Characterization of the Vertical Stiffness Gradient in Cadaveric Human and Excised Canine Larynges.

The elastic properties of the folds govern the characteristics of vocal fold vibrations. This study addresses existing gaps by investigating the Young's modulus along the anterior-posterior direction in excised canine and cadaveric human vocal folds. Micro-indentation testing was conducted on six excised canines and three cadaveric human larynges. Multiple points along the medial glottal wall were indented to determine force-displacement, stress-strain relationships, and Young's modulus as a function of Green's strain. A vertical stiffness gradient was consistently observed in both canine and human samples, with higher stiffness in the inferior aspect compared with the superior aspect. The stiffness increased toward both the anterior and posterior directions from the mid-coronal plane, with a more pronounced increase at the posterior edge. Human vocal folds generally exhibited lower stiffness at low strains but were comparable to canine vocal folds at higher strains. These findings suggest that the canine larynx model is a reasonable representation of the human laryngeal tissues' elastic property trends. This analysis of the vertical stiffness gradient in canine and human vocal folds provides valuable data for improving experimental and numerical models of phonation.

Pullback Pressure Gradient-An Emerging Concept in Patients with Coronary Artery Disease.

Fractional flow reserve fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) is currently recommended in the management of patients with stable coronary artery disease (CAD). Pullback pressure gradient (PPG) index is an emerging concept that provides a fully quantitative measure of the longitudinal distribution of CAD. It can be derived from FFR, as well as other non-hyperemic indices, and is a novel metric of assessing the focality or diffuseness of CAD. PPG adds a second domain to the assessment of CAD, beyond ischemia as measured by FFR, and may enable clinicians to better inform their patients about the status of their CAD but may also predict potential outcomes before revascularization. In this article, we will provide an in-depth review on the concept of PPG index and its correlation to pre and post revascularization ischemia. We will assess the relationship between PPG index and plaque characteristics and how this is translated into any difference in procedural and long-term clinical outcomes.

Risk level and removal performance of antibiotic resistance genes and bacterial pathogens in static composting with different temperatures.

The effect of different levels of temperature on resistance genes is not clear in mesophilic static composting (<50 °C). This study conducted livestock manure composting with different temperature gradients from 20 to 50 °C, it was found that the reduction rates of risk rank-I antibiotic resistance genes (from 3 % to 66 %), metal resistance genes (from -50 % to 76 %) and bacterial pathogens (from 72 % to 91 %) all increased significantly with increasing temperature from 20 to 50°C. The vulnerability of bacterial communities increased significantly, and the assembly process of bacterial communities changed from deterministic to stochastic with the increase of composting temperature. Higher temperature could accelerate the removal of thermolabile resistance genes hosts or pathogenic hosts carrying mobile genetic elements by directly or indirectly affecting organic acids content. Therefore, for soil safety, the temperature of the manure recycling process should be increased as much as possible.

Inflammatory signature-based theranostics for acute lung injury in acute type A aortic dissection.

Acute lung injury (ALI) is a serious adverse event in the management of acute type A aortic dissection (ATAAD). Using a large-scale cohort, we applied artificial intelligence-driven approach to stratify patients with different outcomes and treatment responses. A total of 2,499 patients from China 5A study database (2016-2022) from 10 cardiovascular centers were divided into 70% for derivation cohort and 30% for validation cohort, in which extreme gradient boosting algorithm was used to develop ALI risk model. Logistic regression was used to assess the risk under anti-inflammatory strategies in different risk probability. Eight top features of importance (leukocyte, platelet, hemoglobin, base excess, age, creatinine, glucose, and left ventricular end-diastolic dimension) were used to develop and validate an ALI risk model, with adequate discrimination ability regarding area under the receiver operating characteristic curve of 0.844 and 0.799 in the derivation and validation cohort, respectively. By the individualized treatment effect prediction, ulinastatin use was significantly associated with significantly lower risk of developing ALI (odds ratio [OR] 0.623 [95% CI 0.456, 0.851]; P = 0.003) in patients with a predicted ALI risk of 32.5-73.0%, rather than in pooled patients with a risk of <32.5 and >73.0% (OR 0.929 [0.682, 1.267], P = 0.642) (Pinteraction = 0.075). An artificial intelligence-driven risk stratification of ALI following ATAAD surgery were developed and validated, and subgroup analysis showed the heterogeneity of anti-inflammatory pharmacotherapy, which suggested individualized anti-inflammatory strategies in different risk probability of ALI.

