How Structural Features of a Spring-Based Model of Fibrous Collagen Tissue Govern the Overall Young's Modulus

While much study has been dedicated to investigating biopolymers' stress-strain response at low strain levels, little research has been done to investigate the linear region of biopolymers' stress-strain response and how the microstructure affects it. We propose a mathematical model of fibrous networks which reproduces qualitative features of collagen gel's stress-strain response and provides insight into the key features which impact the Young's Modulus of similar fibrous tissues. This model analyzes the relationship of the Young's Modulus of the lattice to internodal fiber length, number of connection points or nodes per unit area, and average number of connections to each node. Our results show that fiber length, nodal density, and level of connectivity each uniquely impact the Young's Modulus of the lattice. Furthermore, our model indicates that the Young's Modulus of a lattice can be estimated using the effective resistance of the network, a graph theory technique that measures distances across a network. Our model thus provides insight into how the organization of fibers in a biopolymer impact its linear Young's Modulus.

A Method to Compare Heterogeneous Types of Bone and Cartilage Meshes

Accurately capturing the bone and cartilage morphology and generating a mesh remains a critical step in the workflow of computational knee joint modeling. Currently there is no standardized method to compare meshes of different element types and nodal densities, making comparisons across research teams a significant challenge. The aim of this paper is to describe a method to quantify differences in knee joint bone and cartilages meshes, independent of bone and cartilage mesh topology. Bone mesh-to-mesh distances, subchondral bone boundaries and cartilage thicknesses from meshes of any type of mesh are obtained using a series of steps involving registration, resampling, and radial basis function fitting after which the comparisons are performed. Subchondral bone boundaries and cartilage thicknesses are calculated and visualized in a common frame of reference for comparison. The established method is applied to models developed by five modeling teams. Our approach to obtain bone mesh-to-mesh distances decreased the divergence seen in selecting a reference mesh (i.e. comparing mesh A-to-B vs. mesh B-to-A). In general the bone morphology was similar across teams. The cartilage thicknesses for all models were calculated and the mean absolute cartilage thickness difference was presented, the articulating areas had the best agreement across teams. The teams showed disagreement on the subchondral bone boundaries. The method presented in this paper allows for objective comparisons of bone and cartilage geometry that is agnostic to mesh type and nodal density.

Prognostic Factors and Survival Analysis of Patients with Triple Negative Breast Cancer - A Retrospective Study from a Tertiary Care Teaching Hospital in Kerala

BACKGROUND Breast cancer is now the most common cancer in Indian women, having recently surpassed cervical cancer in incidence. Triple negative breast cancer (TNBC), which accounts for 15 % of all the breast cancers is an aggressive type seen in younger women with early signs of metastasis, has a poor prognosis due to systemic recurrence and its refractoriness to conventional adjuvant therapy. The purpose of this study was to look into the various prognostic factors associated with 5 years disease-free survival (DFS) and overall survival (OS) in TNBC. METHODS This retrospective study included 67 patients with complete treatment and followup (median 57 months) presented and treated in the Department of Radiotherapy, Kottayam, between January 2011 and December 2012. The Kaplan-Meier approach was used to analyse survival. Using the log-rank test, univariate analysis of prognostic factors was completed. Using the Cox regression process, multivariate analysis was performed on IBM SPSS version 20. RESULTS The average age was 51.36 ± 11.393 (median, 51.36 years; range 30.0 – 80.0 years), with a median of 50 months, the five-year OS was 65.7 % and DFS was found to be 59.7 % with a median of 45 months, suggesting aggressive nature and poor TNBC survival. Univariate analysis of prognostic factor, clinical stage (cN) and positive nodes (pN) status, clinical tumour size, lympho-vascular invasion (LVI), grade, and nodal density were found to have a significant impact on DFS. Except tumour grade and LVI all were found to be associated with OS. Multivariate analysis, clinical tumour size and pathological nodal status had a significant impact on OS and DFS. CONCLUSIONS TNBC is an aggressive subtype of breast cancer in younger patients with a high risk of metastasis to visceral organs with inherent molecular subtypes and immunological heterogeneity. For treatment of TNBC, targeted estimated glomerular filtration rate (EGFR), fibroblast growth factor receptor 2 (FGFR2), vascular endothelial growth factor (VEGF), and mechanistic target of rapamycin (mTOR) receptor based initial treatment setting will improve the outcome dramatically and will fill the unmet clinical needs. KEYWORDS TNBC, Recurrence, OS, DFS, Nodal Density

Interacting brains revisited: A cross-brain network neuroscience perspective

Elucidating the neural basis of social behavior is a long-standing challenge in neuroscience. Such endeavors are driven by attempts to extend the isolated perspective on the human brain by considering interacting persons’ brain activities, but a theoretical and computational framework for this purpose is still in its infancy. Here, we posit a comprehensive framework based on bipartite graphs for interbrain networks and address whether they provide meaningful insights into the neural underpinnings of social interactions. First, we show that the nodal density of such graphs exhibits nonrandom properties. While the current analyses mostly rely on global metrics, we encode the regions’ roles via matrix decomposition to obtain an interpretable network representation yielding both global and local insights. With Bayesian modeling, we reveal how synchrony patterns seeded in specific brain regions contribute to global effects. Beyond inferential inquiries, we demonstrate that graph representations can be used to predict individual social characteristics, outperforming functional connectivity estimators for this purpose. In the future, this may provide a means of characterizing individual variations in social behavior or identifying biomarkers for social interaction and disorders.

Sparse interaction between oligodendrocyte precursor cells (NG2+ cells) and nodes of Ranvier in the central nervous system

ABSTRACTRegeneration of propagating action potentials at nodes of Ranvier allows nerve impulses to be conducted over long distances. Proper nodal function is believed to rely on intimate associations among axons, myelinating oligodendrocytes, and perinodal astrocytes. Studies in the optic nerve, corpus callosum, and spinal cord suggest that NG2+ cells are also key constituents of CNS nodes and that these glia may influence conduction efficacy and formation of axon collaterals. However, the prevalence of NG2+ cell processes at CNS nodes of Ranvier has not been rigorously quantified. Here we used a transgenic mouse expressing membrane-targeted EGFP to visualize the fine processes of NG2+ cells and to quantify the spatial relationship between NG2+ cells and nodes of Ranvier in four distinct CNS white matter tracts. NG2+ cell processes came within close spatial proximity to a small percentage of nodes of Ranvier and approximately half of these spatial interactions were estimated to occur by chance. The majority of NG2+ cell process tips were not found in close proximity to nodes and gray matter NG2+ cells in regions of low nodal density were as morphologically complex as their white matter counterparts, indicating that attraction to nodes does not critically influence the elaboration of NG2+ cell processes. Finally, there was no difference in nodal density between small regions devoid of NG2+ cell processes and those containing numerous NG2+ cells processes, demonstrating that the function of CNS nodes of Ranvier does not require ongoing interaction with NG2+ cells.Significance StatementEffective propagation of action potentials along neuronal axons is dependent upon periodic regeneration of depolarization at nodes of Ranvier. The position, structural integrity, and function of nodes of Ranvier is believed to be regulated, in part, by intimate physical interactions between nearby glial cells and nodes. Clarifying whether oligodendrocyte precursor cells are obligate members of this nodal support system is critical for defining whether these cells contribute to pathologies in which nodal structure is compromised.

Research of Topology Optimization of Nodal Density Based on Bilinear Interpolation

With the purpose to overcome the numerical instabilities and to generate more distinct structural layouts in the topology optimization, by using bilinear interpolation function, a topology optimization model of density interpolation based on nodal density is established, smooth density field is constructed. This method can ensure that the density field in the fixed design domain owns C0 continuity, and checkerboard patterns are naturally avoided in the nature of mathematics. After adding the sensitivity filtering, the optimal structures are smoother and have lesser details, which is helpful for manufacturing. Two numerical examples show that not only checkerboard pattern can be solved by the proposed method, but also the middle density nodal can be suppressed effectively.

STUDY ON MODAL TOPOLOGY OPTIMIZATION METHOD OF CONTINUUM STRUCTURE BASED ON EFG METHOD

The modal topology optimization method of continuum structure based on element-free Galerkin (EFG) method is presented by integrating solid isotropic material with penalization (SIMP) method with the optimality criteria method, and the penalty method is used to impose essential boundary conditions. The density of Gauss point and nodal density are selected as the design variables respectively, and the maximum of the first-order natural frequency is specified as the objective function. The sensitivity analysis algorithm is derived by using direct differential method. The examples are finished by selecting the two types of design variables respectively. The results obtained show that the checkerboard phenomenon does not appear when nodal density is selected as the design variable, and also verify that topology optimization method presented is feasible.