Concept: Occam's razor
Ancient DNA research is revealing a human history far more complex than that inferred from parsimonious models based on modern DNA. Here, we review some of the key events in the peopling of the world in the light of the findings of work on ancient DNA.
Among lean populations, cardiovascular disease (CVD) is rare. Among those with increased adiposity, CVD is the commonest cause of worldwide death. The “obesity paradox” describes seemingly contrary relationships between body fat and health/ill-health. Multiple obesity paradoxes exist, and include the anatomic obesity paradox, physiologic obesity paradox, demographic obesity paradox, therapeutic obesity paradox, cardiovascular event/procedure obesity paradox, and obesity treatment paradox. Adiposopathy (“sick fat”) is defined as adipocyte/adipose tissue dysfunction caused by positive caloric balance and sedentary lifestyle in genetically and environmentally susceptible individuals. Adiposopathy contributes to the commonest metabolic disorders encountered in clinical practice (high glucose levels, high blood pressure, dyslipidemia, etc.), all major CVD risk factors. Ockham’s razor is a principle of parsimony which postulates that among competing theories, the hypothesis with the fewest assumptions is the one best selected. Ockham’s razor supports adiposopathy as the primary cause of most cases of adiposity-related metabolic diseases, which in turn helps resolve the obesity paradox.
Recent advances in the HMP (human microbiome project) research have revealed profound implications of the human microbiome to our health and diseases. We postulated that there should be distinctive features associated with healthy and/or diseased microbiome networks. Following Occam’s razor principle, we further hypothesized that triangle motifs or trios, arguably the simplest motif in a complex network of the human microbiome, should be sufficient to detect changes that occurred in the diseased microbiome. Here we test our hypothesis with six HMP datasets that cover five major human microbiome sites (gut, lung, oral, skin, and vaginal). The tests confirm our hypothesis and demonstrate that the trios involving the special nodes (e.g., most abundant OTU or MAO, and most dominant OTU or MDO, etc.) and interactions types (positive vs. negative) can be a powerful tool to differentiate between healthy and diseased microbiome samples. Our findings suggest that 12 kinds of trios (especially, dominantly inhibitive trio with mixed strategy, dominantly inhibitive trio with pure strategy, and fully facilitative strategy) may be utilized as in silico biomarkers for detecting disease-associated changes in the human microbiome, and may play an important role in personalized precision diagnosis of the human microbiome associated diseases.
- The Journal of neuroscience nursing : journal of the American Association of Neuroscience Nurses
- Published almost 2 years ago
A collaborative approach was used to ascertain an appropriate stimulus for the patients to remember their stroke-specific education. The stroke education had to stand out amidst the myriad of papers and folders patients are bombarded with in the hospital. The team came up with the simple idea of using a bright red folder. When the patients were called, the call center would prompt the patient by saying, “The stroke education was given to you in a bright red folder.” Before the implementation of the red folders, only 81.5% of the patients remembered receiving stroke education. After the implementation of the red folders, 96.8% remembered receiving stroke education. The principle of Occam’s razor proved to be correct in our study. A very simple idea such as changing the color of the folders to bright red proved to have very meaningful results.
Occam’s razor-the idea that all else being equal, we should pick the simpler hypothesis-plays a prominent role in ordinary and scientific inference. But why are simpler hypotheses better? One attractive hypothesis known as Bayesian Occam’s razor (BOR) is that more complex hypotheses tend to be more flexible-they can accommodate a wider range of possible data-and that flexibility is automatically penalized by Bayesian inference. In two experiments, we provide evidence that people’s intuitive probabilistic and explanatory judgments follow the prescriptions of BOR. In particular, people’s judgments are consistent with the two most distinctive characteristics of BOR: They penalize hypotheses as a function not only of their numbers of free parameters but also as a function of the size of the parameter space, and they penalize those hypotheses even when their parameters can be “tuned” to fit the data better than comparatively simpler hypotheses.
We describe a case of a man with ectopic Cushing’s syndrome, elevated serum beta-D-glucan, and respiratory cultures with Pseudomonas, Aspergillus, and a partially acid-fast organism. Our case highlights challenges in diagnosis and management of coinfection in an immunocompromised host.
There has been an increasing interest in using interval-based Bayesian designs for dose finding, one of which is the modified toxicity probability interval (mTPI) method. We show that the decision rules in mTPI correspond to an optimal rule under a formal Bayesian decision theoretic framework. However, the probability models in mTPI are overly sharpened by the Ockham’s razor, which, while in general helps with parsimonious statistical inference, leads to undesirable decisions from safety perspective. We propose a new framework that blunts the Ockham’s razor, and demonstrate the superior performance of the new method, called mTPI-2. An online web tool is provided for users who can generate the design, conduct clinical trials, and examine operating characteristics of the designs.
Distributed lag non-linear models (DLNMs) are a modelling tool for describing potentially non-linear and delayed dependencies. Here, we illustrate an extension of the DLNM framework through the use of penalized splines within generalized additive models (GAM). This extension offers built-in model selection procedures and the possibility of accommodating assumptions on the shape of the lag structure through specific penalties. In addition, this framework includes, as special cases, simpler models previously proposed for linear relationships (DLMs). Alternative versions of penalized DLNMs are compared with each other and with the standard unpenalized version in a simulation study. Results show that this penalized extension to the DLNM class provides greater flexibility and improved inferential properties. The framework exploits recent theoretical developments of GAMs and is implemented using efficient routines within freely available software. Real-data applications are illustrated through two reproducible examples in time series and survival analysis.
Recent studies have shown that the traditional position weight matrix model is often insufficient for modeling transcription factor binding sites, as intra-motif dependencies play a significant role for an accurate description of binding motifs. Here, we present the Java application InMoDe, a collection of tools for learning, leveraging and visualizing such dependencies of putative higher order. The distinguishing feature of InMoDe is a robust model selection from a class of parsimonious models, taking into account dependencies only if justified by the data while choosing for simplicity otherwise.
There are always rival hypotheses to explain away the one that is posited as the most likely to be true. Context and Occam’s razor - the principle that among competing hypotheses, the one with the fewest assumptions should be selected - ultimately point to which hypothesis is the most likely to be true.