Bi-directional cell-pericellular matrix interactions direct stem cell fate

Modifiable hydrogels have revealed tremendous insight into how physical characteristics of cells’ 3D environment drive stem cell lineage specification. However, in native tissues, cells do not passively receive signals from their niche. Instead they actively probe and modify their pericellular space to suit their needs, yet the dynamics of cells’ reciprocal interactions with their pericellular environment when encapsulated within hydrogels remains relatively unexplored. Here, we show that human bone marrow stromal cells (hMSC) encapsulated within hyaluronic acid-based hydrogels modify their surroundings by synthesizing, secreting and arranging proteins pericellularly or by degrading the hydrogel. hMSC’s interactions with this local environment have a role in regulating hMSC fate, with a secreted proteinaceous pericellular matrix associated with adipogenesis, and degradation with osteogenesis. Our observations suggest that hMSC participate in a bi-directional interplay between the properties of their 3D milieu and their own secreted pericellular matrix, and that this combination of interactions drives fate

Quantile Estimation Based on the Principles of the Search on the Line

Part 10: Learning - IntelligenceInternational audienceThe goal of our research is to estimate the quantiles of a distribution from a large set of samples that arrive sequentially. We propose a novel quantile estimator that requires a finite memory and is simple to implement. Furthermore, the estimator falls under the family of incremental estimators, i.e., it utilizes the previously-computed estimates and only resorts to the last sample for updating these estimates. The estimator estimates the quantile on a set of discrete values. Choosing a low resolution results in fast convergence and low precision of the current estimate after convergence, while a high resolution results in slower convergence, but higher precision. The convergence results are based on the theory of Stochastic Point Location (SPL). The reader should note that the aim of the paper is to demonstrate its salient properties as a novel quantile estimator that uses only finite memory

Multiaction learning automata possessing ergodicity of the mean

Multiaction learning automata which update their action probabilities on the basis of the responses they get from an environment are considered in this paper. The automata update the probabilities according to whether the environment responds with a reward or a penalty. Learning automata are said to possess ergodicity of the mean if the mean action probability is the state probability (or unconditional probability) of an ergodic Markov chain. In an earlier paper [11] we considered the problem of a two-action learning automaton being ergodic in the mean (EM). The family of such automata was characterized completely by proving the necessary and sufficient conditions for automata to be EM. In this paper, we generalize the results of [11] and obtain necessary and sufficient conditions for the multiaction learning automaton to be EM. These conditions involve two families of probability updating functions. It is shown that for the automaton to be EM the two families must be linearly dependent. The vector defining the linear dependence is the only vector parameter which controls the rate of convergence of the automaton. Further, the technique for reducing the variance of the limiting distribution is discussed. Just as in the two-action case, it is shown that the set of absolutely expedient schemes and the set of schemes which possess ergodicity of the mean are mutually disjoint

Text Classification Using Novel “Anti-Bayesian” Techniques

This paper presents a non-traditional “Anti-Bayesian” solution for the traditional Text Classification (TC) problem. Historically, all the recorded TC schemes work using the fundamental paradigm that once the statistical features are inferred from the syntactic/semantic indicators, the classifiers themselves are the well-established statistical ones. In this paper, we shall demonstrate that by virtue of the skewed distributions of the features, one could advantageously work with information latent in certain “non-central” quantiles (i.e., those distant from the mean) of the distributions. We, indeed, demonstrate that such classifiers exist and are attainable, and show that the design and implementation of such schemes work with the recently-introduced paradigm of Quantile Statistics (QS)-based classifiers. These classifiers, referred to as Classification by Moments of Quantile Statistics (CMQS), are essentially “Anti”-Bayesian in their modus operandi. To achieve our goal, in this paper we demonstrate the power and potential of CMQS to describe the very high-dimensional TC-related vector spaces in terms of a limited number of “outlier-based” statistics. Thereafter, the PR task in classification invokes the CMQS classifier for the underlying multi-class problem by using a linear number of pair-wise CMQS-based classifiers. By a rigorous testing on the standard 20-Newsgroups corpus we show that CMQS-based TC attains accuracy that is comparable to the bestreported classifiers. We also propose the potential of fusing the results of a CMQS-based method with those obtained from a traditional scheme

Learning Automata-Based Solutions to the Single Elevator Problem

The field of AI has been a topic of interest for the better part of a century, where the goal is to have computers mimic human behaviour. Researchers have incorporated AI in different problem domains, such as autonomous driving, game playing, diagnosis and security. This paper concentrates on a subfield of AI, i.e., the field of Learning Automata (LA), and to use its tools to tackle a problem that has not been tackled before using AI, namely the problem of the optimally scheduling and parking of elevators. In particular, we are concerned with determining the Elevators’ optimal “parking” location. In this paper, we specifically work with the Single (We consider the more complicated multi-elevator problem in a forthcoming paper.) Elevator Problem (SEP), and show how it can be extended to the solution to Elevator-like Problems (ELPs), which are a family of problems with similar characteristics. Here, the objective is to find the optimal parking floors for the single elevator scenario so as to minimize the passengers’ Average Waiting Time (AWT). Apart from proposing benchmark solutions, we have provided two different novel LA-based solutions for the single-elevator scenario. The first solution is based on the well-known LRI scheme, and the second solution incorporates the Pursuit concept to improve the performance and the convergence speed of the former, leading to the LRI scheme. The simulation results presented demonstrate that our solutions performed much better than those used in modern-day elevators, and provided results that are near-optimal, yielding a performance increase of up to 80%