gebhart/patrepeval huggingface dataset:
High‐power‐density InAlGaN/GaN HEMT using InGaN back barrier for W‐band amplifiers (2023)

Abstract This paper investigated the back‐barrier (BB) effect for gallium nitride (GaN)‐based high‐electron‐mobility transistors with an Fe‐doped buffer and Fe‐buffer + Indium gallium nitride (InGaN)‐BB structure. The authors found that the Fe‐doped buffer + InGaN‐BB structure was effective in reducing the off‐state leakage current compared to the Fe‐doped buffer. Secondary‐ion‐mass spectrometry measurements revealed that the segregated Fe existed with peaks at ∼2 × 10 17 cm −3 around the InGaN‐BB layer. The authors believe that the negative charges which are generated by Fe effectively increased the BB effect as they exist just underneath the 2‐dimensional‐electron‐gas channel and successfully achieved a high output power operation of 4.6 W/mm at 94 GHz.
Stress-induced inflammation evoked by immunogenic cell death is blunted by the IRE1α kinase inhibitor KIRA6 through HSP60 targeting (2021)

Mounting evidence indicates that immunogenic therapies engaging the unfolded protein response (UPR) following endoplasmic reticulum (ER) stress favor proficient cancer cell-immune interactions, by stimulating the release of immunomodulatory/proinflammatory factors by stressed or dying cancer cells. UPR-driven transcription of proinflammatory cytokines/chemokines exert beneficial or detrimental effects on tumor growth and antitumor immunity, but the cell-autonomous machinery governing the cancer cell inflammatory output in response to immunogenic therapies remains poorly defined. Here, we profiled the transcriptome of cancer cells responding to immunogenic or weakly immunogenic treatments. Bioinformatics-driven pathway analysis indicated that immunogenic treatments instigated a NF-κB/AP-1-inflammatory stress response, which dissociated from both cell death and UPR. This stress-induced inflammation was specifically abolished by the IRE1α-kinase inhibitor KIRA6. Supernatants from immunogenic chemotherapy and KIRA6 co-treated cancer cells were deprived of proinflammatory/chemoattractant factors and failed to mobilize neutrophils and induce dendritic cell maturation. Furthermore, KIRA6 significantly reduced the in vivo vaccination potential of dying cancer cells responding to immunogenic chemotherapy. Mechanistically, we found that the anti-inflammatory effect of KIRA6 was still effective in IRE1α-deficient cells, indicating a hitherto unknown off-target effector of this IRE1α-kinase inhibitor. Generation of a KIRA6-clickable photoaffinity probe, mass spectrometry, and co-immunoprecipitation analysis identified cytosolic HSP60 as a KIRA6 off-target in the IKK-driven NF-κB pathway. In sum, our study unravels that HSP60 is a KIRA6-inhibitable upstream regulator of the NF-κB/AP-1-inflammatory stress responses evoked by immunogenic treatments. It also urges caution when interpreting the anti-inflammatory action of IRE1α chemical inhibitors.
Mechanisms Involved in Ceramide-Induced Autophagy in Osteoblasts (2021)

This study aimed to investigate the role of ceramide in inducing autophagy in osteoblasts, then further investigate the involved downstream signaling cascades. The effect of exogenous ceramide on inducing autophagy in MC3T3-E1 and Primary murine osteoblasts (POB) were assessed by (a) Transmission Electron Microscopy (TEM), Western Blot (WB) analysis, (c) Immunofluorescence microscopy, and (d) Propidium Iodide (PI)-uptake/flow cytometry analysis. C2-ceramide induced the formation of cytoplasmic v
Dynamic Routing Optimization in WDM Networks (2010)

We present a multi-objective optimization approach for joint throughput optimization and traffic engineering, where the routing request of traffic arrives one-by-one. We provide an Integer Linear Program (ILP) that simultaneously i) maximizes the aggregate throughput, ii) minimizes the resource consumption, and iii) minimizes the maximum link utilization. We study the impact of optimizing the three different objectives simultaneously in dynamic environments, and show that better solutions than those of mono-objective approaches can be obtained. Because of the complexity of the ILP, we also propose another ILP with reduced complexity, and study its performance and the optimality gap between it and optimal solutions.
Some stereocontrolled ring-opening reactions of 6-substituted 2,3-epoxybicyclo[3.2.0]heptanes (1983)

