General Electric

A turbulent flow can be characterized by Taylor correlation functions which are obtained empirically, understood by statistical mechanics and regarded as universal. Here, we show that Taylor correlations are analytically derived by hypothesizing turbulence as a phenomenon of superfluids at resonance. Leveraging from a recent study on heat transfer at the speed of sound, we derived and fitted the longitudinal and lateral turbulent velocities in an isotropic, turbulent flow. The concept of the boundary of the second law helps to specify the integration constants in the solution. From the velocity profiles, Taylor’s correlation functions are analytically determined. From the linearity of the eigenfunction, we introduce amplitude and frequency factors. These factors are curve-fitted with two experimental dataset. Additional experimental datasets in the public domain are compared to the correlations, which shows that the theory agrees with experiments very well in isotropic flows. The analytical correlation functions help to elucidate observations that experiments and statistical mechanics have challenges to explain.

Based on the observations made in rheumatology clinics, autoimmune disease (AD) patients on immunosuppressive (IS) medications have variable vaccine site inflammation responses, whose study may help predict the long-term efficacy of the vaccine in this at-risk population. However, the quantitative assessment of the inflammation of the vaccine site is technically challenging. In this study analyzing AD patients on IS medications and normal control subjects, we imaged the inflammation of the vaccine site 24 h after mRNA COVID-19 vaccinations were administered using both the emerging photoacoustic imaging (PAI) method and the established Doppler ultrasound (US) method. A total of 15 subjects were involved, including 6 AD patients on IS and 9 normal control subjects, and the results from the two groups were compared. Compared to the results obtained from the control subjects, the AD patients on IS medications showed statistically significant reductions in vaccine site inflammation, indicating that immunosuppressed AD patients also experience local inflammation after mRNA vaccination but not in as clinically apparent of a manner when compared to non-immunosuppressed non-AD individuals. Both PAI and Doppler US were able to detect mRNA COVID-19 vaccine-induced local inflammation. PAI, based on the optical absorption contrast, shows better sensitivity in assessing and quantifying the spatially distributed inflammation in soft tissues at the vaccine site.

Objective This study aims to develop and validate the performance of an unenhanced magnetic resonance imaging (MRI)-based combined radiomics nomogram for discrimination between low-grade and high-grade in chondrosarcoma. Methods A total of 102 patients with 44 in low-grade and 58 in high-grade chondrosarcoma were enrolled and divided into training set (n=72) and validation set (n=30) with a 7:3 ratio in this retrospective study. The demographics and unenhanced MRI imaging characteristics of the patients were evaluated to develop a clinic-radiological factors model. Radiomics features were extracted from T1-weighted (T1WI) images to construct radiomics signature and calculate radiomics score (Rad-score). According to multivariate logistic regression analysis, a combined radiomics nomogram based on MRI was constructed by integrating radiomics signature and independent clinic-radiological features. The performance of the combined radiomics nomogram was evaluated in terms of calibration, discrimination, and clinical usefulness. Results Using multivariate logistic regression analysis, only one clinic-radiological feature (marrow edema OR=0.29, 95% CI=0.11-0.76, P=0.012) was found to be independent predictors of differentiation in chondrosarcoma. Combined with the above clinic-radiological predictor and the radiomics signature constructed by LASSO [least absolute shrinkage and selection operator], a combined radiomics nomogram based on MRI was constructed, and its predictive performance was better than that of clinic-radiological factors model and radiomics signature, with the AUC [area under the curve] of the training set and the validation set were 0.78 (95%CI =0.67-0.89) and 0.77 (95%CI =0.59-0.94), respectively. DCA [decision curve analysis] showed that combined radiomics nomogram has potential clinical application value. Conclusion The MRI-based combined radiomics nomogram is a noninvasive preoperative prediction tool that combines clinic-radiological feature and radiomics signature and shows good predictive effect in distinguishing low-grade and high-grade bone chondrosarcoma, which may help clinicians to make accurate treatment plans.

