gebhart/patrepeval huggingface dataset:
The Transcriptome of the Rat Hypothalamic‐Neurohypophyseal System is Highly Strain‐Dependent (2007)

We have used microarrays to comprehensively describe the transcriptomes of the supraoptic nucleus (SON), the paraventricular nucleus (PVN) and the neurointermediate lobe of adult male Sprague-Dawley (SD) and Wistar-Kyoto (WKY) rats, as well as the PVN of Wistar rats. Comparison of these gene lists has enabled us to identify surprisingly large differences in hypothalamic-neurohypophyseal system gene expression patterns in these three strains. We have also shown that different transcript populations are enriched in the PVN and the SON of SD and WKY rats. The transcriptome differences catalogued here may be molecular substrates for the neuro-humoral phenotypic differences exhibited by different strains of rats.
Cellular automata model for the evolution of inert gas monolayers on a calcium (111) surface (1999)

The development of a simple atomistic simulation of microstructural evolution is illustrated for a two-dimensional system comprising of a monolayer of inert gas atoms on a calcium (111) surface. The simulation results in the formation and growth of domains, the domain size increasing as a function of simulation temperature and flux of atoms incident on the surface. Of particular interest is the observed nonlinear critical behaviour in the surface coverage.
Preparation of Previously Unavailable 6,6-Dialkylhexahydro-1,3,5-triazine-2,4-diones (1992)

Abstract A high-yield, two-step procedure for the preparation of 6,6-dialkylhexahydro-1,3,5-triazine-2,4-diones starting from ketones and dithiobiuret is reported.
Template-Based Fuzzy Systems Modeling (1994)

A methodology is suggested here for the development of fuzzy systems models that is a combination of the AI-expert systems approach, with its heavy dependence on the use of expert knowledge, and the neural network-type systems building, with its emphasis on learning from data observations. We use expert-provided information in the form of template linguistic values to induce potential elemental rules for the knowledge base of the system model. We then introduce input-output observations into a simple learning mechanism to obtain weights characterizing the effect of each of the potential elemental rules on the overall systems model. The development of the learning mechanism is based on a representation of systems models combining fuzzy logic and Dempster-Shafer theory, which we previously introduced.
Correction: Attenuated Recombinant Influenza A Virus Expressing HPV16 E6 and E7 as a Novel Therapeutic Vaccine Approach (2015)

There is an error throughout the Methods section of this manuscript. The virus concentration is recorded incorrectly as 3.75 log TCID 50 . The correct concentration is 7.2 log TCID 50 .
In-Product BTI Aging Sensor for Reliability Screening and Early Detection of Material at Risk (2023)

We have developed a new reliability monitoring suite, within a proprietary IP block that we call a CV® Core, with aging sensors embedded in the product layout and testable through the product I / O interface. We illustrate the application of the sensor suite with an example of the PMOS NBTI monitor, testable at the wafer level during product electrical wafer sort (EWS), as well after packaging at final test or during burn-in. During EWS, the wafer-level stress test can be used to identify a marginal chip, help material dispositioning for burn-in, or support additional grading for chiplet matching for multi-chip modules. The aging sensors can also be used during the chip lifetime to monitor the device wear-out and alarm users about abnormal silicon aging rates against target mission profile. In this work, we show the wafer level test results for PMOS transistor degradation rates under NBTI stress, within wafer variability, and correlation of degradation rates between sensors stressed under different conditions.
Generalized chest CT and lab curves throughout the course of COVID-19 (2021)

