learning ability, to restore a visually pleasing finer mapping. However, the spatial context features of artifacts are usually aggregated and progressively lost during the forward pass of the network without sufficient representation, which make it difficult to be learned and restored. In this article, a semantic information modulated (SIM) deep subpixel mapping network (SIMNet) is proposed, which uses low-resolution semantic images as prior, to reinforce the representation of spatial context features. In SIMNet, SIM module is proposed to parametrically incorporate the semantic prior into the state-of-the-art (SOTA) feed forward network architecture in an end-to-end training fashion. Furthermore, stacked SIM module with residual blocks (SIM_ResBlock) is adopted to pass the representation of spatial context feature to the deep layers, to get it fully learned during backpropagation. Experiments have been implemented on three public urban scenario data sets, and the SIMNet generates a clearer outline of artificial facilities with sufficient spatial context, and is distinctive for even individual building, which is challenging for other SOTA DLSMNet. The results demonstrate that the proposed SIMNet is a promising way for high-resolution urban land use mapping from easily available lower resolution remote sensing images. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) denoising plays an important role in image quality improvement and related applications. Convolutional neural network (CNN)-based image denoising methods have been predominant due to advances made in the field of deep learning in recent years. Spatial and spectral information are crucial to HIS denoising, along with their correlations. However, existing methods fail to consider the global dependence and correlation between spatial and spectral information. Accordingly, in this article, we propose a novel dual-attention denoising network to overcome these limitations. We design two parallel branches to process the spatial and spectral information separately. The position attention module is applied to the spatial branch to formulate the interdependencies on the feature map, while the channel attention module is applied to the spectral branch to simulate the spectral correlation before the two branches are combined. A multiscale structure is also employed to extract and fuse the multiscale features following the fusion of spatial and spectral information. Experimental results on simulated and real data substantiate the superiority of our method both visually and quantitatively when compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive incremental nonlinear backstepping sliding-mode (INBSM) controller, for fault tolerant tracking control of a blended wing body (BWB) aircraft with unknown disturbances and actuator faults. The INBSM controller is based on a nonlinear dynamics model of the BWB aircraft. In addition, a radial basis function neural network disturbance observer (RBF-NNDO) is proposed to enhance the disturbance attenuation ability. A fault estimator is suggested to improve actuator fault tolerant control level. The closed-loop control system of the BWB aircraft is proved to be globally asymptotically stable using Lyapunov theory. Simulations of the combined NNDO-INBSM controller are presented and compared with both the INBSM design and an adaptive fuzzy controller. The results demonstrate an improved capability of the NNDO-INBSM control for the BWB aircraft to execute realistic attitude tracking missions, even in the presence of center of gravity movement, unknown disturbances, model uncertainties and actuator faults. [ABSTRACT FROM AUTHOR]

Bond strength of steel reinforcement embedded in ultra‐high‐performance fiber‐reinforced concrete (UHPFRC) plays a vital role in the post‐cracking behavior of reinforced UHPFRC members in flexure or tension. This paper presents an analytical study on the bond strength of steel reinforcement in UHPFRC. Three different bond‐slip models, namely the modified fib model, Marchand model and the proposed model are compared in the analytical method and explicit equations are also proposed. Comparisons with test data suggest that the proposed model and explicit equations can evaluate the bond‐slip behavior of reinforcement with good accuracy. Marchand model shows similar predictions of bond‐slip behavior to the proposed model at different load levels, whereas the modified fib model is accurate only when the reinforcement nearly yields in tension. The embedment length required for embedded reinforcement to develop its yield and ultimate strengths can also be calculated from the analytical method and explicit equations. [ABSTRACT FROM AUTHOR]

