Current crowding in graphene-silicon schottky diodes.

The performance of the Graphene/Si (Gr/Si) Schottky interface and its potential in future electronics strongly rely on the quality of interconnecting contacts with external circuitry. In this work, we investigate the dominating and limiting factors of Gr/Si interfaces designed for high light absorption, paying particular attention to the nature of the contact failure under high electrostatic discharge (ESD) conditions. Our findings indicate that severe current crowding at contact edges of the graphene is the dominating factor for the device breakdown. Material degradation and electrical breakdown are systematically analyzed by atomic force, Raman, scanning electron, and energy-dispersive x-ray spectroscopies. This work enlists the robustness and limitations of Gr/Si junction in photodiode architecture under high ESD conditions that can be used as general guidelines for 2D-3D electronic and optoelectronic devices.

Improvements on sampling of point spread function in optical transfer function measurement.

Point spread function (PSF), which is the intensity distribution of optical system impulse response and usually acquired by imaging a single pinhole in experiment, can characterize the quality of optical imaging system. Faithful recording of the two-dimensional intensity distribution of PSF is key for accurate measurement of optical transfer function (OTF), however distortions in recorded PSF can be easily caused by a large sampling interval and the electronic noise of the detector. Under a given sampling interval, the position-phase difference between pixels and intensity signals can change the intensity distribution of acquired PSF remarkably, making the computed OTF or MTF (modulation transfer function) error prone. Aiming at problems existing in pinhole based MTF measurement methods, this paper developed a new method with underline physics similar to that of slanted edge method to realize sub-pixel sampling of PSF intensity by using accurate non-integer up-sampling matrix of separate binary pixels and applying random patterns shown on digital micro-mirror device (DMD) as target. Numerical simulations show that improvement on the discrete sampling of point spread function with this method is very helpful to improve anti-noise robustness and the accuracy of optical transfer function measurement.

Recognition of Upper Limb Action Intention Based on IMU.

Using motion information of the upper limb to control the prosthetic hand has become a hotspot of current research. The operation of the prosthetic hand must also be coordinated with the user's intention. Therefore, identifying action intention of the upper limb based on motion information of the upper limb is key to controlling the prosthetic hand. Since a wearable inertial sensor bears the advantages of small size, low cost, and little external environment interference, we employ an inertial sensor to collect angle and angular velocity data during movement of the upper limb. Aiming at the action classification for putting on socks, putting on shoes and tying shoelaces, this paper proposes a recognition model based on the Dynamic Time Warping (DTW) algorithm of the motion unit. Based on whether the upper limb is moving, the complete motion data are divided into several motion units. Considering the delay associated with controlling the prosthetic hand, this paper only performs feature extraction on the first motion unit and the second motion unit, and recognizes action on different classifiers. The experimental results reveal that the DTW algorithm based on motion unit bears a higher recognition rate and lower running time. The recognition rate reaches as high as 99.46%, and the average running time measures 8.027 ms. In order to enable the prosthetic hand to understand the grasping intention of the upper limb, this paper proposes a Generalized Regression Neural Network (GRNN) model based on 10-fold cross-validation. The motion state of the upper limb is subdivided, and the static state is used as the sign of controlling the prosthetic hand. This paper applies a 10-fold cross-validation method to train the neural network model to find the optimal smoothing parameter. In addition, the recognition performance of different neural networks is compared. The experimental results show that the GRNN model based on 10-fold cross-validation exhibits a high accuracy rate, capable of reaching 98.28%. Finally, the two algorithms proposed in this paper are implemented in an experiment of using the prosthetic hand to reproduce an action, and the feasibility and practicability of the algorithm are verified by experiment.

Iterative reconstruction method for the accurate measurement of optical transfer function.

An iterative computation method was proposed to accurately reconstruct the optical transfer function (OTF) of an imaging lens from a series of recorded images of different patterns displayed on a digital micromirror device (DMD). Since several tens of recorded images are adopted in combination, the measurement noise is remarkably reduced in comparison with the conventional measurement method using a point object, and quite high measurement accuracy is reached by taking the known shape of each tiny mirror of the DMD into computation. While theoretical analysis is demonstrated, the validity of this proposed method is verified both numerically and experimentally.

[Research Status and Hot Trends in Atherosclerosis Genomics between 2010 and 2019].

Objective To analyze the research status,hotspots,and frontiers of atherosclerosis genomics from 2010 to 2019.Methods CiteSpace software was used to conduct data statistics and visual analysis on countries,institutions,authors,journals,co-cited papers,and keywords of the related papers published in the Web of Science from 2010 to 2019.Results A total of 1021 papers in English were included,and the annual number of publications generally showed an upward trend.The knowledge base in the research of atherosclerosis mostly focused on the genetic risk sites and biomarkers for coronary artery diseases such as coronary heart disease,myocardial infarction,and dyslipidemia.The related journals mainly involved the fields of molecular biology,biology,genetics,immunology,medicine,pharmacy,and clinical medicine.The latest research in atherosclerosis concentrated on genome-wide association study,DNA methylation,microRNA,messenger RNA and so on.The research frontiers involved long noncoding RNA,DNA methylation,and immune metabolism.Conclusion The studies in atherosclerotic genomics have gradually increased.

Effects of Photoperiod on Acetaminophen-Induced Hepatotoxicity in Mice.

PURPOSE: Acetaminophen (APAP) is a clinically popular analgesic and antipyretic drug, but excessive APAP can cause fatal hepatotoxicity. Many factors affect the degree of APAP-induced liver injury. This study aimed to investigate how circadian rhythm affects the development of APAP-induced hepatotoxicity and to clarify the roles of photoperiod and dietary rhythm on APAP-induced hepatotoxicity in mice. METHODS: APAP-induced hepatotoxicity models were established by intraperitoneal injection of APAP (400 mg/kg) to mice. The mice were then divided into three treatment groups: normal diet, reversed diet, and reversed photoperiod. RESULTS: More severe liver injury was observed at zeitgeber time 12 (ZT12) than at zeitgeber time 0 (ZT0) in all treatment groups, suggesting that photoperiod played a critical role in APAP-induced liver injury. We observed a change in the expression of the circadian gene Per2, which may be responsible for regulation of liver injury by photoperiod. Our results showed negligible change in Per2 expression with diet reversion, whereas Cry1, Cry2, and Dbp expressions were more highly affected by diet reversion than was Per2 expression. Downstream effects including liver enzyme expression, GSH level, and inflammation factors were also examined to identify the mechanism of liver injury. The results indicated that the circadian gene Per2 participated in APAP biometabolism by regulating the expression of Cyp2e1, which may explain the more severe hepatotoxicity at ZT12 than at ZT0. CONCLUSION: APAP-induced hepatotoxicity can be mediated by photoperiod through the circadian gene Per2, suggesting that medicines containing APAP should be administered not only with food but also according to the appropriate photoperiod.

MiR-140 modulates the inflammatory responses of Mycobacterium tuberculosis-infected macrophages by targeting TRAF6.

This study aimed to examine miR-140 expression in clinical samples from tuberculosis (TB) patients and to explore the molecular mechanisms of miR-140 in host-bacterial interactions during Mycobacterium tuberculosis (M tb) infections. The miR-140 expression and relevant mRNA expression were detected by quantitative real-time PCR (qRT-PCR); the protein expression levels were analysed by ELISA and western blot; M tb survival was measured by colony formation unit assay; potential interactions between miR-140 and the 3' untranslated region (UTR) of tumour necrosis factor receptor-associated factor 6 (TRAF6) was confirmed by luciferase reporter assay. MiR-140 was up-regulated in the human peripheral blood mononuclear cells (PBMCs) from TB patients and in THP-1 and U937 cells with M tb infection. Overexpression of miR-140 promoted M tb survival; on the other hand, miR-140 knockdown attenuated M tb survival. The pro-inflammatory cytokines including interleukin 6, tumour necrosis-α, interleukin-1ß and interferon-γ were enhanced by M tb infection in THP-1 and U937 cells. MiR-140 overexpression reduced these pro-inflammatory cytokines levels in THP-1 and U937 cells with M tb infection; while knockdown of miR-140 exerted the opposite actions. TRAF6 was identified to be a downstream target of miR-140 and was negatively modulated by miR-140. TRAF6 overexpression increased the pro-inflammatory cytokines levels and partially restored the suppressive effects of miR-140 overexpression on pro-inflammatory cytokines levels in THP-1 and U937 cells with M tb infection. In conclusion, our results implied that miR-140 promoted M tb survival and reduced the pro-inflammatory cytokines levels in macrophages with M tb infection partially via modulating TRAF6 expression.

[Dual NDVI Ratio Vegetation Index: A Kind of Vegetation Index Assessing Leaf Carotenoid Content Based on Leaf Optical Properties Model].

