Microtubule-binding protein MAP1B regulates interstitial axon branching of cortical neurons via the tubulin tyrosination cycle.

Regulation of directed axon guidance and branching during development is essential for the generation of neuronal networks. However, the molecular mechanisms that underlie interstitial (or collateral) axon branching in the mammalian brain remain unresolved. Here, we investigate interstitial axon branching in vivo using an approach for precise labeling of layer 2/3 callosal projection neurons (CPNs). This method allows for quantitative analysis of axonal morphology at high acuity and also manipulation of gene expression in well-defined temporal windows. We find that the GSK3ß serine/threonine kinase promotes interstitial axon branching in layer 2/3 CPNs by releasing MAP1B-mediated inhibition of axon branching. Further, we find that the tubulin tyrosination cycle is a key downstream component of GSK3ß/MAP1B signaling. These data suggest a cell-autonomous molecular regulation of cortical neuron axon morphology, in which GSK3ß can release a MAP1B-mediated brake on interstitial axon branching upstream of the posttranslational tubulin code.

Microenvironment with NIR-Controlled ROS and Mechanical Tensions for Manipulating Cell Activities in Wound Healing.

The extracellular matrix (ECM) orchestrates cell behavior and tissue regeneration by modulating biochemical and mechanical signals. Manipulating cell-material interactions is crucial for leveraging biomaterials to regulate cell functions. Yet, integrating multiple cues in a single material remains a challenge. Here, near-infrared (NIR)-controlled multifunctional hydrogel platforms, named PIC/CM@NPs, are introduced to dictate fibroblast behavior during wound healing by tuning the matrix oxidative stress and mechanical tensions. PIC/CM@NPs are prepared through cell adhesion-medicated assembly of collagen-like polyisocyanide (PIC) polymers and cell-membrane-coated conjugated polymer nanoparticles (CM@NPs), which closely mimic the fibrous structure and nonlinear mechanics of ECM. Upon NIR stimulation, PIC/CM@NPs composites enhance fibroblast cell proliferation, migration, cytokine production, and myofibroblast activation, crucial for wound closure. Moreover, they exhibit effective and toxin removal antibacterial properties, reducing inflammation. This multifunctional approach accelerates healing by 95%, highlighting the importance of integrating biochemical and biophysical cues in the biomaterial design for advanced tissue regeneration.

Porous Agarose Layered Magnetic Graphene Oxide Nanocomposite for Virus RNA Monitoring in Wastewater.

The detection of virus RNA in wastewater has been established as a valuable method for monitoring Coronavirus disease 2019. Carbon nanomaterials hold potential application in separating virus RNA owing to their effective adsorption and extraction capabilities. However, carbon nanomaterials have limited separability under homogeneous aqueous conditions. Due to the stabilities in their nanostructure, it is a challenge to efficiently immobilize them onto magnetic beads for separation. Here, we develop a porous agarose layered magnetic graphene oxide (GO) nanocomposite that is prepared by agglutinating ferroferric oxide (Fe3O4) beads and GO with agarose into a cohesive whole. With an average porous size of approximately 500 nm, the porous structure enables the unhindered entry of virus RNA, facilitating its interaction with the surface of GO. Upon the application of a magnetic field, the nucleic acid can be separated from the solution within a few minutes, achieving adsorption efficiency and recovery rate exceeding 90% under optimized conditions. The adsorbed nucleic acid can then be preserved against complex sample matrix for 3 days, and quantitatively released for subsequent quantitative reverse transcription polymerase chain reaction (RT-qPCR) detection. The developed method was successfully utilized to analyze wastewater samples obtained from a wastewater treatment plant, detecting as few as 10 copies of RNA molecules per sample. The developed aMGO-RT-qPCR provides an efficient approach for monitoring viruses and will contribute to wastewater-based surveillance of community infections.

Using deep learning to accelerate magnetic resonance measurements of molecular exchange.

