The origin of the 6.4 keV line emission and H2_2 ionization in the diffuse molecular gas of the Galactic center region

We investigate the origin of the diffuse 6.4 keV line emission recently detected by Suzaku and the source of H_2ionization in the diffuse molecular gas of the Galactic Center (GC) region. We show that Fe atoms and H_2 molecules in the diffuse interstellar medium of the GC are not ionized by the same particles. The Fe atoms are most likely ionized by X-ray photons emitted by Sgr A* during a previous period of flaring activity of the supermassive black hole. The measured longitudinal intensity distribution of the diffuse 6.4 keV line emission is best explained if the past activity of Sgr A$* lasted at least several hundred years and released a mean 2-100 keV luminosity > 10^38} erg s^{-1}. The H_2 molecules of the diffuse gas can not be ionized by photons from Sgr A*, because soft photons are strongly absorbed in the interstellar gas around the central black hole. The molecular hydrogen in the GC region is most likely ionized by low-energy cosmic rays, probably protons rather than electrons, whose contribution into the diffuse 6.4 keV line emission is negligible.Comment: 5 pages, 4 figues, accepted for publication in the Astrophysical Journal Letter

Do orthopaedic shoes improve local dynamic stability of gait? An observational study in patients with chronic foot and ankle injuries.

BACKGROUND: Complex foot and ankle fractures, such as calcaneum fractures or Lisfranc dislocations, are often associated with a poor outcome, especially in terms of gait capacity. Indeed, degenerative changes often lead to chronic pain and chronic functional limitations. Prescription footwear represents an important therapeutic tool during the rehabilitation process. Local Dynamic Stability (LDS) is the ability of locomotor system to maintain continuous walking by accommodating small perturbations that occur naturally during walking. Because it reflects the degree of control over the gait, LDS has been advocated as a relevant indicator for evaluating different conditions and pathologies. The aim of this study was to analyze changes in LDS induced by orthopaedic shoes in patients with persistent foot and ankle injuries. We hypothesised that footwear adaptation might help patients to improve gait control, which could lead to higher LDS: METHODS: Twenty-five middle-aged inpatients (5 females, 20 males) participated in the study. They were treated for chronic post-traumatic disabilities following ankle and/or foot fractures in a Swiss rehabilitation clinic. During their stay, included inpatients received orthopaedic shoes with custom-made orthoses (insoles). They performed two 30s walking trials with standard shoes and two 30s trials with orthopaedic shoes. A triaxial motion sensor recorded 3D accelerations at the lower back level. LDS was assessed by computing divergence exponents in the acceleration signals (maximal Lyapunov exponents). Pain was evaluated with Visual Analogue Scale (VAS). LDS and pain differences between the trials with standard shoes and the trials with orthopaedic shoes were assessed. RESULTS: Orthopaedic shoes significantly improved LDS in the three axes (medio-lateral: 10% relative change, paired t-test p < 0.001; vertical: 9%, p = 0.03; antero-posterior: 7%, p = 0.04). A significant decrease in pain level (VAS score -29%) was observed. CONCLUSIONS: Footwear adaptation led to pain relief and to improved foot & ankle proprioception. It is likely that that enhancement allows patients to better control foot placement. As a result, higher dynamic stability has been observed. LDS seems therefore a valuable index that could be used in early evaluation of footwear outcome in clinical settings

Periodic Modulations in an X-ray Flare from Sagittarius A*

We present the highly significant detection of a quasi-periodic flux modulation with a period of 22.2 min seen in the X-ray data of the Sgr A* flare of 2004 August 31. This flaring event, which lasted a total of about three hours, was detected simultaneously by EPIC on XMM-Newton and the NICMOS near-infrared camera on the HST. Given the inherent difficulty in, and the lack of readily available methods for quantifying the probability of a periodic signal detected over only several cycles in a data set where red noise can be important, we developed a general method for quantifying the likelihood that such a modulation is indeed intrinsic to the source and does not arise from background fluctuations. We here describe this Monte Carlo based method, and discuss the results obtained by its application to a other XMM-Newton data sets. Under the simplest hypothesis that we witnessed a transient event that evolved, peaked and decayed near the marginally stable orbit of the supermassive black hole, this result implies that for a mass of 3.5 x 10^{6} Msun, the central object must have an angular momentum corresponding to a spin parameter of a=0.22.Comment: 4 pages, 6 figures, submitted to ApJ