With characteristics of rapidness, non-destructiveness and high precision in detecting plant leaves, hyperspectral technology is promising in assessing the contents of leaf pigments and other biochemical components. Because the spectral absorption features of carotenoid and chlorophyll are overlapped in visible light region and that foliar carotenoid content is far lower than chlorophyll content, studies about constructing vegetation indices (VIs) for carotenoid is rare at home and abroad though carotenoid is one of the most important photosynthetic pigments. Hyperspectral data has abundant spectral information, so this paper proposed a multiple spectral indices collaborative algorithm to construct VIs on the basis of band-combination traversal and correlation analysis. Through a large number of simulated leaf reflectance spectra under different biochemical components contents run on PROSPECT model, a radiative transfer model, we successfully constructed a new kind of stable vegetation index (VI) for assessing carotenoid content at leaf level: RVIDNDVI. Our results indicate that RVIDNDVI is composed of two parts: (1)Narrow band NDVI constructed with 532 and 405 nm is high correlated with both carotenoid content and chlorophyll content while narrow band NDVI constructed with 548 and 498 nm is highly correlated with carotenoid content. The influence of chlorophyll content on RVIDNDVI can be eliminated with the ratio combination of these two indices. (2) The influence of mesophyll structure parameter can be weakened by subtracting the reflectance at 916 nm, which has strong correlation with mesophyll structure parameter. RVIDNDVIonly has high sensitivity to carotenoid content (the correlation coefficient is -0.94) at leaf level and R2 of its exponential fit is 0.834 4. The estimation of RVIDNDVIto carotenoid content can be verified with the validations of both simulated data and measured data.

First- and second-order full-differential in edge analysis of images.

Two concepts of first- and second-order differential of images are presented to deal with the changes of pixels. These are the basic ideas in mathematics. We propose and reformulate them with a uniform definition framework. Based on our observation and analysis with the difference, we propose an algorithm to detect the edge from image. Experiments on Corel5K and PASCAL VOC 2007 are done to show the difference between the first order and the second order. After comparison with Canny operator and the proposed first-order differential, the main result is that the second-order differential has the better performance in analysis of changes of the context of images with good selection of control parameter.

A primal analysis system of brain neurons data.

It is a very challenging work to classify the 86 billions of neurons in the human brain. The most important step is to get the features of these neurons. In this paper, we present a primal system to analyze and extract features from brain neurons. First, we make analysis on the original data of neurons in which one neuron contains six parameters: room type, X, Y, Z coordinate range, total number of leaf nodes, and fuzzy volume of neurons. Then, we extract three important geometry features including rooms type, number of leaf nodes, and fuzzy volume. As application, we employ the feature database to fit the basic procedure of neuron growth. The result shows that the proposed system is effective.

Assessing and Optimizing Large Language Models on Spondyloarthritis Multi-Choice Question Answering: Protocol for Enhancement and Assessment.

BACKGROUND: Spondyloarthritis (SpA), a chronic inflammatory disorder, predominantly impacts the sacroiliac joints and spine, significantly escalating the risk of disability. SpA's complexity, as evidenced by its diverse clinical presentations and symptoms that often mimic other diseases, presents substantial challenges in its accurate diagnosis and differentiation. This complexity becomes even more pronounced in nonspecialist health care environments due to limited resources, resulting in delayed referrals, increased misdiagnosis rates, and exacerbated disability outcomes for patients with SpA. The emergence of large language models (LLMs) in medical diagnostics introduces a revolutionary potential to overcome these diagnostic hurdles. Despite recent advancements in artificial intelligence and LLMs demonstrating effectiveness in diagnosing and treating various diseases, their application in SpA remains underdeveloped. Currently, there is a notable absence of SpA-specific LLMs and an established benchmark for assessing the performance of such models in this particular field. OBJECTIVE: Our objective is to develop a foundational medical model, creating a comprehensive evaluation benchmark tailored to the essential medical knowledge of SpA and its unique diagnostic and treatment protocols. The model, post-pretraining, will be subject to further enhancement through supervised fine-tuning. It is projected to significantly aid physicians in SpA diagnosis and treatment, especially in settings with limited access to specialized care. Furthermore, this initiative is poised to promote early and accurate SpA detection at the primary care level, thereby diminishing the risks associated with delayed or incorrect diagnoses. METHODS: A rigorous benchmark, comprising 222 meticulously formulated multiple-choice questions on SpA, will be established and developed. These questions will be extensively revised to ensure their suitability for accurately evaluating LLMs' performance in real-world diagnostic and therapeutic scenarios. Our methodology involves selecting and refining top foundational models using public data sets. The best-performing model in our benchmark will undergo further training. Subsequently, more than 80,000 real-world inpatient and outpatient cases from hospitals will enhance LLM training, incorporating techniques such as supervised fine-tuning and low-rank adaptation. We will rigorously assess the models' generated responses for accuracy and evaluate their reasoning processes using the metrics of fluency, relevance, completeness, and medical proficiency. RESULTS: Development of the model is progressing, with significant enhancements anticipated by early 2024. The benchmark, along with the results of evaluations, is expected to be released in the second quarter of 2024. CONCLUSIONS: Our trained model aims to capitalize on the capabilities of LLMs in analyzing complex clinical data, thereby enabling precise detection, diagnosis, and treatment of SpA. This innovation is anticipated to play a vital role in diminishing the disabilities arising from delayed or incorrect SpA diagnoses. By promoting this model across diverse health care settings, we anticipate a significant improvement in SpA management, culminating in enhanced patient outcomes and a reduced overall burden of the disease. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57001.

