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The world is experiencing unprecedented urbanization, particularly through the continuous growth of megacities, leading to significant urban land expansion. The spatial layout and growth patterns of urban areas play a critical role in determining cities' sustainable development. However, the optimal path for cities undergoing rapid urbanization remains uncertain. This study examines four megacities—Tokyo, Seoul, Beijing, and Shanghai—and, from the perspective of urban land density, compares their patterns and processes of land expansion in 1990, 2000, 2010, and 2020. Findings reveal that the urban land area of China's megacities has expanded nearly fourfold over the past three decades. Meanwhile, Tokyo and Seoul have adopted a polycentric urban structure, becoming increasingly compact. In contrast, Beijing and Shanghai are only beginning to show signs of polycentric development. The study concludes that cities experiencing rapid growth should not impose excessive limitations on urban expansion. Polycentric and compact development has become a critical strategy for megacities. These insights offer valuable guidance for urban planning and sustainable development in both established and emerging megacities across East Asia.

Anode-free lithium metal batteries (AFLMBs) are expected to achieve high energy density without Li anode. However, their capacities are fading quickly due to the lack of excessive Li resources from the anode side (N/P=0). Previously, cathode pre-lithiation to supplement excess Li in NCM811 was proven feasible to extend the battery lifespan of AFLMB, but deep lithiation suffers from crystal structure damage, resulting in short cycle life. Here, we proposed a hybrid Li-rich cathode by pre-lithiation of spinel structure material LiMn2O4 instead of Li-rich NCM compositing with NCM811, providing a new way to extend the lifespan of AFLMBs. During the first charge process, Li2Mn2O4, as a pre-lithiation reagent, releases excess Li to form a lithium layer on the anode and revert to LiMn2O4, maintaining stable electrochemical reversibility in the following cycles. The anode-free lithium metal pouch cell employing a Li-rich hybrid cathode achieves a high energy density of nearly 400 Wh kg−1 with 80 % capacity retention after 50 cycles.

Abstract Severe mental health problems with the representation of negative affect symptoms (NAS) have been increasingly reported during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to explore the multivariate patterns of brain functional connectome predicting COVID-19-related NAS. This cohort study encompassed a group of university students to undergo neuroimaging scans before the pandemic, and we re-contacted participants for 1-year follow-up COVID-related NAS evaluations during the pandemic. Regularized canonical correlation analysis was used to identify connectome-based dimensions of NAS to compute pairs of canonical variates. The predictive ability of identified functional connectome to NAS dimensional scores was examined with a nested cross-validation. Two dimensions (i.e. mode stress and mode anxiety) were related to distinct patterns of brain functional connectome (r 2 = 0.911, P FDR = 0.048; r 2 = 0.901, P FDR = 0.037, respectively). Mode anxiety was characterized by high loadings in connectivity between affective network (AFN) and visual network (VN), while connectivity of the default mode network with dorsal attention network (DAN) were remarkably prominent in mode stress. Connectivity patterns within the DAN and between DAN and VN, ventral attention network, and AFN was common for both dimensions. The identified functional connectome can reliably predict mode stress (r = 0.37, MAE = 5.1, p

In light of rapid economic and urban growth, the proliferation of structures including transmission towers, signal poles, and wind generators has become evident. Consequently, precise object detection of these structures emerges as a pivotal approach to enhance infrastructure management. This technique establishes a robust foundation for achieving elevated efficiency, precision, optimized energy management, and heightened safety monitoring. This article introduces a novel model for detecting structure based on channel and large-scale selective kernel (CLSK) model using high-resolution images. The method is rooted in a two-stage target detection network, enabling simultaneous identification of both primary structures and their associated shadows. The incorporation of deformable convolutions augments the model's ability to extract intricate features. Moreover, the introduction of the innovative LSK attention module, along with the CLSK attention module, enhances the optimization of features gleaned from the network's core architecture. Simultaneously, the complete intersection over union loss function refines the network's focus by considering parameters such as center point distance and aspect ratio in addition to the conventional overlapping area. This comprehensive approach facilitates improved feedback on detection outcomes. Empirical evaluation of the proposed network underscores its superior performance when juxtaposed with both conventional network models and the rotating detection box network.

A new species of odorous frog, Odorrana damingshanensis sp. nov., was found at the Damingshan National Nature Reserve in Guangxi, China. This species can be distinguished from its congeners by a combination of the following characters: medium body size (SVL 52.3–54.8 mm in males and 74.8–81.2 mm in females), sawtooth spinules on the upper lip, obtusely rounded snout that extends beyond the lower margin, distinct dorsolateral folds, horny tubercles on the rear of the back, presence of outer metatarsal tubercles, dilated nuptial pad with velvety spinules, distinct maxillary gland with tiny spines, and external lateral vocal sac. Through analysis of the 16S mitochondria gene, the new species is closely related to O. nasica and O. yentuensis, but the genetic divergence between the new species and the latter exceeds 7% (uncorrected p-distance). Currently, the new species is only known from its original discovery site. Furthermore, a discussion on the taxonomy of Odorrana (Bamburana) was conducted, identifying seven species within the subgenus Odorrana (Bamburana).

Enteroviruses have been a historical concern since the identification of polioviruses in humans. Wild polioviruses have almost been eliminated, while multiple species of non-polio enteroviruses and their variants co-circulate annually. To date, at least 116 types have been found in humans and are grouped into the species Enterovirus A–D and Rhinovirus A–C. However, there are few available antiviral drugs, especially with a universal pharmaceutical effect. Here, we demonstrate that peptide P25 from EV-D68 has broad antiviral activity against EV A–D enteroviruses in vitro. P25, derived from the HI loop and β-I sheet of VP1, operates through a conserved hydrophilic motif -R---K-K--K- and the hydrophobic F near the N-terminus. It could prevent viral infection of EV-A71 by competing for the heparan sulfate (HS) receptor, binding and stabilizing virions by suppressing the release of the viral genome. P25 also inhibited the generation of infectious viral particles by reducing viral protein synthesis. The molecular docking revealed that P25 might bind to the pocket opening area, a potential target for broad-spectrum antivirals. Our findings implicate the multiple antiviral effects of peptide P25, including blocking viral binding to the HS receptor, impeding viral genome release, and reducing progeny particles, which could be a novel universal anti-enterovirus drug candidate.

Aster yaoshanensis sp. nov., a new species of the family Asteraceae is here described and illustrated. The species is presently known only from rock crevices of mountain valleys in Dayaoshan National Nature Reserve, Guangxi autonomous region, China. Phylogenetic analyses based on ITS sequences and complete plastome data have shown that this new species is a member of genus Aster with high support. Morphologically, it mostly resembles A. jishouensis, but it can be easily distinguished from the latter by bract indumentum (glabrous except margin ciliate vs. villous especially on veins abaxially, glabrous adaxially) and color (green vs. purple), shorter corolla (3.2–3.5 mm vs. 4.5–5.3 mm), bract stalk (obvious, ca.1.2 mm vs. sessile), and different distribution (Guangxi vs. Hunan). The detailed description, distribution map, and photos are provided. This study further elucidates the species identification, phylogeny and characteristic evolution of Aster.

