Splitting Approach Research Articles

Location of the posterior interosseous nerve in relation to common lateral approaches to the elbow

The risk of posterior interosseous nerve (PIN) injury during surgical approaches to the lateral elbow varies depending on chosen approach, level of dissection, and rotational position of the forearm. Previous studies evaluated the trajectory of the PIN in specific surgical applications to reduce iatrogenic nerve injuries. The goal of this study is to examine the location of the PIN using common lateral approaches with varying forearm rotation. The Kaplan, EDC split and Kocher approaches were performed on 18 cadaveric upper extremity specimens. Measurements were recorded with a digital caliper from the radiocapitellar (RC) joint and the lateral epicondyle to the point where the PIN crosses the approach in full supination, neutral, and full pronation with the elbow at 90 degrees. The ratio of the nerve's location in relation to the entire length of the radius was also evaluated to account for different sized specimens. The PIN was not encountered in the Kocher interval. For Kaplan and EDC split, with the forearm in full supination, mean distance from lateral epicondyle to PIN was 52.0 ± 6.1mm and 59.1 ± 5.5mm respectively, and mean distance from RC joint to PIN was 34.7 ± 5.5mm and 39.3 ± 4.7mm respectively; with the forearm in full pronation, mean distance from lateral epicondyle to PIN was 63.3 ± 9.7mm and 71.4 ± 8.3mm respectively, and mean distance from RC joint to PIN was 44.2 ± 7.7mm and 51.1 ± 8.7mm respectively. The PIN is closer to the lateral epicondyle and radiocapitellar joint in the Kaplan than EDC split approach and is not encountered during the Kocher approach. The PIN was not encountered within 26mm from the radiocapitellar joint and 39mm from the lateral epicondyle in any approach and forearm position and is generally safe from iatrogenic injury within these distances.

Evaluation of the treatment of distal radial volar fracture by different methods sparing the pronator quadratus

ObjectiveThe traditional volar approach requires the release of the pronator quadratus (PQ) muscle in the treatment of distal radius fractures. However, intraoperative repair of the PQ muscle often fails due to tissue injury and unstable muscle repair. This study compared the outcomes of different methods of sparing the PQ muscle combined with the volar plate in treating distal radius fractures.MethodsA total of 95 patients with distal radius fractures sparing the PQ muscle were enrolled with the brachioradialis (BR) splitting approach (group A, 33 people), the volar plating insertion PQ muscle approach (group B, 35 people) and traditional Henry approach without sparing PQ muscle (group C, 27 people). Postoperative internal fixation, fracture healing and postoperative complications were observed in the three groups. The visual analog scale (VAS) of postoperative wrist pain was compared between three groups. The Dienst joint scale was used to evaluate the wrist function of patients, and imaging indexes were used to evaluate the surgical efficacy.ResultsA total of 95 patients with distal radius fractures were followed up for more than one year after surgery. All fractures obtained good union, with no vascular injury, nerve injury or wound infection. Outcomes at three days, one month and three months all showed no significant differences in postoperative imaging indexes among three groups and no significant differences in various indexes among three groups during the same period. The mean operative time in group C was significantly lower than that in groups A and B. There was significant difference in the mean operation time between group A and group B. The amount of mean operative blood loss or mean bone union time in groups A and B was significantly lower than those in group C. No significant difference was shown in mean operative blood loss or mean bone union time between group A and group B. No significant differences in limb function scores, VAS scores and the mean range of motion existed among three groups at the 12-month postoperative follow-up. However, outcomes assessed one week, one month and three months after surgery demonstrated significant differences in the VAS scores and the mean range of motion among three groups, and the group B had lower VAS score and greater the mean range of motion. According to Dienst score, the excellent rate in groups A, B and C was 91.0% (30/33), 94.2% (33/35) and 85.2% (23/27), respectively, at 12 months after surgery. Tendon irritation occurred in 2 cases and joint stiffness in 1 case in group A. In group B, there were 2 cases traumatic arthritis and 2 cases delayed carpal tunnel syndrome and 1 case tendon irritation. In group C, tendon irritation and delayed carpal tunnel syndrome occurred, respectively, in 3 cases.ConclusionOur results demonstrated that these two different surgical approaches were effective ways to reserve PQ and had good clinical outcomes. The volar plating insertion PQ muscle approach could reduce early postoperative pain, promote early activity and return to normal life, while the BR splitting approach was more advantageous in intraoperative fracture exposure and could shorten the operative time. However, some defects also existed. At 12 months of follow-up, no significant advantage was seen in sparing the PQ muscle. Therefore, surgeons should be aware of their individual characteristics and choose patients carefully.

