gebhart/patrepeval huggingface dataset:
Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns (2019)

Epigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. Here we examine the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs) in 4 independent cohorts (overall n = 2365). We use Akaike's information criterion to test which factors best explain variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E), genotypes in cis (G), or their additive (G + E) or interaction (GxE) effects. Genetic and environmental factors in combination best explain DNAm at the majority of VMRs. The CpGs best explained by either G, G + E or GxE are functionally distinct. The enrichment of genetic variants from GxE models in GWAS for complex disorders supports their importance for disease risk.
Extending TensorFlow's Semantics with Pipelined Execution (2019)

TensorFlow is a popular cloud computing framework that targets machine learning applications. It separates the specification of application logic (in a dataflow graph) from the execution of the logic. TensorFlow's native runtime executes the application with low overhead across a diverse set of hardware including CPUs, GPUs, and ASICs. Although the underlying dataflow engine supporting these features could be applied to computations beyond machine learning, certain design decisions limit this broader application, such as the inability for an application to differentiate between data items across concurrent requests. This paper introduces Pipelined TensorFlow (PTF), a system that extends TensorFlow's semantics to provide support for a broader variety of application logic. In particular, PTF supports applications that concurrently process finite batches of data on a single instantiation. PTF adds these semantics by partitioning the dataflow graph into a pipeline of smaller graphs and tagging each data item with metadata. These smaller graphs are separated by gates: new data structures in PTF that buffer data items between graphs and interpret the metadata to apply the new semantics. PTF's pipeline architecture executes on an unmodified TensorFlow runtime, maintaining compatibility with many existing TensorFlow library functions. Our evaluation shows that the pipelining mechanism of PTF can increase the throughput of a bioinformatics application by 4$\times$ while only increasing its latency by 0.13$\times$. This results in a sustained genome alignment and sorting rate of 321 megabases/second, using the compute and I/O resources of 20 computers.
ExpertMatcher: Automating ML Model Selection for Clients using Hidden\n Representations (2019)

Recently, there has been the development of Split Learning, a framework for\ndistributed computation where model components are split between the client and\nserver (Vepakomma et al., 2018b). As Split Learning scales to include many\ndifferent model components, there needs to be a method of matching client-side\nmodel components with the best server-side model components. A solution to this\nproblem was introduced in the ExpertMatcher (Sharma et al., 2019) framework,\nwhich uses autoencoders to match raw data to models. In this work, we propose\nan extension of ExpertMatcher, where matching can be performed without the need\nto share the client's raw data representation. The technique is applicable to\nsituations where there are local clients and centralized expert ML models, but\nthe sharing of raw data is constrained.\n
Monolithic integration of InP based heterostructures on silicon using crystalline Gd2O3 buffers (2007)

An original approach of monolithic integration of InP based heterostructures on silicon is proposed based on the peculiar properties of the heterointerface between InP and crystalline Gd2O3. When grown on a crystalline Gd2O3∕Si(111) buffer, InP takes its bulk lattice parameter as soon as the growth begins, and the lattice mismatch (7.9%) is fully accommodated by the formation of a misfit dislocation network at the InP∕Gd2O3 heterointerface. This plastic compliant effect allows the monolithic growth of good quality InAsP∕InP heterostructures on Si, as attested by room-temperature photoluminescence experiments.
The Optimum Layout for Giant Magneto-Resistive Angle Sensors (2010)

360° angle sensors use small permanent magnets attached to the shaft. The magnet is polarized perpendicularly to the axis of rotation, and a magnetic field sensor is placed underneath on the axis. The sensor circuit consists of two orthogonal bridges having four giant magneto-resistive (GMR) elements. In a prior work it was shown that even though the magnetic field sensor may be calibrated perfectly, still significant angle errors may result from assembly tolerances of magnet and sensor. This work investigates how shape and size of the GMRs affect this error. Optimum layouts for GMRs fulfill three constraints: (i) the centers of gravity of all GMRs of both bridges lie on the axis of rotation; (ii) the sum over the deviation moments of all GMRs of each bridge circuit vanishes; and (iii) the sums over the moments of inertia around two perpendicular axes in the die surface are equal. Examples of optimized layouts are given. Layout and assembly tolerances interact to give an overall angle error, which is expanded into a second-order Taylor series. Monte Carlo simulations show that optimum layout reduces typical angle errors significantly and worst case angle errors moderately. For Gaussian distributed assembly tolerances with standard deviations of 0.1 mm and 1° and cylindrical magnets with 5 mm diameter 99.7% of all systems have errors less than +/-0.41°. Magnets with 10 mm diameter have only +/-0.16° error. It is shown that magnetic angle sensors are more robust against eccentricities of the shaft than many optical encoder systems.
Diagnostics of surface layer disordering using optical third harmonic generation of a circular polarized light (2001)

A nonlinear optical technique based on a third-harmonic generation of a circular polarized light is proposed for the diagnostics of order–disorder transformations in semiconductors. Applications of this technique to study both noncentrosymmetric (GaAs) and centrosymmetric (Si) materials are demonstrated.
Ptychographic Nano-Analytical Microscope (PtyNAMi) at PETRA III: signal-to-background optimization for imaging with high sensitivity (2019)

The X-ray scanning microscope PtyNAMi at beamline P06 of PETRA III at DESY in Hamburg, Germany, is designed for high-spatial-resolution 3D imaging with high sensitivity. Besides optimizing the coherent ux density on the sample and the precision mechanics of the scanner, special care has been taken to reduce background signals on the detector. The optical path behind the sample is evacuated up until the sensor of a four-megapixel detector that is placed into the vacuum. In this way, parasitic scattering from air and windows close to the detector is avoided. The instrument has been commissioned and is in user operation. The main commissioning results of the low-background detector system are presented. A signal-to-noise model for small object details is derived that includes incoherent background scattering.
Trends in U.S. Health Care Spending on Inflammatory Bowel Diseases, 1996-2016 (2021)

Even after adjusting for rising prevalence, U.S. health care spending on IBD continues to progressively increase, primarily in middle-aged and older adults, with unplanned health care utilization accounting for the majority of costs.
Shape Transition of Germanium Nanocrystals on a Silicon (001) Surface from Pyramids to Domes (1998)

Chemical vapor deposition of germanium onto the silicon (001) surface at atmospheric pressure and 600 degrees Celsius has previously been shown to produce distinct families of smaller (up to 6 nanometers high) and larger (all approximately 15 nanometers high) nanocrystals. Under ultrahigh-vacuum conditions, physical vapor deposition at approximately the same substrate temperature and growth rate produced a similar bimodal size distribution. In situ scanning tunneling microscopy revealed that the smaller square-based pyramids transform abruptly during growth to significantly larger multifaceted domes, and that few structures with intermediate size and shape remain. Both nanocrystal shapes have size-dependent energy minima that result from the interplay between strain relaxation at the facets and stress concentration at the edges. A thermodynamic model similar to a phase transition accounts for this abrupt morphology change.
Monoclonal Antibodies Mimic Insulin Activation of Ribosomal Protein S6 Kinase without Activation of Insulin Receptor Tyrosine Kinase (1989)

