Photo-Thermal Mediated Li-ion Transport for Solid-State Lithium Metal Batteries.

The development of lithium-based solid-state batteries (SSBs) has to date been hindered by the limited ionic conductivity of solid polymer electrolytes (SPEs), where nonsolvated Li-ions are difficult to migrate in a polymer framework at room temperature. Despite the improved cationic migration by traditional heating systems, they are far from practical applications of SSBs. Here, an innovative strategy of light-mediated energy conversion is reported to build photothermal-based SPEs (PT-SPEs). The results suggest that the nanostructured photothermal materials acting as a powerful light-to-heat converter enable heating within a submicron space, leading to a decreased Li+ migration barrier and a stronger solid electrolyte interface. Via in situ X-ray diffraction analysis and molecular dynamics simulation, it is shown that the generated heating effectively triggers the structural transition of SPEs from a highly crystalline to an amorphous state, that helps mediate lithium-ion transport. Using the assembled SSBs for exemplification, PT-SPEs function as efficient ion-transport media, providing outstanding capacity retention (96% after 150 cycles) and a stable charge/discharge capacity (140 mA g-1 at 1.0 C). Overall, the work provides a comprehensive picture of the Li-ion transport in solid polymer electrolytes and suggests that free volume may be critical to achieving high-performance solid-state batteries.

Application of 5T glutamate chemical exchange saturation transfer imaging in brain tumors: preliminary results.

PURPOSE: Glutamate chemical exchange saturation transfer (GluCEST) is a non-invasive CEST imaging technique for detecting glutamate levels in tissues. We aimed to investigate the reproducibility of the 5T GluCEST technique in healthy volunteers and preliminarily explore its potential clinical application in patients with brain tumors. METHODS: Ten volunteers (4 males, mean age 29 years) underwent three 5T GluCEST imaging scans. The reproducibility of the three imaging GluCEST measurements was assessed using one-way repeated measures analysis of variance (ANOVA), generalized estimating equations, and linear mixed models. Twenty-eight patients with brain tumors (10 males, mean age 54 years) underwent a single GluCEST scan preoperatively, and t-tests were used to compare the differences in GluCEST values between different brain tumors. In addition, the diagnostic accuracy of GluCEST values in differentiating brain tumors was assessed using the receiver work characteristics (ROC) curve. RESULTS: The coefficients of variation of GluCEST values in healthy volunteers were less than 5% for intra-day, inter-day, and within-subjects and less than 10% for between-subjects. High-grade gliomas (HGG) had higher GluCEST values compared to low-grade gliomas (LGG) (P < 0.001). In addition, cerebellopontine angle (CPA) meningiomas had higher GluCEST values than acoustic neuromas (P < 0.001). The area under the curve (AUC) of the GluCEST value for differentiating CPA meningioma from acoustic neuroma was 0.93. CONCLUSION: 5T GluCEST images are highly reproducible in healthy brains. In addition, the 5T GluCEST technique has potential clinical applications in differentiating LGG from HGG and CPA meningiomas from acoustic neuromas.

Raman lidar at 355 nm using low dead time photon counting for atmospheric aerosol measurements.

Photon counting is an effective way to enhance the dynamic range of the data acquisition system (DAQ) in Raman lidars. However, there exists a deficiency of relatively high dead times among current options, which necessitates an additional calibration procedure for the nonlinearity of the photon counting signal, thus leading to unanticipated errors. A field programmable gate array (FPGA)-based photon counting module has been proposed and implemented in a Raman lidar, offering two operational channels. Through observational experiments, it was determined that this module has an overall dead time of 1.13 ns taking advantage of the high-speed amplifier/discriminator pair and the logic design, a significant improvement compared to the 4.35 ns of a commercially used Licel transient recorder within the same counting rate range. This notably low dead time implies that its output maintains sufficient linearity even at substantially high counting rates. As a result, the need for a dead time calibration procedure prior to signal integration with the analog signal is eliminated, reducing uncertainty in the final integrated signal, and even in the retrieval result. The backscattering result of the comparison between this module and a transient recorder indicates that a more precise performance can be acquired benefiting from this hardware upgrading.

Quantifying H&E staining results, grading and predicting IDH mutation status of gliomas using hybrid multi-dimensional MRI.

OBJECTIVE: To assess the performance of hybrid multi-dimensional magnetic resonance imaging (HM-MRI) in quantifying hematoxylin and eosin (H&E) staining results, grading and predicting isocitrate dehydrogenase (IDH) mutation status of gliomas. MATERIALS AND METHODS: Included were 71 glioma patients (mean age, 50.17 ± 13.38 years; 35 men). HM-MRI images were collected at five different echo times (80-200 ms) with seven b-values (0-3000 s/mm2). A modified three-compartment model with very-slow, slow and fast diffusion components was applied to calculate HM-MRI metrics, including fractions, diffusion coefficients and T2 values of each component. Pearson correlation analysis was performed between HM-MRI derived fractions and H&E staining derived percentages. HM-MRI metrics were compared between high-grade and low-grade gliomas, and between IDH-wild and IDH-mutant gliomas. Using receiver operational characteristic (ROC) analysis, the diagnostic performance of HM-MRI in grading and genotyping was compared with mono-exponential models. RESULTS: HM-MRI metrics FDvery-slow and FDslow demonstrated a significant correlation with the H&E staining results (p < .05). Besides, FDvery-slow showed the highest area under ROC curve (AUC = 0.854) for grading, while Dslow showed the highest AUC (0.845) for genotyping. Furthermore, a combination of HM-MRI metrics FDvery-slow and T2Dslow improved the diagnostic performance for grading (AUC = 0.876). DISCUSSION: HM-MRI can aid in non-invasive diagnosis of gliomas.

The feasibility of half-dose contrast-enhanced scanning of brain tumours at 5.0 T: a preliminary study.

PURPOSE: This study investigated and compared the effects of Gd enhancement on brain tumours with a half-dose of contrast medium at 5.0 T and with a full dose at 3.0 T. METHODS: Twelve subjects diagnosed with brain tumours were included in this study and underwent MRI after contrast agent injection at 3.0 T (full dose) or 5.0 T (half dose) with a 3D T1-weighted gradient echo sequence. The postcontrast images were compared by two independent neuroradiologists in terms of the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and subjective image quality score on a ten-point Likert scale. Quantitative indices and subjective quality ratings were compared with paired Student's t tests, and interreader agreement was assessed with the intraclass correlation coefficient (ICC). RESULTS: A total of 16 enhanced tumour lesions were detected. The SNR was significantly greater at 5.0 T than at 3.0 T in grey matter, white matter and enhanced lesions (p < 0.001). The CNR was also significantly greater at 5.0 T than at 3.0 T for grey matter/tumour lesions, white matter/tumour lesions, and grey matter/white matter (p < 0.001). Subjective evaluation revealed that the internal structure and outline of the tumour lesions were more clearly displayed with a half-dose at 5.0 T (Likert scale 8.1 ± 0.3 at 3.0 T, 8.9 ± 0.3 at 5.0 T, p < 0.001), and the effects of enhancement in the lesions were comparable to those with a full dose at 3.0 T (7.8 ± 0.3 at 3.0 T, 8.7 ± 0.4 at 5.0 T, p < 0.001). All subjective scores were good to excellent at both 5.0 T and 3.0 T. CONCLUSION: Both quantitative and subjective evaluation parameters suggested that half-dose enhanced scanning via 5.0 T MRI might be feasible for meeting clinical diagnostic requirements, as the image quality remains optimal. Enhanced scanning at 5.0 T with a half-dose of contrast agents might benefit patients with conditions that require less intravenous contrast agent, such as renal dysfunction.

Potential of Mie-Fluorescence-Raman Lidar to Profile Chlorophyll a Concentration in Inland Waters.

Vertical distribution of phytoplankton is crucial for assessing the trophic status and primary production in inland waters. However, there is sparse information about phytoplankton vertical distribution due to the lack of sufficient measurements. Here, we report, to the best of our knowledge, the first Mie-fluorescence-Raman lidar (MFRL) measurements of continuous chlorophyll a (Chl-a) profiles as well as their parametrization in inland water. The lidar-measured Chl-a during several experiments showed good agreement with the in situ data. A case study verified that MFRL had the potential to profile the Chl-a concentration. The results revealed that the maintenance of subsurface chlorophyll maxima (SCM) was influenced by light and nutrient inputs. Furthermore, inspired by the observations from MFRL, an SCM model built upon surface Chl-a concentration and euphotic layer depth was proposed with root mean square relative difference of 16.5% compared to MFRL observations, providing the possibility to map 3D Chl-a distribution in aquatic ecosystems by integrated active-passive remote sensing technology. Profiling and modeling Chl-a concentration with MFRL are expected to be of paramount importance for monitoring inland water ecosystems and environments.

The association between albumin and mortality in patients with acute kidney injury: a retrospective observational study.

BACKGROUND: While the association between decreased serum albumin (ALB) levels and increased risk of acute kidney injury (AKI) is well established, the risk of death among patients with AKI with low serum ALB levels is unclear. We aimed to evaluate the association between serum ALB levels in patients with AKI and mortality, and help guide their clinical management. METHODS: The included patients were those diagnosed with AKI and admitted to Zhejiang Provincial People's Hospital between January 2018 and December 2020. The clinical endpoint was all-cause mortality rate at 90-days and 1-year. Patients were divided into four groups according to the quartiles (Qs) of ALB measurements at admission. Cumulative survival curves were calculated using Kaplan-Meier analysis, and Cox proportional risk models were used to assess the association between serum ALB levels and 90-day and 1-year all-cause mortality. RESULTS: This study included 740 patients with AKI. Patients with measured ALB values were classified into quartiles: Q1 ≤ 26.0 g/L (n = 188); Q2 = 26.1-30.5 g/L (n = 186); Q3 = 30.6-34.7 g/L (n = 183); Q4 ≥ 34.8 g/L (n = 183). Univariate analysis using Cox regression showed that for every 10 g/L increase in ALB, the 90-day and 1-year mortality decreased by 29%. Among the four subgroups, patients with lower ALB levels had a higher risk of death. After adjusting for demographics, comorbid conditions, inflammatory index, and medicine, the lowest ALB quartile (ALB < 26 g/L) was associated with increased risk of 90-day mortality (hazard ratio [HR], 1.76; 95% confidence interval [CI], 1.30 to 2.38, P < 0.001) and 1-year all-cause mortality (HR, 1.79; 95% CI, 1.33 to 2.41, P < 0.001). CONCLUSIONS: ALB levels in patients with AKI were significantly correlated with prognosis, and the higher the level, the better the prognosis. Compared to patients with ALB ≥ 34.8 g/L, patients with 26.1 g/L < ALB ≤ 30.5 g/L had an increased risk of 90-day and 1-year all-cause mortality of approximately 40%, and patients with ALB ≤ 26.0 g/L had an increased risk of 90-day and 1-year all-cause mortality of approximately 76% and 79%, respectively.

Fundamental efficiency bound for quantum coherent energy transfer in nanophotonics.

We derive a unified quantum theory of coherent and incoherent energy transfer between two atoms (donor and acceptor) valid in arbitrary Markovian nanophotonic environments. Our theory predicts a fundamental bound η m a x =γ a γ d+γ a for energy transfer efficiency arising from the spontaneous emission rates γd and γa of the donor and acceptor. We propose the control of the acceptor spontaneous emission rate as a new design principle for enhancing energy transfer efficiency. We predict an experiment using mirrors to enhance the efficiency bound by exploiting the dipole orientations of the donor and acceptor. Of fundamental interest, we show that while quantum coherence implies the ultimate efficiency bound has been reached, reaching the ultimate efficiency does not require quantum coherence. Our work paves the way towards nanophotonic analogues of efficiency-enhancing environments known in quantum biological systems.

