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State-of-the-art thoracic magnetic resonance imaging (MRI) plays a complementary role in the assessment of pulmonary nodules/masses which potentially indicate to cancer. We aimed to evaluate the sensitivity and specificity of MRI in diagnosis of pulmonary nodules/masses.Sixty-eight patients with computed tomography (CT)-detected pulmonary nodules/masses underwent 3T MRI (T1-VIBE, T1-starVIBE, T2-fBLADE turbo spin-echo, and T2-SPACE). The detection rate was calculated for each of the different subgroups of pulmonary nodules according to lung imaging reporting and data system (Lung-RADS). The four MRI sequences were compared in terms of detection rate and image quality-signal to noise ratio (SNR), contrast to noise ratio (CNR) and 5-point scoring scale. Agreement of lesion size measurement between CT and MRI was assessed by intraclass correlation coefficient (ICC). The picture-SNR, lesion-SNR and CNR of each sequence were analyzed by Mann-Whitney U test.In total, 232 pulmonary lesions were detected by CT. The CT showed 86 solid nodules (SNs)6 mm, 15 SNs between 6-8 mm, 35 SNs between 8-15 mm, and 52 SNs between 15-30 mm. The T1-VIBE, T1-starVIBE, T2-fBLADE TSE and T2-SPACE sequences accurately detected 141 SNs (141/188, 75%/83.3%), 150 SNs (150/188, 79.8%/100%), 166 SNs (166/188, 88.3%/66.7%) and 169 SNs (169/188, 89.9%/53.3%), respectively. Four ground glass nodules (GGNs) (4/6) were detected by T2-fBLADE TSE. Twelve part-solid nodules (PSNs) (12/22) were detected by T1-VIBE and 20 PSNs (20/22) by T2-SPACE. A total of 100 lesions (2.2±1.4 cm, 0.8-7.3 cm) were accurately detected and measured by the four MRI sequences with ICC0.96. The picture-SNR, lesion-SNR and CNR by T1-starVIBE were higher than those by T1-VIBE (P0.001). The lesion-SNR and CNR by T2-fBLADE TSE were higher than those by T2-SPACE (P=0.006, 0.038). 86% of images by T1-starVIBE, 92% by T2-fBLADE TSE, 90% by T2-SPACE and 93% by T1-VIBE were scored 3 or more.MRI achieves high sensitivity and specificity for different type of pulmonary nodules detection and is an effective alternative to CT as a diagnostic tool for pulmonary nodules.

Sub-1 cm-1 high-resolution broadband sum-frequency generation vibrational spectrometer (HR-BB-SFG-VS) using synchronized picosecond and femtosecond lasers at 1 kHz was first reported over a decade ago, and many advantages of the HR-BB-SFG-VS over the conventional BB-SFG-VS have been well-documented. A high-efficient and low-cost version of the HR-BB-SFG-VS is needed for broader adoption of this powerful interface specific spectroscopic technique. Here, we report the realization of such a sub-1 cm-1 HR-BB-SFG-VS with a tunable repetition rate around 100 kHz. Instead of synchronization of an additional expensive 90 picosecond laser for enough power to achieve high spectral resolution SFG measurement, a chirped volume Bragg grating (CVBG) is implemented with the second harmonic band compression (SHBC) unit to generate an intense 90 picosecond laser pulse at 517 nm with a bandwidth of about 0.16 cm-1 from a 150-fs laser pulse at ~1034 nm, with an efficiency of ~26%. The effectiveness of this new SFG system is demonstrated through the SFG spectra obtained with a spectral resolution of 0.6 cm-1 and excellent lineshape from the air/DMSO aqueous solutions interfaces, and the air/water interface, without apparent surface heating effect. This development provides a low-cost and easy-to-implement powerful HR-BB-SFG-VS instrumentation for broad applications in structure and dynamics studies on the illusive molecular surfaces and interfaces.

The current H-type vertical axis wind turbine (VAWT) airfoils are from horizontal axis wind turbine airfoils or symmetry airfoils that are designed at one angle of attack (such as α = 6°) rather than different angles of attack. As a consequence, it cannot, to a certain extent, increase wind power efficiency. Therefore, an optimal method of H-type VAWT blade airfoils in different ranges of angles of attack is presented. It can be expressed by airfoil integrated function. Then, an optimized mathematical model in which the objective function is the average of tangential force coefficients is established. The particle swarm optimization algorithm coupled with RFOIL program is introduced to optimize the H-type VAWT airfoil profiles with high aerodynamic performance. The optimized results show that the new HVAWT-00153 airfoil is more suitable to VAWTs than the other two new airfoils and NACA-0015 airfoil. Besides, by using computational fluid dynamics technology, the superiority of HVAWT-00153 airfoil over NCAC-0015 airfoil is reviewed. The results indicate that the H-type VAWT with new HVAWT-00153 airfoils could exhibit larger torque coefficients and higher power coefficients than that of the original H-type VAWT with NACA-0015 airfoils. The maximum power coefficient can reach 0.362, increased by 8.45% compared with that of the original one. This study has a good guidance to how to design the H-type VAWT airfoils with high wind energy power.

