Motor ability in children treated for idiopathic clubfoot. A controlled pilot study

<p>Abstract</p> <p>Background</p> <p>To study motor ability at seven years of age in children treated for idiopathic clubfoot and its relation to clubfoot laterality, foot status and the amount of surgery performed.</p> <p>Methods</p> <p>Twenty children (mean age 7.5 years, SD 3.2 months) from a consecutive birth cohort from our hospital catchments area (300.000 inhabitants from southern Sweden) were assessed with the Movement Assessment Battery for Children (MABC) and the Clubfoot Assessment Protocol (CAP).</p> <p>Results</p> <p>Compared to typically developing children an increased prevalence of motor impairment was found regarding both the total score for MABC (p < 0.05) and the subtest ABC-Ball skills (p < 0.05). No relationship was found between the child's actual foot status, laterality or the extent of foot surgery with the motor ability as measured with MABC. Only the CAP item "one-leg stand" correlated significantly with the MABC (rs = -0.53, p = 0.02).</p> <p>Conclusions</p> <p>Children with idiopathic clubfoot appear to have an increased risk of motor activity limitations and it is possible that other factors, independent of the clinical status, might be involved. The ability to keep balance on one leg may be a sufficient tool for determining which children in the orthopedic setting should be more thoroughly evaluated regarding their neuromotor functioning.</p

Inferring learning from big data:The importance of a transdisciplinary and multidimensional approach

The use of big data in higher education has evolved rapidly with a focus on the practical application of new tools and methods for supporting learning. In this paper, we depart from the core emphasis on application and delve into a mostly neglected aspect of the big data conversation in higher education. Drawing on developments in cognate disciplines, we analyse the inherent difficulties in inferring the complex phenomenon that is learning from big datasets. This forms the basis of a discussion about the possibilities for systematic collaboration across different paradigms and disciplinary backgrounds in interpreting big data for enhancing learning. The aim of this paper is to provide the foundation for a research agenda, where differing conceptualisations of learning become a strength in interpreting patterns in big datasets, rather than a point of contention

“Hot standards” for the thermoacidophilic archaeon Sulfolobus solfataricus

Within the archaea, the thermoacidophilic crenarchaeote Sulfolobus solfataricus has become an important model organism for physiology and biochemistry, comparative and functional genomics, as well as, more recently also for systems biology approaches. Within the Sulfolobus Systems Biology (“SulfoSYS”)-project the effect of changing growth temperatures on a metabolic network is investigated at the systems level by integrating genomic, transcriptomic, proteomic, metabolomic and enzymatic information for production of a silicon cell-model. The network under investigation is the central carbohydrate metabolism. The generation of high-quality quantitative data, which is critical for the investigation of biological systems and the successful integration of the different datasets, derived for example from high-throughput approaches (e.g., transcriptome or proteome analyses), requires the application and compliance of uniform standard protocols, e.g., for growth and handling of the organism as well as the “–omics” approaches. Here, we report on the establishment and implementation of standard operating procedures for the different wet-lab and in silico techniques that are applied within the SulfoSYS-project and that we believe can be useful for future projects on Sulfolobus or (hyper)thermophiles in general. Beside established techniques, it includes new methodologies like strain surveillance, the improved identification of membrane proteins and the application of crenarchaeal metabolomics

Inhibition of G Protein-Activated Inwardly Rectifying K+ Channels by Different Classes of Antidepressants

Various antidepressants are commonly used for the treatment of depression and several other neuropsychiatric disorders. In addition to their primary effects on serotonergic or noradrenergic neurotransmitter systems, antidepressants have been shown to interact with several receptors and ion channels. However, the molecular mechanisms that underlie the effects of antidepressants have not yet been sufficiently clarified. G protein-activated inwardly rectifying K+ (GIRK, Kir3) channels play an important role in regulating neuronal excitability and heart rate, and GIRK channel modulation has been suggested to have therapeutic potential for several neuropsychiatric disorders and cardiac arrhythmias. In the present study, we investigated the effects of various classes of antidepressants on GIRK channels using the Xenopus oocyte expression assay. In oocytes injected with mRNA for GIRK1/GIRK2 or GIRK1/GIRK4 subunits, extracellular application of sertraline, duloxetine, and amoxapine effectively reduced GIRK currents, whereas nefazodone, venlafaxine, mianserin, and mirtazapine weakly inhibited GIRK currents even at toxic levels. The inhibitory effects were concentration-dependent, with various degrees of potency and effectiveness. Furthermore, the effects of sertraline were voltage-independent and time-independent during each voltage pulse, whereas the effects of duloxetine were voltage-dependent with weaker inhibition with negative membrane potentials and time-dependent with a gradual decrease in each voltage pulse. However, Kir2.1 channels were insensitive to all of the drugs. Moreover, the GIRK currents induced by ethanol were inhibited by sertraline but not by intracellularly applied sertraline. The present results suggest that GIRK channel inhibition may reveal a novel characteristic of the commonly used antidepressants, particularly sertraline, and contributes to some of the therapeutic effects and adverse effects

Codivergence of Mycoviruses with Their Hosts

BACKGROUND: The associations between pathogens and their hosts are complex and can result from any combination of evolutionary events such as codivergence, switching, and duplication of the pathogen. Mycoviruses are RNA viruses which infect fungi and for which natural vectors are so far unknown. Thus, lateral transfer might be improbable and codivergence their dominant mode of evolution. Accordingly, mycoviruses are a suitable target for statistical tests of virus-host codivergence, but inference of mycovirus phylogenies might be difficult because of low sequence similarity even within families. METHODOLOGY: We analyzed here the evolutionary dynamics of all mycovirus families by comparing virus and host phylogenies. Additionally, we assessed the sensitivity of the co-phylogenetic tests to the settings for inferring virus trees from their genome sequences and approximate, taxonomy-based host trees. CONCLUSIONS: While sequence alignment filtering modes affected branch support, the overall results of the co-phylogenetic tests were significantly influenced only by the number of viruses sampled per family. The trees of the two largest families, Partitiviridae and Totiviridae, were significantly more similar to those of their hosts than expected by chance, and most individual host-virus links had a significant positive impact on the global fit, indicating that codivergence is the dominant mode of virus diversification. However, in this regard mycoviruses did not differ from closely related viruses sampled from non-fungus hosts. The remaining virus families were either dominated by other evolutionary modes or lacked an apparent overall pattern. As this negative result might be caused by insufficient taxon sampling, the most parsimonious hypothesis still is that host-parasite evolution is basically the same in all mycovirus families. This is the first study of mycovirus-host codivergence, and the results shed light not only on how mycovirus biology affects their co-phylogenetic relationships, but also on their presumable host range itself

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta