Carotid Vessel Wall Segmentation Through Domain Aligner, Topological Learning, and Segment Anything Model for Sparse Annotation in MR Images.

Medical image analysis poses significant challenges due to limited availability of clinical data, which is crucial for training accurate models. This limitation is further compounded by the specialized and labor-intensive nature of the data annotation process. For example, despite the popularity of computed tomography angiography (CTA) in diagnosing atherosclerosis with an abundance of annotated datasets, magnetic resonance (MR) images stand out with better visualization for soft plaque and vessel wall characterization. However, the higher cost and limited accessibility of MR, as well as time-consuming nature of manual labeling, contribute to fewer annotated datasets. To address these issues, we formulate a multi-modal transfer learning network, named MT-Net, designed to learn from unpaired CTA and sparsely-annotated MR data. Additionally, we harness the Segment Anything Model (SAM) to synthesize additional MR annotations, enriching the training process. Specifically, our method first segments vessel lumen regions followed by precise characterization of carotid artery vessel walls, thereby ensuring both segmentation accuracy and clinical relevance. Validation of our method involved rigorous experimentation on publicly available datasets from COSMOS and CARE-II challenge, demonstrating its superior performance compared to existing state-of-the-art techniques.

Mineral transformation and solidification of heavy metals during co-melting of MSWI fly ash with coal fly ash.

Melting is an efficient method to turn municipal solid waste incineration (MSWI) fly ash (FA) into non-hazardous material. Coal fly ash (CFA) was selected as the silica-alumina source to carry out co-melting research with MSWI FA in this work. The effects of the temperature and the CFA content on mineral transformation and the migration characteristics of heavy metals were analyzed. The results showed that the mixtures of MSWI FA and CFA reacted at high temperatures to mainly generate Ca2Al2SiO7, Ca2SiO4, and CaAl2Si2O8 primarily and then melted and formed the amorphous-phase vitreous body when the CFA content was more than 40% and the temperature was higher than 1300 °C. During the melting process, Cd and Pb were almost volatilized, while Cr, Mn, and Ni were almost retained. Besides, the volatilization rates of Cu and Zn fluctuated with the temperature and the CFA content. Suitable treatment temperature and CFA content were conducive to the transformation of the heavy metals in the FA into stable forms, and the melting products were no longer hazardous wastes because the vitreous body could effectively encapsulate heavy metals. This study aims to help reuse the FA and CFA collaboratively and be more environmentally friendly.

The Curve of Learning With and Without Instructions.

In skill acquisition, instructing individuals the stimulus-response mappings indicating how to perform and act, yields better performance. Additionally, performance is helped by repeated practice. Whether providing instructions and repeated practice interact to achieve optimal performance remains debated. This paper addresses that question by analyzing the learning curves of individuals learning stimulus-response mappings of varying complexity. We particularly focus on the question whether instructions lead to improved performance in the longer run. Via evidence accumulation modeling, we find no evidence for this assertion. Instructions seem to provide individuals with a head start, leading to better initial performance in the early stages of learning, without long-lasting effects on behavior. We discuss the results in light of related studies that do report long-lasting effects of instructions, and propose that the complexity of a skill determines whether long-lasting benefits of initial instructions exist.

Observation of anodic electrochemiluminescence from silicon quantum dots for the detection of hydrogen peroxide.

Silicon quantum dots (QDs) with stable positively charged intermediates are prepared using chemical etching to generate strong anodic electrochemiluminescence (ECL) under a positive potential. Their surfaces could be passivated in the presence of strong oxidants, leading to enhanced ECL and offering the ability to carry out analysis for hydrogen peroxide.

Coupled redox cycling of arsenic and sulfur regulates thioarsenate enrichment in groundwater.