Massively Enhanced Charge Selectivity, Ion Transport, and Osmotic Energy Conversion by Antiswelling Nanoconfined Hydrogels.

Developing a nanofluidic membrane with simultaneously enhanced ion selectivity and permeability for high-performance osmotic energy conversion has largely been unexplored. Here, we tackle this issue by the confinement of highly space-charged hydrogels within an orderedly aligned nanochannel array membrane. The nanoconfinement effect endows the hydrogel-based membrane with excellent antiswelling property. Furthermore, experimental and simulation results demonstrate that such a nanoconfined hydrogel membrane exhibits massively enhanced cation selectivity and ion transport properties. Consequently, an amazingly high power density up to ∼52.1 W/m2 with an unprecedented energy conversion efficiency of 37.5% can be reached by mixing simulated salt-lake water (5 M NaCl) and river water (0.01 M NaCl). Both efficiency indexes surpass those of most of the state-of-the-art nanofluidic membranes. This work offers insights into the design of highly ion-selective membranes to achieve ultrafast ion transport and high-performance osmotic energy harvesting.

Enhancing clinical and public health interpretation of accelerometer-assessed physical activity with age-referenced values based on UK Biobank data.

BACKGROUND: Higher accelerometer-assessed volume and intensity of physical activity (PA) have been associated with a longer life expectancy but can be difficult to translate into recommended doses of PA. We aimed to: (a) improve interpretability by producing UK Biobank age-referenced centiles for PA volume and intensity; (b) inform public-health messaging by examining how adding recommended quantities of moderate and vigorous PA affect PA volume and intensity. METHODS: 92,480 UK Biobank participants aged 43-80 years with wrist-worn accelerometer data were included. Average acceleration and intensity gradient were derived as proxies for PA volume and intensity. We generated sex-specific centile curves using Generalized Additive Models for Location Scale and Shape (GAMLSS) and modeled the effect of adding moderate (walking) or vigorous (running) activity on the combined change in the volume and intensity centiles (change in PA profile). RESULTS: In men, volume was lower as age increased while intensity was lower after age 55; in women, both volume and intensity were lower as age increased. Adding 150 min of moderate PA weekly (5×30 min walking) increased the PA profile by 4 percentage points. Defining moderate PA as brisk walking approximately doubled the increase (9 percentage points) while 75 min of vigorous PA weekly (5×15 min running) trebled the increase (13 percentage points). CONCLUSION: These UK Biobank reference centiles provide a benchmark for interpretation of accelerometer data. Application of our translational methods demonstrate that meeting PA guidelines through shorter duration vigorous activity is more beneficial to the PA profile (volume and intensity) than longer duration moderate activity.

Intensified river salinization alters nitrogen-cycling microbial communities in arid and semi-arid regions of China.

Freshwater salinization is receiving increasing global attention due to its profound influence on nitrogen cycling in aquatic ecosystems and the accessibility of water resources. However, a comprehensive understanding of the changes in river salinization and the impacts of salinity on nitrogen cycling in arid and semi-arid regions of China is currently lacking. A meta-analysis was first conducted based on previous investigations and found an intensification in river salinization that altered hydrochemical characteristics. To further analyze the impact of salinity on nitrogen metabolism processes, we evaluated rivers with long-term salinity gradients based on in situ observations. The genes and enzymes that were inhibited generally by salinity, especially those involved in nitrogen fixation and nitrification, showed low abundances in three salinity levels. The abundance of genes and enzymes with denitrification and dissimilatory nitrate reduction to ammonium functions still maintained a high proportion, especially for denitrification genes/enzymes that were enriched under medium salinity. Denitrifying bacteria exhibited various relationships with salinity, while dissimilatory nitrate reduction to ammonium bacterium (such as Hydrogenophaga and Curvibacter carrying nirB) were more inhibited by salinity, indicating that diverse denitrifying bacteria could be used to regulate nitrogen concentration. Most genera exhibited symbiotic and mutual relationships, and the highest proportion of significant positive correlations of abundant genera was found under medium salinity. This study emphasizes the role of river salinity on environment characteristics and nitrogen transformation rules, and our results are useful for improving the availability of river water resources in arid and semi-arid regions.

Octocoral growth rate and mortality along a eutrophication gradient in Cuban reefs.

Octocorals are showing resilience to local and global stressors, while the decline in zooxanthellate corals continues. One of the processes that helps explain this ecological succession is the vertical growth of octocorals, which allows colonies to avoid stressors occurring at the substrate level. However, the growth and survival of octocorals could be affected by eutrophication, similar to what has happened with zooxanthellate corals. For this reason, the growth rate, mortality and survival of two octocoral species were determined along a eutrophication gradient in Cuba. A permanent band transect (250 × 2 m) was established on seven frontal reefs, and marked colonies were monitored for one year. The growth rates in height, width and colony area of Eunicea flexuosa and Plexaura kükenthali were significantly greater in the reefs near the polluted river basins. The eutrophication gradient, water visibility, and sediment accumulation on the bottom explained 36-78% of the variability in the growth of both species. The positive and significant correlations between the growth rate and stable nitrogen isotopes in both species and the microbiological variables, suggest that the contributions of dissolved inorganic nitrogen and organic matter from sewage discharge favor the growth of colonies. The eutrophication gradient did not explain the variability in mortality of either species in the short term, while hydrodynamic stress did. The results of this research highlight the resilience of both species and their ability to grow more rapidly in areas with eutrophic conditions, low water visibility, and greater sediment accumulation on the bottom, which may help explain the abundance of octocorals in the western tropical Atlantic.

DCSGMDA: A dual-channel convolutional model based on stacked deep learning collaborative gradient decomposition for predicting miRNA-disease associations.

Numerous studies have shown that microRNAs (miRNAs) play a key role in human diseases as critical biomarkers. Its abnormal expression is often accompanied by the emergence of specific diseases. Therefore, studying the relationship between miRNAs and diseases can deepen the insights of their pathogenesis, grasp the process of disease onset and development, and promote drug research of specific diseases. However, many undiscovered relationships between miRNAs and diseases remain, significantly limiting research on miRNA-disease correlations. To explore more potential correlations, we propose a dual-channel convolutional model based on stacked deep learning collaborative gradient decomposition for predicting miRNA-disease associations (DCSGMDA). Firstly, we constructed similarity networks for miRNAs and diseases, as well as an association relationship network. Secondly, potential features were fully mined using stacked deep learning and gradient decomposition networks, along with dual-channel convolutional neural networks. Finally, correlations were scored by a multilayer perceptron. We performed 5-fold and 10-fold cross-validation experiments on DCSGMDA using two datasets based on the Human MicroRNA Disease Database (HMDD). Additionally, parametric, ablation, and comparative experiments, along with case studies, were conducted. The experimental results demonstrate that DCSGMDA performs well in predicting miRNA-disease associations.

Siamese based deep neural network for ADHD detection using EEG signal.

BACKGROUND: Detecting Attention-Deficit/Hyperactivity Disorder (ADHD) in children is crucial for timely intervention and personalized treatment. OBJECTIVE: This study aims to utilize deep learning techniques to analyze brain maps derived from Power Spectral Density (PSD) of Electroencephalography (EEG) signals in pediatric subjects for ADHD detection. METHODS: We employed a Siamese-based Convolutional Neural Network (CNN) to analyze EEG-based brain maps. Gradient-weighted class activation mapping (Grad-CAM) was used as an explainable AI (XAI) visualization method to identify significant features. RESULTS: The CNN model achieved a high classification accuracy of 99.17 %. Grad-CAM analysis revealed that PSD features from the theta band of the frontal and occipital lobes are effective discriminators for distinguishing children with ADHD from healthy controls. CONCLUSION: This study demonstrates the effectiveness of deep learning in ADHD detection and highlights the importance of regional PSD metrics in accurate classification. By utilizing Grad-CAM, we elucidate the discriminative power of specific brain regions and frequency bands, thereby enhancing the understanding of ADHD neurobiology for improved diagnostic precision in pediatric populations.

Prediction of poststroke independent walking using machine learning: a retrospective study.

