Explore the vast range of titles available.

X-linked myotubular myopathy (XLMTM) is a congenital disorder caused by mutations of the myotubularin gene, MTM1. Myotubularin belongs to a large family of conserved lipid phosphatases that include both catalytically active and inactive myotubularin-related proteins (i.e., \"MTMRs\"). Biochemically, catalytically inactive MTMRs have been shown to form heteroligomers with active members within the myotubularin family through protein-protein interactions. However, the pathophysiological significance of catalytically inactive MTMRs remains unknown in muscle. By in vitro as well as in vivo studies, we have identified that catalytically inactive myotubularin-related protein 12 (MTMR12) binds to myotubularin in skeletal muscle. Knockdown of the mtmr12 gene in zebrafish resulted in skeletal muscle defects and impaired motor function. Analysis of mtmr12 morphant fish showed pathological changes with central nucleation, disorganized Triads, myofiber hypotrophy and whorled membrane structures similar to those seen in X-linked myotubular myopathy. Biochemical studies showed that deficiency of MTMR12 results in reduced levels of myotubularin protein in zebrafish and mammalian C2C12 cells. Loss of myotubularin also resulted in reduction of MTMR12 protein in C2C12 cells, mice and humans. Moreover, XLMTM mutations within the myotubularin interaction domain disrupted binding to MTMR12 in cell culture. Analysis of human XLMTM patient myotubes showed that mutations that disrupt the interaction between myotubularin and MTMR12 proteins result in reduction of both myotubularin and MTMR12. These studies strongly support the concept that interactions between myotubularin and MTMR12 are required for the stability of their functional protein complex in normal skeletal muscles. This work highlights an important physiological function of catalytically inactive phosphatases in the pathophysiology of myotubular myopathy and suggests a novel therapeutic approach through identification of drugs that could stabilize the myotubularin-MTMR12 complex and hence ameliorate this disorder.

X-linked myotubular myopathy (XLMTM) is a congenital disorder caused by mutations of the myotubularin gene, MTM1. Myotubularin belongs to a large family of conserved lipid phosphatases that include both catalytically active and inactive myotubularin-related proteins (i.e., \"MTMRs\"). Biochemically, catalytically inactive MTMRs have been shown to form heteroligomers with active members within the myotubularin family through protein-protein interactions. However, the pathophysiological significance of catalytically inactive MTMRs remains unknown in muscle. By in vitro as well as in vivo studies, we have identified that catalytically inactive myotubularin-related protein 12 (MTMR12) binds to myotubularin in skeletal muscle. Knockdown of the mtmr12 gene in zebrafish resulted in skeletal muscle defects and impaired motor function. Analysis of mtmr12 morphant fish showed pathological changes with central nucleation, disorganized Triads, myofiber hypotrophy and whorled membrane structures similar to those seen in X-linked myotubular myopathy. Biochemical studies showed that deficiency of MTMR12 results in reduced levels of myotubularin protein in zebrafish and mammalian C2C12 cells. Loss of myotubularin also resulted in reduction of MTMR12 protein in C2C12 cells, mice and humans. Moreover, XLMTM mutations within the myotubularin interaction domain disrupted binding to MTMR12 in cell culture. Analysis of human XLMTM patient myotubes showed that mutations that disrupt the interaction between myotubularin and MTMR12 proteins result in reduction of both myotubularin and MTMR12. These studies strongly support the concept that interactions between myotubularin and MTMR12 are required for the stability of their functional protein complex in normal skeletal muscles. This work highlights an important physiological function of catalytically inactive phosphatases in the pathophysiology of myotubular myopathy and suggests a novel therapeutic approach through identification of drugs that could stabilize the myotubularin-MTMR12 complex and hence ameliorate this disorder.

Students are exposed to various academic, financial and psychosocial stressors while studying at a university, and have increasingly turned to social media to alleviate stress and access social support. While evidence suggests that social networking sites may promote health awareness and health-protective behaviours, little research has explored TikTok, a relatively new platform with over 800 million active users. Seven university students (6 females, 1 non-binary; all White British; mean age = 20.57) were interviewed about their experiences on TikTok, their motivations behind using it and its effects on their psychological wellbeing. A thematic analysis of this data revealed that while TikTok use temporarily relieved academic stress, it also encouraged addictive behaviour and upward social comparisons. These differential effects were largely mediated by TikTok’s algorithm, which recommended content based on users’ previous interactions.