A better understanding of temporal relationships between chest CT and labs may provide a reference for disease severity over the disease course. Generalized curves of lung opacity volume and density over time can be used as standardized references from well before symptoms develop to over a month after recovery, when residual lung opacities remain. 739 patients with COVID-19 underwent CT and RT-PCR in an outbreak setting between January 21st and April 12th, 2020. 29 of 739 patients had serial exams (121 CTs and 279 laboratory measurements) over 50 ± 16 days, with an average of 4.2 sequential CTs each. Sequential volumes of total lung, overall opacity and opacity subtypes (ground glass opacity [GGO] and consolidation) were extracted using deep learning and manual segmentation. Generalized temporal curves of CT and laboratory measurements were correlated. Lung opacities appeared 3.4 ± 2.2 days prior to symptom onset. Opacity peaked 1 day after symptom onset. GGO onset was earlier and resolved later than consolidation. Lactate dehydrogenase, and C-reactive protein peaked earlier than procalcitonin and leukopenia. The temporal relationships of quantitative CT features and clinical labs have distinctive patterns and peaks in relation to symptom onset, which may inform early clinical course in patients with mild COVID-19 pneumonia, or may shed light upon chronic lung effects or mechanisms of medical countermeasures in clinical trials.
ChemInform Abstract: Tandem Inter (4 + 2)/Intra (3 + 2)Nitroalkene Cycloadditions. Part 5. Origin of the Lewis Acid Dependent Reversal of Stereoselectivity. (1993)

Abstract ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.
Direct Measurement of Light Waves (2004)

The electromagnetic field of visible light performs approximately 10(15) oscillations per second. Although many instruments are sensitive to the amplitude and frequency (or wavelength) of these oscillations, they cannot access the light field itself. We directly observed how the field built up and disappeared in a short, few-cycle pulse of visible laser light by probing the variation of the field strength with a 250-attosecond electron burst. Our apparatus allows complete characterization of few-cycle waves of visible, ultraviolet, and/or infrared light, thereby providing the possibility for controlled and reproducible synthesis of ultrabroadband light waveforms.
Multilineage somatic activating mutations in HRAS and NRAS cause mosaic cutaneous and skeletal lesions, elevated FGF23 and hypophosphatemia (2013)

Pathologically elevated serum levels of fibroblast growth factor-23 (FGF23), a bone-derived hormone that regulates phosphorus homeostasis, result in renal phosphate wasting and lead to rickets or osteomalacia. Rarely, elevated serum FGF23 levels are found in association with mosaic cutaneous disorders that affect large proportions of the skin and appear in patterns corresponding to the migration of ectodermal progenitors. The cause and source of elevated serum FGF23 is unknown. In those conditions, such as epidermal and large congenital melanocytic nevi, skin lesions are variably associated with other abnormalities in the eye, brain and vasculature. The wide distribution of involved tissues and the appearance of multiple segmental skin and bone lesions suggest that these conditions result from early embryonic somatic mutations. We report five such cases with elevated serum FGF23 and bone lesions, four with large epidermal nevi and one with a giant congenital melanocytic nevus. Exome sequencing of blood and affected skin tissue identified somatic activating mutations of HRAS or NRAS in each case without recurrent secondary mutation, and we further found that the same mutation is present in dysplastic bone. Our finding of somatic activating RAS mutation in bone, the endogenous source of FGF23, provides the first evidence that elevated serum FGF23 levels, hypophosphatemia and osteomalacia are associated with pathologic Ras activation and may provide insight in the heretofore limited understanding of the regulation of FGF23.
Power System State Estimation Using Gauss-Newton Unrolled Neural Networks with Trainable Priors (2020)

Power system state estimation (PSSE) aims at finding the voltage magnitudes and angles at all generation and load buses, using meter readings and other available information. PSSE is often formulated as a nonconvex and nonlinear least-squares (NLS) cost function, which is traditionally solved by the Gauss-Newton method. However, Gauss-Newton iterations for minimizing nonconvex problems are sensitive to the initialization, and they can diverge. In this context, we advocate a deep neural network (DNN) based "trainable regularizer" to incorporate prior information for accurate and reliable state estimation. The resulting regularized NLS does not admit a neat closed form solution. To handle this, a novel end-to-end DNN is constructed subsequently by unrolling a Gauss-Newton-type solver which alternates between least-squares loss and the regularization term. Our DNN architecture can further offer a suite of advantages, e.g., accommodating network topology via graph neural networks based prior. Numerical tests using real load data on the IEEE 118-bus benchmark system showcase the improved estimation performance of the proposed scheme compared with state-of-the-art alternatives. Interestingly, our results suggest that a simple feed forward network based prior implicitly exploits the topology information hidden in data.
High-Performance Uncooled Mid-Infrared Detector Based on a Polycrystalline PbSe/CdSe Heterojunction (2023)

Developing high-performance, uncooled mid-wavelength infrared (MWIR) detectors is a challenging task due to the inherent physical properties of materials and manufacturing technologies. In this study, we designed and manufactured an uncooled polycrystalline PbSe/CdSe heterojunction photovoltaic (PV) detector through vapor physical deposition. The resulting 10 μm × 10 μm device exhibited a peak detectivity of 7.5 × 10<sup>9</sup> and 3 × 10<sup>10</sup> cm·Hz<sup>1/2</sup>·W<sup>-1</sup> at 298 and 220 K, respectively, under blackbody radiation. These values are comparable to those of typical PbSe photoconductive detectors fabricated through standard chemical bath deposition. Additionally, the sensitization-free process used to create these PbSe/CdSe PV detectors allows for high replicability and yield, making them promising candidates for low-cost, high-performance, uncooled MWIR focal plane array imaging in commercial applications.
Low -k 1 optical lithography for 100 nm logic technology and beyond (2001)

In this article, we present 193 nm lithography at a k1 factor of 0.37–0.40 and discuss several topics important to 100 nm logic such as optical proximity correction (OPC), control of critical dimension (CD) variation, and lowering of the mask error factor (MEF). In OPC, the best correction results can be achieved by developing accurate models and using reasonable segmentation rules. The technique of variable-dose exposures is demonstrated as a means to reduce interfield CD variation once the cause is known and can be characterized. A more than 30% reduction in CD variation is realized for variation caused by temperature nonuniformity in hot plates. The concept of two dimensional (2D) MEF is introduced to describe situations at feature ends. Higher numerical aperture or more effectively, quadrupole illumination, can be used to lower 1D and 2D MEFs. We also explore the technique of dipole illumination, which may be a candidate for taking optical lithography to k1&amp;lt;0.35.
Understanding deep learning (still) requires rethinking generalization (2021)

Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small gap between training and test performance. Conventional wisdom attributes small generalization error either to properties of the model family or to the regularization techniques used during training. Through extensive systematic experiments, we show how these traditional approaches fail to explain why large neural networks generalize well in practice. Specifically, our experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data. This phenomenon is qualitatively unaffected by explicit regularization and occurs even if we replace the true images by completely unstructured random noise. We corroborate these experimental findings with a theoretical construction showing that simple depth two neural networks already have perfect finite sample expressivity as soon as the number of parameters exceeds the number of data points as it usually does in practice. We interpret our experimental findings by comparison with traditional models. We supplement this republication with a new section at the end summarizing recent progresses in the field since the original version of this paper.
ChemInform Abstract: Poly(ethylene glycol)‐Supported Chiral Imidazolidin‐4‐one: An Efficient Organic Catalyst for the Enantioselective Diels—Alder Cycloaddition. (2002)

Abstract ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.
EcoFusion: Energy-Aware Adaptive Sensor Fusion for Efficient Autonomous Vehicle Perception (2022)

Autonomous vehicles use multiple sensors, large deep-learning models, and powerful hardware platforms to perceive the environment and navigate safely. In many contexts, some sensing modalities negatively impact perception while increasing energy consumption. We propose EcoFusion: an energy-aware sensor fusion approach that uses context to adapt the fusion method and reduce energy consumption without affecting perception performance. EcoFusion performs up to 9.5% better at object detection than existing fusion methods with approximately 60% less energy and 58% lower latency on the industry-standard Nvidia Drive PX2 hardware platform. We also propose several context-identification strategies, implement a joint optimization between energy and performance, and present scenario-specific results.
Cytokine regulation of colony-stimulating factor production in cultured human synovial fibroblasts: I. Induction of GM-CSF and G-CSF production by interleukin-1 and tumor necrosis factor (1990)

