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Caricature generation aims to translate real photos into caricatures with artistic styles and shape exaggerations while maintaining the identity of the subject. Different from the generic image-to-image translation, drawing a caricature automatically is a more challenging task due to the existence of various spacial deformations. Previous caricature generation methods are obsessed with predicting definite image warping from a given photo while ignoring the intrinsic representation and distribution for exaggerations in caricatures. This limits their ability on diverse exaggeration generation. In this paper, we generalize the caricature generation problem from instance-level warping prediction to distribution-level deformation modeling. Based on this assumption, we present the first exploration for unpaired CARIcature generation with Multiple Exaggerations (CariMe). Technically, we propose a Multi-exaggeration Warper network to learn the distribution-level mapping from photo to facial exaggerations. This makes it possible to generate diverse and reasonable exaggerations from randomly sampled warp codes given one input photo. To better represent the facial exaggeration and produce fine-grained warping, a deformation-field-based warping method is also proposed, which helps us to capture more detailed exaggerations than other point-based warping methods. Experiments and two perceptual studies prove the superiority of our method comparing with other state-of-the-art methods, showing the improvement of our work on caricature generation.

Objective Small cell lung cancer (SCLC) transformation had previously been reported mainly in epidermal growth factor receptor (EGFR) mutant adenocarcinoma. However, the underlying genomic profile remains unclear. Our study aim to find the evolution and genotypic characteristic of SCLC transformation. Patients and methods Thirty-one SCLC transformation patients who were initially diagnosed as Non-Small Cell Lung Cancer (NSCLC) patients were included. Whole exome sequencing (WES) of both primary and transformed re-biopsy lesions was conducted on 12 patients. Clinical characteristics were analyzed using R software (v.3.6.1). Results Our study included 31 patients, of whom, three had lung squamous cell carcinoma. Six patients did not carry EGFR mutations. Thirty patients received chemotherapy for SCLCs. The disease control rate (DCR) was 96.7%, and the median progression-free survival (PFS) was 4.03 months. The median time to transformation was 33.07 months, and the median overall survival (OS) was 62.08 months. Somatic mutation analysis showed that besides TP53, RB1, and EGFR, there was a high occurrence of mutations to CSMD3 and ADAMTS19, especially in the EGFR-wild type (EGFR-wt) group. Concerning mutational signature, the EGFR-mutant (EFFR-mut) transformed group favored an APOBEC (apolipoprotein B mRNA editing catalytic polypeptide-like)-associated mutation pattern (P = 0.16). DNA Damage Repair (DDR)-related signatures were significantly enriched in the EGFR-wt transformed group (P = 0.034). Additionally, clonal evolution analysis revealed that all patients had the same main trunk genes in the phylogenetic tree. Transformed SCLCs are not sensitive to immunotherapy, possibly due to increased tumor heterogeneity. Conclusion Our results indicated that the EGFR-wt patients could also transform to SCLCs, but they have different genetic features with EGFR-mut patients. SCLC-transformed patients respond to classical chemotherapy and have a better prognosis than those with classical SCLCs.

The presence of anaplastic lymphoma kinase (ALK) rearrangement defines a molecular subtype of non-small-cell lung cancer (NSCLC). ALK inhibitors (ALKIs) confer significant clinical benefits in patients with ALK-positive advanced NSCLC; therefore, it is of great clinical significance to select accurate, rapid, and appropriate ALK testing methods to screen for patients who are suitable for anti-ALK treatment. In recent years, great progress has been made in the development and clinical application of ALKIs, as well as in our understanding of acquired drug resistance mechanisms. Meanwhile, new ALK companion diagnostic platforms have been developed and applied in clinical practice. Although many studies have shown that there is a high rate of concordance among these platforms, new problems continue to appear during testing. To maximize the benefit for patients, accurate testing results can be obtained by first selecting the appropriate testing method and then formulating, optimizing, and complying with the standardized testing process in accordance with the testing population and specimen types. With the ongoing accumulation of clinical practice data, experience from quality control of ALK testing, and results from multicenter research, an updated expert consensus is necessary. The experts who participated in the discussion and development of this guideline have a rich background in theoretical and clinical testing experience, which ensures the practical value of the information presented in this guideline.

Background Neuroimaging studies in posttraumatic stress disorder (PTSD) have identified various alterations in white matter (WM) microstructural organization. However, it remains unclear whether these are localized to specific regions of fiber tracts, and what diagnostic value they might have. This study set out to explore the spatial profile of WM abnormalities along defined fiber tracts in PTSD. Methods Diffusion tensor images were obtained from 77 treatment-naive noncomorbid patients with PTSD and 76 demographically matched trauma-exposed non-PTSD (TENP) controls. Using automated fiber quantification, tract profiles of fractional anisotropy, axial diffusivity, mean diffusivity, and radial diffusivity were calculated to evaluate WM microstructural organization. Results were analyzed by pointwise comparisons, by correlation with symptom severity, and for diagnosis-by-sex interactions. Support vector machine analyses assessed the ability of tract profiles to discriminate PTSD from TENP. Results Compared to TENP, PTSD showed lower fractional anisotropy accompanied by higher radial diffusivity and mean diffusivity in the left uncinate fasciculus, and lower fractional anisotropy accompanied by higher radial diffusivity in the right anterior thalamic radiation. Tract profile alterations were correlated with symptom severity, suggesting a pathophysiological relevance. There were no significant differences in diagnosis-by-sex interaction. Tract profiles allowed individual classification of PTSD versus TENP with significant accuracy, of potential diagnostic utility. Conclusions These findings add to the knowledge of the neuropathological basis of PTSD. WM alterations based on a tract-profile quantification approach are a potential biomarker for PTSD.

In order to solve the problems of non-uniform gas concentration distribution and serious flooding in the parallel flow field of traditional PEMFC, a stepped flow field of PEMFC bipolar plate is proposed in this paper. The gas concentration distribution, velocity distribution, liquid water distribution and current density distribution of traditional parallel flow field and stepped flow fields are studied by computational fluid dynamics (CFD) simulations. The results suggest that the fuel cells with stepped flow field are superior than the traditional fuel cell with parallel flow field. Compared with fuel cell with parallel flow field, the net power of fuel cell with stepped flow field is increased by 21.5%. Moreover, the stepped flow fields can improve the uniformity of gas concentration and current density distribution, accelerate the discharge of liquid water, alleviate water flooding phenomenon.

In recent years, deep learning techniques have significantly improved the performance of single image super-resolution (SISR). However, this improvement is often achieved at the cost of introducing a large amount of parameters, which limits the real-world applications for SISR. In this paper, we propose a lightweight SISR network called Multi-scale Channel Attention Network for Image Super-Resolution (MCSN). Our contributions are threefold. First of all, the multi-scale feature fusion block (MSFFB) can extract multi-scale features by filters with different receptive fields. Secondly, the channel shuffle attention mechanism (CSAM) encourages the flow of the information across feature channels and enhances the ability of feature selection. Thirdly, the global feature fusion connection (GFFC) can effectively improve feature utilization. Extensive experiments demonstrate that the parameter amount of our method is reduced by 3/4 compared with the current state-of-the-art MSRN method, while both the subjective visual effect and objective quality of the reconstructed high-resolution images are significantly better.

INTRODUCTION: Mood disorders are associated with fronto-limbic structural and functional abnormalities and deficits in omega-3 polyunsaturated fatty acids including eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Emerging evidence also suggests that n-3 PUFA, which are enriched in fish oil, promote cortical plasticity and connectivity. The present study performed a graph-based connectome analysis to investigate the role of n-3 PUFA in emotion-related network organization in medication-free depressed adolescent bipolar offspring. METHODS: At baseline patients (n=53) were compared with healthy controls (n=53), and patients were then randomized to 12-week double-blind treatment with placebo or fish oil. At baseline and endpoint, erythrocyte EPA+DHA levels were measured and fMRI scans (4 Tesla) were obtained while performing a continuous performance task with emotional and neutral distractors (CPT-END). Graph-based analysis was used to characterize topological properties of large-scale brain network organization. RESULTS: Compared with healthy controls, patients exhibited lower erythrocyte EPA+DHA levels (p=0.0001), lower network clustering coefficients (p=0.029), global efficiency (p=0.042), and lower node centrality and connectivity strengths in frontal-limbic regions (p

Highlights Fully inkjet-printed transparent high-performance thin-film transistors (TFTs) with ultrathin indium tin oxide (ITO) as semiconducting channels were achieved. The energy band alignment at ITO/Al2O3 channel/dielectric interface was investigated by in-depth spectroscopy analysis. Fully printed n-type metal–oxide–semiconductor inverters based on ITO TFTs exhibited extremely high gain of 181 at a low-supply voltage of 3 V, promising for applications in advanced electronic devices and circuits. Supplementary Information The online version contains supplementary material available at 10.1007/s40820-021-00694-4., Metal oxide thin-films transistors (TFTs) produced from solution-based printing techniques can lead to large-area electronics with low cost. However, the performance of current printed devices is inferior to those from vacuum-based methods due to poor film uniformity induced by the “coffee-ring” effect. Here, we report a novel approach to print high-performance indium tin oxide (ITO)-based TFTs and logic inverters by taking advantage of such notorious effect. ITO has high electrical conductivity and is generally used as an electrode material. However, by reducing the film thickness down to nanometers scale, the carrier concentration of ITO can be effectively reduced to enable new applications as active channels in transistors. The ultrathin (~10-nm-thick) ITO film in the center of the coffee-ring worked as semiconducting channels, while the thick ITO ridges (>18-nm-thick) served as the contact electrodes. The fully inkjet-printed ITO TFTs exhibited a high saturation mobility of 34.9 cm2 V−1 s−1 and a low subthreshold swing of 105 mV dec−1. In addition, the devices exhibited excellent electrical stability under positive bias illumination stress (PBIS, ΔVth = 0.31 V) and negative bias illuminaiton stress (NBIS, ΔVth = −0.29 V) after 10,000 s voltage bias tests. More remarkably, fully printed n-type metal–oxide–semiconductor (NMOS) inverter based on ITO TFTs exhibited an extremely high gain of 181 at a low-supply voltage of 3 V, promising for advanced electronics applications. Supplementary Information The online version contains supplementary material available at 10.1007/s40820-021-00694-4.

OBJECTIVE To evaluate the effects of fish oil (FO), a source of the omega-3 polyunsaturated fatty acids (n-3 PUFA), eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), on emotion-generated corticolimbic functional connectivity in depressed youth at high risk for developing bipolar I disorder. METHODS Thirty-nine antidepressant-free youth with a current depressive disorder diagnosis and a biological parent with bipolar I disorder were randomized to 12-week double-blind treatment with FO or placebo. At baseline and endpoint, fMRI (4 Tesla) scans were obtained while performing a continuous performance task with emotional and neutral distractors (CPT-END). Seed-to-voxel functional connectivity analyses were performed using bilateral orbitofrontal cortex (OFC) and amygdala (AMY) seeds. Measures of depression, mania, global symptom severity, and erythrocyte fatty acids were obtained. RESULTS Erythrocyte EPA+DHA composition increased significantly in the FO group (+47%, p ≤ 0.0001) but not in the placebo group (-10%, p = 0.11). Significant group by time interactions were found for functional connectivity between the left OFC and the left superior temporal gyrus (STG) and between the right AMY and right inferior temporal gyrus (ITG). OFC-STG connectivity increased in the FO group (p = 0.0001) and decreased in the placebo group (p = 0.0019), and AMY-ITG connectivity decreased in the FO group (p = 0.0014) and increased in the placebo group (p

The charging process of solar power applied leaf venation shape bionic latent heat thermal energy storage (LHTES) system was studied in the present research. n-Eicosane was chosen for the phase cha...

Despite many changes in alternative splicing events (ASEs) are frequently involved in various cancers, prognosis-related ASEs and drug treatment targets in glioblastoma multiforme (GBM) have not been well explored. ASEs participate in many biological behaviors in the initiation and progression of tumors, the aberrant ASE has been considered another hallmark of cancer, and the systematic study of alternative splicing may provide potential biomarkers for malignancies. In this study, we carried out a systematic analysis to characterize the ASE signatures in GBM cohort. Through comparing GBM tissues and nontumor tissues, a total of 48,191 differently expressed ASEs from 10,727 genes were obtained, and these aberrant ASEs play an important role in the oncogenic process. Then, we identified 514 ASEs independently associated with patient survival in GBM by univariate and multivariate Cox regression, including exon skip in CD3D, alternate acceptor site in POLD2, and exon skip in DCN. Those prognostic models built on ASEs of each splice type can accurately predict the outcome of GBM patients, and values for the area under curve were 0.97 in the predictive model based on alternate acceptor site. In addition, the splicing-regulatory network revealed an interesting correlation between survival-associated splicing factors and prognostic ASE corresponding genes. Moreover, these three hub splicing factors in splicing regulation network are the potential targets of some drugs. In conclusion, a systematic analysis of ASE signatures in GBM could serve as an indicator for identifying novel prognostic biomarkers and guiding clinical treatment.

While dense visual SLAM methods are capable of estimating dense reconstructions of the environment, they suffer from a lack of robustness in their tracking step, especially when the optimisation is poorly initialised. Sparse visual SLAM systems have attained high levels of accuracy and robustness through the inclusion of inertial measurements in a tightly-coupled fusion. Inspired by this performance, we propose the first tightly-coupled dense RGB-D-inertial SLAM system. Our system has real-time capability while running on a GPU. It jointly optimises for the camera pose, velocity, IMU biases and gravity direction while building up a globally consistent, fully dense surfel-based 3D reconstruction of the environment. Through a series of experiments on both synthetic and real world datasets, we show that our dense visual-inertial SLAM system is more robust to fast motions and periods of low texture and low geometric variation than a related RGB-D-only SLAM system., Accepted at IROS 2017; supplementary video available at https://youtu.be/-gUdQ0cxDh0

Inspired by the outstanding properties discovered in two-dimensional materials, the bottom-up generation of molecular monolayers is becoming again extremely popular as a route to develop novel functional materials and devices with tailored characteristics and minimal materials consumption. However, achieving a full-coverage over a large-area still represents a grand challenge. Here we report a molecular self-assembly protocol at the water surface in which the monolayers are strained by a novel solvent surface tension balance (SSTB) instead of a physical film balance as in the conventional Langmuir–Blodgett (LB) method. The obtained molecular monolayers can be transferred onto any arbitrary substrate including rigid inorganic oxides and metals, as well as flexible polymeric dielectrics. As a proof-of-concept, their application as ideal modification layers of a dielectric support for high-performance organic field-effect transistors (OFETs) has been demonstrated. The field-effect mobilities of both p- and n-type semiconductors displayed dramatic improvements of 1–3 orders of magnitude on SSTB-derived molecular monolayer, reaching values as high as 6.16 cm2 V–1 s–1 and 0.68 cm2 V–1 s–1 for pentacene and PTCDI-C8, respectively. This methodology for the fabrication of wafer-scale and defect-free molecular monolayers holds potential toward the emergence of a new generation of high-performance electronics based on two-dimensional materials.