Objective This study aimed to explore the effects of different types of xanthophyll extracted from marigold on the growth performance, skin color, and carcass pigmentation. Methods A total of 192 healthy 60-day-old yellow-feathered broilers weighing an average of 1,279±81 g were randomly allocated to 4 groups, each with 6 replicates and 8 broilers. The 4 treatments were as follows: i) CON group, fed with basal diet; ii) LTN group, supplemented with lutein; iii) MDP group, supplemented with monohydroxyl pigment including dehydrated lutein, β-cryptoxanthin, and α-cryptoxanthin; iv) LTN+MDP group, supplemented with lutein and monohydroxyl pigment in proportion to 1:1. The supplementary content of LTN, MDP, and LTN+MDP was 2 g/kg. Skin color was measured after 7, 14, 21, and 28 days of feeding the dietary treatments. The breast, thigh, and abdominal fat of slaughtered chickens were stored in cold storage at 4°C for 24 hours and then the meat color of lightness (L*), redness (a*), and yellowness (b*) values was determined. Results The results showed that all treatments enhanced the yellow scores of subwing skin on day 14, 21, and 28 (p

Abstract Although attention-deficit/hyperactivity disorder (ADHD) and a family history of bipolar I disorder (BD) are associated with increased risk for developing BD, their neuroanatomical substrates remain poorly understood. This study compared cortical and subcortical gray matter morphology in psychostimulant-free ADHD youth with and without a first-degree relative with BD and typically developing healthy controls. ADHD youth (ages 10-18 years) with (‘high-risk’, HR) or without (‘low-risk’, LR) a first-degree relative with BD and healthy comparison youth (HC) were enrolled. High-resolution 3D T1-weighted images were acquired using a Philips 3.0 T MR scanner. The FreeSurfer image analysis suite was used to measure cortical thickness, surface area, and subcortical volumes. A general linear model evaluated group differences in MRI features with age and sex as covariates, and exploratory correlational analyses evaluated associations with symptom ratings. A total of n = 142 youth (mean age: 14.16 ± 2.54 years, 35.9% female) were included in the analysis (HC, n = 48; LR, n = 49; HR, n = 45). The HR group exhibited a more severe symptom profile, including higher mania and dysregulation scores, compared to the LR group. For subcortical volumes, the HR group exhibited smaller bilateral thalamic, hippocampal, and left caudate nucleus volumes compared to both LR and HC, and smaller right caudate nucleus compared with LR. No differences were found between LR and HC groups. For cortical surface area, the HR group exhibited lower parietal and temporal surface area compared with HC and LR, and lower orbitofrontal and superior frontal surface area compared to LR. The HR group exhibited lower left anterior cingulate surface area compared with HC. LR participants exhibited greater right pars opercularis surface area compared with the HC. Some cortical alterations correlated with symptom severity ratings. These findings suggest that ADHD in youth with a BD family history is associated with a more a severe symptom profile and a neuroanatomical phenotype that distinguishes it from ADHD without a BD family history.

Enteroviruses (EVs and RVs) are prevalent worldwide and cause various diseases in humans, of which the VP1-pocket is a target of antivirals, with a lipid molecule as a pocket factor to stabilize the virion. However, the characterization of the structure of the VP1-pocket in EVs is poor. Here, we compared the published capsid crystals of EVs and RVs and proposed a structural framework for the VP1-pocket: Frame 1–4, which is located at the CD loop, GH loop, and C-terminus, presenting with an outward opening appearance or not. The non-outward viral strains—CVB3, Echo 11, RV-A81, and RV-B70—are more thermally stable, with a breakpoint temperature (B.T.) of 51~62 °C for genome releasing, which is 4~10 °C higher than its outward temperature of 41~47 °C, and infectivity preservation when treated at 50 °C for 3 min. Its outward versus non-outward opening is correlated significantly with the B.T. for genome release (r = −0.90; p = 0.0004) and infectivity (r = −0.82, p = 0.0039). The energy of Frames 1, 2, and 4, including Van der Waals attractive and repulsive interactions and hydrogen bonds, showed significant correlations with the B.T. (r = −0.67, 0.75, and −0.8; p = 0.034, 0.013, and 0.006, respectively). These characters of the VP1-pocket could be predictors for virion thermostability and aid in the development of vaccines or antivirals.

Abstract Quick Access Recorders (QARs) provide an important data source for Flight Operation Quality Assurance (FOQA) and flight safety. It is generally characterized by large volume, high-dimensionality and high frequency, and these features result in extreme complexities and uncertainties in its usage and comprehension. In this study, we proposed a Time-Feature Attention (TFA)-based Convolutional Auto-Encoder (TFA-CAE) network model to extract essential flight features from QAR data. As a case study, we used the QAR data landing at the Kunming Changshui International Airport and Lhasa Gonggar International Airport as the experimental data. The results show that (1) the TFA-CAE model performs the best in extracting representative flight features in comparison to some traditional or similar approaches, such as Principal Component Analysis (PCA), Convolutional Auto-Encoder (CAE), Self-Attention-based CAE (SA-CAE), Gate Recurrent Unit based Auto-Encoder (GRU-AE) and TFA-GRU-AE models; (2) flight patterns corresponding to different runways can be recognized; and (3) anomalous flights can effectively deviate from many observations. Overall, the TFA-CAE model provides a well-established technique for further usage of QAR data, such as flight risk detection or FOQA.

As globalization and economic development accelerate, Chinese tea has emerged as an essential export commodity in the international market. China boasts abundant tea resources, which have significantly promoted economic growth through tea exports. This study analyses the relevant literature and the prevailing state of China's tea export trade. Using statistical data on tea exports and empirical research methodologies, this investigation examines the key determinants influencing the high-quality development of China's tea export sector. The research integrates relevant frontier theories from management science and engineering to propose tailored recommendations. The study reveals that while the industry has demonstrated robust economic growth, it is confronted with challenges such as the need for enhanced brand visibility and adherence to international quality benchmarks. The research findings highlight the positive influence of the "Belt and Road" initiative on the tea export trade, which advocates for strategic international collaboration. This study concludes with policy recommendations that underscore the importance of bolstering the international dissemination of Chinese tea culture, standardizing export practices, and fostering global cooperation to augment the industry's high-quality development trajectory. The findings aim to enhance the export capabilities of Chinese tea and foster the robust growth of the tea export industry.

Air pollution has been standing as one of the most pressing global challenges. The changing patterns of air pollutants at different spatial and temporal scales have been substantially studied all over the world, which, however, were intricately disturbed by COVID-19 and subsequent containment measures. Understanding fine-scale changing patterns of air pollutants at different stages over the epidemic’s course is necessary for better identifying region-specific drivers of air pollution and preparing for environmental decision making during future epidemics. Taking China as an example, this study developed a multi-output LightGBM approach to estimate monthly concentrations of the six major air pollutants (i.e., PM2.5, PM10, NO2, SO2, O3, and CO) in China and revealed distinct spatiotemporal patterns for each pollutant over the epidemic’s course. The 5-year period of 2019–2023 was selected to observe changes in the concentrations of air pollutants from the pre-COVID-19 era to the lifting of all containment measures. The performance of our model, assessed by cross-validation R2, demonstrated high accuracy with values of 0.92 for PM2.5, 0.95 for PM10, 0.95 for O3, 0.90 for NO2, 0.79 for SO2, and 0.82 for CO. Notably, there was an improvement in the concentrations of particulate matter, particularly for PM2.5, although PM10 exhibited a rebound in northern regions. The concentrations of SO2 and CO consistently declined across the country over the epidemic’s course (p < 0.001 and p < 0.05, respectively), while O3 concentrations in southern regions experienced a notable increase. Concentrations of air pollutants in the Beijing–Tianjin–Hebei region were effectively controlled and mitigated. The findings of this study provide critical insights into changing trends of air quality during public health emergencies, help guide the development of targeted interventions, and inform policy making aimed at reducing disease burdens associated with air pollution.

