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Digital Image Correlation (DIC) has been widely used as a non-contact deformation measurement technique. Nevertheless, its accuracy is greatly affected by the speckle pattern on the specimen. To systematically evaluate how speckle deformability affects the precision of DIC algorithms. In this study, a test dataset of 2D speckle patterns with various prescribed deformation fields was numerically generated, containing two categories of speckles, i.e., the deformable and the non-deformable (rigid) ones. This dataset was used to evaluate the performance of inverse compositional Gauss-Newton (ICGN)-based DIC algorithms with two types of shape function (first-order and second-order), in the different scenarios of the deformation field. The results showed that imaging noise had a significant influence on the DIC algorithm. The first-order shape function (ICGN-1) performed better when tracking the simple linear deformation field. While the second-order shape function (ICGN-2) was proved to perform better on non-linear deformations. Moreover, the deformability of the speckle was found to have an obvious impact on the performance of the DIC algorithm. ICGN-2 could effectively reduce so-called speckle rigidity induced (SRI) error. Conclusively, ICGN-2 should be chosen as priority, because of its feasibility on non-linear deformation fields and speckle rigidity. While in the linear deformation scenarios, ICGN-1 was still a robust and efficient method.

Abstract Computational fluid dynamics (CFD) simulations are increasingly utilised to evaluate intracranial aneurysm (IA) haemodynamics to aid in the prediction of morphological changes and rupture risk. However, these models vary and differences in published results warrant the investigation of IA-CFD reproducibility. This study aims to explore sources of intra-team variability and determine its impact on the aneurysm morphology and CFD parameters. A team of four operators were given six sets of magnetic resonance angiography data spanning a decade from one patient with a middle cerebral aneurysm. All operators were given the same protocol and software for model reconstruction and numerical analysis. The morphology and haemodynamics of the operator models were then compared. The segmentation, smoothing factor, inlet and outflow branch lengths were found to cause intra-team variability. There was 80% reproducibility in the time-averaged wall shear stress distribution among operators with the major difference attributed to the level of smoothing. Based on these findings, it was concluded that the clinical applicability of CFD simulations may be feasible if a standardised segmentation protocol is developed. Moreover, when analysing the aneurysm shape change over a decade, it was noted that the co-existence of positive and negative values of the wall shear stress divergence (WSSD) contributed to the growth of a daughter sac.

Corrective osteotomy is a standard treatment for distal radius fractures in malunited radius cases. In order to increase the efficiency of the osteotomy pre-operative plan, in this study, a proof-of-concept framework of automatic computer-assisted segmentation and registration tool was developed for the purpose of malunited radius osteotomy pre-operative planning. The program consisted of the functions of segmentation, virtual cutting, automatic alignment and registration. One computed tomography (CT) scanning dataset of a patient's bilateral forearm was employed as an illustration example in this study. Three templates of 3D models including the healthy radius, and the pre- and post-correction injured radius were output as STL geometries for pre-operative plan purposes.

Abstract Abstract Gold nanoparticles (AuNPs) are promising materials for many bioapplications. However, upon contacting with biological media, AuNPs undergo changes. The interaction with proteins results in the so‐called protein corona (PC) around AuNPs, leading to the new bioidentity and optical properties. Understanding the mechanisms of PC formation and its functions can help us to utilise its benefits and avoid its drawbacks. To date, most of the previous works aimed to understand the mechanisms governing PC formation and focused on the spherical nanoparticles, although non‐spherical nanoparticles are designed for a wide range of applications in biosensing. In this work, we investigated the differences in PC formation on spherical and anisotropic AuNPs (nanostars in particular) from the joint experimental (extinction spectroscopy, zeta potential and surface‐enhanced Raman scattering [SERS]) and computational methods (the finite element method and molecular dynamics [MD] simulations). We discovered that protein does not fully cover the surface of anisotropic nanoparticles, leaving SERS hot‐spots at the tips and high curvature edges ‘available’ for analyte binding (no SERS signal after pre‐incubation with protein) while providing protein‐induced stabilization (indicated by extinction spectroscopy) of the AuNPs by providing a protein layer around the particle's core. The findings are confirmed from our MD simulations, the adsorption energy significantly decreases with the increased radius of curvature, so that tips (adsorption energy: 2762.334 kJ/mol) would be the least preferential binding site compared to core (adsorption energy: 11819.263 kJ/mol). These observations will help the development of new nanostructures with improved sensing and targeting ability.

