Authors: Ilija Barukčić
Comments: 28 Pages. pp. 28. Copyright © 2017 by Ilija Barukčić, Jever, Germany. Published by:
Objective: Parvovirus B19 appears to be associated with several diseases, one among those appears to be systemic sclerosis. Still, there is no evidence of a causal link be-tween parvovirus B19 and systemic sclerosis.
Methods: To explore the cause-effect relationship between Parvovirus B19 and sys-temic sclerosis, a systematic review and re-analysis of studies available and suitable was performed. The method of the conditio sine qua non relationship was used to proof the hypothesis without Parvovirus B19 infection no systemic sclerosis. The mathematical formula of the causal relationship k was used to proof the hypothesis, whether there is a cause effect relationship between Parvovirus B19 and systemic sclerosis. Significance was indicated by a p-value of less than 0.05
Result: The data analyzed support the Null-hypothesis that without Parvovirus B19 infection no systemic sclerosis. In the same respect, the studies analyzed provide evi-dence of a (highly) significant cause effect relationship between Parvovirus B19 and systemic sclerosis.
Conclusion: This study supports the conclusion that Parvovirus B19 is the cause of systemic sclerosis.
Parvovirus B19, systemic sclerosis, causal relationship
In this paper, we propose a novel nonconvex penalty function for compressed sensing using integral convolution approximation. It is well known that an unconstrained optimization criterion based on $\ell_1$-norm easily underestimates the large component in signal recovery. Moreover, most methods either perform well only under the measurement matrix satisfied restricted isometry property (RIP) or the highly coherent measurement matrix, which both can not be established at the same time. We introduce a new solver to address both of these concerns by adopting a frame of the difference between two convex functions with integral convolution approximation. What's more, to better boost the recovery performance, a weighted version of it is also provided. Experimental results suggest the effectiveness and robustness of our methods through several signal reconstruction examples in term of success rate and signal-to-noise ratio (SNR).
Authors: Ilija Barukčić
Comments: 37 pages. Copyright © 2018 by Ilija Barukčić, Horandstrasse, Jever, Germany. Published by: Journal of Biosciences and Medicines Vol.6 No.3, March 14, 2018
Objective: Accumulating evidence indicates that the gut microbiome has an increas-ingly important role in human disease and health. Fusobacterium nucleatum has been identified in several studies as the leading gut bacterium which is present in colorectal cancer (CRC). Still it is not clear if Fusobacterium plays a causal role.
Methods: To explore the cause-effect relationship between Fusobacterium nucleatum and colorectal cancer, a systematic review and re-analysis of studies published was performed. The method of the conditio sine qua non relationship was used to proof the hypothesis without Fusobacterium nucleatum infection no colorectal cancer. The mathematical formula of the causal relationship k was used to proof the hypothesis, whether there is a cause effect relationship between Fusobacterium nucleatum and colorectal cancer. Significance was indicated by a p-value of less than 0.05
Result: The data analyzed support the Null-hypothesis that without Fusobacterium nucleatum infection no colorectal cancer. In the same respect, the studies analyzed provide highly significant cause effect relationship between Fusobacterium nucleatum and colorectal cancer.
Conclusion: The findings of this study suggest that Fusobacterium (nucleatum) is the cause of colorectal cancer.
Authors: Robert Bennett
Comments: 14 Pages.
Biased statistics can arise from computational errors, belief in non-existent or unproven correlations ...or acceptance of premises proven invalid scientifically.
It is the latter that will be examined here for the case of human life expectancy, whose values are well-known...and virtually never challenged as to their basic assumptions.
Whether the false premises are accidental, a case of overlooking the obvious...or if they may be deliberate distortions serving a subliminal agenda.... is beyond the scope of this analysis.
Authors: Jason Hou-Liu
Latent Dirichlet Allocation (LDA) is a generative model describing the observed data as being composed of a mixture of underlying unobserved topics, as introduced by Blei et al. (2003). A key hyperparameter of LDA is the number of underlying topics k, which must be estimated empirically in practice. Selecting the appropriate value of k is essentially selecting the correct model to represent the data; an important issue concerning the goodness of fit. We examine in the current work a series of metrics from literature on a quantitative basis by performing benchmarks against a generated dataset with a known value of k and evaluate the ability of each metric to recover the true value, varying over multiple levels of topic resolution in the Dirichlet prior distributions. Finally, we introduce a new metric and heuristic for estimating kand demonstrate improved performance over existing metrics from the literature on several benchmarks.