A data-driven approach to approximate the correlation functions in cluster variation method

The cluster variation method (CVM) is one of the thermodynamic models used to calculate phase diagrams considering short range order (SRO). This method predicts the SRO values through internal variables referred to as correlation functions (CFs), accurately up to the cluster chosen in modeling the system. Determination of these CFs at each thermodynamic state of the system requires solving a set of nonlinear equations using numerical methods. In this communication, a neural network model is proposed to predict the values of the CFs. This network is trained for the BCC phase under tetrahedron approximation for both ordering and phase separating systems. The results show that the network can predict the values of the CFs accurately and thereby Helmholtz energy and the phase diagram with significantly less computational burden than that of conventional methods used.

A New Functional Clustering Method with Combined Dissimilarity Sources and Graphical Interpretation

Clustering is an essential task in functional data analysis. In this study, we propose a framework for a clustering procedure based on functional rankings or depth. Our methods naturally combine various types of between-cluster variation equally, which caters to various discriminative sources of functional data; for example, they combine raw data with transformed data or various components of multivariate functional data with their covariance. Our methods also enhance the clustering results with a visualization tool that allows intrinsic graphical interpretation. Finally, our methods are model-free and nonparametric and hence are robust to heavy-tailed distribution or potential outliers. The implementation and performance of the proposed methods are illustrated with a simulation study and applied to three real-world applications.

Investigating the inequalities in route to diagnosis amongst patients with diffuse large B-cell or follicular lymphoma in England

Introduction Diagnostic delay is associated with lower chances of cancer survival. Underlying comorbidities are known to affect the timely diagnosis of cancer. Diffuse large B-cell (DLBCL) and follicular lymphomas (FL) are primarily diagnosed amongst older patients, who are more likely to have comorbidities. Characteristics of clinical commissioning groups (CCG) are also known to impact diagnostic delay. We assess the association between comorbidities and diagnostic delay amongst patients with DLBCL or FL in England during 2005–2013. Methods Multivariable generalised linear mixed-effect models were used to assess the main association. Empirical Bayes estimates of the random effects were used to explore between-cluster variation. The latent normal joint modelling multiple imputation approach was used to account for partially observed variables. Results We included 30,078 and 15,551 patients diagnosed with DLBCL or FL, respectively. Amongst patients from the same CCG, having multimorbidity was strongly associated with the emergency route to diagnosis (DLBCL: odds ratio 1.56, CI 1.40–1.73; FL: odds ratio 1.80, CI 1.45–2.23). Amongst DLBCL patients, the diagnostic delay was possibly correlated with CCGs that had higher population densities. Conclusions Underlying comorbidity is associated with diagnostic delay amongst patients with DLBCL or FL. Results suggest a possible correlation between CCGs with higher population densities and diagnostic delay of aggressive lymphomas.

Why do strategies to strengthen primary health care succeed in some places and fail in others? Exploring local variation in the effectiveness of a community health worker managed digital health intervention in rural India

IntroductionDigital health interventions (DHIs) have huge potential as support modalities to identify and manage cardiovascular disease (CVD) risk in resource-constrained settings, but studies assessing them show modest effects. This study aims to identify variation in outcomes and implementation of SMARTHealth India, a cluster randomised trial of an ASHA-managed digitally enabled primary healthcare (PHC) service strengthening strategy for CVD risk management, and to explain how and in what contexts the intervention was effective.MethodsWe analysed trial outcome and implementation data for 18 PHC centres and collected qualitative data via focus groups with ASHAs (n=14) and interviews with ASHAs, PHC facility doctors and fieldteam mangers (n=12) Drawing on principles of realist evaluation and an explanatory mixed-methods design we developed mechanism-based explanations for observed outcomes.ResultsThere was substantial between-cluster variation in the primary outcome (overall: I2=62.4%, p<=0.001). The observed heterogeneity in trial outcomes was not attributable to any single factor. Key mechanisms for intervention effectiveness were community trust and acceptability of doctors’ and ASHAs’ new roles, and risk awareness. Enabling local contexts were seen to evolve over time and in response to the intervention. These included obtaining legitimacy for ASHAs’ new roles from trusted providers of curative care; ASHAs’ connections to community and to qualified providers; their responsiveness to community needs; and the accessibility, quality and appropriateness of care provided by higher level medical providers, including those outside of the implementing (public) subsystem.ConclusionLocal contextual factors were significant influences on the effectiveness of this DHI-enabled PHC service strategy intervention. Local adaptions need to be planned for, monitored and responded to over time. By identifying plausible explanations for variation in outcomes between clusters, we identify potential strategies to strengthen such interventions.