The effects of species-specific monoclonal antibodies to the human insulin receptor on ribosomal protein S6 phosphorylation were studied in rodent cell lines transfected with human insulin receptors. First, Swiss mouse 3T3 fibroblasts expressing normal human insulin receptors (3T3/HIR cells) were studied. Three monoclonal antibodies, MA-5, MA-20, and MA-51, activated S6 kinase in these cells but had no effects in untransfected 3T3 cells. Both insulin and MA-5, the most potent antibody, activated S6 kinase in a similar time- and dose-dependent manner. To measure S6 phosphorylation in vivo, 3T3/HIR cells were preincubated with [32P]Pi and treated with insulin and MA-5. Both agents increased S6 phosphorylation, and their tryptic phosphopeptide maps were similar. MA-5 and the other monoclonal antibodies, unlike insulin, failed to stimulate insulin receptor tyrosine kinase activity either in vitro or in vivo. Moreover, unlike insulin, they failed to increase the tyrosine phosphorylation of the endogenous cytoplasmic protein, pp 185. Next, HTC rat hepatoma cells, expressing a human insulin receptor mutant that had three key tyrosine autophosphorylation sites in the beta-subunit changed to phenylalanines (HTC-IR-F3 cells), were studied. In this cell line but not in untransfected HTC cells, monoclonal antibodies activated S6 kinase without stimulating either insulin receptor autophosphorylation or the tyrosine phosphorylation of pp 185. These data indicate, therefore, that monoclonal antibodies can activate S6 kinase and then increase S6 phosphorylation. Moreover, they suggest that activation of receptor tyrosine kinase and subsequent tyrosine phosphorylation of cellular proteins may not be crucial for activation of S6 kinase by the insulin receptor.
Method to Estimate Thermal Shrinkage Behavior of Glasses (2011)

We present a method to estimate the effect of heat treatment on the shrinkage behavior of glasses. As a pre‐requisite, sensitivity of the glass density as a function of glass fictive temperature is measured using the sink–float method and the slope of the relationship is used to determine the linear thermal strain proportionality factor. Evolution of the fictive temperature for different temperature–time history is measured using the infrared spectroscopy method and the results are used to estimate the structural relaxation parameters in the temperature range of interest. The overall shrinkage behavior is predicted using the linear thermal strain factor and estimated change in fictive temperature due to the thermal treatment. The predicted shrinkage behavior is observed to be in good agreement with the independent dimensional change measurements performed on large glass sheets that have undergone similar thermal treatments.
Prostaglandin E2 exacerbates collagen‐induced arthritis in mice through the inflammatory interleukin‐23/interleukin‐17 axis (2007)

Our results indicate that PGE(2) enhances DC-derived IL-6 production and induces a shift in the IL-23/IL-12 balance in favor of IL-23, resulting in increased IL-17 production, presumably through the amplification of self-reactive Th17 cells.
The Vibrio cholerae Quorum-Sensing Protein VqmA Integrates Cell Density, Environmental, and Host-Derived Cues into the Control of Virulence (2020)

Quorum sensing is a chemical communication process in which bacteria use the production, release, and detection of signal molecules called autoinducers to orchestrate collective behaviors. The human pathogen <i>Vibrio cholerae</i> requires quorum sensing to infect the small intestine. There, <i>V. cholerae</i> encounters the absence of oxygen and the presence of bile salts. We show that these two stimuli differentially affect quorum-sensing function and, in turn, <i>V. cholerae</i> pathogenicity. First, during anaerobic growth, <i>V. cholerae</i> does not produce the CAI-1 autoinducer, while it continues to produce the DPO autoinducer, suggesting that CAI-1 may encode information specific to the aerobic lifestyle of <i>V. cholerae</i> Second, the quorum-sensing receptor-transcription factor called VqmA, which detects the DPO autoinducer, also detects the lack of oxygen and the presence of bile salts. Detection occurs via oxygen-, bile salt-, and redox-responsive disulfide bonds that alter VqmA DNA binding activity. We propose that VqmA serves as an information processing hub that integrates quorum-sensing information, redox status, the presence or absence of oxygen, and host cues. In response to the information acquired through this mechanism, <i>V. cholerae</i> appropriately modulates its virulence output.<b>IMPORTANCE</b> Quorum sensing (QS) is a process of chemical communication that bacteria use to orchestrate collective behaviors. QS communication relies on chemical signal molecules called autoinducers. QS regulates virulence in <i>Vibrio cholerae</i>, the causative agent of the disease cholera. Transit into the human small intestine, the site of cholera infection, exposes <i>V. cholerae</i> to the host environment. In this study, we show that the combination of two stimuli encountered in the small intestine, the absence of oxygen and the presence of host-produced bile salts, impinge on <i>V. cholerae</i> QS function and, in turn, pathogenicity. We suggest that possessing a QS system that is responsive to multiple environmental, host, and cell density cues enables <i>V. cholerae</i> to fine-tune its virulence capacity in the human intestine.
Primary Frequency Regulation with Li-Ion Battery Energy Storage System - Evaluation and Comparison of Different Control Strategies (2013)