A channel-fixed delay error estimation method was proposed to tackle the performance degradation of digital beamforming caused by the channel-fixed delay error of wideband digital array radar.First, the integer part of the fixed delay of the channel was estimated through the correlation integral peak of the baseband transmitted signal and the baseband received signal containing unknown delay parameters.Then, the fractional part of the fixed delay of the channel was estimated with parabolic interpolation by using the correlation peak and its adjacent two points.Finally, the multi-channel fixed delay error was calculated and compensated.The simulation results show that the multi-channel fixed delay estimation of wideband digital array radar can be realized on Xilinx xcku115.The estimative precision can reach 0.001～0.014 times the sampling gap when the signal-to-noise ratio of the input signal is 10～30 dB.The proposed method can meet the engineering application requirements and improve the performance of digital beamforming in wideband digital array radar.

Long-time coherent integration is an effective approach to improve detection performance for weak air moving targets (AMTs). The range position variation and azimuth Doppler variation will easily exceed range gate and Doppler resolution in a long observation time, resulting in severe performance degradation, especially for high maneuverability target. Besides, the high detection performance and low computational complexity ability are, most of times, the contradiction requirements by using the existing long-time coherent integration algorithms. To overcome above constraints, a novel subaperture keystone transform matched filtering (SAKTMF) method is developed in this article based on the conventional hybrid integration (HI) algorithm, which can realize coherent integration both within the subaperture and among subapertures, effectively improving the detection performance of a weak moving target. Furthermore, the proposed SAKTMF is applied for weak AMT detection in spaceborne early warning radar, which considers serious extended clutter, range migration, and Doppler migration problems simultaneously. Simulation experiments processing results show that the proposed method can provide improved detection performance compared with the conventional HI methods.

Abstract Background Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. Methods We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. Results We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. Conclusion We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Abstract Influenza B virus circulates yearly with lower activity than that of influenza A virus in China. During winter 2017 to 2018, a sharp surge of influenza activity dominated by type B/Yamagata lineage virus caused unprecedented medical burden in Beijing. This research aimed to understand the underlying mechanism for this circulation and prepare for epidemics in the future. Sera samples collected from the patients in 2016–2017 and 2017–2018 flu seasons were tested for profiling hemagglutinin inhibition (HI) antibodies against both prevailing Victoria and Yamagata lineages of type B influenza viruses. It showed that the seroprevalence against both lineages of the virus in 2017–2018 winter was higher than that in 2016–2017, while no difference of the seroprevalence was observed between the two viruses. Meanwhile, significant elevated geometric mean titer (GMT) against both lineages of influenza B viruses was found in the specimens collected during 2017–2018 flu season than that from 2016 to 2017, suggesting the viruses might undergo antigenic changes. These results also suggested that lower GMT against both type B variants in 2016–2017 might serve as an immunological niche for the dominating of B/Yamagata virus in China during 2017–2018 winter season. Our findings have implication that there was a significantly elevation of HI antibodies to influenza viruses B in 2017–2018 than in 2016–2017. On the other hand, the low level of HI antibodies to both B/Y and B/V in 2016–2017 could contribute to the severe B/Y epidemic in 2017–2018 to some extent.

Accurate assessments of the historical and current status of eco-environmental quality (EEQ) are essential for governments to have a comprehensive understanding of regional ecological conditions, formulate scientific policies, and achieve the United Nations Sustainable Development Goals (SDGs). While various approaches to EEQ monitoring exist, they each have limitations and cannot be used universally. Moreover, previous studies lack detailed examinations of EEQ dynamics and its driving factors at national and local levels. Therefore, this study utilized a remote sensing ecological index (RSEI) to assess the EEQ of China from 2001 to 2021. Additionally, an emerging hot-spot analysis was conducted to study the spatial and temporal dynamics of the EEQ of China. The degree of influence of eight major drivers affecting EEQ was evaluated by a GeoDetector model. The results show that from 2001 to 2021, the mean RSEI values in China showed a fluctuating upward trend; the EEQ varied significantly in different regions of China, with a lower EEQ in the north and west and a higher EEQ in the northeast, east, and south in general. The spatio-temporal patterns of hot/cold spots in China were dominated by intensifying hot spots, persistent cold spots, and diminishing cold spots, with an area coverage of over 90%. The hot spots were concentrated to the east of the Hu Huanyong Line, while the cold spots were concentrated to its west. The oscillating hot/cold spots were located in the ecologically fragile agro-pastoral zone, next to the upper part of the Hu Huanyong Line. Natural forces have become the main driving force for changes in China’s EEQ, and precipitation and soil sand content were key variables affecting the EEQ. The interaction between these factors had a greater impact on the EEQ than individual factors.

Oxidative stress occurs in the process of egg storage. Antioxidants as feed additives can enhance egg quality and extend the shelf life of eggs. Selenium-enriched Cardamine violifolia (SEC) has strongly antioxidant properties. The objective of this study was to assess the effects of dietary supplementation with SEC on egg quality and the yolk antioxidant capacity of eggs stored at 4 °C and 25 °C. Four hundred fifty 65-week-old, Roman hens that were similar in laying rate (90.79 ± 1.69%) and body weight (2.19 ± 0.23 kg) were divided into 5 groups. The birds were fed diets supplemented with 0 mg/kg selenium (Se) (CON), 0.3 mg/kg Se from sodium selenite (SS), 0.3 mg/kg Se from Se-enriched yeast (SEY), 0.3 mg/kg Se for selenium-enriched Cardamine violifolia (SEC) or 0.3 mg/kg Se from Se-enriched Cardamine violifolia and 0.3 mg/kg Se from Se-enriched yeast (SEC + SEY) for 8 weeks. The eggs were collected on the 8th week and were analyzed for egg quality and oxidative stability of yolk during storage at 4 °C or 25 °C for 0, 2, 4, or 6 weeks. Dietary SEC and SEC + SEY supplementation increased the Haugh unit (HU) and albumen foam stability in eggs stored at 4 °C and 25 °C (p < 0.05). SS and SEC supplementation increased the yolk index in eggs stored at 25 °C (p < 0.05). SEC or SEC + SEY slowed down an increase in albumen pH and gel firmness in eggs stored at 4 °C and 25 °C (p < 0.05). Moreover, SEC or SEC + SEY alleviated the increase in malonaldehyde (MDA), and the decrease in total antioxidant capacity (T-AOC) level and total superoxide dismutase (T-SOD) activity in yolks stored at 4 °C and 25 °C (p < 0.05). These results indicate that SEC mitigated egg quality loss and improved the antioxidant capacity of yolks during storage. SEC supplementation would be advantageous to extend the shelf life of eggs.