Quantum analog of the maximum power transfer theorem.

We discover the quantum analog of the well-known classical maximum power transfer theorem. Our theoretical framework considers the continuous steady-state problem of coherent energy transfer through an N-node bosonic network coupled to an external dissipative load. We present an exact solution for optimal power transfer in the form of the maximum power transfer theorem known in the design of electrical circuits. Furthermore, we introduce the concept of quantum impedance matching with Thevenin equivalent networks, which are shown to be exact analogs to their classical counterparts. Our results are applicable to both ordered and disordered quantum networks with graph-like structures ranging from nearest-neighbor to all-to-all connectivities. This work points towards universal design principles adapting ideas from the classical regime to the quantum domain for various quantum optical applications in energy-harvesting, wireless power transfer, and energy transduction.

Deep ultra-violet plasmonics: exploiting momentum-resolved electron energy loss spectroscopy to probe germanium.

Germanium is typically used for solid-state electronics, fiber-optics, and infrared applications, due to its semiconducting behavior at optical and infrared wavelengths. In contrast, here we show that the germanium displays metallic nature and supports propagating surface plasmons in the deep ultraviolet (DUV) wavelengths, that is typically not possible to achieve with conventional plasmonic metals such as gold, silver, and aluminum. We measure the photonic band spectrum and distinguish the plasmonic excitation modes: bulk plasmons, surface plasmons, and Cherenkov radiation using a momentum-resolved electron energy loss spectroscopy. The observed spectrum is validated through the macroscopic electrodynamic electron energy loss theory and first-principles density functional theory calculations. In the DUV regime, intraband transitions of valence electrons dominate over the interband transitions, resulting in the observed highly dispersive surface plasmons. We further employ these surface plasmons in germanium to design a DUV radiation source based on the Smith-Purcell effect. Our work opens a new frontier of DUV plasmonics to enable the development of DUV devices such as metasurfaces, detectors, and light sources based on plasmonic germanium thin films.

Recombinase polymerase amplification assay for rapid detection of Seneca Valley Virus.

Seneca Valley virus (SVV) is related to vesicular disease in pigs, and its clinical symptoms are indistinguishable from other notifiable clinical symptoms of vesicular disease such as foot-and-mouth disease. The rapid and accurate detection of SVV is essential to confirm the pathogenic factors and initiate the implementation of control measures. The development of a rapid, simple, convenient, and low-cost molecular (nucleic acid amplification) test that can be used at the sample collection point has been identified as a key component for controlling SVV. This study describes the development and demonstration of recombinase polymerase amplification (RPA) test targeting the conserved regions of SVV for detection of SVV. The Primers and probes designed by us have shown good sensitivity and specificity in RPA test, which is helpful for RPA to be an effective tool for rapid diagnosis of SVV.

Entropy Generation Analysis of the Flow Boiling in Microgravity Field.

Entropy generation analysis of the flow boiling in microgravity field is conducted in this paper. A new entropy generation model based on the flow pattern and the phase change process is developed in this study. The velocity ranges from 1 m/s to 4 m/s, and the heat flux ranges from 10,000 W/m2 to 50,000 W/m2, so as to investigate their influence on irreversibility during flow boiling in the tunnel. A phase-change model verified by the Stefan problem is employed in this paper to simulate the phase-change process in boiling. The numerical simulations are carried out on ANSYS-FLUENT. The entropy generation produced by the heat transfer, viscous dissipation, turbulent dissipation, and phase change are observed at different working conditions. Moreover, the Be number and a new evaluation number, EP, are introduced in this paper to investigate the performance of the boiling phenomenon. The following conclusions are obtained: (1) a high local entropy generation will be obtained when only heat conduction in vapor occurs near the hot wall, whereas a low local entropy generation will be obtained when heat conduction in water or evaporation occurs near the hot wall; (2) the entropy generation and the Be number are positively correlated with the heat flux, which indicates that the heat transfer entropy generation becomes the major contributor of the total entropy generation with the increase of the heat flux; (3) the transition of the boiling status shows different trends at different velocities, which affects the irreversibility in the tunnel; (4) the critical heat flux (CHF) is the optimal choice under the comprehensive consideration of the first law and the second law of the thermodynamics.

