Conventional Diffusion Research Articles

A Day Saved is a Life Saved: Direct Antimicrobial Susceptibility Testing from Positively Flagged Blood Culture Bottles and their Concordance with the Routine Method.

Sepsis is a major health problem worldwide and is associated with high morbidity and mortality with every hour delay in initiation of therapy. A conventional method of blood culture and Antimicrobial Susceptibility Testing (AST) takes around 48-72 hours. Empirical antibiotics need to be administered until the sensitivity report is made available. It has been estimated that 20-50% of the empirical antibiotics are inappropriate, resulting in prolonged hospital stays, adverse effects, and emergence of drug resistance. Additionally, this also puts an extra financial burden on both the patients and healthcare settings. Performing direct Antimicrobial Sensitivity Testing (dAST) is an important tool to reduce turn-around time (TAT) by at least 18-24 hours, thus reducing morbidity and mortality among critically ill patients. Direct AST (dAST) was performed from the positively flagged blood culture bottles received between December, 2021 to May, 2022 from Intensive Care Units (ICUs) on Mueller- Hinton Agar (MHA) using four drops of withdrawn blood. dAST was performed for six drugs: Ceftriaxone-30 μg (CTR), Piperacillin/Tazobactam-100/10 μg (PIT), Meropenem-10 μg (MRP), Ciprofloxacin-5 μg (CIP), Aztreonam-30 μg (AT), and Colistin (CL). The zone of inhibition was interpreted as per CLSI M100 ed32, 2022 guidelines. A parallel conventional method was also performed to examine for categorical agreement and disagreement. Identification was carried out using MALDI-TOF MS from the colonies that appeared on the dAST plate on the subsequent day. A total of 162 positively flagged blood culture bottles were included in the study. The majority of the Gram-negative organisms were from Enterobacterales (n=109), followed by Acinetobacter spp. (n=28) and Pseudomonas aeruginosa (n=25). Out of the 972 isolate-antimicrobial combinations, overall Categorical Agreement (CA) was seen in 936 (96.3%), whereas disagreement was observed in 36 with minor error (mE) in 21 (2.2%), major error (ME) in 7 (0.7%), and very major error (VME) in 8 (0.8%) when compared to the routine method. Categorical agreement (CA) of > 99% was seen in ceftriaxone (CTR) and ciprofloxacin (CIP). In comparison, the lowest CA was observed with meropenem (MRP) at 92%. Colistin dAST was performed using the E-strip method, and the result obtained was highly convincing, with an overall disagreement of only 1.2%. Rapid dAST from positively flagged blood culture bottles proved to significantly reduce the TAT from the time of sample collection to the first availability of antimicrobial susceptibility report with excellent categorical agreement of > 95% using the conventional disc diffusion method. Results obtained were within the acceptance criteria set by U. S. Food and Drug Administration (FDA) guidelines of > 90% categorical agreement for a new method. We were able to obtain excellent concordance for colistin using the E-strip method. Performing dAST not only saves a "day", but its proper implementation would save a "life".

Flash Sintering of Bimetallic Assemblies for Turbine Disks in Next‐Generation Jet Engines

René 77 and another advanced powder metallurgy nickel‐based superalloy are flash‐sintered and joined to produce a dual‐alloy assembly. This innovative process allows for the sintering of powders to achieve high relative densities or the formation of solid joints in a shorter time than is feasible with conventional diffusion bonding techniques. Examination of the microstructure after heat treatments reveals that diffusion and recrystallization phenomena can modulate the transition zone. The creep properties of both alloys, evaluated using stress relaxation tests, do not seem to differ by more than an order of magnitude, while the higher thermal stability of René 77 compared to Alloy B at 800 °C tips the scales. In tension, base materials show very high properties, while bimetallic specimens exhibit at least higher properties than René 77. However, failure occurs at the bond at high temperatures due to the formation of an oxide film during the bonding process. These encouraging results illustrate the potential and constraints of the flash‐sintering process for producing dissimilar assemblies, which may enhance the properties of next‐generation aero‐engine turbine disks.