Abstract Background Sarcopenia is one of the most frequent syndromes in older adults and one of its main characteristics is low muscle mass. Gastrointestinal tumor is a malignant disease with high incidence. This study aimed to investigate the risk factors of low muscle mass in older adults with gastrointestinal tumor, the prognostic indicators of and short-term outcomes after resection for gastrointestinal tumor, and to explore the relationship between low muscle mass and short-term postoperative prognosis. Method A total of 247 older patients with gastrointestinal tumors who underwent radical resection in 2019 were included in this study. Relevant indexes were calculated using L3 slice image of computed tomography (CT) to evaluate low muscle mass. Short-term postoperative complications and length of stay were considered as short-term outcomes of this study. Results Advanced age, lower higher body mass index (BMI), lower hemoglobin, having history of abdominal surgery and higher visceral fat index (VFI) were risk factors of low muscle mass, while higher BMI and lower subcutaneous fat index (SFI) were protective factors of low muscle mass. Further multivariate logistic regression analysis showed that having history of abdominal surgery, advanced age and lower BMI were independent risk factors. Low muscle mass and higher Charlson comorbidity index were independent risk factors of short-term postoperative complications in older adults with gastrointestinal tumor. Higher Charlson comorbidity index gave rise to longer length of stay. Conclusions Low muscle mass and higher Charlson comorbidity index predict poor short-term prognosis of older patients undergoing gastrointestinal tumor resection.

For a connected graph G with vertex set V, denote by d(v) the degree of vertex v and d(u, v) the distance between u and v. The value Gut(G)=∑{u,v}⊆Vd(u)d(v)d(u,v) is called the Gutman index of G. Recently, the graph minimizing the Gutman index among unicyclic graphs with pendent edges was characterized. Denoted by U(n,m) the set of unicyclic graphs on n vertices with matching number m. Motivated by that work, in this paper, we obtain a sharp lower bound for Gutman index of graphs in U(n,m), and the extremal graph attaining the bound is also obtained. It is worth noticing that all the extremal graphs are of high symmetry, that is, they have large automorphic groups.

Abstract Background Different oscillations of brain networks could carry different dimensions of brain integration. We aimed to investigate oscillation-specific nodal alterations in patients with Parkinson’s disease (PD) across early stage to middle stage by using graph theory-based analysis. Methods Eighty-eight PD patients including 39 PD patients in the early stage (EPD) and 49 patients in the middle stage (MPD) and 36 controls were recruited in the present study. Graph theory-based network analyses from three oscillation frequencies (slow-5: 0.01–0.027 Hz; slow-4: 0.027–0.073 Hz; slow-3: 0.073–0.198 Hz) were analyzed. Nodal metrics (e.g. nodal degree centrality, betweenness centrality and nodal efficiency) were calculated. Results Our results showed that (1) a divergent effect of oscillation frequencies on nodal metrics, especially on nodal degree centrality and nodal efficiency, that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies while the posterolateral neocortex had high values within the relative high oscillation frequency was observed, which visually showed that network was perturbed in PD; (2) PD patients in early stage relatively preserved nodal properties while MPD patients showed widespread abnormalities, which was consistently detected within all three oscillation frequencies; (3) the involvement of basal ganglia could be specifically observed within slow-5 oscillation frequency in MPD patients; (4) logistic regression and receiver operating characteristic curve analyses demonstrated that some of those oscillation-specific nodal alterations had the ability to well discriminate PD patients from controls or MPD from EPD patients at the individual level; (5) occipital disruption within high frequency (slow-3) made a significant influence on motor impairment which was dominated by akinesia and rigidity. Conclusions Coupling various oscillations could provide potentially useful information for large-scale network and progressive oscillation-specific nodal alterations were observed in PD patients across early to middle stages.

