Morphological and Biochemical Characteristics of Wild Loquat (Eriobotrya japonica Lindl.) Genotypes in Turkey

This study was carried out to determine the pomological and biochemical characteristics of eight different loquat genotypes collected from the Black Sea region (Turkey) in 2018. Totally 20 fruits, at the same ripening stage, were collected from the selected genotypes and tested. Results suggested that there were a high (0.768-0.907) positive and statistically significant correlations among all pomological features (P 0.05). According to the PCA (principal component analysis) analysis of the pomological characteristics, the genotype #1 was superior as compared with other genotypes. Contrary to the pomological characteristics, genotype #1 was found to have the lowest phenolic compounds and it was also moderate in sugar content but a high-grade genotype by organic acids, especially citric acid and malic acid. Furthermore, results suggested that genotypes #3, #4, #7 and #8 were identical and were rich in glucose, succinic acid, and total flavonoid. The results suggested that loquat fruits had a high potential for health benefits. The results are also a preliminary key reference for future studies in terms of loquat cultivation throughout the world and have high potential as a functional food

Morphological and Biochemical Characteristics of Wild Loquat (Eriobotrya japonica Lindl.) Genotypes in Turkey

This study was carried out to determine the pomological and biochemical characteristics of eight different loquat genotypes collected from the Black Sea region (Turkey) in 2018. Totally 20 fruits, at the same ripening stage, were collected from the selected genotypes and tested. Results suggested that there were a high (0.768-0.907) positive and statistically significant correlations among all pomological features (P 0.05). According to the PCA (principal component analysis) analysis of the pomological characteristics, the genotype #1 was superior as compared with other genotypes. Contrary to the pomological characteristics, genotype #1 was found to have the lowest phenolic compounds and it was also moderate in sugar content but a high-grade genotype by organic acids, especially citric acid and malic acid. Furthermore, results suggested that genotypes #3, #4, #7 and #8 were identical and were rich in glucose, succinic acid, and total flavonoid. The results suggested that loquat fruits had a high potential for health benefits. The results are also a preliminary key reference for future studies in terms of loquat cultivation throughout the world and have high potential as a functional food

Some phytochemicals and sugar contents of black mulberry (<i>Morus nigra</i> L.) genotypes from Simav District, Kütahya Province, Turkey

The purpose of this research was to determine the biochemical contents in black (Morus nigra L.) mulberry genotypes grown in Kütahya Province. Total soluble solids content, pH, titratable acidity, total phenolics, vitamin C, DPPH radical scavenging activity, and soluble sugars (fructose, glucose, and sucrose) of black mulberry genotypes were determined at the end of the study. The highest total phenolics value was detected in SIM02 (2995.16 mg GAE g-1). The SIM03 genotype had the highest vitamin C content of 31.34 mg 100 g-1. The study indicated that radical scavenging activity (DPPH) of 19.05 (SIM03) was the highest. In terms of the most valuable chemical composition, the SIM01, SIM02 and SIM03 genotypes can be suggested and used for future breeding reasons. It is desirable to take actions in Turkey to conduct an extensive conservation program for Morus nigra biodiversity

Multiplicity dependence of jet-like two-particle correlations in p-Pb collisions at sNN\sqrt{s_{NN}} = 5.02 TeV

Two-particle angular correlations between unidentified charged trigger and associated particles are measured by the ALICE detector in p-Pb collisions at a nucleon-nucleon centre-of-mass energy of 5.02 TeV. The transverse-momentum range 0.7 $< p_{\rm{T}, assoc} < p_{\rm{T}, trig} <$ 5.0 GeV/$c$ is examined, to include correlations induced by jets originating from low momen\-tum-transfer scatterings (minijets). The correlations expressed as associated yield per trigger particle are obtained in the pseudorapidity range $|\eta|<0.9$. The near-side long-range pseudorapidity correlations observed in high-multiplicity p-Pb collisions are subtracted from both near-side short-range and away-side correlations in order to remove the non-jet-like components. The yields in the jet-like peaks are found to be invariant with event multiplicity with the exception of events with low multiplicity. This invariance is consistent with the particles being produced via the incoherent fragmentation of multiple parton--parton scatterings, while the yield related to the previously observed ridge structures is not jet-related. The number of uncorrelated sources of particle production is found to increase linearly with multiplicity, suggesting no saturation of the number of multi-parton interactions even in the highest multiplicity p-Pb collisions. Further, the number scales in the intermediate multiplicity region with the number of binary nucleon-nucleon collisions estimated with a Glauber Monte-Carlo simulation.Comment: 23 pages, 6 captioned figures, 1 table, authors from page 17, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/161

Multi-particle azimuthal correlations in p-Pb and Pb-Pb collisions at the CERN Large Hadron Collider

Measurements of multi-particle azimuthal correlations (cumulants) for charged particles in p-Pb and Pb-Pb collisions are presented. They help address the question of whether there is evidence for global, flow-like, azimuthal correlations in the p-Pb system. Comparisons are made to measurements from the larger Pb-Pb system, where such evidence is established. In particular, the second harmonic two-particle cumulants are found to decrease with multiplicity, characteristic of a dominance of few-particle correlations in p-Pb collisions. However, when a $|\Delta \eta|$ gap is placed to suppress such correlations, the two-particle cumulants begin to rise at high-multiplicity, indicating the presence of global azimuthal correlations. The Pb-Pb values are higher than the p-Pb values at similar multiplicities. In both systems, the second harmonic four-particle cumulants exhibit a transition from positive to negative values when the multiplicity increases. The negative values allow for a measurement of $v_{2}\{4\}$ to be made, which is found to be higher in Pb-Pb collisions at similar multiplicities. The second harmonic six-particle cumulants are also found to be higher in Pb-Pb collisions. In Pb-Pb collisions, we generally find $v_{2}\{4\} \simeq v_{2}\{6\}\neq 0$ which is indicative of a Bessel-Gaussian function for the $v_{2}$ distribution. For very high-multiplicity Pb-Pb collisions, we observe that the four- and six-particle cumulants become consistent with 0. Finally, third harmonic two-particle cumulants in p-Pb and Pb-Pb are measured. These are found to be similar for overlapping multiplicities, when a $|\Delta\eta| > 1.4$ gap is placed.Comment: 25 pages, 11 captioned figures, 3 tables, authors from page 20, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/87

Of Toasters and Molecular Ticker Tapes

Experiments in systems neuroscience can be seen as consisting of three steps: (1) selecting the signals we are interested in, (2) probing the system with carefully chosen stimuli, and (3) getting data out of the brain. Here I discuss how emerging techniques in molecular biology are starting to improve these three steps. To estimate its future impact on experimental neuroscience, I will stress the analogy of ongoing progress with that of microprocessor production techniques. These techniques have allowed computers to simplify countless problems; because they are easier to use than mechanical timers, they are even built into toasters. Molecular biology may advance even faster than computer speeds and has made immense progress in understanding and designing molecules. These advancements may in turn produce impressive improvements to each of the three steps, ultimately shifting the bottleneck from obtaining data to interpreting it

Geometric frustration in compositionally modulated ferroelectrics

Geometric frustration is a broad phenomenon that results from an intrinsic incompatibility between some fundamental interactions and the underlying lattice geometry1-7. Geometric frustration gives rise to new fundamental phenomena and is known to yield intriguing effects, such as the formation of exotic states like spin ice, spin liquids and spin glasses1-7. It has also led to interesting findings of fractional charge quantization and magnetic monopoles5,6. Geometric frustration related mechanisms have been proposed to understand the origins of relaxor behavior in some multiferroics, colossal magnetocapacitive coupling and unusual and novel mechanisms of high Tc superconductivity1-5. Although geometric frustration has been particularly well studied in magnetic systems in the last 20 years or so, its manifestation in the important class formed by ferroelectric materials (that are compounds exhibiting electric rather than magnetic dipoles) is basically unknown. Here, we show, via the use of a first-principles-based technique, that compositionally graded ferroelectrics possess the characteristic "fingerprints" associated with geometric frustration. These systems have a highly degenerate energy surface and exhibit original critical phenomena. They further reveal exotic orderings with novel stripe phases involving complex spatial organization. These stripes display spiral states, topological defects and curvature. Compositionally graded ferroelectrics can thus be considered as the "missing" link that brings ferroelectrics into the broad category of materials able to exhibit geometric frustration. Our ab-initio calculations allow a deep microscopic insight into this novel geometrically frustrated system.Comment: 14 pages, 5 Figures; http://www.nature.com/nature/journal/v470/n7335/full/nature09752.htm

CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains

Recent developments in electrophysiological and optical recording techniques enable the simultaneous observation of large numbers of neurons. A meaningful interpretation of the resulting multivariate data, however, presents a serious challenge. In particular, the estimation of higher-order correlations that characterize the cooperative dynamics of groups of neurons is impeded by the combinatorial explosion of the parameter space. The resulting requirements with respect to sample size and recording time has rendered the detection of coordinated neuronal groups exceedingly difficult. Here we describe a novel approach to infer higher-order correlations in massively parallel spike trains that is less susceptible to these problems. Based on the superimposed activity of all recorded neurons, the cumulant-based inference of higher-order correlations (CuBIC) presented here exploits the fact that the absence of higher-order correlations imposes also strong constraints on correlations of lower order. Thus, estimates of only few lower-order cumulants suffice to infer higher-order correlations in the population. As a consequence, CuBIC is much better compatible with the constraints of in vivo recordings than previous approaches, which is shown by a systematic analysis of its parameter dependence

Efficient Network Reconstruction from Dynamical Cascades Identifies Small-World Topology of Neuronal Avalanches

Cascading activity is commonly found in complex systems with directed interactions such as metabolic networks, neuronal networks, or disease spreading in social networks. Substantial insight into a system's organization can be obtained by reconstructing the underlying functional network architecture from the observed activity cascades. Here we focus on Bayesian approaches and reduce their computational demands by introducing the Iterative Bayesian (IB) and Posterior Weighted Averaging (PWA) methods. We introduce a special case of PWA, cast in nonparametric form, which we call the normalized count (NC) algorithm. NC efficiently reconstructs random and small-world functional network topologies and architectures from subcritical, critical, and supercritical cascading dynamics and yields significant improvements over commonly used correlation methods. With experimental data, NC identified a functional and structural small-world topology and its corresponding traffic in cortical networks with neuronal avalanche dynamics

Functional identification of biological neural networks using reservoir adaptation for point processes

The complexity of biological neural networks does not allow to directly relate their biophysical properties to the dynamics of their electrical activity. We present a reservoir computing approach for functionally identifying a biological neural network, i.e. for building an artificial system that is functionally equivalent to the reference biological network. Employing feed-forward and recurrent networks with fading memory, i.e. reservoirs, we propose a point process based learning algorithm to train the internal parameters of the reservoir and the connectivity between the reservoir and the memoryless readout neurons. Specifically, the model is an Echo State Network (ESN) with leaky integrator neurons, whose individual leakage time constants are also adapted. The proposed ESN algorithm learns a predictive model of stimulus-response relations in in vitro and simulated networks, i.e. it models their response dynamics. Receiver Operating Characteristic (ROC) curve analysis indicates that these ESNs can imitate the response signal of a reference biological network. Reservoir adaptation improved the performance of an ESN over readout-only training methods in many cases. This also held for adaptive feed-forward reservoirs, which had no recurrent dynamics. We demonstrate the predictive power of these ESNs on various tasks with cultured and simulated biological neural networks