A study of non-equilibrium wave groups in two-phase flow in high-contrast porous media with relative permeability hysteresis

Non-equilibrium models are applicable in various physical situations, including phase transitions, hysteresis, and chemical reactions, among others. To model the dynamics of such phenomena, partial differential equations are employed with source terms, as seen in Eq. (1.1). This work focuses on situations where we connect states in equilibrium while allowing for non-equilibrium times. In our model, we model non-equilibrium through relaxation. The source term, in this case, acts as an attractor, and the model’s dynamics allow only a small-time far from equilibrium. We use this theory to model a two-equation system in a two-phase fluid in porous media, where different permeabilities of the flow lead to hysteresis phenomena. We show several examples and study the Riemann solution to gain a better understanding of the solution. We investigate the interplay between nonlinear wave groups of water–oil saturation with different permeabilities in the non-wetting phase and the high-contrast heterogeneity of the porous medium in two-space dimensions. This phenomenon is described by a coupled set of time-dependent partial differential equations of hyperbolic–parabolic–elliptic mixed type. We present and discuss a new projection method with relaxation to obtain 1D-analytical Riemann solutions in an idealized homogenized medium, and we use this solution to validate our numerical method. The primary components of obtaining these solutions are shocks, rarefactions, and bifurcations. We obtain 2D-numerical solutions using an operator splitting approach based on physics, highlighting the discretization methods used. We provide robust analytical–numerical examples to verify the theory and illustrate the approach’s capabilities. In particular, we discuss the 1D-2D behavior of wave groups in situations driven by water–oil infiltration problems of permeable reservoir rocks.

Improving readability of static, straight-line graph drawings: A first look at edge crossing resolution through iterative vertex splitting

We present a novel vertex-splitting approach to iteratively resolve edge crossings in order to improve the readability of graph drawings. Dense graphs, even when small in size (10 to 15 nodes in size) quickly become difficult to read with increasing numbers of edges, and form so-called “hairballs”. The readability of a graph drawing is measured using many different quantitative aesthetic metrics. One such metric of particular importance is the number of edge crossings. Classical approaches to improving readability, such as the minimization of the number of edge crossings, focus on providing overviews of the input graph by aggregating or sampling vertices and/or edges. However, this simplification of the graph drawing does not allow for detailed views into the data, as not all vertices or edges are rendered, and also requires sophisticated interaction approaches to perform well. To avoid this, our locally optimal vertex splitting approach aims to minimize the number of remaining edge crossings while also minimizing the number of vertices that need to be split. In each iteration, we identify the vertex contributing the largest number of edge crossings, remove it, locate the embedding locations of said vertex’s two split copies, and determine each copy’s unique adjacency. We conduct a user study with 52 participants to evaluate whether vertex splitting affects users’ abilities to conduct a set of graph analytical tasks on graphs 12 nodes in size. Users were tasked with identifying a vertex’s adjacency, determining the shared neighbors of two vertices, and checking the validity of a set of paths. We ultimately conclude that within the context of small, dense graphs, systematic vertex splitting is preferred by participants and even positively impacts user performance, though at the cost of the time taken per task.

