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Iodine, is an essential nutrient that may change the occurrence of autoimmune thyroiditis (AIT). Apoptosis and DNA methylation participate in the pathogenesis and destructive mechanism of AIT. We detected the methylation and the expression of mRNA of intrinsic apoptosis-associated genes (YWHAG, ING4, BRSK2, and GJA1) to identify the potential interactions between the levels of methylation in these genes and different levels of iodine. 176 adult patients with AIT in Shandong Province, China, were included. The MethylTargetTM assay was used to verify the levels of methylation. We used PCR to detect the mRNA levels of the candidate genes. Interactions between methylation levels of the candidate genes and iodine levels were evaluated with multiplicative and addictive interaction models and GMDR. In the AIT group, YWHAG_1 and six CpG sites, and BRSK2_1 and eight CpG sites were hypermethylated, whereas ING4_1 and one CpG site were hypomethylated. A negative correlation was found between methylation levels of YWHAG and mRNA expression. The combination of iodine fortification, YWHAG_1 hypermethylation, and BRSK2_1 hypermethylation was significantly associated with elevated AIT risk. A four-locus model (YWHAG_1×ING4_1×BRSK2_1×iodine level) was found to be the best model of the gene-environment interactions. We identified abnormal changes in the methylation status of YWHAG, ING4, and BRSK2 in patients with AIT in different iodine levels. Iodine fortification not only affected the methylation levels of YWHAG and BRSK2 but also interacted with the methylation levels of these genes and may ultimately increase the risk of AIT.

The newly discovered kagome superconductors represent a promising platform for investigating the interplay between band topology, electronic order, and lattice geometry. Despite extensive research efforts on this system, the nature of the superconducting ground state remains elusive. In particular, consensus on the electron pairing symmetry has not been achieved so far, in part owing to the lack of a momentum-resolved measurement of the superconducting gap structure. Here we report the direct observation of a nodeless, nearly isotropic, and orbital-independent superconducting gap in the momentum space of two exemplary CsV$_3$Sb$_5$-derived kagome superconductors -- Cs(V$_{0.93}$Nb$_{0.07}$)$_3$Sb$_5$ and Cs(V$_{0.86}$Ta$_{0.14}$)$_3$Sb$_5$, using ultrahigh resolution and low-temperature angle-resolved photoemission spectroscopy (ARPES). Remarkably, such a gap structure is robust to the appearance or absence of charge order in the normal state, tuned by isovalent Nb/Ta substitutions of V. Moreover, we observe a signature of the time-reversal symmetry (TRS) breaking inside the superconducting state, which extends the previous observation of TRS-breaking CDW in the kagome lattice. Our comprehensive characterizations of the superconducting state provide indispensable information on the electron pairing of kagome superconductors, and advance our understanding of unconventional superconductivity and intertwined electronic orders.

Strategies developed based on nanozyme catalysis for ion sensing.

Racial status, that is, majority/minority identity, affects an individual’s confidence in the criminal justice system, and this effect could vary across social contexts. We analyzed people’s confidence in the criminal justice system comparatively in 88 societies using the World Values Survey (1981–2020). Results from the hierarchical linear models showed the following patterns: (1) Racial majority members display higher confidence in the criminal justice system than minority members; (2) the majority advantage in confidence is greater when racial diversity increases; (3) the majority advantage is most salient in societies with Black or Arabic majorities. The results suggest that majority members’ higher trust in order institutions is associated with perceived advantages and social comparison with minority members. Our findings reveal the profound interactive effects of racial status and context on confidence in the criminal justice system, shed light on racial diversity, and contribute new knowledge to public opinion studies.

This work reports the thermal conductivity of RbV3Sb5 and CsV3Sb5 with three-dimensional charge density wave phase transitions from 80 K to 400 K measured by pump-probe thermoreflectance techniques. At room temperature, the in-plane (basal plane) thermal conductivities are found moderate, i.e., 12 W m-1 K-1 of RbV3Sb5 and 8.8 W m-1 K-1 of CsV3Sb5, and ultralow cross-plane (stacking direction) thermal conductivities are observed, with 0.72 W m-1 K-1 of RbV3Sb5 and 0.49 W m-1 K-1 of CsV3Sb5 at 300 K. A unique glass-like temperature dependence in the cross-plane thermal conductivity is discovered, which decreases monotonically even lower than the Cahill-Pohl limit as the temperature decreases below the phase transition point TCDW. This temperature dependence is found to obey the hopping transport picture. In addition, a peak in cross-plane thermal conductivity is observed at TCDW as a fingerprint of the modulated structural distortion along the stacking direction.

