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Higher fungi can rapidly produce large numbers of spores suitable for aerial dispersal. The efficiency of the dispersal and spore resilience to abiotic stresses correlate with their hydrophobicity provided by the unique amphiphilic and superior surface-active proteins-hydrophobins (HFBs)-that self-assemble at hydrophobic/hydrophilic interfaces and thus modulate surface properties. Using the HFB-enriched mold Trichoderma (Hypocreales, Ascomycota) and the HFB-free yeast Pichia pastoris (Saccharomycetales, Ascomycota), we revealed that the rapid release of HFBs by aerial hyphae shortly prior to conidiation is associated with their intracellular accumulation in vacuoles and/or lipid-enriched organelles. The occasional internalization of the latter organelles in vacuoles can provide the hydrophobic/hydrophilic interface for the assembly of HFB layers and thus result in the formation of HFB-enriched vesicles and vacuolar multicisternal structures (VMSs) putatively lined up by HFBs. These HFB-enriched vesicles and VMSs can become fused in large tonoplast-like organelles or move to the periplasm for secretion. The tonoplast-like structures can contribute to the maintenance of turgor pressure in aerial hyphae supporting the erection of sporogenic structures (e.g., conidiophores) and provide intracellular force to squeeze out HFB-enriched vesicles and VMSs from the periplasm through the cell wall. We also show that the secretion of HFBs occurs prior to the conidiation and reveal that the even spore coating of HFBs deposited in the extracellular matrix requires microscopic water droplets that can be either guttated by the hyphae or obtained from the environment. Furthermore, we demonstrate that at least one HFB, HFB4 in T. guizhouense, is produced and secreted by wetted spores. We show that this protein possibly controls spore dormancy and contributes to the water sensing mechanism required for the detection of germination conditions. Thus, intracellular HFBs have a range of pleiotropic functions in aerial hyphae and spores and are essential for fungal development and fitness.

Identifying the factors influencing airspace resources, quantifying the availability of airspace resources, and mastering their spatial distribution characteristics are the cornerstone of scientific and efficient airspace management. Therefore, this paper investigates the impact of prohibited, restricted, and dangerous areas (PRDs) on airspace resource availability from a traffic flow perspective, proposes a multi-layer network model, and establishes a flow-based sector resource availability (FSRA) calculation model. The FSRA in mainland China is calculated above the standard pressure altitude of 6000 m. The results show that the FSRA is lower when the sector is determined to have a higher PRD density, a more complex traffic flow pattern, and a more sophisticated interaction between the two. China’s mainland airspace is separated into three altitude ranges along the vertical direction according to the FSRA and sector distribution: 6000–7800 m, 7800–8900 m, and 8900–12,500 m. The spatial distribution characteristics of the FSRA are addressed using the ArcGIS software. The results demonstrate that spatial autocorrelation is exhibited for all three altitude ranges. The high–high cluster pattern mainly occurs in the western part of mainland Chinese airspace, while the low–low cluster pattern is distributed in the southeast. The three altitude ranges are divided into three groups, respectively, and suggestions for airspace management are made for each group.

Lysophosphatidylcholine (LPC) is a bioactive lipid that modulates macrophage polarization during immune responses, inflammation, and tissue remodeling. Patatin-like phospholipase domain containing protein 7 (PNPLA7) is a lysophospholipase with a preference for LPC. However, the role of PNPLA7 in macrophage polarization as an LPC hydrolase has not been explored. In the current study, we found that PNPLA7 is highly expressed in naïve macrophages and downregulated upon lipopolysaccharide (LPS)-induced polarization towards the classically activated (M1) phenotype. Consistently, overexpression of PNPLA7 suppressed the expression of proinflammatory M1 marker genes, including interleukin 1β (IL-1β), IL-6, inducible nitric oxide synthase (iNOS), and tumor necrosis factor α (TNF-α), whereas knockdown of PNPLA7 augmented the inflammatory gene expression in LPS-challenged macrophages. PNPLA7 overexpression and knockdown increased and decreased Sirtuin1 (SIRT1) mRNA and protein levels, respectively, and affected the acetylation of the nuclear factor-kappa B (NF-κB) p65 subunit, a key transcription factor in M1 polarization. In addition, the levels of phosphorylated p38 mitogen-activated protein kinase (MAPK) were suppressed and enhanced by PNPLA7 overexpression and knockdown, respectively. Taken together, these findings suggest that PNPLA7 suppresses M1 polarization of LPS-challenged macrophages by modulating SIRT1/NF-κB- and p38 MAPK-dependent pathways.

As new screening tools for sarcopenia, the serum sarcopenia index (SI) and creatinine/cystatin C ratio (CCR) had not been confirmd in a population with a high fragility fracture risk. This study aimed to evaluate whether SI and CCR indicators are useful for diagnosing sarcopenia and to determine their prediction values for future falls and fractures. A total of 404 hospitalized older adults were enrolled in this longitudinal follow-up study (mean age = 66.43 ± 6.80 years). The receiver operating curve (ROC) was used to assess the diagnostic accuracy of SI and CCR. Backward-selection binary logistic regression was applied to develop the optimal models for the diagnosis of new falls and fractures. SI had a significantly higher area under the curve (AUC) than CCR for predicting sarcopenia. The optimal models had acceptable discriminative powers for predicting new falls and fractures. Lower SI and CCR are the independent risks for sarcopenia, new falls, and fractures in the low-BMD population. SI and CCR, as easily accessible biochemical markers, may be useful in the detection of sarcopenia and in predicting the occurrence of new falls and fractures in patients with low BMD who have not previously experienced falls or fractures. However, further external validations are required.

