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End-stage renal disease occurs when there is permanent loss of the kidney’s ability to filter toxins from the blood. Due to the limited number of transplants, dialysis is currently the most common treatment, but it significantly limits a patient’s lifestyle and has significant side effects. One solution is an artificial kidney, but significant challenges remain in its development. One challenge is the separation of glucose from urea. Nanofiltration is ideal for this separation; however, there is little understanding of the important parameters for this separation under physiological conditions. In this study, operating parameters (pressure and temperature) as well as feed conditions (increased glucose/salt) were explored for their effects on the separation of glucose from urea in six commercial membranes. The rejection of monovalent and divalent ions was also characterized. While increasing pressure increased flux, it had little effect on metabolite rejection, except for glucose, which increased above 20 psi. Increasing temperature led to a slight increase in flux and a slight decrease in the rejection of divalent ions. Glucose rejection was sensitive to feed conditions, while urea rejection was less affected. Divalent ions were rejected more strongly than monovalent ions and were also more affected by feed conditions.

Due to the drastic differences in the projects involved, this dissertation will be split into two sections: Alzheimer’s disease and chronic kidney disease. Each chapter will contain an introduction, materials and methods section, results and discussion, conclusions, and works cited section. The Alzheimer’s disease chapter will also have a supplemental figures section containing the polyacrylamide gels that were silver stained and quantified via ImageJ.The presence of elevated levels of amyloid beta oligomeric species in the brain has been related to the pathology of Alzheimer’s disease. As these oligomeric species are transient in nature, their kinetics pose a challenge to study. Also seen in the literature, mutations in the primary sequence of the proteins lead to differences in aggregation kinetics. In this study, we investigated the application of PICUP to study the early aggregation patterns of Wildtype amyloid beta compared to three sequences variants: Flemish, Arctic, and scrambled. To explore the influence of steric hindrance on aggregation kinetics, two naturally occurring mutations of amyloid beta called Arctic and Flemish were selected. To determine if primary amino acid sequence affects aggregation, a lab designed scrambled variant was tested. This study revealed that each mutation of Wildtype amyloid beta did indeed cause variation in aggregation with scrambled differing the most while Arctic and Flemish followed behind and behaved similarly.Chronic kidney disease is a growing healthcare concern inflicting approximately 37 million people in the United States alone. Currently, the only options for patients are either a kidney transplant or dialysis. While dialysis provides a way to separate larger metabolites, there appears to be a limitation with the ability for dialyzers to separate smaller components due to the passive transport performed in dialysis. Unlike the passive transport in dialysis, nanofiltration membranes utilize active transport via pressure to drive separation, allowing for smaller components to be separated. This study explored six commercial nanofiltration membranes with different characteristics on their ability to separate glucose, urea, Na+ , K+ , Mg2+, and Ca2+ while also investigating the effects of feed ( increased glucose and salt concentration) and operating (pressure and temperature) conditions. Depending on the properties of the membrane, feed composition and pressure had varying effects. Finally chemical modification utilizing polyethylenimine to potentially decrease the pore size was explored on one of the commercial membranes for its potential to improve the membranes’ ability to maintain a high glucose rejection while also keeping a low urea rejection. This study confirmed that membranes with different properties like pore size and charge separated components rather differently under the varying feed and operating conditions tested.

There is limited literature examining the adaptive functioning of adolescents with autism spectrum disorder (ASD). This study aimed to (a) document Vineland Adaptive Behavior Scales (VABS-3) and Adaptive Behavior Assessment System (ABAS-3) adaptive behavior profiles of adolescents with ASD; (b) examine the comparability of the two measures; and (c) assess potential discrepancies between IQ and adaptive behaviors. Participants included 14- to 18-year-olds with ASD without intellectual disability. Significant adaptive skills deficits were observed with most scores at least one standard deviation below the mean. Relative weaknesses were observed for social and daily living skills. The absolute magnitude of VABS-3 and ABAS-3 scores differed. There were significant discrepancies between IQ and adaptive functioning. These findings have implications for clinicians and researchers.