The cytokines, interleukin-1 (IL-1) and tumor necrosis factor (TNF), induce a dose-dependent production of both granulocyte-macrophage colony-stimulating factor (GM-CSF) and granulocyte CSF (G-CSF) in cultured human synovial cells, as measured by immunoassay. With IL-1, significant levels of both CSFs were first detected within 6 to 12 hours, with a maximum reached 24 to 48 hours after commencement of stimulation. A synergistic effect was detected between IL-1 and TNF in production of both CSFs in these cells. No evidence was obtained for the IL-1-induced effect to be mediated by induction of endogenous TNF nor for the TNF-induced stimulation to involve IL-1. IL-1-stimulated synovial cells were shown to secrete biologically active GM-CSF and G- CSF, which were specifically inhibited by their respective monoclonal antibodies. The transcription inhibitor, actinomycin D, and protein synthesis inhibitor, cycloheximide, inhibited the increase in GM-CSF and G-CSF production induced by IL-1 and TNF. Finally, other cytokines, IL-3, interferon gamma (IFN gamma), IL-2, platelet-derived growth factor (PDGF), epidermal growth factor (EGF) and transforming growth factor alpha (TGF alpha), failed to stimulate either GM-CSF or G-CSF production, whether alone or in the presence of IL-1. These results suggest that cytokine-stimulated synovial fibroblasts may be a major source of intraarticular CSF production in the joints of patients with inflammatory arthritis; as a result, monocyte/macrophages and granulocytes may be activated, leading to perpetuation of the inflammation and destructive events occurring in these lesions.
Role of Preeclampsia-Related Angiogenic Factors in Sunitinib Cardiotoxicity: Two Cases and Review of the Literature (2012)

Sunitinib is a multi-targeted tyrosine kinase inhibitor widely used in clear cell renal carcinoma and in imatinib-resistant gastrointestinal stromal tumors. Sunitinib-associated cardiotoxicity has been recognized and includes hypertension, left ventricular dysfunction and congestive heart failure; nevertheless, few data exist in the literature regarding the role of preeclampsia-related angiogenic factors in sunitinib cardiotoxicity. We report a case of sunitinib-induced severe left ventricular dysfunction that occurred in a hypertensive woman with metastatic renal carcinoma and a history of preeclampsia, and a case of sunitinib-induced preeclampsia-like syndrome in a normotensive patient with an imatinib-resistant gastrointestinal stromal tumor. Our experience confirms that inhibition of angiogenic factors to treat cancer is a novel challenge for the oncologist and requires the cardiologist's support.
Nickel-catalysed enantioselective hydrovinylation of styrenes in liquid or supercritical carbon dioxide (1999)

Compressed (liquid or supercritical) CO2 is an environmentally benign reaction medium for the highly efficient regio-, chemo- and enantio-selective nickel-catalysed hydrovinylation of styrenes and it allows for catalyst recycling and selective removal of the product from the reaction mixture.
sslGolog: When conditional compositions of web services meet semantic links and causal laws (2011)

Web service composition enhanced by semantic technologies is currently one of the most hyped and addressed issues in Service Oriented Computing. This work focuses on both i) conditional composition, i.e., how to automate the composition of services t
Can nausea and vomiting be treated with ginger extract? (2015)

Ginger (Zingiber officinale) is a spice traditionally used to treat indigestion, nausea and vomiting. Ginger extracts accelerate gastric emptying and stimulate gastric antral contractions. These effects are mainly due to the presence of gingerols and shogaols and their activity on cholinergic M receptors and serotonergic 5-HT and 5-HT receptors. Various researches on this subject have led to controversial results, due to the chemical instability of ginger extracts and particularly of gingerols, which are readily-oxidizable substances. A systematic review of double-blind, placebo-controlled, randomized studies highlighted the potential efficacy of ginger on the prevention and treatment of nausea and vomiting of various origins, even though additional controlled studies are needed. This review focuses on pregnancy-induced nausea and vomiting and on chemotherapy induced nausea, and hypothesizes a therapeutic role for ginger extracts in case of side effects, as an alternative to traditional prokinetic drugs such as domperidone, levosulpiride or metoclopramide.
Progression of Visual Field Defects in Eyes With Different Optic Disc Appearances in Patients With Normal Tension Glaucoma (2012)