The aim of this study was to characterize the outcomes of traumatic abdominal and pelvic vascular injuries. Using the 2012 National Trauma Data Bank, we identified 5858 patients with major abdominal and/or pelvic vascular injury. Patients were stratified by age group, gender, race, Injury Severity Score (ISS), and mechanism of injury. We evaluated the percentage of patients with blunt and penetrating trauma by demographic and correlated the mechanism of injury to the ISS score, emergency room disposition, and hospital disposition. We performed a logistic regression analysis to calculate predictors of death. In the final cohort, 1458 patients (25%) with abdominal/pelvic vascular injury died of trauma. In total, 3368 patients (57%) had a blunt mechanism of injury, whereas 2353 (40%) were victims of a penetrating trauma. Patients with penetrating injuries were 1.72 times more likely to die from their injuries than those with blunt traumas. Patients with higher ISS scores (>16) were more likely to die from their injuries than patients with lower ISS scores. Men were more likely to experience a penetrating vascular injury than women (48% vs 17%). Similarly, 77 per cent of black patients had a penetrating mechanism of injury compared with 20 per cent of white patients. There were 1910 patients with penetrating injuries (81%) that went immediately from the emergency room to the OR, compared with 1287 patients with blunt injuries (38%). Of the patients with blunt injuries, 695 (21%) died, whereas 727 (31%) patients with penetrating injuries died. Abdominal and pelvic traumatic vascular injuries carry a high mortality rate. Penetrating mechanism of injury, ISS score, and race are independent predictors of mortality.

Inferring deep brain activity from noninvasive scalp recordings remains a fundamental challenge in neuroscience. Here, we analyzed concurrent scalp and intracranial recordings from 1918 electrode contacts across 20 patients affected by drug-resistant epilepsy undergoing intracranial depth electrode monitoring for pre-surgical evaluation to establish predictive relationships between surface and deep brain signals. Using regularized and cross-validated linear regression within subjects, we demonstrate that scalp recordings can predict spontaneous intracranial activity, with accuracy varying by region, depth, and frequency. Low-frequency signals (<12 Hz) were most predictable, with our models explaining approximately 10% of intracranial signal variance across contacts. Prediction accuracy decreased with contact depth, particularly for high-frequency signals. Using Bayesian modeling with leave-one-patient-out cross-validation, we observed generalizable prediction of activity in mesial temporal, prefrontal, and orbitofrontal cortices, explaining 10-12% of low-frequency signal variance. This scalp-to-intracranial mapping derived from spontaneous activity was further validated by its correlation with scalp responses evoked by direct electrical stimulation. These findings support the development of improved inverse models of brain activity and potentially more accurate scalp-based markers of disease and treatment response.

The gravitational waveform of a merging stellar-mass binary is described at leading order by a quadrupolar mode. However, the complete waveform includes higher-order modes, which encode valuable information not accessible from the leading-order mode alone. Despite this, the majority of astrophysical inferences so far obtained with observations of gravitational waves employ only the leading order mode because calculations with higher-order modes are often computationally challenging. We show how to efficiently incorporate higher-order modes into astrophysical inference calculations with a two step procedure. First, we carry out Bayesian parameter estimation using a computationally cheap leading-order-mode waveform, which provides an initial estimate of binary parameters. Second, we weight the initial estimate using higher-order mode waveforms in order to fold in the extra information from the full waveform. We use mock data to demonstrate the effectiveness of this method. We apply the method to each binary black hole event in the first gravitational-wave transient catalog GWTC-1 to obtain posterior distributions and Bayesian evidence with higher-order modes. Performing Bayesian model selection on the events in GWTC-1, we find only a weak preference for waveforms with higher order modes. We discuss how this method can be generalized to a variety of other applications.

In General Relativity, the spacetimes of black holes have three fundamental properties: (i) they are the same, to lowest order in spin, as the metrics of stellar objects; (ii) they are independent of mass, when expressed in geometric units; and (iii) they are described by the Kerr metric. In this paper, we quantify the upper bounds on potential black-hole metric deviations imposed by observations of black-hole shadows and of binary black-hole inspirals in order to explore the current experimental limits on possible violations of the last two predictions. We find that both types of experiments provide correlated constraints on deviation parameters that are primarily in the tt-components of the spacetimes, when expressed in areal coordinates. We conclude that, currently, there is no evidence for a deviations from the Kerr metric across the 8 orders of magnitudes in masses and 16 orders in curvatures spanned by the two types of black holes. Moreover, because of the particular masses of black holes in the current sample of gravitational-wave sources, the correlations imposed by the two experiments are aligned and of similar magnitudes when expressed in terms of the far field, post-Newtonian predictions of the metrics. If a future coalescing black-hole binary with two low-mass (e.g., ~3 Msun) components is discovered, the degeneracy between the deviation parameters can be broken by combining the inspiral constraints with those from the black-hole shadow measurements.

We carry out astrophysical inference for compact binary merger events in LIGO-Virgo's first gravitational-wave transient catalog (GWTC-1) using a physically motivated calibration model. We demonstrate that importance sampling can be used to reduce the cost of what would otherwise be a computationally challenging analysis. We show that including the physical estimate for the calibration error distribution has negligible impact on the inference of parameters for the events in GWTC-1. Studying a simulated signal with matched filter signal-to-noise ratio \\(SNR=200\\), we project that a calibration error estimate typical of GWTC-1 is likely to be negligible for the current generation of gravitational-wave detectors. We argue that other sources of systematic error---from waveforms, prior distributions, and noise modelling---are likely to be more important. Finally, using the events in GWTC-1 as standard sirens, we infer an astrophysically-informed improvement on the estimate of the calibration error in the LIGO interferometers.