High­arsenic groundwater is influenced by a combination of processes: reductive dissolution of iron minerals and formation of secondary minerals, metal complexation and redox reactions of organic matter (OM), and formation of more migratory thioarsenate, which together can lead to significant increases in arsenic concentration in groundwater. This study was conducted in a typical sulfur- and arsenic-rich groundwater site within the Datong Basin to explore the conditions of thioarsenate formation and its influence on arsenic enrichment in groundwater using HPLC-ICPMS, hydrogeochemical modeling, and fluorescence spectroscopy. The shallow aquifer exhibited a highly reducing environment, marked by elevated sulfide levels, low concentrations of Fe(II), and the highest proportion of thioarsenate. In the middle aquifer, an optimal ∑S/∑As led to the presence of significant quantities of thioarsenate. In contrast, the deep aquifer exhibited low sulfide and high Fe(II) concentration, with arsenic primarily originating from dissolved iron minerals. Redox fluctuations in the sediment driven by sulfur­iron minerals generated reduced sulfur, thereby facilitating thioarsenate formation. OM played a crucial role as an electron donor for microbial activities, promoting iron and sulfate reduction processes and creating conditions conducive to thioarsenate formation in reduced and high­sulfur environments. Understanding the process of thioarsenate formation and the influencing factors is of paramount importance for comprehending the migration and redistribution of arsenic in groundwater systems.

Overcoming the challenge of potent endogenous interferences in limaprost quantification: An innovative methodology combining differential mobility spectrometry with LC-MS/MS for ultra-high sensitivity, selectivity and significantly enhanced throughput.

Limaprost, an orally administered analogue of prostaglandin E1, possesses potent vasodilatory, antiplatelet, and cytoprotective properties. Due to its extremely low therapeutic doses and exceedingly low plasma concentrations, the pharmacokinetic and bioequivalence studies of limaprost necessitate a highly sensitive quantitative method with a sub-pg/mL level of lower limit of quantification. Moreover, the intensity of endogenous interferences can even exceed the maximum concentration level of limaprost in human plasma, presenting further challenge to the quantification of limaprost. As a result, existing methods have not yet met the necessary level of sensitivity, selectivity, and throughput needed for the quantitative analysis of limaprost in pharmacokinetic and bioequivalence investigations. This study presents a new methodology that combines differential mobility spectrometry (DMS) with liquid chromatography-tandem mass spectrometry (LC-MS/MS) and utilizes a distinctive strategy to achieve more accurate DMS conditions. This integration yields a method that is currently the most sensitive and features the shortest analytical time, making it the sole technique capable of meeting the requirements for limaprost pharmacokinetic and bioequivalence investigations. This method demonstrates robustness and is successfully employed in a pharmacokinetic investigation of limaprost in human subjects, underscoring that the combination of DMS with LC-MS/MS serves as an efficacious strategy for overcoming the challenges inherent in analyzing biological samples afflicted by multiple interferences.

Pharmacokinetic/Pharmacodynamic Assessment of the Structural Refinement of Clopidogrel Focusing on the Balance between Bioactivation and Deactivation.

The delicate balance between ischemic and bleeding risks is a critical factor in antiplatelet therapy administration. Clopidogrel and prasugrel, belonging to the thienopyridine class of antiplatelet drugs, are known for their variability in individual responsiveness and high incidence of bleeding events, respectively. The present study is centered on the development and assessment of a range of deuterated thienopyridine derivatives, leveraging insights from structure-pharmacokinetic relationships of clopidogrel and prasugrel. Our approaches were grounded in the molecular framework of clopidogrel and incorporated the C2-pharmacophore design from prasugrel. The selection of ester or carbamate substituents at the C2-position facilitated the generation of the 2-oxointermediate through hydrolysis, akin to prasugrel, thereby bypassing the issue of CYP2C19 dependency. The bulky C2-pharmacophore in our approach distinguishes itself from prasugrel's acetyloxy substituent by exhibiting a moderated hydrolysis rate, resulting in a more gradual formation of the active metabolite. Excessive and rapid release of the active metabolite, believed to be linked with an elevated risk of bleeding, is thus mitigated. Our proposed structural modification retains the hydrolysis-sensitive methyl ester of clopidogrel but substitutes it with a deuterated methyl group, shown to effectively reduce metabolic deactivation. Three promising compounds demonstrated a pharmacokinetic profile similar to that of clopidogrel at four times the dose, while also augmenting its antiplatelet activity. SIGNIFICANCE STATEMENT: Inspired by the structure-pharmacokinetic relationship of clopidogrel and prasugrel, a range of clopidogrel derivatives were designed, synthesized, and assessed. Among them, three promising compounds have been identified, striking a delicate balance between efficacy and safety for antiplatelet therapy. Additionally, the ozagrel prodrug conjugate was discovered to exert a synergistic therapeutic effect alongside clopidogrel.