Coronavirus disease 2019 has significantly impacted mental health. Urban green spaces’ ecological function can improve citizens’ well-being and mental health; thus, this study explored the value realisation mechanism of ecological products in green space by examining the impact of urban green space soundscape on citizens’ emotions. Additionally, we investigated citizens’ subjective perceptions and emotional changes in soundscape through questionnaires. An A-weighted sound pressure level meter was used to measure the sound pressure levels at three points in a day to obtain the sound pressure level changes during different day periods in a park. Subsequently, the universal conclusion through a comparative analysis of the sound pressure level change in urban parks during the day was roughly ‘M’ shape. Further, a structural equation model analysed the influence of different soundscape on public sentiment and used a multigroup analysis to examine the difference in the impact of natural sounds in summer and winter on the change in public opinion. The results show that natural and living sounds positively affect citizens’ emotions, whereas mechanical sounds negatively affect citizens’ emotions. Furthermore, natural sounds in summer positively affect citizens’ moods significantly. Conversely, natural sounds in winter negatively affect citizens’ attitudes, but this is not obvious. Finally, this study proposed some suggestions for managing and improving urban green space soundscape.

Various machine learning methods have been applied to study regional landslide risk assessment problems, but most of them mainly consider the influencing factors associated with landslide occurrences and ignore the spatial features. Therefore, by combining the advantages of traditional case-based reasoning (CBR) and by fully mining the spatial features, this paper proposes a novel spatial case-based reasoning method for landslide risk assessment. In this method, the spatial proximity and spatial topological relationships were extracted as spatial features, and the influencing factors associated with landslide occurrences were selected as attribute features. Then, the integrated expression of the spatial and attribute features of a landslide case was further constructed. Finally, attribute similarity and spatial similarity were used for joint reasoning. This study then carried out experiments in Lushan, China. Upon comparison of the three CBR models, the proposed integrated CBR outperformed the other models. Furthermore, an optimized support vector machine and a deep convolutional neural network were selected as experimental methods in the control group. The results show that the proposed integrated CBR has better performance. In conclusion, the proposed integrated CBR can be effectively applied to regional landslide risk evaluation and other similar geospatial problems.

Global cerebral ischemia induced by cardiac arrest usually leads to poor neurological outcomes. Numerous studies have focused on ways to prevent ischemic damage in the brain, however clinical therapies are still limited. Our previous studies revealed that delta opioid receptor (DOR) activation with [d-Ala2, d-Leu5] enkephalin (DADLE), a DOR agonist, not only significantly promotes neuronal survival on day 3, but also improves spatial memory deficits on days 5-9 after ischemia. However, the neurological mechanism underlying DADLE-induced cognitive recovery remains unclear. This study first examined the changes in neuronal survival in the CA1 region at the advanced time point (day 7) after ischemia/reperfusion (I/R) injury and found a significant amelioration of damaged CA1 neurons in the rats treated with DADLE (2.5 nmol) when administered at the onset of reperfusion. The structure and function of CA1 neurons on days 3 and 7 post-ischemia showed significant improvements in both the density of the injured dendritic spines and the basic transmission of the impaired CA3-CA1 synapses following DADLE treatment. The molecular changes involved in DADLE-mediated synaptic modulation on days 3 and 7 post-ischemia implied the time-related differential regulation of PKCα-MARCKS on the dendritic spine structure and of BDNF- ERK1/2-synapsin I on synaptic function, in response to ischemic/reperfusion injury as well as to DADLE treatment. Importantly, all the beneficial effects of DADLE on ischemia-induced cellular, synaptic, and molecular deficits were eliminated by the DOR inhibitor naltrindole (2.5 nmol). Taken together, this study suggested that DOR activation-induced protective signaling pathways of PKCα-MARCKS involved in the synaptic morphology and BDNF-ERK-synapsin I in synaptic transmission may be engaged in the cognitive recovery in rats suffering from advanced cerebral ischemia.

Esophageal squamous cell carcinoma (ESCC) persists among the most lethal and broad-spreading malignancies in China. The exosome is a kind of extracellular vesicle (EV) from about 30 to 200 nm in diameter, contributing to the transfer of specific functional molecules, such as metabolites, proteins, lipids, and nucleic acids. The paramount role of exosomes in the formation and development of ESCC, which relies on promoting intercellular communication in the tumor microenvironment (TME), is manifested with immense amounts. Tumor-derived exosomes (TDEs) participate in most hallmarks of ESCC, including tumorigenesis, invasion, angiogenesis, immunologic escape, metastasis, radioresistance, and chemoresistance. Published reports have delineated that exosome-encapsulated cargos like miRNAs may have utility in the diagnosis, as prognostic biomarkers, and in the treatment of ESCC. This review summarizes the function of exosomes in the neoplasia, progression, and metastasis of ESCC, which improves our understanding of the etiology and pathogenesis of ESCC, and presents a promising target for early diagnostics in ESCC. However, recent studies of exosomes in the treatment of ESCC are sparse. Thus, we introduce the advances in exosome-based methods and indicate the possible applications for ESCC therapy in the future.

Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.

In order to strengthen the construction of smart airports and improve the ability of airport managers to identify, intervene and rescue delayed flights, this paper proposes a delay prediction method for the whole process of transit flights through the basic steps of node time and link time prediction and delayed flight identification. By designing the key node time prediction model (ML-DM), the method predicts the important guaranteed node time involved in the process of flight departure from the outstation to the departure from the current station. By constructing the imbalance data classification model, the delayed flight is identified at each predicted guarantee node. The experimental results for a busy airport show that this prediction method can achieve a maximum recognition rate of 96.5% for delayed flights.

Urban parks provide their residents with a space for activities and interactions, and soundscapes play an important role in influencing the residents’ sense of experience of the park environment, with the result that the preferences of residents regarding multiple sound sources can be used as the basis of improving the soundscape quality and optimizing the function of urban parks. Using Shanghai Century Park as the research area, this study focused on the residents’ preference to multiple sound sources based on registered residence differences, and comprehensively used the semantic differential method, importance-performance analysis model, and multinomial logit model to conduct quantitative research on urban park soundscapes and combined the measurement results of the soundscape with the residents’ perception and preference. The results showed that the artificial sounds in Century Park were noisy during the morning and evening peak, while the human sounds were noisy when residents gathered in the park. There was a close relationship between the favorability and subjective loudness of urban park soundscapes, and the residents’ perceptions of urban park soundscapes substantially differed in terms of loudness, tone, and sense of belonging, whereas the differences in the perception of timbre, coverage, and sound source distance were relatively small. Furthermore, it is necessary to enhance the role of natural sounds as well as control the main noise-producing sound sources so that various sounds in the park will not interfere with each other; the optimization of soundscapes should also focus on the residents’ different soundscape preferences due to their individual characteristics.

