QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology.

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber cross-sectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the "patchy" distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigator-specific needs and provides novel analytical approaches for quantifying muscle morphology

The cardiovascular effects of salidroside in the Goto-Kakizaki diabetic rat model

Many factors, including hyperglycemia, hypertension, obesity, dyslipidemia, and a sedentary lifestyle, contribute to a high prevalence of cardiovascular disease. Specific vascular impairment treatments in the context of diabetes and vascular risk need to be improved. Salidroside is the primary active component of Rhodiola rosea and has documented antioxidative, cardioprotective, and vasculoprotective properties. The aim of this study was to test the hypothesis that salidroside has protective effects against hyperglycemia, hypertension, and vasodilation impairment in the Goto-Kakizaki (GK) rat model of diabetes. We evaluated cardiovascular parameters (e.g., daytime/nighttime systolic and diastolic blood pressure, heart rate, and activity), metabolic parameters (e.g., body weight, food and water consumption, serum fructosamine level, glucose tolerance), eNOS / phospho-eNOS expression level and in vitro vascular reactivity of aorta and second-order mesenteric arteries in Wistar-Kyoto (control) and GK (diabetic) rats treated with salidroside (40 mg/kg) or placebo (water) for 5 weeks. GK rats showed hypertension, marked glucose intolerance, and impaired endothelium-dependent and endothelium-independent vasodilation capacity. Salidroside showed beneficial effects on endothelial and non-endothelial vasodilation and likely acts on the endothelium and smooth muscle cells through the soluble guanylyl cyclase pathway. Despite its vascular effects, salidroside had no effect on blood pressure and heart rate in GK and control rats, it did not improve glucose metabolism or limit hypertension in the GK model of type 2 diabetes

Language, Truth, and Logic and the Anglophone reception of the Vienna Circle

A. J. Ayer’s Language, Truth, and Logic had been responsible for introducing the Vienna Circle’s ideas, developed within a Germanophone framework, to an Anglophone readership. Inevitably, this migration from one context to another resulted in the alteration of some of the concepts being transmitted. Such alterations have served to facilitate a number of false impressions of Logical Empiricism from which recent scholarship still tries to recover. In this paper, I will attempt to point to the ways in which LTL has helped to foster the various mistaken stereotypes about Logical Empiricism which were combined into the received view. I will begin by examining Ayer’s all too brief presentation of an Anglocentric lineage for his ideas. This lineage, as we shall see, simply omits the major 19th century Germanophone influences on the rise of analytic philosophy. The Germanophone ideas he presents are selectively introduced into an Anglophone context, and directed towards various concerns that arose within that context. I will focus on the differences between Carnap’s version of the overcoming of metaphysics, and Ayer’s reconfiguration into what he calls the elimination of metaphysics. Having discussed the above, I will very briefly outline the consequences that Ayer’s radicalisation of the Vienna Circle’s doctrines had on the subsequent Anglophone reception of Logical Empiricism

Phase fluctuations in the ABC model

We analyze the fluctuations of the steady state profiles in the modulated phase of the ABC model. For a system of $L$ sites, the steady state profiles move on a microscopic time scale of order $L^3$. The variance of their displacement is computed in terms of the macroscopic steady state profiles by using fluctuating hydrodynamics and large deviations. Our analytical prediction for this variance is confirmed by the results of numerical simulations

Least Squares and Shrinkage Estimation under Bimonotonicity Constraints

In this paper we describe active set type algorithms for minimization of a smooth function under general order constraints, an important case being functions on the set of bimonotone r-by-s matrices. These algorithms can be used, for instance, to estimate a bimonotone regression function via least squares or (a smooth approximation of) least absolute deviations. Another application is shrinkage estimation in image denoising or, more generally, regression problems with two ordinal factors after representing the data in a suitable basis which is indexed by pairs (i,j) in {1,...,r}x{1,...,s}. Various numerical examples illustrate our methods

Young children's research: children aged 4-8 years finding solutions at home and at school

