BackgroundClinical researchers have often preferred to use a fixed effects model for the primary interpretation of a meta-analysis. Heterogeneity is usually assessed via the well known Q and I2 statistics, along with the random effects estimate they imply. In recent years, alternative methods for quantifying heterogeneity have been proposed, that are based on a 'generalised' Q statistic.MethodsWe review 18 IPD meta-analyses of RCTs into treatments for cancer, in order to quantify the amount of heterogeneity present and also to discuss practical methods for explaining heterogeneity.ResultsDiffering results were obtained when the standard Q and I2 statistics were used to test for the presence of heterogeneity. The two meta-analyses with the largest amount of heterogeneity were investigated further, and on inspection the straightforward application of a random effects model was not deemed appropriate. Compared to the standard Q statistic, the generalised Q statistic provided a more accurate platform for estimating the amount of heterogeneity in the 18 meta-analyses.ConclusionsExplaining heterogeneity via the pre-specification of trial subgroups, graphical diagnostic tools and sensitivity analyses produced a more desirable outcome than an automatic application of the random effects model. Generalised Q statistic methods for quantifying and adjusting for heterogeneity should be incorporated as standard into statistical software. Software is provided to help achieve this aim.
Biological engineering, the convergence between engineering and biology, is at the forefront of significant advances in healthcare, agriculture, and environmental sustainability, making it highly relevant to current scientific and societal challenges. We take a comprehensive look at this broad and interdisciplinary domain, structure it into three main areas - bioinspired, biological and biohybrid approaches - and dissect inherent and fundamental challenges along with opportunities, highlighting specific examples. We describe how data-driven discovery and design, in conjunction with artificial intelligence, can mitigate the absence of reductionist models in these areas. Additionally, we address the education of a new generation of biological engineers, emphasizing mathematical, technical, and artificial intelligence frameworks.
Decision-theoretic planning is a popular approach to sequential decision making problems, because it treats uncertainty in sensing and acting in a principled way. In single-agent frameworks like MDPs and POMDPs, planning can be carried out by resorting to Q-value functions: an optimal Q-value function Q* is computed in a recursive manner by dynamic programming, and then an optimal policy is extracted from Q*. In this paper we study whether similar Q-value functions can be defined for decentralized POMDP models (Dec-POMDPs), and how policies can be extracted from such value functions. We define two forms of the optimal Q-value function for Dec-POMDPs: one that gives a normative description as the Q-value function of an optimal pure joint policy and another one that is sequentially rational and thus gives a recipe for computation. This computation, however, is infeasible for all but the smallest problems. Therefore, we analyze various approximate Q-value functions that allow for efficient computation. We describe how they relate, and we prove that they all provide an upper bound to the optimal Q-value function Q*. Finally, unifying some previous approaches for solving Dec-POMDPs, we describe a family of algorithms for extracting policies from such Q-value functions, and perform an experimental evaluation on existing test problems, including a new firefighting benchmark problem.
A bstractWe introduce a new framework for constructing black hole solutions that are holographically dual to strongly coupled field theories with explicitly broken translation invariance. Using a classical gravitational theory with a continuous global symmetry leads to constructions that involve solving ODEs instead of PDEs. We study in detail D = 4 Einstein-Maxwell theory coupled to a complex scalar field with a simple mass term. We construct black holes dual to metallic phases which exhibit a Drude-type peak in the optical conductivity, but there is no evidence of an intermediate scaling that has been reported in other holographic lattice constructions. We also construct black holes dual to insulating phases which exhibit a suppression of spectral weight at low frequencies. We show that the model also admits a novel AdS3 × $ \mathbb{R} $ solution.
Neutron Star Measurements Neutron stars are one of the densest manifestations of matter in the universe. Yagi and Yunes (p. 365) examined the moment of inertia of neutron stars, which determines how fast they can spin, and the quadrupole moment and tidal Love number, which determine how much they can be deformed. The findings suggest that these three quantities obey universal relationships that are independent of the internal structure of the stars, implying that measurements of one of the three could accurately predict the other two. The relation of inertia, Love number, and quadrupole moment is independent of neutron and quark stars’ internal structure. Neutron stars and quark stars are not only characterized by their mass and radius but also by how fast they spin, through their moment of inertia, and how much they can be deformed, through their Love number and quadrupole moment. These depend sensitively on the star’s internal structure and thus on unknown nuclear physics. We find universal relations between the moment of inertia, the Love number, and the quadrupole moment that are independent of the neutron and quark star’s internal structure. These can be used to learn about neutron star deformability through observations of the moment of inertia, break degeneracies in gravitational wave detection to measure spin in binary inspirals, distinguish neutron stars from quark stars, and test general relativity in a nuclear structure–independent fashion.
Complexity in biology is often described using a multi-map architecture, where the genotype, representing the encoded information, is mapped to the functional level, known as the phenotype, which is then connected to a latent phenotype we refer to as fitness. This underlying architecture governs the processes that drive evolution. Moreover, natural selection, along with other neutral forces, can modify these maps. At each hierarchical level, variation is observed. Here, I propose the need to establish principles that can aid in understanding the transformation of variation within this multi-map architecture. Specifically, I will introduce three, related to the presence of modulators, constraints, and the modular channeling of variation. By comprehending these design principles in various biological systems, we can gain better insights into the mechanisms underlying these maps and their evolutionary dynamics.
By making use of the concept of basic (or q-) calculus, various families of q-extensions of starlike functions of order α in the open unit disk U were introduced and studied from many different viewpoints and perspectives. In this paper, we first investigate the relationship between various known classes of q-starlike functions that are associated with the Janowski functions. We then introduce and study a new subclass of q-starlike functions that involves the Janowski functions. We also derive several properties of such families of q-starlike functions with negative coefficients including (for example) distortion theorems.
Colorectal cancer refers to the cancer from the dentate line to the junction of rectosigmoid colon, which is one of the most common malignant tumors of the digestive tract. The treatment of colorectal cancer is controversial, so understanding the risk factors and survival factors of colorectal cancer is of great significance for the diagnosis of patients. This study sampled patients with colorectal cancer from the SEER database. The factors affecting the survival of colorectal cancer patients were analysed by combining principal component analysis and competitive risk model, and the survival time of patients was analysed by combining principal component analysis and linear regression. Finally, the data were predicted. The results show that principal component analysis can effectively reduce the number of variables, and the combination of competitive risk model and linear regression model can effectively analyse and predict the data.