CRISPR/Cas9-Mediated Genome Editing of T4 Bacteriophage for High-Throughput Antimicrobial Susceptibility Testing.

The accurate and effective determination of antimicrobial resistance is essential to limiting the spread of infectious diseases and ensuring human health. Herein, a simple, accurate, and high-throughput phage-based colorimetric sensing strategy was developed for antimicrobial susceptibility testing (AST). Taking advantage of the CRISPR/Cas9 system, the genome of the T4 phage was modularly engineered to carry lacZ-α (lacZa), a marker gene encoding the α-fragment of β-galactosidase (β-gal). T4lacZa phages were identified by blue-white selection and then used for a biosensing application. In this strategy, the bacterial solution is exposed to the T4lacZa phage, causing target bacteria to overexpress β-gal. Upon the addition of a colorimetric substrate, the β-gal initiates an enzymatic reaction, resulting in a solution color change from yellow to red. This sensing strategy offers a visual way to monitor bacterial growth in the presence of antibiotics, enabling the determination of bacterial antimicrobial susceptibility. As a proof of concept, our developed sensing strategy was successfully applied to identify 9 different multidrug-resistant Escherichia coli (E. coli) in urine samples with 100% specificity. Compared with conventional disk diffusion susceptibility tests, the engineered phage-based sensing strategy can shorten the detection time by at least half without losing detection sensitivity, providing an alternative high-throughput method for AST in clinical diagnosis.

Artificial intelligence reveals past climate extremes by reconstructing historical records

The understanding of recent climate extremes and the characterization of climate risk require examining these extremes within a historical context. However, the existing datasets of observed extremes generally exhibit spatial gaps and inaccuracies due to inadequate spatial extrapolation. This problem arises from traditional statistical methods used to account for the lack of measurements, particularly prevalent before the mid-20th century. In this work, we use artificial intelligence to reconstruct observations of European climate extremes (warm and cold days and nights) by leveraging Earth system model data from CMIP6 through transfer learning. Our method surpasses conventional statistical techniques and diffusion models, showcasing its ability to reconstruct past extreme events and reveal spatial trends across an extensive time span (1901-2018) that is not covered by most reanalysis datasets. Providing our dataset to the climate community will improve the characterization of climate extremes, resulting in better risk management and policies.

SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI.

Diffusion models have emerged as a leading methodology for image generation and have proven successful in the realm of magnetic resonance imaging (MRI) reconstruction. However, existing reconstruction methods based on diffusion models are primarily formulated in the image domain, making the reconstruction quality susceptible to inaccuracies in coil sensitivity maps (CSMs). k-space interpolation methods can effectively address this issue but conventional diffusion models are not readily applicable in k-space interpolation. To overcome this challenge, we introduce a novel approach called SPIRiT-Diffusion, which is a diffusion model for k-space interpolation inspired by the iterative self-consistent SPIRiT method. Specifically, we utilize the iterative solver of the self-consistent term (i.e., k-space physical prior) in SPIRiT to formulate a novel stochastic differential equation (SDE) governing the diffusion process. Subsequently, k-space data can be interpolated by executing the diffusion process. This innovative approach highlights the optimization model's role in designing the SDE in diffusion models, enabling the diffusion process to align closely with the physics inherent in the optimization model-a concept referred to as model-driven diffusion. We evaluated the proposed SPIRiT-Diffusion method using a 3D joint intracranial and carotid vessel wall imaging dataset. The results convincingly demonstrate its superiority over image-domain reconstruction methods, achieving high reconstruction quality even at a substantial acceleration rate of 10. Our code are available at https://github.com/zhyjSIAT/SPIRiT-Diffusion.

Bayesian-Learning-Based Diffusion Least Mean Square Algorithms Over Networks.