Risk of postpartum opioid use disorder or overdose is low among women with prenatal opioid analgesic use.

To use structural magnetic resonance imaging and graph theory approaches to investigate the topological organization of the brain morphological network based on gray matter in essential tremor, and its potential relation to disease severity. In this prospective study conducted from November 2018 to November 2019, 36 participants with essential tremor and 37 matched healthy controls underwent magnetic resonance imaging. Brain networks based on the morphological similarity of gray matter across regions were analyzed using graph theory. Nonparametric permutation testing was used to assess group differences in topological metrics. Support vector machine was applied to the gray matter morphological matrices to classify participants with essential tremor vs. healthy controls. Compared with healthy controls, participants with essential tremor showed increased global efficiency (p < 0.01) and decreased path length (p < 0.01); abnormal nodal properties in frontal, parietal, and cerebellar lobes; and disconnectivity in cerebello-thalamo-cortical network. The abnormal brain nodal centralities (left superior cerebellum gyrus; right caudate nucleus) correlated with clinical measures, both motor (Fahn–Tolosa–Marin tremor rating, p = 0.017, r = − 0.41) and nonmotor (Hamilton depression scale, p = 0.040, r = − 0.36; Hamilton anxiety scale, p = 0.008, r = − 0.436). Gray matter morphological matrices classified individuals with high accuracy of 80.0%. Participants with essential tremor showed randomization in global properties and dysconnectivity in the cerebello-thalamo-cortical network. Participants with essential tremor could be distinguished from healthy controls by gray matter morphological matrices.

Neuroimaging studies using a variety of techniques have demonstrated abnormal patterns of spontaneous brain activity in patients with essential tremor (ET). However, the findings are variable and inconsistent, hindering understanding of underlying neuropathology. We conducted a meta‐analysis of whole‐brain resting‐state functional neuroimaging studies in ET compared to healthy controls (HC), using anisotropic effect‐size seed‐based d mapping, to identify the most consistent brain activity alterations and their relation to clinical features. After systematic literature search, we included 13 studies reporting 14 comparisons, describing 286 ET patients and 254 HC. Subgroup analyses were conducted considering medication status, head tremor status, and methodological factors. Brain activity in ET is altered not only in the cerebellum and cerebral motor cortex, but also in nonmotor cortical regions including prefrontal cortex and insula. Most of the results remained unchanged in subgroup analyses of patients with head tremor, medication‐naive patients, studies with statistical threshold correction, and the large subgroup of studies using functional magnetic resonance imaging. These findings not only show consistent and robust abnormalities in specific brain regions but also provide new information on the biology of patient heterogeneity, and thus help to elucidate the pathophysiology of ET., Neuroimaging studies using a variety of techniques have demonstrated abnormal patterns of spontaneous brain activity in patients with essential tremor (ET). However, the findings are variable and inconsistent, hindering understanding of underlying neuropathology. We conducted a meta‐analysis of whole‐brain resting‐state functional neuroimaging studies in ET compared to healthy controls, brain activity in ET is altered not only in the cerebellum and cerebral motor cortex, but also in non‐motor cortical regions including prefrontal cortex and insula.

We propose a data and knowledge driven approach for SPECT by combining a classical iterative algorithm of SPECT with a convolutional neural network. The classical iterative algorithm, such as ART and ML-EM, is employed to provide the model knowledge of SPECT. A modified U-net is then connected to exploit further features of reconstructed images and data sinograms of SPECT. We provide mathematical formulations for the architecture of the proposed networks. The networks are trained by supervised learning using the technique of mini-batch optimization. We apply the trained networks to the problems of simulated lung perfusion imaging and simulated myocardial perfusion imaging, and numerical results demonstrate their effectiveness of reconstructing source images from noisy data measurements.

The goals of the current study were to determine whether topological organization of brain structural networks is altered in youth with bipolar disorder, whether such alterations predict treatment outcomes, and whether they are normalized by treatment. Youth with bipolar disorder were randomized to double-blind treatment with quetiapine or lithium and assessed weekly. High-resolution MRI images were collected from children and adolescents with bipolar disorder who were experiencing a mixed or manic episode (n = 100) and healthy youth (n = 63). Brain networks were constructed based on the similarity of morphological features across regions and analyzed using graph theory approaches. We tested for pretreatment anatomical differences between bipolar and healthy youth and for changes in neuroanatomic network metrics following treatment in the youth with bipolar disorder. Youth with bipolar disorder showed significantly increased clustering coefficient (Cp) (p = 0.009) and characteristic path length (Lp) (p = 0.04) at baseline, and altered nodal centralities in insula, inferior frontal gyrus, and supplementary motor area. Cp, Lp, and nodal centrality of the insula exhibited normalization in patients following treatment. Changes in these neuroanatomic parameters were correlated with improvement in manic symptoms but did not differ between the two drug therapies. Baseline structural network matrices significantly differentiated medication responders and non-responders with 80% accuracy. These findings demonstrate that both global and nodal structural network features are altered in early course bipolar disorder, and that pretreatment alterations in neuroanatomic features predicted treatment outcome and were reduced by treatment. Similar connectome normalization with lithium and quetiapine suggests that the connectome changes are a downstream effect of both therapies that is related to their clinical efficacy.

A 4-bed-8-step pressure swing adsorption (PSA) process has been developed to produce high-purity hydrogen from the steam methane reforming (SMR) gas mixture. The Detailed models have been established for hydrogen purification based on the experimentally determined parameters. Two surrogate models are investigated to optimize the process performance using artificial neural networks (ANN), which have been well trained by the samples, obtaining from the Detailed models using Latin hypercube sampling strategy. The results indicate that ANNs could approximate the performance and dynamic behavior of PSA process with extremely high accuracy. Herein, a robust and fast multi-objective optimization approach of PSA process using genetic algorithm on the basis of different ANN-based surrogate models has also been proposed, in which Dual- and Tri-objective optimizations are taken into account. This research shows that the method can not only find out the optimal operating conditions of the PSA process for hydrogen production with higher than 99% accuracy, namely Pareto-Optimal Fronts, but also provide a reliable reference for operational enhancement.