Active travel to schools in Wales

ObjectivesActive travel to school (ATS), such as walking and cycling, not only reduces carbon emissions and air pollution but also contributes to a myriad of health benefits. Understanding the ‘potential’ for ATS across Wales is poorly understood yet vital to inform policy and practice aimed at increasing ATS.
 MethodsUsing geospatial techniques, network distances have been calculated for all residences to all schools in Wales. Distances for each residence were de-identified and imported into the Secure Anonymised Information Linkage (SAIL) databank using SAIL’s split file approach. Within SAIL, anonymised distances were linked to encrypted school locations and a cohort of approximately 440,000 children attending a state school in Wales as recorded in the Education Wales (EDUW) dataset. Travel distance to school was subsequently filtered for each child within our cohort.
 ResultsThe incorporation of geospatial network derived origin-destination pairs (n=2,205,000,000) allows us to explore how households can engage in ATS, and how this varies by primary/secondary school, welsh-speaking, urban/rural, and socioeconomic status. Initially we will present findings based on distance alone, with future refinements to encompass other factors influencing ATS including: active travel routes, road safety, and topography. We will link our results to anonymised ATS survey responses in SAIL, to calculate specific ATS distance thresholds by age, deprivation, and urban/rural status, including social indicators such as household composition.
 ConclusionCurrently there is limited local or national comparable information on the potential for ATS in Wales. This baseline information is urgently needed to inform ATS policy and planning, and to ensure appropriate ATS interventions are prioritised for schools and communities where need is greatest.

Nitrate prediction in groundwater of data scarce regions: The futuristic fresh-water management outlook

Nitrate contamination in groundwater poses a significant threat to water quality and public health, especially in regions with limited data availability. This study addresses this challenge by employing machine learning (ML) techniques to predict nitrate (NO3−-N) concentrations in Mexico's groundwater. Four ML algorithms—Extreme Gradient Boosting (XGB), Boosted Regression Trees (BRT), Random Forest (RF), and Support Vector Machines (SVM)—were executed to model NO3−-N concentrations across the country. Despite data limitations, the ML models achieved robust predictive performances. XGB and BRT algorithms demonstrated superior accuracy (0.80 and 0.78, respectively). Notably, this was achieved using ∼10 times less information than previous large-scale assessments. The novelty lies in the first-ever implementation of the ‘Support Points-based Split Approach’ during data pre-processing. The models considered initially 68 covariates and identified 13–19 significant predictors of NO3−-N concentration spanning from climate, geomorphology, soil, hydrogeology, and human factors. Rainfall, elevation, and slope emerged as key predictors. A validation incorporated nationwide waste disposal sites, yielding an encouraging correlation. Spatial risk mapping unveiled significant pollution hotspots across Mexico. Regions with elevated NO3−-N concentrations (>10 mg/L) were identified, particularly in the north-central and northeast parts of the country, associated with agricultural and industrial activities. Approximately 21 million people, accounting for 10 % of Mexico's population, are potentially exposed to elevated NO3−-N levels in groundwater. Moreover, the NO3−-N hotspots align with reported NO3−-N health implications such as gastric and colorectal cancer. This study not only demonstrates the potential of ML in data-scarce regions but also offers actionable insights for policy and management strategies. Our research underscores the urgency of implementing sustainable agricultural practices and comprehensive domestic waste management measures to mitigate NO3−-N contamination. Moreover, it advocates for the establishment of effective policies based on real-time monitoring and collaboration among stakeholders.

RapidStream 2.0: Automated Parallel Implementation of Latency–Insensitive FPGA Designs Through Partial Reconfiguration

Field-programmable gate arrays (FPGAs) require a much longer compilation cycle than conventional computing platforms such as CPUs. In this article, we shorten the overall compilation time by co-optimizing the HLS compilation (C-to-RTL) and the back-end physical implementation (RTL-to-bitstream). We propose a split compilation approach based on the pipelining flexibility at the HLS level, which allows us to partition designs for parallel placement and routing. We outline a number of technical challenges and address them by breaking the conventional boundaries between different stages of the traditional FPGA tool flow and reorganizing them to achieve a fast end-to-end compilation. Our research produces RapidStream, a parallelized and physical-integrated compilation framework that takes in a latency-insensitive program in C/C++ and generates a fully placed and routed implementation. We present two approaches. The first approach (RapidStream 1.0) resolves inter-partition routing conflicts at the end when separate partitions are stitched together. When tested on the Xilinx U250 FPGA with a set of realistic HLS designs, RapidStream achieves a 5 to 7× reduction in compile time and up to 1.3× increase in frequency when compared with a commercial off-the-shelf toolchain. In addition, we provide preliminary results using a customized open-source router to reduce the compile time up to an order of magnitude in cases with lower performance requirements. The second approach (RapidStream 2.0) prevents routing conflicts using virtual pins. Testing on Xilinx U280 FPGA, we observed 5 to 7× compile time reduction and 1.3× frequency increase.