To improve the learning performance of the conventional diffusion least mean square (DLMS) algorithms, this article proposes Bayesian-learning-based DLMS (BL-DLMS) algorithms. First, the proposed BL-DLMS algorithms are inferred from a Gaussian state-space model-based Bayesian learning perspective. By performing Bayesian inference in the given Gaussian state-space model, a variable step-size and an estimation of the uncertainty of information of interest at each node are obtained for the proposed BL-DLMS algorithms. Next, a control method at each node is designed to improve the tracking performance of the proposed BL-DLMS algorithms in the sudden change scenario. Then, a lower bound on the variable step-size of each node of the proposed BL-DLMS algorithms is derived to maintain the optimal steady-state performance in the nonstationary scenario (unknown parameter vector of interest is time-varying). Afterward, the mean stability and the transient and steady-state mean square performance of the proposed BL-DLMS algorithms are analyzed in the nonstationary scenario. In addition, two Bayesian-learning-based diffusion bias-compensated LMS algorithms are proposed to handle the noisy inputs. Finally, the superior learning performance of the proposed learning algorithms is verified by numerical simulations, and the simulated results are in good agreement with the theoretical results.

Physics-Informed DeepMRI: k-Space Interpolation Meets Heat Diffusion.

Recently, diffusion models have shown considerable promise for MRI reconstruction. However, extensive experimentation has revealed that these models are prone to generating artifacts due to the inherent randomness involved in generating images from pure noise. To achieve more controlled image reconstruction, we reexamine the concept of interpolatable physical priors in k-space data, focusing specifically on the interpolation of high-frequency (HF) k-space data from low-frequency (LF) k-space data. Broadly, this insight drives a shift in the generation paradigm from random noise to a more deterministic approach grounded in the existing LF k-space data. Building on this, we first establish a relationship between the interpolation of HF k-space data from LF k-space data and the reverse heat diffusion process, providing a fundamental framework for designing diffusion models that generate missing HF data. To further improve reconstruction accuracy, we integrate a traditional physics-informed k-space interpolation model into our diffusion framework as a data fidelity term. Experimental validation using publicly available datasets demonstrates that our approach significantly surpasses traditional k-space interpolation methods, deep learning-based k-space interpolation techniques, and conventional diffusion models, particularly in HF regions. Finally, we assess the generalization performance of our model across various out-of-distribution datasets. Our code are available at https://github.com/ZhuoxuCui/Heat-Diffusion.

Characterization of Classic Meningioma with Use of Conventional Magnetic Resonance and Diffusion Tensor Imaging

Introduction: The conventional magnetic resonance imaging (MRI) method is widely considered ‘with limited success’ in differentiating the meningioma types but may fail to localize the tumor occupation of white-matter fiber bundles accurately. Diffusion tensor imaging (DTI) is considered as an imaging modality that may elucidate the microstructure of brain tumors. We provide characteristics of meningioma using DTI-based-three-dimensional tracing of white matter to portray meningioma in a noninvasive approach and its structural contact to contiguous tumors and elucidate the influence of occupying lesions on white-matter fiber bundles. Case Presentation: A 28-year-old female presented with visuospatial disturbances and persistent headaches for 2 years. Conventional and advanced MRI studies were performed. Diffusion-weighted Images (DWI) and Apparent Diffusion Coefficient (ADC) values were measured in the lesion using routine MRI sequences. Advanced MRI using DTI was also performed. Conventional MRI outcomes showed tumor parenchyma, peritumoral edema, and compression on the circumnavigated brain tissue. There was hyperintense on DW trace image and isointense on ADC map. On T2-weighted image (T2WI) and Fluid-Attenuated Inversion Recovery (FLAIR) images, there was an increased signal intensity that demonstrated an extra-axial lesion, while T1-weighted imaging signals showed hypointensity. DTI fractional anisotropy (FA) marker is an unclear optic radiation in the concerned area, indicating the shift or destruction of the optic radiation. The mean FA values of solid-enhancing areas of meningioma were 0.28 ± 0.17. Mean ADC values (103 mm2/s) were 0.764 ± 0.172. Conclusions: Classic meningioma in this case has low intratumoral FA and high ADC. DTI displayed that intratumoral microscopic water motion is disorganized.

Two-phase modelling for fission gas sweeping in restructuring nuclear oxide fuel

In this work, we propose a modelling approach for the intra-granular fission gas behaviour in UO2 under restructuring process. Leveraging the definition of restructured volume fraction, we consider the fuel matrix transition from the non-restructured to the restructured phase, together with the evolution of the corresponding fission gas concentrations retained in the fuel matrix. Firstly, we derive a sweeping term that exchanges fission gas atoms from the non-restructured to the restructured fuel region. The sweeping term is then included in the conventional intra-granular fission gas diffusion problem. Secondly, the spectral diffusion algorithm is employed to solve two spatially-dimensionless problems, properly representing the non-restructured region with micrometric grains and the restructured region with sub-micrometric grains. The model developed is implemented in SCIANTIX, a 0D meso-scale code for physics-based modelling of fission gas behaviour in nuclear oxide fuel and compared with experimental data and semi-empirical models.

Peak width of skeletonized mean diffusivity as a neuroimaging biomarker in first-episode schizophrenia.

Peak width of skeletonized mean diffusivity (PSMD), a fully automated diffusion tensor imaging (DTI) biomarker of white matter (WM) microstructure damage, has been shown to be associated with cognition in various WM pathologies. However, its application in schizophrenic disease remains unexplored. This study aims to investigate PSMD along with other DTI markers in first-episode schizophrenia patients compared to healthy controls (HCs), and explore the correlations between these metrics and clinical characteristics. A total of 56 first-episode drug-naive schizophrenia patients and 64 HCs were recruited for this study. Participants underwent structural imaging and DTI, followed by comprehensive clinical assessments, including the Positive and Negative Syndrome Scale (PANSS) for patients and cognitive function tests for all participants. We calculated PSMD, peak width of skeletonized fractional anisotropy (PSFA), axial diffusivity (PSAD), radial diffusivity (PSRD) values, skeletonized average mean diffusivity (MD), average fractional anisotropy (FA), average axial diffusivity (AD), and average radial diffusivity (RD) values as well as structural network global topological parameters, and examined between-group differences in these WM metrics. Furthermore, we investigated associations between abnormal metrics and clinical characteristics. Compared to HCs, patients exhibited significantly increased PSMD values (t = 2.467, p = 0.015), decreased global efficiency (Z = -2.188, p = 0.029), and increased normalized characteristic path length (lambda) (t = 2.270, p = 0.025). No significant differences were observed between the groups in the remaining metrics, including PSFA, PSAD, PSRD, average MD, FA, AD, RD, local efficiency, normalized cluster coefficient, small-worldness, assortativity, modularity, or hierarchy (p > 0.05). After adjusting for relevant variables, both PSMD and lambda values exhibited a significant negative correlation with reasoning and problem-solving scores (PSMD: r = -0.409, p = 0.038; lambda: r = -0.520, p = 0.006). No statistically significant correlations were observed between each PANSS score and the aforementioned metrics in the patient group (p > 0.05). Multivariate linear regression analysis revealed that increased PSMD (β = -0.426, t = -2.260, p = 0.034) and increased lambda (β = -0.490, t = -2.994, p = 0.007) were independently associated with decreased reasoning and problem-solving scores respectively ( = 0.295, F = 2.951, p = 0.029). But these significant correlations did not withstand FDR correction (p_FDR > 0.05). PSMD can be considered as a valuable neuroimaging biomarker that complements conventional diffusion measurements for investigating abnormalities in WM microstructural integrity and cognitive functions in schizophrenia.

Enhancing energy and nitrogen removal efficiency through automatic split injection and innovative aeration device: A study of low C/N ratio environments