Degree of luminal stenosis is generally considered to be an important indicator for judging the risk of atherosclerosis burden. However, patients with the same or similar degree of stenosis may have significant differences in plaque morphology and biomechanical factors. This study investigated three patients with carotid atherosclerosis within a similar range of stenosis. Using our developed fluid–structure interaction (FSI) modelling method, this study analyzed and compared the morphological and biomechanical parameters of the three patients. Although their degrees of carotid stenosis were similar, the plaque components showed a significant difference. The distribution range of time-averaged wall shear stress (TAWSS) of patient 2 was wider than that of patient 1 and patient 3. Patient 2 also had a much smaller plaque stress compared to the other two patients. There were significant differences in TAWSS and plaque stresses among three patients. This study suggests that plaque vulnerability is not determined by a single morphological factor, but rather by the combined structure. It is necessary to transform the morphological assessment into a structural assessment of the risk of plaque rupture.

Titanium carbides attract attention from both academic and industry fields because of their intriguing mechanical properties and proven potential as appealing candidates in the variety of fields such as nanomechanics, nanoelectronics, energy storage and oil/water separation devices. A recent study revealed that the presence of Ti8C5 not only improves the impact strength of composites as coatings, but also possesses significant strengthening performance as an interlayer material in composites by forming strong bonding between different matrices, which sheds light on the design of impact protection composite materials. To further investigate the impact resistance and strengthening mechanism of Ti8C5, a pilot Molecular Dynamics (MD) study utilizing comb3 potential is carried out on a Ti8C5 nanosheet by subjecting it to hypervelocity impacts. The deformation behaviour of Ti8C5 and the related impact resist mechanisms are assessed in this research. At a low impact velocity ~0.5 km/s, the main resonance frequency of Ti8C5 is 11.9 GHz and its low Q factor (111.9) indicates a decent energy damping capability, which would eliminate the received energy in an interfacial reflection process and weaken the shock waves for Ti8C5 strengthened composites. As the impact velocity increases above the threshold of 1.8 km/s, Ti8C5 demonstrates brittle behaviour, which is signified by its insignificant out-of-plane deformation prior to crack initiation. When tracking atomic Von Mises stress distribution, the elastic wave propagation velocity of Ti8C5 is calculated to be 5.34 and 5.90 km/s for X and Y directions, respectively. These figures are inferior compared with graphene and copper, which indicate slower energy delocalization rates and thus less energy dissipation via deformation is expected prior to bond break. However, because of its relatively small mass density comparing with copper, Ti8C5 presents superior specific penetration. This study provides a fundamental understanding of the deformation and penetration mechanisms of titanium carbide nanosheets under impact, which is crucial in order to facilitate emerging impact protection applications for titanium carbide-related composites.

There is a need to develop a validated algorithm for plaque characterization which can help to facilitate the standardization of optical coherence tomography (OCT) image interpretation of plaque morphology, and improve the efficiency and accuracy in the application of OCT imaging for the quantitative assessment of plaque vulnerability. In this study, a machine learning algorithm was implemented for characterization of atherosclerotic plaque components by intravascular OCT using ex vivo carotid plaque tissue samples. A total of 31 patients underwent carotid endarterectomy and the ex vivo carotid plaques were imaged with OCT. Optical parameter, texture features and relative position of pixels were extracted within the region of interest and then used to quantify the tissue characterization of plaque components. The potential of individual and combined feature set to discriminate tissue components was quantified using sensitivity, specificity, accuracy. The results show there was a lower classification accuracy in the calcified tissue than the fibrous tissue and lipid tissue. The pixel-wise classification accuracy obtained by the developed method, to characterize the fibrous, calcified and lipid tissue by comparing with histology, were 80.0, 62.0, and 83.1, respectively. The developed algorithm was capable of characterizing plaque components with an excellent accuracy using the combined feature set.