Design of inception architecture for skin melanoma classification

The number of people diagnosed with skin cancer is increasing sharply. Both invasive and non-invasive methods of examination may be used to investigate it. However, the invasive method is more difficult for the patient because samples must be taken from the lesion itself, or the whole lesion must be cut out. It also requires more time and cost. To avoid invasive procedures, computer-based analysis and diagnosis have the potential to increase diagnostic accuracy and turnaround time. This study develops a unique discriminative deep learning architecture (DDLA) for dermoscopic image classification (DIC), called DDLA-DIC, which uses the concept of inception. Using this concept, the proposed DDLA-DIC system is designed wider and deeper and the network learns from various spatial patterns. The proposed DDLA-DIC system can extract image characteristics from dermoscopic images for skin cancer diagnosis in an effective and efficient way. The proposed DDLA-DIC system is evaluated by utilizing the dermoscopic images from the PH2 database, and the obtained classification results are based on a random split approach. The simulation results indicate that the framework has a great deal of potential with 99.79% accuracy.

Comparative study between deltoid split approach versus delto pectoral approach for proximal humerus fracture fixation

Background: Proximal humeral fracture is defined as fractures occurring at or proximal to the surgical neck of the humerus. It affects 2.4% of women over the age of 75 years. It is the commonest fracture affecting the shoulder girdle in adults and its incidence is rising. For displaced fractures that meet surgical indications, deltopectoral approach is the most common approach for plate fixation of proximal humeral fractures. Deltoid splitting approach offers advantages of better exposure and implant placement for fixation of the fracture and early mobilization of the shoulder joint. Methods: This is a comparative study of 30 cases of proximal humerus fractures treated by surgical fixation through deltoid splitting approach and 30 cases treated by delto pectoral approach. Patient of both sexes belonging to adult age group in general population presenting with proximal humerus fracture to the orthopaedic department of Bapuji Hospital and Chigateri General hospital attached to J.J.M. Medical College, Davangere. The period of survey extends from December 2020 to December 2022. The final results were evaluated using constant Murley score periodically. This constant Murley score ranges from 0 to 100 points representing worst and shoulder function respectively. Results: No statistically significant difference was observed in outcome with regard to mechanism of injury, side of fracture and type of fracture with both deltoid splitting approach and delto pectoral approach. Conclusions: The treatment of proximal humeral fractures with a proximal locking plate is reliable using both approaches. No significant difference found in functional outcome of proximal humerus fracture following deltoid split and deltopectoral approach on serial follow up.

Global Sound Absorption Prediction for a Composite Coating Laid on an Underwater Submersible in Debonding States

To address the problem that anechoic coatings frequently fall off from modern submersible hulls and are detrimental to the realization of underwater acoustic stealth, this paper focuses on the broadband sound absorbing of acoustic coverings in debonding states from fully bonded span to fully shedded conditions. Based on the non-uniform waveguide theory, subdomain splitting approach, and wave propagation theory in layered media, a global transfer matrix method (TMM) is developed for predicting the sound absorption of a composite overburden with periodic cavities in all peeling situations. Meanwhile, the corresponding acoustic-structure fully coupled finite element (FE) simulation and hydroacoustic impedance tube-based absorption experiment are sequentially performed for the lining in a semi-bonded state to comprehensively verify the accuracy and reliability of the present analytical methodology. Then, the influence laws of debonding states, material properties, and geometric parameters on the global absorption performance are investigated in depth to reveal the multiple energy dissipation mechanisms. The results show that the shedding state primarily affects the sound absorption characteristics of anechoic coatings in the low- to mid-frequency band below 7 kHz.

Cost-efficiency enhancement of X- and Ku-band split waveguides for industrial accelerators

Compact microwave electron linear accelerators must operate at high frequencies in order to maintain tight SWaP (size, weight, and power) requirements, set by modern applications. Currently, the frequencies are practically limited to X- and Ku-bands because of the availability of MW-class magnetrons, required to achieve beam energies of several MeV, and tight fabrication tolerances. The latter is associated with micron-level accuracy and further waveguide frequency tuning process, both increasing its costs dramatically. By implying a novel split structure fabrication approach, it is possible to relax these tolerances to the values of 10–20 μm and eliminate the tuning step, greatly reducing their total costs. This paper presents the results of microwave measurements of split X- and Ku-band accelerating structures of different types and discusses the benefits of our fabrication approach.