Background: Although Ontario has an established trauma system, it experiences a substantial burden of morbidity and mortality from injury. Our objective was to describe patterns of fatal injury in Ontario, with a focus on location of death (out of hospital, trauma or non–trauma centre) and receipt of surgical intervention before death. Methods: We conducted a retrospective population-based cohort study using linked administrative data on fatal injuries in children and adults (no age restrictions) in Ontario between 2000 and 2016. We identified injury-related deaths in the Ontario Registrar General Death database. We developed descriptive statistics for injury characteristics and causes of death. We calculated the fatal injury incidence rate for each year of the study. The primary outcome was cause of death; the secondary outcome was receipt of surgical intervention. Results: The analysis included 19 408 people. The mean annual incidence of fatal injury averaged 8.7 (95% confidence interval 7.7–9.6) per 100 000. The most common mechanisms of injury were motor vehicle collisions (12 065, 62.2%), followed by gunshot wounds (3134, 16.1%) and falls (2387, 12.3%). Deaths frequently occurred out of hospital (72.6%), rather than at a trauma centre (14.2%) or non–trauma centre (13.2%). Patients treated at trauma centres were significantly more likely to receive a surgical intervention (standardized difference 0.6) than those treated at non–trauma centres. Interpretation: Most injury deaths in Ontario occur in the out-of-hospital setting or are managed at non–trauma centres; many patients receive no surgical intervention before death. There are likely opportunities to improve access to specialized injury care in Ontario’s trauma system.

Flow field structure can largely determine the output performance of Polymer electrolyte membrane fuel cell. Excellent channel configuration accelerates electrochemical reactions in the catalytic layer, effectively avoiding flooding on the cathode side. In present study, a three-dimensional, multi-phase model of PEMFC with a 3D wave flow channel is established. CFD method is applied to optimize the geometry constructions of three-dimensional wave flow channels. The results reveal that 3D wave flow channel is overall better than straight channel in promoting reactant gases transport, removing liquid water accumulated in microporous layer and avoiding thermal stress concentration in the membrane. Moreover, results show the optimal flow channel minimum depth and wave length of the 3D wave flow channel are 0.45 mm and 2 mm, respectively. Due to the periodic geometric characteristics of the wave channel, the convective mass transfer is introduced, improving gas flow rate in through-plane direction. Furthermore, when the cell output voltage is 0.4 V, the current density in the novel channel is 23.8% higher than that of conventional channel.

Supplemental Digital Content is available in the text., Background: It is unclear whether confounding accounts for the increased risk of preterm birth and small for gestational age (SGA) birth in opioid analgesic exposed pregnancies. Methods: Using universal coverage health data for Ontario, we assembled a cohort of mother–infant pairs without opioid use disorder (627,172 pregnancies and 509,522 women). We estimated risk ratios (RRs) between opioid analgesics and preterm birth, SGA birth, and stillbirth; neonatal abstinence syndrome was a secondary outcome. We used high-dimensional propensity scores and sensitivity analyses for confounding adjustment. Results: 4% of pairs were exposed, mainly to codeine (2%), morphine (1%), and oxycodone (1%). Compared with unexposed, the adjusted risk of preterm birth was higher with any (1.3, 95% confidence interval [CI] = 1.2, 1.3), first- (RR: 1.2, 95% CI = 1.2, 1.3), and second-trimester (RR: 1.3, 95% CI = 1.2, 1.4) opioid analgesic exposure. Preterm birth risk was higher for first- and second-trimester codeine, morphine, and oxycodone exposure, and for third-trimester morphine. There was a small increase in SGA with first-trimester exposure to any opioid analgesic or to codeine. Exposed pregnancies had an elevated stillbirth risk with any (RR: 1.6, 95% CI = 1.4, 1.8), first- and second-trimester exposure. Few infants had neonatal abstinence syndrome (N = 143); the risk was higher in exposed (RR: 3.6, 95% CI = 2.1, 6.0). In sensitivity analyses of unmeasured confounding, an elevated risk in exposed pregnancies persisted for preterm birth but not SGA. Conclusions: Opioid analgesic-exposed pregnancies had a small increased risk of preterm birth and possibly stillbirth after accounting for confounding by indication and sociodemographic factors.

In this work, we present the construction of a multilayered PtSe2/Ge heterostructure-based photodetector array comprising 1 × 10 device units operating in the short-wavelength infrared (SWIR) spectrum region. The as-fabricated heterostructures show an obvious photovoltaic effect, providing the devices with the ability to work as self-driven photodetectors. Upon 1550 nm illumination, a typical photodetector exhibits prominent photoresponse performance with the current on/off ratio, responsivity, external quantum efficiency and specific detectivity reaching 1.08 × 103, 766 mA W−1, 61.3% and 1.1 × 1011 Jones, respectively. The device also has a fast response speed with rise/fall times of 54.9 μs/56.6 μs. Thanks to the respectable homogeneity in device performance, the photodetector array can reliably record an image of a “diode symbol” produced by SWIR irradiation. What is more, the photodetector is successfully integrated into a SWIR optical communication system serving as an optical receiver to transmit a text signal. The above results imply a huge possibility of the present heterostructure-based photodetector array for some optoelectronic purposes such as SWIR image sensing and optical communication applications.

As the main forest firefighting equipment, portable wind extinguisher plays a major role in forest and grassland firefighting. The same as other hand-held vibration machines, portable wind extinguisher inevitably causes hand-transmitted vibration which is an important cause of discomfort for operators, decreased work efficiency, and even health hazards. In this paper, a biomechanical response model of hand-transmitted vibration is established based on the operating posture of the wind fire extinguisher. A standard vibration test platform was set up for the test, and the model parameters were identified through the standard vibration test data. The vibration response predicted by the model is compared with the measured response of portable wind extinguisher to verify the feasibility of the model prediction. The vibration response of the standard vibration experiment platform was used to compare the influence of hand force on the hand-transmitted vibration response. The results show that the goodness of fit between the vibration transmissibility response predicted by the arm model and the response measured by the standard vibration test platform is between 75% and 92%, and the goodness of fit between the vibration transmissibility response predicted by the arm model and the response measured by the portable wind fire machine is within the range of 77%. The research in this article can provides theory and data basis for improvement of vibration reduction protection strategy and device of portable pneumatic extinguisher.

Objectives: Allogeneic myoblast transplantation (AMT), cyclosporine immunosuppression and coronary artery bypass grafting (CABG) were used to treat end-stage heart failure (HF) subjects without hope of obtaining a heart transplant. Background: Severe myocardial infarction conveys serious complications such as ventricular aneurysm, wall thinning and rupture with fatal consequences. Methods: After meeting Inclusion/Exclusion criteria and signing Patient Informed Consents, 10 HF subjects having mean thinnest wall thickness of 2.21 ± 0.55 mm and ventricular aneurysms were admitted under intensive care. Each subject took daily cyclosporine for three weeks. On the third day of cyclosporine administration, approximately 1 billion myoblasts were implanted through 20 injections into the infarcted myocardium following CABG. Results: Safety No subject suffered death, viral infection, malignant arrhythmia, reduction in cardiac output, immune rejection, or aneurysm growth. No significant difference was found before versus after treatment in the mean levels of blood routine, liver and kidney enzymes, electrolytes and fibrinogen. Efficacy Emission computed tomography (ECT) and magnetic resonance (MR) demonstrated significant increases in viability and perfusion. Mean left ventricular ejection fraction (LVEF) significantly increased (P Conclusions: For the first time, AMT in adjunct use with CABG and cyclosporine demonstrated that cell survived and engrafted in patients with ischemic cardiomyopathy; in this small study the cell transplant was safe. The improvement in heart function and quality of life could be secondary to combined effect of bypass and cell transplant. A larger randomized clinical trial is required to confirm the efficacy.