Mortality statistics are fundamental to public health decision making. Mortality varies by time and location, and its measurement is affected by well known biases that have been exacerbated during the COVID-19 pandemic. This paper aims to estimate excess mortality from the COVID-19 pandemic in 191 countries and territories, and 252 subnational units for selected countries, from Jan 1, 2020, to Dec 31, 2021. All-cause mortality reports were collected for 74 countries and territories and 266 subnational locations (including 31 locations in low-income and middle-income countries) that had reported either weekly or monthly deaths from all causes during the pandemic in 2020 and 2021, and for up to 11 year previously. In addition, we obtained excess mortality data for 12 states in India. Excess mortality over time was calculated as observed mortality, after excluding data from periods affected by late registration and anomalies such as heat waves, minus expected mortality. Six models were used to estimate expected mortality; final estimates of expected mortality were based on an ensemble of these models. Ensemble weights were based on root mean squared errors derived from an out-of-sample predictive validity test. As mortality records are incomplete worldwide, we built a statistical model that predicted the excess mortality rate for locations and periods where all-cause mortality data were not available. We used least absolute shrinkage and selection operator (LASSO) regression as a variable selection mechanism and selected 15 covariates, including both covariates pertaining to the COVID-19 pandemic, such as seroprevalence, and to background population health metrics, such as the Healthcare Access and Quality Index, with direction of effects on excess mortality concordant with a meta-analysis by the US Centers for Disease Control and Prevention. With the selected best model, we ran a prediction process using 100 draws for each covariate and 100 draws of estimated coefficients and residuals, estimated from the regressions run at the draw level using draw-level input data on both excess mortality and covariates. Mean values and 95% uncertainty intervals were then generated at national, regional, and global levels. Out-of-sample predictive validity testing was done on the basis of our final model specification. Although reported COVID-19 deaths between Jan 1, 2020, and Dec 31, 2021, totalled 5·94 million worldwide, we estimate that 18·2 million (95% uncertainty interval 17·1–19·6) people died worldwide because of the COVID-19 pandemic (as measured by excess mortality) over that period. The global all-age rate of excess mortality due to the COVID-19 pandemic was 120·3 deaths (113·1–129·3) per 100 000 of the population, and excess mortality rate exceeded 300 deaths per 100 000 of the population in 21 countries. The number of excess deaths due to COVID-19 was largest in the regions of south Asia, north Africa and the Middle East, and eastern Europe. At the country level, the highest numbers of cumulative excess deaths due to COVID-19 were estimated in India (4·07 million [3·71–4·36]), the USA (1·13 million [1·08–1·18]), Russia (1·07 million [1·06–1·08]), Mexico (798 000 [741 000–867 000]), Brazil (792 000 [730 000–847 000]), Indonesia (736 000 [594 000–955 000]), and Pakistan (664 000 [498 000–847 000]). Among these countries, the excess mortality rate was highest in Russia (374·6 deaths [369·7–378·4] per 100 000) and Mexico (325·1 [301·6–353·3] per 100 000), and was similar in Brazil (186·9 [172·2–199·8] per 100 000) and the USA (179·3 [170·7–187·5] per 100 000). The full impact of the pandemic has been much greater than what is indicated by reported deaths due to COVID-19 alone. Strengthening death registration systems around the world, long understood to be crucial to global public health strategy, is necessary for improved monitoring of this pandemic and future pandemics. In addition, further research is warranted to help distinguish the proportion of excess mortality that was directly caused by SARS-CoV-2 infection and the changes in causes of death as an indirect consequence of the pandemic. Bill & Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom

The type 1 cannabinoid receptor (CB1R) mediates neurotransmitter release and synaptic plasticity in the central nervous system. Endogenous, plant-derived, synthetic cannabinoids bind to CB1R, initiating the inhibitory G-protein (G i ) and the β-arrestin signaling pathways. Within the G i signaling pathway, CB1R activates G protein-gated, inwardly-rectifying potassium (GIRK) channels. The β-arrestin pathway reduces CB1R expression on the cell surface through receptor internalization. Because of their association with analgesia and drug tolerance, GIRK channels and receptor internalization are of interest to the development of pharmaceuticals. This research used immortalized mouse pituitary gland cells transduced with a pH-sensitive, fluorescently-tagged human CB1R (AtT20-SEPCB1) to measure GIRK channel activity and CB1R internalization. Cannabinoid-induced GIRK channel activity is measured by using a fluorescent membrane-potential sensitive dye. We developed a kinetic imaging assay that visualizes and measures CB1R internalization. All cannabinoids stimulated a GIRK channel response with a rank order potency of WIN55,212-2 > (±)CP55,940 > Δ 9 -THC > AEA. Efficacy was expressed relative to (±)CP55,940 with a rank order efficacy of (±)CP55,940 > WIN55, 212-2 > AEA > Δ 9 -THC. All cannabinoids stimulated CB1R internalization with a rank order potency of (±)CP55,940 > WIN55, 212-2 > AEA > Δ 9 -THC. Internalization efficacy was normalized to (±)CP55,940 with a rank order efficacy of WIN55,212-2 > AEA > (±)CP55,940 > Δ 9 -THC. (±)CP55,940 was significantly more potent and efficacious than AEA and Δ 9 -THC at stimulating a GIRK channel response; no significant differences between potency and efficacy were observed with CB1R internalization. No significant differences were found when comparing a cannabinoid’s GIRK channel and CB1R internalization response. In conclusion, AtT20-SEPCB1 cells can be used to assess cannabinoid-induced CB1R internalization. While cannabinoids display differential G i signaling when compared to each other, this did not extend to CB1R internalization.

Pulsar timing arrays (PTAs) are designed to detect low-frequency gravitational waves (GWs). GWs induce achromatic signals in PTA data, meaning that the timing delays do not depend on radio frequency. However, pulse arrival times are also affected by radio-frequency-dependent “chromatic” noise from sources such as dispersion measure (DM) and scattering delay variations. Furthermore, the characterization of GW signals may be influenced by the choice of chromatic noise model for each pulsar. To better understand this effect, we assess if and how different chromatic noise models affect the achromatic noise properties in each pulsar. The models we compare include existing DM models used by the North American Nanohertz Observatory for Gravitational waves (NANOGrav) and noise models used for the European PTA Data Release 2 (EPTA DR2). We perform this comparison using a subsample of six pulsars from the NANOGrav 15 yr data set, selecting the same six pulsars as from the EPTA DR2 six-pulsar data set. We find that the choice of chromatic noise model noticeably affects the achromatic noise properties of several pulsars. This is most dramatic for PSR J1713+0747, where the amplitude of its achromatic red noise lowers from log10ARN=−14.1−0.1+0.1 to −14.7−0.5+0.3 , and the spectral index broadens from γRN=2.6−0.4+0.5 to γRN=3.5−0.9+1.2 . We also compare each pulsar's noise properties with those inferred from the EPTA DR2, using the same models. From the discrepancies, we identify potential areas where the noise models could be improved. These results highlight the potential for custom chromatic noise models to improve PTA sensitivity to GWs.

Parent-of-origin effects (POE) exist when there is differential expression of alleles inherited from the two parents. A genome-wide scan for POE on DNA methylation at 639,238 CpGs in 5,101 individuals identifies 733 independent methylation CpGs potentially influenced by POE at a false discovery rate ≤ 0.05 of which 331 had not previously been identified. Cis and trans methylation quantitative trait loci (mQTL) regulate methylation variation through POE at 54% (399/733) of the identified POE-influenced CpGs. The combined results provide strong evidence for previously unidentified POE-influenced CpGs at 171 independent loci. Methylation variation at 14 of the POE-influenced CpGs is associated with multiple metabolic traits. A phenome-wide association analysis using the POE mQTL SNPs identifies a previously unidentified imprinted locus associated with waist circumference. These results provide a high resolution population-level map for POE on DNA methylation sites, their local and distant regulators and potential consequences for complex traits. Parent-of-origin effects (POE) are observed when there are different effects from alleles inherited from the two parents on phenotypic measures. Here, Zeng et al. study POE on DNA methylation in 5,101 individuals and identify genetic variants that associate with methylation variation via POE and their potential phenotypic consequences.