To investigate the feasibility and accuracy of applying a computed tomography (CT) texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images for classifying pulmonary nodules.CT images of 102 patients, with a total of 118 pulmonary nodules (52 benign, 66 malignant) were retrospectively reconstructed with a deep-learning reconstruction (artificial intelligence iterative reconstruction [AIIR]) and a hybrid iterative reconstruction (HIR) technique. The AIIR data were divided into a training (n = 96) and a validation set (n = 22), and the HIR data were set as the test set (n = 118). Extracted texture features were compared using the Mann-Whitney U test and t-test. The diagnostic performance of the classification model was analyzed with the receiver operating characteristic curve (ROC), the area under ROC (AUC), sensitivity, specificity, and accuracy.Among the obtained 68 texture features, 51 (75.0%) were not influenced by the change of reconstruction algorithm (p 0.05). Forty-four features were significantly different between benign and malignant nodules (p 0.05) for the AIIR dataset, which were selected to build the classification model. The accuracy and AUC of the classification model were 92.3% and 0.91 (95% confidence interval [CI], 0.74-0.90) with the validation set, which were 80.0% and 0.80 (95% CI, 0.68-0.86) with the test set.With the CT texture analysis model trained with deep-learning reconstruction (AIIR) images showing favorable diagnostic accuracy in discriminating benign and malignant pulmonary nodules, it also has certain applicability to the iterative reconstruction (HIR) images.

Objectives: To evaluate image quality and detection rate of four 3T magnetic resonance imaging (MRI) sequences and MRI performances in pulmonary tuberculosis (TB) when compared to computed tomography (CT). Methods: Forty patients with pulmonary tuberculosis separately underwent CT and 3T-MRI with T1-weighted free-breathing star-volumetric interpolated breath-hold examination (Star-VIBE) and standard VIBE, T2-weighted two-dimensional fast BLADE turbo spin-echo (2D-fBLADE TSE) and three-dimensional isotropic turbo spin-echo (3D-SPACE). Four MRI sequences were compared in terms of detection rate and image quality, which consisted of signal to noise ratio (SNR), contrast to noise ratio (CNR) and 5-point scoring scale. The total sensitivity was also compared between CT and MRI. Inter-observer agreement on 5-point scoring scale was calculated by Cohen’s kappa (k). SNR, CNR and 5-point scoring scale were compared using two-tailed pared t-test. Using CT as a reference, the MRI detection rate of pulmonary abnormality was evaluated by Pearson’s Chi-square test. Furthermore, the sizes of the nodules (≥5 mm) were compared using intraclass correlation coefficient. Results: In this study, Free-breathing Star-VIBE had significantly better SNR and identical CNR compared with standard VIBE. 2D-fBLADE TSE had statistically higher SNR but uniform or inferior CNR compared with 3D-SPACE. Inter-observers showed excellent agreement on 5-point scoring scale. The average score of Star-VIBE and VIBE had no difference. The average score of 2D-fBLADE TSE was higher than 3D-SPACE. There were no statistical differences in the detection rates of non-calcified parenchymal lesions between Star-VIBE and standard VIBE, 2D-fBALDE TSE and 3D-SPACE. MRI is comparable to CT in detecting consolidation, cavity, non-calcified nodules of ≥5 mm and tree-in-bud signs compared to CT. MRI detected non-calcified nodules of ＜5 mm, 5–10 mm, ≥10 mm and calcified nodules with sensitivity of 69.6%, 90.6%, 100% and 89.5% respectively. In addition, the sizes of the nodules (≥5 mm) had statistical consistency. MRI is more sensitive in detecting caseous necrosis, liquefaction, active cavity, abnormalities of lymph nodes and pleura. Conclusions: T1-weighted free-breathing Star-VIBE and T2-weighted 2D-fBLADE TSE, both with satisfactory image quality, are suitable for patients with pulmonary TB who need long-term follow-ups in clinical routine, especially for children, young women and pregnant women. Keywords: Pulmonary tuberculosis, 3T MRI, Image quality, Detection