BACKGROUND: Accurately predicting the walking independence of stroke patients is important. Our objective was to determine and compare the performance of logistic regression (LR) and three machine learning models (eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest (RF)) in predicting walking independence at discharge in stroke patients, as well as to explore the variables that predict prognosis. METHODS: 778 (80% for the training set and 20% for the test set) stroke patients admitted to China Rehabilitation Research Center between February 2020 and January 2023 were retrospectively included. The training set was used for training models. The test set was used to validate and compare the performance of the four models in terms of area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. RESULTS: Among the three ML models, the AUC of the XGBoost model is significantly higher than that of the SVM and RF models (P < 0.001, P = 0.024, respectively). There was no significant difference in the AUCs between the XGBoost model and the LR model (0.891 vs. 0.880, P = 0.560). The XGBoost model demonstrated superior accuracy (87.82% vs. 86.54%), sensitivity (50.00% vs. 39.39%), PPV (73.68% vs. 73.33%), NPV (89.78% vs. 87.94%), and F1 score (59.57% vs. 51.16%), with only slightly lower specificity (96.09% vs. 96.88%). Together, the XGBoost model and the stepwise LR model identified age, FMA-LE at admission, FAC at admission, and lower limb spasticity as key factors influencing independent walking. CONCLUSION: Overall, the XGBoost model performed best in predicting independent walking after stroke. The XGBoost and LR models together confirm that age, admission FMA-LE, admission FAC, and lower extremity spasticity are the key factors influencing independent walking in stroke patients at hospital discharge. TRIAL REGISTRATION: Not applicable.

Digital analysis of fabrication accuracy and fit in additively and subtractively manufactured implant-supported fixed complete dentures: Trueness and fit of implant-supported complete-arch frameworks.

OBJECTIVES: . To digitally evaluate the trueness and fit of additively and subtractively manufactured fixed complete dentures in materials intended for definitive use. METHODS: . An edentulous maxillary model with implants at the left first molar, left canine, right canine, and right first molar site was digitized and a fixed complete denture was designed. This design was used to fabricate fixed dentures in an additively manufactured resin for definitive use (AM), a high-impact polymer composite (SM-CR), and a strength gradient zirconia (SM-ZR) (n = 10). Each fixed denture was digitized and the surface (overall, occlusal, except occlusal, and abutments), linear, and interimplant distance deviations were analyzed. The fit was assessed with the triple-scan protocol. Data were analyzed with Welch analysis of variance and Games-Howell tests (α = 0.05). RESULTS: . SM-ZR led to lower overall deviations than AM, which had the highest occlusal and the lowest abutments deviations (P ≤ 0.007). SM-ZR had the lowest occlusal and SM-CR had the highest except occlusal deviations (P ≤ 0.002). AM mostly had higher linear and SM-CR mostly had higher interimplant distance deviations (P ≤ 0.043). AM led to the highest marginal gap at the left canine site, while SM-CR had the highest and SM-ZR had the lowest gaps at the right canine site (P ≤ 0.022). CONCLUSIONS: . SM-ZR dentures mostly had trueness and marginal fit similar to or better than the other groups. Tested fixed complete dentures were mostly smaller than the design file in terms of interimplant distances.

Release of contaminants from polymer surfaces under condition of organized fluid flows.

The use of polymers for water storage or distribution is closely monitored, especially with regard to the possible contamination with substances coming from the material's surfaces. Different standards are practiced across countries according to type of applied materials and such test methods are prevalently based on constant temperature conditions. However, these polymers systems could be located in diverse environment which does not necessarily provide constant conditions. Experimental findings show that exposure of liquid inside polymeric materials to specific temperature gradients, and consequently to emerging organized flows, can result in an accelerated leaching of undesirable substances from the solid surface. In presented work model steady-state and organized flow conditions are used to compare release of contaminates from polyethylene by measuring of surface tension, UV-Vis spectroscopy, FTIR, scanning electron microscopy and elemental analysis of polymer surfaces and water leachates. The pilot study shows that convective flow generated via temperature gradient significantly affects contaminant release in comparison to a steady state and mixing flow conditions.

Highly Sensitive Detection of Tumor Cell-Derived Exosomes Using Solid-State Nanopores Assisted with a Slight Salt Gradient.