Atherosclerotic plaque rupture is a catastrophic event that contributes to mortality and long-term disability. A better understanding of the plaque mechanical behavior is essential for the identification of vulnerable plaques pre-rupture. Plaque is subjected to a natural dynamic mechanical environment under hemodynamic loading. Therefore, it is important to understand the mechanical response of plaque tissue under cyclic loading conditions. Moreover, experimental data of such mechanical properties are fundamental for more clinically relevant biomechanical modeling and numerical simulations for risk stratification. This study aims to experimentally and numerically characterize the stress-relaxation and cyclic mechanical behavior of carotid plaque tissue. Instron microtester equipped with a custom-developed setup was used for the experiments. Carotid plaque samples excised at endarterectomy were subjected to uniaxial tensile, stress-relaxation, and cyclic loading protocols. Thirty percent of the underlying load level obtained from the uniaxial tensile test results was used to determine the change in mechanical properties of the tissue over time under a controlled testing environment (Control tests). The stress-relaxation test data was used to calibrate the hyperelastic (neo-Hookean, Ogden, Yeoh) and linear viscoelastic (Prony series) material parameters. The normalized relaxation force increased initially and slowly stabilized toward the end of relaxation phase, highlighting the viscoelastic behavior. During the cyclic tests, there was a decrease in the peak force as a function of the cycle number indicating mechanical distension due to repeated loading that varied with different frequencies. The material also accumulated residual deformation, which increased with the cycle number. This trend showed softening behavior of the samples. The results of this preliminary study provide an enhanced understanding of in vivo stress-relaxation and cyclic behavior of the human atherosclerotic plaque tissue.

A patient-specific carotid bifurcation with tandem stenosis found at both internal carotid artery (ICA) and common carotid artery (CCA) was studied. The in vivo pre-carotid endarterectomy (pre-CEA) multi-spectral magnetic resonance imaging (MRI) were performed and in vitro post-CEA carotid plaque tissue sample was collected. MR imaging data and tissue sample staining histology were used to recognize the plaque components. Further, the computational fluid dynamics (CFD) were performed on four MR-based reconstructed 3D carotid bifurcation models (the patient-specific geometry with tandem stenosis and three presumptive geometries by removing the stenosis part). The flow and shear stress behavior affected by the tandem stenosis was analyzed. From the results of MR segmentation and histology analysis, plaque lipid pool and calcification were found at both ICA and CCA. From the result of CFD simulation, the flow shear stress behavior suggested the tandem stenosis as a more “dangerous” situation than a single-stenosis artery. Besides, the CFD results deduced that the stenosis at the CCA location formed initially and led to the subsequent formation of stenosis at ICA. This study suggests that when planning CEA, CFD simulation on the presumptive models could help clinicians to estimate the blood flow behavior after surgery. Particular attention should be paid to the case of tandem stenosis, as the local hemodynamic environment is more complex and treatment of one stenosis may lead to a variation in the hemodynamic loading on the second plaque, which may result in either a higher risk of plaque rupture or restenosis.

In the social-collaboration scenario, the social-collaboration tasks need to be completed/coordinated by multiple people. For example, in the field of open-source software development, most software developments require multiple developers to collaborate with each other. With the increase in the number and variety of social-collaboration tasks, it is difficult for individuals to discover social-collaboration tasks that they can participate. If we can help match the social-collaboration tasks with appropriate users, the quality and speed of these tasks will be improved, thus social organizations (e.g., companies, teams, and research institutions) and individuals can improve productivity, which is very significant. However, most related work in recommending individuals to participate in social-collaboration tasks mainly focus on individual's data features/characteristics (e.g., personal behaviors and attributes), and little work is based on the social collaborative data features/characteristics within the individual's participation in the social-collaboration tasks, such as the types of social-collaboration tasks that individuals participate, collaborative content, collaborative behavior, collaborative intensity, and so on. This paper proposes a universal recommendation method based on personal social-collaboration preferences, which is used to recommend the social-collaboration tasks that individuals can participate. The characteristics of social-collaboration data contain a large number of individuals' preferences for social-collaboration, which helps improve recommendation performance. We perform a large number of experiments to verify the effectiveness of our proposed method, based on our collected real-world data sets in the open source software development services (Bugzilla and Github). Experimental results show that the proposed algorithm can be well applied to the social-collaboration task recommendation.