Stress cardiovascular magnetic resonance (CMR) imaging is a well-validated non-invasive stress test to diagnose significant coronary artery disease (CAD), with higher diagnostic accuracy than other common functional imaging modalities. One-stop assessment of myocardial ischemia, cardiac function, and myocardial viability qualitatively and quantitatively has been proven to be a cost-effective method in clinical practice for CAD evaluation. Beyond diagnosis, stress CMR also provides prognostic information and guides coronary revascularisation. In addition to CAD, there is a large body of literature demonstrating CMR’s diagnostic performance and prognostic value in other common cardiovascular diseases (CVDs), especially coronary microvascular dysfunction (CMD). This review focuses on the clinical applications of stress CMR, including stress CMR scanning methods, practical interpretation of stress CMR images, and clinical utility of stress CMR in a setting of CVDs with possible myocardial ischemia.

Aim: To evaluate image quality acquired at lung imaging using magnetic resonance imaging (MRI) sequences using short and ultra-short (UTE) echo times (TEs) with different acquisition strategies (breath-hold, prospective, and retrospective gating) in paediatric patients and in healthy volunteers. Materials and methods: End-inspiratory and end-expiratory three-dimensional (3D) spoiled gradient (SPGR3D) and 3D zero echo-time (ZTE3D), and 3D UTE free-breathing (UTE3D), prospective projection navigated radial ZTE3D (ZTE3D vnav), and four-dimensional ZTE (ZTE4D) were performed using a 1.5 T MRI system. For quantitative assessment, the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) values were calculated. To evaluate image quality, qualitative scoring was undertaken on all sequences to evaluate depiction of intrapulmonary vessels, fissures, bronchi, imaging noise, artefacts, and overall acceptability. Results: Eight cystic fibrosis (CF) patients (median age 14 years, range 13-17 years), seven children with history of prematurity with or without bronchopulmonary dysplasia (BPD; median 10 years, range 10-11 years), and 10 healthy volunteers (median 32 years, range 20-52 years) were included in the study. ZTE3D vnav provided the most reliable output in terms of image quality, although scan time was highly dependent on navigator triggering efficiency and respiratory pattern. Conclusions: Best image quality was achieved with prospective ZTE3D and UTE3D readouts both in children and volunteers. The current implementation of retrospective ZTE3D readout (ZTE4D) did not provide diagnostic image quality but rather introduced artefacts over the entire imaging volume mimicking lung pathology.

PurposeUltrasound is often the preferred modality for image-guided therapy or treatment in organs such as liver due to real-time imaging capabilities. However, the reduced conspicuity of tumors in ultrasound images adversely impacts the precision and accuracy of treatment delivery. This problem is compounded by deformable motion due to breathing and other physiological activity. This creates the need for a fusion method to align interventional US with pre-interventional modalities that provide superior soft-tissue contrast (e.g., MRI) to accurately target a structure-of-interest and compensate for liver motion.Method In this work, we developed a hybrid deformable fusion method to align 3D pre-interventional MRI and 3D interventional US volumes to target the structures-of-interest in liver accurately in real-time. The deformable multimodal fusion method involved an offline alignment of a pre-intervention MRI with a pre-intervention US volume using a traditional registration method, followed by real-time prediction of deformation using a trained deep-learning model between interventional US volumes across different respiratory states. This framework enables motion-compensated MRI-US image fusion in real-time for image-guided treatment.ResultsThe proposed hybrid deformable registration method was evaluated on three healthy volunteers across the pre-intervention MRI and 20 US volume pairs in the free-breathing respiratory cycle. The mean Euclidean landmark distance of three homologous targets in all three volunteers was less than 3 mm for percutaneous liver procedures.Conclusions Preliminary results show that clinically acceptable registration accuracies for near real-time, deformable MRI-US fusion can be achieved by our proposed hybrid approach. The proposed combination of traditional and deep-learning deformable registration techniques is thus a promising approach for motion-compensated MRI-US fusion to improve targeting in image-guided liver interventions.

