gebhart/patrepeval huggingface dataset:
Aligning Needs: Integrating Citizen Science Efforts into Schools Through Service Requirements. (2019)

Citizen science is an increasingly valuable tool for both scientists and educators. For researchers, citizen science is a means of more quickly investigating questions which would otherwise be time-consuming and costly to study. For educators, citizen science offers a means to engage students in actual research and improve learning outcomes. Since most citizen science projects are usually designed with research goals in mind, many lack the necessary educator materials for successful integration in a formal science education (FSE) setting. In an ideal world, researchers and educators would build the necessary materials together; however, many researchers lack the time, resources, and networks to create these materials early on in the life of a citizen science project. For resource-poor projects, we propose an intermediate entry point for recruiting from the educational setting: community service or service learning requirements (CSSLRs). Many schools require students to participate in community service or service learning activities in order to graduate. When implemented well, CSSLRs provide students with growth and development opportunities outside the classroom while contributing to the community and other worthwhile causes. However, CSSLRs take time, resources, and effort to implement well. Just as citizen science projects need to establish relationships to transition well into formal science education, schools need to cultivate relationships with community service organizations. Students and educators at schools with CSSLRs where implementation is still a work in progress may be left with a burdensome requirement and inadequate support. With the help of a volunteer fulfilling a CSSLR, we investigated the number of students impacted by CSSLRs set at different levels of government and explored the qualifications needed for citizen science projects to fulfill CSSLRs by examining the explicitly-stated justifications for having CSSLRs, surveying how CSSLRs are verified, and using these qualifications to demonstrate how an online citizen science project, Mark2Cure, could use this information to meet the needs of students fulfilling CSSLRs.
Effects of Microstructures on Fatigue Behavior of an Al-Mg-Sc Alloy at an Elevated Temperature (2012)

Polycrystalline Al-1wt%Mg-0.27wt%Sc alloys bearing Al 3 Sc particles with different average sizes of 4 and 11nm in diameter have been cyclically deformed at 423K under various constant stress amplitudes, and the relationship between fatigue characteristics and microstructure of the alloy has been investigated. The specimen bearing 11 nm particles exhibited a cyclic hardening to saturation, while in specimens with the small particles a cyclic softening was observed after initial hardening. In the specimen with large particles, dislocations were uniformly distributed under all applied stress amplitudes, whereas the specimens bearing small particles, in which cyclic softening occurred exhibited clearly developed slip bands. The cyclic softening for the latter specimen was explained by particle shearing within the strongly strained slip bands. The width of precipitate free zones (PFZs) has been found to be one of the factors affecting the fatigue life of the specimens at 423K. The two-step aging decreases the width of PFZs, resulting in increase in the fatigue life.
Fast time domain physical optics for the analysis of non linear phenomena in large reflector antennas (2008)

International audience
Deep Learning–Based COVID-19 Pneumonia Classification Using Chest CT Images: Model Generalizability (2021)

Since the outbreak of the COVID-19 pandemic, worldwide research efforts have focused on using artificial intelligence (AI) technologies on various medical data of COVID-19-positive patients in order to identify or classify various aspects of the disease, with promising reported results. However, concerns have been raised over their generalizability, given the heterogeneous factors in training datasets. This study aims to examine the severity of this problem by evaluating deep learning (DL) classification models trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries. We collected one dataset at UT Southwestern (UTSW) and three external datasets from different countries: CC-CCII Dataset (China), COVID-CTset (Iran), and MosMedData (Russia). We divided the data into two classes: COVID-19-positive and COVID-19-negative patients. We trained nine identical DL-based classification models by using combinations of datasets with a 72% train, 8% validation, and 20% test data split. The models trained on a single dataset achieved accuracy/area under the receiver operating characteristic curve (AUC) values of 0.87/0.826 (UTSW), 0.97/0.988 (CC-CCCI), and 0.86/0.873 (COVID-CTset) when evaluated on their own dataset. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better. However, the performance dropped close to an AUC of 0.5 (random guess) for all models when evaluated on a different dataset outside of its training datasets. Including MosMedData, which only contained positive labels, into the training datasets did not necessarily help the performance of other datasets. Multiple factors likely contributed to these results, such as patient demographics and differences in image acquisition or reconstruction, causing a data shift among different study cohorts.
Combined Antiretroviral Therapy and Immune Pressure Lead to In Vivo HIV-1 Recombination With Ancestral Viral Genomes (2011)