PHLDA2 overexpression facilitates senescence and apoptosis via the mitochondrial route in human nucleus pulposus cells by regulating Wnt/ß-catenin signalling pathway.

Low back pain is a common clinical symptom of intervertebral disc degeneration (IVDD), which seriously affects the quality of life of the patients. The abnormal apoptosis and senescence of nucleus pulposus cells (NPCs) play important roles in the pathogenesis of IVDD. PHLDA2 is an imprinted gene related to cell apoptosis and tumour progression. However, its role in NPC degeneration is not yet clear. Therefore, this study was set to explore the effects of PHLDA2 on NPC senescence and apoptosis and the underlying mechanisms. The expression of PHLDA2 was examined in human nucleus pulposus (NP) tissues and NPCs. Immunohistochemical staining, magnetic resonance imaging imaging and western blot were performed to evaluate the phenotypes of intervertebral discs. Senescence and apoptosis of NPCs were assessed by SA-ß-galactosidase, flow cytometry and western blot. Mitochondrial function was investigated by JC-1 staining and transmission electron microscopy. It was found that the expression level of PHLDA2 was abnormally elevated in degenerated human NP tissues and NPCs. Furthermore, knockdown of PHLDA2 can significantly inhibit senescence and apoptosis of NPCs, whereas overexpression of PHLDA2 can reverse senescence and apoptosis of NPCs in vitro. In vivo experiment further confirmed that PHLDA2 knockdown could alleviate IVDD in rats. Knockdown of PHLDA2 could also reverse senescence and apoptosis in IL-1ß-treated NPCs. JC-1 staining indicated PHLDA2's knockdown impaired disruption of the mitochondrial membrane potential and also ameliorated superstructural destruction of NPCs as showed by transmission electron microscopy. Finally, we found the PHLDA2 knockdown promoted Collagen-II expression and suppressed MMP3 expression in NPCs by repressing wnt/ß-catenin pathway. In conclusion, the results of the present study showed that PHLDA2 promotes IL-1ß-induced apoptosis and senescence of NP cells via mitochondrial route by activating the Wnt/ß-catenin pathway, and suggested that therapy targeting PHLDA2 may provide valuable insights into possible IVDD therapies.

The role of pre-onset hair hormone in predicting the prognosis of patients with severe pneumonia and acute COVID-19 outbreak.

Numerous research works have investigated the potential impact of endocrine hormones on the severity of COVID-19-related pneumonia in individuals. However, there are few studies on the effect of pre-onset neuroendocrine hormones on the prognosis of COVID-19 patients. This study looked into the prognostic value of pre-onset hair hormone levels in COVID-19 infected individuals. This study included 27 patients with COVID-19 and collected patient information and laboratory indicators. The hormone levels in hair were determined by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Within 28 days, 63 % of the patients in this study passed away. With 28-day mortality as the outcome index, urea nitrogen, CURB-65 score and pneumonia severity score (PSI) of 2 groups were statistically significant (P < 0.05). Among all hormone levels detected in hair, only progesterone level was substantially correlated negatively with COVID-19 patients' 28-day mortality(P < 0.05). The level of progesterone in hair was substantially adversely connected with the death rate at 28 days of COVID-19 patients, according to correlation and logistic regression analysis(P < 0.05). Among patients with COVID-19 pneumonia, hair progesterone levels were strongly associated with 28-day mortality, which emphasizes hair progesterone's importance as a prognostic factor.

Sluggish and Ion-Resilient Behavior of Interfacial Aqueous Layer on Single-Layer Graphene Oxide: Insights from In Situ Atomic Force Microscopy.