Oxidation of the iron(II) precursor [(L 1 )Fe II Cl 2 ] with soluble iodosylbenzene s PhIO, where L 1 is a tetradentate bispidine, leads to the extremely reactive ferryl oxidant [(L 1 )(Cl)Fe IV =O] + with a cis disposition of the chlorido and oxido coligands as observed in nonheme halogenase enzymes. Experimental data indicate that, with cyclohexane as substrate, there is selective formation of chlorocyclohexane, where the halogenation is initiated by C-H abstraction and the result of a rebound of the ensuing radical to an iron-bound Cl - . The time-resolved formation of the halogenation product indicates that this primarily results from s PhIO oxidation of an initially formed oxido-bridged diiron(III) resting state. The high yield of up to over 70% (stoichiometric reaction) as well as the differing reactivities of free Fe 2+ and Fe 3+ in comparison with [(L 1 )Fe II Cl 2 ] indicate a high complex stability of the bispidine-iron complexes. A DFT analysis shows that, due to a large driving force and small triplet-quintet gap, in comparison to other small molecule halogenase models [(L 1 )(Cl)Fe IV =O] + is the most reactive, that the Fe III / radical rebound intermediate has a relatively long lifetime, supported by experimentally observed cage escape, and that this intermediate has, as observed experimentally, a lower energy barrier to the halogenation than the hydroxylation product, and this is shown to primarily be due to steric effects.

BACKGROUND: Computer-aided diagnosis based on chest X-ray (CXR) is an exponentially growing field of research owing to the development of deep learning, especially convolutional neural networks (CNNs). However, due to the intrinsic locality of convolution operations, CNNs cannot model long-range dependencies. Although vision transformers (ViTs) have recently been proposed to alleviate this limitation, those trained on patches cannot learn any dependencies for inter-patch pixels and thus, are insufficient for medical image detection. To address this problem, in this paper, we propose a CXR detection method which integrates CNN with a ViT for modeling patch-wise and inter-patch dependencies. METHODS: We experimented on the ChestX-ray14 dataset and followed the official training-test set split. Because the training data only had global annotations, the detection network was weakly supervised. A DenseNet with a feature pyramid structure was designed and integrated with an adaptive ViT to model inter-patch and patch-wise long-range dependencies and obtain fine-grained feature maps. We compared the performance using our method with that of other disease detection methods. RESULTS: For disease classification, our method achieved the best result among all the disease detection methods, with a mean area under the curve (AUC) of 0.829. For lesion localization, our method achieved significantly higher intersection of the union (IoU) scores on the test images with bounding box annotations than did the other detection methods. The visualized results showed that our predictions were more accurate and detailed. Furthermore, evaluation of our method in an external validation dataset demonstrated its generalization ability. CONCLUSIONS: Our proposed method achieves the new state of the art for thoracic disease classification and weakly supervised localization. It has potential to assist in clinical decision-making.

BACKGROUND Radiomics can yield minable information from medical images, which can facilitate computer-aided diagnosis. However, the lack of repeatability and reproducibility of radiomic features (RFs) may hinder their generalizability in clinical applications. OBJECTIVES The aims of this study were to explore 3 main sources of variability in RFs, investigate their influencing magnitudes and patterns, and identify a subset of robust RFs for further studies. MATERIALS AND METHODS A chest phantom with nodules was scanned with different computed tomography (CT) scanners repeatedly with varying acquisition and reconstruction parameters (April-May 2019) to evaluate 3 sources of variability: test-retest, inter-CT, and intra-CT protocol variability. The robustness of the RFs was measured using the concordance correlation coefficient, dynamic range, and intraclass correlation coefficient (ICC). The influencing magnitudes and patterns were analyzed using the Friedman test and Spearman rank correlation coefficient. Stable and informative RFs were selected, and their redundancy was eliminated using hierarchical clustering. Clinical validation was also performed to verify the clinical effectiveness and potential enhancement of the generalizability of radiomics research. RESULTS A total of 1295 RFs that showed all 3 sources of variability were included. The reconstruction kernel and the iteration level showed the greatest (ICC, 0.35 ± 0.31) and the least (ICC, 0.63 ± 0.27) influence on magnitudes. The different sources of variability showed relatively consistent patterns of influence (false discovery rate

The needle-like TiO2 obtained by hydrothermal treatment combines with Graphene by adding cetyltrimethylammonium bromide (CTAB) as auxiliary dispersant. On this basis, the needle-like TiO2/Graphene composite conductive material was prepared which is increasing the compatibility of conductive Graphene. The growth mechanism of the needle-like TiO2/Graphene was discussed and supported by scanning electron microscopy, transmission electron microscopy and other test methods. Experimental results show that hydrothermal temperature has a great influence on the formation of needle-like TiO2. The quick electron transportation properties between TiO2 and Graphene make the TiO2/Graphene have excellent conductive ability. Needle-like TiO2 can facilitate electron transport on the layer of Graphene. The three-dimensional mesh structure formed by crisscross of needle-like TiO2 is attached to the Graphene surface by the auxiliary effect of CTAB, is forming a conductive network to increase the transmission rate of electrons, so that the TiO2/Graphene is endowed with good electrical conductivity. Finally, the resistivity of TiO2/Graphene is as low as 1.655 × 10−3 Ω m at 7 wt% Graphene, which conforms to the electrical conductive standard of the materials prepared by the factory.

Zirconium dioxide loaded montmorillonite (ZrO 2 -MMT) composites have been prepared using an ion-exchange process at room temperature followed by a thermal treatment. The as-prepared composites are used to adsorb Cr 3+ ions in the tanning wastewater with a lower initial Cr 3+ concentration, reducing residual Cr 3+ concentration to less than 1 mg L −1 . XRD, TEM, SEM-EDS and XPS analyses are made to explore the structure and morphology of the composites. ZrO 2 is found to load on the interlayer and surface of MMT. The adsorption process is appropriately described by the pseudo-second-order kinetic equation and Langmuir equation. The ZrO 2 -MMT composites exhibit a better adsorption capacity than adsorption materials reported in the literature, and the maximum adsorption capacity for the initial Cr 3+ concentration of 10, 20, 30 mg L −1 is as large as 142.86, 158.73 and 172.41 mg g −1 , respectively. The removal efficiency of ZrO 2 -MMT for Cr 3+ remains above 92% after six cycles.

While SARS-CoV-2 specific neutralizing antibodies have been developed for therapeutic purposes, the specific viral triggers that drive the generation of SARS-CoV-2 specific IgG and IgM antibodies remain only partially characterized. Moreover, it is unknown whether endogenously derived antibodies drive viral clearance that might result in mitigation of clinical severity during natural infection. We developed a series of non-linear mathematical models to investigate whether SARS-CoV-2 viral and antibody kinetics are coupled or governed by separate processes. Patients with severe disease had a higher production rate of IgG but not IgM antibodies. Maximal levels of both isotypes were governed by their production rate rather than different saturation levels between people. Our results suggest that an exponential surge in IgG levels occurs approximately 5–10 days after symptom onset with no requirement for continual antigenic stimulation. SARS-CoV-2 specific IgG antibodies appear to have limited to no effect on viral dynamics but may enhance viral clearance late during primary infection resulting from the binding effect of antibody to virus, rather than neutralization. In conclusion, SARS-CoV-2 specific IgG antibodies may play only a limited role in clearing infection from the nasal passages despite providing long-term immunity against infection following vaccination or prior infection.

Neuroinflammation has been recognized as a major cause for neurocognitive diseases. Although the hippocampus has been considered an important region for cognitive dysfunction, the influence of hippocampal neuroinflammation on brain functional connectivity (FC) has been rarely studied. In this study, lipopolysaccharide (LPS) was used to induce systemic inflammation and neuroinflammation in the aged rat brain, while elamipretide (SS-31) was used for treatment. Systemic and hippocampal inflammation were determined using ELISA, while astrocyte responses during hippocampal neuroinflammation were determined by interleukin 1 beta (IL-1β)/tumor necrosis factor alpha (TNFα) double staining immunofluorescence. Oxidative stress was determined by reactive oxidative species (ROS), electron transport chain (ETC) complex, and superoxide dismutase (SOD). Short- (30 days) learning and spatial working memory were tested by the Morris water maze (MWM). Resting-state functional magnetic resonance imaging (rs-fMRI) was used to analyze the brain FC by placing seed voxels on the left and right hippocampus. Compared with the vehicle group, rats with the LPS exposure showed an impaired MWM performance, higher oxidative stress, higher levels of inflammatory cytokines, and astrocyte activation in the hippocampus. The neuroimaging examination showed decreased FC on the right orbital cortex, right olfactory bulb, and left hippocampus on day 3, 7, and 31, respectively, after treatment. In contrast, rats with SS-31 treatment showed lower levels of inflammatory cytokines, less astrocyte activation in the hippocampus, and improved MWM performance. Neuroimaging examination showed increased FC on the left-parietal association cortex (L-PAC), left sensory cortex, and left motor cortex on day 7 with the right flocculonodular lobe on day 31 as compared with those without SS-31 treatment. Our study demonstrated that inhibiting neuroinflammation in the hippocampus not only reduces inflammatory responses in the hippocampus but also improves the brain FC in regions related to the hippocampus. Furthermore, early anti-inflammatory treatment with SS-31 has a long-lasting effect on reducing the impact of LPS-induced neuroinflammation.