These findings demonstrate the relevance of HIV-1 reservoirs in adaptive evolution throughout recombination in response to selective pressure, such as antiretroviral therapy and immune responses. This result might assist in the design of new treatment strategies for patients experiencing treatment failure.
Mechanisms of cellular retention of melanin bound drugs: Experiments and computational modeling (2022)

Melanin binding of drugs is known to increase drug concentrations and retention in pigmented eye tissues. Even though the correlation between melanin binding in vitro and exposure to pigmented eye in vivo has been shown, there is a discrepancy between rapid drug release from melanin particles in vitro and the long in vivo retention in the pigmented tissues. We investigated mechanisms and kinetics of pigment-related drug retention experimentally using isolated melanin particles from porcine retinal pigment epithelium and choroid, isolated porcine eye melanosomes, and re-pigmented ARPE-19 cells in a dynamic flow system. The experimental studies were supplemented with kinetic simulations. Affinity and capacity of levofloxacin, terazosin, papaverine, and timolol binding to melanin revealed K<sub>d</sub> values of ≈ 50-150 μM and B<sub>max</sub> ≈ 40-112 nmol.mg<sup>-1</sup>. The drugs were released from melanin in <1 h (timolol) or in 6-12 h (other drugs). The drugs were released slower from the melanosomes than from melanin; the experimental differences ranged from 1.2-fold (papaverine) to 7.4-fold (timolol). Kinetic simulations supported the role of the melanosomal membrane in slowing down the release of melanin binders. In release studies from the pigmented ARPE-19 cells, drugs were released from the cellular melanin to the extracellular space in ≈ 1 day (timolol) and ≈ 11 days (levofloxacin), i.e., much slower than the release from melanin or melanosomes. Simulations of drug release from pigmented cells in the flow system matched the experimental data and enabled further sensitivity analyses. The simulations demonstrated a significant prolongation of drug retention in the cells as a function of decreasing drug permeability in the melanosomal membranes and increasing melanin content in the cells. Overall, we report the impact of cellular factors in prolonging drug retention and release from melanin-containing cells. These data and simulations will facilitate the design of melanin binding drugs with prolonged ocular actions.
Joint Distributed Compression and Encryption of Correlated Data in Sensor Networks (2006)

In this paper, we propose, formulate, and study a joint distributed data compression and encryption scheme suitable for wireless sensor networks where we adopt the structured encryption system of advanced encryption standard (AES). The distributed compression is achieved as per the Slepian-Wolf coding theorem, using channel codes. Core to achieving optimal compression in the joint compression and encryption is the preservation of correlation among different blocks of data despite applying cryptographic primitives. We establish that the correlation between sources remains unchanged when cryptographic primitives, namely key addition and substitution are applied. However, as a requirement of security in the encryption, any correlation between two inputs to a encryption system is removed with diffusion techniques. Compliance to the requirements of diffusion layer of AES cipher is achieved by designing the compression function so as to maintain branch number property. We establish the necessary and sufficient condition for achieving a compression function with branch number property and show that distributed compression using non-systematic Reed Solomon (RS) code can satisfy this condition
A Probabilistic Model for Online Document Clustering with Application to Novelty Detection (2004)

In this paper we propose a probabilistic model for online document clustering. We use non-parametric Dirichlet process prior to model the growing number of clusters, and use a prior of general English language model as the base distribution to handle the generation of novel clusters. Furthermore, cluster uncertainty is modeled with a Bayesian Dirichletmultinomial distribution. We use empirical Bayes method to estimate hyperparameters based on a historical dataset. Our probabilistic model is applied to the novelty detection task in Topic Detection and Tracking (TDT) and compared with existing approaches in the literature. 1
Ultrafast Integrated Optical Signal Processing (2010)

Abstract : We investigated the fabrication and optical characterization of photonic integrated circuit (PIC) designed for telecommunication applications using ultrafast (> 100Gbits/s) optical logic. The basic components in the PIC are active components, such as semiconductor optical amplifiers (SOAs), integrated with passive, light-routing waveguides and beam splitter/combiners. Optical switching is accomplished with SOAs embedded within the arms of Mach-Zehnder interferometers (MZIs), whereas power splitting/combining of propagating signals is carried out within multimode interferometers (MMIs). Figure 1 shows a schematic of the basic unit cell under investigation.
Bioactive α-Pyrone Derivatives from the Endolichenic Fungus <i>Dothideomycetes</i> sp. EL003334 (2018)

Two new α-pyrones, dothideopyrones E (1) and F (2), were isolated from a culture of the endolichenic fungus Dothideomycetes sp. EL003334. Their structures were elucidated by spectroscopic data analysis. Their absolute configurations were established by the modified Mosher's method. Compound 2 inhibited nitric oxide (NO) production with IC<sub>50</sub> values of 15.0 ± 2.8 μM in lipopolysaccharide (LPS)-induced BV2 cells. Compound 2 diminished the protein expression levels of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2). Additionally, 2 decreased the mRNA expression levels of pro-inflammatory cytokines, such as tumor necrosis factor-α (TNF-α), interleukin (IL)-1β, and IL-6.
Four and Half Lim Protein 2 (FHL2) Stimulates Osteoblast Differentiation (2006)

FHL2 and actin-binding proteins, palladin and filamin A, were identified as proteins interacting with beta5 cytoplasmic domain. FHL2 co-localized with alphavbeta5 at the focal adhesion sites in association with palladin and filamin A. FHL2 was also present in nuclei. Osteoblasts overexpressing FHL2 exhibited increased adhesion to and migration on matrix proteins. Conversely, FHL2 stimulation of CREB activity was dependent on integrin function because it was inhibited by Gly-Arg-Gly-Asp-Ser (GRGDS) peptide. The expression of osteoblast differentiation markers and Msx2 was upregulated, and bone matrix mineralization was increased in FHL2 overexpressing cells. In contrast, FHL2-deficient bone marrow cells and osteoblasts displayed decreased osteoblast colony formation and differentiation, respectively, compared with wildtype cells. Moreover, FHL2-deficient female mice exhibited greater bone loss than the wildtype littermates after ovariectomy. Thus, FHL2 plays an important role in osteoblast differentiation and bone formation.
Cellular and Molecular Biology of Lipoprotein Metabolism in Atherosclerosis (1981)

Despite a significant decrease in mortality from cardiovascular disease in the US over the past decade, coronary artery disease, more specifically myocardial infarction, remains the leading cause of death in our country, and in most Westernized countries of the world. The disease process of atherosclerosis causes coronary heart disease; yet, despite considerable progress, much remains to be learned about the basic mechanisms responsible for this disease process. It is the purpose of this paper to review the recent advances that are contributing to our understanding of the important role of plasma lipoproteins in atherosclerosis and the role of dietary fat and cholesterol in altering plasma lipoprotein metabolism.
Spin-Split Band Hybridization in Graphene Proximitized with α-RuCl<sub>3</sub> Nanosheets (2019)