In this study, we achieved fully-printed flexible n-type tin oxide (SnO2) thin-film transistors (TFTs) and logic inverters. The SnO2 transistors exhibit outstanding performance with high saturation mobility of 13.3 cm2 V−1 s−1, small subthreshold swing (SS) of 130 mV dec−1, large current on/off ratio (Ion/Ioff) of >105, low turn-on voltage (Von) of −0.04 V, and enhancement-mode operations (threshold voltage, Vth = 0.14 V). Moreover, the devices exhibited excellent electrical stability under positive bias stress (PBTS, ΔVth = 0.16 V) and negative bias stress (NBIS, ΔVth = −0.18 V) after 10 000 s voltage bias tests. The thickness-dependent bandgap widening together with TFT performance in SnO2 thin films were also studied, illustrating that the nanometer-thin channel thickness played an important role in determining the switching performance of SnO2 TFTs. Furthermore, the fully-printed SnO2 TFTs exhibited robust mechanical flexibility with the minimum bending radius of 0.5 cm, promising for constructing advanced low-cost electronic devices and circuits.

Gliomas, the most common primary tumors in the central nervous system (CNS), can be categorized into 4 grades according to the World Health Organization. The most malignant glioma type is grade Ⅳ, also named glioblastoma multiforme (GBM). However, the standard treatment of concurrent temozolomide (TMZ) chemotherapy and radiotherapy after maximum resection does not improve overall survival in patients with GBM. Targeting components of the CNS microenvironment represents a new strategy for improving the efficacy of glioma treatment. Most recent studies focused on T cells. However, there is a growing body of evidence that tumor‐associated macrophages (TAMs) play an important role in tumor progression and can be regulated by a wide array of cytokines or chemokines. New TAM‐associated immunotherapies may improve clinical outcomes by blocking tumor progression and prolonging survival. However, understanding the exact roles and possible mechanisms of TAMs in the tumor environment is necessary for developing this promising therapeutic target and identifying potential diagnostic markers for improved prognosis. This review summarizes the possible interactions between TAMs and glioma progression and discusses the potential therapeutic directions for TAM‐associated immunotherapies.

The formed structure is of importance in determining the surface quality of a component made by droplet-based 3D printing. In the present work, the molten droplet solicitation process was simulated under an axisymmetric system where the smallest length scale and time scale were fully resolved. Evolutions of sharp droplet interfaces were captured through the front tracking method. Parametric studies have been carried out to explore how the dynamic metrics, which include the Ohnesorge number (Oh) and Weber number (We), can affect the structure of depositing droplet. The effect of the superheat parameter on the cooling rate was also investigated in the final section. Numerical results show that the inertial resisting force is critical dynamics in the variation of horizontal dimensionless length at the early deposition process. Three levels of Oh numbers and stages of We numbers were classified according to the deformation behavior. Flattening degree under different Oh number and We number cases were both fitted well by the exponential function. This paper also reveals that the variation law of the cooling rate and solidification time is affected by the superheat parameter, resulting in a feasible and promising method to predict droplet deformation time through the fully resolved numerical simulations during the manufacturing process.

Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread around the world. However, approaches to distinguish COVID-19 from pneumonia caused by other pathogens have not yet been reported. We retrospectively analyzed the clinical data of 97 children with probable COVID-19. A total of 13 (13.4%) patients were confirmed positive for SARS-CoV-2 infection by nucleic acid RT-PCR testing, and 41 (42.3%) patients were found to be infected with other pathogens. Notably, no pathogen was detected in 43 (44.3%) patients. Among all patients, 25 (25.8%) had familial cluster exposure history, and 52 (53.6%) had one or more coexisting conditions. Fifteen (15.5%) patients were admitted or transferred to the PICU. In the 11 confirmed COVID-19 cases, 5 (45.5%) and 7 (63.6%) were positive for IgM and IgG against SARS-CoV-2, respectively. In 22 patients with suspected COVID-19, 1 (4.5%) was positive for IgG but negative for IgM. The most frequently detected pathogen was Mycoplasma pneumonia (29, 29.9%). One patient with confirmed COVID-19 died. Our results strongly indicated that the detection of asymptomatic COVID-19 or coexisting conditions must be strengthened in pediatric patients. These cases may be difficult to diagnose as COVID-19 unless etiologic analysis is conducted. A serologic test can be a useful adjunctive diagnostic tool in cases where SARS-CoV-2 infection is highly suspected but the nucleic acid test is negative. Electronic Supplementary Material Supplementary material is available in the online version of this article at 10.1007/s11684-020-0820-7 and is accessible for authorized users.

A systematic review and meta-analysis was conducted in an attempt to systematically collect and evaluate the associations of epidemiological, comorbidity factors with the severity and prognosis of coronavirus disease 2019 (COVID-19). The systematic review and meta-analysis was conducted according to the guidelines proposed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Sixty nine publications met our study criteria, and 61 studies with more than 10,000 COVID-19 cases were eligible for the quantitative synthesis. We found that the males had significantly higher disease severity (RR: 1.20, 95% CI: 1.13-1.27, P

Multi-view classification optimally integrates various features from different views to improve classification tasks. Though most of the existing works demonstrate promising performance in various computer vision applications, we observe that they can be further improved by sufficiently utilizing complementary view-specific information, deep interactive information between different views, and the strategy of fusing various views. In this work, we propose a novel multi-view learning framework that seamlessly embeds various view-specific information and deep interactive information and introduces a novel multi-view fusion strategy to make a joint decision during the optimization for classification. Specifically, we utilize different deep neural networks to learn multiple view-specific representations, and model deep interactive information through a shared interactive network using the cross-correlations between attributes of these representations. After that, we adaptively integrate multiple neural networks by flexibly tuning the power exponent of weight, which not only avoids the trivial solution of weight but also provides a new approach to fuse outputs from different deterministic neural networks. Extensive experiments on several public datasets demonstrate the rationality and effectiveness of our method.

LiNi0.8Co0.1Mn0.1O2 (NCM) is a highly prospective cathode material for high energy density Li-ion batteries (LIBs). Nevertheless, poor cycling performance and rate capability at high cut-off voltag...

In recent years, the deoxycytidine analogue gemcitabine (2′,2′,-difluorodeoxycytidine) has become the first-line chemotherapeutic agent for patients with pancreatic cancer. However, due to the intrinsic resistance of pancreatic cancer cells, gemcitabine-based chemotherapy yields limited disease control, with >85% disease progression at 6 months from diagnosis. Therefore, elucidating the mechanisms of chemoresistance is a critical step in improving cancer therapy, especially for the treatment of pancreatic cancer. We show PROM2, a transmembrane glycoprotein, is ubiquitously upregulated in pancreatic cancer cell. We also found higher PROM2 expression is associated with shortened overall and disease-free survival times in patients diagnosed with pancreatic cancer. We provide evidence that PROM2 promotes chemoresistance to gemcitabine both in vivo and in vitro. Mechanistically, we demonstrate that PROM2 could directly interacted with Akt and activates the Akt signaling pathway, which thus inhibiting gemcitabine-induced apoptosis. As further evidence, we show PROM2 expression and Akt phosphorylation both promote gemcitabine chemoresistance, and cause poorer survival in clinical samples with pancreatic cancer. Combining gemcitabine with the Akt inhibitor MK-2206 facilitated significant tumor shrinkage and dramatically elevated the survival status in mice xenografted with pancreatic cancer cells. Our findings not only establish PROM2 as a novel positive regulator of the Akt signaling pathway and a candidate prognostic indicator of gemcitabine response, but also provide a neo-therapeutic approach for patients resistant to gemcitabine treatment., Pancreatic cancer: Exploring a pathway to drug resistance A cell membrane protein called PROM2 promotes the resistance of pancreatic cancer to the anti-cancer drug gemcitabine, suggesting PROM2 and the molecular signaling pathway it stimulates could be targeted by new treatments. Researchers in China led by Jian Sun at Sun Yat-Sen University, Guangzhou, investigated the role of PROM2 in cultured human pancreatic cancer cells and in a mouse model of pancreatic cancer. Production and activity of PROM2 were increased in cancer cells, leading to increased resistance to gemcitabine. The researchers found that PROM2’s promotion of gemcitabine resistance was linked to its ability to bind to another protein called Akt. This interaction stimulates the Akt signaling pathway, sustaining cancer cells. Combining gemcitabine therapy with an Akt pathway inhibitor restored cancer cell sensitivity to gemcitabine, revealing a potential approach to developing drugs to overcome gemcitabine resistance.

The stable exchange of energy and the effective conduction of heat are particularly critical to ensuring the stable operation of thermoelectric components in entire thermoelectric conversion processes. Poor heat exchange and conduction will cause thermal energy in a thermoelectric system to accumulate, leading to the consequence of burning the energy conversion modules. To solve these problems, a thermoelectric energy conversion system equipped with a water-cooling thermal energy adjustment structure settled on the surface of the heat source was proposed. The working temperature of the heat source, the output voltage, current, and power of the thermoelectric system with and without the structure were obtained and compared. Through comparative analysis, benefiting from the above structure, the temperature fluctuation of the heat sink surface of the heat source was found to drop from 10.36 K to 1.18 K, and the maximum output of the system increased from 255 to 290 mV through the process of input voltage from 1 to 6 V. Furthermore, in the process of gradually increasing the load from 0 to 180 ohms, the system achieves an increased output of 53.8 mV, 1.81 mA and 52.1 mV, 1.83 mA when the input voltage was 4 and 5 V, respectively. In conclusion, the application and design of the above structure obviously promotes the stability and output capacity during thermoelectric energy conversion. The combination of the water-cooling thermal energy adjustment structure with pulse-width modulation (PWM) or maximum power point tracking (MPPT) theory is worth further study.

A micro-combined cooling heating and power (CCHP) system integrated with geothermal-assisted methanol reforming and incorporating a proton exchange membrane fuel cell (PEMFC) stack is presented. The novel CCHP system consists of a geothermal-based methanol steam reforming subsystem, PEMFC, micro gas turbine and lithium bromide (LiBr) absorption chiller. Geothermal energy is used as a heat source to drive methanol steam reforming to produce hydrogen. The unreacted methanol and hydrogen are efficiently utilized via the gas turbine and PEMFC to generate electricity, respectively. For thermodynamic and economic analysis, the effects of the thermodynamic parameters (geothermal temperature and molar ratio of water to methanol) and economic factors (such as methanol price, hydrogen price and service life) on the proposed system performance are investigated. The results indicate that the ExUF (exergy utilization factor the exergy utilization factor), TPES (trigeneration primary energy saving) and energy efficiency of the novel system can be reached at 8.8%, 47.24% and 66.3%, respectively; the levelized cost of energy is 0.0422 $/kWh, and the annual total cost saving ratio can be reached at 20.9%, compared with the conventional system. The novel system achieves thermodynamic and economic potential, and provides an alternative and promising way for efficiently utilizing abundant geothermal energy and methanol resources.

To improve the thermoelectric conversion efficiency of solar thermoelectric power, a concentration solar thermoelectric generator (CTEG) unit based on concentrating and storing energy is designed. A Fresnel lens is used to concentrate thermal energy, and a phase change material (PCM) is used to store thermal energy to increase the temperature difference between the hot and cold ends of thermoelectric (TE) sheets. The heat stored in the PCM container will help to generate continuous solar energy at night and improve the thermal power conversion efficiency of the TEGs. The energy conversion equilibrium equation is established for the CTEG unit. By numerical calculation, we conclude that the absorption rate of the coating surface is reduced by 0.1 and the maximum thermoelectric conversion efficiency is reduced by approximately 0.4%. By selecting different heights of the heat exchanger, the unit supplies 1.025 v, 0.4204 v and 0.299 v open circuit voltages on average for approximately 30, 44 and 53 minutes, respectively. Therefore, the height of the heat exchanger affects the rate of heat energy absorption and release. The reversible operation of the CTEG-PCM unit is conducive to the day and night operation of solar power generation and is more suitable to solving the self-supplying power problem for wireless temperature sensors in forests.

As the population and the distribution of Crested Ibis (Nipponia nippon) become larger, it is necessary to propose a highly efficient census method to estimate the population size of the Crested Ibis. Passive acoustic monitoring (PAM) has a very good prospect for the Crested Ibis monitoring. To realize the automatic census of the Crested Ibis with PAM, the automatic individual identification method based on the vocalization is the key technology. A novel individual identification model was proposed in this paper, which built the autoencoder based on LSTM to obtain the meaningful latent representation from the raw recording directly, further, embedded self-attention and putted forward a combined training mode to achieve distinctive latent representation. With this model, nine Crested Ibis individuals were identified accurately, the highest accuracy is 0.971, and the average accuracy reaches 0.958. As for other three species, Little owl (Athene noctua), Chiffchaff (Phylloscopus collybita) and Tree pipit (Anthus trivialis), the better performances were achieved than the existing method, which means the proposed model can provide an alternative method for the individual identification of other bird species.