Children's research capacities have become increasingly recognised by adults, yet children remain excluded from the academy, with reports of their research participation generally located in adults' agenda. Such practice restricts children's freedom to make choices in matters affecting them, underestimates children’s capabilities and denies children particular rights. The present paper reports on one aspect of a small-scale critical ethnographic study adopting a constructivist grounded approach to conceptualise ways in which children's naturalistic behaviours may be perceived as research. The study builds on multi-disciplinary theoretical perspectives, embracing 'new' sociology, psychology, economics, philosophy and early childhood education and care (ECEC). Research questions include: 'What is the nature of ECEC research?' and 'Do children’s enquiries count as research?' Initially, data were collected from the academy: professional researchers (n=14) confirmed 'finding solutions' as a research behaviour and indicated children aged 4-8 years, their practitioners and primary carers as 'theoretical sampling'. Consequently, multi-modal case studies were constructed with children (n=138) and their practitioners (n=17) in three ‘good’ schools, with selected children and their primary carers also participating at home. This paper reports on data emerging from children aged 4-8 years at school (n=17) and at home (n=5). Outcomes indicate that participating children found diverse solutions to diverse problems, some of which they set themselves. Some solutions engaged children in high order thinking, whilst others did not; selecting resources and trialing activities engaged children in 'finding solutions'. Conversely, when children's time, provocations and activities were directed by adults, the quality of their solutions was limited, they focused on pleasing adults and their motivation to propose solutions decreased. In this study, professional researchers recognised 'finding solutions' as research behaviour and children aged 4-8 years naturalistically presented with capacities for finding solutions; however, the children's encounters with adults affected the solutions they found

Hip fracture risk assessment: Artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study

Copyright @ 2013 Tseng et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background - Osteoporotic hip fractures with a significant morbidity and excess mortality among the elderly have imposed huge health and economic burdens on societies worldwide. In this age- and sex-matched case control study, we examined the risk factors of hip fractures and assessed the fracture risk by conditional logistic regression (CLR) and ensemble artificial neural network (ANN). The performances of these two classifiers were compared. Methods - The study population consisted of 217 pairs (149 women and 68 men) of fractures and controls with an age older than 60 years. All the participants were interviewed with the same standardized questionnaire including questions on 66 risk factors in 12 categories. Univariate CLR analysis was initially conducted to examine the unadjusted odds ratio of all potential risk factors. The significant risk factors were then tested by multivariate analyses. For fracture risk assessment, the participants were randomly divided into modeling and testing datasets for 10-fold cross validation analyses. The predicting models built by CLR and ANN in modeling datasets were applied to testing datasets for generalization study. The performances, including discrimination and calibration, were compared with non-parametric Wilcoxon tests. Results - In univariate CLR analyses, 16 variables achieved significant level, and six of them remained significant in multivariate analyses, including low T score, low BMI, low MMSE score, milk intake, walking difficulty, and significant fall at home. For discrimination, ANN outperformed CLR in both 16- and 6-variable analyses in modeling and testing datasets (p?<?0.005). For calibration, ANN outperformed CLR only in 16-variable analyses in modeling and testing datasets (p?=?0.013 and 0.047, respectively). Conclusions - The risk factors of hip fracture are more personal than environmental. With adequate model construction, ANN may outperform CLR in both discrimination and calibration. ANN seems to have not been developed to its full potential and efforts should be made to improve its performance.National Health Research Institutes in Taiwa

Evidence based medicine as science

Evidence based medicine has claimed to be science on a number of occasions but it is not clear that this status is deserved. Within philosophy of science four main theories about the nature of science are historically recognised: inductivism, falsificationism, Kuhnian paradigms and research programmes. If evidence based medicine is science knowledge claims should be derived using a process that corresponds to one of these theories. This paper analyses whether this is the case. In the first section, different theories about the nature of science are introduced. In the second section, the claim that evidence based medicine is science is reinterpreted as the claim that knowledge claims derived from randomised controlled trails and meta-analyses are science. In the third section the knowledge claims valued within evidence based medicine are considered from the perspective of inductivism, falsificationism, Kuhnian paradigms and research programmes. In the final section possible counter arguments are considered. It is argued that the knowledge claims valued by evidence based medicine are not justified using inductivism, falsificationism, Kuhnian paradigms or research programmes. If these are the main criteria for evaluating if something is science or not, evidence based medicine does not meet these criteria

QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber crosssectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the “patchy” distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigatorspecific needs and provides novel analytical approaches for quantifying muscle morphology

Whose Science and whose Religion? Reflections on the Relations between Scientific and Religious Worldviews

Arguments about the relationship between science and religion often proceed by identifying a set of essential characteristics of scientific and religious worldviews and arguing on the basis of these characteristics for claims about a relationship of conflict or compatibility between them. Such a strategy is doomed to failure because science, to some extent, and religion, to a much larger extent, are cultural phenomena that are too diverse in their expressions to be characterized in terms of a unified worldview. In this paper I follow a different strategy. Having offered a loose characterization of the nature of science, I pose five questions about specific areas where religious and scientific worldviews may conflict - questions about the nature of faith, the belief in a God or Gods, the authority of sacred texts, the relationship between scientific and religious conceptions of the mind/soul, and the relationship between scientific and religious understandings of moral behavior. My review of these questions will show that they cannot be answered unequivocally because there is no agreement amongst religious believers as to the meaning of important religious concepts. Thus, whether scientific and religious worldviews conflict depends essentially upon whose science and whose religion one is considering. In closing, I consider the implications of this conundrum for science education