Intra-brain microinjection of human mesenchymal stem cells decreases allodynia in neuropathic mice

Neuropathic pain is a very complex disease, involving several molecular pathways. Current available drugs are usually not acting on the several mechanisms underlying the generation and propagation of pain. We used spared nerve injury model of neuropathic pain to assess the possible use of human mesenchymal stem cells (hMSCs) as anti-neuropathic tool. Human MSCs were transplanted in the mouse lateral cerebral ventricle. Stem cells injection was performed 4 days after sciatic nerve surgery. Neuropathic mice were monitored 7, 10, 14, 17, and 21 days after surgery. hMSCs were able to reduce pain-like behaviors, such as mechanical allodynia and thermal hyperalgesia, once transplanted in cerebral ventricle. Anti-nociceptive effect was detectable from day 10 after surgery (6 days post cell injection). Human MSCs reduced the mRNA levels of the pro-inflammatory interleukin IL-1β mouse gene, as well as the neural β-galactosidase over-activation in prefrontal cortex of SNI mice. Transplanted hMSCs were able to reduce astrocytic and microglial cell activation.     

Gravitational microlensing by low-mass objects in the globular cluster M22.

Gravitational microlensing offers a means of determining directly the masses of objects ranging from planets to stars, provided that the distances and motions of the lenses and sources can be determined. A globular cluster observed against the dense stellar field of the Galactic bulge presents ideal conditions for such observations because the probability of lensing is high and the distances and kinematics of the lenses and sources are well constrained. The abundance of low-mass objects in a globular cluster is of particular interest, because it may be representative of the very early stages of star formation in the Universe, and therefore indicative of the amount of dark baryonic matter in such clusters. Here we report a microlensing event associated with the globular cluster M22. We determine the mass of the lens to be 0.13(+0.03)(-0.02) solar masses. We have also detected six events that are unresolved in time. If these are also microlensing events, they imply that a non-negligible fraction of the cluster mass resides in the form of free-floating planetary-mass objects.     

Transiting extrasolar planetary candidates in the Galactic bulge

More than 200 extrasolar planets have been discovered around relatively nearby stars, primarily through the Doppler line shifts owing to reflex motions of their host stars, and more recently through transits of some planets across the faces of the host stars. The detection of planets with the shortest known periods, 1.2-2.5 days, has mainly resulted from transit surveys which have generally targeted stars more massive than 0.75 M(o), where M(o) is the mass of the Sun. Here we report the results from a planetary transit search performed in a rich stellar field towards the Galactic bulge. We discovered 16 candidates with orbital periods between 0.4 and 4.2 days, five of which orbit stars of masses in the range 0.44-0.75 M(o). In two cases, radial-velocity measurements support the planetary nature of the companions. Five candidates have orbital periods below 1.0 day, constituting a new class of ultra-short-period planets, which occur only around stars of less than 0.88 M(o). This indicates that those orbiting very close to more-luminous stars might be evaporatively destroyed or that jovian planets around stars of lower mass might migrate to smaller radii.     

Filtering and prediction of noisy and unstable signals: The case of Google Trends data

Google Trends data is a dataset increasingly employed for many statistical investigations. However, care should be placed in handling this tool, especially when applied for quantitative prediction purposes. Being by design Internet user dependent, estimators based on Google Trends data embody many sources of uncertainty and instability. They are related, for example, to technical (e.g., cross-regional disparities in the degree of computer alphabetization, time dependency of Internet users), psychological (e.g., emotionally driven spikes and other form of data perturbations), linguistic (e.g., noise generated by double-meaning words). Despite the stimulating literature available today on how to use Google Trends data as a forecasting tool, surprisingly, to the best of the author's knowledge, it appears that to date no articles specifically devoted to the prediction of these data have been published. In this paper, a novel forecasting method, based on a denoiser of the wavelet type employed in conjunction with a forecasting model of the class SARIMA (seasonal autoregressive integrated moving average), is presented. The wavelet filter is iteratively calibrated according to a bounded search algorithm, until a minimum of a suitable loss function is reached. Finally, empirical evidence is presented to support the validity of the proposed method.     

Long gamma-ray bursts and core-collapse supernovae have different environments

When massive stars exhaust their fuel, they collapse and often produce the extraordinarily bright explosions known as core-collapse supernovae. On occasion, this stellar collapse also powers an even more brilliant relativistic explosion known as a long-duration gamma-ray burst. One would then expect that these long gamma-ray bursts and core-collapse supernovae should be found in similar galactic environments. Here we show that this expectation is wrong. We find that the gamma-ray bursts are far more concentrated in the very brightest regions of their host galaxies than are the core-collapse supernovae. Furthermore, the host galaxies of the long gamma-ray bursts are significantly fainter and more irregular than the hosts of the core-collapse supernovae. Together these results suggest that long-duration gamma-ray bursts are associated with the most extremely massive stars and may be restricted to galaxies of limited chemical evolution. Our results directly imply that long gamma-ray bursts are relatively rare in galaxies such as our own Milky Way.