Objectives: To establish and verify radiomics models based on multiparametric MRI for preoperatively identifying the microsatellite instability (MSI) status of rectal cancer (RC) by comparing different machine learning algorithms. Methods: This retrospective study enrolled 383 (training set, 268; test set, 115) RC patients between January 2017 and June 2022. A total of 4148 radiomics features were extracted from multiparametric MRI, including T2-weighted imaging, T1-weighted imaging, apparent diffusion coefficient, and contrast-enhanced T1-weighted imaging. The analysis of variance, correlation test, univariate logistic analysis, and a gradient-boosting decision tree were used for the dimension reduction. Logistic regression, Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and tree machine learning algorithms were used to build different radiomics models. The relative standard deviation (RSD) and bootstrap method were used to quantify the stability of these five algorithms. Then, predictive performances of different models were assessed using area under curves (AUCs). The performance of the best radiomics model was evaluated using calibration and discrimination. Results: Among these 383 patients, the prevalence of MSI was 14.62% (56/383). The RSD value of logistic regression algorithm was the lowest (4.64%), followed by Bayes (5.44%) and KNN (5.45%), which was significantly better than that of SVM (19.11%) and tree (11.94%) algorithms. The radiomics model based on logistic regression algorithm performed best, with AUCs of 0.827 and 0.739 in the training and test sets, respectively. Conclusions: We developed a radiomics model based on the logistic regression algorithm, which could potentially be used to facilitate the individualized prediction of MSI status in RC patients.

The real-time models of offshore wind turbine generators complied with industry standards are presented in this paper. The developed models provide essential capabilities for future electromagnetic transient controls and testing offshore wind farms, such as low-voltage ride-through, active/reactive power support, transient current limiting. Furthermore, the proposed models provide an additional capability of controlling negative sequence current, which meets the future requirements for protecting wind farm systems. This paper presents average-value and detailed switching real-time models in the Opal-RT real-time simulator. The efficacy of the proposed real-time models in terms of computational efficiency is demonstrated by comparing them with the non-real-time models. Extensive case studies are performed to demonstrate the control functions of the proposed models, such as dynamic-wind condition, power curtailment, low-voltage and fault ride-through, negative sequence current control, etc. The wind turbine model validation against the generic model presented by the Western Electricity Coordinating Council (WECC) is conducted to show their compliance with industry standards utilized by WECC. A real-time model of a 450 MW offshore wind farm is developed to show the feasibility of large-scale wind farm modeling.

Muscle weakness is common in many neurological, neuromuscular, and musculoskeletal conditions. Muscle size only partially explains muscle strength as adaptions within the nervous system also contribute to strength. Brain-based biomarkers of neuromuscular function could provide diagnostic, prognostic, and predictive value in treating these disorders. Therefore, we sought to characterize and quantify the brain's contribution to strength by developing multimodal MRI pipelines to predict grip strength. However, the prediction of strength was not straightforward, and we present a case of sex being a clear confound in brain decoding analyses. While each MRI modality—structural MRI (i.e., gray matter morphometry), diffusion MRI (i.e., white matter fractional anisotropy), resting state functional MRI (i.e., functional connectivity), and task-evoked functional MRI (i.e., left or right hand motor task activation)—and a multimodal prediction pipeline demonstrated significant predictive power for strength ( R ² = 0.108–0.536, p ≤ 0.001), after correcting for sex, the predictive power was substantially reduced ( R ² = −0.038–0.075). Next, we flipped the analysis and demonstrated that each MRI modality and a multimodal prediction pipeline could significantly predict sex (accuracy = 68.0%−93.3%, AUC = 0.780–0.982, p < 0.001). However, correcting the brain features for strength reduced the accuracy for predicting sex (accuracy = 57.3%−69.3%, AUC = 0.615–0.780). Here we demonstrate the effects of sex-correlated confounds in brain-based predictive models across multiple brain MRI modalities for both regression and classification models. We discuss implications of confounds in predictive modeling and the development of brain-based MRI biomarkers, as well as possible strategies to overcome these barriers.