The bicyclo[3.2.0]heptenes (7), (9), (10), (12), and (13) were prepared and treated with a brominating agent in an aqueous medium. The alkenes 99) and (10) gave only the bromohydrins (15) and (16), respectively. The bicycloalkenes (7), (12), and (13) gave mixtures of bromohydrins. The mixture obtained from compound (12) gave the epoxides (32) and (35) on treatment with base: similarly the bromohydrins obtained from the alkenes (7) and (13) were converted into the isomeric epoxide pairs (29)/(34), and (33)/(36), respectively. The epoxides (30) and (31) were furnished by base-treatment of the bromohydrins (15) and (16), respectively. The epoxides (29)–(33) were treated with lithium dibutyl cuprate. The epoxides (30) and (31) reacted with high selectivity to give the corresponding bicycloalkan-3-ols (19) and (20) almost exclusively. The epoxides (32) and (33) reacted non-selectively with this cuprate reagent. The parent epoxide (29) displayed an intermediate behaviour, giving a mixture of the alcohols (18) and (23) with the former compound predominating. These results are explained by assuming that a substituent at the 6-position in this ring-system exerts a perannular conformational control which affects the selectively of reaction of an alkene and epoxide unit at C-2 and also influences the conformation of 2,3-disubstituted derivatives.
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud (2021)

Understanding the predictions made by machine learning (ML) models and their potential biases remains a challenging and labor-intensive task that depends on the application, the dataset, and the specific model. We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into data and ML models by identifying biases and explaining predictions. It is deeply integrated into Amazon SageMaker, a fully managed service that enables data scientists and developers to build, train, and deploy ML models at any scale. Clarify supports bias detection and feature importance computation across the ML lifecycle, during data preparation, model evaluation, and post-deployment monitoring. We outline the desiderata derived from customer input, the modular architecture, and the methodology for bias and explanation computations. Further, we describe the technical challenges encountered and the tradeoffs we had to make. For illustration, we discuss two customer use cases. We present our deployment results including qualitative customer feedback and a quantitative evaluation. Finally, we summarize lessons learned, and discuss best practices for the successful adoption of fairness and explanation tools in practice.
Application of Taxonomic Modeling to Microbiota Data Mining for Detection of Helminth Infection in Global Populations (2016)

Human microbiome data from genomic sequencing technologies is fast accumulating, giving us insights into bacterial taxa that contribute to health and disease. The predictive modeling of such microbiota count data for the classification of human infection from parasitic worms, such as helminths, can help in the detection and management across global populations. Real-world datasets of microbiome experiments are typically sparse, containing hundreds of measurements for bacterial species, of which only a few are detected in the bio-specimens that are analyzed. This feature of microbiome data produces the challenge of needing more observations for accurate predictive modeling and has been dealt with previously, using different methods of feature reduction. To our knowledge, integrative methods, such as transfer learning, have not yet been explored in the microbiome domain as a way to deal with data sparsity by incorporating knowledge of different but related datasets. One way of incorporating this knowledge is by using a meaningful mapping among features of these datasets. In this paper, we claim that this mapping would exist among members of each individual cluster, grouped based on phylogenetic dependency among taxa and their association to the phenotype. We validate our claim by showing that models incorporating associations in such a grouped feature space result in no performance deterioration for the given classification task. In this paper, we test our hypothesis by using classification models that detect helminth infection in microbiota of human fecal samples obtained from Indonesia and Liberia countries. In our experiments, we first learn binary classifiers for helminth infection detection by using Naive Bayes, Support Vector Machines, Multilayer Perceptrons, and Random Forest methods. In the next step, we add taxonomic modeling by using the SMART-scan module to group the data, and learn classifiers using the same four methods, to test the validity of the achieved groupings. We observed a 6% to 23% and 7% to 26% performance improvement based on the Area Under the receiver operating characteristic (ROC) Curve (AUC) and Balanced Accuracy (Bacc) measures, respectively, over 10 runs of 10-fold cross-validation. These results show that using phylogenetic dependency for grouping our microbiota data actually results in a noticeable improvement in classification performance for helminth infection detection. These promising results from this feasibility study demonstrate that methods such as SMART-scan can be utilized in the future for knowledge transfer from different but related microbiome datasets by phylogenetically-related functional mapping, to enable novel integrative biomarker discovery.
Evaluation of the Market Potential for DG SOFC Systems at Military Installations (2016)

