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7SK RNA is a key player in the regulation of polymerase II transcription. 7SK RNA was considered as a highly conserved vertebrate innovation. The discovery of poorly conserved homologs in several insects and lophotrochozoans, however, implies a much earlier evolutionary origin. The mechanism of 7SK function requires interaction with the proteins HEXIM and La-related protein 7. Here, we present a comprehensive computational analysis of these two proteins in metazoa, and we extend the collection of 7SK RNAs by several additional candidates. In particular, we describe 7SK homologs in Caenorhabditis species. Furthermore, we derive an improved secondary structure model of 7SK RNA, which shows that the structure is quite well-conserved across animal phyla despite the extreme divergence at sequence level.

Tong et al . comment on the accuracy of the dating analysis presented in our work on the phylogeny of insects and provide a reanalysis of our data. They replace log-normal priors with uniform priors and add a “roachoid” fossil as a calibration point. Although the reanalysis provides an interesting alternative viewpoint, we maintain that our choices were appropriate.

Clinical decision rules can help to determine the need for CT imaging in children with head injuries. We aimed to validate three clinical decision rules (PECARN, CATCH, and CHALICE) in a large sample of children. In this prospective observational study, we included children and adolescents (aged <18 years) with head injuries of any severity who presented to the emergency departments of ten Australian and New Zealand hospitals. We assessed the diagnostic accuracy of PECARN (stratified into children aged <2 years and ≥2 years), CATCH, and CHALICE in predicting each rule-specific outcome measure (clinically important traumatic brain injury [TBI], need for neurological intervention, and clinically significant intracranial injury, respectively). For each calculation we used rule-specific predictor variables in populations that satisfied inclusion and exclusion criteria for each rule (validation cohort). In a secondary analysis, we compiled a comparison cohort of patients with mild head injuries (Glasgow Coma Scale score 13–15) and calculated accuracy using rule-specific predictor variables for the standardised outcome of clinically important TBI. This study is registered with the Australian New Zealand Clinical Trials Registry, number ACTRN12614000463673. Between April 11, 2011, and Nov 30, 2014, we analysed 20 137 children and adolescents attending with head injuries. CTs were obtained for 2106 (10%) patients, 4544 (23%) were admitted, 83 (<1%) underwent neurosurgery, and 15 (<1%) died. PECARN was applicable for 4011 (75%) of 5374 patients younger than 2 years and 11 152 (76%) of 14 763 patients aged 2 years and older. CATCH was applicable for 4957 (25%) patients and CHALICE for 20 029 (99%). The highest point validation sensitivities were shown for PECARN in children younger than 2 years (100·0%, 95% CI 90·7–100·0; 38 patients identified of 38 with outcome [38/38]) and PECARN in children 2 years and older (99·0%, 94·4–100·0; 97/98), followed by CATCH (high-risk predictors only; 95·2%; 76·2–99·9; 20/21; medium-risk and high-risk predictors 88·7%; 82·2–93·4; 125/141) and CHALICE (92·3%, 89·2–94·7; 370/401). In the comparison cohort of 18 913 patients with mild injuries, sensitivities for clinically important TBI were similar. Negative predictive values in both analyses were higher than 99% for all rules. The sensitivities of three clinical decision rules for head injuries in children were high when used as designed. The findings are an important starting point for clinicians considering the introduction of one of the rules. National Health and Medical Research Council, Emergency Medicine Foundation, Perpetual Philanthropic Services, WA Health Targeted Research Funds, Townsville Hospital Private Practice Fund, Auckland Medical Research Foundation, A + Trust.

An understanding of the energy dependence of gamma-ray sources can yield important information on the underlying emission mechanisms. However, despite the detection of energy-dependent morphologies in many TeV sources, we lack a proper quantification of such measurements. We introduce an estimation tool within the Gammapy landscape, an open-source Python package for the analysis of gamma-ray data, for quantifying the energy-dependent morphology of a gamma-ray source. The proposed method fits the spatial morphology in a global fit across all energy slices (null hypothesis) and compares this to separate fits for each energy slice (alternative hypothesis). These are modelled using forward-folding methods, and the significance of the variability is quantified by comparing the test statistics of the two hypotheses. We present a general tool for probing changes in the spatial morphology with energy, employing a full forward-folding approach with a 3D likelihood. We present its usage on a real dataset from H.E.S.S. and on a simulated dataset to quantify the significance of the energy dependence for sources of different sizes. In the first example, which utilises a subset of data from HESSJ1825-137, we observe extended emission at lower energies that becomes more compact at higher energies. The tool indicates a very significant variability (9.8{\\sigma}) in the case of the largely extended emission. In the second example, a source with a smaller extent (~0.1{\\deg}), simulated using the CTAO response, shows the tool can still provide a statistically significant variation (9.7{\\sigma}) on small scales.