Real-time monitoring and quantitative measurement of molecular exchange between different microdomains are useful to characterize the local dynamics in porous media and biomedical applications of magnetic resonance. Diffusion exchange spectroscopy (DEXSY) is a noninvasive technique for such measurements. However, its application is largely limited by the involved long acquisition time and complex parameter estimation. In this study, we introduce a physics-guided deep neural network that accelerates DEXSY acquisition in a data-driven manner. The proposed method combines sampling pattern optimization and physical parameter estimation into a unified framework. Comprehensive simulations and experiments based on a two-site exchange system are conducted to demonstrate this new sampling optimization method in terms of accuracy, repeatability, and efficiency. This general framework can be adapted for other molecular exchange magnetic resonance measurements.

Convolutional neural network for accelerating the computation of the extended Tofts model in dynamic contrast-enhanced magnetic resonance imaging.

Quantitative physiological parameters can be obtained from nonlinear pharmacokinetic models, such as the extended Tofts (eTofts) model, applied to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). However, the computation of such nonlinear models is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for accelerating the computation of fitting eTofts model without sacrificing agreement with conventional nonlinear-least-square (NLLS) fitting. This was a retrospective study, which included 13 patients with brain glioma for training (75%) and validation (25%), and 11 patients (three glioma, four brain metastases, and four lymphoma) for testing. CAIPIRINHA-Dixon-TWIST DCE-MRI and double flip angle T1 map acquired at 3 T were used. A CNN with both local pathway and global pathway modules was designed to estimate the eTofts model parameters, the volume transfer constant (Ktrans ), blood volume fraction (vp ), and volume fraction of extracellular extravascular space (ve ), from DCE-MRI data of tumor and normal-appearing voxels. The CNN was trained on mixed dataset consisting of synthetic and patient data. The CNN result and computation speed were compared with NLLS fitting. The robustness to noise variations and generalization to brain metastases and lymphoma data were also evaluated. Statistical tests used were Student's t test on mean absolute error, concordance correlation coefficient (CCC), and normalized root mean squared error. Including global pathway modules in the CNN and training the network with mixed data significantly (p < 0.05) improved the CNN performance. Compared with NLLS fitting, CNN yields an average CCC greater than 0.986 for Ktrans , greater than 0.965 for vp , and greater than 0.948 for ve . The CNN accelerated computation speed approximately 2000 times compared to NLLS, showed robustness to noise (signal-to-noise ratio >34.42 dB), and had no significant (p > 0.21) difference applied to brain metastases and lymphoma data. In conclusion, the proposed CNN to estimate eTofts parameters showed comparable result as NLLS fitting while significantly reducing the computation time. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 1.

Immobilization of horseradish peroxidase on ZnO nanowires/macroporous SiO2 composites for the complete decolorization of anthraquinone dyes.

A zinc oxide (ZnO) nanowires/macroporous silicon dioxide composite was used as support to immobilize horseradish peroxidase (HRP) simply by in situ cross-linking method. As cross-linker was adsorbed on the surface of ZnO nanowires, the cross-linked HRP was quite different from the traditional cross-linking enzyme aggregates on both structure and catalytic performance. Among three epoxy compounds, diethylene glycol diglycidyl ether (DDE) was the best cross-linker, with which the loading amount of HRP with pI of 5.3 reached as high as 118.1 mg/g and specific activity was up to 14.9 U/mg-support. The mass loss of HRP cross-linked with DDE was negligible during 50-H leaching at 4 °C, and the thermal stability of the immobilized HRP was also quite good. The catalytic performance of immobilized HRP to decolorize anthraquinone dye was explored by using Reactive Blue 19 (RB 19) and Acid Violet 109 (AV 109) as model substrates. The results indicated that the immobilized HRP exhibited high decolorization efficiency and good reusability. The decolorization efficiency reached 94.3% and 95.9% for AV 109 and RB 19 within the first 30 Min, respectively. A complete decolorization of these two dyes has been realized within 2-3 H by using this new biocatalysis system.

Efficacy of polysaccharide iron complex in IDA rats: A comparative study with iron protein succinylate and ferrous succinate.

Iron deficiency anemia (IDA) is the most common nutrient-related health problem in the world. There is still a lack of comprehensive comparative study on the efficacies of commonly used iron supplements such as polysaccharide iron complex (PIC), iron protein succinylate (IPS) and ferrous succinate (FS) for IDA. In this study, we compared the PIC, IPS and FS efficacies in IDA rats via intragastric administration. The results showed that the three iron supplements had similar efficacies. PIC/IPS/FS at a dose of 15 mg Fe/kg/d for 10 d increased the hematological and serum biochemical parameters to 2.15/2.12/2.18 (Hb), 1.71/1.67/1.69 (RBC), 2.10/2.11/2.12 (HCT), 1.26/1.22/1.22 (MCV), all 1.34 (MCH), 1.15/1.15/1.14 (MCHC), 1.94/1.82/1.91 (SF), 9.75/9.67/9.53 (SI), and 23.30/22.68/21.64 (TS) times, and reduced TIBC to 0.42/0.43/0.44 times, compared to untreated IDA rats. PIC performed slightly better than IPS and FS in restoring MCV level. Meanwhile, the heart, spleen and kidney coefficients reduced to 67%/74%/65% (heart), all 59% (spleen) and 87%/88%/88% (kidney), and the liver coefficient increased to 116%/115%/116%, compared to untreated IDA rats. The liver iron content was found to be more affected by IDA than the spleen iron content. PIC/IPS/FS at 15 mg Fe/kg/d increased organ iron contents to 4.20/3.97/4.03 times (liver) and 1.36/1.24/1.41 times (spleen) within 10 d compared to untreated IDA rats, and PIC-H and FS were slightly better than IPS in restoring spleen iron content. The results of this study can provide useful data information for the comparison of three iron supplements, PIC, IPS and FS.

Uncommon presentation of gastric mucosa-associated lymphoid tissue lymphoma in a 13-year-old girl: acute vomiting of blood as the initial symptom.

Introduction and importance: Extranodal marginal zone lymphoma (EMZL lymphoma), also known as mucosa-associated lymphoid tissue (MALT) lymphoma, is a rare B-cell lymphoma that rarely affects children. The involvement of infectious agents, especially H. pylori, has been observed in the formation and progression of MALT lymphoma in the stomach. Hematemesis as the primary clinical manifestation is uncommon, highlighting the need for case studies with this presentation. This article uses SCARE2023 criteria as a framework to sort out a case report in order. Case presentation: A 13-year-old female patient was admitted in August 2022 with an episode of hematemesis. She had a prior diagnosis of anaemia and was found positive for H. pylori. Despite treatment, she developed symptoms of chronic non-atrophic gastritis and had recurring episodes of hematemesis. Physical and diagnostic examinations revealed B-cell lymphoma localized in the gastric antrum. The primary diagnosis was extranodal MALT lymphoma with unique plasma cell differentiation. Clinical discussion: The presentation of gastric MALT lymphoma can be variable, with definitive diagnosis often achieved via endoscopic biopsy. H. pylori plays a significant role in the onset and progression of this lymphoma, emphasizing the importance of its eradication for treatment. Effective outcomes can be achieved through anti-H. pylori treatment, although it is essential for clinicians to ensure its complete eradication post-treatment. Conclusion: Paediatric presentation of gastric MALT lymphoma, especially with hematemesis as the primary symptom, is rare and can be easily misdiagnosed. Compared to adults, children generally exhibit a better prognosis with effective H. pylori treatment. It is vital for medical professionals to recognize the differences in presentation between children and adults to ensure accurate diagnosis and treatment.