Understanding interfacial interactions of graphene oxide (GO) is important to evaluate its colloidal behavior and environmental fate. Single-layer GO is the fundamental unit of GO colloids, and its interfacial aqueous layers critically dictate these interfacial interactions. However, conventional techniques like X-ray diffraction are limited to multilayer systems and are inapplicable to single-layer GO. Therefore, our study employed atomic force microscopy to precisely observe the in situ dynamic behaviors of interfacial aqueous layers on single-layer GO. The interfacial aqueous layer height was detected at the subnanometer level. In real-time monitoring, the single-layer height increased from 1.17 to 1.70 nm within 3 h immersion. This sluggish process is different from the rapid equilibration of multilayer GO in previous studies, underscoring a gradual transition in hydration kinetics. Ion strength exhibited negligible influence on the single-layer height, suggesting a resilient response of the interfacial aqueous layer to ion-related perturbations due to intricate ion interactions and electrical double-layer compression. Humic acid led to a substantial increase in the interfacial aqueous layers, improving the colloidal stability of GO and augmenting its potential for migration. These findings hold considerable significance regarding the environmental behaviors of the GO interfacial aqueous layer in ion- and organic-rich water and soil.

A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural representation.

Quantitative susceptibility mapping (QSM) is an MRI-based technique that estimates the underlying tissue magnetic susceptibility based on phase signal. Deep learning (DL)-based methods have shown promise in handling the challenging ill-posed inverse problem for QSM reconstruction. However, they require extensive paired training data that are typically unavailable and suffer from generalization problems. Recent model-incorporated DL approaches also overlook the non-local effect of the tissue phase in applying the source-to-field forward model due to patch-based training constraint, resulting in a discrepancy between the prediction and measurement and subsequently suboptimal QSM reconstruction. This study proposes an unsupervised and subject-specific DL method for QSM reconstruction based on implicit neural representation (INR), referred to as INR-QSM. INR has emerged as a powerful framework for learning a high-quality continuous representation of the signal (image) by exploiting its internal information without training labels. In INR-QSM, the desired susceptibility map is represented as a continuous function of the spatial coordinates, parameterized by a fully-connected neural network. The weights are learned by minimizing a loss function that includes a data fidelity term incorporated by the physical model and regularization terms. Additionally, a novel phase compensation strategy is proposed for the first time to account for the non-local effect of tissue phase in data consistency calculation to make the physical model more accurate. Our experiments show that INR-QSM outperforms traditional established QSM reconstruction methods and the compared unsupervised DL method both qualitatively and quantitatively, and is competitive against supervised DL methods under data perturbations.

The Roles of Circular RNAs in Ischemic Stroke through Modulating Neuroinflammation.

Ischemic stroke (IS) remains a serious threat to human health. Neuroinflammatory response is an important pathophysiological process after IS. Circular RNAs (circRNAs), a member of the non-coding RNA family, are highly expressed in the central nervous system and widely involved in regulating physiological and pathophysiological processes. This study reviews the current evidence on neuroinflammatory responses, the role of circRNAs in IS and their potential mechanisms in regulating inflammatory cells, and inflammatory factors affecting IS damage. This review lays a foundation for future clinical application of circRNAs as novel biomarkers and therapeutic targets.

Facile and efficient synthesis of sweater-ball shaped metal-organic framework/nickel sulfide nanoheterojunction for boosting electrochemical glucose sensing.

The synthesis of heterojunction materials is regarded as an efficient way to enhance catalytic activities in various catalytic reactions. However, the existing fabrication approaches often rely on complex multi-step synthesis process. In this work, we fabricate sweater-ball shaped nanostructured MOF/TMS (Ni-MOF/NiS1.03) heterojunction by one-pot, one-step solvothermal method. According to the results of discrete Fourier transform (DFT) calculations and experiments, the formation of Ni-MOF/NiS1.03 heterojunction interfaces improves electron transfer and charge redistribution, and increases the adsorption energy of glucose molecules as well, which is conducive to enhance electrochemical activity of electrode materials. The as-prepared Ni-MOF/NiS1.03 heterojunction exhibit enhanced glucose sensitivity, wide detection range and low detection limit. This study paves the way towards the development of MOF-based heterojunctions for electrochemical applications.