This work established a numerical model to investigate the heat transfer characteristics and wall film formation of spray impinging on the wall in the selective catalytic reduction (SCR) system. The model is developed by the Eulerian–Lagrangian approach, where the Lagrangian approach is used to represent the spray generated by a commercial non-air-assisted pressure-driven injector and the Eulerian approach is adopted to represent exhaust gas. The Stochastic Kuhnke Model is applied to spray/wall interaction. The model considers relevant processes, which include mass transfer, momentum transfer, heat transfer, droplet phase change, spray/wall interaction, and wall film formation. The numerical results compared with that of the experiment indicate that the model can accurately estimate the heat transfer characteristics of the wall surface during the spray impingement. Based on the numerical results, the causes of the spray local cooling effect and the rapid cooling effect are analyzed. The correlation between the critical transition temperature and the critical heat flux temperature for wall film formation is derived from the trends of wall temperature and heat flux. In this work, the Stochastic Kuhnke Model is applied and compared with the Kuhnke Model, which proves that it can improve the disadvantage of sudden change during the wall film formation. When the wall temperature is below the critical transition temperature, the wall film mass is sensitive to the wall temperature and increases as the wall temperature decreases.

All-inorganic Cs3Bi2I9 (CBI) halide perovskites are sought to be candidate for photoelectrical materials because of their low toxicity and satisfactory stability. Unfortunately, the discrete molecular [Bi2I9]3− clusters limit the charge-transport behaviors. Herein, the defect halide perovskite based on trivalent Bi3+ is expanded to Cs3Bi2I6Br3 (CBIB). Centimeter-size CBIB single crystal (Φ 15 × 70 mm3) was grown by the vertical Bridgeman method. The powder X-ray diffraction analysis shows that CBIB has P 3 ¯ m 1 structure with lattice parameters of a = b = 8.223 A, c = 10.024 A, α = β = 90° and γ = 120°. The density functional theory (DFT) calculations demonstrate that the charge density distribution was enhanced after the dimensional expansion. The enhancement of carrier transport ability of (00l) in-plane is characterized before and after dimensional improvement. The obtained CBIB(001) exhibited an electron mobility up to 40.03 cm2 V−1 s−1 by time-of-flight (TOF) technique, higher than 26.46 cm2 V−1 s−1 of CBI(001). Furthermore, the X-ray sensitivity increases from 707.81 μC Gy−1 cm−2 for CBI(001) to 3194.59 μC Gy−1 cm−2 for CBIB(001). This research will deepen our understanding of Bi-based perovskite materials and afford more promising strategies for lead-free perovskite optoelectronic devices modification.

PurposeThis research aims to examine which cooperative contract (wholesale-price contract or cost-sharing contract) can more effectively upgrade the green degree of product and promote demand when considering consumer reference price effect under different power structures.Design/methodology/approachThis research investigates a dyadic green supply chain composed of one manufacturer and one retailer. Four Stackelberg game models with a cost-sharing contract or a wholesale-price contract are built in retailer-led and manufacturer-led scenarios, respectively. Using backward induction, the optimal green decision under each model is obtained. In addition, the optimal cooperative contract is proposed by comparing these four models.FindingsIt is found that under consumer reference price effect, a cost-sharing contract outperforms a wholesale-price contract in upgrading product greenness and promoting demand. Under any single contract, the retailer-led situation is more conducive to improving product greenness than the manufacturer-led situation. Moreover, consumer reference price effect would reduce the sharing ratio of a cost-sharing contract when the manufacturer dominates, but it could mitigate the problem of double marginalization by reducing wholesale and retail prices under both types of contracts, which would enhance consumer surplus.Originality/valueIt is a new attempt to incorporate consumer reference price effect and power structure into a green supply chain framework and proposes a novel demand function that simultaneously emphasizes consumer reference price effect, consumer environmental awareness and product green attribute. In addition, it provides managerial insights for business managers to choose green cooperative contracts with consumer reference price effect under different power structures.

