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Clinical studies have proved that both structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are implicitly associated with neuropsychiatric disorders (NDs), and integrating multi-modal to the binary classification of NDs has been thoroughly explored. However, accurately classifying multiple classes of NDs remains a challenge due to the complexity of disease subclass. In our study, we develop a heterogeneous neural network (H-Net) that integrates sMRI and fMRI modes for classifying multi-class NDs. To account for the differences between the two modes, H-Net adopts a heterogeneous neural network strategy to extract information from each mode. Specifically, H-Net includes an multi-layer perceptron based (MLP-based) encoder, a graph attention network based (GAT-based) encoder, and a cross-modality transformer block. The MLP-based and GAT-based encoders extract semantic features from sMRI and features from fMRI, respectively, while the cross-modality transformer block models the attention of two types of features. In H-Net, the proposed MLP-mixer block and cross-modality alignment are powerful tools for improving the multi-classification performance of NDs. H-Net is validate on the public dataset (CNP), where H-Net achieves 90% classification accuracy in diagnosing multi-class NDs. Furthermore, we demonstrate the complementarity of the two MRI modalities in improving the identification of multi-class NDs. Both visual and statistical analyses show the differences between ND subclasses.

Non-small cell lung cancer (NSCLC) is a major disease with high incidence, low survival rate and prone to develop drug resistance to chemotherapy. The mechanism of secondary drug resistance in NSCLC chemotherapy is very complex, and studies have shown that the abnormal activation of STAT3 (Signal Transducer and Activator of Transcription 3) plays an important role in it. In this study, the pGPU6/GFP/Neo STAT3-shRNA recombinant plasmid was constructed with STAT3 as the precise target. By modifying hydrophilic and hydrophobic blocks onto chitosan, a multifunctional vitamin E succinate-chitosan-polyethylene glycol monomethyl ether histidine (VES-CTS-mPEG-His) micelles were synthesized. The micelles could encapsulate hydrophobic drug doxorubicin through self-assembly, and load the recombinant pGPU6/GFP/Neo STAT3-shRNA (pDNA) through positive and negative charges to form dual-loaded nanoparticles DOX/VCPH/pDNA. The co-delivery and synergistic effect of DOX and pDNA could up-regulate the expression of PTEN (Phosphatase and Tensin Homolog), down-regulate the expression of CD31, and induce apoptosis of tumor cells. The results of precision targeted therapy showed that DOX/VCPH/pDNA could significantly down-regulate the expression level of STAT3 protein, further enhancing the efficacy of chemotherapy. Through this study, precision personalized treatment of NSCLC could be effectively achieved, reversing its resistance to chemotherapy drugs, and providing new strategies for the treatment of drug-resistant NSCLC.

A Metal-Organic Framework (MOF) based on a tricarboxylic acid ligand (H3L = biphenyl-2,4′,5-tricarboxylic acid), namely [Cd3(L)2(H2O)4] (1), had been prepared and characterized by elemental analysis, IR spectroscopy, powder X-ray diffraction (PXRD), thermogravimetric analyses (TGA), and single-crystal X-ray diffraction. Fluorescence studies showed that 1has high quenching efficiency for testing acetone and Fe3+ions in aqueous systems. Therefore, it can be used as a new type of fluorescence probe to detect solvent molecules and metal ions in water.

This study provides a solution for robust and early detection of Oscillatory Failure Cases in aircraft control surface servo loops. Extending the results from literature, an improved model-based monitoring approach based on a nonlinear actuator model is presented including novel band-pass filters, sliding windows, and an enhanced signal processing method for faster detection of high-frequency OFC. To specifically cope with the effects of actuator model uncertainties, filter time delay, and noise, an integrated method combining model-based and data-driven methods employing neural networks, distance-complexity correlation, and information entropy is proposed. Verification of this novel scheme is carried out using an industrial benchmark provided by Airbus. The promising results indicate that the proposed scheme is able to detect any OFC ensuring a very low false alarm rate, even in the presence of uncertainties.

Using first-principles calculations, we predict the coexistence of mechanical superhardness and the controllable magnetism in the clathrate material β-C3N2through the implant of the external atom into the intrinsic cage structure. Taking hydrogen-doping (H@β-C3N2) and fluorine-doping (F@β-C3N2) as examples, our calculations indicate these two doped configurations are stable and discover that they belong to antiferromagnetic semiconductor and ferromagnetic semi-metal, respectively. These intriguing magnetic phase transitions originate from their distinctive band structures around the Fermi level and can be well understood by the three-dimensional Hubbard model with half-filling occupation and the Stoner model. Moreover, the high Vickers hardness of 49.0 GPa for H@β-C3N2and 48.2 GPa for F@β-C3N2are obtained, suggesting they are clathrate superhard materials as their host. Therefore, the incorporations of H and F in β-C3N2give rise to new types of superhard antiferromagnetic semiconductor and superhard ferromagnetic semimetal, respectively, which could have potential applications in harsh conditions. Our work provides an effective strategy to design a new class of highly desirable multifunctional materials with excellent mechanical properties and magnetic properties, which may arouse spintronic applications in superhard materials in the future.