Detection of very-high-energy gamma-ray emission from the vicinity of PSR B1706-44 with H.E.S.S

The energetic pulsar PSR B1706-44 and the adjacent supernova remnant (SNR) candidate G 343.1-2.3 were observed by H.E.S.S. during a dedicated observational campaign in 2007. A new source of very-high-energy (VHE; E > 100 GeV) gamma-ray emission, HESS J1708-443, was discovered with its centroid at RA(J2000) = 17h08m10s and Dec(J2000) = -44d21', with a statistical error of 3 arcmin on each axis. The VHE gamma-ray source is significantly more extended than the H.E.S.S. point-spread function, with an intrinsic Gaussian width of 0.29 +/- 0.04 deg. Its energy spectrum can be described by a power law with a photon index Gamma = 2.0 +/- 0.1 (stat) +/- 0.2 (sys). The integral flux measured between 1-10 TeV is ~17% of the Crab Nebula flux in the same energy range. The possible associations with PSR B1706-44 and SNR G343.1-2.3 are discussed.Comment: 4+ pages, 2 figures; v1 submitted to ICRC Proceedings on 15 May 2009; v2 has additional references and minor change

Improvement of walking speed prediction by accelerometry and altimetry, validated by satellite positioning

Activity monitors based on accelerometry are used to predict the speed and energy cost of walking at 0% slope, but not at other inclinations. Parallel measurements of body accelerations and altitude variation were studied to determine whether walking speed prediction could be improved. Fourteen subjects walked twice along a 1.3km circuit with substantial slope variations (−17% to +17%). The parameters recorded were body acceleration using a uni-axial accelerometer, altitude variation using differential barometry, and walking speed using satellite positioning (DGPS). Linear regressions were calculated between acceleration and walking speed, and between acceleration/altitude and walking speed. These predictive models, calculated using the data from the first circuit run, were used to predict speed during the second circuit. Finally the predicted velocity was compared with the measured one. The result was that acceleration alone failed to predict speed (meanr=0.4). Adding altitude variation improved the prediction (meanr=0.7). With regard to the altitude/acceleration-speed relationship, substantial inter-individual variation was found. It is concluded that accelerometry, combined with altitude measurement, can assess position variations of humans provided inter-individual variation is taken into account. It is also confirmed that DGPS can be used for outdoor walking speed measurements, opening up new perspectives in the field of biomechanic

Influenza A viruses alter the stability and antiviral contribution of host E3-ubiquitin ligase Mdm2 during the time-course of infection

International audienceThe interplay between influenza A viruses (IAV) and the p53 pathway has been reported in several studies, highlighting the antiviral contribution of p53. Here, we investigated the impact of IAV on the E3-ubiquitin ligase Mdm2, a major regulator of p53, and observed that IAV targets Mdm2, notably via its non-structural protein (NS1), therefore altering Mdm2 stability, p53/Mdm2 interaction and regulatory loop during the time-course of infection. This study also highlights a new antiviral facet of Mdm2 possibly increasing the list of its many p53-independent functions. Altogether, our work contributes to better understand the mechanisms underlining the complex interactions between IAV and the p53 pathway, for which both NS1 and Mdm2 arise as key players

XMM-Newton observations of HESS J1813-178 reveal a composite Supernova remnant

We present X-ray and 12CO(J=1-0) observations of the very-high-energy (VHE) gamma-ray source HESS J1813-178 with the aim of understanding the origin of the gamma-ray emission. Using this dataset we are able to undertake spectral and morphological studies of the X-ray emission from this object with greater precision than previous studies. NANTEN 12CO(J=1-0) data are used to search for correlations of the gamma-ray emission with molecular clouds which could act as target material for gamma-ray production in a hadronic scenario. The NANTEN 12CO(J=1-0) observations show a giant molecular cloud of mass 2.5 10^5 M_{\sun} at a distance of 4 kpc in the vicinity of HESS J1813-178. Even though there is no direct positional coincidence, this giant cloud might have influenced the evolution of the gamma-ray source and its surroundings. The X-ray data show a highly absorbed non-thermal X-ray emitting object coincident with the previously known ASCA source AX J1813-178 showing a compact core and an extended tail towards the north-east, located in the centre of the radio shell-type Supernova remnant (SNR) G12.82-0.2. This central object shows morphological and spectral resemblance to a Pulsar Wind Nebula (PWN) and we therefore consider that the object is very likely to be a composite SNR. We discuss the scenario in which the gamma-rays originate in the shell of the SNR and the one in which they originate in the central object. We demonstrate, that in order to connect the core X-ray emission to the VHE gamma-ray emission electrons have to be accelerated to energies of at least 1 PeV.Comment: Submitted to A&