Abstract As two common mental disorders during the period of adolescence that extend to early adulthood, attention-deficit/hyperactivity disorder (ADHD) and substance use disorders (SUDs) have considerable diagnostic co-occurrence and shared neuropsychological impairments. Our study aimed to identify overlapping and distinct brain structural abnormalities associated with ADHD and SUDs among adolescents and young adults. A systematic literature search on voxel-based morphometry (VBM) studies of ADHD and SUDs was conducted in PubMed and Web of Science. Data were extracted and analyzed to identify brain abnormalities using Seed-based d-Mapping software. Data-driven functional decoding was conducted to identify the psychophysiological functioning associated with brain alterations. 13 and 14 VBM studies for ADHD (619 patients and 483 controls) and SUDs (516 patients and 413 controls), respectively, were included. Patterns of decreased gray matter volume (GMV) were found in the left precentral gyrus, bilateral superior frontal gyri, and left inferior frontal gyrus in the ADHD group compared to the control group. In contrast, individuals with SUDs, relative to controls, were characterized by increased GMV in the left putamen and insula. Comparative analysis indicated larger regional GMV in the right inferior parietal lobule and smaller volumes in the left putamen and left precentral gyrus in the ADHD group than in the SUDs group. Dissociable brain structural abnormalities in adolescents and young adults with ADHD and SUDs potentially implicate different pathogeneses and provide a reference for differential diagnosis and early detection for shared symptomology and comorbidity.

Humanities and Social Sciences (HSS) are undergoing the transformation of spatialization and quantification. Geo-computation, with geoinformatics (including RS: Remote Sensing; GIS: Geographical Information System; GNSS: Global Navigation Satellite System), provides effective computational and spatialization methods and tools for HSS. Spatial Humanities and Geo-computation for Social Sciences (SH&GSS) is a field coupling geo-computation, and geoinformatics, with HSS. This special issue accepted a set of contributions highlighting recent advances in methodologies and applications of SH&GSS, which are related to sentiment spatial analysis from social media data, emotional change spatial analysis from news data, spatial analysis of social media related to COVID-19, crime spatiotemporal analysis, “double evaluation” for Land Use/Land Cover (LUCC), Specially Protected Natural Areas (SPNA) analysis, editing behavior analysis of Volunteered Geographic Information (VGI), electricity consumption anomaly detection, First and Last Mile Problem (FLMP) of public transport, and spatial interaction network analysis for crude oil trade network. Based on these related researches, we aim to present an overview of SH&GSS, and propose some future research directions for SH&HSS.

Abstract Magnetization transfer imaging (MTI) may provide more sensitivity and mechanistic understanding of neuropathological changes associated with schizophrenia than volumetric MRI. This study aims to identify brain magnetization transfer ratio (MTR) changes in antipsychotic-naïve first-episode schizophrenia (FES), and to correlate MTR findings with clinical symptom severity. A total of 143 individuals with antipsychotic-naïve FES and 147 healthy controls (HCs) were included and underwent 3.0 T brain MTI between August 2005 and July 2014. Voxelwise analysis was performed to test for MTR differences with family-wise error corrections. Relationships of these differences to symptom severity were assessed using partial correlations. Exploratory analyses using a support vector machine (SVM) classifier were conducted to discriminate FES from HCs using MTR maps. Model performance was examined using a 10-fold stratified cross-validation. Compared with HCs, individuals with FES exhibited higher MTR values in left thalamus, precuneus, cuneus, and paracentral lobule, that were positively correlated with schizophrenia symptom severity [precuneus (r = 0.34, P = 0.0004), cuneus (r = 0.33, P = 0.0006) and paracentral lobule (r = 0.37, P = 0.001)]. Whole-brain MTR maps identified individuals with FES with overall accuracy 75.5% (219 of 290 individuals) based on SVM approach. In antipsychotic-naïve FES, clinically relevant biophysical abnormalities detected by MTI mainly in the left parieto-occipital regions are informative about local brain pathology, and have potential as diagnostic markers.

Abstract Background Children exposed to natural disasters are vulnerable to developing posttraumatic stress disorder (PTSD). Previous studies using resting-state functional neuroimaging have revealed alterations in graph-based brain topological network metrics in pediatric PTSD patients relative to healthy controls (HC). Here we aimed to apply deep learning (DL) models to neuroimaging markers of classification which may be of assistance in diagnosis of pediatric PTSD. Methods We studied 33 pediatric PTSD and 53 matched HC. Functional connectivity between 90 brain regions from the automated anatomical labeling atlas was established using partial correlation coefficients, and the whole-brain functional connectome was constructed by applying a threshold to the resultant 90 * 90 partial correlation matrix. Graph theory analysis was used to examine the topological properties of the functional connectome. A DL algorithm then used this measure to classify pediatric PTSD vs HC. Results Graphic topological measures using DL provide a potentially clinically useful classifier for differentiating pediatric PTSD and HC (overall accuracy 71.2%). Frontoparietal areas (central executive network), cingulate cortex, and amygdala contributed the most to the DL model’s performance. Conclusions Graphic topological measures based on fMRI data could contribute to imaging models of clinical utility in distinguishing pediatric PTSD from HC. DL model may be a useful tool in the identification of brain mechanisms PTSD participants.

BackgroundMalignant mesothelioma (MMe) is a rare and fatal cancer with a poor prognosis. Our study aimed to compare the overall survival (OS) of MMe patients across various sites and develop a prognostic model to provide a foundation for individualized management of MMe patients.MethodsFrom the Surveillance, Epidemiology, and End Results (SEER) database, 1,772 individuals with malignant mesothelioma (MMe) were identified. The X-tile software was used to identify the optimal cut-off point for continuous variables. The Kaplan–Meier method was employed to compare the survival of MMe across different sites. The Cox proportional hazards model was applied to identify the independent risk factors of overall survival (OS) and a nomogram was constructed.ResultsIn the survival analysis, MMe originating from the reproductive organs and hollow organs showed a relatively better prognosis than those originating from soft tissue, solid organs, and pleura. Age, gender, location, histological type, grade of differentiation, extent of disease, lymph node status, lymph node ratio (LNR), and chemotherapy were all found to be independent risk variables for the prognosis of MMe patients (P

Abstract Objective There is increasing evidence that amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease impacting large-scale brain networks. However, it is still unclear which structural networks are associated with the disease and whether the network connectomics are associated with disease progression. This study was aimed to characterize the network abnormalities in ALS and to identify the network-based biomarkers that predict the ALS baseline progression rate. Methods Magnetic resonance imaging was performed on 73 patients with sporadic ALS and 100 healthy participants to acquire diffusion-weighted magnetic resonance images and construct white matter (WM) networks using tractography methods. The global and regional network properties were compared between ALS and healthy subjects. The single-subject WM network matrices of patients were used to predict the ALS baseline progression rate using machine learning algorithms. Results Compared with the healthy participants, the patients with ALS showed significantly decreased clustering coefficient C p (P = 0.0034, t = 2.98), normalized clustering coefficient γ (P = 0.039, t = 2.08), and small‐worldness σ (P = 0.038, t = 2.10) at the global network level. The patients also showed decreased regional centralities in motor and non-motor systems including the frontal, temporal and subcortical regions. Using the single-subject structural connection matrix, our classification model could distinguish patients with fast versus slow progression rate with an average accuracy of 85%. Conclusion Disruption of the WM structural networks in ALS is indicated by weaker small-worldness and disturbances in regions outside of the motor systems, extending the classical pathophysiological understanding of ALS as a motor disorder. The individual WM structural network matrices of ALS patients are potential neuroimaging biomarkers for the baseline disease progression in clinical practice.

