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Denosumab is a monoclonal antibody used to reduce risk of fractures in osteoporosis. ROSALIA was a multicenter, double-blind, randomized, integrated phase I/phase III study comparing the efficacy, pharmacokinetics (PK), pharmacodynamics (PD), immunogenicity, and safety of proposed biosimilar denosumab GP2411 with reference denosumab (REF-DMAb) (Prolia®; Amgen). Postmenopausal women with osteoporosis were randomized 1:1 to 2 60-mg doses of GP2411 or REF-DMAb, one at study start and one at week 26. At week 52, the REF-DMAb group was re-randomized 1:1 to a third dose of REF-DMAb or switch to GP2411. The primary efficacy endpoint was percentage change from baseline (%CfB) in LS-BMD at week 52. Secondary efficacy endpoints were %CfB in LS-BMD, FN-BMD, and TH-BMD at weeks 26 and 78 (and week 52 for FN-BMD and TH-BMD). Primary PK and PD endpoints were the area under the serum concentration-time curve extrapolated to infinity and maximum drug serum concentration at week 26, and the area under the effect-time curve of the %CfB in serum CTX at week 26. Secondary PK and PD endpoints included drug serum concentrations and %CfB in serum CTX and P1NP during the study period. Similar efficacy was demonstrated at week 52, with 95% CIs of the difference in %CfB in LS-BMD between treatment groups fully contained within prespecified equivalence margins. Similarity in PK and PD was demonstrated at week 26. Immunogenicity was similar between groups and was not impacted by treatment switch. The rate of new vertebral fractures was comparable. Treatment-emergent adverse events were comparable between groups (63.6% [GP2411/GP2411]; 76.0% [REF-DMAb/REF-DMAb]; 76.6% [REF-DMAb/GP2411]). In conclusion, ROSALIA showed similar efficacy, PK and PD, and comparable safety and immunogenicity of GP2411 to REF-DMAb in postmenopausal osteoporosis., SCOPUS: ar.j, info:eu-repo/semantics/published

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

Neuroscientific data analysis has traditionally relied on linear algebra and stochastic process theory. However, the tree-like shapes of neurons cannot be described easily as points in a vector space (the subtraction of two neuronal shapes is not a meaningful operation), and methods from computational topology are better suited to their analysis. Here we introduce methods from Discrete Morse (DM) Theory to extract the tree-skeletons of individual neurons from volumetric brain image data, and to summarize collections of neurons labelled by tracer injections. Since individual neurons are topologically trees, it is sensible to summarize the collection of neurons using a consensus tree-shape that provides a richer information summary than the traditional regional 'connectivity matrix' approach. The conceptually elegant DM approach lacks hand-tuned parameters and captures global properties of the data as opposed to previous approaches which are inherently local. For individual skeletonization of sparsely labelled neurons we obtain substantial performance gains over state-of-the-art non-topological methods (over 10% improvements in precision and faster proofreading). The consensus-tree summary of tracer injections incorporates the regional connectivity matrix information, but in addition captures the collective collateral branching patterns of the set of neurons connected to the injection site, and provides a bridge between single-neuron morphology and tracer-injection data., Comment: 20 pages (14 pages main-text and 6 pages supplementary information). 5 main-text figures. 5 supplementary figures. 2 supplementary tables

Certain facial parts are salient (unique) in appearance, which substantially contribute to the holistic recognition of a subject. Occlusion of these salient parts deteriorates the performance of face recognition algorithms. In this paper, we propose a generative model to reconstruct the missing parts of the face which are under occlusion. The proposed generative model (SD-GAN) reconstructs a face preserving the illumination variation and identity of the face. A novel adversarial training algorithm has been designed for a bimodal mutually exclusive Generative Adversarial Network (GAN) model, for faster convergence. A novel adversarial "structural" loss function is also proposed, comprising of two components: a holistic and a local loss, characterized by SSIM and patch-wise MSE. Ablation studies on real and synthetically occluded face datasets reveal that our proposed technique outperforms the competing methods by a considerable margin, even for boosting the performance of Face Recognition., Comment: Recommended for revision in Neurocomputing, Elsevier

Face Recognition (FR) has been the interest to several researchers over the past few decades due to its passive nature of biometric authentication. Despite high accuracy achieved by face recognition algorithms under controlled conditions, achieving the same performance for face images obtained in surveillance scenarios, is a major hurdle. Some attempts have been made to super-resolve the low-resolution face images and improve the contrast, without considerable degree of success. The proposed technique in this paper tries to cope with the very low resolution and low contrast face images obtained from surveillance cameras, for FR under surveillance conditions. For Support Vector Machine classification, the selection of appropriate kernel has been a widely discussed issue in the research community. In this paper, we propose a novel kernel selection technique termed as MFKL (Multi-Feature Kernel Learning) to obtain the best feature-kernel pairing. Our proposed technique employs a effective kernel selection by Multiple Kernel Learning (MKL) method, to choose the optimal kernel to be used along with unsupervised domain adaptation method in the Reproducing Kernel Hilbert Space (RKHS), for a solution to the problem. Rigorous experimentation has been performed on three real-world surveillance face datasets : FR\_SURV, SCface and ChokePoint. Results have been shown using Rank-1 Recognition Accuracy, ROC and CMC measures. Our proposed method outperforms all other recent state-of-the-art techniques by a considerable margin., Comment: 13 pages, 15 figures, 4 tables. Kernel Selection, Surveillance, Multiple Kernel Learning, Domain Adaptation, RKHS, Hallucination

Face recognition (FR) is the most preferred mode for biometric-based surveillance, due to its passive nature of detecting subjects, amongst all different types of biometric traits. FR under surveillance scenario does not give satisfactory performance due to low contrast, noise and poor illumination conditions on probes, as compared to the training samples. A state-of-the-art technology, Deep Learning, even fails to perform well in these scenarios. We propose a novel soft-margin based learning method for multiple feature-kernel combinations, followed by feature transformed using Domain Adaptation, which outperforms many recent state-of-the-art techniques, when tested using three real-world surveillance face datasets., Comment: This is an extended version of the paper accepted in CVPR Biometric Workshop, 2016. arXiv admin note: text overlap with arXiv:1610.00660