• The expression levels of 30 autoantibodies were detected by protein chip technology, and 11 autoantibodies (MAGE-A4, NY-ESO-1, HuD, Survivin, IMDH2, ubiquilin-1, IMP1, PGP9.5, c-Myc, IMP3, Cyclin B1) were significantly different between NPC patients and the control group. • We established different models by different combinations of potential biomarkers by means of logistic regression for the diagnosis of NPC as past research. Four molecules (Cyclin B1, NY-ESO-1, Survivin, IMP3) were finally entered into the regression model with highest AUC of 0.779. The early symptoms of nasopharyngeal carcinoma (NPC) are not obvious, and it is difficult to make early diagnosis. A case-control study was conducted to identify potential biomarkers and established a diagnosis model for nasopharyngeal carcinoma. Plasma samples of 131 cases of NPC and 132 cases of healthy individuals were incubated with the Ray Biotech Human Lung Cancer IgG Autoantibody Detection Array G1, and signal values were used to develop a risk prediction model for NPC diagnosis. Of the 30 autoantibodies, high expression of MAGE-A4, NY-ESO-1, HuD, Survivin, IMDH2, Ubiquilin-1, IMP1, PGP9.5, IMP3, C-Myc and low expression of Cyclin B1 were potential biomarkers for NPC diagnosis (p <0.05), among which Survivin, MAGE-A4 and IMP3 shows higher AUC of 0.674, 0.652 and 0.650 respectively, the specificity of them was 89.39% (95% CI: 82.85–94.08%), 90.15% (95% CI: 83.75–94.65%) and 88.64% (81.95–93.50%).The risk probability analysis for NPC diagnosis based on the panel of Cyclin B1, NY-ESO-1, Survivin, and IMP3 displayed the best diagnosis performance with an AUC of 0.779, p (Yi = 1) = 1/(1+EXP[8.316+1.672*CyclinB1-1.152*NY-ESO-1-2.052*Survivin-0.950*IMP3]), the specificity of that was 86.36% (95% CI: 79.31–91.71%). Our findings demonstrated that the panel of Cyclin B1, NY-ESO-1, Survivin, and IMP3 has a good performance in the detection of NPC, and all 11 autoantibodies may also have a certain significance for the prognosis of NPC. [ABSTRACT FROM AUTHOR]

Background: To investigate the effect of flavaspidic acid BB (BB) on biofilms and quorumsensing-related genes of drug-resistant Staphylococcus epidermidis (SE) and to provideatheoretical basis for the development of BB as a new type of quorumsensing inhibitor. Methods: The microdilution method was applied to screen 15 clinical isolates of S. epidermidis for drug-resistant S. epidermidis to be used as the test strain. The effects of BBonthe biofilms of drug-resistant S. epidermidis at different growth stages were studied usingthecell counting kit-8 assay and scanning electron microscopy. The gene expression of sigB, sarA, and luxS, regulators involved in quorum sensing of biofilm formation, was measuredby PCR. Results: The minimum inhibitory concentrations of BB against 15 clinical strains of S. epidermidis ranged 11.67–66.67 µg/mL. BB significantly inhibited biofilmformationbydrug-resistant SE06 in the aggregation and maturation stages, with an activity superior tothose of mupirocin and fusidic acid (P < 0.05). At 40 µg/mL, BB reduced the secretionof extracellular polymers and promoted the disruption of biofilm structure. At 80 µg/mL, BBspecifically interfered with the quorum sensing system of drug-resistant SE06bydownregulating sarA and luxS, thereby inhibiting this microbe’s biofilmformation. Conclusion: BB exhibited strong inhibitory activity against SE06 by suppressing biofilmformation and downregulating quorum sensing-related genes and was identified as anewpotential quorum sensing inhibitor against S. epidermidis. [ABSTRACT FROM AUTHOR]

Safety is one of the key requirements for automated vehicles and fault diagnosis is an effective technique to enhance the vehicle safety. The model-based fault diagnosis method models the fault into the system model and estimates the faults by observer. In this article, to avoid the complexity of designing observer, we investigate the problem of steering actuator fault diagnosis for automated vehicles based on the approach of model-based support vector machine (SVM) classification. The system model is utilized to generate the residual signal as the training data and the data-based algorithm of the SVM classification is employed to diagnose the fault. Due to the phenomena of data unbalance induced poor performance of the data-driven method, an undersampling procedure with the approach of linear discriminant analysis and a threshold adjustment using the algorithm of grey wolf optimizer are proposed to modify and improve the performance of classification and fault diagnosis. Various comparisons are carried out based on widely used datasets. The comparison results show that the proposed algorithm has superiority on the classification over existing methods. Experimental results and comparisons of an automated vehicle illustrate the effectiveness of the proposed algorithm on the steering actuator fault diagnosis. [ABSTRACT FROM AUTHOR]

