Microstructural modification of recycled aluminium alloys by high-intensity ultrasonication: Observations from custom Al–2Si–2Mg–1.2Fe–(0.5,1.0)Mn alloys

The effect of ultrasonication on the solidification microstructure of recycled Al-alloys is investigated using custom Al–2Si–2Mg-1.2Fe-xMn alloys (x = 0.5 and 1%, in wt.%) through cooling curve measurement, optical and electron microscopy, X-ray diffraction, differential scanning calorimetry and computational thermodynamic calculations. Applying ultrasonication throughout the primary-Al nucleation stage resulted in refined non-dendritic grain structure. Cooling curves indicate a noticeable reduction in primary-Al nucleation undercooling and reduction of the recalescence peak under ultrasonication. However, terminating ultrasonication prior to the nucleation of primary-Al led to dendritic grains with marginal refinement. Without ultrasonication, coarse Chinese-script α−Al15(Fe,Mn)3Si2 intermetallics developed from initially polygonal particles due to interface growth instability under thermo-solutal undercooling. In contrast, ultrasonication produced refined and polygonal α−Al15(Fe,Mn)3Si2 particles by promoting nucleation and growth stabilisation under strong fluid flow. The enhanced nucleation from ultrasonication is presumably due to the pressure-induced shift of freezing point along with improved wetting of insoluble inclusions under cavitation. The present results show that ultrasonication can effectively modify the Fe-intermetallics and refine the grain structure in recycled Al-alloys

Anisotropic Cardiac Conduction.

Anisotropy is the property of directional dependence. In cardiac tissue, conduction velocity is anisotropic and its orientation is determined by myocyte direction. Cell shape and size, excitability, myocardial fibrosis, gap junction distribution and function are all considered to contribute to anisotropic conduction. In disease states, anisotropic conduction may be enhanced, and is implicated, in the genesis of pathological arrhythmias. The principal mechanism responsible for enhanced anisotropy in disease remains uncertain. Possible contributors include changes in cellular excitability, changes in gap junction distribution or function and cellular uncoupling through interstitial fibrosis. It has recently been demonstrated that myocyte orientation may be identified using diffusion tensor magnetic resonance imaging in explanted hearts, and multisite pacing protocols have been proposed to estimate myocyte orientation and anisotropic conduction in vivo. These tools have the potential to contribute to the understanding of the role of myocyte disarray and anisotropic conduction in arrhythmic states

Solidification behavior of intensively sheared hypoeutectic Al-Si alloy liquid

The official published version of this article can be found at the link below.The effect of the processing temperature on the microstructural and mechanical properties of Al-Si (hypoeutectic) alloy solidified from intensively sheared liquid metal has been investigated systematically. Intensive shearing gives a significant refinement in grain size and intermetallic particle size. It also is observed that the morphology of intermetallics, defect bands, and microscopic defects in high-pressure die cast components are affected by intensive shearing the liquid metal. We attempt to discuss the possible mechanism for these effects.Funded by the EPSRC

Variation with lunar phase of midday critical frequencies and heights of the F2 layer over Ahmedabad and other low latitude stations

The paper contains an analysis of the variation of the midday values of foF2, h'F2 and hpF2 with lunar phase at Ahmedabad during the years 1954 and 1955 and of foF2 alone at Bombay, Madras and Tiruchirapalli during 1954. It is found that while the semidiurnal lunar tidal variations at Ahmedabad and Bombay agree in phase with those observed at middle latitudes, the phase reverses in direction between Bombay and Madras. The results are compared with those relating to Huancayo and Singapore

Microstructural modification of Sn–Bi and Sn–Bi–Al immiscible alloys by shearing

Sn–20 wt-%Bi and immiscible Sn–20 wt-%Bi–1 wt-%Al alloys were used to understand the effect of high-intensity shearing on microstructural refinement. Novel ACME (Axial Centrifugal Metal Expeller) shearing device, based on axial compressor and rotor–stator mechanism to generate high shear rate and intense turbulence, was used to condition the melts prior to solidification. Microstructure in the Sn–Bi alloy deviated from dendritic grains with coarse eutectic pockets under conventional solidification to compact grains with well-dispersed eutectic under semisolid-state shearing. Decreasing the shearing temperature and increasing shearing time increased the globularity of grains. Following shearing, remnant liquid solidified into fine grain structure. In the immiscible Sn–Bi–Al alloy, shearing produced uniform dispersion of refined Al-rich particles in Sn-rich matrix as opposed to severe segregation under conventional solidification. The primary effect of shearing appears to originate from the thermo-solutal homogenisation of the melt and its effect on interface stability during solidification

Expression of expanded FMR1-CGG repeats alters mitochondrial miRNAs and modulates mitochondrial functions and cell death in cellular model of FXTAS

