gebhart/patrepeval huggingface dataset:
Investigation of the dynamics of matrix-assisted laser desorption/ionization ion formation using an electrostatic analyzer/time-of-flight mass spectrometer (1999)

A hybrid electrostatic analyzer/time-of-flight mass spectrometer was used to examine the matrix-assisted laser desorption/ionization (MALDI) ion kinetic energy distributions for Na+, protonated 7-hydroxy-4-methylcoumarin matrix ions and protonated bradykinin under a variety of source region accelerating electric field conditions. Broad kinetic energy distributions are observed for the three targeted ions with the ions having both positive and negative kinetic energies relative to the full acceleration potential. The positive kinetic energy ions are shown to have flight times in good agreement with flight times calculated from first principles while the flight times of the negative kinetic energy ions diverge positively from the calculated values. Analysis of the flight time shifts for the negative kinetic energy ions allows an entrainment velocity of ∼550 m s−1 for the material to be calculated. A composite picture of the dynamics of MALDI ion formation is presented which combines promptly formed protonated matrix and analyte ions with ions formed/accelerated at delayed times in an expanding, constant velocity plume of laser desorbed material. Copyright © 1999 John Wiley & Sons, Ltd.
Bioinstrumental Analysis (2013)

An undergraduate course in bioinstrumental analysis was developed at the University of Washington and taught for six years between 2005 and 2010. The course consisted of 18 lectures, two exams, and eight laboratory experiments during a 10-week quarter. The first half of the course dealt with nucleic acid analysis and the second half dealt with protein analysis. Instrumentation used in the laboratory included gel electrophoresis and a gel reader, a PCR thermocycler, and a capillary electrophoresis system. Access was required to a core protein mass spectrometer and a DNA sequencing facility. Finally, bioinformatics portions of the laboratory require access to both BLAST for nucleic acid analysis and SYQUEST or Mascot for protein analysis. Enrollment was capped at 32 students, which was limited by laboratory space. The class was typically made up of roughly 2/3 biochemistry and 1/3 chemistry majors with an occasional student from biology and computer science.
Usefulness of the Surprise Question on an Inpatient Oncology Service (2018)

Regular discussion of prognosis of patients with cancer in an inpatient medical setting did not increase referrals to inpatient or outpatient palliative care or hospice. Increased clinical experience impacted hospital medicine providers and bedside nurses' estimation of prognosis differently than oncology providers. Medical oncologists were significantly more optimistic than hospital medicine providers.
Evaluation of para-18F-fluorohippurate PET renography to predict future disease progression in a rat model of ADPKD (2015)

1077 Objectives Prognostic markers for progression of polycystic kidney disease are limited. Our objective for this pilot study was to evaluate the potential of early para-18F-fluorohippurate (18F-PFH) PET renography to predict future progression of disease in Han:SPRD rats1 with slowly progressive autosomal dominant polycystic kidney disease (ADPKD) . Methods Four groups (n = 2 to 7) of genotyped male and female cystic (Cy/+) and non-cystic (+/+) Han:SPRD rats underwent PET renography using 18F-PFH, a renal tubular agent,2,3 and blood sampling at ages 6 and 26 weeks. T2 and T20 values, which represent the percent of the injected dose of 18F-PFH in kidneys at 2 minutes and 20 minutes after injection, were determined from imaging data. The T20/T2 ratio was assessed as a prognostic marker. Rats were euthanized after renography at age 26 weeks, and kidney weight/body weight ratios (KW/BW%) were determined as a measure of PKD progression. Results Serum creatinine (S-Cr) and serum urea nitrogen (SUN) concentrations did not differ in female and male +/+ and Cy/+ rats at age 6 weeks, but they were significantly higher in Cy/+ than +/+ rats at age 26 weeks of age and male Cy/+ rats displayed significantly higher values than female Cy/+ rats. Importantly, male Cy/+ rats displayed higher T20/T2 values than female Cy/+ at age 6 weeks, which correlated with S-Cr, SUN, and KW/BW% at age 26 weeks, indicating that 18F-PFH PET at 6 weeks of age accurately predicted disease outcome at 26 weeks of age. Conclusions The results indicate that male Cy/+ rats exhibit more severe disease than female Cy/+ rats, which is consistent with literature reports in this model. This study indicates that T20/T2 ratio obtained from 18F-PFH PET renography could be used as a surrogate marker to predict disease progression at an early stage. Research Support This work was funded by an OU College of Pharmacy startup grant and the John S. Gammill Endowment for PKD Research.
Psychomotor function measured via online activity predicts motor vehicle fatality risk (2017)