Talking about chronic pain in family settings: a glimpse of older persons' everyday realities.

The expression of chronic pain remains a delicate matter for those older persons who suffer from this condition. If many studies highlight the difficulties of putting pain into words, scarce are those that take into account how given social networks can facilitate or prevent its expression. Based on a qualitative study that explores the communication about chronic pain in older persons' social network, this article reports on this key issue of talking about health in later life within family settings and provides clinicians with information about the way older persons with chronic conditions perceive their everyday realities and social relations. A multidisciplinary research team (medicine, linguistics and psychology) interviewed 49 persons with chronic pain, all from the French-speaking part of Switzerland, aged 75 and older, without any major cognitive or auditory impairments. After transcription, the interviews were analyzed by combining content and discourse analysis with social network theories. Communication about chronic pain depends significantly on the position of the interlocutors within the family structure, with a preference for direct relatives or individuals with similar difficulties. In social networks, the ability to communicate about chronic pain is both a resource (by allowing older persons to get help or by strengthening interpersonal relations) and a challenge (by threatening their autonomy, social relations or self-esteem). The study shows the predominance of the nuclear family (partner, children) in communication relating specifically to the everyday management of chronic pain. This state of affairs is, nevertheless, balanced by issues of (loss of) autonomy. These findings, in line with current trends in geriatrics, could benefit future reflections on the scope and limits of including relatives in the care of older patients with chronic conditions

Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets.

This study aimed at developing a convolutional neural network (CNN) able to automatically quantify and characterize the level of degeneration of rotator cuff (RC) muscles from shoulder CT images including muscle atrophy and fatty infiltration. One hundred three shoulder CT scans from 95 patients with primary glenohumeral osteoarthritis undergoing anatomical total shoulder arthroplasty were retrospectively retrieved. Three independent radiologists manually segmented the premorbid boundaries of all four RC muscles on standardized sagittal-oblique CT sections. This premorbid muscle segmentation was further automatically predicted using a CNN. Automatically predicted premorbid segmentations were then used to quantify the ratio of muscle atrophy, fatty infiltration, secondary bone formation, and overall muscle degeneration. These muscle parameters were compared with measures obtained manually by human raters. Average Dice similarity coefficients for muscle segmentations obtained automatically with the CNN (88% ± 9%) and manually by human raters (89% ± 6%) were comparable. No significant differences were observed for the subscapularis, supraspinatus, and teres minor muscles (p > 0.120), whereas Dice coefficients of the automatic segmentation were significantly higher for the infraspinatus (p < 0.012). The automatic approach was able to provide good-very good estimates of muscle atrophy (R <sup>2</sup> = 0.87), fatty infiltration (R <sup>2</sup> = 0.91), and overall muscle degeneration (R <sup>2</sup> = 0.91). However, CNN-derived segmentations showed a higher variability in quantifying secondary bone formation (R <sup>2</sup> = 0.61) than human raters (R <sup>2</sup> = 0.87). Deep learning provides a rapid and reliable automatic quantification of RC muscle atrophy, fatty infiltration, and overall muscle degeneration directly from preoperative shoulder CT scans of osteoarthritic patients, with an accuracy comparable with that of human raters. • Deep learning can not only segment RC muscles currently available in CT images but also learn their pre-existing locations and shapes from invariant anatomical structures visible on CT sections. • Our automatic method is able to provide a rapid and reliable quantification of RC muscle atrophy and fatty infiltration from conventional shoulder CT scans. • The accuracy of our automatic quantitative technique is comparable with that of human raters