Tong et al. comment on the accuracy of the dating analysis presented in our work on the phylogeny of insects and provide a reanalysis of our data. They replace log-normal priors with uniform priors and add a \"roachoid\" fossil as a calibration point. Although the reanalysis provides an interesting alternative viewpoint, we maintain that our choices were appropriate.

Dichloromethylene diphosphonate (clodronate), a new compound, has powerful activity against osteoclasts and has been used successfully to treat hypercalcemia associated with cancer. We studied its effects on calcium balance in patients with malignant osteolytic lesions. Ten normocalcemic patients with advanced metastatic bone disease or myeloma were evaluated in a baseline 20-day balance and calcium kinetic study. They were then randomized to a clodronate or placebo regimen, treated intravenously for two weeks and orally for a month, and finally reevaluated in another 20-day balance and kinetic study, conducted while they were still receiving treatment. The results show that both calcium balance and calcium absorption increased from base line in the clodronate group and that these changes were significantly different from those in the placebo group (mean change [±S.D.] in calcium balance [clodronate vs. placebo], 203.8±140.1 vs. -65.2±98.8 mg [5.1+3.5 vs. -1.6+2.5 mmol] of calcium per day, P<0.01; change in calcium absorption, 158.8±158 vs. -38.2±96.0 mg [4.0±4.0 vs. -1.0±2.4 mmol] per day, P<0.05). There was a marginal decrease in bone resorption in the clodronate group and no change in bone accretion. Our results suggest that clodronate may be a useful adjuvant in managing metastatic bone disease. (N Engl J Med 1983; 308:1499–1501.) THE diphosphonate clodronate inhibits the growth and dissolution of hydroxyapatite crystals. It effectively prevents bone resorption in a variety of in vivo and in vitro experimental systems. Rat-calvaria culture-system studies have demonstrated that the agent inhibits nonstimulated and parathyroid hormone-stimulated resorption, 1 and the same action has occurred in tumor-conditioned medium. 2 , 3 In rats, clodronate also decreases bone-mineral mobilization secondary to disuse after sciatic-nerve section 4 and bone destruction due to Walker tumor. 5 This effect of clodronate on bone resorption has also been documented in human beings. Measurements of bone histology in patients with Paget's disease showed that oral doses of 400 to . . .

Since its start in 2014, the lightweight open source Python library Gammapy has come a long way to become a popular data analysis package for high-energy astrophysics. Selected as the official CTAO Science Analysis tool, it is also an approved analysis software within the H.E.S.S. and MAGIC collaborations. The first long-term version, Gammapy v1.0 was released on late 2022. It is compliant with several well-established data conventions in high-energy astrophysics, and provides serialised data products that are interoperable with other software. Event lists and instrument response functions curated within the same format from various instruments can be reduced to data binned in energy, time or spatial coordinates. Thereafter, the flux and morphology of one or more gamma-ray sources can be estimated using Poisson maximum likelihood fitting and assuming a variety of spectral, temporal and spatial models. Flux points, likelihood profiles and light curves extractions are supported. Complex user defined likelihoods and models can also be implemented. In this contribution, we will highlight the main features of Gammapy v1.0, including data reduction and analysis examples from different space and ground-based instruments, applications of various background rejection techniques, and a simultaneous fitting across multiple instruments with astrophysical models. We will also present our plans for the future, showcasing new features such as the support of different event types, unbinned likelihood analysis, spectral unfolding and transient source detections. In addition to an improved API with distributed computing for scalable analysis, enhanced support for all-sky instruments like Fermi-LAT and HAWC is foreseen.

