Tetrasomy 9p is a rare genetic syndrome resulting from two additional copies of the short arm of chromosome 9. Symptoms often present in the form of congenital abnormalities including cognitive disabilities, growth retardation, abnormal earlobes, congenital heart disease, and dysmorphia of the skull and face. Current literature suggests patients with tetrasomy 9p may exhibit any combination of these symptoms or, in rare instances, none at all. Although karyotyping, chromosomal microarray, and galactose-1-phosphate uridyltransferase activity analyses are the definitive diagnostic methods used, there remains a need for more robust clinical recognition in cases of mild phenotypic expression. Herein, we present a rare case of mosaic tetrasomy 9p in a long-term survival patient with multiple and recurrent pilomatrixomas, rare benign growths more commonly found in individuals under the age of 20. To our knowledge, only two previous reports have noted concurrent tetrasomy 9p with pilomatrixomas. We are the first to identify this phenotype in an adult tetrasomy 9p patient. Dermatopathology evaluation was conducted to verify our diagnoses. Our aim is to present a unique, additional case suggesting multiple pilomatrixomas as a new defining clinical presentation of mosaic tetrasomy 9p and to review the literature underlying the genetic changes associated with this syndrome.

To ensure the reliability of power transformers, they are monitored for partial discharge (PD) events, which are symptoms of transformer failure. Our goal is to classify PDs to gain an understanding of the location of failure. We develop a small set of features and a stacking ensemble that outperform larger feature sets and other models in both accuracy and variance.

The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating the positions of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determining the signal arrival time, and then locating the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transformers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.

The goal of our study is to analyze massive high-frequency streaming sensor data to accurately locate the source of partial discharges (PD) in transformers. The PD signal is collected by ultra-high frequency sensors at a resolution of 0.4 ns per record resulting in a data streaming rate of 12 GB/s. A voltage threshold is applied to the data stream to extract 400 ns signal samples. We develop a voltage threshold method based on the Savitzky-Golay filter for signal arrival timing, and localize the PD with arrival time differences using Finite-Difference Time-Domain (FDTD) simulation. The Savitzky-Golay filter is able to preserve features better than other methods, resulting in improved signal-to-noise ratios and more accurate signal timing. FDTD accounts for the travel path of signals inside the transformer, allowing for more precise PD localization. Our resulting method localizes PDs more accurately than existing methods, particularly in high noise cases.

Our society depends heavily on the electric power infrastructure. To ensure its reliability, key power grid components such as transformers are extensively monitored for signs of failures and errors. This work concentrates on a type of event known as the partial discharge (PD) because it is the symptom of insulation weakness, the most common cause of transformer failures. More specifically, our work is to locate the position of a partial discharge to provide information for preventive maintenance. Our method utilizes the information from a set of ultra-high frequency (UHF) sensors inside the transformer, and proceeds in two steps: first determine the signal arrival time and then locate the position based on time differences. To determine the arrival time, we develop a convolutional filtering method based on the Savitzky-Golay filter. To provide accurate locations, we simulate the electromagnetic wave propagation using finite-difference time-domain (FDTD) to generate a reference table of the travel time from each FDTD mesh point to the sensors. We exercise our method using two sets of UHF measurements with different signal to noise ratios. In both cases, our method provides more accurate locations than other methods. The difference is particularly prominent when the signal is weak. With weak signals, the best existing method, the cumulative energy method, was only able to predict the PD location within 300 mm of the known sources in 13% of the test cases, while our method is correct in 48% of the test cases.

Purpose

Determine whether patient pain and function are similar following hip abductor repair in patients without and with total hip arthroplasty (THA).

Methods

Patients who underwent hip abductor repair were categorized as to whether they had a THA or not. Pre- and postoperative pain and Harris Hip Score (HHS) were recorded and compared between groups.

Results

There were no differences in improvement in pain level, improvement in HHS, satisfaction with surgery.

Conclusions

Hip abductor repair leads to similar pain, function, and satisfaction in patients without and with THA.

Wildfire modifies the short- and long-term exchange of carbon between terrestrial ecosystems and the atmosphere, with impacts on ecosystem services such as carbon uptake. Dry western US forests historically experienced low-intensity, frequent fires, with patches across the landscape occupying different points in the fire-recovery trajectory. Contemporary perturbations, such as recent severe fires in California, could shift the historic stand-age distribution and impact the legacy of carbon uptake on the landscape. Here, we combine flux measurements of gross primary production (GPP) and chronosequence analysis using satellite remote sensing to investigate how the last century of fires in California impacted the dynamics of ecosystem carbon uptake on the fire-affected landscape. A GPP recovery trajectory curve of more than five thousand fires in forest ecosystems since 1919 indicated that fire reduced GPP by [Formula: see text] g C m[Formula: see text] y[Formula: see text]([Formula: see text]) in the first year after fire, with average recovery to prefire conditions after [Formula: see text] y. The largest fires in forested ecosystems reduced GPP by [Formula: see text] g C m[Formula: see text] y[Formula: see text] (n = 401) and took more than two decades to recover. Recent increases in fire severity and recovery time have led to nearly [Formula: see text] MMT CO[Formula: see text] (3-y rolling mean) in cumulative forgone carbon uptake due to the legacy of fires on the landscape, complicating the challenge of maintaining California's natural and working lands as a net carbon sink. Understanding these changes is paramount to weighing the costs and benefits associated with fuels management and ecosystem management for climate change mitigation.