The Department of Defense (DoD) is a major consumer of energy in the United States (U.S.) and is responsible for nearly 80 percent of U.S. government energy consumption. The DoD facility energy consumption profile reflects the U.S. commercial sector with electricity and natural gas accounting for nearly 80 percent of the energy consumed. The high-efficiency Solid Oxide Fuel Cell (SOFC) systems, which can support distributed generation (DG) and combined heat and power (CHP) applications, are well suited to support the DoD energy strategy and respond to the federal sustainability executive order. As a direct consequence of these policies, the DoD facility power sector was found to not only significantly supplement the early market opportunities for DG SOFC systems in the commercial sector, but also provide a significant longer-term market opportunity for commercial DG SOFC system developers. The DoD facilities power sector is capable of supporting the SOFC commercialization roadmap by providing increased production capacity that results in SOFC technology cost reduction. Apart from the expectation of typical readiness of commercially mature systems, there does not appear to be any significant issues that could hinder the market entry of SOFCs into the DoD facilities power sector. The expeditionary military bases, which are not considered in this report, provide yet another DoD market arena for DG SOFC systems.
Granular Convection Observed by Magnetic Resonance Imaging (1995)

Vibrations in a granular material can spontaneously produce convection rolls reminiscent of those seen in fluids. Magnetic resonance imaging provides a sensitive and noninvasive probe for the detection of these convection currents, which have otherwise been difficult to observe. A magnetic resonance imaging study of convection in a column of poppy seeds yielded data about the detailed shape of the convection rolls and the depth dependence of the convection velocity. The velocity was found to decrease exponentially with depth; a simple model for this behavior is presented here.
A New Ion Mobility–Linear Ion Trap Instrument for Complex Mixture Analysis (2014)

A new instrument that couples a low-pressure drift tube with a linear ion trap mass spectrometer is demonstrated for complex mixture analysis. The combination of the low-pressure separation with the ion trapping capabilities provides several benefits for complex mixture analysis. These include high sensitivity, unique ion fragmentation capabilities, and high reproducibility. Even though the gas-phase separation and the mass measurement steps are each conducted in an ion filtering mode, detection limits for mobility-selected peptide ions are in the tens of attomole range. In addition to ion separation, the low-pressure drift tube can be used as an ion fragmentation cell yielding mobility-resolved fragment ions that can be subsequently analyzed by multistage tandem mass spectrometry (MS(n)) methods in the ion trap. Because of the ion trap configuration, these methods can be comprised of any number (limited by ion signal) of collision-induced dissociation (CID) and electron transfer dissociation (ETD) processes. The high reproducibility of the gas-phase separation allows for comparison of two-dimensional ion mobility spectrometry (IMS)-MS data sets in a pixel-by-pixel fashion without the need for data set alignment. These advantages are presented in model analyses representing mixtures encountered in proteomics and metabolomics experiments.
Asymmetric Catalytic Addition of Diorganozinc Reagents to Imines: Scope and Application (2005)

Abstract For Abstract see ChemInform Abstract in Full Text.
Development of an Automated Microfluidic Reaction Platform for Multidimensional Screening: Reaction Discovery Employing Bicyclo[3.2.1]octanoid Scaffolds (2009)

An automated, silicon-based microreactor system has been developed for rapid, low-volume, multidimensional reaction screening. Use of the microfluidic platform to identify transformations of densely functionalized bicyclo[3.2.1]octanoid scaffolds will be described.
Novel energy recovery systems for the efficient cooling of data centers using absorption chillers and renewable energy resources (2012)

The study proposes an efficient energy recovery system for the cooling of a data center using renewable energy resources. The cooling system consists of an absorption chiller driven by the thermal energy recovered from the data center components and additional extra solar energy. The heat dissipated to the ambient by the data center racks is used to vaporize the cooling agents and thus activate the absorption Br-water refrigeration unit and cool the water which in turn will be used to cool the data center environment. When the thermal energy received from the data centers servers is not sufficient to activate the refrigeration unit, a dual system is proposed to heat the water needed for the absorption chiller, using solar energy or other available regenerative heat source. The current study will provide a solution for the optimal design of the data center/server cooling systems, the appropriate selection and the functional analysis of the absorption chiller together with the complementary heat exchangers required by the overall system. The efficiency of the system is defined in terms of the Coefficient of Performance (COP), the ratio between the recovered energy from the system and the total power dissipated by the system. The COP values can reach 0.33 for a typical system. The conclusions highlight the efficiency of the system and comparison to the classical solutions.
Persistence of immunogenic pulmonary metastases in the presence of protective tumor immunity in a murine melanoma model (1997)

Departments of 1Immunology and 2Cell Biology, University of Texas, M.D. Anderson Cancer Center, Houston, Texas, USA
A broader view on EUV-masks: adding complementary imaging modes to the SHARP microscope (2014)

The authors are expanding the capabilities of the SHARP microscope by implementing complementary imaging modes. SHARP (the SEMATECH High-NA Actinic Reticle review Project) is an actinic, synchrotron-based microscope dedicated to extreme ultraviolet (EUV) photomask research. SHARP’s programmable Fourier Synthesis Illuminator and its use of Fresnel zoneplate lenses as imaging optics provide a versatile framework, facilitating the implementation of diverse modes beyond conventional imaging. In addition to SHARP’s set of standard zoneplates, we have created more than 100 zoneplates for complementary imaging modes, all designed to extract additional information from photomasks, improve navigation and enhance defect detection. More than 50 new zoneplates are installed in the tool; the remaining lenses are currently in production. In this paper we discuss the design and fabrication of zoneplates for complementary imaging modes and present image data, obtained using Zernike Phase Contrast and different implementations of Differential Interference Contrast.
Unlocking the elusive binding cavity in p-sulfonatocalix[8]arene (2006)

For the first time the utility of p-sulfonatocalix[8]arene to form a chalice-like cavity for the solid state inclusion of a classical coordination complex [Co(phen)3]3+ has been demonstrated. In the presence of Yb3+ ions, a new crystalline material is formed which possesses large diameter, negatively charged channels which propagate through the material in three dimensions.
Polymer-Mediated Particle Coarsening within Hollow Silica Shell Nanoreactors (2022)

Inspired by the scanning probe block copolymer lithography process, hollow silica shells were loaded with polymer─metal ink mixtures and investigated as solution-based nanoreactors for the synthesis of gold nanoparticles. The incorporation of poly(ethylene oxide) (PEO) into these hollow silica nanoreactors (approximately 40 nm in size) and the use of a two-step reductive annealing process (first at 200 °C and then at 600 °C) results in a high yield (76%) of larger (∼6 nm) single nanoparticles; when the polymer is not used, smaller (∼3 nm) particles dominate, and the yield of single particles is only 6%. It was determined that particle coarsening mostly occurs in the temperature range where the polymer is present and not degraded (i.e., <400 °C for PEO), as indicted by correlative in situ scanning/transmission electron microscopy in a reductive gas-phase environment. Thus, polymer incorporation in this nanoreactor system, which is amenable to scale up, drives the complete conversion of nanoreactor contents without excessive metal loss, highlighting the impact of nanoreactor composition and structural design on particle synthesis.
ChemInform Abstract: An Efficient One‐Pot Synthesis of Coumarins Mediated by Propylphosphonic Anhydride (T3P) via the Perkin Condensation. (2012)