Normalizing Flow-Based Distribution Estimation of Pharmacokinetic Parameters in Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

OBJECTIVE: The pharmacokinetic (PK) parameters estimated from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide valuable information for clinical research and diagnosis. However, these estimated PK parameters suffer from many sources of variability. Thus, the estimation of the posterior distributions of these PK parameters could provide a way to simultaneously quantify the values and uncertainties of the PK parameters. Our objective is to develop an efficient and flexible method to more closely approximate and estimate the underlying posterior distributions of the PK parameters. METHODS: The normalizing flow model-based parameters distribution estimation neural network (FPDEN) is proposed to adaptively learn and estimate the posterior distributions of the PK parameters. The maximum likelihood estimation (MLE) loss is directly constructed based on the parameter distributions learned by the normalizing flow model, rather than pre-defined distributions. RESULTS: Experimental analysis shows that the proposed method can improve parameter estimation accuracy. Moreover, the uncertainty derived from the parameter distribution constitutes an effective indicator to exclude unreliable parametric results. A successful demonstration is the improved classification performance of the glioma World Health Organization (WHO) grading task, specifically in terms of distinguishing between low and high grades, as well as between Grade III and Grade IV. CONCLUSION: The FPDEN method offers improved accuracy for estimation of PK parameters and boosts the performance of the glioma grading task. SIGNIFICANCE: By enhancing the precision and reliability of DCE-MRI, the proposed method promotes its further applications in clinical practice.

Effect of heat-moisture treatment (HMT) on thermal stability of starch gel and the surface adhesiveness of vermicelli.

The molecular structure has an important influence on the surface adhesion of starch gel. In the present study, the surface adhesiveness of vermicelli after cooking was reduced by heat-moisture treatment (HMT), and the mechanism underlying the increased thermal stability was explored by measuring the changes in short-range order, crystallinity, the thickness of the crystalline layer, and the length of the double helix in the dry starch gel. The surface adhesiveness decreased by 72.12 % when the moisture content was 26 %. HMT increased the crystallinity, and the thickness of the crystalline layer of the starch gel increased from 14.61 nm to 14.83-17.30 nm at 20-26 % moisture content. The molecular rearrangement and destruction of unstable short double helixes increased the proportion of long double helixes, resulting in an increased crystallinity and layer thickness.

Efficacy of Faecal Microbiota Transplantation for the Treatment of Autism in Children: Meta-Analysis of Randomised Controlled Trials.

Objective: Evidence-based research methods were applied to assess the efficacy of faecal microbiota transplantation (FMT) for the treatment of autism in children. Methods: We searched the Chinese Biomedical Literature, CNKI, Wanfang, PubMed, Embase, Web of Science, and the Cochrane Library databases to collect randomised controlled trials on faecal microbiota transplantation for the treatment of autism in children. The search included studies published from the creation of the respective database to 5 April 2022. Literature screening, data extraction, and quality evaluation were implemented by three investigators according to the inclusion and exclusion criteria. The meta-analysis was performed using the RevMan 5.1 software. Results: Nine studies with population-based subjects and four studies with animal-based subjects were included. Five papers were screened for the meta-analysis. The results showed that FMT markedly reduced Autism Behaviour Checklist (ABC) scores in children with autism spectrum disorder (weighted mean difference (WMD) = -14.96; 95% confidence intervals (CI), -21.68 to -8.24; P < 0.001; I 2 = 0%). FMT also reduced Childhood Autism Rating Scale (CARS) scores (WMD = -6.95; 95% CI, -8.76 to -5.14; P < 0.001; I 2 = 28.1%). Conclusion: Our results indicate that FMT can benefit children with autism by reducing ABC and CARS scores, but more high-quality studies are needed to verify these results.

Fe(II)/LXQ-10 bifunctional resin materials for boosting synergistic adsorption/oxidation of benzene in industrial waste gas.