Proximity effects induced in the two-dimensional Dirac material graphene potentially open access to novel and intriguing physical phenomena. Thus far, the coupling between graphene and ferromagnetic insulators has been experimentally established. However, only very little is known about graphene's interaction with antiferromagnetic insulators. Here, we report a low-temperature study of the electronic properties of high quality van der Waals heterostructures composed of a single graphene layer proximitized with α-RuCl<sub>3</sub>. The latter is known to become antiferromagnetically ordered below 10 K. Shubnikov-de Haas oscillations in the longitudinal resistance together with Hall resistance measurements provide clear evidence for a band realignment that is accompanied by a transfer of electrons originally occupying the graphene's spin degenerate Dirac cones into α-RuCl<sub>3</sub> band states with in-plane spin polarization. Left behind are holes in two separate Fermi pockets, only the dispersion of one of which is distorted near the Fermi energy due to spin selective hybridization with these spin polarized α-RuCl<sub>3</sub> band states. This interpretation is supported by our density functional theory calculations. An unexpected damping of the quantum oscillations as well as a zero-field resistance upturn close to the Néel temperature of α-RuCl<sub>3</sub> suggest the onset of additional spin scattering due to spin fluctuations in the α-RuCl<sub>3</sub>.
Predicting Treatment Efficacy via Quantitative Magnetic Resonance Imaging: A Bayesian Joint Model (2011)

The prognosis for patients with high grade gliomas is poor, with a median survival of 1 year. Treatment efficacy assessment is typically unavailable until 5-6 months post diagnosis. Investigators hypothesize that quantitative magnetic resonance imaging can assess treatment efficacy 3 weeks after therapy starts, thereby allowing salvage treatments to begin earlier. The purpose of this work is to build a predictive model of treatment efficacy by using quantitative magnetic resonance imaging data and to assess its performance. The outcome is 1-year survival status. We propose a joint, two-stage Bayesian model. In stage I, we smooth the image data with a multivariate spatiotemporal pairwise difference prior. We propose four summary statistics that are functionals of posterior parameters from the first-stage model. In stage II, these statistics enter a generalized non-linear model as predictors of survival status. We use the probit link and a multivariate adaptive regression spline basis. Gibbs sampling and reversible jump Markov chain Monte Carlo methods are applied iteratively between the two stages to estimate the posterior distribution. Through both simulation studies and model performance comparisons we find that we can achieve higher overall correct classification rates by accounting for the spatiotemporal correlation in the images and by allowing for a more complex and flexible decision boundary provided by the generalized non-linear model.
Single Quadrupole Multiple Reaction Monitoring Q-MRM enables Quantitative Mass Spectrometry (2021)

&lt;p&gt; &lt;/p&gt;&lt;p&gt;A single quadrupole combined with enhanced in-source fragmentation/annotation (EISA) was used to perform multiple reaction monitoring (Q-MRM) for quantitative mass spectrometry analysis. EISA amplifies fragmentation of traditional soft electrospray ionization to produce fragment ions that have been found to be identical to those generated in tandem mass spectrometry. We have combined EISA fragmentation data with criteria established by the European Union Commission Directive 2002/657/EC for electron ionization single quadrupole quantitative analysis to perform quantitative Q-MRM experiments. These experiments were performed on multiple types of complex samples that included a mixture of 50 standards, as well as cell and plasma extracts. The dynamic range for Q-MRM quantitative analysis was comparable to triple quadrupole multiple reaction monitoring (QqQ-MRM) analyses at up to 5 orders of magnitude with the cell and plasma extracts showing similar matrix effects across both platforms. Amino acid and fatty acid measurements performed from certified NIST 1950 plasma with isotopically labelled standards demonstrated Q-MRM accuracy in the range of 91-110% for the amino acids, 76-129% for the fatty acids, and good precision (coefficient of variation &lt; 10%). In order to enhance specificity, a newly developed Correlated Ion Monitoring (CIM) algorithm was designed to facilitate these analyses. CIM autonomously processes, aligns, filters, and compiles multiple ions within one chromatogram enabling both precursor and in-source fragment ions to be correlated within a single chromatogram, also enabling the detection of co-eluting species based on precursor and fragment ion ratios. Q-MRM and CIM with single quadrupole instrumentation has been designed as an alternative to QqQ-MRM technology by correlating precursor and fragment ions to facilitate high sensitivity Q-MRM quantitative analysis on existing instrumentation that are generally inexpensive, easy to operate, and technically less complex. &lt;/p&gt; &lt;p&gt;@font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:3 0 0 0 1 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0in; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman";}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-size:11.0pt; mso-ansi-font-size:11.0pt; mso-bidi-font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:DengXian; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-fareast-language:ZH-CN;}.MsoPapDefault {mso-style-type:export-only; margin-bottom:8.0pt; line-height:107%;}div.WordSection1 {page:WordSection1;}&lt;/p&gt;
Ribozymes and Their Medical Implications (1988)