The effect of a hybrid filler on the thermal conductivity of an epoxy composite is described. The hybrid filler, containing boron nitride (BN) particles and silicon nitride (Si3N4) whiskers with different wt% ratios (20:0, 18:2, 15:5, 10:10, 5:15, 2:18 and 0:20), was loaded into the epoxy resin. The thermal conductivities of the doped composites were higher than that of the pure EP (0.4287 W·m−1·K−1); with addition of BN and Si3N4 fillers the 10%BN/10%Si3N4/EP composites exhibited the highest thermal conductivity (1.1486 W·m−1·K−1), which was 185% and 214% that of the 20%BN/EP composite (0.6200 W·m−1·K−1) and the 20%Si3N4/EP (0.5374 W·m−1·K−1), respectively. The impact strength of the composite material 10%BN/10%Si3N4/EP also was the maximum (3.48 KJ/m2). Thus, the 10%BN/10%Si3N4/EP composites not only exhibited excellent thermal conductivity, but also slight improvements in thermal stability and impact strength. Suggesting it would be useful for thermal sensor interconnections of electronic circuits. [ABSTRACT FROM AUTHOR]

Current hyperspectral image classification assumes that a predefined classification system is closed and complete, and there are no unknown or novel classes in the unseen data. However, this assumption may be too strict for the real world. Often, novel classes are overlooked when the classification system is constructed. The closed nature forces a model to assign a label given a new sample and may lead to overestimation of known land covers (e.g., crop area). To tackle this issue, we propose a multitask deep learning method that simultaneously conducts classification and reconstruction in the open world (named MDL4OW) where unknown classes may exist. The reconstructed data are compared with the original data; those failing to be reconstructed are considered unknown based on the assumption that they are not well represented in the latent features due to the lack of labels. A threshold needs to be defined to separate the unknown and known classes; we propose two strategies based on the extreme value theory for few- and many-shot scenarios. The proposed method was tested on real-world hyperspectral images; state-of-the-art results were achieved, e.g., improving the overall accuracy by 4.94% for the Salinas data. By considering the existence of unknown classes in the open world, our method achieved more accurate hyperspectral image classification, especially under the few-shot context. [ABSTRACT FROM AUTHOR]

The application of convolutional neural networks has been shown to significantly improve the accuracy of building extraction from very high-resolution (VHR) remote sensing images. However, there exist so-called semantic gaps among different kinds of buildings due to the large intraclass variance of buildings, and most of the present-day methods are ineffective in extracting various buildings in large areas that cover different scenes, for example, urban villages and high-rise buildings, because existing building extraction strategies are the same for various scenes. With the improvement of the resolution of remote sensing images, it is feasible to improve the image interpretation based on the scene prior. However, this idea has not been fully utilized in building extraction from VHR remote sensing imagery. This study proposes a scene-driven multitask parallel attention convolutional network (MTPA-Net) to resolve these limitations. The proposed approach classifies the input image into multilabel scenes and further separately maps the buildings in pixel level under different scenes. In addition, a simple postprocessing method is applied to integrate the building extraction results and scene prior. Our proposed method does not require multimodel training and the network can learn in an end-to-end manner. The performance of our proposed method is evaluated on a data set that includes various urban and rural scenes with diverse landscapes. The experimental results show that the proposed MTPA-Net outperforms state-of-the-art algorithms by reducing misclassification areas and maintaining improved robustness. [ABSTRACT FROM AUTHOR]

Signals of radars with fixed, stagger and other conventional pulse repetition intervals (PRI) occupy a large proportion in most radar reconnaissance datasets. Automatic and accurate reconstruction of the PRI patterns of these radars is an important task in realizing intelligent processing of radar reconnaissance data. In order to address this problem, this article proposes an automatic method based on the extraction and extension of frequent differential time‐of‐arrival (DTOA) items, which can be combined with other pulse parameters such as pulse width. The values and ordering of the frequent items are determined to reconstruct a complete PRI cycle, and the radar pulses are deinterleaved at the same time as a by‐product. Compared with the commonly used one‐dimensional radar reconnaissance parameters such as framework PRI or typical PRI set, the reconstructed PRI pattern contains more information about radar operational modes and can be applied to realize online and parallel pulse deinterleaving and more accurate radar type classification. [ABSTRACT FROM AUTHOR]

Importance: Aspirin and cyclooxygenase 2 (COX-2) inhibitors have been associated with a reduced risk of colorectal polyps and cancer in observational and randomized studies. The effect of celecoxib, a COX-2 inhibitor, as treatment for nonmetastatic colon cancer is unknown.Objective: To determine if the addition of celecoxib to adjuvant chemotherapy with fluorouracil, leucovorin, and oxaliplatin (FOLFOX) improves disease-free survival in patients with stage III colon cancer.Design, Setting, and Participants: Cancer and Leukemia Group B (Alliance)/Southwest Oncology Group 80702 was a 2 × 2 factorial design, phase 3 trial conducted at 654 community and academic centers throughout the United States and Canada. A total of 2526 patients with stage III colon cancer were enrolled between June 2010 and November 2015 and were followed up through August 10, 2020.Interventions: Patients were randomized to receive adjuvant FOLFOX (every 2 weeks) for 3 vs 6 months with or without 3 years of celecoxib (400 mg orally daily; n = 1263) vs placebo (n = 1261). This report focuses on the results of the celecoxib randomization.Main Outcomes and Measures: The primary end point was disease-free survival, measured from the time of randomization until documented recurrence or death from any cause. Secondary end points included overall survival, adverse events, and cardiovascular-specific events.Results: Of the 2526 patients who were randomized (mean [SD] age, 61.0 years [11 years]; 1134 women [44.9%]), 2524 were included in the primary analysis. Adherence with protocol treatment, defined as receiving celecoxib or placebo for more than 2.75 years or continuing treatment until recurrence, death, or unacceptable adverse events, was 70.8% for patients treated with celecoxib and 69.9% for patients treated with placebo. A total of 337 patients randomized to celecoxib and 363 to placebo experienced disease recurrence or died, and with 6 years' median follow-up, the 3-year disease-free survival was 76.3% for celecoxib-treated patients vs 73.4% for placebo-treated patients (hazard ratio [HR] for disease recurrence or death, 0.89; 95% CI, 0.76-1.03; P = .12). The effect of celecoxib treatment on disease-free survival did not vary significantly according to assigned duration of adjuvant chemotherapy (P for interaction = .61). Five-year overall survival was 84.3% for celecoxib vs 81.6% for placebo (HR for death, 0.86; 95% CI, 0.72-1.04; P = .13). Hypertension (any grade) occurred while treated with FOLFOX in 14.6% of patients in the celecoxib group vs 10.9% of patients in the placebo group, and a grade 2 or higher increase in creatinine levels occurred after completion of FOLFOX in 1.7% vs 0.5% of patients, respectively.Conclusions and Relevance: Among patients with stage III colon cancer, the addition of celecoxib for 3 years, compared with placebo, to standard adjuvant chemotherapy did not significantly improve disease-free survival.Trial Registration: ClinicalTrials.gov Identifier: NCT01150045. [ABSTRACT FROM AUTHOR]

Morphological transitions in Candida species are key factors in facilitating invasion and adapting to environmental changes. N-acetylglucosamine (GlcNAc) is a monosaccharide signalling molecule that can regulate morphological transitions in Candida albicans and Candida tropicalis. Interestingly, although the uptake and metabolic pathways of GlcNAc and GlcNAc-mediated white-to-opaque cell switching are similar between the two Candida species, GlcNAc induces hyphal development in C. albicans, whereas it suppresses hyphal development in C. tropicalis. These findings indicate that the characteristics of C. albicans and C. tropicalis in response to GlcNAc are remarkably different. Here, we compare the conserved and divergent GlcNAc-mediated signalling pathways and catabolism between the two Candida species. Deletion of NGT1, a GlcNAc transportation gene, inhibited hyphal formation in C. albicans but promoted hyphal development in C. tropicalis. To further understand these opposite effects on filamentous growth in response to GlcNAc in the two Candida species, the cyclic adenosine monophosphate/protein kinase A (cAMP/PKA) signalling pathways in both C. albicans and C. tropicalis were compared. Interestingly, GlcNAc activated the cAMP/PKA signalling pathway of the two Candida species, suggesting that the hyphal development-regulated circuit is remarkably diverse between the two species. Indeed, the Ndt80-like gene REP1, which is critical for regulating GlcNAc catabolism, exhibits distinct roles in the hyphal development of C. albicans and C. tropicalis. These data suggest possible reasons for the divergent hyphal growth response in C. albicans and C. tropicalis upon GlcNAc induction. [ABSTRACT FROM AUTHOR]