International audience; Semi-supervised learning is crucial in many applications where accessing class labels is unaffordable or costly. The most promising approaches are graph-based but they are transductive and they do not provide a generalized model working on inductive scenarios. To address this problem, we propose a generic framework, ESA☆, for inductive semi-supervised learning based on three components: an ensemble of semi-supervised autoencoders providing a new data representation that leverages the knowledge supplied by the reduced amount of available labels; a graph-based step that helps augmenting the training set with pseudo-labeled instances and, finally, a classifier trained with labeled and pseudo-labeled instances. Additionally, we also introduce two variants of our framework adopting different graph-based pseudo-labeling strategies: the first, ESALP, is based on a confidence-aware label propagation algorithm, while the second, ESAGAT, on a graph convolutional attention network. The experimental results show that our framework outperforms state-of-the-art inductive semi-supervised methods.

AIM To evaluate the lung function of coronavirus disease 2019 (COVID-19) patients using oxygen-enhanced (OE) ultrashort echo time (UTE) MRI. MATERIALS AND METHODS Forty-nine patients with COVID-19 were included in the study. The OE-MRI was based on a respiratory-gated three-dimensional (3D) radial UTE sequence. For each patient, the percent signal enhancement (PSE) map was calculated using the expression PSE = (S100% – S21%)/S21%, where S21% and S100% are signals acquired during room air and 100% oxygen inhalation, respectively. Agreement of lesion detectability between UTE-MRI and computed tomography (CT) was performed using the kappa test. The Mann–Whitney U-test was used to evaluate the difference in the mean PSE between mild-type COVID-19 and common-type COVID-19. Spearman's test was used to assess the relationship between lesion mean PSE and lesion size. Furthermore, the Mann–Whitney U-test was used to evaluate the difference in region of interest (ROI) mean PSE between normal pulmonary parenchyma and lesions. The Kruskal–Wallis test was applied to test the difference in the mean PSE between different lesion types. RESULTS CT and UTE-MRI reached good agreement in lesion detectability. Ventilation measures in mild-type patients (5.3 ± 5.5%) were significantly different from those in common-type patients (3 ± 3.9%). Besides, there was no significant correlation between lesion mean PSE and lesion size. The mean PSE of COVID-19 lesions (3.2 ± 4.9%) was significantly lower than that of the pulmonary parenchyma (5.4 ± 3.9%). No significant difference was found among different lesion types. CONCLUSION OE-UTE-MRI could serve as a promising method for the assessment of lung function or treatment management of COVID-19 patients.

BACKGROUND: To investigate the accuracy of size estimation and potential diagnosis efficacy of native T1-mapping in focal pulmonary lesion, compared to T1-star 3D-volumetric interpolated breath-hold sequence (VIBE), T2-fBLADE turbo-spin echo (TSE), and computed tomography (CT). METHODS: Thirty-nine patients with CT-detected focal pulmonary lesions underwent thoracic 3.0-T magnetic resonance imaging (MRI) using axial free-breathing 3D T1-star VIBE, respiratory triggered T2-fBLADE TSE, breath-hold T1-Turbo fast low angle shot (FLASH) and T1-FLASH 3D. Native T1-mapping images were generated by T1-FLASH 3D with B1-filed correction by T1-Turbo FLASH. The intraclass correlation coefficient (ICC) and Bland-Altman plots were used to evaluate intra-observer agreement and inter-method reliability of diameter measurements. Native T1-values were measured and compared among the malignancy, tuberculosis, non-tuberculosis benign groups using Mann-Whitney U tests. RESULTS: Forty-five focal pulmonary lesions were displayed by CT, native T1-mapping, T1-star VIBE, and T2-fBLADE TSE. T1-mapping-based diameter measurements yielded an intra-observer ICC of 0.995. Additionally, inter-method measurements were highly consistent (T1-mapping & T1-star VIBE: ICC 0.982, T1-mapping & T2-fBLADE TSE: ICC 0.978, T1-mapping & CT: ICC 0.972). For lesions

To evaluate the clinical features and radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of the presence of spread through air spaces (STAS) in patients with lung adenocarcinoma. A total of 107 STAS-positive lung adenocarcinomas were selected and matched to 105 STAS-negative lung adenocarcinomas. Thin-slice CT imaging annotation and region of interest (ROI) segmentation were performed with semi-automatic in-house software. Radiomics features were extracted from all nodules and incremental distances of 5, 10, and 15 mm outside the lesion segmentation. A radiomics nomogram was established with multivariable logistic regression based on clinical and radiomics features. The maximum diameter of the solid component and mediastinal lymphadenectasis were selected as independent predictors of STAS. The radiomics nomogram of lung nodules showed especially good prediction in the training set [area under the curve (AUC), 0.98; 95% confidence interval (CI), 0.97–1.00] and test set (AUC, 0.99; 95% CI, 0.97–1.00). The radiomics nomogram of peritumoral regions also showed good prediction, but the fitting degrees of the calibration curves were not good. Our study may provide guidance for surgical methods in patients with lung adenocarcinoma.