There are indicates that raphe nuclei may be involved in the occurrence of chronic pain in Parkinson's disease (PD). In the study, we investigated the functional connectivity pattern of raphe nuclei in Parkinson's disease with chronic pain (PDP) to uncover its possible pathophysiology.Fifteen PDP, who suffered from pain, lasted longer than 3 months, sixteen Parkinson's disease patients with no pain (nPDP) and eighteen matched normal health controls (NCs) were recruited. All subjects completed the King's Parkinson's Pain Scale (KPPS) besides Parkinson-related scale and demographics. We performed a seed-based resting-state analysis of functional magnetic resonance imaging to explore whole-brain functional connectivity of the raphe nuclei. Multiple regression model was used to explore the related factors of pain including disease duration, disease severity, Hamilton Depression Rating Scale, age, sex, levodopa equivalent dose and the strength of network functional connectivity.Compared with the nPDP, the PDP group showed stronger functional connectivity between raphe nuclei and pain-related brain regions, including parietal lobe, insular lobe, cingulum cortex and prefrontal cortex, and the functional connectivity values of those areas were significantly positively correlated with KPPS independent of the clinical variables. Compared with NCs, the combined PD groups showed decreased functional connectivity including prefrontal cortex and cingulum cortex.Abnormal functional connectivity model of raphe nuclei may be partly involved in pathophysiological mechanism of pain in PD.

Regarding the global energy crisis, it is of profound significance to develop spontaneous power generators that harvest natural energy. Fabricating humidity-responsive actuators that can conduct such energy transduction is of paramount importance. Herein, we incorporate covalent organic frameworks with flexible polyethylene glycol to fabricate rigid-flexible coupled membrane actuators. This strategy significantly improves the mechanical properties and humidity-responsive performance of the actuators, meanwhile the existence of ordered structures enables us to unveil the actuation mechanism. These high-performance actuators can achieve various actuation applications and exhibit interesting self-oscillation behavior above a water surface. Finally, after being coupled with a piezoelectric film, the bilayer device can spontaneously output electricity over 2 days. This work paves a new avenue to fabricate rigid-flexible coupled actuators for self-sustained energy transduction.

About 75% of epithelial ovarian cancer (EOC) patients suffer from relapsing and develop drug resistance after primary chemotherapy. The commonly used clinical examinations and biological tumor tissue models for chemotherapeutic sensitivity are time-consuming and expensive. Research studies showed that the cell morphology-based method is promising to be a new route for chemotherapeutic sensitivity evaluation. Here, we offer how the drug resistance of EOC cells can be assessed through a label-free and high-throughput microfluidic flow cytometer equipped with a digital holographic microscope reinforced by machine learning. It is the first time that such type of assessment is performed to the best of our knowledge. Several morphologic and texture features at a single-cell level have been extracted from the quantitative phase images. In addition, we compared four common machine learning algorithms, including naive Bayes, decision tree, K-nearest neighbors, support vector machine (SVM), and fully connected network. The result shows that the SVM classifier achieves the optimal performance with an accuracy of 92.2% and an area under the curve of 0.96. This study demonstrates that the proposed method achieves high-accuracy, high-throughput, and label-free assessment of the drug resistance of EOC cells. Furthermore, it reflects strong potentialities to develop data-driven individualized chemotherapy treatments in the future.

Recent works based on deep learning and facial priors have performed well in superresolving severely degraded facial images. However, due to the limitation of illumination, pixels of the monitoring probe itself, focusing area, and human motion, the face image is usually blurred or even deformed. To address this problem, we properly propose Face Restoration Generative Adversarial Networks to improve the resolution and restore the details of the blurred face. They include the Head Pose Estimation Network, Postural Transformer Network, and Face Generative Adversarial Networks. In this paper, we employ the following: (i) Swish-B activation function that is used in Face Generative Adversarial Networks to accelerate the convergence speed of the cross-entropy cost function, (ii) a special prejudgment monitor that is added to improve the accuracy of the discriminant, and (iii) the modified Postural Transformer Network that is used with 3D face reconstruction network to correct faces at different expression pose angles. Our method improves the resolution of face image and performs well in image restoration. We demonstrate how our method can produce high-quality faces, and it is superior to the most advanced methods on the reconstruction task of blind faces for in-the-wild images; especially, our 8 × SR SSIM and PSNR are, respectively, 0.078 and 1.16 higher than FSRNet in AFLW.

Selective catalytic reduction (SCR) is currently one of the most efficient denitration technologies to reduce nitrogen oxide (NOx) emissions of diesel engines. AdBlue (urea water solution, UWS) is the carrier of the reducing agent of SCR, and the spray process of UWS is one of the critical factors affecting denitration efficiency. In this paper, a non-air-assisted pressure-driven full process spray (NPFPS) model is proposed to illustrate the breakup mechanism and the spray distribution properties of UWS through computational fluid dynamics (CFD). In the NPFPS model, the mechanism of the primary breakup is described by the volume of fluid (VOF) approach, which realizes the quantitative study of the critical parameters determining spray characteristics such as the breakup length, inclination angle, droplet size of the primary breakup, and primary velocity. The distribution of the spray after the primary breakup is depicted by the discrete phase model (DPM) coupled with the Taylor analogy breakup (TAB) model, through which the degree of secondary breakup can be obtained including quantitative studies of the droplet size distribution and velocity distribution in the different cross-sections. To verify the accuracy and feasibility of the NPFPS model, the experimental data are employed to compare with the simulation data. The results are in good agreement, which indicate the practical value of the model.

Large-scale and low-cost synthesis of covalent organic frameworks (COFs) to meet the demands of industrial application remains formidably challenge. Here we report using 2,4,6-collidine as monomer to produce a series of highly crystalline olefin-linked COFs by a melt polymerization method. This method enables the kilogram-scale fabrication of self-shaped monolithic robust foams. The afforded COFs possess extremely low cost (50 USD/kg), superior to all the reported COFs. Furthermore, using one-pot or post-modification methods can conveniently transform neutral COFs to ionic COFs, which can be applied as highly efficient ion-exchange sorbents for scavenging oxoanion pollutants. Remarkably, the superior adsorption capacity of a model oxoanion (ReO

Antiretroviral therapy (ART) regimens containing integrase strand transfer inhibitors (INSTIs) have become the recommended treatment for human immunodeficiency virus type 1 (HIV-1)-infected patients in the updated guidelines in China. In this study, we investigated the prevalence of acquired and transmitted INSTI-associated resistance of HIV-1 strains in the Henan Province (China) to provide guidance on the implementation of routine INSTI-associated HIV-1 genotypic resistance testing. Serum samples from HIV-1-infected patients seeking treatment in our hospital from August 2018 to December 2020 were collected and the HIV-1 integrase gene coding sequence was amplified, sequenced and analyzed for INSTI resistance. We obtained integrase sequence data from a total of 999 HIV-1-infected patients, including 474 ART-naive patients, 438 ART-treated patients, and 87 patients with unknown treatment history. We detected INSTI resistance in 12 patients (1.2%, 12/999) of the study group, which included 9 ART-treated patients (2.05%, 9/438), with 6 being INSTI-treated (14.63%, 6/41) and 3 INSTI-naive (0.76%, 3/397) and 3 ART-naive (0.63%, 3/474) patients. The most common major resistance mutation was E138AK (0.5%, 5/999), while the most common accessory resistance mutation was E157Q (1.8%, 18/999). Phylogenetic analysis based on the HIV-1 integrase gene indicated that INSTI resistance was primarily detected in patients infected with HIV-1 subtype B. In conclusion, our study reveals that INSTI resistance is observed in INSTI-treated patients, as expected, and the prevalence of INSTI resistance in ART-naive patients in Henan Province is low. However, baseline INSTI resistance testing should be considered, as the prescription of INSTI-based regimens is anticipated to increase considerably in the near future.

Objectives Construct and validate a nomogram model integrating the radiomics features and the clinical risk factors to differentiating axial spondyloarthritis (axSpA) in low back pain patients undergone sacroiliac joint (SIJ)-MRI. Methods A total of 638 patients confirmed as axSpA (n = 424) or non-axSpA (n = 214) who were randomly divided into training (n = 447) and validation cohorts (n = 191). Optimal radiomics signatures were constructed from the 3.0 T SIJ-MRI using maximum relevance–minimum redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithm in the training cohort. We also included six clinical risk predictors to build the clinical model. Incorporating the independent clinical factors and Rad-score, a nomogram model was constructed by multivariable logistic regression analysis. The performance of the clinical, Rad-score, and nomogram models were evaluated by ROC analysis, calibration curve and decision curve analysis (DCA). Results A total of 1316 features were extracted and reduced to 15 features to build the Rad-score. The Rad-score allowed a good discrimination in the training (AUC, 0.82; 95% CI: 0.77, 0.86) and the validation cohort (AUC, 0.82; 95% CI: 0.76, 0.88). The clinical-radiomics nomogram model also showed favourable discrimination in the training (AUC, 0.90; 95% CI: 0.86, 0.93) and the validation cohort (AUC, 0.90; 95% CI: 0.85, 0.94). Calibration curves (P >0.05) and DCA demonstrated the nomogram was useful for axSpA diagnosis in the clinical environment. Conclusion The study proposed a radiomics model was able to separate axSpA and non-axSpA. The clinical-radiomics nomogram can increase the efficacy for differentiating axSpA, which might facilitate clinical decision-making process.

Coordination-driven crosslinking networks with reversible and dynamic characteristics are gaining increasing interest in diverse application fields. Herein, we use a coordination crosslinking approach using metal-organic polyhedra (MOPs) as high-connectivity building blocks to post-assemble a class of coordination hypercrosslinked MOP (CHMOP) polymers. The introduction of 12-connected MOP nodes to the polymeric networks is critical to producing membranes that overcome the trade-off between mechanical properties and dynamic healing, and meanwhile possess multifunctionalities including shape memory, solution processability, and 3D printing. The CHMOPs can also be used for anticorrosion coating and achieve function couplings, e.g., shape memory-assisted self-healing (SMASH), which have not been achieved in the MOP-based hybrid materials yet. This work not only offers a feasible strategy to construct new multifunctional materials but also greatly expands the application scopes of MOPs.

Target identification is essential for developing novel therapeutic strategies in diseases. Thioredoxin-interacting protein (TXNIP), also known as thioredoxin-binding protein-2, is a member of the α-arrestin protein family and is regulated by several cellular stress factors. TXNIP overexpression coupled with thioredoxin inhibits its antioxidant functions, thereby increasing oxidative stress. TXNIP is directly involved in inflammatory activation by interacting with Nod-like receptor protein 3 inflammasome. Bone metabolic disorders are associated with aging, oxidative stress, and inflammation. They are characterized by an imbalance between bone formation involving osteoblasts and bone resorption by osteoclasts, and by chondrocyte destruction. The role of TXNIP in bone metabolic diseases has been extensively investigated. Here, we discuss the roles of TXNIP in the regulatory mechanisms of transcription and protein levels and summarize its involvement in bone metabolic disorders such as osteoporosis, osteoarthritis, and rheumatoid arthritis. TXNIP is expressed in osteoblasts, osteoclasts, and chondrocytes and affects the differentiation and functioning of skeletal cells through both redox-dependent and -independent regulatory mechanisms. Therefore, TXNIP is a potential regulatory and functional factor in bone metabolism and a possible new target for the treatment of bone metabolism-related diseases.

ObjectiveSmall intracranial aneurysms are increasingly being detected; however, a prediction model for their rupture is rare. Random forest modeling was used to predict the rupture status of small middle cerebral artery (MCA) aneurysms with morphological features.MethodsFrom January 2009 to June 2020, we retrospectively reviewed patients with small MCA aneurysms (ResultsA total of 426 consecutive patients with 454 small MCA aneurysms (p = 0.048). A nomogram was developed to assess the rupture of small MCA aneurysms.ConclusionRandom forest modeling is a good tool for evaluating the rupture status of small MCA aneurysms and may be considered for the management of small aneurysms.

The newly discovered kagome superconductors represent a promising platform for investigating the quantum interplay between band topology, electronic order, and lattice geometry. Despite extensive research efforts on this system, the nature of the superconducting ground state remains elusive. In particular, consensus on the electron pairing symmetry has not been achieved so far, in part owing to the lack of a momentum-resolved measurement of the superconducting gap structure. Here we report the direct observation of a nodeless, nearly isotropic and orbital-independent superconducting gap in the momentum space of two exemplary CsV3Sb5-derived kagome superconductors — Cs(V0.93Nb0.07)3Sb5 and Cs(V0.86Ta0.14)3Sb5, using ultrahigh resolution and low temperature angle-resolved photoemission spectroscopy (ARPES). Remarkably, we find that such a gap structure is robust to the appearance or absence of charge order in the normal state, tuned by isovalent Nb/Ta substitutions of V. Moreover, the scaling ratio of gap versus superconducting transition temperature is close to the Bardeen-Cooper-Schrieffer (BCS) value, indicating a pairing in the weak-coupling regime. Our direct observation of the superconducting gap structure points to a persistent s-wave pairing in such kagome superconductors.

China has implemented the low-carbon city pilot (LCCP) policy in the hopes of efficiently limiting carbon emission intensity to combat global warming and promote green economic growth. Urban land utilization, the second-largest source of carbon emissions, is key to the LCCP policy being able to have the desired effect, which has attracted widespread attention. Based on the panel data from prefecture-level cities in China from 2006 to 2019, this study used the propensity score matching difference-in-differences method (PSM-DID) to examine the impacts of LCCP policy on green utilization efficiency of urban land (GUEUL). The results reveal that LCCP policy has a beneficial impact on GUEUL and can effectively boost the future possibilities of green and low-carbon city development. Due to variances in regional economic and resource endowment level, the impacts of LCCP are different. The pilot has pushed GUEUL in the eastern region, western region, and growing resource-based cities, but has failed to improve GUEUL in other regions. Policymakers should adhere to the long-term sustainability of the LCCP policy and adopt differentiated action strategies to promote GUEUL when implementing it in different regions.