A new aeration device based on Bernoulli's principle, Jetventrumixer (JVM), was introduced into an aeration tank in denitrification process, which involved an automatic split injection system (ASIS) into two denitrification tanks every 10 minutes. Real-time monitoring of influent water allowed the calculation of the C/N ratio, enhancing the utilization efficiency of internal carbon sources while reducing the need for external carbon. The comparison of the JVM with the conventional air diffuser for 100 days operation showed that the removal efficiency for NH4+-N in both systems was approximately 98 %, but the nitrification efficiencies were 84 % and 80 %, respectively. This indicates that the JVM achieves an high enough removal efficiency and nitrification efficiency compared with conventional air diffuser system with dramatic reduction in energy consumption by 52.1 %. When the influent wastewater was split and injected into duplicate denitrification tanks at ratios of 3:7, 5:5, and 7:3, the total nitrogen (TN) removal efficiencies were 77 %, 73 %, and 72 %, respectively. In contrast, with the implementation of the ASIS, the TN removal efficiency increased up to 82 %. The increase in TN removal indicates that real-time monitoring could stably track changes chemical composition in wastewater influent over 24 h and introducing ASIS facilitate the efficient utilization of internal carbon sources, thereby enhancing denitrification efficiency and improving TN removal efficiency. Finally, the greenhouse gas (GHG) emissions from the JVM and air diffuser were 9.39401 and 19.60488 tCO2eq year-1, respectively, representing a 52% reduction. Therefore, JVM and ASIS successfully reduced energy consumption and enhanced both nitrification and denitrification efficiencies.

Atlas-based assessment of hypomyelination: Quantitative MRI in Pelizaeus-Merzbacher disease.

Pelizaeus-Merzbacher disease (PMD) is a rare childhood hypomyelinating leukodystrophy. Quantification of the pronounced myelin deficit and delineation of subtle myelination processes are of high clinical interest. Quantitative magnetic resonance imaging (qMRI) techniques can provide in vivo insights into myelination status, its spatial distribution, and dynamics during brain maturation. They may serve as potential biomarkers to assess the efficacy of myelin-modulating therapies. However, registration techniques for image quantification and statistical comparison of affected pediatric brains, especially those of low or deviant image tissue contrast, with healthy controls are not yet established. This study aimed first to develop and compare postprocessing pipelines for atlas-based quantification of qMRI data in pediatric patients with PMD and evaluate their registration accuracy. Second, to apply an optimized pipeline to investigate spatial myelin deficiency using myelin water imaging (MWI) data from patients with PMD and healthy controls. This retrospective single-center study included five patients with PMD (mean age, 6 years ± 3.8) who underwent conventional brain MRI and diffusion tensor imaging (DTI), with MWI data available for a subset of patients. Three methods of registering PMD images to a pediatric template were investigated. These were based on (a) T1-weighted (T1w) images, (b) fractional anisotropy (FA) maps, and (c) a combination of T1w, T2-weighted, and FA images in a multimodal approach. Registration accuracy was determined by visual inspection and calculated using the structural similarity index method (SSIM). SSIM values for the registration approaches were compared using a t test. Myelin water fraction (MWF) was quantified from MWI data as an assessment of relative myelination. Mean MWF was obtained from two PMDs (mean age, 3.1 years ± 0.3) within four major white matter (WM) pathways of a pediatric atlas and compared to seven healthy controls (mean age, 3 years ± 0.2) using a Mann-Whitney U test. Our results show that visual registration accuracy estimation and computed SSIM were highest for FA-based registration, followed by multimodal, and T1w-based registration (SSIMFA = 0.67 ± 0.04 vs. SSIMmultimodal = 0.60 ± 0.03 vs. SSIMT1 = 0.40 ± 0.14). Mean MWF of patients with PMD within the WM pathways was significantly lower than in healthy controls MWFPMD = 0.0267 ± 0.021 vs. MWFcontrols = 0.1299 ± 0.039. Specifically, MWF was measurable in brain structures known to be myelinated at birth (brainstem) or postnatally (projection fibers) but was scarcely detectable in other brain regions (commissural and association fibers). Taken together, our results indicate that registration accuracy was highest with an FA-based registration pipeline, providing an alternative to conventional T1w-based registration approaches in the case of hypomyelinating leukodystrophies missing normative intrinsic tissue contrasts. The applied atlas-based analysis of MWF data revealed that the extent of spatial myelin deficiency in patients with PMD was most pronounced in commissural and association and to a lesser degree in brainstem and projection pathways.

Exploring Solitary Waves and Nonlinear Dynamics in the Fractional Chaffee–Infante Equation: A Study Beyond Conventional Diffusion Models

Exploring Solitary Waves and Nonlinear Dynamics in the Fractional Chaffee–Infante Equation: A Study Beyond Conventional Diffusion Models

A new strategy for preparing high strength and high precision diffusion bonding GH536 joints via pulsed current and subsequent heat treatment

GH536 as a typical solid solution strengthened Ni-based superalloy, has been widely used in the preparation of laminated cooling structure. Diffusion bonding, which is highly promising as a joining process in laminated cooling structure, generally requires high strength and high precision. However, the high strength joint fabricated by the conventional hot pressure diffusion bonding (HPDB) often leads to severe deformation of base materials. Here, we developed a new diffusion bonding strategy to overcome this issue in GH536 via pulsed current diffusion bonding (PCDB) and subsequent heat treatment. At low temperature and short time (900 °C/30 min), a joint with a high shear strength of 535 MPa and a low deformation rate of 0.42 % was obtained using the PCDB, and these were 1.60 times and 0.28 times that of the conventional HPDB joint, respectively. Meanwhile, the low bonding temperature and short bonding time hindered the formation and coarsening of carbides, allowing them to be eliminated in a short time heat treatment. The unbonded zones healing, carbide dissolution and recrystallization during heat treatment significantly improved the joint shear strength, and the joint shear strength after heat treatment reached 561 MPa.

Identification of Local Overpotentials and Their Spatial Distributions in Polymer Electrolyte Water Electrolyzers

Polymer electrolyte water electrolyzers (PEWEs) are pivotal in the transition to a clean energy economy, promising green and efficient hydrogen production. Understanding the complexities of overpotentials within PEWEs is crucial for optimizing their performance and efficiency1. For PEWEs to attain the highest possible system efficiency, considering a specific material set and operating parameters, meticulous design is imperative. The goal is to achieve a high reaction rate that is not only optimal but also uniform across the entire active surface area of the electrodes. This uniformity should ideally be maintained with minimal sensitivity to variations in operating conditions.This study focuses on the contribution of different overpotential categories and reveals their spatial variations through in-situ analysis to understand and overcome the issues caused by non-uniformity in reaction. Employing a segmented cell and printed circuit board (PCB) approach2, current density distribution (CDD) and distributed area-specific resistance (DASR) measurements are obtained, enabling a detailed examination of the mass transport overpotentials (as shown in Figure 1). Typically, in the fuel cell community, modeling mass transport limitations is thought of as diffusion-limited transport3. In the anode porous transport layer (PTL), oxygen accumulation (i.e. product transport limitation) can cause membrane dry-out and an increase in ASR4. The experimental results elucidate the intricate relationship between ohmic and mass transport losses, emphasizing the deep coupling induced by the two-phase nature of the system. Figure 1 shows that, as mass transport limitation grows, the highest increase in overpotential comes from mass transport-coupled ohmic losses. Beyond conventional diffusion limited mass transport losses, bubble dynamics significantly impact local hydration and ASR, highlighting the need for a holistic approach in device optimization5.This research contributes to the understanding of PEWE dynamics, providing a basis for informed engineering decisions. By unraveling the intricate interdependencies between overpotentials, this work empowers the efficient utilization of expensive catalyst materials. Moreover, it offers insights into mitigating the challenges associated with bubble removal, accomplishing this with lower pumping requirements that scale non-linearly. These insights enrich both theoretical understanding and are paramount for enhancing system efficiency and economic viability of PEWEs, thereby advancing the prospect of sustainable hydrogen production. Reference N. Danilovic and I. Zenyuk, Electrochem. Soc. Interface, 30 (2021).F. H. Roenning, A. Roy, D. S. Aaron, and M. M. Mench, J. Power Sources, 542, 231749 (2022) https://linkinghub.elsevier.com/retrieve/pii/S037877532200742X.A. Z. Weber et al., J. Electrochem. Soc., 161, F1254–F1299 (2014) https://iopscience.iop.org/article/10.1149/2.0751412jes.C. Immerz, B. Bensmann, P. Trinke, M. Suermann, and R. Hanke-Rauschenbach, J. Electrochem. Soc., 165, F1292–F1299 (2018).P. Satjaritanun et al., iScience, 23, 101783 (2020) https://doi.org/10.1016/j.isci.2020.101783. Figure 1

