The influence of the precipitation rate on the properties of porous chromia

The properties were studied of heated (320°C) chromia samples, prepared by two precipitation methods: \ud \ud 1. (1) addition of ammonia to chromium salt solutions,\ud 2. (2) OH− formation in chromium salt solutions through hydrolysis of urea.\ud \ud Samples formed by means of the first method are macro or mesoporous and have a lower specific surface area (~200 m2·g−1) than those formed by urea hydrolysis (~300 m2·g−1). Only in the case of a very slow addition of the ammonia solution these properties of the chromia's become equal. Experiments show that the micro porous type samples with high surface area are only formed if the pH range 5.1 to 5.7 is passed slowly. The formation of polychromium complexes of uniform size is suggested.\ud \u

How Noisy Data Affects Geometric Semantic Genetic Programming

Noise is a consequence of acquiring and pre-processing data from the environment, and shows fluctuations from different sources---e.g., from sensors, signal processing technology or even human error. As a machine learning technique, Genetic Programming (GP) is not immune to this problem, which the field has frequently addressed. Recently, Geometric Semantic Genetic Programming (GSGP), a semantic-aware branch of GP, has shown robustness and high generalization capability. Researchers believe these characteristics may be associated with a lower sensibility to noisy data. However, there is no systematic study on this matter. This paper performs a deep analysis of the GSGP performance over the presence of noise. Using 15 synthetic datasets where noise can be controlled, we added different ratios of noise to the data and compared the results obtained with those of a canonical GP. The results show that, as we increase the percentage of noisy instances, the generalization performance degradation is more pronounced in GSGP than GP. However, in general, GSGP is more robust to noise than GP in the presence of up to 10% of noise, and presents no statistical difference for values higher than that in the test bed.Comment: 8 pages, In proceedings of Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, German

Short and random: Modelling the effects of (proto-)neural elongations

To understand how neurons and nervous systems first evolved, we need an account of the origins of neural elongations: Why did neural elongations (axons and dendrites) first originate, such that they could become the central component of both neurons and nervous systems? Two contrasting conceptual accounts provide different answers to this question. Braitenberg's vehicles provide the iconic illustration of the dominant input-output (IO) view. Here the basic role of neural elongations is to connect sensors to effectors, both situated at different positions within the body. For this function, neural elongations are thought of as comparatively long and specific connections, which require an articulated body involving substantial developmental processes to build. Internal coordination (IC) models stress a different function for early nervous systems. Here the coordination of activity across extended parts of a multicellular body is held central, in particular for the contractions of (muscle) tissue. An IC perspective allows the hypothesis that the earliest proto-neural elongations could have been functional even when they were initially simple short and random connections, as long as they enhanced the patterning of contractile activity across a multicellular surface. The present computational study provides a proof of concept that such short and random neural elongations can play this role. While an excitable epithelium can generate basic forms of patterning for small body-configurations, adding elongations allows such patterning to scale up to larger bodies. This result supports a new, more gradual evolutionary route towards the origins of the very first full neurons and nervous systems.Comment: 12 pages, 5 figures, Keywords: early nervous systems, neural elongations, nervous system evolution, computational modelling, internal coordinatio

Effects of flywheel training on strength-related variables in female populations. A systematic review

This study aimed to evaluate the effect of flywheel training on female populations, report practical recommendations for practitioners based on the currently available evidence, underline the limitations of current literature, and establish future research directions. Studies were searched through the electronic databases (PubMed, SPORTDiscus, and Web of Science) following the preferred reporting items for systematic reviews and meta-analysis statement guidelines. The methodological quality of the seven studies included in this review ranged from 10 to 19 points (good to excellent), with an average score of 14-points (good). These studies were carried out between 2004 and 2019 and comprised a total of 100 female participants. The training duration ranged from 5 weeks to 24 weeks, with volume ranging from 1 to 4 sets and 7 to 12 repetitions, and frequency ranged from 1 to 3 times a week. The contemporary literature suggests that flywheel training is a safe and time-effective strategy to enhance physical outcomes with young and elderly females. With this information, practitioners may be inclined to prescribe flywheel training as an effective countermeasure for injuries or falls and as potent stimulus for physical enhancement