ObjectiveTo develop and validate a radiomics nomogram that could incorporate clinicopathological characteristics and ultrasound (US)-based radiomics signature to non-invasively predict Ki-67 expression level in patients with breast cancer (BC) preoperatively.MethodsA total of 328 breast lesions from 324 patients with BC who were pathologically confirmed in our hospital from June 2019 to October 2020 were included, and they were divided into high Ki-67 expression level group and low Ki-67 expression level group. Routine US and shear wave elastography (SWE) were performed for each lesion, and the ipsilateral axillary lymph nodes (ALNs) were scanned for abnormal changes. The datasets were randomly divided into training and validation cohorts with a ratio of 7:3. Correlation analysis and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomics features obtained from gray-scale US images of BC patients, and each radiomics score (Rad-score) was calculated. Afterwards, multivariate logistic regression analysis was used to establish a radiomics nomogram based on the radiomics signature and clinicopathological characteristics. The prediction performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC), the calibration curve, and decision curve analysis (DCA) using the results of immunohistochemistry as the gold standard.ResultsThe radiomics signature, consisted of eight selected radiomics features, achieved a nearly moderate prediction efficacy with AUC of 0.821 (95% CI:0.764-0.880) and 0.713 (95% CI:0.612-0.814) in the training and validation cohorts, respectively. The radiomics nomogram, incorporating maximum diameter of lesions, stiff rim sign, US-reported ALN status, and radiomics signature showed a promising performance for prediction of Ki-67 expression level, with AUC of 0.904 (95% CI:0.860-0.948) and 0.890 (95% CI:0.817-0.964) in the training and validation cohorts, respectively. The calibration curve and DCA indicated promising consistency and clinical applicability.ConclusionThe proposed US-based radiomics nomogram could be used to non-invasively predict Ki-67 expression level in BC patients preoperatively, and to assist clinicians in making reliable clinical decisions.

Determining the rupture risk of unruptured intracranial aneurysm is crucial for treatment strategy. The purpose of this study was to predict the rupture risk of middle cerebral artery (MCA) aneurysms using a machine learning technique. We retrospectively reviewed 403 MCA aneurysms and randomly partitioned them into the training and testing datasets with a ratio of 8:2. A generalized linear model with logit link was developed using training dataset to predict the aneurysm rupture risk based on the clinical variables and morphological features manually measured from computed tomography angiography. To facilitate the clinical application, we further constructed an easy-to-use nomogram based on the developed model. Ruptured MCA aneurysm had larger aneurysm size, aneurysm height, perpendicular height, aspect ratio, size ratio, bottleneck factor, and height-width ratio. Presence of a daughter-sac was more common in ruptured than in unruptured MCA aneurysms. Six features, including aneurysm multiplicity, lobulations, size ratio, bottleneck factor, height-width ratio, and aneurysm angle, were adopted in the model after feature selection. The model achieved a relatively good performance with areas under the receiver operating characteristic curves of 0.77 in the training dataset and 0.76 in the testing dataset. The nomogram provided a visual interpretation of our model, and the rupture risk probability of MCA aneurysms can be directly read from it. Our model can be used to predict the rupture risk of MCA aneurysm.

Rationale and Objectives Noncontrast CT-based radiomics signature has shown ability for detecting hematoma expansion (HE) in spontaneous intracerebral hemorrhage (ICH). We sought to compare its predictive performance with clinical risk factors and develop a clinical-radiomics nomogram to assess the risk of early HE. Materials and Methods In total, 1153 patients with ICH who underwent baseline cranial CT within 6 hours and follow-up scans within 72 hours of stroke onset were enrolled, of whom 864 (75%) were assigned to the derivation cohort and 289 (25%) to the validation cohort. Based on LASSO algorithm or stepwise logistic regression analysis, three models (clinical model, radiomics model, and hybrid model) were constructed to predict HE. The Akaike information criterion (AIC) and likelihood ratio test (LRT) were used for comparing the goodness of fit of the three models, and the AUC was used to evaluate their discrimination ability for HE. Results The hybrid model (AIC = 681.426; χ2= 128.779) was the optimal model with the lowest AIC and highest chi-square values compared to the radiomics model (AIC = 767.979; χ2 = 110.234) or the clinical model (AIC = 753.757; χ2 = 56.448). The radiomics model was superior in the prediction of HE to the clinical model in both derivation (p = 0.009) and validation (p = 0.022) cohorts. In both datasets, the clinical-radiomics nomogram showed satisfactory discrimination and calibration for detecting HE (AUC = 0.771, Sensitivity = 87.0%; AUC = 0.820, Sensitivity = 88.1%; respectively). Conclusion Among patients with acute ICH, noncontrast CT-based radiomics model outperformed the clinical-only model in the prediction of HE, and the established clinical-radiomics nomogram with favorable performance can offer a noninvasive tool for the risk stratification of HE.