Impaired psychomotor performance severely increases the risk of fatal and non-fatal car accidents. However, we currently lack methods to continuously and non-intrusively monitor psychomotor performance. We show we can estimate psychomotor function at population scale from 16 billion observations of typing speeds during the input of web search queries. We show that these estimates exhibit diurnal variation with a substantial increase during typical sleep times, matching published accident risk rates. Further, we show that psychomotor impairment, as measured by keystroke timing, predicts motor vehicle fatality risk on a population level (Spearman <i>ρ</i> = 0.61; <i>p</i> « 10<sup>-10</sup>). The methods and results highlight a promising direction of harnessing ambient streams of data, such as patterns of interactions with devices, as large-scale sensors to continuously and non-intrusively monitor human psychomotor performance at population scale.
The use of plasma proteomic markers to understand the biology of immunotherapy response. (2020)

10062 Background: Despite recent successes with immune checkpoint blockade (ICB) in melanoma, the prognosis for most patients remains dire. Whereas small fraction of patients are able to achieve disease control, most do not respond or are limited by immune-related toxicities. Robust non-invasive predictors of ICB response have the potential to guide clinical decision and alter management of patients, however, no such predictors currently exist. Methods: We applied a highly-multiplex Proximity Extension Assay to simultaneously detect &gt; 1000 proteins in the plasma of anti-PD-1 treated melanoma patients. Our cohort comprised 116 patients, 66 responders (R) and 50 non-responders (NR). Additional 65 patients comprised a validation cohort with 30 R and 35 NR, and included 50 patients who developed treatment-related toxicities. Plasma samples were collected at baseline, 6-weeks and 6-months after starting the treatment. A subset of patients had single-cell RNA-seq performed on tumor tissue. Group differences and treatment effects were evaluated by linear model with maximum likelihood estimation for model parameters and Benjamini and Hochberg multiple hypothesis correction. Results: At baseline, 6 significantly differentially expressed (DE) proteins were identified between R and NR. Elevated expression of ST2 and IL-6, two key immunoregulatory proteins were found in NR. At 6-weeks, more dynamic changes occurred and 79 significantly DE proteins were identified between R and NR, including proteins implicated in primary or acquired resistance as IL-8, MIA, TNFR1 and potential novel targets as MCP-4/CCL13, ICOSLG and VEGF. Proteomic changes identified at baseline and 6-weeks were more profound at 6-months, and moreover 238 proteins were confirmed significant between R and NR. Importantly, we were able to leverage these differences to build classifiers of R and NR subsets. We compared mRNA expression of DE proteins within the tumor microenvironment by leveraging scRNAseq data from a subset of these patients. Enriched expression of these genes was uncovered in certain myeloid and exhausted T cell subsets, thus shedding insight into the potential role of these cell subsets in ICB response. Conclusions: Plasma proteomic profiling of anti-PD1 treated patients identified important tumor and immune changes associated with response. Non-invasive means discovery of circulatory protein biomarkers may predict sensitivity to immunotherapy and uncover biological insights underlying primary resistance.
Surgical Stress Abrogates Pre-Existing Protective T Cell Mediated Anti-Tumor Immunity Leading to Postoperative Cancer Recurrence (2016)