Sizing of Fuel Cell/Supercapacitor Hybrid System based on Frequency Splitting of required Energy

Optimal power source sizing and energy management strategies are crucial in the problem of component sizing for hybrid electric vehicles fuel cell. Ensuring cost-effective sizing while meeting power demand necessitates consideration of these factors as well, thereby ensuring a good driving range, reduced energy-loss and consumption, and minimal degradation of fuel cells and batteries for hybrid power sources. The purpose of this work concerns the sizing and the modelling of a power source utilized in a fuel cell hybrid vehicle, the principal source of energy is a Proton Exchange Membrane Fuel Cell, while an Ultra-Capacitor bank serves as an auxiliary source. The sizing algorithm initiates by computing the power demand, which is determined by the mechanical characteristics of the vehicle. This calculation involves considering the instantaneous speed of the chosen drive cycle and the instantaneous road gradient. Subsequently, the algorithm proceeds to determine the mechanical power needed by the motor. In this article, a frequency splitting approach is employed to determine the power distribution between the SC and the fuel-cell for Worldwide Harmonised Light Vehicles Test Procedure (WLTP) driving cycles. The fuel cell operates effectively at low frequencies, whereas the supercapacitor provides power at high frequencies. The efficiencies of every power transformer, including the motor, gearbox, differential, and DC-DC converters, are considered in our work. The data analysis is conducted using the MATLAB software environment. The obtained results demonstrated that the approach outlined in this research article offers a more efficient sizing and energy management between sources in terms of simplicity and adherence to operational conditions of the fuel cell and supercapacitor.

A second-order bulk–surface splitting for parabolic problems with dynamic boundary conditions

Abstract This paper introduces a novel approach for the construction of bulk–surface splitting schemes for semilinear parabolic partial differential equations with dynamic boundary conditions. The proposed construction is based on a reformulation of the system as a partial differential–algebraic equation and the inclusion of certain delay terms for the decoupling. To obtain a fully discrete scheme, the splitting approach is combined with finite elements in space and a backward differentiation formula in time. Within this paper, we focus on the second-order case, resulting in a $3$-step scheme. We prove second-order convergence under the assumption of a weak CFL-type condition and confirm the theoretical findings by numerical experiments. Moreover, we illustrate the potential for higher-order splitting schemes numerically.

Controlled Power System Separation Using Generator PMU Data and System Kinetic Energy

With the growing number of severe system disturbances and blackouts around the world, controlled system separation is becoming an increasingly important system integrity protection scheme (SIPS) to save the electric power system from a complete or partial disintegration. A successful controlled splitting approach should at least tackle the following two well-known and interrelated problems: "when to split?" and "where to split?". Multiple previous publications consider these problems separately, and even those pursuing a combined approach propose solutions with limited applicability. In this paper, we are proposing a novel PMU-based detector of loss of synchronism that utilizes generator rotor angles and speeds to promptly detect rotor angle instabilities over a wide area. Moreover, we are showing how our loss of synchronism detection principle can be coupled with the known controlled splitting techniques to form an integrated defense scheme against unintentional loss of synchronism. The performance of this wide-area SIPS is demonstrated on the IEEE 39-bus test power system for various types of unstable conditions.

An innovative ensemble model based on deep learning for predicting COVID-19 infection