Relationship Between Serum Severe Acute Respiratory Syndrome Coronavirus 2 Nucleic Acid and Organ Damage in Coronavirus 2019 Patients: A Cohort Study.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide and has the ability to damage multiple organs. However, information on serum SARS-CoV-2 nucleic acid (RNAemia) in patients affected by coronavirus disease 2019 (COVID-19) is limited. METHODS: Patients who were admitted to Zhongnan Hospital of Wuhan University with laboratory-confirmed COVID-19 were tested for SARS-COV-2 RNA in serum from 28 January 2020 to 9 February 2020. Demographic data, laboratory and radiological findings, comorbidities, and outcomes data were collected and analyzed. RESULTS: Eighty-five patients were included in the analysis. The viral load of throat swabs was significantly higher than of serum samples. The highest detection of SARS-CoV-2 RNA in serum samples was between 11 and 15 days after symptom onset. Analysis to compare patients with and without RNAemia provided evidence that computed tomography and some laboratory biomarkers (total protein, blood urea nitrogen, lactate dehydrogenase, hypersensitive troponin I, and D-dimer) were abnormal and that the extent of these abnormalities was generally higher in patients with RNAemia than in patients without RNAemia. Organ damage (respiratory failure, cardiac damage, renal damage, and coagulopathy) was more common in patients with RNAemia than in patients without RNAemia. Patients with vs without RNAemia had shorter durations from serum testing SARS-CoV-2 RNA. The mortality rate was higher among patients with vs without RNAemia. CONCLUSIONS: In this study, we provide evidence to support that SARS-CoV-2 may have an important role in multiple organ damage. Our evidence suggests that RNAemia has a significant association with higher risk of in-hospital mortality.

Development of functional connectivity within and among the resting-state networks in anesthetized rhesus monkeys.

OBJECTIVE: The age-related changes in the resting-state networks (RSNs) exhibited temporally specific patterns in humans, and humans and rhesus monkeys have similar RSNs. We hypothesized that the RSNs in rhesus monkeys experienced similar developmental patterns as humans. METHODS: We acquired resting-state fMRI data from 62 rhesus monkeys, which were divided into childhood, adolescence, and early adulthood groups. Group independent component analysis (ICA) was used to identify monkey RSNs. We detected the between-group differences in the RSNs and static, dynamic, and effective functional network connections (FNCs) using one-way variance analysis (ANOVA) and post-hoc analysis. RESULTS: Eight rhesus RSNs were identified, including cerebellum (CN), left and right lateral visual (LVN and RVN), posterior default mode (pDMN), visuospatial (VSN), frontal (FN), salience (SN), and sensorimotor networks (SMN). In internal connections, the CN, SN, FN, and SMN mainly matured in early adulthood. The static FNCs associated with FN, SN, pDMN primarily experienced fast descending slow ascending type (U-shaped) developmental patterns for maturation, and the dynamic FNCs related to pDMN (RVN, CN, and SMN) and SMN (CN) were mature in early adulthood. The effective FNC results showed that the pDMN and VSN (stimulated), SN (inhibited), and FN (first inhibited then stimulated) chiefly matured in early adulthood. CONCLUSION: We identified eight monkey RSNs, which exhibited similar development patterns as humans. All the RSNs and FNCs in monkeys were not widely changed but fine-tuned. Our study clarified that the progressive synchronization, exploration, and regulation of cognitive RSNs within the pDMN, FN, SN, and VSN denoted potential maturation of the RSNs throughout development. We confirmed the development patterns of RSNs and FNCs would support the use of monkeys as a best animal model for human brain function.