BACKGROUND: The efficient and accurate diagnosis of pulmonary adenocarcinoma before surgery is of considerable significance to clinicians. Although computed tomography (CT) examinations are widely used in practice, it is still challenging and time-consuming for radiologists to distinguish between different types of subcentimeter pulmonary nodules. Although there have been many deep learning algorithms proposed, their performance largely depends on vast amounts of data, which is difficult to collect in the medical imaging area. Therefore, we propose an automatic classification system for subcentimeter pulmonary adenocarcinoma, combining a convolutional neural network (CNN) and a generative adversarial network (GAN) to optimize clinical decision-making and to provide small dataset algorithm design ideas. METHODS: A total of 206 nodules with postoperative pathological labels were analyzed. Among them were 30 adenocarcinomas in situ (AISs), 119 minimally invasive adenocarcinomas (MIAs), and 57 invasive adenocarcinomas (IACs). Our system consisted of two parts, a GAN-based image synthesis, and a CNN classification. First, several popular existing GAN techniques were employed to augment the datasets, and comprehensive experiments were conducted to evaluate the quality of the GAN synthesis. Additionally, our classification system processes were based on two-dimensional (2D) nodule-centered CT patches without the need of manual labeling information. RESULTS: For GAN-based image synthesis, the visual Turing test showed that even radiologists could not tell the GAN-synthesized from the raw images (accuracy: primary radiologist 56%, senior radiologist 65%). For CNN classification, our progressive growing wGAN improved the performance of CNN most effectively (area under the curve =0.83). The experiments indicated that the proposed GAN augmentation method improved the classification accuracy by 23.5% (from 37.0% to 60.5%) and 7.3% (from 53.2% to 60.5%) in comparison with training methods using raw and common augmented images respectively. The performance of this combined GAN and CNN method (accuracy: 60.5%±2.6%) was comparable to the state-of-the-art methods, and our CNN was also more lightweight. CONCLUSIONS: The experiments revealed that GAN synthesis techniques could effectively alleviate the problem of insufficient data in medical imaging. The proposed GAN plus CNN framework can be generalized for use in building other computer-aided detection (CADx) algorithms and thus assist in diagnosis.

BACKGROUND: Chest computed tomography (CT) has shown tremendous clinical potential for screening, diagnosis, and surveillance of COVID-19. However, safety concerns are warranted due to repeated exposure of X-rays over a short period of time. Recent advances in MRI suggested that ultrashort echo time MRI (UTE-MRI) was valuable for pulmonary applications. PURPOSE: To evaluate the effectiveness of UTE-MRI for assessing COVID-19. STUDY TYPE: Prospective. POPULATION: In all, 23 patients with COVID-19 and with an average interval of 2.81 days between hospital admission and image examination. FIELD STRENGTH/SEQUENCE: 3T; Respiratory-gated three-dimensional radial UTE pulse sequence. ASSESSMENT: Image quality score. Patient- and lesion-based interobserver and intermethod agreement for identifying the representative image findings of COVID-19. STATISTICAL TESTS: Wilcoxon-rank sum test, Kendall's coefficient of concordance (Kendall's W), intraclass coefficients (ICCs), and weighted kappa statistics. RESULTS: There was no significant difference between the image quality of CT and UTE-MRI (CT vs. UTE-MRI: 4.3 ± 0.4 vs. 4.0 ± 0.5, P = 0.09). Moreover, both patient- and lesion-based interobserver agreement of CT and UTE-MRI for evaluating the image signs of COVID-19 were determined as excellent (ICC: 0.939-1.000, P < 0.05; Kendall's W: 0.894-1.000, P < 0.05.). In addition, the intermethod agreement of two image modalities for assessing the representative findings of COVID-19 including affected lobes, total severity score, ground glass opacities (GGO), consolidation, GGO with consolidation, the number of crazy paving pattern, and linear opacities, as well as pseudocavity were all determined as substantial or excellent (kappa: 0.649-1.000, P < 0.05; ICC: 0.913-1.000, P < 0.05). DATA CONCLUSION: Pulmonary MRI with UTE is valuable for assessing the representative image findings of COVID-19 with a high concordance to CT. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 3 J. Magn. Reson. Imaging 2020;52:397-406.