Nowadays, global public health crises are occurring more frequently, and accurate prediction of these diseases can reduce the burden on the healthcare system. Taking COVID-19 as an example, accurate prediction of infection can assist experts in effectively allocating medical resources and diagnosing diseases. Currently, scholars worldwide use single model approaches or epidemiology models more often to predict the outbreak trend of COVID-19, resulting in poor prediction accuracy. Although a few studies have employed ensemble models, there is still room for improvement in their performance. In addition, there are only a few models that use the laboratory results of patients to predict COVID-19 infection. To address these issues, research efforts should focus on improving disease prediction performance and expanding the use of medical disease prediction models. In this paper, we propose an innovative deep learning model Whale Optimization Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN) called WOCLSA which incorporates three models ANN, CNN and LSTM. The WOCLSA model utilizes the Whale Optimization Algorithm to optimize the neuron number, dropout and batch size parameters in the integrated model of ANN, CNN and LSTM, thereby finding the global optimal solution parameters. WOCLSA employs 18 patient indicators as predictors, and compares its results with three other ensemble deep learning models. All models were validated with train-test split approaches. We evaluate and compare our proposed model and other models using accuracy, F1 score, recall, AUC and precision metrics. Through many studies and tests, our results show that our prediction models can identify patients with COVID-19 infection at the AUC of 91%, 91%, and 93% respectively. Other prediction results achieve a respectable accuracy of 92.82%, 92.79%, and 91.66% respectively, f1-score of 93.41%, 92.79%, and 92.33% respectively, precision of 93.41%, 92.79%, and 92.33% respectively, recall of 93.41%, 92.79%, and 92.33% respectively. All of these exceed 91%, surpassing those of comparable models. The execution time of WOCLSA is also an advantage. Therefore, the WOCLSA ensemble model can be used to assist in verifying laboratory research results and predict and to judge various diseases in public health events.

Federated Learning for Computationally Constrained Heterogeneous Devices: A Survey

With an increasing number of smart devices like internet of things devices deployed in the field, offloading training of neural networks (NNs) to a central server becomes more and more infeasible. Recent efforts to improve users’ privacy have led to on-device learning emerging as an alternative. However, a model trained only on a single device, using only local data, is unlikely to reach a high accuracy. Federated learning (FL) has been introduced as a solution, offering a privacy-preserving tradeoff between communication overhead and model accuracy by sharing knowledge between devices but disclosing the devices’ private data. The applicability and the benefit of applying baseline FL are, however, limited in many relevant use cases due to the heterogeneity present in such environments. In this survey, we outline the heterogeneity challenges FL has to overcome to be widely applicable in real-world applications. We especially focus on the aspect of computation heterogeneity among the participating devices and provide a comprehensive overview of recent works on heterogeneity-aware FL. We discuss two groups: works that adapt the NN architecture and works that approach heterogeneity on a system level, covering Federated Averaging, distillation, and split learning–based approaches, as well as synchronous and asynchronous aggregation schemes.

Autoimmune nodo-paranodopathies 10 years later: Clinical features, pathophysiology and treatment.

Autoimmune neuropathies are classified, on the basis of pathophysiology, as demyelinating or axonal. The term nodo-paranodopathy, introduced in 2013 to better categorize the neuropathies with antiganglioside antibodies and later expanded to include neuropathies with antibodies to nodal and paranodal axoglial complexes, characterizes disorders in which the nodal region is critical in the pathogenesis. These neuropathies, although presenting electrophysiologic demyelinating features do not show pathologic evidence of segmental demyelination, or, although being classified as axonal, can show reversible nerve conduction failure and rapid recovery contrary with the communal concept of an axonal neuropathy. In this personal view is reported, with a splitting approach, an update on autoimmune nodo-paranodopathies, classified according to the domains of peripheral nerve fiber, the target antigens and the antibody class and subclass involved. The clinical features, the electrophysiologic findings, the results of the immunopathological and ultrastructural studies, the pathophysiology and treatment are also described. The nodo-paranodopathy category integrates the clinical classification of autoimmune neuropathies and expands the traditional dichotomous demyelinating and axonal classification. It helps to a better systematization pointing to the domain and target antigens of the autoimmune process, it resolves conflicting pathologic and electrophysiologic findings, reconciles the contradiction that axonal neuropathies may be rapidly reversible, avoids taxonomical confusion and possible misdiagnoses. Finally this categorization, through the identification of the specific antibody and its prevalent class and subclass, clarifies the pathophysiological mechanisms and addresses to a more targeted therapeutic approach.