AbstractBackgroundThe resting full-cycle ratio (RFR), a new non-congestive resting index, is commonly used for physiological evaluations of coronary arteries.AimsThis study aims to evaluate the accuracy of RFR in detecting coronary artery stenosis with hemodynamic significance using fractional flow reserve (FFR) as the reference standard.MethodsUsing ‘RFR, resting full-cycle ratio’ as the search term, we searched PubMed, Embase, Cochrane Library, and Web of Science databases, screening the literature according to the inclusion and exclusion criteria. By applying FFR ≤ 0.80 and RFR ≤ 0.89 as the diagnostic criteria for ischaemia, we analysed the synthetic sensitivity, specificity, and corresponding 95% confidence intervals, then synthesised the summary receiver operating characteristic curve (SROC).ResultsThree studies were included in this meta-analysis, comprising 1,084 patients with 1,312 lesions. When we used FFR ≤ 0.80 as the reference standard, the synthesised sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+), and negative likelihood ratio (LR-) of RFR in the diagnosis of coronary ischaemia were 73%, 81%, 67%, 85%, 3.95, and 0.33, respectively. Besides, the area under the curve (AUC) was 0.8276.ConclusionUsing FFR as the reference standard, RFR has good diagnostic accuracy in detecting coronary ischaemic lesions and may be an effective alternative to FFR in the future, to some extent.

As a phenylpyrazole insecticide, flufiprole is an important substitute for fipronil in the agricultural field of China. However, its bioaccumulation and metabolism in terrestrial organisms especially in the lizards living in the agricultural area have rarely been investigated. As an ectothermic animal, lizards are also sensitive to temperature changes. Considering global warming, this study measured bioaccumulation, metabolism, and hepatotoxicity of flufiprole in the Chinese native lizard (Eremias argus) under different temperature stresses. Lizards exposed to flufiprole-contaminated soil adsorbed flufiprole through the skin and flufiprole was preferred to accumulate in lizard liver and brain. The oxidation product fipronil sulfone was the main metabolite of flufiprole in both lizard liver and human liver microsomes, which were mainly metabolized by lizard CYP3A19 or human CYP3A4. The fipronil sulfone concentration increased with increased temperature in lizard tissues. In addition, more serious oxidative damage was shown under higher temperature as the glutathione (GSH), malondialdehyde (MDA), and 8-hydroxy-2′-deoxyguanosine (8-OHdG) levels in lizards increased with increased temperature after flufiprole exposure. Flufiprole exposure also induced lizard liver lesions, and these lesions became more serious in the higher-temperature groups. This study provided new insights into the risk assessment of flufiprole in lizards under global warming.

Stroke is a cerebrovascular disease with high mortality and disability rates. The occurrence of the stroke typically produces lesions of different sizes, with the accurate segmentation and detection of small-size stroke lesions being closely related to the prognosis of patients. However, the large lesions are usually correctly identified, the small-size lesions are usually ignored. This article provides a hybrid contextual semantic network (HCSNet) that can accurately and simultaneously segment and detect small-size stroke lesions from magnetic resonance images. HCSNet inherits the advantages of the encoder–decoder architecture and applies a novel hybrid contextual semantic module that generates high-quality contextual semantic features from the spatial and channel contextual semantic features through the skip connection layer. Moreover, a mixing-loss function is proposed to optimize HCSNet for unbalanced small-size lesions. HCSNet is trained and evaluated on 2D magnetic resonance images produced from the Anatomical Tracings of Lesions After Stroke challenge (ATLAS R2.0). Extensive experiments demonstrate that HCSNet outperforms several other state-of-the-art methods in its ability to segment and detect small-size stroke lesions. Visualization and ablation experiments reveal that the hybrid semantic module improves the segmentation and detection performance of HCSNet.

Health insurance fraud accounts for 3–10% of total medical expenditures every year. If the growth of fraud activities is allowed, it will cause irreversible consequences to the medical system. However, medical-related data is too large and complex, and it is difficult to process such a large amount of data with traditional statistical methods. Therefore, machine learning algorithms have become one of important solutions. When faced with different data, whether the learning method can maintain its stability and give a more appropriate answer is a big question. Many related studies focused on medical insurance fraud and assessment, but few studies attempts to discover the important factors of medical fraud, and find optimal machines learning method. Therefore, this study used two unpublished datasets that might discover novel knowledge, and four machine learning methods, including Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF) and Multilayer Perceptron (MLP) to find the best machine learning method that can effectively detect medical fraud. From results of DT, we also extracted 19 crucial characteristics of medical insurance fraud, and grouped them into 4 categories, which are medical service providers, applied insurance claims amount, Healthcare Common Procedure Coding System (HCPCS), and beneficiary. Results of experiments could provide valuable suggestions for insurance management to establish an automatic audit mechanism to eliminate medical frauds.