Improving the transfer of dissolved oxygen in a biological Fe2+ oxidation process using a venturi jet as an intensive aeration system

The low bio-production of Fe3+ as a leaching agent is one of the main limitations to implementing industrial bio-processes at feasibility conditions. The main limitation of the bio-oxidation process of Fe2+ is the low oxygen transfer to the aqueous phase because of the low oxygen solubility. This study assesses the effectiveness of the venturi jet as an innovative and intensive aeration device for overcoming the oxygen limitation in a continuous ferrous oxidation process in a fixed-bed reactor with immobilized acidithiobacillus ferrooxidans, in contrast to the conventional diffuser aeration device. Firstly, the influence of the airflow and the influence of the medium concentration were determined for the following parameters for both aeration devices; Volumetric mass transfer coefficient (kLa), Standard Oxygen Transfer Rate (SOTR), Standard Aeration Efficiency (SAE), and Standard Oxygen Transfer Efficiency (SOTE). Then, both aeration devices were compared in a continuous bio-oxidation process in an up-flow packed bio-reactor (UFPB). The system performance was assessed by monitoring temperature, pH, oxidation-reduction potential, and dissolved oxygen concentration for 69 days. Findings displayed that when aerating with the diffuser, the ferrous oxidation rate was restricted by the low dissolved oxygen availability, being about 1 ppm (1 mg L−1). Under these oxygen-limiting conditions, the maximum ferrous (Fe2+) oxidation rate was 9.09 g L−1 h−1. However, when aerating with the venturi jet, the dissolved oxygen concentration increased up to 2.70 mg L−1, achieving a maximum of 29.11 g L−1 h−1. So, this study has demonstrated that the change in the aeration device has resulted in an improvement in the process, achieving a 3.5-fold increase in the oxidation rate. Furthermore, the venturi jet offered additional advantages over the diffuser, such as requiring less power to deliver the same amount of air, being unaffected by jarosite precipitates, and not requiring a supply of compressed air.

Diffusion kurtosis imaging for different brain masses characterization

BackgroundDiffusion kurtosis imaging is an advanced magnetic resonance imaging technique that reveals additional information on the microstructure and micro-dynamics of different brain masses without the need for contrast agents. The aim of this study was to provide a comprehensive analysis of the role of MRI diffusion kurtosis and to compare it with magnetic resonance spectroscopy (MRS) and dynamic susceptibility contrast perfusion (DSC) in characterizing different brain masses, including gliomas, recurrent tumors, radiation necrosis, abscesses, and infarctions. Sixty-six patients with intracranial brain masses were enrolled in this prospective study. All patients were examined by conventional MRI sequences, DSC perfusion, MRS, and diffusion kurtosis imaging, with implemented b values which were 200, 500, 1000, 1500 and 2000s/mm2.ResultsMean kurtosis (MK) was higher (P < 0.001) in recurrent brain tumors than in radiation-induced necrosis; the optimal MK cutoff value for differentiation between them was 642 with 91.3% sensitivity and 85.7% specificity. Mean kurtosis was also higher (P < 0.001) in high-grade gliomas than in low-grade gliomas; the optimal MK cutoff value for differentiation between them was 639 with 91.6% sensitivity and 85.71% specificity. There was a good level of agreement between ADC and MD within the studied cases, with a correlation coefficient r = 0.815. MK had more sensitivity and specificity in differentiation between high- and low-grade gliomas, as well as RIN and tumoral recurrence, than MRS and DSC.ConclusionsDiffusion kurtosis imaging stands as an integral, noninvasive, and noncontrast tool for the characterization of various brain masses. It augments the capabilities of traditional and advanced MRI techniques, providing a deeper understanding of the microstructural changes in brain tissues.