Defect Engineering Enables Synergistic Action of Enzyme-Mimicking Active Centers for High-Efficiency Tumor Therapy.

Perusing redox nanozymes capable of disrupting cellular homeostasis offers new opportunities to develop cancer-specific therapy, but remains challenging, because most artificial enzymes lack enzyme-like scale and configuration. Herein, for the first time, we leverage a defect engineering strategy to develop a simple yet efficient redox nanozyme by constructing enzyme-mimicking active centers and investigated its formation and catalysis mechanism thoroughly. Specifically, the partial Fe doping in MoOx (donated as Fe-MoOv) was demonstrated to activate structure reconstruction with abundant defect site generation, including Fe substitution and oxygen vacancy (OV) defects, which significantly enable the binding capacity and catalytic activity of Fe-MoOv nanozymes in a synergetic fashion. More intriguingly, plenty of delocalized electrons appear due to Fe-facilitated band structure reconstruction, directly contributing to the remarkable surface plasmon resonance effect in the near-infrared (NIR) region. Under NIR-II laser irradiation, the designed Fe-MoOv nanozymes are able to induce substantial disruption of redox and metabolism homeostasis in the tumor region via enzyme-mimicking cascade reactions, thus significantly augmenting therapeutic effects. This study that takes advantage of defect engineering offers new insights into developing high-efficiency redox nanozymes.

A multi-dimensional integration (MDI) strategy for MR T2 * mapping.

MRI signals are intrinsically multi-dimensional, and signal behavior may be orthogonal among different dimensions. Such dimensional orthogonality can be utilized to eliminate unwanted effects and facilitate mathematical simplicity during image processing for improved outcomes. In this work, we will demonstrate and analyze the principles and performance of a newly developed multi-dimensional integration (MDI) strategy in MR T2 * mapping. By constructing a complex signal function to extract the inter-echo signal changes, MDI solves an optimization problem by processing all signal dimensions (eg echoes, flip angles and coil channels) in one integrative step. MDI was compared with routine curve fitting methods on noise behavior, quantification accuracy and computational efficiency. All methods were tested and compared on simulation, phantom and knee data. Monte Carlo simulations were performed on simulation and all MRI data to investigate noise propagation from k space to T2 * maps. For phantom tests, T2 * values in regions of interest were extracted on a voxel-wise basis and analyzed using a paired t-test between scanning parameters and mapping methods, with p < 0.05 being significantly different. MDI facilitated a straightforward processing procedure, yielding homogeneous, high-signal-to-noise-ratio (SNR) and artifact-free T2 * maps without explicit coil combination or additional measures. Compared with routine fitting methods, MDI offered significantly (p < 0.05) improved SNR and quantitative accuracy/robustness, with two to three orders higher computational efficiency. MDI also represented low-SNR signals with low T2 * values, avoiding misinterpretation with long-T2 * species.

Active remote sensing of atmospheric dust using relationships between their depolarization ratios and reflectivity.

The backscattered light from agglomerated debris particles shows that an approximate linear correlation exists between the logarithm of the geometric albedo $ \log(A )$ of polydispersions of agglomerated debris particles and their lidar linear or circular depolarization ratios, $ \unicode{x00B5}_L$ and $ \unicode{x00B5}_C$. The nature of the relationship depends on the complex refractive index of the particles in the distribution. This extension of the Umov law can be used for lidar and radar characterizations by placing constraints on the reflectivity of the particles. It suggests that an approximate inverse relationship exists between the lidar ratio and the lidar depolarization ratios whose scaling parameter depends on the refractive index of the aerosol population.

Glioma grading, molecular feature classification, and microstructural characterization using MR diffusional variance decomposition (DIVIDE) imaging.