Anti-tumor CD8+ T cells are a key determinant for overall survival in patients following surgical resection for solid malignancies. Using a mouse model of cancer vaccination (adenovirus expressing melanoma tumor-associated antigen (TAA)-dopachrome tautomerase (AdDCT) and resection resulting in major surgical stress (abdominal nephrectomy), we demonstrate that surgical stress results in a reduction in the number of CD8+ T cell that produce cytokines (IFNγ, TNFα, Granzyme B) in response to TAA. This effect is secondary to both reduced proliferation and impaired T cell function following antigen binding. In a prophylactic model, surgical stress completely abrogates tumor protection conferred by vaccination in the immediate postoperative period. In a clinically relevant surgical resection model, vaccinated mice undergoing a positive margin resection with surgical stress had decreased survival compared to mice with positive margin resection alone. Preoperative immunotherapy with IFNα significantly extends survival in surgically stressed mice. Importantly, myeloid derived suppressor cell (MDSC) population numbers and functional impairment of TAA-specific CD8+ T cell were altered in surgically stressed mice. Our observations suggest that cancer progression may result from surgery-induced suppression of tumor-specific CD8+ T cells. Preoperative immunotherapies aimed at targeting the prometastatic effects of cancer surgery will reduce recurrence and improve survival in cancer surgery patients.
Unfolding Simulations Reveal the Mechanism of Extreme Unfolding Cooperativity in the Kinetically Stable α-Lytic Protease (2010)

Kinetically stable proteins, those whose stability is derived from their slow unfolding kinetics and not thermodynamics, are examples of evolution's best attempts at suppressing unfolding. Especially in highly proteolytic environments, both partially and fully unfolded proteins face potential inactivation through degradation and/or aggregation, hence, slowing unfolding can greatly extend a protein's functional lifetime. The prokaryotic serine protease alpha-lytic protease (alphaLP) has done just that, as its unfolding is both very slow (t(1/2) approximately 1 year) and so cooperative that partial unfolding is negligible, providing a functional advantage over its thermodynamically stable homologs, such as trypsin. Previous studies have identified regions of the domain interface as critical to alphaLP unfolding, though a complete description of the unfolding pathway is missing. In order to identify the alphaLP unfolding pathway and the mechanism for its extreme cooperativity, we performed high temperature molecular dynamics unfolding simulations of both alphaLP and trypsin. The simulated alphaLP unfolding pathway produces a robust transition state ensemble consistent with prior biochemical experiments and clearly shows that unfolding proceeds through a preferential disruption of the domain interface. Through a novel method of calculating unfolding cooperativity, we show that alphaLP unfolds extremely cooperatively while trypsin unfolds gradually. Finally, by examining the behavior of both domain interfaces, we propose a model for the differential unfolding cooperativity of alphaLP and trypsin involving three key regions that differ between the kinetically stable and thermodynamically stable classes of serine proteases.
A short 5'-flanking region mediates glucose repression of amylase gene expression in Drosophila melanogaster. (1993)

Expression of the alpha-amylase gene is highly repressed by dietary glucose in Drosophila melanogaster larvae. Here, we show that glucose repression is controlled by DNA sequences that are located upstream of the transcribed region. Recombinant gene constructions, in which the amylase promoter sequences were fused with the transcribed region of the Adh gene, were expressed in transgenic Drosophila larvae. The expression of ADH from the recombinant gene was shown to be subject to glucose repression. The function of potential regulatory cis-acting elements within the glucose responsive upstream region was examined by deletion analysis and by site-directed mutagenesis, coupled with expression assays in transformed larvae. The upstream deletion analysis showed that essential elements, both for overall activity and for glucose repression of the amylase gene, are located within a 109-bp region upstream of the transcription start site. Site-directed mutagenesis of these upstream sequences showed that the TATA motif, at position -31, and a novel 36-bp element, at position -109, were necessary for full activity of the amylase promoter. None of the introduced mutations resulted in loss of glucose responsiveness. These results indicate that glucose repression, in Drosophila, is mediated by transcriptional mechanisms that involve multiple, functionally redundant DNA elements.
Filtering and Quantization Effects on GNSS Successive Interference Cancellation (2019)