The toxicity of lead and the instability of lead-based perovskite materials in the air are two key challenges in emerging Pb-based inorganic perovskite solar cells. Thus, the development of lead-free and stabilized inorganic perovskite materials is of great interest. In this work, the structural, ductility, and optoelectronic properties of Se-based double perovskites A2SeX6(A = Rb, K; X = Cl, Br, I) have been studied for the first time by first-principles calculations to determine their potential use in optoelectronic sectors. Results reveal that these new series of A2SeX6(A = Rb, K; X = Cl, Br, I) double perovskites are structural and mechanical stability. Their ductility increases as the halogen atom goes from Cl to I. Especially, the K2SeI6possesses better ductility than other double perovskites. Electronic structure calculations exhibit that these new perovskite compounds are indirect band gap semiconductors. The Rb2SeI6and K2SeI6have more suitable bandgaps (1.24 and 1.16 eV) and smaller carrier masses, which are suitable for absorber layers of solar cells, while A2SeCl6and A2SeBr6are suitable for fabricating the optoelectronic devices in the ultraviolet region due to the wide gap. Furthermore, all these A2SeX6compounds exhibit excellent optical properties with light absorption coefficients over 105cm–1. Especially, the Rb2SeI6and K2SeI6have more excellent absorption spectra throughout the whole visible region. These results show that both Rb2SeI6and K2SeI6are potential candidates for absorption layer materials of solar cells, and A2SeCl6and A2SeBr6are suitable for other optoelectronic devices, such as photodetectors, light-emitting diodes, and so on.

Michael Berry and Chiho Sawada, editors. Divided Lenses: Screen Memories of War in East Asia. Honolulu: University of Hawai'i Press, 2016. viii, 331 pp. Hardcover $58.00, ISBN 978-0-8248-5151-4. Bringing together [...]

Weihong Bao. Fiery Cinema: The Emergence of an Affective Medium in China, 1915-1945. Minneapolis: University of Minnesota Press, 2015. 479 pp. Paperback $30.00, ISBN 978-0-8166-8134-1. At the core of Weihong [...]

This article exposes that although some sensor-based nonlinear fault-tolerant control frameworks including incremental nonlinear dynamic inversion control can passively resist a wide range of actuator faults and structural damage without requiring an accurate model of the dynamic system, their stability heavily relies on a sufficient condition, which is unfortunately violated if the control direction is unknown. Consequently, it is proved in this article that no matter, which perturbation compensation technique (adaptive, disturbance observer, sliding-mode) is implemented, none of the existing nonlinear incremental control methods can guarantee closed-loop stability. Therefore, this article proposes a Nussbaum function-based adaptive incremental control framework for nonlinear dynamic systems with partially known (control direction is unknown) or even completely unknown control effectiveness. Its effectiveness is proved in the Lyapunov sense and is also verified via numerical simulations of an aircraft attitude tracking problem in the presence of sensing errors, parametric model uncertainties, structural damage, actuator faults, as well as inversed and unknown control effectiveness.

The development of new green fungicides is an effective way to solve the resistance of agricultural pathogens and plays an important role in promoting high-quality and sustainable development of modern agriculture. In this project, a series of aryloxy-, arylthio-, and arylamino-containing acethydrazide derivatives were designed, synthesized, and characterized by 1H nuclear magnetic resonance (NMR), 13C NMR, and high-resolution mass spectrometry (HRMS). The fungicidal bioassays indicated that some compounds showed excellent and broad-spectrum fungicidal activity, and the structure–activity relationship was discussed. The in vivo fungicidal activity demonstrated that compounds C4and D8exhibited good preventative effects against Fusarium graminearuminfecting wheat leaves, of which the preventative activity of compound D8was almost equal to that of the positive agents. Transmission electron microscopy (TEM) observation revealed that the plasma membrane in the C4-treated F. graminearumhyphal cells was severely contracted and separated with the cell wall, coupling with the visible lysosomes and the disappeared cytoplasm and organelles, which may be the reasons for the shriveled and even ruptured hyphae observed by scanning electron microscopy (SEM). Subsequently, transcriptomics and metabolomics were performed to further elucidate the fungicidal mechanism. The regulatory networks of differential genes and metabolites in plasma membrane-related sphingolipid metabolism, linoleic acid metabolism, α-linoleic acid metabolism, and arachidonic acid metabolism were constructed and elaborated. Additionally, preliminary investigation of seeding growth suggested that compounds C4and D8may have different degrees of influence on the growth indicators of wheat seedlings; however, this effect may be negligible as the plant grows.