A computational macroscopic model of piezomagnetoelectric materials using Generalized Multiscale Finite Element Method

Piezomagnetoelectric materials have many applications in various areas of science and engineering. For example, such materials can be used in sensors, transducers, energy harvesters, etc. The mathematical model of piezomagnetoelectricity consists of a coupled system of partial differential equations for the electric potential, magnetic potential, and mechanical displacements.We develop multiscale algorithms based on the Generalized Multiscale Finite Element Method for solving the piezomagnetoelectricity problem. We consider two ways of constructing multiscale basis functions: split and coupled. In the split approach, we construct the basis functions separately for each field. The coupled approach constructs multiscale basis functions by solving coupled local problems. The split way requires fewer computational resources than the coupled one, but it may give a less accurate solution in the case of a strongly coupled medium. The coupled approach considers the mutual influence of the fields, which is especially important when they are strongly coupled. We use the multiscale partition of unity functions to obtain conforming basis functions for both approaches.We consider two- and three-dimensional model problems to test the effectiveness of the proposed multiscale approaches. On average, the coupled basis functions show better results than the split ones. We also consider the application of the single-step hermitian and skew-hermitian splitting for solving the discrete problem on a coarse grid. The results have demonstrated that one can successfully apply this technique to solving the piezomagnetoelectricity problem on a coarse grid.

Analysis of RAS and drug induced homo- and heterodimerization of RAF and KSR1 proteins in living cells using split Nanoluc luciferase

The dimerization of RAF kinases represents a key event in their activation cycle and in RAS/ERK pathway activation. Genetic, biochemical and structural approaches provided key insights into this process defining RAF signaling output and the clinical efficacy of RAF inhibitors (RAFi). However, methods reporting the dynamics of RAF dimerization in living cells and in real time are still in their infancy. Recently, split luciferase systems have been developed for the detection of protein–protein-interactions (PPIs), incl. proof-of-concept studies demonstrating the heterodimerization of the BRAF and RAF1 isoforms. Due to their small size, the Nanoluc luciferase moieties LgBiT and SmBiT, which reconstitute a light emitting holoenzyme upon fusion partner promoted interaction, appear as well-suited to study RAF dimerization. Here, we provide an extensive analysis of the suitability of the Nanoluc system to study the homo- and heterodimerization of BRAF, RAF1 and the related KSR1 pseudokinase. We show that KRASG12V promotes the homo- and heterodimerization of BRAF, while considerable KSR1 homo- and KSR1/BRAF heterodimerization already occurs in the absence of this active GTPase and requires a salt bridge between the CC-SAM domain of KSR1 and the BRAF-specific region. We demonstrate that loss-of-function mutations impairing key steps of the RAF activation cycle can be used as calibrators to gauge the dynamics of heterodimerization. This approach identified the RAS-binding domains and the C-terminal 14–3-3 binding motifs as particularly critical for the reconstitution of RAF mediated LgBiT/SmBiT reconstitution, while the dimer interface was less important for dimerization but essential for downstream signaling. We show for the first time that BRAFV600E, the most common BRAF oncoprotein whose dimerization status is controversially portrayed in the literature, forms homodimers in living cells more efficiently than its wildtype counterpart. Of note, Nanoluc activity reconstituted by BRAFV600E homodimers is highly sensitive to the paradox-breaking RAFi PLX8394, indicating a dynamic and specific PPI. We report the effects of eleven ERK pathway inhibitors on RAF dimerization, incl. third-generation compounds that are less-defined in terms of their dimer promoting abilities. We identify Naporafenib as a potent and long-lasting dimerizer and show that the split Nanoluc approach discriminates between type I, I1/2 and II RAFi.Ah1kwifp3aoZnF_eXdbERzVideo

Aortic arch replacement with simultaneous antegrade cerebral and systemic perfusion using a single centrifugal pump driven 'split arterial line' extracorporeal circuit design.

The ability to provide antegrade cerebral and systemic perfusion simultaneously may negate the requirement for any prolonged period of circulatory arrest during complex aortic arch reconstruction procedures, depending on the cannulation strategy. We describe the development and successful implementation of a custom 'split arterial line' extracorporeal circuit configuration to facilitate complex aortic surgery. This circuit design offers a wide range of cannulation and perfusion strategies, is safe, adaptable, simple to manage, and avoids the use of roller pumps for blood delivery, which are associated with deleterious haematological complications during prolonged cardiopulmonary bypass cases. The split arterial line approach has now become the standardised methodology for facilitating complex aortic surgery at our institution.