In the presence of unknown disturbances and model parameter uncertainties, this paper develop a nonlinear backstepping sliding-mode controller (BSMC) for trajectory tracking control of a stratospheric airship using a disturbance-observer (DO). Compared with the conventional sliding mode surface (SMS) constructed by a linear combination of the errors, the new SMS manifold is selected as the last back-step error to improve independence of the adjustment of the controller gains. Furthermore, a nonlinear disturbance-observer is designed to process unknown disturbance inputs and improve the BSMC performances. The closed-loop system of trajectory tracking control plant is proved to be globally asymptotically stable by using Lyapunov theory. By comparing with traditional backstepping control and SMC design, the results obtained demonstrate the capacity of the airship to execute a realistic trajectory tracking mission, even in the presence of unknown disturbances, and aerodynamic coefficient uncertainties. [ABSTRACT FROM AUTHOR]

Sub-pixel mapping is the prevailing approach for dealing with the mixed pixel effect in urban land use/land cover classification, by reconstructing the sub-pixel-scale distribution inside each mixed-pixel based on spatial autocorrelation. However, 1) traditional spatial autocorrelation is limited to a local window, which cannot model the teleconnection between two locations or objects that are far apart and 2) autocorrelation is based on the idea of "the more proximate, the more similar", which relies on a distance-weight decay parameter and cannot characterize the rich variety of mutual information in spatially heterogenous areas in urban. In this research, we develop and demonstrate a learnable correlation-based sub-pixel mapping (LECOS) method. 1) We use the "mutual retrieval" mechanism of the self-attention operation to model teleconnections that enable more distant locations or objects to be mutually correlated and 2) we design a parameter-free "self-attention in self-attention" operation to learn adaptively the diverse global correlation patterns between pixel and sub-pixel. The learned spatial correlations are then used for reasoning the sub-pixel-scale distribution of each class. We validated our method on the most challenging public datasets of urban scenes, which exhibit considerable spatial heterogeneity with complex structures and broken objects. The learned building-tree, building-road and road-tree correlation patterns contributed most to the sub-pixel reconstruction result of the urban scenes, consistent with in-situ reference data. We further explored the model's explicability in a large-area of several metropolises in China, by mapping land cover in these cities at a 2 m very fine spatial resolution using 10 m Sentinel-2 input images, and found that the derived result not only revealed rich urban spatial heterogeneity, but also that the learned correlation was indicative of urban pattern dynamics, suggesting the potential for greater understanding of issues such as urban fairness, accessibility, human exposure and sustainability. • An explicable deep learning is developed for urban land use/land cover mapping. • The spatial teleconnection of urban components is embedded into neural network. • Sub-pixel mapping network is used to restore 10 m Sentinel image to 2 m urban map. • Our results reveal unprecedented urban patterns with rich spatial heterogeneity. • The learned correlation is explainable for urban fairness and human exposure. [ABSTRACT FROM AUTHOR]

The lack of in situ data has always posed challenges to remote-sensing-data product validation. Herein, the products of sea-ice concentration (SIC) data derived using the arctic radiation and turbulence interaction study (ARTIST) sea ice (ASI) algorithm were evaluated by comparing them with SICs from a high-resolution sea-ice aerial image obtained during the 27th China Antarctic expedition in January 2011. Results suggest that data obtained from the advanced microwave scanning radiometer for the earth-observing system (AMSR-E) underestimate SICs by 19%. We performed step-by-step comparisons among the aerial image, moderate-resolution-imaging spectroradiometer (MODIS), and AMSR-E SIC. These types of comparisons have not been made in previous validation studies. First, SICs acquired from MODIS-Terra imagery were acquired using a tie-point method and corrected by SICs derived from the aerial photography. Second, SICs of MODIS-Aqua images were trained based on the consistency of SIC results between MODIS-Terra and MODIS-Aqua over the selected region on the same day. Finally, the MODIS-Aqua SICs were employed to validate synchronous AMSR-E swath SIC products. The results show that the AMSR-E products underestimate SICs by 8.5% in the marginal ice zone in comparison with MODIS SICs. According to our further analysis between sea-ice types and AMSR-E biases, the higher the proportion of first-year ice, the smaller the AMSR-E SIC bias. In other words, results suggest that the higher the thin ice proportion, the more the AMSR-E underestimates the SIC. [ABSTRACT FROM AUTHOR]