Nature-based climate solutions are a vital component of many climate mitigation strategies, including California's, which aims to achieve carbon neutrality by 2045. Most carbon offsets in California's cap-and-trade program come from improved forest management (IFM) projects. Since 2012, various landowners have set up IFM projects following the California Air Resources Board's IFM protocol. As many of these projects approach their 10th year, we now have the opportunity to assess their effectiveness, identify best practices, and suggest improvements toward future protocol revisions. In this study, we used remote sensing-based datasets to evaluate the carbon trends and harvest histories of 37 IFM projects in California. Despite some current limitations and biases, these datasets can be used to quantify carbon accumulation and harvest rates in offset project lands relative to nearby similar "control" lands before and after the projects began. Five lines of evidence suggest that the carbon accumulated in offset projects to date has generally not been additional to what might have otherwise occurred: (1) most forests in northwestern California have been accumulating carbon since at least the mid-1980s and continue to accumulate carbon, whether enrolled in offset projects or not; (2) harvest rates were high in large timber company project lands before IFM initiation, suggesting they are earning carbon credits for forests in recovery; (3) projects are often located on lands with higher densities of low-timber-value species; (4) carbon accumulation rates have not yet increased on lands that enroll as offset projects, relative to their pre-enrollment levels; and (5) harvest rates have not decreased on most project lands since offset project initiation. These patterns suggest that the current protocol should be improved to robustly measure and reward additionality. In general, our framework of geospatial analyses offers an important and independent means to evaluate the effectiveness of the carbon offsets program, especially as these data products continue improving and as offsets receive attention as a climate mitigation strategy.

Predicting whether and how populations will adapt to rapid climate change is a critical goal for evolutionary biology. To examine the genetic basis of fitness and predict adaptive evolution in novel climates with seasonal variation, we grew a diverse panel of the annual plant Arabidopsis thaliana (multiparent advanced generation intercross lines) in controlled conditions simulating four climates: a present-day reference climate, an increased-temperature climate, a winter-warming only climate, and a poleward-migration climate with increased photoperiod amplitude. In each climate, four successive seasonal cohorts experienced dynamic daily temperature and photoperiod variation over a year. We measured 12 traits and developed a genomic prediction model for fitness evolution in each seasonal environment. This model was used to simulate evolutionary trajectories of the base population over 50 y in each climate, as well as 100-y scenarios of gradual climate change following adaptation to a reference climate. Patterns of plastic and evolutionary fitness response varied across seasons and climates. The increased-temperature climate promoted genetic divergence of subpopulations across seasons, whereas in the winter-warming and poleward-migration climates, seasonal genetic differentiation was reduced. In silico "resurrection experiments" showed limited evolutionary rescue compared with the plastic response of fitness to seasonal climate change. The genetic basis of adaptation and, consequently, the dynamics of evolutionary change differed qualitatively among scenarios. Populations with fewer founding genotypes and populations with genetic diversity reduced by prior selection adapted less well to novel conditions, demonstrating that adaptation to rapid climate change requires the maintenance of sufficient standing variation.

Nicotinic partial agonists provide an accepted aid for smoking cessation and thus contribute to decreasing tobacco-related disease. Improved drugs constitute a continued area of study. However, there remains no reductionist method to examine the cellular and subcellular pharmacokinetic properties of these compounds in living cells. Here, we developed new intensity-based drug-sensing fluorescent reporters (iDrugSnFRs) for the nicotinic partial agonists dianicline, cytisine, and two cytisine derivatives - 10-fluorocytisine and 9-bromo-10-ethylcytisine. We report the first atomic-scale structures of liganded periplasmic binding protein-based biosensors, accelerating development of iDrugSnFRs and also explaining the activation mechanism. The nicotinic iDrugSnFRs detect their drug partners in solution, as well as at the plasma membrane (PM) and in the endoplasmic reticulum (ER) of cell lines and mouse hippocampal neurons. At the PM, the speed of solution changes limits the growth and decay rates of the fluorescence response in almost all cases. In contrast, we found that rates of membrane crossing differ among these nicotinic drugs by >30-fold. The new nicotinic iDrugSnFRs provide insight into the real-time pharmacokinetic properties of nicotinic agonists and provide a methodology whereby iDrugSnFRs can inform both pharmaceutical neuroscience and addiction neuroscience.