Trio-One: Layering Uncertainty and Lineage on a Conventional DBMS

Trio is a new kind of database system that supports data, uncertainty, and lineage in a fully integrated manner. The first Trio prototype, dubbed Trio-One, is built on top of a conventional DBMS using data and query translation techniques together with a small number of stored procedures. This paper describes Trio-One's translation scheme and system architecture, showing how it efficiently and easily supports the Trio data model and query language

Discovery and application of colorectal cancer protein markers for disease stratification

Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise methods of CRC stratification are needed. The intracellular protein expression from 28 CRC primary tumours and corresponding normal intestinal mucosa was analysed using saturation-DIGE/MS and Explorer antibody microarrays. Changes in protein abundance were identified at each stage of CRC. Proteins associated with proliferation, glycolysis, reduced adhesion, endoplasmic reticulum stress, angiogenesis, and response to hypoxia represent changes to CRC and its microenvironment during development. Molecular changes in CRC cells and their microenvironment can be incorporated into clinic-pathological data to help sub-classify tumours and personalise treatment. DotScan antibody microarray analysis was used to profile the surface proteome of cells derived from 50 CRC samples and corresponding normal intestinal mucosa. Fluorescence multiplexing enabled the analysis of two different sub-populations of cells from each sample: EpCAM+ cells (CRC cells or normal epithelial cells in normal mucosa) and CD3+ T-cells (tumour-infiltrating lymphocytes). Unsupervised hierarchical clustering of the CRC and T-cell surface profiles defined four clinically relevant clusters, which showed some correlation with histopathological and clinical characteristics such as cancer cell differentiation, peri-tumoural inflammation and stimulation of infiltrating T-cells. The observed relationship between the surface antigen expression profiles of patients’ CRC cells and their corresponding tumour infiltrating T-cells suggests that CRC surface proteins may play a direct role in influencing the activity (and hence surface protein expression) of neighbouring T-cells and/or vice versa. We conclude that the application of surface profiling may provide improved patient stratification, allowing more reliable prediction of disease progression and patient outcome

Modeling spontaneous activity across an excitable epithelium: Support for a coordination scenario of early neural evolution

Internal coordination models hold that early nervous systems evolved in the first place to coordinate internal activity at a multicellular level, most notably the use of multicellular contractility as an effector for motility. A recent example of such a model, the skin brain thesis, suggests that excitable epithelia using chemical signaling are a potential candidate as a nervous system precursor.We developed a computational model and a measure for whole body coordination to investigate the coordinative properties of such excitable epithelia. Using this measure we show that excitable epithelia can spontaneously exhibit body-scale patterns of activation. Relevant factors determining the extent of patterning are the noise level for exocytosis, relative body dimensions, and body size. In smaller bodies whole-body coordination emerges from cellular excitability and bidirectional excitatory transmission alone.Our results show that basic internal coordination as proposed by the skin brain thesis could have arisen in this potential nervous system precursor, supporting that this configuration may have played a role as a proto-neural system and requires further investigation

Variational method for learning Quantum Channels via Stinespring Dilation on neutral atom systems

The state $|\psi(t)\rangle$ of a closed quantum system evolves under the Schr\"{o}dinger equation, where the reversible evolution of the state is described by the action of a unitary operator $U(t)$ on the initial state $|\psi_0\rangle$, i.e.\ $|\psi(t)\rangle=U(t)|\psi_0\rangle$. However, realistic quantum systems interact with their environment, resulting in non-reversible evolutions, described by Lindblad equations. The solution of these equations give rise to quantum channels $\Phi_t$ that describe the evolution of density matrices according to $\rho(t)=\Phi_t(\rho_0)$, which often results in decoherence and dephasing of the state. For many quantum experiments, the time until which measurements can be done might be limited, e.g. by experimental instability or technological constraints. However, further evolution of the state may be of interest. For instance, to determine the source of the decoherence and dephasing, or to identify the steady state of the evolution. In this work, we introduce a method to approximate a given target quantum channel by means of variationally approximating equivalent unitaries on an extended system, invoking the Stinespring dilation theorem. We report on an experimentally feasible method to extrapolate the quantum channel on discrete time steps using only data on the first time steps. Our approach heavily relies on the ability to spatially transport entangled qubits, which is unique to the neutral atom quantum computing architecture. Furthermore, the method shows promising predictive power for various non-trivial quantum channels. Lastly, a quantitative analysis is performed between gate-based and pulse-based variational quantum algorithms.Comment: 11 pages, 7 figure