Identifying the subtypes of low-grade glioma (LGG) can help prevent brain tumor progression and patient death. However, the complicated non-linear relationship and high dimensionality of 3D brain MRI limit the performance of machine learning methods. Therefore, it is important to develop a classification method that can overcome these limitations. This study proposes a self-attention similarity-guided graph convolutional network (SASG-GCN) that uses the constructed graphs to complete multi-classification (tumor-free (TF), WG, and TMG). In the pipeline of SASG-GCN, we use a convolutional deep belief network and a self-attention similarity-based method to construct the vertices and edges of the constructed graphs at 3D MRI level, respectively. The multi-classification experiment is performed in a two-layer GCN model. SASG-GCN is trained and evaluated on 402 3D MRI images which are produced from the TCGA-LGG dataset. Empirical tests demonstrate that SASG-GCN accurately classifies the subtypes of LGG. The accuracy of SASG-GCN achieves 93.62%, outperforming several other state-of-the-art classification methods. In-depth discussion and analysis reveal that the self-attention similarity-guided strategy improves the performance of SASG-GCN. The visualization revealed differences between different gliomas.

Segmenting stroke lesions and assessing the thrombolysis in cerebral infarction (TICI) grade are two important but challenging prerequisites for an auxiliary diagnosis of the stroke. However, most previous studies have focused only on a single one of two tasks, without considering the relation between them. In our study, we propose a simulated quantum mechanics-based joint learning network (SQMLP-net) that simultaneously segments a stroke lesion and assesses the TICI grade. The correlation and heterogeneity between the two tasks are tackled with a single-input double-output hybrid network. SQMLP-net has a segmentation branch and a classification branch. These two branches share an encoder, which extracts and shares the spatial and global semantic information for the segmentation and classification tasks. Both tasks are optimized by a novel joint loss function that learns the intra- and inter-task weights between these two tasks. Finally, we evaluate SQMLP-net with a public stroke dataset (ATLAS R2.0). SQMLP-net obtains state-of-the-art metrics (Dice:70.98% and accuracy:86.78%) and outperforms single-task and existing advanced methods. An analysis found a negative correlation between the severity of TICI grading and the accuracy of stroke lesion segmentation.

This work presents an indicator-based switching control for a class of strict-feedback systems to exploit the potential performance improvement. The proposed approach is designed by injecting a switch mechanism into the backstepping control. An indicator is introduced to reflect the impact of the integral part on the system. The indicator-triggered control method is capable of wisely switching the control structure. Thus, the transient performance can be improved. In addition, the parameters of the proposed control scheme are optimized by Cuckoo Search algorithm to achieve the better performance. The stability is analyzed in the Lyapunov sense and the effectiveness is also verified by numerical simulations.

The evaluated data with multiplicative or linguistic preferences should be aggregated with information fusion and aggregation that are important research topics in many fields, such as neural networks, fuzzy logic controllers, expert systems, group decision-making, and multi-criteria decision-making (MCDM). Ordered weighted averaging operators have been extensively adopted to handle MCDM problems. However, previous operators are usually independent of their situations and cannot reflect the change in decision situations. Besides, how to solve MCDM problems with feasible operators is still an interesting direction. To resolve above problems, a linguistic MCDM aggregation model is proposed in this paper, which contained two linguistic and situational operators, that is, situational maximal entropy linguistic ordered weighted averaging and maximal entropy linguistic ordered weighted geometric averaging operators. This proposed model has their ability to handle MCDM problems under different decision situations according to the decision-maker’s preference toward criteria. The proposed model is applied to two previous datasets, which are the evaluation of the best main battle tank and the best school in a university. The results of this paper indicate that the proposed model can deal with the situational group MCDM problems with linguistic or multiplicative and linguistic preferences based on proposed operators.

Poor antitumor drug penetration into tumor tissues is a global challenge in clinical cancer treatment. Here, we reported a smart multistage “Trojan Horse”-inspired bovine serum albumin (BSA)-coated liposome (HBM), including the mimics of capsid and secondary BSA-coated polymeric nanoparticles (NPs) for enhancing tumor penetration and antitumor efficacy. These drug-loaded polymeric NPs possess a capsid-like component, a well-distributed nanostructure (size: 190.1 ± 4.98 nm, PDI: 0.259), and an excellent drug loading content (15.85 ± 1.36%). Meaningfully, after the smart multistage BSA-coated liposome targeted the tumor tissue, the mimics of capsid were “taken off” under the condition of tumor-specific enzymes, releasing “Heart” BSA-modified secondary NPs to increase the ability to penetrate tumor cells for enhancing antitumor efficacy. As expected, the HBM efficiently achieves high drug penetration into PAN02 tumor cells. Moreover, compared to free DOX and HM (HBM without BSA) NPs, DOX/HBM NPs exhibited the strongest tumor penetration and the highest cytotoxicity against PAN02 tumor cells both in vitro (IC50 = 0.141 μg/mL) and in vivo. This smart multistage “Trojan Horse”-inspired BSA-coated liposome should provide a new hathpace for further development of polymeric NPs in clinical treatment.

Low temperatures could inhibit the activity of anaerobic ammonium oxidizing bacteria (AnAOB). This study investigated the effect of exogenous zero-valent iron (ZVI) on Anammox-hydroxyapatite (HAP) system at 17 ± 1 °C. The results showed that long-term addition of ZVI increased the nitrogen removal efficiency by 8.59 %, and alleviated the inhibition of AnAOB under the condition of low temperature. After addition of ZVI, the activity of AnAOB and the heme ccontent increased by 58.20 % and 4.83 %, respectively. Moreover, the functional enzyme activities of NIR, HZS, and HZO increased by 94.12 %, 36.36 %, and 16.67 %, respectively. The concentrations of quorum sensing molecules, i.e., C6-HSL and C8-HSL, increased by 191.17 % and 41.54 %, respectively, after ZVI addition. Metagenomic analysis revealed that the addition of 5 g/L ZVI stimulated the relative abundance of Fe(II) metabolism-related genes in AnAOB at 17 ± 1 °C, thereby contributing to the uptake, transport, utilization, and storage of Fe(II). In summary, the addition of ZVI could improve the nitrogen removal performance of the anammox-HAP system at the low temperature.

Uterine smooth muscle tumors of uncertain malignant potential (STUMPs) are rare tumors of the uterine myometrium that are often misdiagnosed, owing to limited knowledge of their characteristics on ultrasonography (US) and magnetic resonance imaging (MRI). We report a woman in her mid-30s who was hospitalized because of a pelvic tumor. A 6-cm mass was found in her lower left abdomen. US and MRI revealed a well-demarcated mass in the left adnexal area, with both cystic and solid elements, visible blood flow within the septa, a strong signal across >50% of the volume on T2-weighted imaging (T2WI), and a strong signal on diffusion-weighted imaging (DWI). After hysterectomy and bilateral salpingectomy, immunohistochemical examination confirmed STUMP. A review of the literature revealed characteristic imaging features of STUMP. Ultrasonography reveals STUMP as a solitary, well-circumscribed lesion with isoechoic or mixed echogenicity, the absence of posterior shadowing, and variations in blood flow. STUMP is characterized by strong signal intensity on T2WI, small areas of strong signal on T1WI, and non-enhancing cystic areas on contrast-enhanced MRI scans. Early diagnosis is crucial for the management and treatment of STUMP, and here we have summarized the imaging features of the lesion, thereby providing a valuable diagnostic reference.

The emergence of crowdfunding has given many capital demanders a new fund-raising channel, but the overall project success rate is very low. Many scholars have begun to discover key suscessful factors of crowdfunding projects. Previous studies have used questionnaires survey to identify important project features. In addition to requiring a lot of manpower and time, there may also be sampling bias. Moreover, related studies also reported that the project description will affect the success of the crowdfunding project, but there is no research to tell fundraisers which success factors should be included in the content of the project description. Besides, in recent years, game crowdfunding projects have been attracted lots of attention in terms of total fundraising amount and number of projects. Moreover, in traditional feature selection and text mining approaches, the selected terms are un-organized and hard to be explained. Therefore, this study will focus on real video and mobile game project descriptions to replace conventional questionnaires. To solve these issues, we present a lexicon-based feature selection method. We attempt to define “content features” and build lexicons to determine the attributes’ values. Three feature selection methods including decision tree (DT), Least Absolute Shrinkage and Selection Operator (LASSO), and support vector machine–recursive feature elimination (SVM–RFE) will be employed to find organized candidate key successful factors. Then, support vector machines (SVM) will be used to evaluate the performances of candidate feature subsets. Finally, this study has identified the key successful factors for video and mobile games, respectively. Based on the experimental results, we can give fundraisers some useful suggestions to improve the success rate of crowdfunding projects.

Silent information regulator type-1 (SIRT1), a nicotinamide adenine dinucleotide+-dependent deacetylase, is a member of the sirtuins family and has unique protein deacetylase activity. SIRT1 participates in physiological as well as pathophysiological processes by targeting a wide range of protein substrates and signalings. In this review, we described the latest progress of SIRT1 in pulmonary diseases. We have introduced the basic information and summarized the prominent role of SIRT1 in several lung diseases, such as acute lung injury, acute respiratory distress syndrome, chronic obstructive pulmonary disease, lung cancer, and aging-related diseases.