The COVID-19 epidemic widely spread across China from Wuhan, Hubei Province, because of huge migration before 2020 Chinese New Year. Previous studies demonstrated that population outflows from Wuhan determined COVID-19 cases in other cities but neglected spatial heterogeneities of their relationships. Here, we use Geographically Weighted Regression (GWR) model to investigate the spatially varying influences of outflows from Wuhan. Overall, the GWR model increases explanatory ability of outflows from Wuhan by 20%, with the adjusted R2 increasing from ~0.6 of Ordinary Least Squares (OLS) models to ~0.8 of GWR models. The coefficient between logarithmic of outflows from Wuhan and COVID-19 cases in other cities is generally less than 1. The sub-linear scaling relationship indicates the increasing returns of outflows was restrained, proving the epidemic was efficiently controlled outside Hubei at the beginning without obvious local transmissions. Coefficients in GWR models vary in cities. Not only cities around Wuhan but also cities having close connections with Wuhan experienced higher coefficients, showing a higher vulnerability of these cities. The secondary or multi-level transmission networks deserve to be further explored to fully uncover influences of migrations on the COVID-19 pandemic.

In order to support the development of the Geared turbofan （GTF） engine in China， the five-branch-power-split herringbone star gearbox for Fan drive gear system（FDGS） is studied. The manufacturing and assembling errors that affects the load sharing coefficient of the star gear system are analyzed. The calculation model for static load sharing coefficient of the star gear system is established. By calculating and analyzing the static load sharing coefficient of a star gear system test piece， the sensitivity of the manufacturing and assembling errors is obtained， the method of reducing the static load sharing coefficient by adjusting offset phase of star gears is proposed， and how to synchronize the eccentricity phase of planetary gear during assembly process illustrated by a star gear system test piece.

BackgroundAlthough the roles of m6A modification in the immune responses to human diseases have been increasingly revealed, their roles in immune microenvironment regulation in coronary heart disease (CHD) are poorly understood.MethodsThe GSE20680 and GSE20681 datasets related to CHD were acquired from the Gene Expression Omnibus (GEO) database. A total of 30 m6A regulators were used to perform LASSO regression to identify the significant genes involved in CHD. Unsupervised clustering analysis was conducted using the m6A regulators to distinguish the m6A RNA methylation patterns in patients with CHD. The differentially expressed genes (DEGs) and biological characteristics, including GO and KEGG enrichment results, were assessed for the different m6A patterns to analyse the impacts of m6A regulators on CHD. Hub genes were identified, and subsequent microRNAs-mRNAs (miRNAs–mRNAs) and mRNAs-transcriptional factors (mRNA-TFs) interaction networks were constructed by the protein and protein interaction (PPI) network method using Cytoscape software. The infiltrating proportion of immune cells was assessed by ssGSEA and the CIBERSORT algorithm. Quantitative real-time PCR (qRT-PCR) was performed to detect the expression of the significant m6A regulators and hub genes.ResultsFour of 30 m6A regulators (HNRNPC, YTHDC2, YTHDF3, and ZC3H13) were identified to be significant in the development of CHD. Two m6A RNA methylation clusters were distinguished by unsupervised clustering analysis based on the expression of the 30 m6A regulators. A total of 491 genes were identified as DEGs between the two clusters. A PPI network including 308 mRNAs corresponding to proteins was constructed, and 30 genes were identified as hub genes that were enriched in the bioprocesses of peptide cross-linking, keratinocyte differentiation. Twenty-seven hub genes were found to be related to miRNAs, and seven hub genes were found to be related to TFs. Moreover, among the 30 hub genes, eight genes were found to be upregulated in CHD, and three were found to be downregulated in CHD compared to the normal people. The high m6A modification pattern was associated with a higher infiltrated abundance of immune cells.ConclusionOur findings demonstrated that m6A modification plays crucial roles in the diversity and complexity of the immune microenvironment in CHD.

Background: Abnormal chromosome segregation is identified to be a common hallmark of cancer. However, the specific predictive value of it in lung adenocarcinoma (LUAD) is unclear.Method: The RNA sequencing and the clinical data of LUAD were acquired from The Cancer Genome Atlas (TACG) database, and the prognosis-related genes were identified. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were carried out for functional enrichment analysis of the prognosis genes. The independent prognosis signature was determined to construct the nomogram Cox model. Unsupervised clustering analysis was performed to identify the distinguishing clusters in LUAD-samples based on the expression of chromosome segregation regulators (CSRs). The differentially expressed genes (DEGs) and the enriched biological processes and pathways between different clusters were identified. The immune environment estimation, including immune cell infiltration, HLA family genes, immune checkpoint genes, and tumor immune dysfunction and exclusion (TIDE), was assessed between the clusters. The potential small-molecular chemotherapeutics for the individual treatments were predicted via the connectivity map (CMap) database.Results: A total of 2,416 genes were determined as the prognosis-related genes in LUAD. Chromosome segregation is found to be the main bioprocess enriched by the prognostic genes. A total of 48 CSRs were found to be differentially expressed in LUAD samples and were correlated with the poor outcome in LUAD. Nine CSRs were identified as the independent prognostic signatures to construct the nomogram Cox model. The LUAD-samples were divided into two distinct clusters according to the expression of the 48 CSRs. Cell cycle and chromosome segregation regulated genes were enriched in cluster 1, while metabolism regulated genes were enriched in cluster 2. Patients in cluster 2 had a higher score of immune, stroma, and HLA family components, while those in cluster 1 had higher scores of TIDES and immune checkpoint genes. According to the hub genes highly expressed in cluster 1, 74 small-molecular chemotherapeutics were predicted to be effective for the patients at high risk.Conclusion: Our results indicate that the CSRs were correlated with the poor prognosis and the possible immunotherapy resistance in LUAD.

Abstract Background Given that psychopharmacological approaches routinely used to treat mood-related problems may result in adverse outcomes in mood dysregulated adolescents at familial risk for bipolar disorder (BD), Mindfulness-Based Cognitive Therapy for Children (MBCT-C) provides an alternative effective and safe option. However, little is known about the brain mechanisms of beneficial outcomes from this intervention. Herein, we aimed to investigate the network-level neurofunctional effects of MBCT-C in mood dysregulated adolescents. Methods Ten mood dysregulated adolescents at familial risk for BD underwent a 12-week MBCT-C intervention. Resting-state functional magnetic resonance imaging (fMRI) was performed prior to and following MBCT-C. Topological metrics of three intrinsic functional networks (default mode network (DMN), fronto-parietal network (FPN) and cingulo-opercular network (CON)) were investigated respectively using graph theory analysis. Results Following MBCT-C, mood dysregulated adolescents showed increased global efficiency and decreased characteristic path length within both CON and FPN. Enhanced functional connectivity strength of frontal and limbic areas were identified within the DMN and CON. Moreover, change in characteristic path length within the CON was suggested to be significantly related to change in the Emotion Regulation Checklist score. Conclusions 12-week MBCT-C treatment in mood dysregulated adolescents at familial risk for BD yield network-level neurofunctional effects within the FPN and CON, suggesting enhanced functional integration of the dual-network. Decreased characteristic path length of the CON may be associated with the improvement of emotion regulation following mindfulness training. However, current findings derived from small sample size should be interpreted with caution. Future randomized controlled trials including larger samples are critical to validate our findings.