Background Past studies have demonstrated that goal-directed fluid therapy (GDFT) may be more marginal than previously believed. However, beneficial effects of alpha-1 adrenergic agonists combined with appropriate fluid administration is getting more and more attention. This study aimed to systematically review the effects of goal-directed fluid therapy (GDFT) combined with the application of alpha-1 adrenergic agonists on postoperative outcomes following noncardiac surgery. Methods This meta-analysis included randomized controlled trials (RCTs) on GDFT combined with the application of alpha-1 adrenergic agonists in patients undergoing noncardiac surgery. The primary outcomes included the postoperative mortality rate and length of hospital stay (LOS). The secondary outcome indexes were the incidence of postoperative complications and recovery of postoperative gastrointestinal (GI) function. The traditional pairwise meta-analysis was conducted to compare the effect of fluid therapy. The quality of included RCTs was evaluated according to the Cochrane Collaboration’s risk-of-bias tool. Also, the publication bias was detected using funnel plots, Egger’s regression test, and Begg’s adjusted rank correlation test. The meta-analysis was conducted using the RevMan 5.3 and Stata 14.0 software. Results Thirty-two eligible RCTs were included in this meta-analysis. Perioperative GDFT combined with the application of alpha-1 adrenergic agonists was associated with a significant reduction in LOS (P = 0.002; I2 = 69%), and overall complication rates (P = 0.04; I2 = 41%). It facilitated gastrointestinal function recovery, as demonstrated by shortening the time to first flatus by 6.30 h (P

Premise of the StudyFor many crops, research into the origin and partitioning of genetic variation is limited and this can slow or prevent crop improvement programs. Many of these underutilized crops have traits that could be of benefit in a changing climate due to stress tolerance or nutritional properties. Winged bean (Psophocarpus tetragonolobus (L.) DC.) is one such crop. All parts of the plant can be eaten, from the roots to the seeds, and is high in protein as well as other micronutrients. The goal of our study was to identify the wild progenitor and analyze the partitioning of genetic variation in the crop.MethodsWe used molecular phylogenetic analyses (cpDNA and nuclear ITS sequencing) to resolve relationships between all species in the genus, and population genetics (utilizing microsatellites) to identify genetic clusters of winged bean accessions and compare this to geography.Key ResultsWe find that winged bean is genetically distinct from all other members of the genus. We also provide support for four groups of species in the genus, largely, but not completely, corresponding to the results of previous morphological analyses. Within winged bean, population genetic analysis using 10 polymorphic microsatellite markers suggests four genetic groups; however, there is little correspondence between the genetic variation and the geography of the accessions.ConclusionsThe true wild progenitor of winged bean remains unknown (or is extinct). There has likely been large‐scale cross‐breeding, trade, and transport of winged bean and/or multiple origins of the crop.

In this study, GO-TiO2 composite material, with abundant oxygen-containing functional groups is prepared in way of loading TiO2 on the surface of GO, binding with C–O–Ti chemical bonds. Especially the resulted anatase-type TiO2 is originated from hydrolyzing butyl titanate in condition of mild water bath (80 ​°C), so that the oxygen-containing functional groups onto GO-TiO2 could be greatly retained to improve its performance for adsorbing Cr3+ (10–30 ​mg/L) in wastewater. A variety of characterizations, including SEM, TEM, FT-IR, XRD, XPS, Zeta and Raman are employed to demonstrate that TiO2 is uniformly dispersed on the surface of GO and the oxygen-containing functional groups onto GO have been greatly retained. Moreover, the adsorptive process of Cr3+ on GO-TiO2 corresponds to the quasi-second-order kinetic equation and Langmuir equation (maximum potential of adsorption could reach up to 149.24 ​mg/g). Furthermore, the thermodynamics studies imply that the adsorption process toward Cr3+ is spontaneous and endothermic. Importantly, density functional theory (DFT) calculations have also been performed to illustrate the order of oxygen-containing functional groups which bind with Cr3+: E0(-OH) ​> ​E0(-O-) ​> ​E0(-COOH). After five cycles, the removal efficiency is still over 85%, suggesting that the resulted adsorbent performs great potential in removing Cr3+ from wastewater.