Abstract The mild and low toxic coupling agent T3P allows the synthesis of various functionalized coumarin structures.
RF attenuation (1967)

A tutorial review of RF attenuation measurement methods and standards is presented. Accepted and proposed definitions and attenuator models are discussed. Commonly used standards operating from dc through most waveguide bands are compared with the "ideal" interlaboratory standard. Characteristics of fixed resistive, waveguide below-cutoff, and rotary-vane standards are included. Measurement methods are classified and described, including comments concerning convenience and accuracy of various methods, and references are given which cover most of the basic important research in the field.
Unsupervised analysis of a chromatographic signal based on an infinite Gaussian mixture model (2017)

We address the issue of remote processing of micro-chromatographic signals for point-of-care body fluid analysis in a home-care environment, or for on-site air and water quality assessment. We propose to use a random walk model considering the chromatographic signal as a distribution of molecule arrival time (MAT) at the chromatography column output and we associate to each analyte a cluster of MAT. The signal processing should realize automatically an unsupervised clustering of the MAT to identify the cluster list, and a classification to assign a cluster label to each MAT. On such complex fluids, the list is only partially known. Thus, we propose to introduce an infinite list of cluster candidates described as an Infinite Gaussian Mixture Model (IGMM) and to estimate a finite list using a variational non parametric Bayesian inference. Experimental results are given for the gas chromatographic analysis of polycyclic aromatic hydrocarbons (PAHs) pollutants in solution.
Noninvasive photoluminescence imaging of silicon PV modules in daylight (2022)

Outdoor photoluminescence imaging on field-deployed solar cell modules has been conducted to an increasing extent in recent years. Photoluminescence images provide more details about defects than thermal infrared images, while the imaging procedure has the potential to be faster than electroluminescence imaging because it does not require electrical connection to the modules. However, when conducted with sunlight excitation, it is based on lock-in technique, which implies switching between the modules' two operating points. This often results in the need to connect additional electrical equipment dimensioned for the task and leads to production disruption for the investigated modules. The present study demonstrates image acquisition based on the string inverter's ability to sweep the IV curve. The advantage of this approach is twofold. This is a noninvasive method, which only requires imaging apparatus and, therefore, allows for a flexible imaging procedure on the string, module, or cell level without having to take into account additional equipment. Furthermore, photoluminescence images spanning the whole IV curve can be obtained during one sweep, as opposed to obtaining images in two operating points when using lock-in technique. Such image series can be used to investigate the state of the cells and modules by looking at the photoluminescence images acquired on different current extraction levels. This has been done for a healthy string and a broken module.
Effect of Nanoparticle Composition, Size, Shape, and Stiffness on Penetration Across the Blood–Brain Barrier (2020)

The delivery of therapeutics to the brain in an efficient, noninvasive manner continues to be a major unmet need in the field of drug delivery. One significant impediment to brain delivery results from the existence of the physical yet dynamic blood-brain barrier (BBB). Despite the many, often complex strategies that currently exist to breach the BBB, adequate delivery of effective therapeutics from the bloodstream continues to remain quite low. Nanotechnology has emerged as a promising tool for brain delivery, but little is known about the important particle parameters that influence delivery. Here, we synthesized and characterized a library of nanoparticles with distinct properties ranging from size, shape, stiffness, and composition to investigate and identify the key attributes influencing particle uptake and transport for brain delivery. To accomplish this task, an <i>in vitro</i> human BBB model was developed and validated using human cerebral microvascular endothelial cells (hCMEC/D3). Particle uptake and apparent permeability coefficients (<i>P</i><sub>app</sub>) were then determined for each particle group. To elucidate the roles of different parameters on particle uptake and transport across the BBB, the predominant mechanisms of endocytosis were also investigated. Our results show that particle composition yielded the greatest impact on penetration across the BBB model. This work lays the foundation and provides new insights into the role of particle parameters on penetration across the BBB.