Background: Most prior studies have explored surgery for the treatment of failed autologous arteriovenous fistulas (AVFs) with limited follow-up times and a lack of end point mortality. Accordingly, we conducted a retrospective cohort study to evaluate the clinical outcomes of the surgery of new AVF proximal to the failed forearm AVF. Methods: In this study, 538 end-stage renal disease patients (group A, 418 with primary AVF; and group B, 120 with failed AVF) were consecutively enrolled between January 2013 and June 2016, with a median follow-up time of 41 months. Primary and secondary patency, all-cause mortality, and risk factors associated with AVF failure were explored by the Kaplan–Meier method or Cox proportional hazards model. Results: In group A (n = 418), the primary and secondary patencies of AVF were 85.6% vs. 96.8%, 79.7% vs. 95.0%, 75.1% vs.93.9%, 73.2% vs. 93.6% and 73.2% vs. 93.6% at 12, 24, 36, 48 and 60 months, respectively. The primary patencies of AVF in group B were 95.0%, 91.7%, 89.2%, 88.3% and 88.3% at 12, 24, 36, 48 and 60 months, respectively. After adjusting for potential confounders, age, angiotensin-converting inhibitors or angiotensin-receptor blockers (anti-RAAS) drugs and D-dimer were independent predictors of AVF failure. However, there were no differences between functional and failed AVF regarding all-cause mortality. Conclusions: The study revealed that the primary and secondary patiencies of the surgery of new AVF proximal to the failed ones were ideal operations to restore failed forearm AVF.

BackgroundGlioblastoma multiforme (GBM) is the most common malignant tumor of the central nervous system. GBM with primitive neuronal component (GBM-PNC) is an aggressive variant identified in 0.5% of GBMs. Extracranial metastasis from GBM-PNC is a rare and challenging situation.MethodsA special case of early-onset GBM with systemic bone metastasis was enrolled. Clinical data, including patient characteristics, disease course, and serial radiological images were retrieved and analyzed. Tumor tissues were obtained by surgical resections and were made into formalin-fixed paraffin-embedded sections. Histopathological examinations and genetic testing were performed for both the primary and metastatic tumor specimens.ResultsA 20-year-old man suffered from GBM with acute intratumoral hemorrhage of the left temporal lobe. He was treated by gross total resection and chemoradiotherapy following the Stupp protocol. Seven months later, he returned with a five-week history of progressive neck pain and unsteady gait. The radiographic examinations identified vertebral collapse at C4 and C6. Similar osteolytic lesions were also observed at the thoracolumbar spine, pelvic, and left femur. Anterior spondylectomy of C4 and C6 was performed. The resected vertebral bodies were infiltrated with greyish, soft, and ill-defined tumor tissue. One month later, he developed mechanical low-back pain and paraplegia caused by thoracolumbar metastases. Another spine surgery was performed, including T10 total en-bloc spondylectomy, T7-9, L2-3, and L5-S1 laminectomy. After the operation, the patient’s neurological function and spinal stability remained stable. However, he finally succumbed to the rapidly increased tumor burden and died 15 months from onset because of cachexia and multiple organ failure. In addition to typical GBM morphology, the histological examinations identified monomorphic small-round cells with positive immunohistochemical staining of synaptophysin and CD99, indicating the coexistence of PNC. The next-generation sequencing detected pathogenic mutations in TP53 and DNMT3A. Based on above findings, a confirmed diagnosis of systemic metastases from GBM-PNC (IDH-wild type, WHO grade IV) was made.ConclusionsThe present case highlights the occurrence and severity of extensive axial skeletal metastases from GBM-PNC. This rare variant of GBM requires aggressive multimodal treatment including surgery and chemoradiotherapy targeting PNC. The pathological screening of PNC is recommended in patients with early-onset GBM and intratumoral hemorrhage. Surgery for spinal metastasis is appropriate in patients with chemoradioresistance and relatively good general status, with the objectives of restoring spinal stability and relieving spinal cord compression.

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.

Background Mindfulness-based cognitive therapy for children (MBCT-C), as a psychotherapeutic intervention, has been shown to be effective for treating mood dysregulation (MD). While previous neuroimaging studies of MD have reported both pre-treatment structural and functional alterations, the effects of MBCT-C on brain morphological network organisation has not been investigated. Methods We investigated brain morphological network organisation in 10 mood-dysregulated youth with familial risk for bipolar disorder and 15 matched healthy comparison youth (HC). Effects of 12 weeks of MBCT-C were examined in the mood-dysregulated youth. Topological properties of brain networks used for analyses were constructed based on morphological similarities in regional grey matter using a graph-theory approach using MRI data. Results At baseline, compared with the HC group, the mood-dysregulated group exhibited increased global efficiency (Eglob ), decreased path length (Lp ), and abnormal nodal properties, mainly in the limbic system. Right temporal pole alterations at baseline predicted change in Child and Adolescent Mindfulness Measure scores after treatment. The mood-dysregulated group showed significant decreases in both the Eglob and Lp metrics after MBCT-C, suggesting an improved capacity for optimal information processing. Changes in Lp were correlated with changes in Emotion Regulation Checklist scores. Our results show significant topological alterations in the mood-dysregulated group as compared to controls at baseline. After MBCT-C, disrupted topological properties in the mood-dysregulated group were significantly reduced. Conclusion MBCT-C may facilitate clinically meaningful changes in the brain structural network in mood-dysregulated individuals.

The low spatial resolution of acquired depth maps is a major drawback of most RGBD sensors. However, there are many scenarios in which fast acquisition of high-resolution and high-quality depth maps would be desirable. One approach to achieve higher quality depth maps is through super-resolution. However, edge preservation is challenging, and artifacts such as depth confusion and blurring are easily introduced near boundaries. In view of this, we propose a method for fast, high-quality hierarchical depth-map super-resolution (HDS). In our method, a high-resolution RGB image is degraded layer by layer to guide the bilateral filtering of the depth map. To improve the upsampled depth map quality, we construct a feature-based bilateral filter (FBF) for the interpolation, by using the extracted RGB shallow and multi-layer features. To accelerate the process, we perform filtering only near depth boundaries and through matrix operations. We also propose an extension of our HDS model to a Classification-based Hierarchical Depth-map Super-resolution (C-HDS) model, where a context-aware trilateral filter reduces the contributions of unreliable neighbors to the current missing depth location. Experimental results show that the proposed method is significantly faster than existing methods for generating high-resolution depth maps, while also significantly improving depth quality compared to the current state-of-the-art approaches, especially for large-scale 16x super-resolution.

Patients with Parkinson's disease with mild cognitive impairment (PD-M) progress to dementia more frequently than those with normal cognition (PD-N), but the underlying neurobiology remains unclear. This study aimed to define the specific morphological brain network alterations in PD-M, and explore their potential diagnostic value. Twenty-four PD-M patients, 17 PD-N patients, and 29 healthy controls (HC) underwent a structural MRI scan. Similarity between interregional gray matter volume distributions was used to construct individual morphological brain networks. These were analyzed using graph theory and network-based statistics (NBS), and their relationship to neuropsychological tests was assessed. Support vector machine (SVM) was used to perform individual classification. Globally, compared with HC, PD-M showed increased local efficiency (p = .001) in their morphological networks, while PD-N showed decreased normalized path length (p = .008). Locally, similar nodal deficits were found in the rectus and lingual gyrus, and cerebellum of both PD groups relative to HC; additionally in PD-M nodal deficits involved several frontal and parietal regions, correlated with cognitive scores. NBS found that similar connections were involved in the default mode and cerebellar networks of both PD groups (to a greater extent in PD-M), while PD-M, but not PD-N, showed altered connections involving the frontoparietal network. Using connections identified by NBS, SVM allowed discrimination with high accuracy between PD-N and HC (90%), PD-M and HC (85%), and between the two PD groups (65%). These results suggest that default mode and cerebellar disruption characterizes PD, more so in PD-M, whereas frontoparietal disruption has diagnostic potential.