Kinase-targeted drug discovery for cancer therapy has advanced significantly in the last three decades. Currently, diverse kinase inhibitors or degraders have been reported, such as allosteric inhibitors, covalent inhibitors, macrocyclic inhibitors, and PROTAC degraders. Out of these, covalent kinase inhibitors (CKIs) have been attracting attention due to their enhanced selectivity and exceptionally strong affinity. Eight covalent kinase drugs have been FDA-approved thus far. Here, we review current developments in CKIs. We explore the characteristics of the CKIs: the features of nucleophilic amino acids and the preferences of electrophilic warheads. We provide systematic insights into privileged warheads for repurposing to other kinase targets. Finally, we discuss trends in CKI development across the whole proteome.

Height estimation from a single Synthetic Aperture Radar (SAR) image has demonstrated a great potential in real-time environmental monitoring and scene understanding. The projection of a single 2D SAR image from multiple 3D height maps is an ill-posed problem in mathematics. Although Unet has been widely used for height estimation from a single image, the ill-posed problem cannot be completely resolved, and it leads to deteriorated performance with limited training data. This paper tackles the problem by Unet with the help of supplementary sparse height information and proxyless neural architecture search (PDPNAS) for Unet. The sparse height, which can be accepted from low-resolution SRTM or LiDAR products, is included as the supplementary information and is helpful to improve the accuracy of the estimated height map, especially in mountain areas with a wide range of elevations. In order to explore the effect of sparsity of sparse height on the estimated height map, a parameterized method is proposed to generate sparse height with a different sparse ratio. In order to further improve the accuracy of the estimated height map from a single SAR imagery, PDPNAS for Unet is proposed. The optimal architecture for Unet can be searched by PDPNAS automatically with the help of a depth-aware penalty term p. The effectiveness of our approach is evaluated by visual and quantitative analysis on three datasets from mountain areas. The root mean squared error (RMSE) is reduced by 90.30% through observing only 0.0109% of height values from a low-resolution SRTM product. Furthermore, the RMSE is reduced by 3.79% via PDPNAS for Unet. The research proposes a reliable method for estimating height and an alternative method for wide-area DEM mapping from a single SAR image, especially for the implementation of real-time DEM estimation in mountain areas.

We study the problem of indoor positioning, which is a fundamental service in managing and analyzing objects in indoor environments. Unpredictable signal interference sources increase the degeneration of the accuracy and robustness of existing solutions. Deep learning approaches have recently been widely studied to overcome these challenges and attain better performance. In this paper, we aim to develop efficient algorithms, such as the dual-encoder-condensed convolution (DECC) method, which can achieve high-precision positioning for indoor services. In particular, firstly, we develop a convolutional module to add the original channel state information to the location information. Secondly, to explore channel differences between different antennas, we adopt a dual-encoder stacking mechanism for parallel calculation. Thirdly, we develop two different convolution kernels to speed up convergence. Performance studies on the indoor scenario and the urban canyon scenario datasets demonstrate the efficiency and effectiveness of our new approach.

Abstract The pandemic of coronavirus disease 2019 (COVID‐19) is continually worsening. Clinical treatment for COVID‐19 remains primarily supportive with no specific medicines or regimens. Here, the development of multifunctional alveolar macrophage (AM)‐like nanoparticles (NPs) with photothermal inactivation capability for COVID‐19 treatment is reported. The NPs, made by wrapping polymeric cores with AM membranes, display the same surface receptors as AMs, including the coronavirus receptor and multiple cytokine receptors. By acting as AM decoys, the NPs block coronavirus from host cell entry and absorb various proinflammatory cytokines, thus achieving combined antiviral and anti‐inflammatory treatment. To enhance the antiviral efficiency, an efficient photothermal material based on aggregation‐induced emission luminogens is doped into the NPs for virus photothermal disruption under near‐infrared (NIR) irradiation. In a surrogate mouse model of COVID‐19 caused by murine coronavirus, treatment with multifunctional AM‐like NPs with NIR irradiation decreases virus burden and cytokine levels, reduces lung damage and inflammation, and confers a significant survival advantage to the infected mice. Crucially, this therapeutic strategy may be clinically applied for the treatment of COVID‐19 at early stage through atomization inhalation of the NPs followed by NIR irradiation of the respiratory tract, thus alleviating infection progression and reducing transmission risk.

Background and Purpose: The effect of Iguratimod in the treatment of rheumatoid arthritis was confirmed in past studies. In terms of the mechanism of the effect and clinical application experience, Iguratimod has a potential value in the treatment of spondyloarthritis (SpA). This study evaluated the efficacy and safety of Iguratimod on active SpA.Methods: Subjects with active SpA were enrolled and randomly divided into two groups at a ratio of 1:2 (placebo vs. Iguratimod). On the basis of non-steroidal anti-inflammatory drugs, combined treatment with Iguratimod or placebo, followed by follow-up every 4 weeks for 24 weeks. The primary efficacy endpoint was to evaluate the alleviation rate of ASAS20; the important improvement of ASDAS and the efficacy of spinal mobility, physical function and quality of life at the 24th week.Results: A total of 48 cases in the Iguratimod group and 25 cases in the placebo group were included in the final analysis. On the 24th week, the percentage of responders to ASAS20 (80 vs. 44%) and ASAS40 (56 vs. 20%) treated with Iguratimod were significantly higher than that in the placebo group (P < 0.05). Twelve cases had gastrointestinal discomfort, of which eight were in the Iguratimod group (16.7%, one case withdrew from the study due to diarrhoea) and four were in the placebo group (16.0%). No significant difference was found between the two groups (P < 0.05). Three cases of elevated transaminase were observed in the Iguratimod group and none in the placebo group, with no significant difference (P < 0.05).Conclusion: Iguratimod could significantly reduce the symptoms and signs of patients with active SpA. It could improve the physical function and quality of life of these patients and the overall safety and tolerance are good.

