Enhancement of K+ conductance improves in vitro the contraction force of skeletal muscle in hypokalemic periodic paralysis

An abnormal ratio between Na+ and K+ conductances seems to be the cause for the depolarization and paralysis of skeletal muscle in primary hypokalemic periodic paralysis. Recently we have shown that the k+ channel opener cromakalim hyperpolarizes mammalian skeletal muscle fibers. Now we have studied the effects of this drug on the twitch force of muscle biopsies from normal and diseased human skeletal muscle. Cromakalim had little effect on the twitch force of normal muscle whereas it strongly improved the contraction force of fibers from patients suffering from hypokalemic periodic paralysis. Recordings of intracellular K+ and Cl- activities in human muscle and isolated rat soleus muscle support the view that cromakalim enhances the membrane K+ conductance (gK+). These data indicate that K+ channel openers may have a beneficial effect in primary hypokalemic periodic paralysis

Expression and Differential Responsiveness of Central Nervous System Glial Cell Populations to the Acute Phase Protein Serum Amyloid A

Acute-phase response is a systemic reaction to environmental/inflammatory insults and involves hepatic production of acute-phase proteins, including serum amyloid A (SAA). Extrahepatically, SAA immunoreactivity is found in axonal myelin sheaths of cortex in Alzheimer's disease and multiple sclerosis (MS), although its cellular origin is unclear. We examined the responses of cultured rat cortical astrocytes, microglia and oligodendrocyte precursor cells (OPCs) to master pro-inflammatory cytokine tumour necrosis factor (TNF)-\u3b1 and lipopolysaccaride (LPS). TNF-\u3b1 time-dependently increased Saa1 (but not Saa3) mRNA expression in purified microglia, enriched astrocytes, and OPCs (as did LPS for microglia and astrocytes). Astrocytes depleted of microglia were markedly less responsive to TNF-\u3b1 and LPS, even after re-addition of microglia. Microglia and enriched astrocytes showed complementary Saa1 expression profiles following TNF-\u3b1 or LPS challenge, being higher in microglia with TNF-\u3b1 and higher in astrocytes with LPS. Recombinant human apo-SAA stimulated production of both inflammatory mediators and its own mRNA in microglia and enriched, but not microglia-depleted astrocytes. Co-ultramicronized palmitoylethanolamide/luteolin, an established anti-inflammatory/neuroprotective agent, reduced Saa1 expression in OPCs subjected to TNF-\u3b1 treatment. These last data, together with past findings suggest that co-ultramicronized palmitoylethanolamide/luteolin may be a novel approach in the treatment of inflammatory demyelinating disorders like MS

Identifying probabilistic weather regimes targeted to a local-scale impact variable

Large-scale atmospheric circulation patterns, so-called weather regimes, modulate the occurrence of extreme events such as heatwaves or extreme precipitation. In their role as mediators between long-range teleconnections and local impacts, weather regimes have demonstrated potential in improving long-term climate projections as well as sub-seasonal to seasonal forecasts. However, existing methods for identifying weather regimes are not specifically designed to capture the relevant physical processes responsible for variations in the impact variable in question. This paper introduces a novel probabilistic machine learning method, RMM-VAE, for identifying weather regimes targeted to a local-scale impact variable. Based on a variational autoencoder architecture, the method combines non-linear dimensionality reduction with a prediction task and probabilistic clustering in one coherent architecture. The new method is applied to identify circulation patterns over the Mediterranean region targeted to precipitation over Morocco and compared to three existing approaches: two established linear methods and another machine-learning approach. The RMM-VAE method identifies regimes that are more predictive of the target variable compared to the two linear methods, both in terms of terciles and extremes in precipitation, while also improving the reconstruction of the input space. Further, the regimes identified by the RMM-VAE method are also more robust and persistent compared to the alternative machine learning method. The results demonstrate the potential benefit of the new method for use in various climate applications such as sub-seasonal forecasting, and illustrate the trade-offs involved in targeted clustering

Human muscle-derived CLEC14A-positive cells regenerate muscle independent of PAX7