OBJECTIVE: To evaluate the potential of diffusional variance decomposition (DIVIDE) for grading, molecular feature classification, and microstructural characterization of gliomas. MATERIALS AND METHODS: Participants with suspected gliomas underwent DIVIDE imaging, yielding parameter maps of fractional anisotropy (FA), mean diffusivity (MD), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), total mean kurtosis (MKT), MKA/MKT, and microscopic fractional anisotropy (µFA). Tumor type and grade, isocitrate dehydrogenase (IDH) 1/2 mutant status, and the Ki-67 labeling index (Ki-67 LI) were determined after surgery. Statistical analysis included 33 high-grade gliomas (HGG) and 17 low-grade gliomas (LGG). Tumor diffusion metrics were compared between HGG and LGG, among grades, and between wild and mutated IDH types using appropriate tests according to normality assessment results. Receiver operating characteristic and Spearman correlation analysis were also used for statistical evaluations. RESULTS: FA, MD, MKA, MKI, MKT, µFA, and MKA/MKT differed between HGG and LGG (FA: p = 0.047; MD: p = 0.037, others p < 0.001), and among glioma grade II, III, and IV (FA: p = 0.048; MD: p = 0.038, others p < 0.001). All diffusion metrics differed between wild-type and mutated IDH tumors (MKI: p = 0.003; others: p < 0.001). The metrics that best discriminated between HGG and LGGs and between wild-type and mutated IDH tumors were MKT and FA respectively (area under the curve 0.866 and 0.881). All diffusion metrics except FA showed significant correlation with Ki-67 LI, and MKI had the highest correlation coefficient (rs = 0.618). CONCLUSION: DIVIDE is a promising technique for glioma characterization and diagnosis. KEY POINTS: • DIVIDE metrics MKI is related to cell density heterogeneity while MKA and µFA are related to cell eccentricity. • DIVIDE metrics can effectively differentiate LGG from HGG and IDH mutation from wild-type tumor, and showed significant correlation with the Ki-67 labeling index. • MKI was larger than MKA which indicates predominant cell density heterogeneity in gliomas. • MKA and MKI increased with grade or degree of malignancy, however with a relatively larger increase in the cell eccentricity metric MKA in relation to the cell density heterogeneity metric MKI.

Behcet's disease with peripheral nervous system involvement successfully treated with golimumab: a case report and review of the literature.

Nervous system involvement is a rare and serious complication of Behcet's disease (BD), and the peripheral type is rarer. This article aimed to describe a case of BD with the peripheral nervous system (PNS) involvement and present a comprehensive literature review. One case of BD with PNS involvement was reported and related literature was retrospectively reviewed via PubMed/MEDLINE and Scopus database. The patient was resistant to traditional treatments, such as glucocorticoids and immunosuppressants, but had rapid quiescence after using golimumab. Our literature review suggests that the involved peripheral nerves in BD were diverse, the most common were the tibial nerves and peroneal nerves, vasculitis might be the main cause, and prednisone was still the cornerstone of treatment. TNF-α inhibitors have been increasingly used for refractory BD in recent years. This well-illustrated case demonstrates the potential benefit of golimumab to the patient with PNS involvement. Given the diversity and complexity of PNS involvement, we recommend golimumab as a new trial treatment in clinical practice.

Semantic Segmentation Leveraging Simultaneous Depth Estimation.

Semantic segmentation is one of the most widely studied problems in computer vision communities, which makes a great contribution to a variety of applications. A lot of learning-based approaches, such as Convolutional Neural Network (CNN), have made a vast contribution to this problem. While rich context information of the input images can be learned from multi-scale receptive fields by convolutions with deep layers, traditional CNNs have great difficulty in learning the geometrical relationship and distribution of objects in the RGB image due to the lack of depth information, which may lead to an inferior segmentation quality. To solve this problem, we propose a method that improves segmentation quality with depth estimation on RGB images. Specifically, we estimate depth information on RGB images via a depth estimation network, and then feed the depth map into the CNN which is able to guide the semantic segmentation. Furthermore, in order to parse the depth map and RGB images simultaneously, we construct a multi-branch encoder-decoder network and fuse the RGB and depth features step by step. Extensive experimental evaluation on four baseline networks demonstrates that our proposed method can enhance the segmentation quality considerably and obtain better performance compared to other segmentation networks.