Ground-based gamma-ray astronomy is still a rather young field of research, with strong historical connections to particle physics. This is why most observations are conducted by experiments with proprietary data and analysis software, as it is usual in the particle physics field. However in recent years, this paradigm has been slowly shifting towards the development and use of open-source data formats and tools, driven by upcoming observatories such as the Cherenkov Telescope Array (CTA). In this context, a community-driven, shared data format (the gamma-astro-data-format or GADF) and analysis tools such as Gammapy and ctools have been developed. So far these efforts have been led by the IACT community, leaving out other types of ground-based gamma-ray instruments.We aim to show that the data from ground particle arrays, such as the High-Altitude Water Cherenkov (HAWC) observatory, is also compatible with the GADF and can thus be fully analysed using the related tools, in this case Gammapy. We reproduce several published HAWC results using Gammapy and data products compliant with GADF standard. We also illustrate the capabilities of the shared format and tools by producing a joint fit of the Crab spectrum including data from six different gamma-ray experiments. We find excellent agreement with the reference results, a powerful check of both the published results and the tools involved. The data from particle detector arrays such as the HAWC observatory can be adapted to the GADF and thus analysed with Gammapy. A common data format and shared analysis tools allow multi-instrument joint analysis and effective data sharing. Given the complementary nature of pointing and wide-field instruments, this synergy will be distinctly beneficial for the joint scientific exploitation of future observatories such as the Southern Wide-field Gamma-ray Observatory and CTA.

Cosmological \\(N\\)-body simulations show that Milky Way-sized galaxies harbor a population of unmerged dark matter subhalos. These subhalos could shine in gamma-rays and be eventually detected in gamma-ray surveys as unidentified sources. We performed a thorough selection among unidentified Fermi-LAT Objects (UFOs) to identify them as possible TeV-scale dark matter subhalo candidates. We search for very-high-energy (E \\(\\gtrsim\\) 100 GeV) gamma-ray emissions using H.E.S.S. observations towards four selected UFOs. Since no significant very-high-energy gamma-ray emission is detected in any dataset of the four observed UFOs nor in the combined UFO dataset, strong constraints are derived on the product of the velocity-weighted annihilation cross section \\(\\langle \\sigma v \\rangle\\) by the \\(J\\)-factor for the dark matter models. The 95% C.L. observed upper limits derived from combined H.E.S.S. observations reach \\(\\langle \\sigma v \\rangle J\\) values of 3.7\\(\\times\\)10\\(^{-5}\\) and 8.1\\(\\times\\)10\\(^{-6}\\) GeV\\(^2\\)cm\\(^{-2}\\)s\\(^{-1}\\) in the \\(W^+W^-\\) and \\(\\tau^+\\tau^-\\) channels, respectively, for a 1 TeV dark matter mass. Focusing on thermal WIMPs, the H.E.S.S. constraints restrict the \\(J\\)-factors to lie in the range 6.1\\(\\times\\)10\\(^{19}\\) - 2.0\\(\\times\\)10\\(^{21}\\) GeV\\(^2\\)cm\\(^{-5}\\), and the masses to lie between 0.2 and 6 TeV in the \\(W^+W^-\\) channel. For the \\(\\tau^+\\tau^-\\) channel, the \\(J\\)-factors lie in the range 7.0\\(\\times\\)10\\(^{19}\\) - 7.1\\(\\times\\)10\\(^{20}\\) GeV\\(^2\\)cm\\(^{-5}\\) and the masses lie between 0.2 and 0.5 TeV. Assuming model-dependent predictions from cosmological N-body simulations on the \\(J\\)-factor distribution for Milky Way-sized galaxies, the dark matter models with masses greater than 0.3 TeV for the UFO emissions can be ruled out at high confidence level.

The analysis and combination of data from different gamma-ray instruments involves the use of collaboration proprietary software and case-by-case methods. The effort of defining a common data format for high-level data, namely event lists and instrument response functions (IRFs), has recently started for very-high-energy gamma-ray instruments, driven by the upcoming Cherenkov Telescope Array (CTA). In this work we implemented this prototypical data format for a small set of MAGIC, VERITAS, FACT, and H.E.S.S. Crab nebula observations, and we analyzed them with the open-source gammapy software package. By combining data from \\(Fermi\\)-LAT, and from four of the currently operating imaging atmospheric Cherenkov telescopes, we produced a joint maximum likelihood fit of the Crab nebula spectrum. Aspects of the statistical errors and the evaluation of systematic uncertainty are also commented upon, along with the release format of spectral measurements. The results presented in this work are obtained using open-access on-line assets that allow for a long-term reproducibility of the results.