As an important biomarker, tumor cell-derived exosomes have substantial application prospects in early cancer screening and diagnosis. However, the unsatisfactory sensitivity and complicated sample pretreatment processes of conventional detection approaches have limited their use in clinical diagnosis. Nanopore sensors, as a highly sensitive, label-free, single-molecule technology, are widely utilized in molecule and bioparticle detection. Nevertheless, the exosome capture rate through nanopores is extremely low due to the low surface charge densities of exosomes and the effects of electrolyte concentration on their structural stability, thereby reducing the detection throughput. Here, we report an approach to improve the capture rate of exosome translocations using silicon nitride (SiNx) nanopores assisted by a slight salt electrolyte gradient. Improvements in exosome translocation event frequency are assessed in electrolyte solutions with different concentration gradients. In the case of asymmetric electrolytes (cis1× PBS and trans0.2 M NaCl, 1× PBS), the event frequency of tumor cell (HepG2)-derived exosome translocations is enhanced by nearly 2 orders of magnitude while maintaining vesicle structure stability. Furthermore, benefiting from the salt gradient effect, tumor cell (AsPC-1 and HCT116)-derived exosome translocations could be discriminated from those of HepG2 cell-derived exosomes. The developed highly sensitive detection method for tumor cell-derived exosomes at the single-particle level provides an approach for early cancer diagnosis.

Compressed cerebro-cerebellar functional gradients in children and adolescents with attention-deficit/hyperactivity disorder.

Both cortical and cerebellar developmental differences have been implicated in attention-deficit/hyperactivity disorder (ADHD). Recently accumulating neuroimaging studies have highlighted hierarchies as a fundamental principle of brain organization, suggesting the importance of assessing hierarchy abnormalities in ADHD. A novel gradient-based resting-state functional connectivity analysis was applied to investigate the cerebro-cerebellar disturbed hierarchy in children and adolescents with ADHD. We found that the interaction of functional gradient between diagnosis and age was concentrated in default mode network (DMN) and visual network (VN). At the same time, we also found that the opposite gradient changes of DMN and VN caused the compression of the cortical main gradient in ADHD patients, implicating the co-occurrence of both low- (visual processing) and high-order (self-related thought) cognitive dysfunction manifesting in abnormal cerebro-cerebellar organizational hierarchy in ADHD. Our study provides a neurobiological framework to better understand the co-occurrence and interaction of both low-level and high-level functional abnormalities in the cortex and cerebellum in ADHD.

Multimodal medical image fusion based on interval gradients and convolutional neural networks.

Many image fusion methods have been proposed to leverage the advantages of functional and anatomical images while compensating for their shortcomings. These methods integrate functional and anatomical images while presenting physiological and metabolic organ information, making their diagnostic efficiency far greater than that of single-modal images. Currently, most existing multimodal medical imaging fusion methods are based on multiscale transformation, which involves obtaining pyramid features through multiscale transformation. Low-resolution images are used to analyse approximate image features, and high-resolution images are used to analyse detailed image features. Different fusion rules are applied to achieve feature fusion at different scales. Although these fusion methods based on multiscale transformation can effectively achieve multimodal medical image fusion, much detailed information is lost during multiscale and inverse transformation, resulting in blurred edges and a loss of detail in the fusion images. A multimodal medical image fusion method based on interval gradients and convolutional neural networks is proposed to overcome this problem. First, this method uses interval gradients for image decomposition to obtain structure and texture images. Second, deep neural networks are used to extract perception images. Three methods are used to fuse structure, texture, and perception images. Last, the images are combined to obtain the final fusion image after colour transformation. Compared with the reference algorithms, the proposed method performs better in multiple objective indicators of Q EN , Q NIQE , Q SD , Q SSEQ and Q TMQI .

Reflexivity gradient-Consciousness knowing itself.

Some consider phenomenal consciousness to be the great achievement of the evolution of life on earth, but the real achievement is much more than mere phenomenality. The real achievement is that consciousness has woken up within us and has recognized itself, that within us humans, consciousness knows that it is conscious. This short review explores the reflexivity of consciousness from the perspective of consciousness itself-a non-conceptual nondual awareness, whose main property is its non-representational reflexivity. In light of this nondual reflexivity, different types of reflexivity proposed by current theories can be seen as a gradation of relational or transitive distances between consciousness as the knower and consciousness as the known, from fully representational and dual, through various forms of qualified monism, to fully non-representational and nondual.

Influence of surface water and groundwater gradient on spatial distribution of typical vegetation in the hinterland of Taklamakan desert.