Hemiparkinsonism duration in patients with Parkinson's disease (PD) is a key time window to study early pathology of PD. We aimed to comprehensively explore the alterations of deformation and structural network in PD patients with hemiparkinsonism, which could potentially disclose the early biomarker for PD. Thirty-one PD patients with hemiparkinsonism and 37 age- and gender- matched normal controls were included in the present study. First of all, we normalized the left hemisphere of structural images as the contralateral side to the affected limbs. Deformation-based morphometry (DBM) was conducted to evaluate the brain atrophy and/or enlargement. structural networks were constructed by thresholding gray matter volume correlation matrices of 116 regions and analyzed using graph theoretical approaches (e.g., small-worldness, global, and nodal measures). Significantly decreased deformation values were observed in the temporoparietal regions like bilateral middle temporal gyri, ipsilateral precuneus and contralateral Rolandic operculum extending to supramarginal and postcentral gyri. Lower deformation values in contralateral middle temporal gyrus were negatively correlated with higher motor impairment which was dominated by akinesia/rigidity. Moreover, nodal reorganization of structural network mainly located in frontal, temporal, subcortex and cerebellum was bilaterally explored in PD patients with hemiparkinsonism. Increased nodal properties could be commonly observed in frontal lobes. Disruption of subcortex including basal ganglia and amygdala was detected by nodal local efficiency and nodal clustering coefficient. Twelve hubs, mainly from paralimbic-limbic and heteromodal networks, were disrupted and, alternatively, 14 hubs, most of which were located in frontal lobes, were additionally detected in PD patients with hemiparkinsonism. In conclusion, during hemiparkinsonism period, mild brain atrophy in the temporoparietal regions and widespread reorganization of structural network, e.g., enhanced frontal function and disruption of basal ganglia nodes, occurred in both hemispheres. With our data, we can also argue that MTG contralateral to the affected limbs (expressing clinically verified brain atrophy) might be a potential living biomarker to monitor disease progression. Therefore, the combination of DBM and structural network analyses can provide a comprehensive and sensitive evaluation for potential pathogenesis of early PD patients with hemiparkinsonism.

It is well known that disruption of basal ganglia function generates the motor symptoms in PD, however, these are presented in a heterogeneous manner; patients can be divided into tremor-dominant and akinesia/rigidity-dominant subtypes. To date, it is unknown if these differences in the motor symptoms could be explained by differences on the functional connectivity of basal ganglia with specific brain regions. In this study, we aimed to explore the alterations of the network-based and global functional connectivity linking to basal ganglia between the PD-TD and PD-AR patients. One hundred and six PD patients and 52 normal controls were recruited. According to the subscales of UPDRS motor scale, PD patients were divided into the PD-TD (n = 57) and PD-AR (n = 49) subtypes. We performed independent component analysis to identify basal ganglia network (BGN) involving connected brain regions having coactivation with basal ganglia. Eigenvector centrality mapping were processed and the eigenvector centrality in the subcortical component of BGN including the bilateral caudate nuclei, putamen, thalami and pallidum were extracted to measure the global connectivity. Compared with controls, whole PD patients or PD subtypes showed decreases of functional connectivity within the subcortical component of BGN, e.g., thalamus, pallidum and putamen. Compared with controls, decreased functional connectivity of precuneus and amygdala with basal ganglia was observed in the PD-TD while that of occipital lobule and precuneus was observed in the PD-AR. Compared with the PD-TD, significantly decreased functional connectivity between occipital lobule and cerebellum posterior lobule and basal ganglia was observed in the PD-AR, and such connectivity had positive correlations with tremor and negative correlations with akinesia/rigidity. We also observed enhanced global connectivity in the caudate nucleus and thalamus in the PD subtypes compared with controls. In conclusion, PD patients independent of motor subtypes consistently express similar alterations of functional connectivity within the subcortical component of BGN including network-based connectivity and global connectivity. Functional connectivity of cerebellum posterior lobule and occipital lobule with basal ganglia play important roles in the modulation of parkinsonian motor symptoms.