The advancement of the additive manufacturing (AM) process in the past few years has offered an opportunity to generate arbitrary typologies with fewer constraints compared to conventional production methods; thus, the manufacturing conformity of complex tunable metamaterials has been enabled. In this chapter, the current state of research on the additively manufactured metamaterials in the field of electromagnetism, optics, and mechanics (vibration, seismic, thermal, acoustics, and structural) is reviewed. A comprehensive outlook of the manufacturing methods, areas, and applications according to the trends in literature for the purpose of underlining the state of the art and interdisciplinarity is represented.

In additive manufacturing (AM) technologies, support structures are used to anchor a part to the base plate and to prevent the part from distortions and dimensional deviations due to high thermal gradients during manufacturing. Because the support structures do not contribute any value to the part and need to be removed after manufacturing with extra costs and time, different studies have focused on minimizing the use of such structures. However, it is almost impossible to totally eliminate the need for support structures, especially in very complex parts with different overhang surfaces. Therefore, it is very important to optimize the support structure geometry to reduce support volume and consequently costs and time. Thus, the aim of this study is to investigate the effect of tooth support geometrical parameters, namely tooth height, top length, base length, and base interval on the part's dimensional accuracy, surface roughness, microhardness through thickness, and support volume used in overhangs produced by laser powder bed fusion AM technology from Inconel 718 material. The L9 Taguchi design method was used to reduce the number of experiments. The efficiency of the parameters was determined by analysis of variance. Analyses of signal-to-noise ratios were used to obtain the optimum support parameter combination. The study reveals that tooth height has the highest effect on support volume and dimensional accuracy. Tooth base length was found to be the most effective parameter on surface roughness and microhardness through thickness.

There is currently an urgent need to identify factors predictive of immunogenicity in colorectal cancer (CRC). Mucinous CRC is a distinct histological subtype of CRC, associated with a poor response to chemotherapy. Recent evidence suggests the commensal facultative anaerobe Fusobacterium may be especially prevalent in mucinous CRC. The objectives of this study were to assess the impact of Fusobacterium prevalence on immune cell expression and prognosis in mucinous CRC. Our study included two independent colorectal cancer patient cohorts, The Cancer Genome Atlas (TCGA) cohort, and a cohort of rectal cancers from the Beaumont RCSI Cancer Centre (BRCC). Multiplexed immunofluorescence staining of a tumor microarray (TMA) from the BRCC cohort was undertaken using Cell DIVE technology. Our cohorts included 87 cases (13.3%) of mucinous and 565 cases (86.7%) of non-mucinous CRC. Mucinous CRC in the TCGA dataset was associated with increased CD8 + lymphocyte (p = 0.018), regulatory T-cell (p = 0.001) and M2 macrophage (p = 0.001) expression. Similarly in the BRCC cohort, mucinous RC was associated with enhanced CD8 + lymphocyte (p = 0.022), regulatory T-cell (p = 0.047), and B-cell (p = 0.025) counts. Elevated Fusobacterium expression was associated with increased CD4+ (p = 0.031) and M1 macrophage (p = 0.006) expression, whilst M2 macrophages (p = 0.043) were under-expressed in the TCGA cohort. Increased Fusobacterium relative abundance in mucinous CRC was associated with improved clinical outcomes in our TCGA cohort despite having no association with MSI status (DSS: likelihood ratio p = 0.04, logrank p = 0.052). Fusobacterium abundance is associated with improved outcomes in mucinous CRC, possibly due its modulatory effect on the host immune response.