In glioma, kinesin family member 23 (KIF23) is up-regulated and plays a vital role in oncogenesis. However, the mechanism underlying KIF23 overexpression in malignant glioma remains to be elucidated. This study aims to find potential causes of KIF23 high expression at genome level. To clarify this issue, we obtained point mutation and copy number alterations (CNAs) of KIF23 in 319 gliomas using whole-exome sequencing. Only two glioma samples with missense mutations in KIF23 coding region were identified, while 7 patients were detected with amplification of KIF23. Additional analysis showed that KIF23 amplification was significantly associated with higher expression of KIF23. Gene ontology analysis indicated that higher copy number of KIF23 was associated TNF-α signaling pathway and mitotic cell circle checkpoint, which probably caused by subsequent upregulated expression of KIF23. Moreover, pan-cancer analysis showed that gaining of copy number was significantly associated with higher expression of KIF23, consolidating our findings in glioma. Thus, it was deduced that elevated KIF23 expression in glioma tended to be caused by DNA copy number amplification, instead of mutation.

Aim: To compare the clinical and radiological characteristics of osteomalacia and spondyloarthritis/ankylosing spondylitis (SpA/AS) in order to provide a basis for differential diagnosis.Methods: We carried out a retrospective analysis of patients who were diagnosed with osteomalacia at the First Medical Center of 301 Hospital (Beijing, China) from January 2012 to January 2019. The clinical and radiological data of all patients were collected; at the same time, we selected age- and gender-matched patients with SpA/AS for comparison.Results: We enrolled a total of 76 patients, 38 with osteomalacia, and 38 with SpA/AS. The mean ages of the two groups were, respectively 44.62 ± 14.90 years and 44.85 ± 9.76 years (P > 0.05). Of patients with osteomalacia, 65.79% (n = 25) had previously been misdiagnosed with SpA/AS. In the osteomalacia and SpA/AS groups, there were, respectively 31 and 33 patients with low back pain, 22 and 13 patients with peripheral arthralgia, and 13 and 3 patients with heel pain. Alkaline phosphatase (ALP) level was significantly higher in the osteomalacia than in the SpA/AS group (P < 0.05). Serum phosphorus levels, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) levels, and bone mineral density (BMD) were significantly lower in the osteomalacia group than the SpA/AS group (P < 0.05). Twenty-five patients in the osteomalacia group underwent sacroiliac-joint magnetic-resonance imaging (SIJ–MRI); abnormalities were found in 10 of these patients, seven of whom met the definition for positive SIJ–MRI according to 2009 Assessment of SpondyloArthritis international Society (ASAS) criteria. All seven presented with bilateral sacral involvement. Logistic-regression analysis found that the odds ratio (OR) for bone erosion score was 0.551; the higher this score, the lower the possibility of osteomalacia.Conclusion: Clinical and radiological presentations of patients with osteomalacia could highly simulate those of patients with spondyloarthritis; identifying the differences between these two diseases could effectively decrease the misdiagnosis rate.

Background/Aim: Macrophage polarization and phenotypic switching of smooth muscle cells (SMCs) are multi-faceted events dominating atherosclerosis (AS) progression. TGF-β was proved to been one of the bridge on the crosstalk between macrophage and SMC. ShenLian (SL) was extracted from a potent anti-atherosclerotic formula. However, its exact mechanism rebalancing inflammatory microenvironment of AS remain largely unknown. Within the entirety of macrophage and SMC, this study investigated the pharmacological effects of SL on stabilizing atherosclerotic plaques.Methods: The main components of SL were examined by high performance liquid chromatography. Co-culture and conditioned medium models of macrophage/SMC interactions were designed to identify the relationship between macrophage polarization and switching of SMC phenotypes. Flow cytometry, immunofluorescent staining, RT-PCR, western blotting, and ELISA were used to determine the expression of molecules relating to AS progression. An atherosclerosis animal model, established by placing a perivascular collar on the right common carotid artery in ApoE−/− mice, was used to investigate whether TGF-β is the key molecular mediator of SL in crosstalk between macrophage and SMC. Plaque size was defined by nuclear magnetic resonance imaging. Key markers related to phenotypic transformation of macrophage and SMC were determined by immunohistochemical staining.Results: Results revealed that, accompanied by rebalanced M2 macrophage polarization, SL supported SMC phenotypic transformation and functionally reconstruct the ECM of plaques specifically in macrophage-SMC co-cultural model. Molecularly, such activity of SL closely related to the activation of STAT3/SOCS3 pathway. Furthermore, in co-culture system, up-regulation of α-SMA induced by SL could neutralized by 1D11, a TGF-β neutralizing antibody, indicating that SL mediated Macrophage-SMC communication by enhancing TGF-β. In the AS model constructed by ApoE−/− mice, effects of SL on phenotypic transformation of macrophage and SMC has been well verified. Specific blocking of TGF-β largely attenuated the aforementioned effects of SL.Conclusion: Our findings highlighted that TGF-β might be the responsive factor of SL within macrophage and SMC communication. This study revealed that crosstalk between macrophage and SMC forms a holistic entirety promoting atherosclerotic plaque stability.

Purpose – The purpose of this paper is to measure the influence factors of their preferences for urban forest, marginal values of various properties and relative values of different scheme portfolios, thus arriving indirectly at the city residents’ demand for urban forest improvement. Design/methodology/approach – This paper, based on the data from the 2015–2017 field survey questionnaire of city residents over the radius of Beijing’s 5th Ring Road, uses the choice experiment method (CEM) to conduct a study of its residents’ demand for urban forest. Findings – Beijing’s city residents are generally inclined to accept a relatively low payment of urban forest while hoping to access a relatively high urban afforestation coverage with the construction of relatively many city parks, especially focusing on the specialized park management; the marginal values of biodiversity and greenery coverage are far higher than those of greenbelts in quantity and the maximum marginal value of biodiversity remains as high as RMB29.42, indicating that the city residents do not favor much the number of greenbelts over other aspects but they generally hope to achieve a higher greenery coverage and a richer biodiversity. Research limitations/implications – Generally speaking, what Beijing City needs most is not continuing the increase in the number of greenbelts, but engaging in the rational retrofit of its existing greenbelts and optimizing its urban forest structure. Originality/value – This paper may provide reference for determining the city residents’ payment criteria for urban forest and will be of equally great significance to developing cities and their urban forest.

