A comparison of neuroimaging abnormalities in multiple sclerosis, major depression and chronic fatigue syndrome (Myalgic Encephalomyelitis): is there a common cause?

There is copious evidence of abnormalities in resting-state functional network connectivity states, grey and white matter pathology and impaired cerebral perfusion in patients afforded a diagnosis of multiple sclerosis, major depression or chronic fatigue syndrome (CFS) (myalgic encephalomyelitis). Systemic inflammation may well be a major element explaining such findings. Inter-patient and inter-illness variations in neuroimaging findings may arise at least in part from regional genetic, epigenetic and environmental variations in the functions of microglia and astrocytes. Regional differences in neuronal resistance to oxidative and inflammatory insults and in the performance of antioxidant defences in the central nervous system may also play a role. Importantly, replicated experimental findings suggest that the use of high-resolution SPECT imaging may have the capacity to differentiate patients afforded a diagnosis of CFS from those with a diagnosis of depression. Further research involving this form of neuroimaging appears warranted in an attempt to overcome the problem of aetiologically heterogeneous cohorts which probably explain conflicting findings produced by investigative teams active in this field. However, the ionising radiation and relative lack of sensitivity involved probably preclude its use as a routine diagnostic tool

Dimension-independent Harnack inequalities for subordinated semigroups

Dimension-independent Harnack inequalities are derived for a class of subordinate semigroups. In particular, for a diffusion satisfying the Bakry-Emery curvature condition, the subordinate semigroup with power $\alpha$ satisfies a dimension-free Harnack inequality provided $\alpha \in(1/2, 1)$, and it satisfies the log-Harnack inequality for all $\alpha \in (0,1).$ Some infinite-dimensional examples are also presented

How self-organization can guide evolution

Self-organization and natural selection are fundamental forces that shape the natural world. Substantial progress in understanding how these forces interact has been made through the study of abstract models. Further progress may be made by identifying a model system in which the interaction between self-organization and selection can be investigated empirically. To this end, we investigate how the self-organizing thermoregulatory huddling behaviours displayed by many species of mammals might influence natural selection of the genetic components of metabolism. By applying a simple evolutionary algorithm to a wellestablished model of the interactions between environmental, morphological, physiological and behavioural components of thermoregulation, we arrive at a clear, but counterintuitive, prediction: rodents that are able to huddle together in cold environments should evolve a lower thermal conductance at a faster rate than animals reared in isolation. The model therefore explains how evolution can be accelerated as a consequence of relaxed selection, and it predicts how the effect may be exaggerated by an increase in the litter size, i.e. by an increase in the capacity to use huddling behaviours for thermoregulation. Confirmation of these predictions in future experiments with rodents would constitute strong evidence of a mechanism by which self-organization can guide natural selection

Low temperature scattering with the R-matrix method: the Morse potential

Experiments are starting to probe collisions and chemical reactions between atoms and molecules at ultra-low temperatures. We have developed a new theoretical procedure for studying these collisions using the R-matrix method. Here this method is tested for the atom -- atom collisions described by a Morse potential. Analytic solutions for continuum states of the Morse potential are derived and compared with numerical results computed using an R-matrix method where the inner region wavefunctions are obtained using a standard nuclear motion algorithm. Results are given for eigenphases and scattering lengths. Excellent agreement is obtained in all cases. Progress in developing a general procedure for treating ultra-low energy reactive and non-reactive collisions is discussed.Comment: 18 pages, 6 figures, 3 tables, conferenc

Noncommutative geometry and stochastic processes

The recent analysis on noncommutative geometry, showing quantization of the volume for the Riemannian manifold entering the geometry, can support a view of quantum mechanics as arising by a stochastic process on it. A class of stochastic processes can be devised, arising as fractional powers of an ordinary Wiener process, that reproduce in a proper way a stochastic process on a noncommutative geometry. These processes are characterized by producing complex values and so, the corresponding Fokker-Planck equation resembles the Schroedinger equation. Indeed, by a direct numerical check, one can recover the kernel of the Schroedinger equation starting by an ordinary Brownian motion. This class of stochastic processes needs a Clifford algebra to exist. In four dimensions, the full set of Dirac matrices is needed and the corresponding stochastic process in a noncommutative geometry is easily recovered as is the Dirac equation in the Klein-Gordon form being it the Fokker--Planck equation of the process.Comment: 16 pages, 2 figures. Updated a reference. A version of this paper will appear in the proceedings of GSI2017, Geometric Science of Information, November 7th to 9th, Paris (France

ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks

Hash codes are efficient data representations for coping with the ever growing amounts of data. In this paper, we introduce a random forest semantic hashing scheme that embeds tiny convolutional neural networks (CNN) into shallow random forests, with near-optimal information-theoretic code aggregation among trees. We start with a simple hashing scheme, where random trees in a forest act as hashing functions by setting `1' for the visited tree leaf, and `0' for the rest. We show that traditional random forests fail to generate hashes that preserve the underlying similarity between the trees, rendering the random forests approach to hashing challenging. To address this, we propose to first randomly group arriving classes at each tree split node into two groups, obtaining a significantly simplified two-class classification problem, which can be handled using a light-weight CNN weak learner. Such random class grouping scheme enables code uniqueness by enforcing each class to share its code with different classes in different trees. A non-conventional low-rank loss is further adopted for the CNN weak learners to encourage code consistency by minimizing intra-class variations and maximizing inter-class distance for the two random class groups. Finally, we introduce an information-theoretic approach for aggregating codes of individual trees into a single hash code, producing a near-optimal unique hash for each class. The proposed approach significantly outperforms state-of-the-art hashing methods for image retrieval tasks on large-scale public datasets, while performing at the level of other state-of-the-art image classification techniques while utilizing a more compact and efficient scalable representation. This work proposes a principled and robust procedure to train and deploy in parallel an ensemble of light-weight CNNs, instead of simply going deeper.Comment: Accepted to ECCV 201

Dark Matter Deficient Galaxies Produced via High-velocity Galaxy Collisions in High-resolution Numerical Simulations

Abstract The recent discovery of diffuse dwarf galaxies that are deficient in dark matter appears to challenge the current paradigm of structure formation in our universe. We describe numerical experiments to determine if so-called dark matter deficient galaxies (DMDGs) could be produced when two gas-rich, dwarf-sized galaxies collide with a high relative velocity of ∼300 km s−1. Using idealized high-resolution simulations with both mesh-based and particle-based gravito-hydrodynamics codes, we find that DMDGs can form as high-velocity galaxy collisions and separate dark matter from the warm disk gas, which subsequently is compressed by shock and tidal interaction to form stars. Then using the large simulated universe IllustrisTNG, we discover a number of high-velocity galaxy collision events in which DMDGs are expected to form. However, we did not find evidence that these types of collisions actually produced DMDGs in the TNG100-1 run. We argue that the resolution of the numerical experiment is critical to realizing the “collision-induced” DMDG formation scenario. Our results demonstrate one of many routes in which galaxies could form with unconventional dark matter fractions.</jats:p

Time separation as a hidden variable to the Copenhagen school of quantum mechanics

The Bohr radius is a space-like separation between the proton and electron in the hydrogen atom. According to the Copenhagen school of quantum mechanics, the proton is sitting in the absolute Lorentz frame. If this hydrogen atom is observed from a different Lorentz frame, there is a time-like separation linearly mixed with the Bohr radius. Indeed, the time-separation is one of the essential variables in high-energy hadronic physics where the hadron is a bound state of the quarks, while thoroughly hidden in the present form of quantum mechanics. It will be concluded that this variable is hidden in Feynman's rest of the universe. It is noted first that Feynman's Lorentz-invariant differential equation for the bound-state quarks has a set of solutions which describe all essential features of hadronic physics. These solutions explicitly depend on the time separation between the quarks. This set also forms the mathematical basis for two-mode squeezed states in quantum optics, where both photons are observable, but one of them can be treated a variable hidden in the rest of the universe. The physics of this two-mode state can then be translated into the time-separation variable in the quark model. As in the case of the un-observed photon, the hidden time-separation variable manifests itself as an increase in entropy and uncertainty.Comment: LaTex 10 pages with 5 figure. Invited paper presented at the Conference on Advances in Quantum Theory (Vaxjo, Sweden, June 2010), to be published in one of the AIP Conference Proceedings serie

Breast cancer stem cell markers – the rocky road to clinical applications

Lately, understanding the role of cancer stem cells in tumor initiation and progression became a major focus in stem cell biology and in cancer research. Considerable efforts, such as the recent studies by Honeth and colleagues, published in the June issue of Breast Cancer Research, are directed towards developing clinical applications of the cancer stem cell concepts. This work shows that the previously described CD44+CD24- stem cell phenotype is associated with basal-type breast cancers in human patients, in particular BRCA1 inherited cancers, but does not correlate with clinical outcome. These very interesting findings caution that the success of our efforts in translating cancer stem cell research into clinical practice depends on how thorough and rigorous we are at characterizing these cells

Preparation and characterization of in situ polymerized cyclic butylene terephthalate/graphene nanocomposites

Graphene reinforced cyclic butylene terephthalate (CBT) matrix nanocomposites were prepared and characterized by mechanical and thermal methods. These nanocomposites containing different amounts of graphene (up to 5 wt%) were prepared by melt mixing with CBT that was polymerized in situ during a subsequent hot pressing. The nanocomposites and the neat polymerized CBT (pCBT) as reference material were subjected to differential scanning calorimetry (DSC), dynamical mechanical analysis (DMA), thermogravimetrical analysis (TGA) and heat conductivity measurements. The dispersion of the grapheme nanoplatelets was characterized by transmission electron microscopy (TEM). It was established that the partly exfoliated graphene worked as nucleating agent for crystallization, acted as very efficient reinforcing agent (the storage modulus at room temperature was increased by 39 and 89% by incorporating 1 and 5 wt.% graphene, respectively). Graphene incorporation markedly enhanced the heat conductivity but did not influence the TGA behaviour due to the not proper exfoliation except the ash content