A series of adsorption/oxidation bifunctional material with different Fe(II) loading amounts was prepared by using ultrahigh crosslinking adsorption resin (LXQ-10) as a carrier and FeCl2 as an impregnating solution. The bifunctional material was characterized by BET, SEM, XRD, XPS, and EPR. The effects of Fe loading, reaction temperature, and space velocity on benzene adsorption efficiency were investigated using self-made experimental equipment to explore the optimal reaction condition. The adsorption results were fitted and analyzed by using four typical models: the quasi-first-order kinetic model, the quasi-second-order kinetic model, Elovich's kinetic model, and the Weber and Morris kinetic model. The quasi-first-order kinetic model had the highest R2 value (0.998) and the best applicability. The fitting effect of the Freundlich equation (R2 = 0.997) was better than that of the Langmuir equation (R2 = 0.919). Furthermore, the effects of Fe loading, H2O2 concentration, benzene inlet concentration, and temperature on the catalytic oxidation efficiency of benzene were studied. The catalytic oxidation efficiency of 3-Fe(II)/LXQ-10 was maintained at about 95% at a temperature of 303 K and an H2O2 concentration of 150 mmol/L. Compared with the adsorption efficiency, the catalytic oxidation efficiency of bifunctional resin materials in a heterogeneous Fenton system was remarkably improved and had excellent stability. A possible migration and transformation path during benzene removal was proposed according to the results of the analysis of GC-MS intermediates. This study provided a novel process for the adsorption and oxidative degradation of VOCs.

Synthesizing MR Image Contrast Enhancement Using 3D High-Resolution ConvNets.

OBJECTIVE: Gadolinium-based contrast agents (GBCAs) have been widely used to better visualize disease in brain magnetic resonance imaging (MRI). However, gadolinium deposition within the brain and body has raised safety concerns about the use of GBCAs. Therefore, the development of novel approaches that can decrease or even eliminate GBCA exposure while providing similar contrast information would be of significant use clinically. METHODS: In this work, we present a deep learning based approach for contrast-enhanced T1 synthesis on brain tumor patients. A 3D high-resolution fully convolutional network (FCN), which maintains high resolution information through processing and aggregates multi-scale information in parallel, is designed to map pre-contrast MRI sequences to contrast-enhanced MRI sequences. Specifically, three pre-contrast MRI sequences, T1, T2 and apparent diffusion coefficient map (ADC), are utilized as inputs and the post-contrast T1 sequences are utilized as target output. To alleviate the data imbalance problem between normal tissues and the tumor regions, we introduce a local loss to improve the contribution of the tumor regions, which leads to better enhancement results on tumors. RESULTS: Extensive quantitative and visual assessments are performed, with our proposed model achieving a PSNR of 28.24 dB in the brain and 21.2 dB in tumor regions. CONCLUSION AND SIGNIFICANCE: Our results suggest the potential of substituting GBCAs with synthetic contrast images generated via deep learning.

Research Progress in the Design and Synthesis of Herbicide Safeners: A Review.

Detoxification plays an important role in herbicide action. Herbicide safeners selectively protect crops from herbicide injury without reducing the herbicidal efficiency against the target weeds. With the large-scale use of herbicides, herbicide safeners have been widely used in sorghum, wheat, rice, corn, and other crops. In recent years, an increasing number of unexpected new herbicide safeners have been designed. The varieties, structural characteristics, uses, and synthetic routes of commercial herbicide safeners are reviewed in this paper. The design ideas and structural characteristics of novel herbicide safeners are summarized, which provide a basis for the design of bioactive molecules as new herbicide safeners in the future.

The Effect of Acidity Coefficient on the Crystallization Properties and Viscosity of Modified Blast Furnace Slag for Mineral Wool Production.

The crystallization and viscosity of modified blast furnace slag are key factors in fiber forming conditions. In this paper, the crystallization behavior of modified blast furnace slag under continuous cooling conditions was studied by differential scanning calorimetry, and its crystallization kinetics with different acidity coefficients were established. On this basis, the evolution law of the crystallization phase and the influence of crystallization on the viscosity of modified blast furnace slag with different acidity coefficients were analyzed. The results indicated that the crystallization phases of slag with acidity coefficients of 1.05 and 1.20 were, respectively, Melilite and Anorthite. During the cooling process at the acidity coefficient of 1.05, the critical rates of precipitation of Melilite and Anorthite were 50 °C/s and 20 °C/s, respectively, while they were 20 °C/s and 15 °C/s, respectively, at the acidity coefficient of 1.20. With the increase of the acidity coefficient, the crystal growth mode of slag changed from two-dimensional and three-dimensional mixed crystallization to surface nucleation and one-dimensional crystallization. The crystallization activation energy of slag with acidity coefficients of 1.05 and 1.20 were 698.14 kJ/mol and 1292.50 kJ/mol, respectively. In addition, the change trend of viscosity was related to crystal size and content.

Self-assembled Cupric Oxide Nanoclusters for Highly efficient chemodynamic therapy.

Chemodynamic therapy (CDT) based on Fenton and Fenton-like reactions induces cancer cell killing via in situ catalyzing H2 O2 and generating highly oxidative hydroxyl radicals (⋅OH) in tumor sites. Their application is not limited by tumor grown depth or hypoxic microenvironment. However, the reaction efficiency is still hampered due to the structure of catalytic agents and the requirement for low pH environment. Here, we design a porous CuO nanocluster (CuO NC) through self-assembly of oleylamine stabilized CuO NPs (OAm-CuO NPs), and functionalize it with folic acid (CuO NC-FA) for specific tumor cell targeting. It can catalyze H2 O2 with high efficiency in nearly neutral environment. Besides, the porous structure of CuO NC also helps the diffusion of H2 O2 to the interior of nanocluster to further improve Fenton-like reaction efficiency. The convenient synthesis of CuO NC-FA with good Fenton-like reaction efficiency at neutral environment demonstrates good chemodynamic therapy effect.

Attention-Guided Discriminative Region Localization and Label Distribution Learning for Bone Age Assessment.

Bone age assessment (BAA) is clinically important as it can be used to diagnose endocrine and metabolic disorders during child development. Existing deep learning based methods for classifying bone age use the global image as input, or exploit local information by annotating extra bounding boxes or key points. However, training with the global image underutilizes discriminative local information, while providing extra annotations is expensive and subjective. In this paper, we propose an attention-guided approach to automatically localize the discriminative regions for BAA without any extra annotations. Specifically, we first train a classification model to learn the attention maps of the discriminative regions, finding the hand region, the most discriminative region (the carpal bones), and the next most discriminative region (the metacarpal bones). Guided by those attention maps, we then crop the informative local regions from the original image and aggregate different regions for BAA. Instead of taking BAA as a general regression task, which is suboptimal due to the label ambiguity problem in the age label space, we propose using joint age distribution learning and expectation regression, which makes use of the ordinal relationship among hand images with different individual ages and leads to more robust age estimation. Extensive experiments are conducted on the RSNA pediatric bone age data set. Without using extra manual annotations, our method achieves competitive results compared with existing state-of-the-art deep learning-based methods that require manual annotation. Code is available at https://github.com/chenchao666/Bone-Age-Assessment.

2D probabilistic undersampling pattern optimization for MR image reconstruction.