In order to ensure the safety of dual manipulator in public space， an accurate and efficient collision detection algorithm is proposed. The sphere and capsule bounding boxes are used to simplify the manipulator model， and the collision is judged according to the intersection between the bounding boxes. The second projection method is used to achieve fast detection. The bounding box is projected into line segments. If the projection line segments are not overlapped， the bounding box will not intersect. If the projection line segments are overlapped， the problem is further transformed into the shortest distance solution from point to point， point to line and line to line. Through the analysis of the simulation software and physical manipulator experimental platform， the results show that the algorithm has high detection efficiency and can be directly implanted into the manipulator controller program， meet the safety requirements.

Background: Stanford type A aortic dissection (ATAAD) is a common life-threatening event in the aorta. Recently, immune disorder has been linked to the risk factors that cause ATAAD at the molecular level. However, the specific immune-related gene signature during the progression is unclear.Methods: The GSE52093 and GSE98770 datasets related to ATAAD from the Gene Expression Omnibus (GEO) database were acquired. The immune gene expression levels were analyzed by single sample gene set enrichment analysis (ssGSEA). The correlations between gene networks and immune scores were determined by weighted gene correlation network analysis (WGCNA). The different immune subgroups were finally divided by consensus clustering. The differentially expressed genes (DEGs) were identified and subsequent functional enrichment analyses were conducted. The hub genes were identified by protein–protein interaction (PPI) network and functional similarities analyses. The immune cell infiltration proportion was determined by the CIBERSORT algorithm.Results: According to the ssGSEA results, the 13 ATAAD samples from the GEO database were divided into high- and low-immune subgroups according to the ssGSEA, WGCNA, and consensus clustering analysis results. Sixty-eight immune-related DEGs (IRDEGs) between the two subgroups were enriched in inflammatory-immune response biological processes, including leukocyte cell–cell adhesion, mononuclear cell migration, and myeloid leukocyte migration. Among these IRDEGs, 8 genes (CXCR4, LYN, CCL19, CCL3L3, SELL, F11R, DPP4, and VAV3) were identified as hub genes that represented immune-related signatures in ATAAD after the PPI and functional similarities analyses. The proportions of infiltrating CD8 T cells and M1 macrophages were significantly higher in ATAAD patients in the immune-high group than the immune-low group.Conclusion: Eight immune-related genes were identified as hub genes representing potential biomarkers and therapeutic targets linked to the immune response in ATAAD patients.

Summary: Background: Establishing objective and quantitative neuroimaging biomarkers at individual level can assist in early and accurate diagnosis of major depressive disorder (MDD). However, most previous studies using machine learning to identify MDD were based on small sample size and did not account for the brain connectome that is associated with the pathophysiology of MDD. Here, we addressed these limitations by applying graph convolutional network (GCN) in a large multi-site MDD dataset. Methods: Resting-state functional MRI scans of 1586 participants (821 MDD vs. 765 controls) across 16 sites of Rest-meta-MDD consortium were collected. GCN model was trained with individual whole-brain functional network to identify MDD patients from controls, characterize the most salient regions contributing to classification, and explore the relationship between topological characteristics of salient regions and clinical measures. Findings: GCN achieved an accuracy of 81·5% (95%CI: 80·5–82·5%, AUC: 0·865), which was higher than other common machine learning classifiers. The most salient regions contributing to classification were primarily identified within the default mode, fronto-parietal, and cingulo-opercular networks. Nodal topologies of the left inferior parietal lobule and left dorsolateral prefrontal cortex were associated with depressive severity and illness duration, respectively. Interpretation: These findings based on a large, multi-site dataset support the feasibility and effectiveness of GCN in characterizing MDD, and also illustrate the potential utility of GCN for enhancing understanding of the neurobiology of MDD by detecting clinically-relevant disruption in functional network topology. Funding: This study was supported by the National Natural Science Foundation of China (Grant Nos. 81621003, 82027808, 81820108018).

ABSTRACTInterferon-induced transmembrane protein 3 (IFITM3) as an antiviral factor can inhibit replication of several viruses including influenza virus. A single-nucleotide polymorphism rs12252-C of IFITM3 results in a truncated IFITM3 protein lacking its first 21 amino acids, which is much higher in the Han Chinese population and associated with severe illness in adults infected with pandemic influenza H1N1/09 virus. To investigate if IFITM3 or IFITM3 rs12252-C could affect the antibody response after influenza vaccination, we detected the haemagglutination inhibition (HI) of 171 healthy young adult volunteers (IFITM3 rs12252-C/C, C/T, T/T carriers) and in an IFITM3-deletion mouse model (Ifitm3-/-) after trivalent inactivated vaccine (TIV) immunization. Seroconversion rates for H1N1, H3N2 and B viruses in IFITM3 rs12252-C/C genotype carriers was lower compared with C/T and T/T donors. Significantly lower levels of specific antibodies to H1N1, H3N2 and B viruses and total IgG were observed in Ifitm3-/- mice. Correspondingly, the numbers of splenic germinal centre (GC) B cells, plasma cells, TIV-specific IgG+ antibody secreting cells and T follicular helper cells in Ifitm3-/- mice were lower compared with wild type mice. However, the number of memory B cells was higher in Ifitm3-/- mice at day 7 after booster. The HI level of Ifitm3-/- mice remained lower than WT mice after third vaccination. Moreover, the transcriptional network regulating GC B cell and plasma cell differentiation was abnormal in Ifitm3-/- mice. Our results indicate that IFITM3 deletion attenuated the antibody response. The mechanism of influenza-IFITM3 interactions affecting the antibody response requires further investigation.

Seasonal influenza A (H3N2) virus has been a concern since its first introduction in humans in 1968. Accumulating antigenic changes in viral hemagglutinin (HA), particularly recent cocirculations of multiple HA genetic clades, allow H3N2 virus evade into humans annually. From 2010, the binding of neuraminidase (NA) to sialic acid made the traditional assay for HA inhibition antibodies (Abs) unsuitable for antigenicity characterization. Here, we investigated the serum anti-NA response in a cohort with a seroconversion of microneutralizing (MN) Abs targeting the circulating strain, A/Singapore/INFIMH-16-0019/2016 (H3N2, 3C.2a1)-like, a virus during 2018/2019 flu seasons. We discovered that MN Ab titers show no difference between children and adults. Nevertheless, higher titers of Abs with NA activity inhibition (NI) activity of 129 and seroconversion rate of 68.42% are presented in children aged 7–17 years (n = 19) and 73.47 and 41.17% in adults aged 21–59 years (n = 17), respectively. The MN Abs generated in children display direct correlations with HA- and NA-binding Abs or NI Abs. The NI activity exhibited cross-reactivity to N2 of H3N2 viruses of 2007 and 2013, commonly with 329-N-glycosylation and E344 in N2, a characteristic of earlier 3C.2a H3N2 virus in 2014. The percentage of such viruses pronouncedly decreased and was even replaced by those dominant H3N2 viruses with E344K and 329 non-glycosylation, which have a significantly low activity to the tested antisera. Our findings suggest that NI assay is a testable assay applied in H3N2 infection in children, and the antigenic drift of current N2 should be considered for vaccine selection.