Molecular systems displaying aggregation-induced emission (AIE) have important biomedical and optoelectronic applications. Here the authors report a further mechanism for AIE, through aromaticity reversal from the ground state to the excited state, in the non-aromatic annulene derivative of cyclooctatetrathiophene.

The mesoporous hydroxyapatite (HAP) powder was synthesized by using the microemulsion (hexadecyltrimethyl ammonium bromide (CTAB)/cyclohexane/n-octyl alcohol) system. The optimal ratios of concentrations of CTAB, cyclohexane and n-octyl alcohol in the microemulsion on morphology of the HAP were studied. The effects of pHs and calcination temperatures on the phase composition of the HAP powder were discussed. The phase composition, morphology, mesoporous structure and the optical density (OD) were characterized and confirmed by X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, field emission scanning electron microscopy (FESEM), N2 adsorption–desorption isotherms and Multiskan Spectrum, respectively. We further investigated the effect of the HAP powder with meso-structure on the proliferation of bone marrow mesenchymal stem cells (BMSCs) and MC3T3 in vitro, and in vitro study of drug release of mesoporous HAP powder. Through the optimized microemulsion system (CTAB 25 g/L/cyclohexane 125 mL/L/n-octyl alcohol 250 mL/L) at pH of 11.0, the mesoporous HAP was formed. The specific surface area of the HAP powder product is 13.62 m2/g with an average pore size of 19.56 nm after calcinating at 850 °C. Keywords: Hydroxyapatite, Mesoporous, Microemulsion, Cells, pH, Calcinating

Abstract Background Mitochondrial Ca2+ plays a critical role in tumorigenesis, including cell proliferation and metastasis. Mitochondrial calcium uniporter regulator 1 (MCUR1) has been shown to be frequently upregulated in HCC and promote cancer cell survival. However, whether MCUR1 is involved in the metastasis of HCC and its underlying mechanisms remain unknown. Methods The effect of MCUR1 expression on epithelial-mesenchymal transition (EMT) in HCC cells was first evaluated by immunofluorescent staining and Western blot. Then, in vitro invasion and in vivo metastasis assays were used to evaluate the function of MCUR1 in HCC metastasis. The underlying mechanism has also been explored by investigating the effect of MCUR1 on ROS/Nrf2/Notch1 pathway. Results MCUR1 expression was significantly higher in HCC with metastasis and associated with tumor progression. MCUR1 promoted in vitro invasion and in vivo metastasis of HCC cells by promoting EMT via Snail. Mechanistically, MCUR1-mediated mitochondrial Ca2+ signaling promoted the EMT of HCC cells by activating ROS/Nrf2/Notch1 pathway. Inhibition of ROS production, mitochondrial Ca2+ uptake, Nrf2 expression or Notch1 activity significantly suppressed MCUR1-induced EMT of HCC cells. In addition, treatment with the mitochondrial Ca2+-buffering protein parvalbumin significantly inhibited ROS/Nrf2/Notch pathway and MCUR1-induced EMT and HCC metastasis. Conclusions Our study provides evidence supporting a metastasis-promoting role for MCUR1-dependent mitochondrial Ca2+ uptake in HCC. Our findings suggest that MCUR1 may be a potential therapeutic target for HCC treatment.

Molecular motion has attracted a wide range of interest for different applications. Here, the authors develop nanoparticles with internal molecular motion upon near infrared absorption and use the nanoparticles for photoacoustic imaging and demonstrate this application in vivo.

With the rapid development of mobile Internet and sharing economy, carpooling over real-time taxi-calling service has attracted more and more attention. Many popular taxi-calling service platforms, e.g., didi and Uber, have developed carpooling service to the passengers. Their goal is maximizing overall profit while meeting passengers' convenience and economic benefits. Many researchers have deeply studied this problem. They usually focus on minimizing the total travel distance of drivers or making passengers' cost as fair as possible. However, these efforts ignore the fact that a “high quality” planning path crowed with potential passengers is more valuable than a “low quality” path for both workers and passengers. An effective high quality path planning algorithm is more desired. In this paper, we propose an efficient framework, named PPVF(short for path prediction and verification-based framework) for path planning over the road network. We first select a group of high quality paths from the historical transaction record set and manage them by proposing a novel index named PCR-Tree. Then we use them for supporting the path planning. Furthermore, we propose a searching and verification-based algorithm for further improving the path planning quality. Theoretical analysis and extensive experimental results demonstrate the effectiveness of the proposed algorithms.

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s13046-022-02409-y.

The incineration process in waste-to-energy plants is characterized by high levels of inertia, large delays, strong coupling, and nonlinearity, which makes accurate modeling difficult. Therefore, an intelligent modeling method for the incineration process in waste-to-energy plants based on deep learning is proposed. First, the output variables were selected from the three aspects of safety, stability and economy. The initial variables related to the output variables were determined by mechanism analysis and the input variables were finally determined by removing invalid and redundant variables through the Lasso algorithm. Secondly, each delay time was calculated, and a multi-input and multi-output model was established on the basis of deep learning. Finally, the deep learning model was compared and verified with traditional models, including LSSVM, CNN, and LSTM. The simulation results show that the intelligent model of the incineration process in the waste-to-energy plant based on deep learning is more accurate and effective than the traditional LSSVM, CNN and LSTM models.

Ground-based interferometric synthetic aperture radar (GB-InSAR) technology can be applied to generate a digital elevation model (DEM) with high spatial resolution and high accuracy. Phase unwrapping is a critical procedure, and unwrapping errors cannot be effectively avoided in the interferometric measurements of terrains with discontinuous heights. In this paper, an improved multi-baseline phase unwrapping (MB PU) method for GB-InSAR is proposed. This method combines the advantages of the cluster-analysis-based MB PU algorithm and the minimum cost flow (MCF) method. A cluster-analysis-based MB PU algorithm (CA-based MB PU) is firstly utilized to unwrap the clustered pixels with high phase quality. Under the topological constraints of a triangulation network, the connectivity graph of any non-clustered pixel is established with its adjacent unwrapped cluster pixels. Then, the absolute phase of these non-clustered pixels can be identified using the MCF method. Additionally, a spatial-distribution-based denoising algorithm is utilized to denoise the data in order to further improve the accuracy of the phase unwrapping. The DEM generated by one GB-InSAR is compared with that generated by light detection and ranging (LiDAR). Both simulated and experimental datasets are utilized to verify the effectiveness and robustness of this improved method.