With 3D magnetic resonance imaging (MRI), a tradeoff exists between higher image quality and shorter scan time. One way to solve this problem is to reconstruct high-quality MRI images from undersampled k-space. There have been many recent studies exploring effective k-space undersampling patterns and designing MRI reconstruction methods from undersampled k-space, which are two necessary steps. Most studies separately considered these two steps, although in theory, their performance is dependent on each other. In this study, we propose a joint optimization model, trained end-to-end, to simultaneously optimize the undersampling pattern in the Fourier domain and the reconstruction model in the image domain. A 2D probabilistic undersampling layer was designed to optimize the undersampling pattern and probability distribution in a differentiable manner. A 2D inverse Fourier transform layer was implemented to connect the Fourier domain and the image domain during the forward and back propagation. Finally, we discovered an optimized relationship between the probability distribution of the undersampling pattern and its corresponding sampling rate. Further testing was performed using 3D T1-weighted MR images of the brain from the MICCAI 2013 Grand Challenge on Multi-Atlas Labeling dataset and locally acquired brain 3D T1-weighted MR images of healthy volunteers and contrast-enhanced 3D T1-weighted MR images of high-grade glioma patients. The results showed that the recovered MR images using our 2D probabilistic undersampling pattern (with or without the reconstruction network) significantly outperformed those using the existing start-of-the-art undersampling strategies for both qualitative and quantitative comparison, suggesting the advantages and some extent of the generalization of our proposed method.

Development and validation of a meta-learning-based multi-modal deep learning algorithm for detection of peritoneal metastasis.

PURPOSE: The existing medical imaging tools have a detection accuracy of 97% for peritoneal metastasis(PM) bigger than 0.5 cm, but only 29% for that smaller than 0.5 cm, the early detection of PM is still a difficult problem. This study is aiming at constructing a deep convolution neural network classifier based on meta-learning to predict PM. METHOD: Peritoneal metastases are delineated on enhanced CT. The model is trained based on meta-learning, and features are extracted using multi-modal deep Convolutional Neural Network(CNN) with enhanced CT to classify PM. Besides, we evaluate the performance on the test dataset, and compare it with other PM prediction algorithm. RESULTS: The training datasets are consisted of 9574 images from 43 patients with PM and 67 patients without PM. The testing datasets are consisted of 1834 images from 21 testing patients. To increase the accuracy of the prediction, we combine the multi-modal inputs of plain scan phase, portal venous phase and arterial phase to build a meta-learning-based multi-modal PM predictor. The classifier shows an accuracy of 87.5% with Area Under Curve(AUC) of 0.877, sensitivity of 73.4%, specificity of 95.2% on the testing datasets. The performance is superior to routine PM classify based on logistic regression (AUC: 0.795), a deep learning method named ResNet3D (AUC: 0.827), and a domain generalization (DG) method named MADDG (AUC: 0.834). CONCLUSIONS: we proposed a novel training strategy based on meta-learning to improve the model's robustness to "unseen" samples. The experiments shows that our meta-learning-based multi-modal PM predicting classifier obtain more competitive results in synchronous PM prediction compared to existing algorithms and the model's improvements of generalization ability even with limited data.

Using molybdenum carbiding to induce digestion of carbon in H2O2: A sustainable approach to eliminate radioactivity for hazardous graphite waste inherited from nuclear enterprise.

To properly manage nuclear wastes is critical to sustainable utilization of nuclear power and environment health. Here, we show an innovative carbiding strategy for sustainable management of radioactive graphite through digestion of carbon in H2O2. The combined action of intermolecular oxidation of graphite by MoO3 and molybdenum carbiding demonstrates success in gasifying graphite and sequestrating uranium for a simulated uranium-contaminated graphite waste. The carbiding process plays a triple role: (1) converting graphite into atomic carbon digestible in H2O2, (2) generating oxalic ligands in the presence of H2O2 to favor U-precipitation, and (3) delivering oxalic ligands to coordinate to MoVI-oxo anionic species to improve sample batching capacity. We demonstrate > 99% of uranium to be sequestrated for the simulated waste with graphite matrix completely gasifying while no detectable U-migration occurred during operation. This method has further been extended to removal of surface carbon layers for graphite monolith and thus can be used to decontaminate monolithic graphite waste with emission of a minimal amount of secondary waste. We believe this work not only provides a sustainable approach to tackle the managing issue of heavily metal contaminated graphite waste, but also indicates a promising methodology toward surface decontamination for irradiated graphite in general.