To discover regularities in human mobility is of fundamental importance to our understanding of urban dynamics, and essential to city and transport planning, urban management and policymaking. Previous research has revealed universal regularities at mainly aggregated spatio-temporal scales but when we zoom into finer scales, considerable heterogeneity and diversity is observed instead. The fundamental question we address in this paper is at what scales are the regularities we detect stable, explicable, and sustainable. This paper thus proposes a basic measure of variability to assess the stability of such regularities focusing mainly on changes over a range of temporal scales. We demonstrate this by comparing regularities in the urban mobility patterns in three world cities, namely London, Singapore and Beijing using one-week of smart-card data. The results show that variations in regularity scale as non-linear functions of the temporal resolution, which we measure over a scale from 1 minute to 24 hours thus reflecting the diurnal cycle of human mobility. A particularly dramatic increase in variability occurs up to the temporal scale of about 15 minutes in all three cities and this implies that limits exist when we look forward or backward with respect to making short-term predictions. The degree of regularity varies in fact from city to city with Beijing and Singapore showing higher regularity in comparison to London across all temporal scales. A detailed discussion is provided, which relates the analysis to various characteristics of the three cities. In summary, this work contributes to a deeper understanding of regularities in patterns of transit use from variations in volumes of travellers entering subway stations, it establishes a generic analytical framework for comparative studies using urban mobility data, and it provides key points for the management of variability by policy-makers intent on for making the travel experience more amenable.

The success or failure of the street network depends on its reliability. In this article, using resilience analysis, the author studies how the shape and appearance of street networks in self-organised and top-down planned cities influences urban transport. Considering London and Beijing as proxies for self-organised and top-down planned cities, the structural properties of London and Beijing networks first are investigated based on their primal and dual representations of planar graphs. The robustness of street networks then is evaluated in primal space and dual space by deactivating road links under random and intentional attack scenarios. The results show that the reliability of London street network differs from that of Beijing, which seems to rely more on its architecture and connectivity. It is found that top-down planned Beijing with its higher average degree in the dual space and assortativity in the primal space is more robust than self-organised London using the measures of maximum and second largest cluster size and network efficiency. The article offers an insight, from a network perspective, into the reliability of street patterns in self-organised and top-down planned city systems.

Personal data represent the e-history of a person and are of great significance to the person, but they are essentially produced and governed by various distributed services and there lacks a global and centralized view. In recent years, researchers pay attention to Personal Data Cloud (PDC) which aggregates the heterogeneous personal data scattered in different clouds into one cloud, so that a person could effectively store, acquire, and share their data. This paper makes a short survey on PDC research by summarizing related papers published in recent years. The concept, classification, and significance of personal data are elaborately introduced and then the semantics correlation and semantics representation of personal data are discussed. A multilayer reference architecture of PDC, including its core components and a real-world operational scenario showing how the reference architecture works, is introduced in detail. Existing commercial PDC products/prototypes are listed and compared from several perspectives. Five open issues to improve the shortcomings of current PDC research are put forward.

Gold nanoparticles (AuNPs) are promising materials for many bioapplications. However, upon contacting with biological media, AuNPs undergo changes. The interaction with proteins results in the so-called protein corona (PC) around AuNPs, leading to the new bioidentity and optical properties. Understanding the mechanisms of PC formation and its functions can help us to utilise its benefits and avoid its drawbacks. To date, most of the previous works aimed to understand the mechanisms governing PC formation and focused on the spherical nanoparticles, although non-spherical nanoparticles are designed for a wide range of applications in biosensing. In this work, we investigated the differences in PC formation on spherical and anisotropic AuNPs (nanostars in particular) from the joint experimental (extinction spectroscopy, zeta potential and surface-enhanced Raman scattering [SERS]) and computational methods (the finite element method and molecular dynamics [MD] simulations). We discovered that protein does not fully cover the surface of anisotropic nanoparticles, leaving SERS hot-spots at the tips and high curvature edges 'available' for analyte binding (no SERS signal after pre-incubation with protein) while providing protein-induced stabilization (indicated by extinction spectroscopy) of the AuNPs by providing a protein layer around the particle's core. The findings are confirmed from our MD simulations, the adsorption energy significantly decreases with the increased radius of curvature, so that tips (adsorption energy: 2762.334 kJ/mol) would be the least preferential binding site compared to core (adsorption energy: 11819.263 kJ/mol). These observations will help the development of new nanostructures with improved sensing and targeting ability. [ABSTRACT FROM AUTHOR]