Skeletal muscle stem cells, called satellite cells and defined by the transcription factor PAX7, are responsible for postnatal muscle growth, homeostasis and regeneration. Attempts to utilize the regenerative potential of muscle stem cells for therapeutic purposes so far failed. We previously established the existence of human PAX7-positive cell colonies with high regenerative potential. We now identified PAX7-negative human muscle-derived cell colonies also positive for the myogenic markers desmin and MYF5. These include cells from a patient with a homozygous PAX7 c.86-1G > A mutation (PAX7null). Single cell and bulk transcriptome analysis show high intra- and inter-donor heterogeneity and reveal the endothelial cell marker CLEC14A to be highly expressed in PAX7null cells. All PAX7-negative cell populations, including PAX7null, form myofibers after transplantation into mice, and regenerate muscle after reinjury. Transplanted PAX7neg cells repopulate the satellite cell niche where they re-express PAX7, or, strikingly, CLEC14A. In conclusion, transplanted human cells do not depend on PAX7 for muscle regeneration

Identifying probabilistic weather regimes targeted to a local-scale impact variable

Large-scale atmospheric circulation patterns, so-called weather regimes, modulate the occurrence of extreme events such as heatwaves or extreme precipitation. In their role as mediators between long-range teleconnections and local impacts, weather regimes have demonstrated potential in improving long-term climate projections as well as sub-seasonal to seasonal forecasts. However, existing methods for identifying weather regimes are not specifically designed to capture the relevant physical processes responsible for variations in the impact variable in question. This paper introduces a novel probabilistic machine learning method, RMM-VAE, for identifying weather regimes targeted to a local-scale impact variable. Based on a variational autoencoder architecture, the method combines non-linear dimensionality reduction with a prediction task and probabilistic clustering in one coherent architecture. The new method is applied to identify circulation patterns over the Mediterranean region targeted to precipitation over Morocco and compared to three existing approaches: two established linear methods and another machine-learning approach. The RMM-VAE method identifies regimes that are more predictive of the target variable compared to the two linear methods, both in terms of terciles and extremes in precipitation, while also improving the reconstruction of the input space. Further, the regimes identified by the RMM-VAE method are also more robust and persistent compared to the alternative machine learning method. The results demonstrate the potential benefit of the new method for use in various climate applications such as sub-seasonal forecasting, and illustrate the trade-offs involved in targeted clustering

Final Report on HOLODEC 2 Technology Readiness Level

During the period of this project, the Holographic Detector for Clouds 2 (HOLODEC 2) instrument has advanced from a laboratory-proven instrument with some initial field testing to a fully flight-tested instrument capable of providing useful cloud microphysics measurements. This can be summarized as 'Technology Readiness Level 8: Technology is proven to work - Actual technology completed and qualified through test and demonstration.' As part of this project, improvements and upgrades have been made to the optical system, the instrument power control system, the data acquisition computer, the instrument control software, the data reconstruction and analysis software, and some of the basic algorithms for estimating basic microphysical variables like droplet diameter. Near the end of the project, the instrument flew on several research flights as part of the IDEAS 2011 project, and a small sample of data from the project is included as an example. There is one caveat in the technology readiness level stated above: the upgrades to the instrument power system were made after the flight testing, so they are not fully field proven. We anticipate that there will be an opportunity to fly the instrument as part of the IDEAS project in fall 2012

Steering the climate system: an extended comment

Lemoine and Rudik (2017) argue that it is efficient to delay reducing carbon emissions, because there is substantial inertia in the climate system. However, this conclusion rests upon misunderstanding the relevant climate physics: there is no substantial lag between CO2 emissions and warming, which policy could rely upon. Applying a mainstream climate physics model to the economics of Lemoine and Rudik (2017) invalidates the article’s implications for climate policy: the cost-effective carbon price that limits warming to a range of targets including 2 oC starts high and increases at the interest rate

MicroPulse DIAL (MPD) – a diode-laser-based lidar architecture for quantitative atmospheric profiling

Continuous water vapor and temperature profiles are critically needed for improved understanding of the lower atmosphere and potential advances in weather forecasting skill. Ground-based, national-scale profiling networks are part of a suite of instruments to provide such observations; however, the technological method must be cost-effective and quantitative. We have been developing an active remote sensing technology based on a diode-laser-based lidar technology to address this observational need. Narrowband, high-spectral-fidelity diode lasers enable accurate and calibration-free measurements requiring a minimal set of assumptions based on direct absorption (Beer–Lambert law) and a ratio of two signals. These well-proven quantitative methods are known as differential absorption lidar (DIAL) and high-spectral-resolution lidar (HSRL). This diode-laser-based architecture, characterized by less powerful laser transmitters than those historically used for atmospheric studies, can be made eye-safe and robust. Nevertheless, it also requires solar background suppression techniques such as narrow-field-of-view receivers with an ultra-narrow bandpass to observe individual photons backscattered from the atmosphere. We discuss this diode-laser-based lidar architecture's latest generation and analyze how it addresses a national-scale profiling network's need to provide continuous thermodynamic observations. The work presented focuses on general architecture changes that pertain to both the water vapor and the temperature profiling capabilities of the MicroPulse DIAL (MPD). However, the specific subcomponent testing and instrument validation presented are for the water vapor measurements only. A fiber-coupled seed laser transmitter optimization is performed and shown to meet all of the requirements for the DIAL technique. Further improvements – such as a fiber-coupled near-range receiver, the ability to perform quality control via automatic receiver scanning, advanced multi-channel scalar capabilities, and advanced processing techniques – are discussed. These new developments increase narrowband DIAL technology readiness and are shown to allow higher-quality water vapor measurements closer to the surface via preliminary intercomparisons within the MPD network itself and with radiosondes.</p

Base editing repairs an SGCA mutation in human primary muscle stem cells

Skeletal muscle can regenerate from muscle stem cells and their myogenic precursor cell progeny, myoblasts. However, precise gene editing in human muscle stem cells for autologous cell replacement therapies of untreatable genetic muscle diseases has not yet been reported. Loss-of-function mutations in SGCA, encoding α-sarcoglycan, cause limb-girdle muscular dystrophy 2D/R3, an early onset, severe and rapidly progressive form of muscular dystrophy affecting equally girls and boys. Patients suffer from muscle degeneration and atrophy affecting the limbs, respiratory muscles, and the heart. We isolated human muscle stem cells from two donors with the common SGCA c.157G>A mutation affecting the last coding nucleotide of exon 2. We found that c.157G>A is an exonic splicing mutation that induces skipping of two co-regulated exons. Using adenine base editing, we corrected the mutation in the cells from both donors with >90% efficiency, thereby rescuing the splicing defect and α-sarcoglycan expression. Base edited patient cells regenerated muscle and contributed to the Pax7 positive satellite cell compartment in vivo in mouse xenografts. We hereby provide the first evidence that autologous gene repaired human muscle stem cells can be harnessed for cell replacement therapies of muscular dystrophies. ONE SENTENCE SUMMARY: Patient primary muscle stems cells gene repaired with >90% efficiency by base editing maintain their regenerative properties for autologous cell replacement therapies of muscular dystrophy

Delay in diagnosis of muscle disorders depends on the subspecialty of the initially consulted physician

<p>Abstract</p> <p>Background</p> <p>New therapeutic strategies in muscular dystrophies will make a difference in prognosis only if they are begun early in the course of the disease. Therefore, we investigated factors that influence the time to diagnosis in muscle dystrophy patients.</p> <p>Methods</p> <p>A sample of 101 patients (mean age 49 years; range 19-80; 44% women) with diagnosed muscle dystrophies from neurological practices and the neuromuscular specialty clinic in Berlin, Germany, was invited to participate. Time from first consultation to diagnosis, subspecialty of physician, and sociodemographic data were assessed with self-report questionnaires. The association between time to diagnosis and potential predictors (subspecialty of initially consulted physician, diagnoses, gender, and age at onset) was modeled with linear regression analysis.</p> <p>Results</p> <p>The mean time span between first health-care contact and diagnosis was 4.3 years (median 1). The diagnostic delay was significantly longer if patients were initially seen by a non-neurological specialist compared to a general practitioner (5.2 vs. 3.5 years, p = 0.047). Other factors that were independently associated with diagnostic delay were female gender and inherited muscle disease.</p> <p>Conclusion</p> <p>Action to improve clinical awareness of muscle diseases in non-neurological specialists is needed.</p