Pulse profile stability is a central assumption of standard pulsar timing methods. Thus, it is important for pulsar timing array experiments such as the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) to account for any pulse profile variability present in their data sets. We show that in the NANOGrav 15 yr data set, the integrated pulse profile of PSR J1022+1001 as seen by the Arecibo radio telescope at 430, 1380, and 2030 MHz varies considerably in its shape from observation to observation. We investigate the possibility that this is due to the “ideal feed assumption” (IFA), on which NANOGrav’s routine polarization calibration procedure relies. PSR J1022+1001 is ∼90% polarized in one pulse profile component, and also has significant levels of circular polarization. Time-dependent deviations in the feed’s polarimetric response (PR) could cause mixing between the intensity I and the other Stokes parameters, leading to the observed variability. We calibrate the PR using a mixture of measurement equation modeling and measurement equation template matching techniques. The resulting profiles are no less variable than those calibrated using the IFA method, nor do they provide an improvement in the timing quality of this pulsar. We observe the pulse shape in 25 MHz bandwidths to vary consistently across the band, which cannot be explained by interstellar scintillation in combination with profile evolution with frequency. Instead, we favor phenomena intrinsic to the pulsar as the cause.

We report multiple lines of evidence for a stochastic signal that is correlated among 67 pulsars from the 15 yr pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves. The correlations follow the Hellings–Downs pattern expected for a stochastic gravitational-wave background. The presence of such a gravitational-wave background with a power-law spectrum is favored over a model with only independent pulsar noises with a Bayes factor in excess of 1014, and this same model is favored over an uncorrelated common power-law spectrum model with Bayes factors of 200–1000, depending on spectral modeling choices. We have built a statistical background distribution for the latter Bayes factors using a method that removes interpulsar correlations from our data set, finding p = 10−3 (≈3σ) for the observed Bayes factors in the null no-correlation scenario. A frequentist test statistic built directly as a weighted sum of interpulsar correlations yields p = 5 × 10−5 to 1.9 × 10−4 (≈3.5σ–4σ). Assuming a fiducial f −2/3 characteristic strain spectrum, as appropriate for an ensemble of binary supermassive black hole inspirals, the strain amplitude is 2.4−0.6+0.7×10−15 (median + 90% credible interval) at a reference frequency of 1 yr−1. The inferred gravitational-wave background amplitude and spectrum are consistent with astrophysical expectations for a signal from a population of supermassive black hole binaries, although more exotic cosmological and astrophysical sources cannot be excluded. The observation of Hellings–Downs correlations points to the gravitational-wave origin of this signal.

Pulsar timing array experiments have recently uncovered evidence for a nanohertz gravitational wave background by precisely timing an ensemble of millisecond pulsars. The next significant milestones for these experiments include characterizing the detected background with greater precision, identifying its source(s), and detecting continuous gravitational waves from individual supermassive black hole binaries. To achieve these objectives, generating accurate and precise times of arrival of pulses from pulsar observations is crucial. Incorrect polarization calibration of the observed pulsar profiles may introduce errors in the measured times of arrival. Further, previous studies have demonstrated that robust polarization calibration of pulsar profiles can reduce noise in the pulsar timing data and improve timing solutions. In this paper, we investigate and compare the impact of different polarization calibration methods on pulsar timing precision using three distinct calibration techniques: the Ideal Feed Assumption (IFA), Measurement Equation Modeling (MEM), and Measurement Equation Template Matching (METM). Three NANOGrav pulsars—PSRs J1643−1224, J1744−1134, and J1909−3744—observed with the 800 MHz and 1.5 GHz receivers at the Green Bank Telescope (GBT) are utilized for our analysis. Our findings reveal that all three calibration methods enhance timing precision compared to scenarios where no polarization calibration is performed. Additionally, among the three calibration methods, the IFA approach generally provides the best results for timing analysis of pulsars observed with the GBT receiver system. We attribute the comparatively poorer performance of the MEM and METM methods to potential instabilities in the reference noise diode coupled to the receiver and temporal variations in the profile of the reference pulsar, respectively.