The cross correlation between GPS L1 coarse acquisition signals manifests as an increased noise floor in the receiver. Reducing the amount of added noise for enhancing tracking performance is important. Successive interference cancellation (SIC) is a method, which can reduce cross-correlation noise, but its performance is dependent on the receiver bandwidth and quantization. This research analyzes the SIC performance influenced by receiver bandwidth and quantization. Results from experiments with live data supporting the analysis are presented.
Prediction of Cutting Concentration in Horizontal and Deviated Wells Using Support Vector Machine (2018)

Abstract Hole cleaning, or drilled-cutting transportation, is one of the main concern in the petroleum industry. This is due to the high impact of improper downhole cleaning during drilling operations. To illustrate, many drilling problems can happen because of inefficient cleaning of the wellbore. These problems may include premature bit wear, slow drilling rate (i.e. low ROP) and, in most severe cases, a stuck pipe which in some cases can lead to complete loss of the well. Moreover, measuring the cleaning efficiency using field or experimental measurements is highly costly and time-consuming which makes it a very complicated problem. Therefore, a lot of studies have been conducted to understand cutting transport efficiency in drilling operations. However, most of these studies are experimental and try to come up with the best measures including experimental models or empirical correlations. In this study, artificial intelligence techniques were used to estimate the cutting concentration in the wellbore. The purpose of this study is to use Support Vector Machine (SVM) technique to indirectly measure the hole cleaning efficiency by predicting the cutting concentration in the wellbore from other operational parameters. Based on 116 experimental data points collected from the literature, the cutting concentration in the borehole was predicted from the properties of the mud itself such as the mud rheological properties (e.g. yield point and plastic viscosity) and mud density (mud weight) and other operational parameters during drilling including the drill pipe rotary speed (RPM), pipe eccentricity (i.e. the axial location of the drill pipe), hole inclination angle, the rate of penetration (ROP), flow rate (GPM), temperature and annular size. The results obtained from SVM show the ability of this method to accurately predict the cutting concentration in the wellbore with an average absolute error (AAE) of less than 5% and a correlation coefficient (R) of more than 0.9. For this specific group of data, comparing the results obtained from this technique with a correlation presented in the literature shows that the SVM method provides a better prediction of cutting concentration and higher accuracy than that in the literature. Finally, the method developed in this study to predict the cutting concentration is based on continuously measured parameters during any drilling operation. Therefore, integration of the developed model into the drilling system will allow for real-time prediction of the concentration of the cuttings (i.e. the amount of cuttings present in the wellbore) and, hence, the cleaning efficiency during the drilling operation.
Max-Min SINR Dependence on Channel Correlation in Line-of-Sight Massive MIMO (2017)

Under LoS (line-of-sight) propagation and the assumption of perfect CSI (channel state information), for either MR (maximum-ratio) or ZF (zero-forcing) precoding/decoding, one can readily obtain Massive MIMO (multi-input multi-output) per-user effective SINR for single-cell scenarios. LoS channels are typically less correlated than IID (independent and identically distributed) Rayleigh channels, but the maximum correlation for LoS is typically much greater than for IID Rayleigh. This motivates an investigation of the dependence of max-min SINR on the maximum channel correlation. Perron-Frobenius theory and the classical Fischer inequality are used to establish some rigorous and explicit upper bounds on the effective max-min SINR (signal to interference plus noise ratio) that depend on the maximum channel correlation. These upper bounds provide an accurate description of this dependence relationship, and readily facilitate system performance analyses and scheduler designs without simulations. In high channel correlation environment, ZF can perform substantially better than MR in the downlink but the opposite is true for the uplink.
Chemistry for Enhancing the Production of Oil and Gas (2013)

Providing an overview of the science and technology of the use of production chemicals to enhance and maintain oil and gas production, Chemistry for Enhancing the Production of Oil and Gas is geared towards a technically trained audience. Readers will find a review of the important chemical and physical principles that are common to most, if not all, of the enhancement treatments. The authors also discuss aspects of the use and mechanisms of the complex chemistries that take place with the application of flow assurance chemicals, during stimulation (reactive chemistry and prop fracturing) and chemically improved oil recovery, including the use of chemical tracers. A final chapter emphasizes the importance of health, safety, and environmental compliance in all aspects of oilfield treatments. Most of the chapters found within end with a section where successful chemical enhancement or control methods have been used to solve specific production problems.
Link Selection in Hybrid RF/VLC Systems Under Statistical Queueing Constraints (2018)