A Diffusion‐Based Uncertainty Quantification Method to Advance E3SM Land Model Calibration

AbstractCalibrating land surface models and accurately quantifying their uncertainty are crucial for improving the reliability of simulations of complex environmental processes. This, in turn, advances our predictive understanding of ecosystems and supports climate‐resilient decision‐making. Traditional calibration methods, however, face challenges of high computational costs and difficulties in accurately quantifying parameter uncertainties. To address these issues, we develop a diffusion‐based uncertainty quantification (DBUQ) method. Unlike conventional generative diffusion methods, which are computationally expensive and memory‐intensive, DBUQ innovates by formulating a parameterized generative model and approximates this model through supervised learning, which enables quick generation of parameter posterior samples to quantify its uncertainty. DBUQ is effective, efficient, and general‐purpose, making it suitable for site‐specific ecosystem model calibration and broadly applicable for parameter uncertainty quantification across various earth system models. In this study, we applied DBUQ to calibrate the Energy Exascale Earth System Model land model at the Missouri Ozark AmeriFlux forest site. Results indicated that DBUQ produced accurate parameter posterior distributions similar to those from Markov Chain Monte Carlo sampling but with 30 times less computing time. This significant improvement in efficiency suggests that DBUQ can enable rapid, site‐level model calibration at a global scale, enhancing our predictive understanding of climate impacts on terrestrial ecosystems.

Development of Rapid Disk Diffusion Device Using Laser Speckle Formation Technology for Rapid Antimicrobial Susceptibility Testing

The escalation of antimicrobial resistance (AMR) due to the excessive and inappropriate use of antimicrobials has prompted the urgent need for more rapid and effective antimicrobial susceptibility testing (AST) methods. Conventional AST techniques often take 16–24 h, leading to empirical prescription practices and the potential emergence of AMR. The study aimed to develop a rapid disk diffusion (RDD) method utilizing laser speckle formation (LSF) technology to expedite AST results. The study aimed to evaluate the performance of LSF technology in determining antimicrobial susceptibility. In this study, preclinical and clinical settings were established to compare the LSF technology with conventional disk diffusion (DD) methods to measure the inhibition zones. Preclinical experiments with different bacterial strains demonstrated more than 70% categorical agreement (CA) against most antimicrobials. Further, clinical experiments with multiple strains and antibiotics revealed CA ranging from 40 to 79%, while major and minor discrepancies were observed around 30% and 11%, respectively. These observations revealed high concordance between RDD and DD for multiple antimicrobials in multiple species. The results underscore the potential of RDD-based LSF technology for hastening AST procedures. The current study is marked by a unique equipment setup and analysis approach. Collectively, the suggested laser-based RDD showed greater potential than previously developed comparable methods. The proposed method and design have a higher application potential than formerly developed similar technologies. Together, the study contributes to the ongoing development of rapid AST methods.

Ultrasonic liquid crystal tunable light diffuser

Conventional light diffusers have periodic surface profiles, periodic refractive index distributions, or light scattering layers containing colloids. In all such structures the optical directivity of the light diffuser is cannot typically be controlled. Here we propose an electrically tunable light diffuser based on the application of ultrasound to a nematic liquid crystal (LC) material. The ultrasonic LC diffuser consists of an LC layer sandwiched by two glass discs and an ultrasonic transducer. The electrodes of the transducer are divided in a circumferential direction so that a resonant non-coaxial flexural vibration mode can be generated on the diffuser by controlling the electrical input signals. A continuous reversed-phase sinusoidal electric signal to the transducer generates the non-coaxial resonant flexural vibration mode on the glass disc, inducing an acoustic radiation force acting on the boundary between the LC layer and glass discs. This effect changes the molecular orientation of the LC and the transmitted light distribution. The diffusion angle of the transmitted light depends on the input voltage amplitude, and the diffusion angle was maximized at 16.0 V. The vibrational distribution and the diffusion directivity could be rotated by adjusting the input voltages to different electrodes, meaning that an ultrasonic LC diffuser with a thin structure and no moving mechanical parts provided a tunable light-diffusing functionality with rotatable directivity.