FSHD muscular dystrophy Region Gene 1 binds Suv4-20h1 histone methyltransferase and impairs myogenesis

Facioscapulohumeral Muscular Dystrophy (FSHD) is an autosomal dominant myopathy with a strong epigenetic component. It is associated with deletion of a macrosatellite repeat leading to over-expression of the nearby genes. Among them, we focused on FSHD Region Gene 1 (FRG1) since its over-expression in mice, X. laevis and C. elegans leads to muscular dystrophy-like defects, suggesting that FRG1 plays a relevant role in muscle biology. Here we show that, when overexpressed, FRG1 binds and interferes with the activity of the histone methyltransferase Suv4-20h1 both in mammals and Drosophila. Accordingly, FRG1 over-expression or Suv4-20h1 knockdown inhibits myogenesis. Moreover, Suv4-20h KO mice develop muscular dystrophy signs. Finally, we identify the FRG1/Suv4-20h1 target Eid3 as a novel myogenic inhibitor that contributes to the muscle differentiation defects. Our study suggests a novel role of FRG1 as epigenetic regulator of muscle differentiation and indicates that Suv4-20h1 has a gene-specific function in myogenesis

Comments on two papers by Kapusta and Wong

We critically examine recently published results on the thermal production of massive vector bosons in a quark-gluon plasma. We claim the production rate is a collinear safe observable.Comment: 6 pages LATEX documen

Towards flavour diffusion coefficient and electrical conductivity without ultraviolet contamination

By subtracting from a recent lattice measurement of the thermal vector-current correlator the known 5-loop vacuum contribution, we demonstrate that the remainder is small and shows no visible short-distance divergence. It can therefore in principle be subjected to model-independent analytic continuation. Testing a particular implementation, we obtain estimates for the flavour-diffusion coefficient (2 pi T D \gsim 0.8) and electrical conductivity which are significantly smaller than previous results. Although systematic errors remain beyond control at present, some aspects of our approach could be of a wider applicability.Comment: 7 pages. v2: clarifications added, published versio

Thermal quark production in ultra-relativistic nuclear collisions

We calculate thermal production of u, d, s, c and b quarks in ultra-relativistic heavy ion collisions. The following processes are taken into account: thermal gluon decay (g to ibar i), gluon fusion (g g to ibar i), and quark-antiquark annihilation (jbar j to ibar i), where i and j represent quark species. We use the thermal quark masses, $m_i^2(T)\simeq m_i^2 + (2g^2/9)T^2$, in all the rates. At small mass ($m_i(T)<2T$), the production is largely dominated by the thermal gluon decay channel. We obtain numerical and analytic solutions of one-dimensional hydrodynamic expansion of an initially pure glue plasma. Our results show that even in a quite optimistic scenario, all quarks are far from chemical equilibrium throughout the expansion. Thermal production of light quarks (u, d and s) is nearly independent of species. Heavy quark (c and b) production is quite independent of the transition temperature and could serve as a very good probe of the initial temperature. Thermal quark production measurements could also be used to determine the gluon damping rate, or equivalently the magnetic mass.Comment: 14 pages (latex) plus 6 figures (uuencoded postscript files); CERN-TH.7038/9

Colour-electric spectral function at next-to-leading order

The spectral function related to the correlator of two colour-electric fields along a Polyakov loop determines the momentum diffusion coefficient of a heavy quark near rest with respect to a heat bath. We compute this spectral function at next-to-leading order, O(alpha_s^2), in the weak-coupling expansion. The high-frequency part of our result (omega >> T), which is shown to be temperature-independent, is accurately determined thanks to asymptotic freedom; the low-frequency part of our result (omega << T), in which Hard Thermal Loop resummation is needed in order to cure infrared divergences, agrees with a previously determined expression. Our result may help to calibrate the overall normalization of a lattice-extracted spectral function in a perturbative frequency domain T << omega << 1/a, paving the way for a non-perturbative estimate of the momentum diffusion coefficient at omega -> 0. We also evaluate the colour-electric Euclidean correlator, which could be directly compared with lattice simulations. As an aside we determine the Euclidean correlator in the lattice strong-coupling expansion, showing that through a limiting procedure it can in principle be defined also in the confined phase of pure Yang-Mills theory, even if a practical measurement could be very noisy there.Comment: 38 page

DUX4 role in normal physiology and in FSHD muscular dystrophy

In the last decade, the sequence-specific transcription factor double homeobox 4 (DUX4) has gone from being an obscure entity to being a key factor in important physiological and pathological processes. We now know that expression of DUX4 is highly regulated and restricted to the early steps of embryonic development, where DUX4 is involved in transcriptional activation of the zygotic genome. While DUX4 is epigenetically silenced in most somatic tissues of healthy humans, its aberrant reactivation is associated with several diseases, including cancer, viral infection and facioscapulohumeral muscular dystrophy (FSHD). DUX4 is also translocated, giving rise to chimeric oncogenic proteins at the basis of sarcoma and leukemia forms. Hence, understanding how DUX4 is regulated and performs its activity could provide relevant information, not only to further our knowledge of human embryonic development regulation, but also to develop therapeutic approaches for the diseases associated with DUX4. Here, we summarize current knowledge on the cellular and molecular processes regulated by DUX4 with a special emphasis on FSHD muscular dystrophy

A hybrid approach integrating genetic algorithm and machine learning to solve the order picking batch assignment problem considering learning and fatigue of pickers

Modeling human behaviors has become increasingly relevant to improving the performance of manual order-picking systems. However, although a vast corpus of literature has recently started to consider the human factors in these systems, several gaps remain uncovered. Specifically, mental and physical human factors, like learning and fatigue, and quantitative and spatial features of picking orders have never been considered jointly to estimate the time a human order picker requires to execute a specific picking mission. Furthermore, little attention has been given to assigning and sequencing orders to pickers to minimize the picking time acting on their individual learning and fatigue characteristics. This study thus proposes a novel approach integrating machine learning and genetic algorithms to solve the problem. A non-linear machine learning-based predictive model has been adopted to predict the picking time of batches of orders based on quantitative and spatial features of batches and learning and fatigue indicators of pickers. These predictions have thus been adopted to guide a genetic algorithm to find the best assignment of future planned batches of orders to pickers. One year of picking data collected from the warehouse of a grocery retailer has been adopted to investigate the potential of the proposed approach. Furthermore, multiple comparisons have been performed. First, the advantages of predicting the batch-picking time with the proposed non-linear model have been compared with predictions executed based on linear models. In addition, an ablation analysis has been performed to investigate the advantages of predicting the batch picking time while simultaneously considering the quantitative and spatial features of batches and the learning and fatigue indicators of pickers. Moreover, the advantages of the proposed batch assignment strategy, which considers learning and fatigue indicators, have been compared with an assignment strategy that does not optimize these elements. Lastly, an explainability analysis of the predictive model has been performed to understand how and how much quantitative and spatial features of batches and learning and fatigue indicators of pickers affect the batch picking time