For optimal design of anti-amyloid-β (Aβ) and anti-tau clinical trials, we need to better understand the pathophysiological cascade of Aβ- and tau-related processes. Therefore, we set out to investigate how Aβ and soluble phosphorylated tau (p-tau) relate to the accumulation of tau aggregates assessed with PET and subsequent cognitive decline across the Alzheimer’s disease (AD) continuum. Using human cross-sectional and longitudinal neuroimaging and cognitive assessment data, we show that in early stages of AD, increased concentration of soluble CSF p-tau is strongly associated with accumulation of insoluble tau aggregates across the brain, and CSF p-tau levels mediate the effect of Aβ on tau aggregation. Further, higher soluble p-tau concentrations are mainly related to faster accumulation of tau aggregates in the regions with strong functional connectivity to individual tau epicenters. In this early stage, higher soluble p-tau concentrations is associated with cognitive decline, which is mediated by faster increase of tau aggregates. In contrast, in AD dementia, when Aβ fibrils and soluble p-tau levels have plateaued, cognitive decline is related to the accumulation rate of insoluble tau aggregates. Our data suggest that therapeutic approaches reducing soluble p-tau levels might be most favorable in early AD, before widespread insoluble tau aggregates. The interplay between amyloid and tau pathology in Alzheimer’s disease is still not well understood. Here, the authors show that amyloid-related increased in soluble p-tau is related to subsequent accumulation of tau aggregates and cognitive decline in early stage of the disease.

A FeCrAl (Fe12Cr6Al2Mo) alloy was prepared by two methods, (a) traditional wrought and (b) powder metallurgy (PM). Both alloys exhibited the same average grain size (∼40 um), but the PM alloy displayed a wider grain size distribution. Charpy impact testing showed that the ductile to brittle transition temperature width is dependent on the grain size distribution width.

We introduce the stochastic pseudo-star degree centrality problem, which focuses on a novel probabilistic group-based centrality metric. The goal is to identify a feasible induced pseudo-star, which is defined as a collection of nodes forming a star network with a certain probability, such that it maximizes the sum of the individual probabilities of unique assignments between the star and its open neighborhood. The feasibility is measured as the product of the existence probabilities of edges between the center node and leaf nodes and the product of one minus the existence probabilities of edges among the leaf nodes. First, the problem is shown to be NP-complete. We then propose a non-linear binary optimization model subsequently linearized via McCormick inequalities. We test both classical and modern Benders Decomposition algorithms together with both two- and three-phase decomposition frameworks. Logic-based-Benders cuts are examined as alternative feasibility cuts when needed. The performance of our implementations is tested on small-world (SW) graphs and a real-world protein-protein interaction network. The SW networks resemble large-scale protein-protein interaction networks for which the deterministic star degree centrality has been shown to be an efficient centrality metric to detect essential proteins. Our computational results indicate that Benders implementations outperforms solving the model directly via a commercial solver in terms of both the solution time and the solution quality in every test instance. More importantly, we show that this new centrality metric plays an important role in the identification of essential proteins in real-world networks.

The aim of this study is to make a comparative assessment of the compression and tensile behavior of two strut-based (body-centered cubic, BCC, and face-centered cubic, FCC) and three triply periodic minimum surfaces (gyroid, primitive, diamond) lattice structures produced by electron beam melting method from Ti6Al4V powder material. Compression and tension tests were performed and compared with finite element analysis results. Moreover, scanning electron microscope analysis for dimensional variation and optical microscope analysis for microstructural changes were performed. Gibson-Ashby relations and related coefficients were also calculated. Results showed that gyroid specimens showed the highest (21.7%) and diamond specimens showed the lowest dimensional error (6.5%). Tensile test results showed that the highest and the lowest ultimate tensile strength were observed on diamond (422 MPa) and BCC (192 MPa) specimens, respectively. Compression test results showed that the diamond had the highest yield stress (427 MPa) and first maximum compressive strength (526 MPa). The highest and the lowest energy absorption capabilities were observed on gyroid (180.2 MJ/m3) and BCC (84.2 MJ/m3) topologies, respectively.