Water resources are essential for desert oases and are key drivers of local ecological processes critical to the growth of desert vegetation. In this study, the oasis in the hinterland of the Taklamakan Desert, China, was selected as the research subject. Using high-precision classification of oasis vegetation through machine learning, surface water within the oasis was identified and extracted from multi-year Landsat remote sensing data. The spatial distribution patterns of the main community-building species, Populus euphratica and Tamarix ramosissima, were studied under different moisture gradients using environmental covariates and measured groundwater depth to invert its spatial distribution and K-mean clustering to construct surface water and groundwater moisture gradients. The results indicated that the classification accuracy for the two species reached 0.917. Gradients 1-5 were used to categorize the water resources, dividing surface water and groundwater into five gradients. Gradient 3 exhibited the optimal moisture conditions, with a high surface water distribution frequency (0.017) and shallow groundwater depth (3.158 m), while Gradient 4 showed the least optimal moisture conditions, characterized by a low surface water distribution frequency (0.008) and deep groundwater depth (4.820 m). The water gradient decreased in the following order: Gradient 3 > Gradient 5 > Gradient 1 > Gradient 2 > Gradient 4. The optimum gradients for growth of P. euphratica and T. ramosissima were gradients 5, 1, and 2. The normalized vegetation index spatial distribution patterns of the two species were consistent with that of the moisture gradient. Tamarix ramosissima was found to be more tolerant to salinity and drought than P. euphratica. Overall, this study provides valuable information on the effect of the spatial distribution of water resource gradients on oasis vegetation and can guide future water delivery policies in oases.

Electric-field assisted cascade reactions to create alginate/carboxymethyl chitosan composite hydrogels with gradient architecture and reconfigurable mechanical properties.

Rational designs of polysaccharide-based hydrogels with organ-like three-dimensional architecture provide a great possibility for addressing the shortages of allograft tissues and organs. However, spatial-temporal control over structure in bulk hydrogel and acquire satisfied mechanical properties remain an intrinsic challenge to achieve. Here, we show how electric-field assisted molecular self-assembly can be coupled to a directional reaction-diffusion (RD) process to produce macroscopic hydrogel in a controllable manner. The electrical energy input was not only to generate complex molecule gradients and initiate the molecular self-assembly, but also to guide/facilitate the RD processes for the gel rapid growth via a cascade construction interaction. The hydrogel mechanical properties can be tuned and enhanced by using an interpenetrating biopolymer network and multiple ionic crosslinkers, leading to a wide-range of mechanical modulus to match with biological organs or tissues. We demonstrate diverse 3D macroscopic hydrogels can be easily prepared via field-assisted directional reaction-diffusion and specific joint interactions. The humility-triggered dissipation of functional gradients and antibacterial performance confirm that the hydrogels can serve as an optically variable soft device for wound management. Therefore, this work provides a general approach toward the rational fabrication of soft hydrogels with controlled architectures and functionality for advanced biomedical systems.

Advancements in MR hardware systems and magnetic field control: B0 shimming, RF coils, and gradient techniques for enhancing magnetic resonance imaging and spectroscopy.

High magnetic field homogeneity is critical for magnetic resonance imaging (MRI), functional MRI, and magnetic resonance spectroscopy (MRS) applications. B0 inhomogeneity during MR scans is a long-standing problem resulting from magnet imperfections and site conditions, with the main issue being the inhomogeneity across the human body caused by differences in magnetic susceptibilities between tissues, resulting in signal loss, image distortion, and poor spectral resolution. Through a combination of passive and active shim techniques, as well as technological advances employing multi-coil techniques, optimal coil design, motion tracking, and real-time modifications, improved field homogeneity and image quality have been achieved in MRI/MRS. The integration of RF and shim coils brings a high shim efficiency due to the proximity of participants. This technique will potentially be applied to high-density RF coils with a high-density shim array for improved B0 homogeneity. Simultaneous shimming and image encoding can be achieved using multi-coil array, which also enables the development of novel encoding methods using advanced magnetic field control. Field monitoring enables the capture and real-time compensation for dynamic field perturbance beyond the static background inhomogeneity. These advancements have the potential to better use the scanner performance to enhance diagnostic capabilities and broaden applications of MRI/MRS in a variety of clinical and research settings. The purpose of this paper is to provide an overview of the latest advances in B0 magnetic field shimming and magnetic field control techniques as well as MR hardware, and to emphasize their significance and potential impact on improving the data quality of MRI/MRS.