As a selenium-enriched plant, Cardamine violifolia (SEC) has an excellent antioxidant function. The edibility of SEC is expected to develop new sources of organic Se supplementation for human and animal nutrition. This study was conducted to investigate the effects of SEC on laying performance and ovarian antioxidant capacity in aging laying hens. A total of 450 laying hens were assigned to five treatments. Dietary treatments included the following: a basal diet (diet without Se supplementation, CON) and basal diets supplemented with 0.3 mg/kg Se from sodium selenite (SS), 0.3 mg/kg Se from Se-enriched yeast (SEY), 0.3 mg/kg Se from SEC, or 0.3 mg/kg Se from SEC and 0.3 mg/kg Se from SEY (SEC + SEY). Results showed that supplementation with SEC tended to increase the laying rate, increased the Haugh unit of eggs, and reduced the FCR. SEC promoted ovarian cell proliferation, inhibited apoptosis, and ameliorated the maintenance of follicles. SEC, SEY, or SEC + SEY increased ovarian T-AOC and decreased MDA levels. SEC increased the mRNA abundance of ovarian selenoproteins. SEC and SEC + SEY increased the mRNA abundance of Nrf2, HO-1, and NQO1, and decreased the mRNA abundance of Keap1. These results indicate that SEC could potentially to improve laying performance and egg quality via the enhancement of ovarian antioxidant capacity. SEC exerts an antioxidant function through the modulation of the Nrf2/Keap1 signaling pathway.

Traditional lithium-ion batteries with graphite anodes have gradually been limited by the glass ceiling of energy density. As a result, lithium metal batteries (LMBs), regarded as the ideal alternative, have attracted considerable attention. However, lithium is highly reactive and susceptible to most electrolytes, resulting in poor cycle performance. In addition, lithium grows Li dendrites during charging, adversely affecting the safety of LMBs. Therefore, LMBs are more sensitive to the chemical composition of electrolytes and their relative ratios (concentrations). Recently, concentrated electrolytes have been widely demonstrated to be friendly to lithium metal anodes (LMAs). This review focuses on the progress of concentrated electrolytes in LMBs, including the solvation structure varying with concentration, unique functions in stabilizing the LMA, and their interfacial chemistry with LMA.

A mitochondria-targeted NIR probe based on the FRET mechanism was developed. It shows ultra-large Stokes shifts (460 nm) and emission shifts (285 nm). Furthermore, we also realized the imaging of SO2 in living SKOV-3 cells, zebrafish and living mice which may be useful for understanding the biological roles of SO2 in mitochondria and in vivo.

According to the phenomenon of the external meshing herringbone teeth test of fan drive gearbox, based on the knowledge of dynamics analysis and failure theory, a set of method for purifying and filtering axis trajectory is established. The failure type is determined by comparing the variation trend of the purified filter axis trajectory diagram and the vibration data. The problem can be detected earlier and serious accidents can be avoided by the purified filter axis trajectory.

Antibodies that target the N-terminal domain (NTD) of the MERS-CoV spike remain poorly characterized. Here, Zhou et al. report the structural and functional analysis of the NTD-targeting mAb 7D10 and show that it synergizes with antibodies targeting the receptor-binding domain against different MERS-CoV strains.

Magnetic iron nanoparticle-based theranostics agents have attracted much attention due to their good magnetism and biocompatibility. However, efficiently enriching tumors with iron nanoparticles to enhance the treatment effect remains a pressing challenge. Herein, based on the targeting and high phagocytosis of macrophages, an Fe nanoparticle-loaded macrophage delivery system was designed and constructed to efficiently deliver iron nanoparticles to tumors. Hydrophilic Fe@Fe3O4 nanoparticles with a core-shell structure were synthesized by pyrolysis and ligand exchange strategy. Subsequently, they were loaded into macrophages (RAW264.7 cells) using a co-incubation method. After loading into RAW264.7, the photothermal performance of Fe@Fe3O4 nanoparticles were significantly enhanced. In addition, Fe@Fe3O4 nanoparticles loaded into the macrophage RAW264.7 (Fe@Fe3O4@RAW) exhibited a good T2-weighted MRI contrast effect and clear tumor imaging in vivo due to the tumor targeting tendency of macrophages. More importantly, after being intravenously injected with Fe@Fe3O4@RAW and subjected to laser irradiation, the tumor growth was effectively inhibited, indicating that macrophage loading could enhance the tumor photothermal ablation ability of Fe@Fe3O4. The macrophage mediated delivery strategy for Fe@Fe3O4 nanoparticles was able to enhance the treatment effect, and has great potential in tumor theranostics.

In the civil aviation industry, security risk management has shifted from post-accident investigations and analyses to pre-accident warnings in an attempt to reduce flight risks by identifying currently untracked flight events and their trends and effectively preventing risks before they occur. The use of flight monitoring data for flight anomaly detection is effective in discovering unknown and potential flight incidents. In this paper, we propose a time-feature attention mechanism and construct a deep hybrid model for flight anomaly detection. The hybrid model combines a time-feature attention-based convolutional autoencoder with the HDBSCAN clustering algorithm, where the autoencoder is constructed and trained to extract flight features while the HDBSCAN works as an anomaly detector. Quick access record (QAR) flight data containing information of aircraft landing at Kunming Changshui International and Chengdu Shuangliu International airports are used as the experimental data, and the results show that (1) the time-feature-based convolutional autoencoder proposed in this paper can better extract the flight features and further discover the different landing patterns; (2) in the representation space of the flights, anomalous flight objects are better separated from normal objects to provide a quality database for subsequent anomaly detection; and (3) the discovered flight patterns are consistent with those at the airports, resulting in anomalies that could be interpreted with the corresponding pattern. Moreover, several examples of anomalous flights at each airport are presented to analyze the characteristics of anomalies.

ABSTRACT: Objective The aim of this study is to evaluate the embolization techniques, as well as the effects and complications, using the non-adhesive liquid embolic material Onyx in intracranial arteriovenous malformations (AVMs).Methods The study comprises a retrospective analysis of 250 patients with intracranial AVMs treated with Onyx in Guangdong General Hospital from Jan 2010 to Dec 2017. The therapeutic strategies, as well as embolization effects and complications, of Onyx are summarized.Results Of 250 cases, 170 were male and 80 were female. Following the Spetzler–Martin (S-M) grading system, there were 35 cases of grade I, 77 of grade II, 72 of grade III, 39 of grade IV, and 27 of grade V. All cases were treated with Onyx. In addition, 69 cases were treated with Glubran glue. The injected volume of Onyx per patient ranged from 1 mL to 10 mL. The largest volume of Onyx injected in one procedure was 10 mL. The cure rate was 67.9% (76/112) of grade I–II patients, 15.0% (11/72) of grade III patients, 7.7% (3/39) of grade IV patients, and 0% (0/27) of grade V patients. The total cure rate was 36.0% (90/250). The average number of targeted vascular branches per patient was 2.28. The microcatheter broke off in two cases. There were two patients who suffered an intracranial hemorrhage during the embolic procedure; in one of these two patients, the microcatheter also broke off. There were two patients who suffered an intracranial hemorrhage after the embolic procedure; one of them died. Seven cases suffered new-onset neurological deficits or their original symptoms deteriorated. Six of them improved or recovered within 3–6 months. The total complication rate was 5.2% (13/250).Conclusion The application of Onyx in intracranial AVMs is flexible, effective and safe, and may also reduce the complications. Keywords: intracranial arteriovenous malformations, embolization treatment, treatment outcome, complication, Onyx