The co-deployment of radio frequency (RF) and visible light communication (VLC) technologies has been investigated in indoor environments to enhance network performances and to address specific quality-of-service (QoS) constraints. In this paper, we explore the benefits of employing both technologies when the QoS requirements are imposed as limits on the buffer overflow and delay violation probabilities, which are important metrics in designing low latency wireless networks. Particularly, we consider a multi-mechanism scenario that utilizes RF and VLC links for data transmission in an indoor environment, and then propose a link selection process through which the transmitter sends data over the link that sustains the desired QoS guarantees the most. Considering an ON-OFF data source, we employ the maximum average data arrival rate at the transmitter buffer and the non-asymptotic bounds on data buffering delay as the main performance measures. We formulate the performance measures under the assumption that both links are subject to average and peak power constraints. Furthermore, we investigate the performance levels when either one of the two links is used for data transmission, or when both are used simultaneously. Finally, we show the impacts of different physical layer parameters on the system performance through numerical analysis.
Direct Measurement of Velocity Space Transport in a Plasma (1992)

The direct measurement of the velocity space transport of ions in a plasma is reported. Measured diffusion and convection coefficients are compared to the calculated Fokker-Planck coefficients for a fully ionized plasma. The measurements were made in a Q-machine barium plasma (Ti= T e = 0.17 eV, 5 × 108 ≤n≤ 8 × 10 9 cm-3) with both high and low fluctuation levels. At low fluctuation levels the measured coefficients agree with classical collision theory. Coefficients measured in the presence of large amplitude fluctuations generally are larger and have different velocity dependences. Test particle distributions are created and interrogated using the technique of optical tagging. The short-time (≪90° collision time) relaxation of the test particle distribution function was measured as a function of density and temperature of the background plasma and as a function of the velocity of the test particle distribution. The values of the convection and diffusion coefficients were extracted from these measurements. Longer time relaxations (∼90° time) also were measured. © 1994 American Institute of Physics.
Artificial Intelligent Pattern Recognition for Optical Fiber Distributed Acoustic Sensing Systems Based on Phase-OTDR (2018)

In this paper, we present an integrated approach to improve measurement efficacy of fiber optical distributed acoustic sensing (DAS) systems using a combined approach of ultrafast laser manufacturing and neural network pattern recognition.
Distributed Joint Power, Association and Flight Control for Massive-MIMO Self-Organizing Flying Drones (2020)

This article studies distributed algorithms to control self-organizing flying drones with massive MIMO networking capabilities - a network scenario referred to as mDroneNet. We attempt to answer the following fundamental question: what is the optimal way to provide spectrally-efficient wireless access to a multitude of ground nodes with mobile hotspots mounted on drones and endowed with a large number of antennas; when we can control the position of the drone hotspots, the association between the ground users and the drone hotspots, as well as the pilot sequence assignment and transmit power for the ground users? To the best of our knowledge, this is the first time that massive MIMO capabilities are considered in self-organizing flying drone networks. We first derive a mathematical formulation of the problem of joint power, association and movement control in mDroneNet, with the objective of maximizing the aggregate spectral efficiency of the ground users. It is shown that the resulting network control problem is a mixed integer nonlinear nonconvex programming (MINLP) problem. Then, a distributed solution algorithm with polynomial time complexity is designed by solving three closely-coupled subproblems: access association, joint pilot sequence assignment and power control, and drone movement control. As a performance benchmark, a globally-optimal but centralized solution algorithm is also designed based on a combination of the branch and bound framework and convex relaxation techniques. Results indicate that the distributed solution algorithm converges fast (within tens of iterations) and achieves a network spectral efficiency very close to the global optimum obtained by the centralized solution algorithm (over 90% in average).