In response to the cap-and-trade policy, many manufacturers begin to reduce emissions by various measures, such as investing in technologies and using clean materials, which will affect their operation strategies and further affect the whole supply chain. Based on this, we explore how the cap-and-trade policy affects firms’ operation decisions under different retailing formats. We study a supply chain with an e-commerce platform and one manufacturer, in which they can cooperate via reseller or marketplace formats. Meanwhile, we employ a new cost function of emission reduction by incorporating the manufacturer’s emission reduction efficiency (ERE). We find that in the absence of low-carbon preference, the manufacturer’s and the platform’s profits both increase as the manufacturer’s ERE improves in reseller format; but in marketplace format, the platform is worse off as the manufacturer’s ERE improves if the efficiency is larger than a threshold. Besides, under which format the platform or the manufacturer is better off is related to the product category. For some product categories, low-carbon preference could alter the retailing format under which the platform or the manufacturer gains a higher profit. Moreover, the platform and the manufacturer are both better off in the presence of low-carbon preference, regardless of retailing formats.

Crystalline porous materials (CPMs) have been widely studied over the past few decades due to their well-defined, customizable, and porous structures. However, the limited stability and machinability restrict their particle applications. Recently, a new trend to use hybrid strategies to bridge the gap between CPMs and flexible polymers has emerged as an effective solution to tackle the above issues. Polymers can be either cross-linked within the framework or anchored to the outer surface of the materials, that endow CPMs with polymer features, such as flexibility, ductility, and machinability. Such resulting polymer-tethered CPMs (named as polyCPMs) have demonstrated improved performances in many application scopes such as membrane separation, heterogeneous catalysis and smart response. This minireview highlights state of the art for the hybridization strategies of CPMs with flexible polymers, and provide a perspective on their potential applications and future directions.

Summary Cutaneous treatment possesses advantages, including the decrease of transient drug overdosage or systematic side effects. The evolution of biomedical materials with multifunctionalities to overcome the limitations of existing materials can bring revolutionary development in this field. A promising solution can turn to the emerging materials known as metal-organic frameworks (MOFs), featuring high porosity, easy modification, and multifunctionality. The work herein developed a systemic assay package to seek suitable MOFs for cutaneous applications. Versatile platforms based on MOFs were built, which integrated superb functions and features, including biocompatibilities, sebum adsorbability, selective adsorption, antimicrobial activity, controlled release, and skin permeability promotion. Such features endow MOFs as high-performance matrixes for cutaneous applications. This study paves a new avenue for the reformation of cosmetic and biomedicinal materials. Moreover, this comprehensive MOF screening system can be broadly applied for material evaluation in this field, which also provides valuable guidance for the design of advanced biomedical materials.

This study aimed to access the performance of apparent diffusion coefficient (ADC) as a predictor for treatment response to whole-brain radiotherapy (WBRT) in patients with brain metastases (BMs) from non-small-cell lung cancer (NSCLC). A retrospective analysis was conducted of 102 NSCLC patients with BMs who underwent WBRT between 2012 and 2016. Diffusion-weighted MRI were performed pre-WBRT and within 12 weeks after WBRT started. Mean single-plane ADC value of ROIs was evaluated by two radiologists blinded to results of each other. The treatment response rate, intracranial progression-free survival (PFS), and overall survival (OS) were analyzed based on the ADC value and ΔADC respectively. At last, we used COX and logistic regression to do the multivariate analysis. There was good inter-observer agreement of mean ADC value pre-WBRT, post-WBRT, and ΔADC between the 2 radiologists (Pearson correlation 0.915 [pre-WBRT], 0.950 [post-WBRT], 0.937 [ΔADC], p < 0.001, for each one). High mean ADC value were related with better response rate (72.2% vs 37.5%, p = 0.001) and iPFS (7.6 vs 6.4 months, p = 0.031). High ΔADC were related with better response rate (73.6% vs 36.7%, p < 0.001). Multivariate analysis shows that histopathology, BMs number, high ADC value pre-WBRT, and high ΔADC post-WBRT were related to better treatment response of WBRT, and KPS, BMs number, and low ADC value pre-WBRT increased the risk of developing intracranial relapse. The mean single-plane ADC value pre-WBRT and ΔADC post-WBRT were potential predictor for intracranial tumor response to WBRT in NSCLC patients with brain metastases. • ADC value is a potential predictor of intracranial treatment response to WBRT in NSCLC patients with brain metastases. • Higher mean ADC value pre-WBRT and ΔADC post-WBRT of brain metastases were related to better intracranial tumor response. • Prediction of response before WBRT using ADC value can help oncologists to make better therapy plans and avoid missing opportunities for rescue therapy.