BackgroundDiagnosing primary central nervous system lymphoma (PCNSL) remains a challenge. MicroRNAs (miRNAs) are promising noninvasive markers for the identification of PCNSL. The present study aims to assess the diagnostic value of miRNAs for PCNSL patients as biomarkers.MethodsWe systematically searched PubMed, Embase, and the Cochrane library from inception to January 31, 2021. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), together with the summary receiver operator characteristic (SROC) curve, and the area under the SROC curve (AUC) value were used to estimate the overall diagnostic performance. We used Q statistic and I2 to test heterogeneity and used subgroup analyses to investigate the source of heterogeneity. The statistical analyses were independently performed by two investigators using Stata 14.0 and Revman 5.3.ResultsIn total, 11 studies from 6 records were included in the current meta-analysis with 281 PCNSL patients and 367 controls. Our statistical analysis demonstrated that the pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.91 (95% CI 0.84–0.95), 0.88 (95% CI 0.84–0.91), 7.48 (95% CI 5.71–9.78), 0.11 (95% CI 0.06–0.19), 70 (95% CI 35–142), and 0.90 (95% CI 0.87–0.92), respectively. The studies had substantial heterogeneity (I2 = 54%, 95% CI 0–100). Two subgroup analyses were conducted based on the type of specimen and miRNAs profiled.ConclusionsThis meta-analysis indicated that miRNAs were suitable as noninvasive diagnostic biomarkers for PCNSL with high accuracy. In addition, both cerebrospinal fluid-based and blood-based miRNAs assays for PCNSL detection were considered reliable for clinical application. MicroRNA-21 assays also seemed to be more accurate in the diagnosis of PCNSL. Good quality studies with large samples should be conducted to verify our results.

Aims: To determine how consistently Chinese glioblastoma multiforme (GBM) patients were treated according to the Stupp regimen. Patients and methods: The proportion of treatments conforming to the Stupp regimen and reasons for nonconformity were evaluated in 202 newly diagnosed GBM patients. Results: Only 15.8% of GBM patients received treatments compliant with the Stupp regimen. The main deviations were temozolomide dosages >75 mg/m2 (58/120; 48.3%) and treatment durations 2 (89/101; 88.1%) in the maintenance phase. Median overall survival (27.09 vs 18.21 months) and progression-free survival (14.27 vs 12.10 months) were longer in patients who received Stupp regimen-compliant treatments. Conclusion: Increased conformity to the Stupp regimen is needed for GBM patients in China.

Flexibility of the films and the limited ion transport in the vertical direction of film highly restrict the development of flexible supercapacitors. Herein, we have developed hybrid porous films consisting of N-doped holey graphene nanosheets (NHGR) with abundant in-plane nanopores and the vertically aligned polyaniline nanowires arrays on polypyrrole nanotubes (PPy@PANI) via a two-step oxidative polymerization strategy and vacuum filtration. The rational design can efficiently shorten the diffusion path of electrons/ions, alleviate volume variation of electrodes during cycling, enhance electric conductivity of the hybrids, and while offer abundant active interfacial sites for electrochemical reaction. Benefiting from the distinctive structural and compositional merits, the obtained PPy@PANI/NHGR film electrode manifests an excellent electrochemical properties in terms of specific capacity (1348 mF cm−2 at a current density of 1 mA cm−2), rate capability (81.2% capacitance retention from 1 to 30 mA cm−2), and cycling stability (capacitance retention of 73.7% at 20 mA cm−2 after 7000 cycles). Matched with NHGR negative electrode, the assembled flexible all-solid-state asymmetric supercapacitor displays a remarkable areal capacitance of 359 mF cm−2 at 5 mA cm−2, maximum areal energy density of 112.2 μWh cm−2 at 3.747 mW cm−2, and good flexibility at various bending angles while preserving stable cycling performance. The result shows the PPy@PANI/NHGR film with high flexibility and 3D ions transport channels is highly attractive for flexible energy storage devices.

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 Cp (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.

For the issue of collapse susceptibility prediction (CSP), minimal attention has been paid to explore the uncertainty characteristics of different machine learning models predicting collapse susceptibility. In this study, six kinds of typical machine learning methods, namely, logistic regression (LR), radial basis function neural network (RBF), multilayer perceptron (MLP), support vector machine (SVM), chi-square automatic interactive detection decision tree (CHAID), and random forest (RF) models, are constructed to do CSP. In this regard, An’yuan County in China, with a total of 108 collapses and 11 related environmental factors acquired through remote sensing and GIS technologies, is selected as a case study. The spatial dataset is first constructed, and then these machine learning models are used to implement CSP. Finally, the uncertainty characteristics of the CSP results are explored according to the accuracies, mean values, and standard deviations of the collapse susceptibility indexes (CSIs) and the Kendall synergy coefficient test. In addition, Huichang County, China, is used as another study case to avoid the uncertainty of different study areas. Results show that 1) overall, all six kinds of machine learning models reasonably and accurately predict the collapse susceptibility in An’yuan County; 2) the RF model has the highest prediction accuracy, followed by the CHAID, SVM, MLP, RBF, and LR models; and 3) the CSP results of these models are significantly different, with the mean value (0.2718) and average rank (2.72) of RF being smaller than those of the other five models, followed by the CHAID (0.3210 and 3.29), SVM (0.3268 and 3.48), MLP (0.3354 and 3.64), RBF (0.3449 and 3.81), and LR (0.3496 and 4.06), and with a Kendall synergy coefficient value of 0.062. Conclusively, it is necessary to adopt a series of different machine learning models to predict collapse susceptibility for cross-validation and comparison. Furthermore, the RF model has the highest prediction accuracy and the lowest uncertainty of the CSP results of the machine learning models.

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. SET and MYND domain-containing protein 3 (SMYD3) has been shown to promote the progression of various types of human cancers, including liver cancer; however, the detailed molecular mechanism is still largely unknown. Here, we report that SMYD3 expression in HCC is an independent prognostic factor for survival and promotes the proliferation and migration of HCC cells. We observed that SMYD3 upregulated sphingosine-1-phosphate receptor 1 (S1PR1) promoter activity by methylating histone 3 (H3K4me3). S1PR1 was expressed at high levels in HCC samples, and high S1PR1 expression was associated with shorter survival. S1PR1 expression was also positively correlated with SMYD3 expression in HCC samples. We confirmed that SMYD3 promotes HCC cell growth and migration in vitro and in vivo by upregulating S1PR1 expression. Further investigations revealed that SMYD3 affects critical signaling pathways associated with the progression of HCC through S1PR1. These findings strongly suggest that SMYD3 has a crucial function in HCC progression that is partially mediated by histone methylation at the downstream gene S1PR1, which affects key signaling pathways associated with carcinogenesis and the progression of HCC.