Abstract Background RNA editing by adenosine deaminases acting on RNA (ADARs) converts adenosines to inosines (A-to-I) in RNA, that alters gene expression and generates protein diversity. Dysregulation of A-to-I editing has been found associated with a number of nervous system diseases. However, the role of ADAR3, a brain specific high expression adenosine deaminase, in gliomas has rarely been investigated. In this study we illuminated the clinical significance and molecular features of ADAR3 in patients with glioma. Methods 309 glioma samples from Chinese Glioma Genome Atlas were enrolled into this study. In validation sets, 601 glioma samples in TCGA, 410 glioma samples in REMBRANDT and 258 glioma samples in GSE16011 were obtained. Relationships between ADAR3 expression and prognosis-related genomic alteration, outcome and gene ontology analysis were investigated. Moreover, the characteristic of GRIA2Q607R editing in gliomas has been investigated. Graphpad Prism 5.0, SPSS 16.0 and R language were used to perform statistical analysis and graphical work. Results ADAR3 expression was down regulated along with glioma grade progression in CGGA dataset. ADAR3 was characteristically highly expressed in neural subtype and IDH1/2 mutant preference. Moreover, high expression of ADAR3 predicted a better prognosis in lower-grade glioma (LGG) patients and multivariate analysis suggested ADAR3 expression was an independent prognostic indicator. The results of the three other validation datasets showed similar findings. Bioinformatics analyses suggested that ADAR3 may play a role in the malignant transformation of glioma cells by affecting cell proliferation, angiogenesis or cell adhesion. Furthermore, the editing level of GRIA2Q607R was significantly correlated with ADAR3 expression. Conclusions Our study demonstrated the clinical and molecular characterization of ADAR3 in glioma development and progression. ADAR3 expression was negatively associated with tumor malignant in the overall glioma patients. And it was a favorable independent prognostic indicator of LGG patients. ADAR3 appeared to act as a tumor suppressor in glioma cells. Therefore, ADAR3 represented a potential therapeutic target and useful prognostic factor for glioma patients.

Abstract Background An increasing number of studies reported that exogenous miRNAs (xenomiRs) can be detected in animal bodies, however, some others reported negative results. Some attributed this divergence to the selective absorption of plant-derived xenomiRs by animals. Results Here, we analyzed 166 plant-derived xenomiRs reported in our previous study and 942 non-xenomiRs extracted from miRNA expression profiles of four species of commonly consumed plants. Employing statistics analysis and cluster analysis, our study revealed the potential sequence specificity of plant-derived xenomiRs. Furthermore, a random forest model and a one-dimensional convolutional neural network model were trained using miRNA sequence features and raw miRNA sequences respectively and then employed to predict unlabeled plant miRNAs in miRBase. A total of 241 possible plant-derived xenomiRs were predicted by both models. Finally, the potential functions of these possible plant-derived xenomiRs along with our previously reported ones in human body were analyzed. Conclusions Our study, for the first time, presents the systematic plant-derived xenomiR sequences analysis and provides evidence for selective absorption of plant miRNA by human body, which could facilitate the future investigation about the mechanisms underlying the transference of plant-derived xenomiR.

COVID-19, the most severe public health problem to occur in the past 10 years, has greatly impacted people's mental health. Colleges in China have reopened, and how to prevent college students from suffering secondary damage due to school reopening remains elusive. This cross-sectional study was aimed to evaluate the psychological impact of COVID-19 after school reopening and explore via machine learning the factors that influence anxiety and depression among students. Among the 478 valid online questionnaires collected between September 14th and September 20th, 74 (15.5%) showed symptoms of anxiety (by the Self-Rating Anxiety Scale), and 155 (32.4%) showed symptoms of depression (by Patient Health Questionnaire-9). Descriptive analysis of basic personal characteristics indicated that students at a higher grade, having relatives or friends who have been infected, fearing being infected, and having a pessimistic attitude to COVID-19 easily experience anxiety or depression. The Synthetic Minority Oversampling Technique (SMOTE) was utilized to counteract the imbalance of retrieved data. The Akaike Information Criterion (AIC) and multivariate logistic regression were performed to explore significant influence factors. The results indicate that exercise frequency, alcohol use, school reopening, having relatives or friends who have been infected, self-quarantine, quarantine of classmates, taking temperature routinely, wearing masks routinely, sleep quality, retaining holiday, availability of package delivery, take-out availability, lockdown restriction, several areas in school closed due to COVID-19, living conditions in the school, taking the final examinations after school reopening, and the degree to which family economic status is influenced by COVID-19 are the primary influence factors for anxiety or depression. To evaluate the effect of our model, we used 5-fold cross-validation, and the average area under the curve (AUC) values of the receiver operating characteristic (ROC) curves of anxiety and depression on the test set reached 0.885 and 0.806, respectively. To conclude, we examined the presence of anxiety and depression symptoms among Chinese college students after school reopening and explored many factors influencing students' mental health, providing reasonable school management suggestions.

Mutation of the telomerase reverse transcriptase (TERT) promoter has been demonstrated as an unfavorable prognostic marker in patients with isocitrate dehydrogenase wild-type (IDHwt) glioma. This study aimed to investigate the immune role of TERT promoter mutation status which could improve prognostic prediction in IDHwt. TERT mutation status, IDH mutation, and 1p-19q codeletion status data were obtained from 614 glioma cases from the Cancer Genome Atlas, and 325 cases from the Chinese Glioma Genome Atlas. The same information was obtained from 49 clinical glioma tissues. TERT mutation is preferentially present in glioblastoma and IDH-wt gliomas and is associated with poor prognosis. Moreover, TERT mutation was associated with infiltration of neutrophils and expression of neutrophil chemokines. which might partially contribute to the poor outcome in IDH-wt glioma. Furthermore, patients with IDH-wt glioma did not harbor increased peripheral neutrophils, implying that the infiltrated neutrophil in the tumor environment might due to cytokine chemotaxis. In this study, we hereby propose that TERT mutation might be a molecular driver of the dysfunctional immune microenvironment in IDH-wt glioma. TERT mutation may be a potential immune therapeutic target for optimizing treatment combinations and patient selection for glioma immunotherapy.