Fragile X-associated tremor/ataxia syndrome (FXTAS) is a progressive neurodegenerative disorder caused by an expansion of 55 to 200 CGG repeats located within 5′UTR of FMR1.These CGG repeats are transcribed into RNAs, which sequester several RNA binding proteins and alter the processing of miRNAs. CGG repeats are also translated into a toxic polyglycine-containing protein, FMRpolyG, that affects mitochondrial and nuclear functions reported in cell and animal models and patient studies. Nuclear-encoded small non-coding RNAs, including miRNAs, are transported to mitochondria; however, the role of mitochondrial miRNAs in FXTAS pathogenesis is not understood. Here, we analyzed mitochondrial miRNAs from HEK293 cells expressing expanded CGG repeats and their implication in the regulation of mitochondrial functions. The analysis of next generation sequencing (NGS) data of small RNAs from HEK293 cells expressing CGG premutation showed decreased level of cellular miRNAs and an altered pattern of association of miRNAs with mitochondria (mito-miRs). Among such mito-miRs, miR-320a was highly enriched in mitoplast and RNA immunoprecipitation of Ago2 (Argonaute-2) followed by Droplet digital PCR (ddPCR)suggested that miR-320a may form a complex with Ago2 and mitotranscripts. Finally, transfection of miR-320a mimic in cells expressing CGG permutation recovers mitochondrial functions and rescues cell death. Overall, this work reveals an altered translocation of miRNAs to mitochondria and the role of miR-320a in FXTAS pathology

OpenEP: A Cross-Platform Electroanatomic Mapping Data Format and Analysis Platform for Electrophysiology Research.

BACKGROUND: Electroanatomic mapping systems are used to support electrophysiology research. Data exported from these systems is stored in proprietary formats which are challenging to access and storage-space inefficient. No previous work has made available an open-source platform for parsing and interrogating this data in a standardized format. We therefore sought to develop a standardized, open-source data structure and associated computer code to store electroanatomic mapping data in a space-efficient and easily accessible manner. METHODS: A data structure was defined capturing the available anatomic and electrical data. OpenEP, implemented in MATLAB, was developed to parse and interrogate this data. Functions are provided for analysis of chamber geometry, activation mapping, conduction velocity mapping, voltage mapping, ablation sites, and electrograms as well as visualization and input/output functions. Performance benchmarking for data import and storage was performed. Data import and analysis validation was performed for chamber geometry, activation mapping, voltage mapping and ablation representation. Finally, systematic analysis of electrophysiology literature was performed to determine the suitability of OpenEP for contemporary electrophysiology research. RESULTS: The average time to parse clinical datasets was 400 ± 162 s per patient. OpenEP data was two orders of magnitude smaller than compressed clinical data (OpenEP: 20.5 ± 8.7 Mb, vs clinical: 1.46 ± 0.77 Gb). OpenEP-derived geometry metrics were correlated with the same clinical metrics (Area: R 2 = 0.7726, P < 0.0001; Volume: R 2 = 0.5179, P < 0.0001). Investigating the cause of systematic bias in these correlations revealed OpenEP to outperform the clinical platform in recovering accurate values. Both activation and voltage mapping data created with OpenEP were correlated with clinical values (mean voltage R 2 = 0.8708, P < 0.001; local activation time R 2 = 0.8892, P < 0.0001). OpenEP provides the processing necessary for 87 of 92 qualitatively assessed analysis techniques (95%) and 119 of 136 quantitatively assessed analysis techniques (88%) in a contemporary cohort of mapping studies. CONCLUSIONS: We present the OpenEP framework for evaluating electroanatomic mapping data. OpenEP provides the core functionality necessary to conduct electroanatomic mapping research. We demonstrate that OpenEP is both space-efficient and accurately representative of the original data. We show that OpenEP captures the majority of data required for contemporary electroanatomic mapping-based electrophysiology research and propose a roadmap for future development

Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models.

BACKGROUND: Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more interindividual variability. METHODS: Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, and 16 long-standing persistent), undergoing first ablation. Patients were followed for 1 year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fiber orientation maps, electrical properties, and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were postprocessed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging, and atrial fibrillation simulation metrics. RESULTS: We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging, and simulation stress tests (average 10-fold cross-validation area under the curve, 0.85±0.09; recall, 0.80±0.13; precision, 0.74±0.13) outperformed those trained to history and imaging (area under the curve, 0.66±0.17) or history alone (area under the curve, 0.61±0.14). CONCLUSION: A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation

Predicting atrial fibrillation recurrence by combining population data and virtual cohorts of patient-specific left atrial models

Background: Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more interindividual variability. Methods: Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, and 16 long-standing persistent), undergoing first ablation. Patients were followed for 1 year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fiber orientation maps, electrical properties, and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were postprocessed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging, and atrial fibrillation simulation metrics. Results: We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging, and simulation stress tests (average 10-fold cross-validation area under the curve, 0.85±0.09; recall, 0.80±0.13; precision, 0.74±0.13) outperformed those trained to history and imaging (area under the curve, 0.66±0.17) or history alone (area under the curve, 0.61±0.14). Conclusion: A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation