Statistical Learning Analysis in Neuroscience: Aiming for Transparency

Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods, neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires “neuroscience-aware” technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here, we review its features and applicability to various neural data modalities

Визначення напрямків аудиторської перевірки запасів підприємств текстильної промисловості

At about 20 % of total GHG emissions, land use, land use change and the forestry (LULUCF) sectors contribute significantly to global green house gas (GHG) emissions. This percentage may be significantly higher in countries with huge forest resources, like Indonesia. In Indonesia, forests are increasingly converted to satisfy the growing demand for commercial agricultural products, most notably oil palm (Elaeis guineensis), not only for food but also for biofuels. Although forest losses caused by oil palm expansion are considered to be one of major contributors to land use change (LUC), oil palm expansion has less visible additional indirect effects in accelerating forest transformation. These are hardly studied, as they require an in depth knowledge and understanding of socio-economic changes caused by oil palm expansion at the grass-root level, the household level. These complex indirect effects receive no or only scant attention. This is striking to note, since they may become a major cause of forest conversion in the (near) future. Oil palm production leads to complex population redistribution. Local people are displaced not only by large scale investors, but also sold out by in-migrants. Large numbers of migrants are entering the Indonesia oil palm producing regions, hoping to benefit from the economic opportunities oil palm plantations provide. The search for arable land by a fast growing population puts increasing pressure on remaining (protected) forest areas, when they start investing in land for small scale oil palm plantations. Many of the remaining areas consist of peatlands. GHG emissions are therefore expected to rise tremendously. Analyzing these indirect socio-economic land use effects associated with oil palm expansion is therefore urgently required and is the main objective of this chapter

Linear quantum quench in the Heisenberg XXZ chain: Time-dependent Luttinger-model description of a lattice system

We study variable-rate linear quenches in the anisotropic Heisenberg (XXZ) chain, starting at the XX point. This is equivalent to switching on a nearest-neighbor interaction for hard-core bosons or an interaction quench for free fermions. The physical observables we investigate are the energy pumped into the system during the quench, the spin-flip correlation function, and the bipartite fluctuations of the z component of the spin in a box. We find excellent agreement between exact numerics (infinite system time-evolving block decimation) and analytical results from bosonization, as a function of the quench time, spatial coordinate, and interaction strength. This provides a stringent and much-needed test of Luttinger liquid theory in a nonequilibrium situation. DOI: 10.1103/PhysRevB.87.04110

The N-end rule pathway controls multiple functions during Arabidopsis shoot and leaf development

The ubiquitin-dependent N-end rule pathway relates the in vivo half-life of a protein to the identity of its N-terminal residue. This proteolytic system is present in all organisms examined and has been shown to have a multitude of functions in animals and fungi. In plants, however, the functional understanding of the N-end rule pathway is only beginning. The N-end rule has a hierarchic structure. Destabilizing activity of N-terminal Asp, Glu, and (oxidized) Cys requires their conjugation to Arg by an arginyl–tRNA–protein transferase (R-transferase). The resulting N-terminal Arg is recognized by the pathway's E3 ubiquitin ligases, called “N-recognins.” Here, we show that the Arabidopsis R-transferases AtATE1 and AtATE2 regulate various aspects of leaf and shoot development. We also show that the previously identified N-recognin PROTEOLYSIS6 (PRT6) mediates these R-transferase-dependent activities. We further demonstrate that the arginylation branch of the N-end rule pathway plays a role in repressing the meristem-promoting BREVIPEDICELLUS (BP) gene in developing leaves. BP expression is known to be excluded from Arabidopsis leaves by the activities of the ASYMMETRIC LEAVES1 (AS1) transcription factor complex and the phytohormone auxin. Our results suggest that AtATE1 and AtATE2 act redundantly with AS1, but independently of auxin, in the control of leaf development

Chain-based order and quantum spin liquids in dipolar spin ice

Recent experiments on the spin–ice material Dy2Ti2O7 suggest that the Pauling “ice entropy”, characteristic of its classical Coulombic spin-liquid state, may be lost at low temperatures [D. Po- maranski et al., Nature Phys. 9, 353 (2013)]. However, despite nearly two decades of intensive study, the nature of the equilibrium ground state of spin ice remains uncertain. Here we explore how long-range dipolar interactions D, short-range exchange interactions, and quantum fluctuations combine to determine the ground state of dipolar spin ice. We identify a new organisational prin- ciple, namely that ordered ground states are selected from a set of “chain states” in which dipolar interactions are exponentially screened. Using both quantum and classical Monte Carlo simulation, we establish phase diagrams as a function of quantum tunneling g, and temperature T , and find that only a very small gc D is needed to stabilize a quantum spin-liquid ground state. We discuss the implications of these results for Dy2Ti2O7

PyMVPA: A Unifying Approach to the Analysis of Neuroscientific Data

The Python programming language is steadily increasing in popularity as the language of choice for scientific computing. The ability of this scripting environment to access a huge code base in various languages, combined with its syntactical simplicity, make it the ideal tool for implementing and sharing ideas among scientists from numerous fields and with heterogeneous methodological backgrounds. The recent rise of reciprocal interest between the machine learning (ML) and neuroscience communities is an example of the desire for an inter-disciplinary transfer of computational methods that can benefit from a Python-based framework. For many years, a large fraction of both research communities have addressed, almost independently, very high-dimensional problems with almost completely non-overlapping methods. However, a number of recently published studies that applied ML methods to neuroscience research questions attracted a lot of attention from researchers from both fields, as well as the general public, and showed that this approach can provide novel and fruitful insights into the functioning of the brain. In this article we show how PyMVPA, a specialized Python framework for machine learning based data analysis, can help to facilitate this inter-disciplinary technology transfer by providing a single interface to a wide array of machine learning libraries and neural data-processing methods. We demonstrate the general applicability and power of PyMVPA via analyses of a number of neural data modalities, including fMRI, EEG, MEG, and extracellular recordings

Tensor network states and geometry

Tensor network states are used to approximate ground states of local Hamiltonians on a lattice in D spatial dimensions. Different types of tensor network states can be seen to generate different geometries. Matrix product states (MPS) in D=1 dimensions, as well as projected entangled pair states (PEPS) in D>1 dimensions, reproduce the D-dimensional physical geometry of the lattice model; in contrast, the multi-scale entanglement renormalization ansatz (MERA) generates a (D+1)-dimensional holographic geometry. Here we focus on homogeneous tensor networks, where all the tensors in the network are copies of the same tensor, and argue that certain structural properties of the resulting many-body states are preconditioned by the geometry of the tensor network and are therefore largely independent of the choice of variational parameters. Indeed, the asymptotic decay of correlations in homogeneous MPS and MERA for D=1 systems is seen to be determined by the structure of geodesics in the physical and holographic geometries, respectively; whereas the asymptotic scaling of entanglement entropy is seen to always obey a simple boundary law -- that is, again in the relevant geometry. This geometrical interpretation offers a simple and unifying framework to understand the structural properties of, and helps clarify the relation between, different tensor network states. In addition, it has recently motivated the branching MERA, a generalization of the MERA capable of reproducing violations of the entropic boundary law in D>1 dimensions.Comment: 18 pages, 18 figure

Subcellular localization and tissue specific expression of amidase 1 from Arabidopsis thaliana

Amidase 1 (AMI1) from Arabidopsis thaliana converts indole-3-acetamide (IAM), into indole-3-acetic acid (IAA). AMI1 is part of a small isogene family comprising seven members in A. thaliana encoding proteins which share a conserved glycine- and serine-rich amidase-signature. One member of this family has been characterized as an N-acylethanolamine-cleaving fatty acid amidohydrolase (FAAH) and two other members are part of the preprotein translocon of the outer envelope of chloroplasts (Toc complex) or mitochondria (Tom complex) and presumably lack enzymatic activity. Among the hitherto characterized proteins of this family, AMI1 is the only member with indole-3-acetamide hydrolase activity, and IAM is the preferred substrate while N-acylethanolamines and oleamide are not hydrolyzed significantly, thus suggesting a role of AMI1 in auxin biosynthesis. Whereas the enzymatic function of AMI1 has been determined in vitro, the subcellular localization of the enzyme remained unclear. By using different GFP-fusion constructs and an A. thaliana transient expression system, we show a cytoplasmic localization of AMI1. In addition, RT-PCR and anti-amidase antisera were used to examine tissue specific expression of AMI1 at the transcriptional and translational level, respectively. AMI1-expression is strongest in places of highest IAA content in the plant. Thus, it is concluded that AMI1 may be involved in de novo IAA synthesis in A. thaliana