A galvanized steel is used to join Mg AZ31B alloy and Al A6061-T6 alloy as a joining transition layer by cold metal transfer (CMT) method. Firstly, to optimize the process variables, extensive welding tests were performed by a design of experiment method. Then, microstructures, joining mechanisms and tensile properties were characterized and analyzed. Results indicated that Mg and Al alloys can be joined by using galvanized steel as a joining transition layer and cold metal transfer welding method. The formed joint is called as a Mg–steel–Al CMT joint. By using galvanized steel transition joining layer, Mg–Al brittle intermetallics Al12Mg17and Al3Mg2were inhibited. The properties of Mg–steel–Al CMT joints have been improved after optimizing the welding variables. The strength of Mg–steel–Al CMT joint is comparable to those of Al–Al joint and Mg–Mg joint. The strength of Mg–steel–Al CMT joint can reach 100% that of Al–Al joint and Al–steel joint, and reach 90% that of Mg–Mg joint and Mg–steel joint. The optimized Mg–steel–Al CMT welded joint with galvanized steel transition layer is fractured at the heat affected zone of Al base metal rather than at the weld-brazed interface, due to softening of Al base metal.

The development of laser-induced graphene (LIG) has been regarded as an effective method for satisfying the substantial requirements for the scalable fabrication of graphene-based electrode materials. Despite the rapid progress in fabricating LIG-based supercapacitors, the incompatibility between material modification and the device planarization process remains a challenging problem to be resolved. In this study, we demonstrate the attributes of novel LIG-MXene (LIG-M) composite electrodes for flexible planar supercapacitors fabricated by direct laser writing (DLW) of MXene-coated polyimide (PI) films. During the DLW process, PI was transformed into LIG, while MXene was simultaneously introduced to produce LIG-M. Combining the porous structure of LIG and the high conductivity of MXene, the as-prepared LIG-M-based supercapacitor exhibited superior specific capacitance, five times higher than that of the pristine LIG-based supercapacitor. The enhanced capacitance of LIG-M also benefited from the pseudocapacitive performance of the abundant active sites offered by MXene. Moreover, the planar LIG-M-based device delivered excellent cycling stability and flexibility. No significant performance degradation was observed after bending tests. Arbitrary electrode patterns could be obtained using the DLW technique. The patterned in-series LIG-M supercapacitor was able to power a light-emitting diode, demonstrating significant potential for practical applications.

Mangiferin (MGF) is found in many natural plants, such as Rhizoma Anemarrhenae, and has anti-diabetes effects. However, its clinical applications and development are limited by poor solubility and low-concentration enrichment in pancreatic islets. In this paper, targeted polymeric nanoparticles were constructed for MGF delivery with the desired drug loading content (6.86 ± 0.60%), excellent blood circulation, and missile-like delivery to the pancreas. Briefly, Glucagon-like peptide 1 (GLP-1) as an active targeting agent to the pancreas was immobilized on the block copolymer polyethyleneglycol-polycaprolactone (PEG-PCL) to obtain final GLP-1-PEG-PCL amphiphiles. Spherical MGF-loaded polymeric nanoparticles were acquired from the self-assembly of the targeted GDPP nanoparticles and MGF with a homogeneous size of 158.9 ± 1.7 nm and a negative potential for a good steady state in circulation. In this drug vehicle, GLP-1 acts as the missile vanguard via the GLP-1 receptor on the surface of the pancreas for improving the accumulation and efficiency of MGF in the pancreas, the hypoglycemic effect of MGF, and the restorative effect on pancreatic islets, which were investigated. As compared to free MGF, MGF/GDPP nanoparticles appeared to be more concentrated in the pancreas, with better blood glucose and glucose tolerance, enhanced insulin levels, increased β-cell proliferation, reduced β-cell apoptosis, and islet repair in vivo. This targeted drug delivery system provided a novel strategy and hope for enhancing MGF delivery and anti-diabetes efficacy.

The sensor-based Incremental Nonlinear Dynamic Inversion (INDI) control has shown promising robustness in the aerospace research field. This control framework only requires a partial knowledge of plant (control effectiveness) because of its usage of angular accelerations and actuator output measurements. However, there are still un-negligible uncertainties of the control effectiveness model in the flight control system, especially when the aircraft is subjected to structural damage/actuator faults. This paper shows that the conventional INDI control fails to satisfy the sufficient conditions for closed-loop stability in the presence of severe damage. Therefore, this paper also proposes a predictor-based gain adaptive INDI control (named PGA-INDI) which can successfully deal with control effectiveness parametric errors caused by structural damage, actuator faults, and model uncertainties. Various simulations using a public aircraft model have demonstrated the effectiveness of the proposed approach