Molecular phylogenetics of the Umbelopsis genus-identification of new species and evaluation of their oil application value.

AIMS: The aim of this study was to reconstruct the evolutionary framework of the genus Umbelopsis by using modern taxonomic strategies and evaluating the quality of oil and prospective uses of three distinct species. METHODS AND RESULTS: Three species of Umbelopsis were identified based on morphological characteristics and phylogenetic evidence obtained from three genes (ITS, LSU, and ACT). A new species of Umbelopsis was described and illustrated, and subsequently named U. ophiocordycipiticola. The characteristics of U. ophiocordycipiticola exhibited sporangia with a diameter ranging from 8 to 17 µm. and sporangiospores that were oval to ellipsoidal in shape, irregularly angular, with dimensions of ∼1.9-2.9 × 1.7-3.0 µm. Gas chromatography and mass spectrometry (GC-MS) were used to examine the composition of fatty acids. Notably, U. ophiocordycipiticola showed a significantly higher oil content of 50.89% in dry cell weight (DCW) compared to U. vinacea and U. ramanniana. The mean proportion of polyunsaturated fatty acids (PUFAs) in U. ophiocordycipiticola was 32.38%, and the maximum levels of γ-linolenic acid (GLA), arachidonic acid (ARA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) in U. ophiocordycipiticola were found to be 14.51, 0.24, 0.54, and 0.53%, respectively. The biodiesel quality from all three species complied with applicable standards set by the American Association for Testing and Materials (ASTM 6751) and the Brazilian National Petroleum Agency (ANP 255). CONCLUSIONS: The establishment of a novel species, U. ophiocordycipiticola, was strongly supported by morphological and molecular evidence. Umbelopsis ophiocordycipiticola exhibited a high-value PUFA content. Additionally, three Umbelopsis species demonstrated good quality for biodiesel production.

Therapeutic effect of notoginseng saponins before and after fermentation on blood deficiency rats.

Notoginseng saponins (NS) are the active ingredients in Panax notoginseng (Burk.) F.H. Chen (PN). NS can be transformed depending on how the extract is processed. Fermentation has been shown to produce secondary ginsenosides with increased bioavailability. However, the therapeutic effect of fermented NS (FNS) requires further study. The present study compared the compositions and activities of FNS and NS in blood deficiency rats, which resembles the symptoms of anemia in modern medicine, induced by acetylphenylhydrazine and cyclophosphamide. A total of 32 rats were randomly divided into control, model, FNS and NS groups. A blood deficiency model was established and then treatment was orally administered for 21 days. The results of component analysis indicated that some saponins transformed during the fermentation process resulting in a decrease of notoginsenoside R1, and ginsenosides Rg1, Rb1 and Re, and an increase in ginsenosides Rd, Rh2, compound K, protopanaxadiol and protopanaxatriol. The animal results showed that both FNS and NS increased the number of white blood cells (WBCs), red blood cells, hemoglobin, platelets and reticulocytes, and the levels of granulocyte-macrophage colony-stimulating factor (GM-CSF), erythropoietin (EPO) and thrombopoietin (TPO), decreased the G0/G1 phase and increased G2/M phase, and decreased the apoptosis rate of bone marrow (BM) cells, which suggested a contribution to the recovery of hematopoietic function of the BM cells. FNS and NS increased the protein expression levels of the cytokines IL-4, IL-10, IL-12, IL-13, TGF-ß, IL-6, IFN-γ and TNF-α, and the mRNA expression levels of transcription factors GATA binding protein 3 and T-box expressed in T cell (T-bet). FNS and NS treatment also increased the number of CD4+ T cells, and decreased the enlargement of the rat spleen and thymus atrophy, which indicated a protective effect on the organs of the immune system. The results of the present study demonstrated that compared with NS, FNS showed an improved ability to increase the levels of WBCs, lymphocytes, GM-CSF, EPO, TPO, aspartate aminotransferase, IL-10, IL-12, IL-13 and TNF-α, and the mRNA expression levels of T-bet, and decrease alanine aminotransferase levels. The differences seen for FNS treatment could arise from their improved bioavailability compared with NS, due to the larger proportion of hydrophobic ginsenosides produced during fermentation.