In this work, we introduced TiH 2 into the 17MgH 2 -12Al composite by ball milling for improving the dehydrogenation performance of the reactive system. According to the measurements of non-isothermal dehydrogenation, the onset temperature of hydrogen release from 17MgH 2 -12Al-TiH 2 was reduced by 50 °C from that of 17MgH 2 -12Al. The investigations by Kissinger's method showed that the activation energy of hydrogen desorption of 17MgH 2 -12Al-TiH 2 (107 kJ mol −1 ) is much lower than 17MgH 2 -12Al (168 kJ mol −1 ), leading to enhanced dehydrogenation kinetics due to the TiH 2 additive. X-ray diffraction (XRD) analysis suggests that TiH 2 catalyzed the reaction between MgH 2 with Al of the system during the dehydrogenation below 280 °C. At temperatures between 280 °C to 400 °C, however, TiH 2 will thoroughly react with produced Mg 17 Al 12 and liberate hydrogen. The overall reaction of the system may be represented by 17 MgH 2 + 12 Al + TiH 2 → 3/4 Mg 17 Al 12 + Al 3 Ti + 17/4 Mg + 18 H 2 . Its theoretical dehydrogenation capacity (4.38 wt% H 2 ) agrees well with that achieved by temperature programmed dehydrogenation (TPD) measurements, demonstrating the occurrence of such reaction(s). The dehydrogenation enthalpy of 17MgH 2 -12Al-TiH 2 (71.7 kJ mol −1 H 2 ) proceeding at lower hydrogen desorption plateau was significantly lowered from that of 17MgH 2 -12Al (81.9 kJ mol −1 H 2 ) according to the measurements of pressure-composition isotherms (PCI) and van't Hoff plots. It means that TiH 2 was involved in the end reaction proceeding in the 17MgH 2 -12Al system. We believe that TiH 2 improves the dehydrogenation kinetics of 17MgH 2 -12Al initially and tailors its thermodynamics finally.

TiH2 was introduced into the 4MgH2-NaAlH4 composite by ball milling to improve the dehydrogenation and re-dehydrogenation performance of the composite. The non-isothermal dehydrogenation curve shows that the onset temperature of hydrogen release from 4MgH2-NaAlH4-TiH2 is at around 100 °C, which is lower by 50 °C than that of 4MgH2-NaAlH4. The investigation by Kissinger's method indicates that 4MgH2-NaAlH4-TiH2 possesses much lower activation energy (68 kJ mol−1) than 4MgH2-NaAlH4 (159 kJ mol−1), indicating an enhanced dehydrogenation kinetics due to the additional TiH2. TiH2 catalyzes the decomposition of MgH2 and NaAlH4, respectively, and also the reaction between their products during the dehydrogenation process below 375 °C. At temperatures above 375 °C, TiH2 will react with Mg17Al12 to form Al3Ti and liberate contained hydrogen. TiH2 also improved the isothermal hydrogenation kinetics of 4MgH2-NaAlH4 desorbed system. The activation energy of re-hydrogenation for 4MgH2-NaAlH4-TiH2 system was greatly reduced from 161.62 kJ mol−1 H2 to 125.80 kJ mol−1 H2 of 4MgH2-NaAlH4. The kinetics enhancement of hydrogen absorption is probably due to the intermediate phase Al3Ti, which is beneficial to the recombination of MgH2.

Trade credit is a payment extension granted by a selling firm to its customer. Companies typically respond to late payments from their customers by delaying payments to suppliers, thus generating a ripple through the transaction network. Therefore, trade credit is as a potential vehicle of propagation of losses in case of default events. The goal of this work is to leverage information on the trade credit among connected firms to predict imminent defaults of firms. We use a unique dataset of client firms of a major Italian bank to investigate firm bankruptcy between October 2016 to March 2018. We develop a model to capture network spillover effects originating from the supply chain on the probability of default of each firm via a sequential approach: the output of a first model component on single firm features is used in a subsequent model which captures network spillovers. While the first component is the standard econometrics way to predict such dynamics, the network module represents an innovative way to look into the effect of trade credit on default probability. This module looks at the transaction network of the firm, as inferred from the payments transiting via the bank, in order to identify the trade partners of the firm. By using several features extracted from the network of transactions, this model is able to predict a large fraction of the defaults, thus showing the value hidden in the network information. Finally, we merge firm and network features with a machine learning model to create a ‘hybrid’ model, which improves the recall for the task by almost 20 percentage points over the baseline.

BACKGROUND: The assessment of the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far. We aimed to establish a deep learning (DL) model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of COVID-19. METHODS: One hundred ninety-six hospitalized patients with confirmed COVID-19 were enrolled from January 20 to February 10, 2020 in our centre, and were divided into severe and non-severe groups. The clinico-radiological data on admission were retrospectively collected and compared between the two groups. The optimal clinico-radiological features were determined based on least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and a predictive nomogram model was established by five-fold cross-validation. Receiver operating characteristic (ROC) analyses were conducted, and the areas under the receiver operating characteristic curve (AUCs) of the nomogram model, quantitative CT parameters that were significant in univariate analysis, and pneumonia severity index (PSI) were compared. RESULTS: In comparison with the non-severe group (151 patients), the severe group (45 patients) had a higher PSI (P