The development of dark pro‐aggregation‐induced‐emission luminogens (pro‐AIEgens) based on the solid‐state intramolecular motion–caused quenching effect has been rarely reported in biochemical analysis. Herein, an azo‐conjugated pro‐AIEgen derived from tetraphenylethene is constructed. Due to the active molecular motion of the azo bond, it retains a nonemissive property in both solution and solid forms. The obtained pro‐AIEgen can be internalized by living cells and undergo reductase‐modulated derotation under hypoxia to emit light. It shows increasing fluorescence when the intracellular oxygen concentration becomes lower, making it a potential probe to sense the hypoxic environment. The present probe can also visualize the hypoxic environment created by photodynamic therapy treatment.

Iron is one of the essential trace elements in the human body. An increasing amount of evidence indicates that the imbalance of iron metabolism is related to the occurrence and development of cancer. Here, we obtained the gene expression and clinical data of sarcoma patients from TCGA and the GEO database. The prognostic value of iron metabolism-related genes (IMRGs) in patients with sarcoma and the relationship between these genes and the immune microenvironment were studied by comprehensive bioinformatics analyses. Two signatures based on IMRGs were generated for the overall survival (OS) and disease-free survival (DFS) of sarcoma patients. At 3, 5, and 7 years, the areas under the curve (AUCs) of the OS signature were 0.708, 0.713, and 0.688, respectively. The AUCs of the DFS signature at 3, 5, and 7 years were 0.717, 0.689, and 0.702, respectively. Kaplan–Meier survival analysis indicated that the prognosis of high-risk patients was worse than that of low-risk patients. In addition, immunological analysis showed that there were different patterns of immune cell infiltration among patients in different clusters. Finally, we constructed two nomograms that can be used to predict the OS and DFS of sarcoma patients. The C-index was 0.766 (95% CI: 0.697–0.835) and 0.763 (95% CI: 0.706–0.820) for the OS and DFS nomograms, respectively. Both the ROC curves and the calibration plots showed that the two nomograms have good predictive performance. In summary, we constructed two IMRG-based prognostic models that can effectively predict the OS and DFS of sarcoma patients.

We propose an agent-based model for predicting individual flight delays in an entire air traffic network. In contrast to previous work, more detailed parameter estimation methods were incorporated into the agent-based model, acting on the state transitions of agents. Specifically, a conditional probability model was proposed for modifying the expected departure time, which was used to indicate whether a flight had experienced the necessary waiting due to Ground Delay Programs (GDPs) or carrier-related reasons. Additionally, two random forest regression models were presented for estimating the turnaround time and the elapsed time of flight agents in the agent-based delay prediction model. The parameter models were trained and fitted using the flight data for 2017 in the United States. The performance of the delay prediction model was tested for thirty days with three types of delay levels (low, medium, and high), which were randomly selected from 2018. The experimental results showed that the average absolute error in the test days was 6.8 min, and the classification accuracy with a 15 min threshold for a two-hour forecast horizon was 89.5%. The performance of our model outperformed that of existing research. Additionally, the positive effect of introducing parameter models and the negative impact of increasing the prediction horizon on the prediction performance were further studied.

Background and Purpose: Accurate prediction of functional outcome after stroke would provide evidence for reasonable post-stroke management. This study aimed to develop a machine learning-based prediction model for 6-month unfavorable functional outcome in Chinese acute ischemic stroke (AIS) patient.Methods: We collected AIS patients at National Advanced Stroke Center of Nanjing First Hospital (China) between September 2016 and March 2019. The unfavorable outcome was defined as modified Rankin Scale score (mRS) 3–6 at 6-month. We developed five machine-learning models (logistic regression, support vector machine, random forest classifier, extreme gradient boosting, and fully-connected deep neural network) and assessed the discriminative performance by the area under the receiver-operating characteristic curve. We also compared them to the Houston Intra-arterial Recanalization Therapy (HIAT) score, the Totaled Health Risks in Vascular Events (THRIVE) score, and the NADE nomogram.Results: A total of 1,735 patients were included into this study, and 541 (31.2%) of them had unfavorable outcomes. Incorporating age, National Institutes of Health Stroke Scale score at admission, premorbid mRS, fasting blood glucose, and creatinine, there were similar predictive performance between our machine-learning models, while they are significantly better than HIAT score, THRIVE score, and NADE nomogram.Conclusions: Compared with the HIAT score, the THRIVE score, and the NADE nomogram, the RFC model can improve the prediction of 6-month outcome in Chinese AIS patients.

Abstract Intelligent stimulus–response (S/R) systems are the basis of natural process and machine control, which are intensively explored in biomimetic design and analytical/biological applications. However, nonmonotonic multi‐S/R systems are still rarely studied so far. In this work, a rational design strategy is proposed to achieve such a unique S/R system by integrating opposite luminescence behaviors in one molecule. When solvent polarity increases, many heterocyclic or carbonyl‐containing compounds often become more emissive due to the suppression of the proximity effect, whereas molecules with donor–acceptor (D–A) structures tend to be less emissive because of the twisted intramolecular charge transfer. Meanwhile, protonation on D/A moieties will weaken/strengthen the D–A interaction to result in blue/redshifted emissions. By combining a protonatable heterocyclic acceptor and a protonatable donor together in one molecule, nonmonotonic brightness responses to polarity stimuli and nonmonotonic color responses to pH stimuli can be achieved. The design strategy is successfully verified by a simple molecule named 4‐(dimethylamino)styryl)quinoxalin‐2(1H)‐one (ASQ). ASQ exhibits nonmonotonic responses to polarity and pH stimuli, and aggregation‐induced emission (AIE) with a nonmonotonic AIE curve. Meanwhile, ASQ can be adjusted to emit white light in an acidic environment, and it shows multivalent functionalities including albumin protein sensing, ratiometric pH sensing, and amine gas sensing.

Besides pathogen evading, Acute Lung Injury (ALI), featuring the systematic inflammation and severe epithelial damages, is widely believed to be the central non-infectious factor controlling the progression of infectious diseases. ALI is partly caused by host immune responses. Under the inspiration of unsuccessful treatment in COVID-19, recent insights into pathogen–host interactions are leading to identification and development of a wide range of host-directed therapies with different mechanisms of action. The interaction unit consisting of macrophages and the alveolar epithelial cells has recently revealed as the therapeutic basis targeting ALI. Lian Hua Qing Wen capsule is the most effective and commonly-used clinical formula in treating respiratory infection for thousands of years in China. However, little is known about its relevance with ALI, especially its protective role against ALI-induced alveolar tissue damages. Aiming to evaluate its contribution in antibiotics-integrating therapies, this study pharmacologically verified whether LHQW could alleviate lipopolysaccharide (LPS)-induced ALI and explore its potential mechanisms in maintaining the physiology of macrophage-epithelial unit. In ALI mouse model, the pathological parameters, including the anal temperature, inflammation condition, lung edema, histopathological structures, have all been systematically analyzed. Results consistently supported the effectiveness of the combined strategy for LHQW and low-dose antibiotics. Furthermore, we established the macrophages-alveolar epithelial cells co-culture model and firstly proved that LHQW inhibited LPS-induced ER stress and TRAIL secretion in macrophages, thereby efficiently protected epithelial cells against TRAIL-induced apoptosis. Mechanistically, results showed that LHQW significantly deactivated NF-κB and reversed the SOCS3 expression in inflammatory macrophages. Furthermore, we proved that the therapeutic effects of LHQW were highly dependent on JNK-AP1 regulation. In conclusion, our data proved that LHQW is an epithelial protector in ALI, implying its promising potential in antibiotic alternative therapy.

Background and Purpose: Accurate prediction of functional outcomes after stroke would provide evidence for reasonable poststroke management. This study aimed to develop and validate a nomogram for individualized prediction of 1-year unfavorable outcomes in Chinese acute ischemic stroke (AIS) patients.Methods: We gathered AIS patients at the National Advanced Stroke Center of Nanjing First Hospital (China) between August 2014 and May 2017 within 12 h of symptom onset. The outcome measure was 1-year unfavorable outcomes (modified Rankin Scale 3–6). The patients were randomly stratified into the training (66.7%) and testing (33.3%) sets. With the training data, pre-established predictors were entered into a logistic regression model to generate the nomogram. Predictive performance of the nomogram model was evaluated in the testing data by calculating the area under the receiver operating characteristic curve (AUC-ROC), Brier score, and a calibration plot.Results: A total of 807 patients were included into this study, and 262 (32.5%) of them had unfavorable outcomes. Systolic blood pressure, Creatinine, Age, National Institutes of Health Stroke Scale (NIHSS) score on admission, and fasting blood glucose were significantly associated with unfavorable outcomes and entered into the SCANO nomogram. The AUC-ROC of the SCANO nomogram in the testing set was 0.781 (Brier score: 0.166; calibration slope: 0.936; calibration intercept: 0.060).Conclusions: The SCANO nomogram is developed and validated in Chinese AIS patients to firstly predict 1-year unfavorable outcomes, which is simple and convenient for the management of stroke patients.

Methylation of DNA, RNA or protein is a reversible modification. The proteins and genes that regulate this modification can be a candidate target for tumor therapy. However, the characteristics of methyltransferase related genes in glioma remain obscure. In this study, we systematically analyzed the relationship between methyltransferase-related genes expression profiles and outcomes in glioma patients based on The Cancer Genome Atlas and Chinese Glioma Genome Atlas RNA sequencing datasets. Consensus clustering identified two robust groups with significantly different pathological features and prognosis. Then a methyltransferase-related risk signature was built by a Cox proportional hazards model with elastic net penalty. Moreover, the risk score is associated with patients' clinical and molecular features and can be used as an independent prognostic indicator for patients with glioma. Furthermore, genes associated with the high-risk group were involved in various aspects of the malignant progression of glioma via Gene Ontology analysis and Gene Set Enrichment Analysis. In summary, our study identified a methyltransferase-related risk signature for predicting the prognosis of gliomas.

Introduction Little is known about the relationship between smoking and clinical outcomes in acute ischemic stroke (AIS) patients undergoing mechanical thrombectomy (MT). The outcomes could depend on different stroke subtypes. The aim of this study was to investigate whether smoking affected differently the outcomes in patients with different stroke subtypes who received MT. Methods AIS patients who underwent MT were prospectively enrolled from three hospitals between January 2014 and December 2018. Smokers were defined as current users of cigarettes. The stroke subtypes were classified according to TOAST criteria. Outcome measurements included treatment effects, intracerebral hemorrhage (ICH), and functional outcomes at 3 months. The effects of smoking on outcomes were assessed by logistic regression analysis. Results A total of 128 AIS patients with MT were enrolled, including 64 smokers and 64 non-smokers. Logistic regression analysis indicated that smoking was related to higher risk of In-hospital ICH (OR=4.31; 95% CI: 1.10–16.96; p=0.036) in patients with cardioembolism subtype. Furthermore, smoking was also associated with lower rates of mild stroke at discharge (OR=0.07; 95% CI: 0.02–0.31; p

Repetitively pulsed gas volume and dielectric surface discharges have gained growing attention because of peculiar and exciting physics phenomena, high efficiency, high reactivity, and potential to obtain conventionally unachievable plasma properties. Nevertheless, incomplete understanding of fundamental mechanisms renders the repetitively pulsed discharge far less predictable and controllable, where the inherent memory effect and the discharge mode transition are universal challenges. In this topical review, the authors will explore the macroscopic characteristics of the gas gap breakdown and the surface flashover, state-of-the-art mechanisms and dominant agents of discharge memory effects, operation regimes and transitions of the discharge mode, and how waveform parameters affect the pulsed discharge properties. Challenges and potential approaches for further understanding the memory effect and the discharge mode transition in the repetitively pulsed discharge are discussed.