Introduction of defects in hexagonal boron nitride for vacancy-based 2D memristors.

Two-dimensional (2D) materials have become potential resistive switching (RS) layers to prepare emerging non-volatile memristors. The atomically thin thickness and the highly controllable defect density contribute to the construction of ultimately scaled memory cells with stable switching behaviors. Although the conductive bridge random-access memory based on 2D hexagonal boron nitride has been widely studied, the realization of RS completely relying on vacancies in 2D materials has performance superiority. Here, we synthesize carbon-doped h-BN (C-h-BN) with a certain number of defects by controlling the weight percentage of carbon powder in the source. These defects can form a vacancy-based conductive filament under an applied electric field. The memristor displays bipolar non-volatile memory with a low SET voltage of 0.85 V and shows a long retention time of up to 104 s at 120 °C. The response times of the SET and RESET process are less than 80 ns and 240 ns, respectively. The current mapping by conductive atomic force microscopy demonstrates the electric-field-induced current tunneling from defective sites of the C-h-BN flake, revealing the defect-based RS in the C-h-BN memristor. Moreover, C-h-BN with excellent flexibility can be applied to wearable devices, maintaining stable RS performance in a variety of bending environments and after multiple bending cycles. The vacancy-based 2D memristor provides a new strategy for developing ultra-scaled memory units with high controllability.

2D Ferroionics: Conductive Switching Mechanisms and Transition Boundaries in Van der Waals Layered Material CuInP2 S6.

The recently unfolded ferroionic phenomena in 2D van der Waals (vdW) copper-indium-thiophosphate (CuInP2 S6 or CIPS) have received widespread interest as they allow for dynamic control of conductive switching properties, which are appealing in the paradigm-shift computing. The intricate couplings between ferroelectric polarization and ionic conduction in 2D vdW CIPS facilitate the manipulation and dynamic control of conductive behaviors. However, the complex interplays and underlying mechanisms are not yet fully explored and understood. Here, by investigating polarization switching and ionic conduction in the temperature and applied electric field domains, it is discovered that the conducting mechanisms of CIPS can be divided into four distinctive states (or modes) with transitional boundaries, depending on the dynamics of Cu ions in the material. Further, it demonstrates that dynamically-tunable synaptic responsive behavior can be well implemented by governing the working-state transition. This research provides an in-depth, quantitative understanding of the complex phenomena of conductive switching in 2D vdW CIPS with coexisting ferroelectric order and ionic disorder. The developed insights in this work lay the ground for implementing high-performance, function-enriched devices for information processing, data storage, and neuromorphic computing based on the 2D ferroionic material systems.

T-G-A Deficiency Pattern in Protein-Coding Genes and Its Potential Reason.

If a stop codon appears within one gene, then its translation will be terminated earlier than expected. False folding of premature protein will be adverse to the host; hence, all functional genes would tend to avoid the intragenic stop codons. Therefore, we hypothesize that there will be less frequency of nucleotides corresponding to stop codons at each codon position of genes. Here, we validate this inference by investigating the nucleotide frequency at a large scale and results from 19,911 prokaryote genomes revealed that nucleotides coinciding with stop codons indeed have the lowest frequency in most genomes. Interestingly, genes with three types of stop codons all tend to follow a T-G-A deficiency pattern, suggesting that the property of avoiding intragenic termination pressure is the same and the major stop codon TGA plays a dominant role in this effect. Finally, a positive correlation between the TGA deficiency extent and the base length was observed in start-experimentally verified genes of Escherichia coli (E. coli). This strengthens the proof of our hypothesis. The T-G-A deficiency pattern observed would help to understand the evolution of codon usage tactics in extant organisms.