The protection of trade secrets is a more complex problem. Compared with the past, the difficulty of the protection of trade secrets in high-tech industry now has increased a lot. Based on the characteristics of high-tech industry, the practicability, confidentiality and economy of trade secrets, and combining with the current market environment in China, this article tries to put forward the legal protection advice of trade secrets in line with the characteristics of high-tech industry in China, in order to provide some ideas for future research.

Photothermal ablation could be considered an effective treatment for tumors, but accurate administration and enrichment of photothermal agents remain a huge challenge. Herein, a mussel-inspired photothermal polymeric hydrogel (PPH) was synthesized through a ferric iron-triggered simultaneous metal–catechol coordination reaction and oxidative polymerization of covalently linked pyrrole. The PPH with rapid gelation (less than 10 s) exhibited high photothermal conversion efficiency (49.3%), which enabled effective hyperthermal therapy in situ. Besides, the introduced iron could be used as a T2-weighted contrast agent for real-time MR imaging to explore the retention and bio-degradation of PPH in vivo. Overall, our findings evidence that the resultant PPH, which possesses potential application in tumor ablation in situ, and metal–catechol coordination strategy inspired by mussel adhesion may stimulate biomedical hydrogel development.

Traditional white light-emitting diodes (LEDs) (blue chip + YAG:Ce3+ yellow phosphor) have the limitation of red deficiency, which limits their application in the illumination field. The single cation/anion substitution or co-doping of activators can increase the red component; however, the large energy loss is attributed to the ultra-long Stokes shift and energy transfer. This work attempts to utilize the short-distance Stokes shift and a small amount of energy transfer to increase the red component in two steps. First, based on a large number of previous research results, the Mg2Y2Al2Si2O12:Ce3+ phosphor is selected. Second, additional enhancement of the red component in the emission spectrum was achieved by ion co-doping Mn2+ into Mg2Y2Al2Si2O12:Ce3+. The emission peaks for samples Mg2Y2Al2Si2O12:Ce3+,Mn2+ shift from 600 to 635 nm with increase in the concentration of Mn2+, and the emission spectra intensity of Mg1.97Y1.93Al2Si2O12:0.07 Ce3+,0.03 Mn2+ anomalously increased by ∼37%, which was attributed to the increase in the distance between Ce3+ ions because of the doping of Mn2+ ions, and reduction in the concentration of defects in the crystal, resulting in the energy loss decreases of Ce3+. The emission peak of Mg1.97Y1.93Al2Si2O12:0.07 Ce3+,0.03 Mn2+ shifts to 618 nm and the quantum efficiency was as high as 83.07%. Furthermore, this sample has high thermal stability and the emission intensity was still 80.14% at 120 °C. As such, it has great potential in the application of white LEDs.

An optimization method of structure parameters based on the quadratic regression orthogonal combination (QROC) and Genetic Algorithm (GA) is proposed in this work. The following work has been conducted to improve the performance of the coal pyrolysis filtration system and prolong the service life of the filter tubes based on QROC-GA method. Firstly, a simulation model is established and two factors always are chosen as optimization objectives. Then one single factor regression prediction algorithm is used to optimize each factor separately while the result was not satisfactory. Secondly, QROC is introduced to achieve the optimization of two factors in the filtration system. The regression relationship is obtained proved to be effective by statistical test and back propagation neural network (BPNN). Finally, a QROC-GA method is established to find the optimization points. Then a verification calculation is done with CFD again. The optimal result has the parameters that φ=40° and ψ=25°. From the simulation results, the mean square error is 0.401. The mean square error is 0.4992 by the QROC-GA results. The errors are within 0.1 between CFD and QROC-GA. The QROC-GA model has a good effect in the prediction of models under changing parameters, and the significance is also confirmed.