Unlocking the link between temporomandibular disorders and suicide ideation in pre-orthodontic patients: A moderated mediation model of depression and anxiety.

BACKGROUND: Temporomandibular disorders (TMD) are a series of musculoskeletal diseases with high prevalence. A few studies have reported the correlation between TMD and suicide ideation (SI). However, the underlying mechanism of the relationship lacks in-depth exploration. METHODS: A cross-sectional study was conducted among 954 pre-orthodontic patients. TMD assessment was based on the quintessential five TMD symptoms (5Ts) questionnaire. Anxiety, depression and pain catastrophizing was evaluated by Seven-item Generalized Anxiety Disorder Scale (GAD-7), Nine-item Patient Health Questionnaire (PHQ-9), and Pain Catastrophizing Scale (PCS), respectively. Correlational and moderated mediated analysis was preformed to demonstrate the relationship between TMD and SI. RESULTS: In pre-orthodontic patients, 31.87 % reported having TMD symptoms and 6.50 % declared SI during the past two weeks. The SI prevalence was 10.53 % in participants with TMD and 4.62 % in those without TMD. Intra-articular TMD, rather than pain-related TMD were especially related with SI. Individuals with TMD had higher risk to SI (rs = 0.112, adjusted OR = 2.213, p < 0.001). The effect of TMD on SI was fully mediated through depression (ß = 0.445, 95 % CI = [0.326, 0.563]). Anxiety exerted a negative moderating effect on the depression-SI relation (ß = -0.033, 95 % CI = [-0.047, -0.019]). LIMITATIONS: This study was a single-centered and cross-sectional survey. The data collection relied on self-reporting methods. CONCLUSIONS: A positive link between TMD and SI was disclosed. The effect of TMD on SI was fully mediated through depression with anxiety as a negative moderator.

Lactic acid promotes nucleus pulposus cell senescence and corresponding intervertebral disc degeneration via interacting with Akt.

The accumulation of metabolites in the intervertebral disc is considered an important cause of intervertebral disc degeneration (IVDD). Lactic acid, which is a metabolite that is produced by cellular anaerobic glycolysis, has been proven to be closely associated with IVDD. However, little is known about the role of lactic acid in nucleus pulposus cells (NPCs) senescence and oxidative stress. The aim of this study was to investigate the effect of lactic acid on NPCs senescence and oxidative stress as well as the underlying mechanism. A puncture-induced disc degeneration (PIDD) model was established in rats. Metabolomics analysis revealed that lactic acid levels were significantly increased in degenerated intervertebral discs. Elimination of excessive lactic acid using a lactate oxidase (LOx)-overexpressing lentivirus alleviated the progression of IVDD. In vitro experiments showed that high concentrations of lactic acid could induce senescence and oxidative stress in NPCs. High-throughput RNA sequencing results and bioinformatic analysis demonstrated that the induction of NPCs senescence and oxidative stress by lactic acid may be related to the PI3K/Akt signaling pathway. Further study verified that high concentrations of lactic acid could induce NPCs senescence and oxidative stress by interacting with Akt and regulating its downstream Akt/p21/p27/cyclin D1 and Akt/Nrf2/HO-1 pathways. Utilizing molecular docking, site-directed mutation and microscale thermophoresis assays, we found that lactic acid could regulate Akt kinase activity by binding to the Lys39 and Leu52 residues in the PH domain of Akt. These results highlight the involvement of lactic acid in NPCs senescence and oxidative stress, and lactic acid may become a novel potential therapeutic target for the treatment of IVDD.

Brain Age Prediction Based on Quantitative Susceptibility Mapping Using the Segmentation Transformer.