The increased grid penetration levels of renewable sources are at the expense of the conventional power plants. This means that the grid support functions, traditionally achieved by the conventional power plants, need to be provided by new technologies. Since grid support with energy storage devices is becoming more attractive, the aim of this paper is to analyse the viability of providing primary frequency regulation with Lithium-ion based energy storage systems. Three control strategies of the energy storage system are analysed and compared in terms of economic benefits on the Danish energy market. The revenues and degradation of the Lithium-ion batteries are obtained by simulations. Furthermore, an energy management strategy based on variable state-of-charge (SOC) set-point is evaluated. Preliminary, the influence of different state-of-charge levels on the cycle lifetime is estimated. Finally, the economic analysis is based on simulations performed with different bidding ratios, i.e. different SOC set-points, for primary upward and primary downward frequency regulation. The results showed that all three control strategies were associated with considerable economic benefits even though different degradation levels of the Lithium-ion batteries were observed. Furthermore,<br/>it was found that the economic benefits are declining by increasing the batteries' SOC set-point.
Characterization of Bio‐oil from Hydrothermal Liquefaction of Organic Waste by NMR Spectroscopy and FTICR Mass Spectrometry (2012)

Solid wastes of organic origins are potential feedstocks for the production of liquid biofuels, which could be suitable alternatives to fossil fuels for the transport and heating sectors, as well as for industrial use. By hydrothermal liquefaction, the wet biomass is partially transformed into a water-immiscible, oil-like organic matter called bio-oil. In this study, an integrated NMR spectroscopy/mass spectrometry approach has been developed for the characterization of the hydrothermal liquefaction of bio-oil at the molecular level. (1)H and (13)C NMR spectroscopy were used for the identification of functional groups and gauging the aromatic carbon content in the mixture. GC-MS analysis revealed that the volatile fraction was rich in fatty acids, as well as in amides and esters. High-resolution Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) has been applied in a systematic way to fully categorize the bio-oil in terms of different classes of components, according to their molecular formulas. Most importantly, for the first time, by using this technique, and for the liquefaction bio-oil characterization in particular, FT-MS data have been used to develop a methodology for the determination of the aromatic versus aliphatic carbon and nitrogen content. It is well known that, because they resist hydrogenation and represent sources of polluting species, both aromatic molecules and nitrogen-containing species raise concerns for subsequent upgrading of bio-oil into a diesel-like fuel.
Cholecystokinin Antagonists: Fluorinated 5-Hydroxy-5-Aryl-Pyrrol-2-Ones as Experimental Agents for Brain, Colon and Pancreatic Cancer (2018)

Asperlicin was the first non-peptidal lead structure from nature 12 and analogues thereof were studied as CCK ligands. Simplification of this lead structure by Merck led to Devazepide, 14 a potent CCK 1 selective cholecystokinin antagonist (Figure Proglumide 15 was the first glutamic acid based agent, marketed as Milid for the treatment of ulcer. Lorglumide, a derivative of proglumide, 16 (Figure The indolyl amide of devazepide was replaced by a urea linkage and Merck's L-365,260 resulted in a CCK 2 selective antagonist. 18 Z-360 is a CCK 2 -gastrin receptor antagonist and progressed into phase 2 trial with pancreatic cancer. 19 Z-360 is the most recent derivative derived from this original lead structure, with improved selectivity and bioavailability.
OPTICAL WAVEGUIDES IN ANALYTICAL CHEMISTRY: RECENT ADVANCES (1992)