Anti-CRISPRdb v2.2: an online repository of anti-CRISPR proteins including information on inhibitory mechanisms, activities and neighbors of curated anti-CRISPR proteins.

We previously released the Anti-CRISPRdb database hosting anti-CRISPR proteins (Acrs) and associated information. Since then, the number of known Acr families, types, structures and inhibitory activities has accumulated over time, and Acr neighbors can be used as a candidate pool for screening Acrs in further studies. Therefore, we here updated the database to include the new available information. Our newly updated database shows several improvements: (i) it comprises more entries and families because it includes both Acrs reported in the most recent literatures and Acrs obtained via performing homologous alignment; (ii) the prediction of Acr neighbors is integrated into Anti-CRISPRdb v2.2, and users can identify novel Acrs from these candidates; and (iii) this version includes experimental information on the inhibitory strength and stage for Acr-Cas/Acr-CRISPR pairs, motivating the development of tools for predicting specific inhibitory abilities. Additionally, a parameter, the rank of codon usage bias (CUBRank), was proposed and provided in the new version, which showed a positive relationship with predicted result from AcRanker; hence, it can be used as an indicator for proteins to be Acrs. CUBRank can be used to estimate the possibility of genes occurring within genome island-a hotspot hosting potential genes encoding Acrs. Based on CUBRank and Anti-CRISPRdb, we also gave the first glimpse for the emergence of Acr genes (acrs). DATABASE URL: http://guolab.whu.edu.cn/anti-CRISPRdb.

The phyllosphere microbiome shifts toward combating melanose pathogen.

BACKGROUND: Plants can recruit beneficial microbes to enhance their ability to defend against pathogens. However, in contrast to the intensively studied roles of the rhizosphere microbiome in suppressing plant pathogens, the collective community-level change and effect of the phyllosphere microbiome in response to pathogen invasion remains largely elusive. RESULTS: Here, we integrated 16S metabarcoding, shotgun metagenomics and culture-dependent methods to systematically investigate the changes in phyllosphere microbiome between infected and uninfected citrus leaves by Diaporthe citri, a fungal pathogen causing melanose disease worldwide. Multiple microbiome features suggested a shift in phyllosphere microbiome upon D. citri infection, highlighted by the marked reduction of community evenness, the emergence of large numbers of new microbes, and the intense microbial network. We also identified the microbiome features from functional perspectives in infected leaves, such as enriched microbial functions for iron competition and potential antifungal traits, and enriched microbes with beneficial genomic characteristics. Glasshouse experiments demonstrated that several bacteria associated with the microbiome shift could positively affect plant performance under D. citri challenge, with reductions in disease index ranging from 65.7 to 88.4%. Among them, Pantoea asv90 and Methylobacterium asv41 identified as "recruited new microbes" in the infected leaves, exhibited antagonistic activities to D. citri both in vitro and in vivo, including inhibition of spore germination and/or mycelium growth. Sphingomonas spp. presented beneficial genomic characteristics and were found to be the main contributor for the functional enrichment of iron complex outer membrane receptor protein in the infected leaves. Moreover, Sphingomonas asv20 showed a stronger suppression ability against D. citri in iron-deficient conditions than iron-sufficient conditions, suggesting a role of iron competition during their antagonistic action. CONCLUSIONS: Overall, our study revealed how phyllosphere microbiomes differed between infected and uninfected citrus leaves by melanose pathogen, and identified potential mechanisms for how the observed microbiome shift might have helped plants cope with pathogen pressure. Our findings provide novel insights into understanding the roles of phyllosphere microbiome responses during pathogen challenge. Video abstract.

Multi-Stimuli-Responsive Synapse Based on Vertical van der Waals Heterostructures.