Certain RNA molecules can mediate their own cleavage or splicing or act as enzymes to promote reactions on substrate RNA molecules. Thus, RNA is not restricted to being a passive carrier of genetic information but can have an active role in directing cellular biochemistry. These findings suggest the possibility that other cellular RNAs, including the RNA components of small nuclear ribonucleoproteins, of the ribosome, and of various ribonucleoprotein enzymes, are catalysts. RNA enzymes (ribozymes) can be used as sequence-specific RNA cleavage agents in vitro, providing useful tools for biochemical studies of RNA. On a more speculative note, ribozymes directed against viral RNAs have the potential of serving as therapeutic agents. Finally, some infectious agents, including hepatitis delta virus and perhaps poliovirus and rhinoviruses, are themselves ribozymes, providing potential targets for pharmaceuticals.
Detection of flow limitation in obstructive sleep apnea with an artificial neural network (2007)

During sleep, the development of a plateau on the inspiratory airflow/time contour provides a non-invasive indicator of airway collapsibility. Humans recognize this abnormal contour easily, and this study replicates this with an artificial neural network (ANN) using a normalized shape. Five 10 min segments were selected from each of 18 sleep records (respiratory airflow measured with a nasal cannula) with varying degrees of sleep disordered breathing. Each breath was visually scored for shape, and breaths split randomly into a training and test set. Equally spaced, peak amplitude normalized flow values (representing breath shape) formed the only input to a back propagation ANN. Following training, breath-by-breath agreement of the ANN with the manual classification was tabulated for the training and test sets separately. Agreement of the ANN was 89% in the training set and 70.6% in the test set. When the categories of 'probably normal' and 'normal', and 'probably flow limited' and 'flow limited' were combined, the agreement increased to 92.7% and 89.4% respectively, similar to the intra- and inter-rater agreements obtained by a visual classification of these breaths. On a naive dataset, the agreement of the ANN to visual classification was 57.7% overall and 82.4% when the categories were collapsed. A neural network based only on the shape of inspiratory airflow succeeded in classifying breaths as to the presence/absence of flow limitation. This approach could be used to provide a standardized, reproducible and automated means of detecting elevated upper airway resistance.
Electrolyte selection for supercapacitive devices: a critical review (2019)

Electrolytes are one of the vital constituents of electrochemical energy storage devices and their physical and chemical properties play an important role in these devices' performance, including capacity, power density, rate performance, cyclability and safety. This article reviews the current state of understanding of the electrode-electrolyte interaction in supercapacitors and battery-supercapacitor hybrid devices. The article discusses factors that affect the overall performance of the devices such as the ionic conductivity, mobility, diffusion coefficient, radius of bare and hydrated spheres, ion solvation, viscosity, dielectric constant, electrochemical stability, thermal stability and dispersion interaction. The requirements needed to design better electrolytes and the challenges that still need to be addressed for building better supercapacitive devices for the competitive energy storage market have also been highlighted.
Robonaut 2 (R2) Overview (2013)

This slide presentation reviews the development and uses of the second robot designed to work in space. The presentation reviews the motivation for developing Robonaut, the cooperative commercial relationship with General Motors, the evolution design of Robonaut Robonaut 2 improvements over the first robonaut (R1), Robonaut 2's hand dexterity, finger impedance control, tactile system, finger haptics, arm control, strength, neck movement and head sensors, human interaction, and the controller. Also the plans for use on board the International Space Station are reviewed. The patent for the Phalange Tactile Load Cell is included.