Background: Although the functional redundancy of catechol 1,2-dioxygenase (C12O) genes has been reported in several microorganisms, limited enzymes were characterised, let alone the advantage of the coexistence of the multiple copies of C12O genes. Results: In this study, four novel C12O genes, designated catA, catAI, catAII and catAIII, in the naphthalene-degrading strain Pseudomonas putida ND6, were cloned and characterised. Phylogenetic analysis of their deduced amino acid sequences revealed that the four C12O isozymes each formed independent subtrees, together with homologues from other organisms. All four enzymes exhibited maximum activity at pH 7.4 and higher activity in alkaline than in acidic conditions. Furthermore, CatA, CatAI and CatAIII were maximally active at a temperature of 45°C, whereas a higher optimum temperature was observed for CatAII at a temperature of 50°C. CatAI exhibited superior temperature stability compared with the other three C12O isozymes, and kinetic analysis indicated similar enzyme activities for CatA, CatAI and CatAII, whereas that of CatAIII was lower. Significantly, among metal ions tested, only Cu2+ substantially inhibited the activity of these C12O isozymes, thus indicating that they have potential to facilitate bioremediation in environments polluted with aromatics in the presence of metals. Moreover, gene expression analysis at the mRNA level and determination of enzyme activity clearly indicated that the redundancy of the catA genes has increased the levels of C12O. Conclusion: The results clearly imply that the redundancy of catA genes increases the available amount of C12O in P. putida ND6, which would be beneficial for survival in challenging environments.How to cite: Li S, Qin K, Li H, et al. Cloning and characterisation of four catA genes located on the chromosome and large plasmid of Pseudomonas putida ND6. Electron J Biotechnol 2018;34. Keywords: Biodegradation pathways, Bioremediation, C12O, catA genes redundancy, Catechol 1,2-dioxygenase genes, Catechol dioxygenases, Gene redundancy, Isozymes, Microbial degradation, Pseudomonas putida ND6

The key to fine-grained aircraft recognition is discovering the subtle traits that can distinguish different subcategories. Early approaches leverage part annotations of fine-grained objects to derive rich representations. However, manual labeling part information is cumbersome. In response to this issue, previous CNN-based methods reuse the backbone network to extract part-discrimination features, the inference process of which consumes much time. Therefore, we introduce generalized multiple instance learning (MIL) into fine-grained recognition. In generalized MIL, an aircraft is assumed to consist of multiple instances (such as head, tail, and body). Firstly, instance-level representations are obtained by the feature extractor and instance conversion component. Secondly, the obtained instance features are scored by an MIL classifier, which can yield high-level part semantics. Finally, a fine-grained object label is inferred by a MIL pooling function that aggregates multiple instance scores. The proposed approach is trained end-to-end without part annotations and complex location networks. Experimental evidence is conducted to prove the feasibility and effectiveness of our approach on combined aircraft images (CAIs).

Previous studies using resting-state functional MRI (rs-fMRI) have revealed alterations in graphical metrics in groups of individuals with posttraumatic stress disorder (PTSD). To explore the ability of graph measures to diagnose PTSD and capture its essential features in individual patients, we used a deep learning (DL) model based on a graph-theoretic approach to discriminate PTSD from trauma-exposed non-PTSD at the individual level and to identify its most discriminant features. Our study was performed on rs-fMRI data from 91 individuals with PTSD and 126 trauma-exposed non-PTSD patients. To evaluate our DL method, we used the traditional support vector machine (SVM) classifier as a reference. Our results showed that the proposed DL model allowed single-subject discrimination of PTSD and trauma-exposed non-PTSD individuals with higher accuracy (average: 80%) than the traditional SVM (average: 57.7%). The top 10 DL features were identified within the default mode, central executive, and salience networks; the first two of these networks were also identified in the SVM classification. We also found that nodal efficiency in the left fusiform gyrus was negatively correlated with the Clinician Administered PTSD Scale score. These findings demonstrate that DL based on graphical features is a promising method for assisting in the diagnosis of PTSD.

Middle East respiratory syndrome coronavirus (MERS-CoV) has caused fatal infections, some through hospital-acquired transmission, in affected regions since its emergence in 2012. Although the virus is not pandemic among humans, it poses a great threat to public health due to its zoonotic origin. Thus, both preventative and therapeutic countermeasures are urgently needed. In this study, we discovered a panel of neutralizing monoclonal antibodies (mAbs) against MERS-CoV, which mapped to a wide range of regions on the spike (S) protein of the virus. In addition to mAbs with neutralizing epitopes located on the receptor-binding domain, one mAb, 5F9, which binds to the N-terminal domain (NTD) of the MERS-CoV S1 subunit, showed efficient neutralizing activity against the wild-type MERS-CoV strain EMC/2012, with a half maximal inhibitory concentration of 0.2 μg/mL. We concluded that a novel neutralizing epitope for MERS-CoV also resides on the NTD of the S protein, indicating that the NTD might be important during the viral infection process. Our findings have significant implications for further vaccine design and for the development of prophylactic and therapeutic monoclonal immunotherapies against MERS-CoV infection.Emerging Microbes & Infections (2017) 6, e37; doi:10.1038/emi.2017.18; published online 24 May 2017

Background: Cerebral arteriovenous malformation (cAVM) is a type of vascular malformation associated with vascular remodeling, hemodynamic imbalance, and inflammation. We detected four angioarchitecture-related cytokines to make a better understanding of the potential aberrant signaling in the pathogenesis of cAVM and found useful proteins in predicting the risk of cerebral hemorrhage. Methods: Immunohistochemical analysis was conducted on specimens from twenty patients with cAVM diagnosed via magnetic resonance imaging and digital subtraction angiography and twenty primary epilepsy controls using antibodies against vascular endothelial growth factor receptor-2 (VEGFR-2), matrix metalloproteinase-9 (MMP-9), vascular cell adhesion molecule (VCAM-1), and endothelial nitric oxide synthase (eNOS). Western blotting and real-time fluorescent quantitative polymerase chain reaction (PCR) were performed to determine protein and mRNA expression levels. Student's t-test was used for statistical analysis. Results: VEGFR-2, MMP-9, VCAM-1, and eNOS expression levels increased in patients with cAVM compared with those in normal cerebral vascular tissue, as determined by immunohistochemical analysis. In addition, Western blotting and real-time PCR showed that the protein and mRNA expression levels of VEGFR-2, MMP-9, VCAM-1, and eNOS were higher in the cAVM group than in the control group, all the differences mentioned were statistically significant (P < 0.05). Conclusions: VEGFR-2, MMP-9, VCAM-1, and eNOS are upregulated in patients with cAVM and might play important roles in angiogenesis, vascular remodeling, and migration in patients with cAVM. MMP-9, VEGFR-2, VCAM-1, and eNOS might be potential excellent group proteins in predicting the risk of cerebral hemorrhage at arteriovenous malformation.