The 2-D Cluster Variation Method: Topography Illustrations and Their Enthalpy Parameter Correlations

One of the biggest challenges in characterizing 2-D image topographies is finding a low-dimensional parameter set that can succinctly describe, not so much image patterns themselves, but the nature of these patterns. The 2-D cluster variation method (CVM), introduced by Kikuchi in 1951, can characterize very local image pattern distributions using configuration variables, identifying nearest-neighbor, next-nearest-neighbor, and triplet configurations. Using the 2-D CVM, we can characterize 2-D topographies using just two parameters; the activation enthalpy (ε0) and the interaction enthalpy (ε1). Two different initial topographies (“scale-free-like” and “extreme rich club-like”) were each computationally brought to a CVM free energy minimum, for the case where the activation enthalpy was zero and different values were used for the interaction enthalpy. The results are: (1) the computational configuration variable results differ significantly from the analytically-predicted values well before ε1 approaches the known divergence as ε1→0.881, (2) the range of potentially useful parameter values, favoring clustering of like-with-like units, is limited to the region where ε0<3 and ε1<0.25, and (3) the topographies in the systems that are brought to a free energy minimum show interesting visual features, such as extended “spider legs” connecting previously unconnected “islands,” and as well as evolution of “peninsulas” in what were previously solid masses.

The 2-D Cluster Variation Method: Initial Findings and Topography Illustrations

One of the biggest challenges in characterizing 2-D image topographies is finding a low-dimensional parameter set that can succinctly describe, not so much image patterns themselves, but the nature of these patterns. The 2-D Cluster Variation Method (CVM), introduced by Kikuchi in 1951, can characterize very local image pattern distributions using configuration variables, identifying nearest-neighbor, next-nearest-neighbor, and triplet configurations. Using the 2-D CVM, we can characterize 2-D topographies using just two parameters; the activation enthalpy and the interaction enthalpy. Initial investigations with two different representative topographies (``scale-free-like'' and ``rich club-like'') produce interesting results when brought to a CVM free energy minimum. Additional phase space investigations, where one of these two parameters has been set to zero, identify useful parameter ranges. Careful comparison of the analytically-predicted configuration variables versus those obtained when performing computational free energy minimization on a 2-D grid show that the computational results differ significantly from the analytic solution. The 2-D CVM can potentially function as a secondary free energy minimization within the hidden layer of a neural network, providing a basis for extending node activations over time and allowing temporal correlation of patterns.

Genetic diversity analysis through cluster constellation in Brinjal (Solanum melongena L.)

In present investigation, 110 locally developed genotypes from different breeding programmes in brinjal were classified into eleven clusters on the basis of their D2 values computed from Mahalanobis D2 statistics of twelve morphological traits, wherein inter-and intra-cluster distances highlighted the genetic divergence of the genotypes grouped among and within different clusters. Among all, fourth cluster was the largest with 33 genotypes; however, each of second, fifth, ninth, tenth and eleventh clusters contained only single genotype. The genotypes of eighth and tenth clusters were highly diverse (1584.40) followed by third and eighth (1431.31), eighth and ninth (1302.69), sixth and eighth (1126.33) and first and eighth (1042.91) clusters. Intra-cluster (within cluster) variation was the highest in seventh cluster (74.43) followed by eighth (61.20) and sixth (54.36) that described the diverse nature of eighteen, five and nineteen genotypes in these groups, respectively. However, PBL-268, PBGL-401, PBL-243, PSR 308 and PBOB-518 were grouped individually in IInd, Vth, IXth, Xth and XIth clusters, respectively. Overall, fifth cluster had most vigorous and high yielding ((2.82 kg/plant) genotype (PBGL-405); eighth cluster included genotypes with big round fruits and maximum fruit weight (317.43g); and tenth cluster had the earliest genotype (PSR-308) with the maximum number of fruits per plant (43.17). Out of twelve morphological traits, 94.19% diversity was brought by average fruit weight (67.86%), number of fruits per plant (17.26%), fruit yield per plant (5.37%) and fruit breadth (3.70%), however, other traits had negligible share towards the variation. This study created the foundation for future hybridization programmes in brinjal, where the parents can be selected on the basis of highly diverse groups as well as traits.