The success of chimeric antigen receptor T cell (CAR-T) therapy in the treatment of hematologic malignancies has prompted the development of numerous CAR-T technologies, including switchable CAR-T (sCAR-T) systems that combine a universal CAR-T with bispecific adapter proteins. Owing to their controllability and versatility, sCAR-Ts have received considerable attention. To explore the therapeutic utility of sCAR-Ts targeting the receptor tyrosine kinase ROR1, which is expressed in hematologic and solid malignancies, and to identify bispecific adaptor proteins that efficiently mediate universal CAR-T engagement, a panel of switches based on ROR1-targeting Fabs with different epitopes and affinities was compared in in vitro and in vivo models of ROR1-expressing cancers. For switches targeting overlapping or identical epitopes, potency correlated with affinity. Surprisingly, however, we identified a switch targeting a unique epitope with low affinity but mediating potent and selective antitumor activity in vitro and in vivo. Converted to a conventional CAR-T, the same anti-ROR1 mAb (324) outperformed a clinically investigated conventional CAR-T that is based on an anti-ROR1 mAb (R12) with ~200-fold higher affinity. Thus, demonstrating therapeutic utility on their own, sCAR-Ts also facilitate higher throughput screening for the identification of conventional CAR-T candidates for preclinical and clinical studies.

Non-orthogonal multiple access (NOMA) has been integrated in millimeter-wave (mmWave) Massive MIMO systems to further enhance the spectrum efficiency, but NOMA has not been fully considered in lens mmWave systems. The fusion of these two technologies requires the joint design of beam selection and interference cancellation. In addition, when users follow the spatial cluster distribution, although the user clustering schemes based on K-means algorithm have been applied, the influence of the virtual and actual cluster center users on achievable sum rate has not been differentiated and analyzed in detail. To solve the above problems, a joint optimization scheme is proposed to maximize the achievable sum rate. The optimization problem is NP-hard, which is solved by using the divide-and-conquer approach. Concretely, based on the signal power loss analysis of directional deviation, a beam selection algorithm is designed for inter-cluster interference cancellation based on the K-means algorithm. Further for intra-cluster interference cancellation, a power allocation algorithm based on the bisection method is designed to guarantee the fairness of users in each cluster. The simulation results show that the proposed scheme offers a significant performance improvement in terms of both achievable sum rate and energy efficiency, and it is suitable for the large-scale user scenario due to its low complexity.

As a new noninvasive diagnostic technique, computed tomography (CT)-based fraction flow reserve (FFR) has been used to identify hemodynamically significant coronary artery stenosis. This meta-analysis used invasive FFR as the standard to evaluate the diagnostic performance of FFRCT.

Angelina Chin. Bound to Emancipate: Working Women and Urban Citizenship in Early Twentieth-Century China and Hong Kong. Lanham, MD: Rowman and Littlefield Publishers, 2012. xiii, 278 pp. Hardcover $85.00, ISBN [...]

As simple and ubiquitous redox-active organic molecules, quinones participate in diverse electron transfer processes in chemistry and biological systems for energy transformation and signal transduction. We introduce here a practical exercise to study the redox potentials of benzoquinone and its two derivatives by combining the electrochemistry method with quantum chemistry computation. The practical reduction potentials of three quinones were measured by cyclic voltammetry experiment. Quantum chemistry computation, on the other hand, provided theoretical Gibbs free energy change and reduction potentials of the three quinones, which were found to be in line with the experimental results. Detailed thermodynamic energy analysis based on the computation results revealed that the reduction Gibbs free energy changes of the three quinones were mainly contributed from the electronic energies change of the molecules, while thermal energy and entropy played a relatively minor role. The energy levels of the lowest unoccupied molecular orbital (LUMO) of the three quinones, which were modulated by substituent groups and conjugation structures, were further identified as the main origin of the reduction potential. This experiment provides practical and theoretical training on the fundamental ideas involved in major courses like thermodynamics, quantum chemistry, electrochemistry, and organic chemistry, and promotes students to find the tie between macroscopic redox properties of chemicals and their microscopic molecular structures, a key topic in chemistry education.

Paul G. Pickowicz. China on Film: A Century of Exploration, Confrontation, and Controversy. Lanham, MD: Rowman and Littlefield, 2012. 376 pp. Hardcover $90.00, ISBN 978-1-4422-1178-0. Here in my hand lies [...]

Coronavirus disease 2019 (COVID-19) has become an increasingly severe public health emergency. Although traditional Chinese medicine (TCM) has helped to combat COVID-19, public perception of TCM remains controversial. We used the theory of planned behavior (TPB) to identify factors that affect the intention to use TCM.

The in vivoformation of Cu(DDC)2complex was induced by disulfiram and Cu2+released from PLGA nanoparticles. This strategy avoids many obstacles in the use of Cu(DDC)2complex as a chemotherapeutic and exhibits excellent inhibition to mesothelioma.

Effective integration of stimulation and direction in bionic scaffolds by materials and microstructure design has been the focus in the advancement of nerve regeneration. Hydrogels are the most promising biomimicked materials used in developing nerve grafts, but the highly hydrated networks limit the fabrication of hydrogel materials into complex biomedical devices. Herein, facile lithography-free and spontaneously micropatterned techniques were used to fabricate a smart protein hydrogel-based scaffold, which carried topographical, electrical, and chemical induction for neural regulation. The synthesized tissue-mimicked silk-gelatin (SG)/polylactic acid bilayer system can self-form three-dimensional ordered corrugation micropatterns with well-defined dimensions (wavelength, λ) based on the stress-induced topography. Through magnetically and topographically guided deposition of the synthesized nerve growth factor-incorporated Fe3O4–graphene nanoparticles (GFPNs), a biologically and electrically conductive cell passage with one-dimensional directionality was constructed to allow for a controllable constrained geometric effect on neuronal adhesion, differentiation, and neurite orientation. Particularly, the SG with corrugation patterns of λ ≈ 30 μm resulted in the optimal cell adhesion and differentiation in response to the pattern guidance. Furthermore, the additional electrical stimulation applied on GFPN-deposited SG resulted in a 1.5-fold increase in the neurite elongation by day 7, finally leading to the neuronal connection by day 21. Such a hydrogel device with synergistic effects of physical and chemical enhancement on neuronal activity provides an expectable opportunity in the development of next-generation nerve conduits.

Smooth basal surfaces of illite were prepared successfully, and the interaction forces between a Si3N4tip and illite basal surface with various concentrations of divalent cations in 10 mM KCl solution at pH 8.0 and 10.8 were measured using an atomic force microscope (AFM). The influence of divalent cation concentration on the interaction forces was investigated, and the surface potentials of the basal surfaces in varying concentrations of divalent cation solutions were determined by fitting the interaction forces with the classical Derjaguin–Landau–Verwey–Overbeek (DLVO) theory. With an increasing CaIIor MgIIconcentration, the surface potentials became less negative in pH 8.0 solution, while the surface potentials reversed from negative to positive after addition of 1.0 mM or more divalent cations in pH 10.8 solution. Electrostatic interaction and specific adsorption were the major contributors to the interaction between divalent cations and illite basal surface in solutions at pH 8.0 and 10.8, respectively. CaIIand MgIIcations had a similar effect magnitude on the illite basal surfaces at pH 8.0. In contrast, MgIIcations had a more profound effect than CaIIon the surface potential of the illite basal surface in pH 10.8 solution, likely due to the low solubility of Mg(OH)2at this pH. This quantitative description of the illite basal surface charge influenced by divalent cations can provide fundamental insight into the mechanisms of tailings treatment and slime coatings in minerals processing.

An active fault-tolerant control scheme is proposed in this paper for the strongly coupled MIMO systems which subject to total actuator failures. A control-oriented interaction indicator is defined in the Lyapunov stability sense and then is utilized to design the cooperative fault-tolerant control law. The proposed scheme can achieve robust tracking performance with globally uniformly ultimate boundlessness and is capable of improving transient performance (fault-tolerant ability) by wisely using the interactions. Simulation results obtained on a flight attitude control system illustrates the benefit of the proposed techniques.

This paper investigates the issue of control design for linear systems with prescribed performance. The novelty of the proposed solution relies on a switching control scheme that uses the interval theory. Different from the existing prescribed performance control approaches, the error transformation and the logarithmic/tangent function are no longer required in the presented control. Alternatively, a switching scheme inspired by the interval observer technique is introduced to ensure the errors not violating performance bound functions. The proposed control establishes an unified prescribed performance control framework which covers the bounded stability, asymptotic stability and finite-time stability only via selecting the corresponding performance bound functions. Numerical examples are simulated to demonstrate the effectiveness of the proposed approach.

Graphical AbstractSFG composite haemostatic sponges were prepared using gelatine and silk fibroin, which can address the mechanical defects of gelatine and silk fibroin when used alone.Adding thrombin can further improved the haemostatic performance of the SFG@TB composite haemostatic sponge.The SFG@TB composite hemostatic sponge with outstanding mechanical properties, good hemostatic performance and high biosafety can promote wound hemostasis and tissue repair.

Fast and efficient sample pretreatment is the prerequisite for realizing surface-enhanced Raman spectroscopy (SERS) detection of trace targets in complex matrices, which is still a big issue for the practical application of SERS. Recently, we have proposed a highly performed liquid–liquid extraction (LLE)–back extraction (BE) for weak acids/bases extraction in drinking water and beverage samples. However, the performance efficiency decreased drastically on facing matrices like food and biological blood. Based on the total interaction energies among target, interferent, and extractant molecules, solid-phase extraction (SPE) with a higher selectivity was introduced in advance of LLE–BE, which enabled the sensitive (μg L–1level) and rapid (within 10 min) SERS detection of both koumine (a weak base) and celastrol (a weak acid) in different food and biological samples. Further, the high SERS sensitivity was determined unmanned by Vis-CAD (a machine learning algorithm), instead of the highly demanded expert recognition. The generality of SPE–LLE–BE for various weak acids/bases (2 < pKa< 12), accompanied by the high efficiency, easy operation, and low cost, offers SERS as a powerful on-site and efficient inspection tool in food safety and forensics.