Brain-inspired intelligent systems demand diverse neuromorphic devices beyond simple functionalities. Merging biomimetic sensing with weight-updating capabilities in artificial synaptic devices represents one of the key research focuses. Here, we report a multiresponsive synapse device that integrates synaptic and optical-sensing functions. The device adopts vertically stacked graphene/h-BN/WSe2 heterostructures, including an ultrahigh-mobility readout layer, a weight-control layer, and a dual-stimuli-responsive layer. The unique structure endows synapse devices with excellent synaptic plasticity, short response time (3 µs), and excellent optical responsivity (105 A/W). To demonstrate the application in neuromorphic computing, handwritten digit recognition was simulated based on an unsupervised spiking neural network (SNN) with a precision of 90.89%, well comparable with the state-of-the-art results. Furthermore, multiterminal neuromorphic devices are demonstrated to mimic dendritic integration and photoswitching logic. Different from other synaptic devices, the research work validates multifunctional integration in synaptic devices, supporting the potential fusion of sensing and self-learning in neuromorphic networks.

[Efficacy of interferon alpha with ribavirin for treatment of chronic Hepatitis C].

OBJECTIVE: To investigate the prevalence and distribution of HCV genotypes and the clinical effect of interferon-alpha combined with ribavirin treatment in chronic hepatitis C patients in Kunming. METHODS: 60 patients were divided into two groups based on drug therapies: PEG-interferon-a plus ribavirin treatment group for HCV 1b and interferon-a plus ribavirin treatment group for non-HCV-1b. Serum ALT levels and HCV RNA quantitations of the patients were detected during treatment and follow-up. RESULTS: The HCV genotypes of 60 patients were determined by type specific probe assay, and five different types were found. Their overall prevalence were 21.7% for type 1b, 5% for type 2a, 16.7% for type 3a, 48.3% for type 3b, and 8.3% for type 6a. Sustained viral response rates for PEG-interferon treatment group were 46.1%, for interferon treatment group were 74.4%. The abnormal rate of serum ALT after the treatment had no significant difference between HCV-1b and non-HCV-1b patients (P>0.05). All patients with early viral responses got sustained viral response. CONCLUSION: HCV-3b is the most dominant genotype in Kunming. The effect of PEG-interferon-a plus ribavirin treatment for genotype 1b is unsatisfactory. The early viral response is a good predictor for the responses to antiviral therapy in chronic hepatitis C patients.

Single-electron detection utilizing coupled nonlinear microresonators.

Since the discovery of the electron, the accurate detection of electrical charges has been a dream of the scientific community. Owing to some remarkable advantages, micro/nanoelectromechanical system-based resonators have been used to design electrometers with excellent sensitivity and resolution. Here, we demonstrate a novel ultrasensitive charge detection method utilizing nonlinear coupling in two micromechanical resonators. We achieve single-electron charge detection with a high resolution up to 0.197 ± 0.056 e / Hz at room temperature. Our findings provide a simple strategy for measuring electron charges with extreme accuracy.

The Utility of Diffusion and Perfusion Magnetic Resonance Imaging in Target Delineation of High-Grade Gliomas.

BACKGROUND: The tumor volume of high-grade glioma (HGG) after surgery is usually determined by contrast-enhanced MRI (CE-MRI), but the clinical target volume remains controversial. Functional magnetic resonance imaging (multimodality MRI) techniques such as magnetic resonance perfusion-weighted imaging (PWI) and diffusion-tensor imaging (DTI) can make up for CE-MRI. This study explored the survival outcomes and failure patterns of patients with HGG by comparing the combination of multimodality MRI and CE-MRI imaging with CE-MRI alone. METHODS: 102 patients with postoperative HGG between 2012 and 2016 were included. 50 were delineated based on multimodality MRI (PWI, DTI) and CE-MRI (enhanced T1), and the other 52 were delineated based on CE-MRI as control. RESULTS: The median survival benefit was 6 months. The 2-year overall survival, progression-free survival, and local-regional control rates were 48% vs. 25%, 42% vs. 13.46%, and 40% vs. 13.46% for the multimodality MRI and CE-MRI cohorts, respectively. The two cohorts had similar rates of disease progression and recurrence but different proportions of failure patterns. The univariate analysis shows that characteristics of patients such as combined with epilepsy, the dose of radiotherapy, the selection of MRI were significant influence factors for 2-year overall survival. However, in multivariate analyses, only the selection of MRI was an independent significant predictor of overall survival. CONCLUSIONS: This study was the first to explore the clinical value of multimodality MRI in the delineation of radiotherapy target volume for HGG. The conclusions of the study have positive reference significance to the combination of multimodality MRI and CE-MRI in guiding the delineation of the radiotherapy target area for HGG patients.