The process of brain aging is intricate, encompassing significant structural and functional changes, including myelination and iron deposition in the brain. Brain age could act as a quantitative marker to evaluate the degree of the individual's brain evolution. Quantitative susceptibility mapping (QSM) is sensitive to variations in magnetically responsive substances such as iron and myelin, making it a favorable tool for estimating brain age. In this study, we introduce an innovative 3D convolutional network named Segmentation-Transformer-Age-Network (STAN) to predict brain age based on QSM data. STAN employs a two-stage network architecture. The first-stage network learns to extract informative features from the QSM data through segmentation training, while the second-stage network predicts brain age by integrating the global and local features. We collected QSM images from 712 healthy participants, with 548 for training and 164 for testing. The results demonstrate that the proposed method achieved a high accuracy brain age prediction with a mean absolute error (MAE) of 4.124 years and a coefficient of determination (R2) of 0.933. Furthermore, the gaps between the predicted brain age and the chronological age of Parkinson's disease patients were significantly higher than those of healthy subjects (P<0.01). We thus believe that using QSM-based predicted brain age offers a more reliable and accurate phenotype, with the potentiality to serve as a biomarker to explore the process of advanced brain aging.

IMJENSE: Scan-Specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI.

Parallel imaging is a commonly used technique to accelerate magnetic resonance imaging (MRI) data acquisition. Mathematically, parallel MRI reconstruction can be formulated as an inverse problem relating the sparsely sampled k-space measurements to the desired MRI image. Despite the success of many existing reconstruction algorithms, it remains a challenge to reliably reconstruct a high-quality image from highly reduced k-space measurements. Recently, implicit neural representation has emerged as a powerful paradigm to exploit the internal information and the physics of partially acquired data to generate the desired object. In this study, we introduced IMJENSE, a scan-specific implicit neural representation-based method for improving parallel MRI reconstruction. Specifically, the underlying MRI image and coil sensitivities were modeled as continuous functions of spatial coordinates, parameterized by neural networks and polynomials, respectively. The weights in the networks and coefficients in the polynomials were simultaneously learned directly from sparsely acquired k-space measurements, without fully sampled ground truth data for training. Benefiting from the powerful continuous representation and joint estimation of the MRI image and coil sensitivities, IMJENSE outperforms conventional image or k-space domain reconstruction algorithms. With extremely limited calibration data, IMJENSE is more stable than supervised calibrationless and calibration-based deep-learning methods. Results show that IMJENSE robustly reconstructs the images acquired at 5× and 6× accelerations with only 4 or 8 calibration lines in 2D Cartesian acquisitions, corresponding to 22.0% and 19.5% undersampling rates. The high-quality results and scanning specificity make the proposed method hold the potential for further accelerating the data acquisition of parallel MRI.

Revolutionizing Solid-State NASICON Sodium Batteries: Enhanced Ionic Conductivity Estimation through Multivariate Experimental Parameters Leveraging Machine Learning.

Na superionic conductor (NASICON) materials hold promise as solid-state electrolytes due to their wide electrochemical stability and chemical durability. However, their limited ionic conductivity hinders their integration into sodium-ion batteries. The conventional approach to electrolyte design struggles with comprehending the intricate interactions of factors impacting conductivity, encompassing synthesis parameters, structural characteristics, and electronic descriptors. Herein, we explored the potential of machine learning in predicting ionic conductivity in NASICON. We compile a database of 211 datasets, covering 160 NASICON materials, and employ facile descriptors, including synthesis parameters, test conditions, molecular and structural attributes, and electronic properties. Random forest (RF) and neural network (NN) models were developed and optimized, with NN performing notably better, particularly with limited data (R2=0.820). Our analysis spotlighted the pivotal role of Na stoichiometric count in ionic conductivity. Furthermore, the NN algorithm highlighted the comparable significance of synthesis parameters to structural factors in determining conductivity. In contrast, the impact of electronegativity on doped elements appears less significant, underscoring the importance of dopant size and quantity. This work underscores the potential of machine learning in advancing NASICON electrolyte design for sodium-ion batteries, offering insights into conductivity drivers and a more efficient path to optimizing materials.