The rate of progression of the field defects, the MD slope, in patients with NTG is possibly dependent on the baseline optic disc appearance. Thus, the optic disc appearance may be useful for the management of patients with NTG.
Nano-Spectroscopy in the 2.5-10 μm Wavelength Range Using Atomic Force Microscope (2009)

We performed nanoscale atomic force microscope surface imaging with chemical identification and spatial resolution of 200 nm. The detection mechanism was based on recording resonant oscillations in the cantilever induced by a tunable mid-IR excitation.
Preparation and Characterization of PETI-330/Multiwalled Carbon Nanotube (2005)

As part of an ongoing effort to incorporate multifunctionality into advanced composites, blends of PETI-330 and multi-walled carbon nanotubes (MWCNTs) were prepared, characterized and fabricated into moldings. The PETI-330/MWCNT mixtures were prepared at concentrations ranging from 3 to 25 weight percent by dry mixing the components in a ball mill. The resulting powders were characterized for degree of mixing, thermal and rheological properties. Based on the characterization results, PETI-330/MWCNT samples were scaled up to ~300 g and used to fabricate moldings 10.2 cm x 15.2 cm x 0.32 cm thick. The moldings were made by injecting the mixtures at 260-280 C into an Invar tool followed by curing for 1 h at 371 C. The tool was designed to impart shear during the injection process in an attempt to achieve some alignment of the MWCNTs in the flow direction. Good quality moldings were obtained that were subsequently characterized for thermal, mechanical and electrical properties. The degree of dispersion and alignment of the MWCNTs were investigated using high-resolution scanning electron microscopy. The preparation and preliminary characterization of PETI-330/MWCNT composites will be discussed. Keywords: phenylethynyl terminated imides, high temperature polymers, nanocomposites, moldings
Bispecific antibodies targeting distinct regions of the spike protein potently neutralize SARS-CoV-2 variants of concern (2021)

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern threatens the efficacy of existing vaccines and therapeutic antibodies and underscores the need for additional antibody-based tools that potently neutralize variants by targeting multiple sites of the spike protein. We isolated 216 monoclonal antibodies targeting SARS-CoV-2 from plasmablasts and memory B cells collected from patients with coronavirus disease 2019. The three most potent antibodies targeted distinct regions of the receptor binding domain (RBD), and all three neutralized the SARS-CoV-2 Alpha and Beta variants. The crystal structure of the most potent antibody, CV503, revealed that it binds to the ridge region of SARS-CoV-2 RBD, competes with the angiotensin-converting enzyme 2 receptor, and has limited contact with key variant residues K417, E484, and N501. We designed bispecific antibodies by combining nonoverlapping specificities and identified five bispecific antibodies that inhibit SARS-CoV-2 infection at concentrations of less than 1 ng/ml. Through a distinct mode of action, three bispecific antibodies cross-linked adjacent spike proteins using dual N-terminal domain–RBD specificities. One bispecific antibody was greater than 100-fold more potent than a cocktail of its parent monoclonals in vitro and prevented clinical disease in a hamster model at a dose of 2.5 mg/kg. Two bispecific antibodies in our panel comparably neutralized the Alpha, Beta, Gamma, and Delta variants and wild-type virus. Furthermore, a bispecific antibody that neutralized the Beta variant protected hamsters against SARS-CoV-2 expressing the E484K mutation. Thus, bispecific antibodies represent a promising next-generation countermeasure against SARS-CoV-2 variants of concern.