Achievements in guided wave optics have had a great influence on many areas of technology for several years. Fibre optic communication links, sensors for various parameters, recently developed distributed temperature sensors, integrated optical switches, etc. are all applications that are commercially available. The field of analytical chemistry is no exception in this growing technology. In order to compete with well‐established chemical‐sensing instrumentation, optical waveguide chemical sensors (OWCSs) must show all the qualities of such instrumentation. OWCSs combine well‐known features of sensors, based on waveguide optics, with optical methods of chemical analysis and offer advantages over other types of chemical sensor. OWCSs are electrically passive, corrosion‐resistant, can respond to analytes for which other chemical sensors are not available, and referencing can be carried out optically. They allow multicomponent measurements at several wavelengths, have a common technology for fabrication of sensors for different chemical and physical parameters and are easily compatible with telemetry etc. Further, only OWCSs are capable of distributed sensing. However, interference from ambient light, temperature, long‐term instability, relatively slow response time, and limited dynamic range may be a problem for some types of OWCS. These disadvantages can be considerably reduced using various methods.
Compact Certificates of Collective Knowledge. (2020)

We introduce compact certificate schemes, which allow any party to take a large number of signatures on a message M, by many signers of different weights, and compress them to a much shorter certificate. This certificate convinces the verifiers that signers with sufficient total weight signed M, even though the verifier will not see—let alone verify—all of the signatures. Thus, for example, a compact certificate can be used to prove that parties who jointly have a sufficient total account balance have attested to a given block in a blockchain.After defining compact certificates, we demonstrate an effi-cient compact certificate scheme. We then show how to implement such a scheme in a decentralized setting over an unreliable network and in the presence of adversarial parties who wish to disrupt certificate creation. Our evaluation shows that compact certificates are 50–280× smaller and 300–4000 cheaper to verify than a natural baseline approach.
Hybrid Look-Up-Tables Based Behavioral Model for Dynamic Nonlinear Power Amplifiers (2020)

A new look-up table based behavioral model for dynamic nonlinear power amplifiers is proposed. This model labelled as hybrid look-up tables model is based on the combination of a memoryless look-up table sub-model and a nested look-up tables one. It is demonstrated that the proposed model circumvents the computational complexity associated with the parameters identification in analytically defined behavioral models. Moreover, the proposed model reduces the size of the standalone nested look-up tables model by approximately 80% while maintaining its accuracy. Furthermore, a novel slew-rate based trimming and indexing technique to reduce the nested look-up tables model size is developed and corroborated experimentally. Additionally, the two-box structure of the hybrid look-up tables model makes it suitable for bandwidth scalability. Experimental validation using LTE-advanced test signals with up to 120MHz bandwidth demonstrates the ability of the proposed hybrid look-up tables model to be bandwidth scalable with less than 0.5dB degradation in the normalized mean-squared error.
Efficient Pipelined Execution of CNNs Based on In-Memory Computing and Graph Homomorphism Verification (2021)

In-memory computing is an emerging computing paradigm enabling deep-learning inference at significantly higher energy-efficiency and reduced latency. The essential idea is mapping the synaptic weights of each layer to one or more in-memory computing (IMC) cores. During inference, these cores perform the associated matrix-vector multiplications in place with O(1) time complexity, obviating the need to move the synaptic weights to additional processing units. Moreover, this architecture enables the execution of these networks in a highly pipelined fashion. However, a key challenge is designing an efficient communication fabric for the IMC cores. In this work, we present one such communication fabric based on a graph topology that is well-suited for the widely successful convolutional neural networks (CNNs). We show that this communication fabric facilitates the pipelined execution of all state-of-the-art CNNs by proving the existence of a homomorphism between the graph representations of these networks and that corresponding to the proposed communication fabric. We then present a quantitative comparison with established communication topologies and show that our proposed topology achieves the lowest bandwidth requirements per communication channel. Finally, we present one hardware implementation and show a concrete example of mapping ResNet-32 onto an IMC core array interconnected via the proposed communication fabric.
Electrochemical Formation of a Polyviologen–ZnO Composite with an Efficient Charging Capability (2015)

Abstract Concurrent and cathodic electrolysis of tris(cyanopyridinio)mesitylene and zinc chloride gave redox-active polyviologen, densely formed around the zinc oxide nanorod, on a substrate, which displayed a large charging–discharging capacity and an excellent rate performance based on a large diffusivity of charge, and was useful as an anode-active material.