gebhart/patrepeval huggingface dataset:
Multiple View Stereo by Reflectance Modeling (2012)

Multiple view stereo is typically formulated as an optimization problem over a data term and a prior term. The data term is based on the consistency of images projected on a hypothesized surface. This consistency is based on a measure denoted a visual metric, e.g. normalized cross correlation. Here we argue that a visual metric based on a surface reflectance model should be founded on more observations than the degrees of freedom (dof) of the reflectance model. If (partly) specular surfaces are to be handled, this implies a model with at least two dof. In this paper, we propose to construct visual metrics of more than one dof using the DAISY methodology, which compares favorably to the state of the art in the experiments carried out. These experiments are based on a novel data set of eight scenes with diffuse and specular surfaces and accompanying ground truth. The performance of six different visual metrics based on the DAISY framework is investigated experimentally, addressing whether a visual metric should be aggregated from a set of minimal images, which dof is best, or whether a combination of one and two dof should be used. Which metric performs best is dependent on the viewed scene, although there are clear tendencies for the two dof minimal metric to be the preferred one.
Inference from Stationary Time Sequences via Learned Factor Graphs. (2020)

The design of methods for inference from time sequences has traditionally relied on statistical models that describe the relation between a latent desired sequence and the observed one. A broad family of model-based algorithms have been derived to carry out inference at controllable complexity using recursive computations over the factor graph representing the underlying distribution. An alternative model-agnostic approach utilizes machine learning (ML) methods. Here we propose a framework that combines model-based inference algorithms and data-driven ML tools for stationary time sequences. In the proposed approach, neural networks are developed to separately learn specific components of a factor graph describing the distribution of the time sequence, rather than the complete inference task. By exploiting stationary properties of this distribution, the resulting approach can be applied to sequences of varying temporal duration. Additionally, this approach facilitates the use of compact neural networks which can be trained with small training sets, or alternatively, can be used to improve upon existing deep inference systems. We present an inference algorithm based on learned stationary factor graphs, referred to as StaSPNet, which learns to implement the sum product scheme from labeled data, and can be applied to sequences of different lengths. Our experimental results demonstrate the ability of the proposed StaSPNet to learn to carry out accurate inference from small training sets for sleep stage detection using the Sleep-EDF dataset, as well as for symbol detection in digital communications with unknown channels.
Factoring Hard Integers on a Parallel Machine (1997)

this paper we describe the results obtained from a refinement to MPQS known as the Hypercube variation (HMPQS). HMPQS was developed independently by the first author [7] and by Pomerance and Alford [1]. We implemented HMPQS on a Parsytec GC with 128 processors (at 80 Mhz) at the Japan Advanced Institute of Science and Technology (JAIST). We used our pro- Manuscript received , 1996.
Protein Expression Profile of Celiac Disease Patient with Aberrant T Cell by Two‐dimensional Difference Gel Electrophoresis (2007)

One complication of celiac disease (CD) is refractory CD. These patients frequently show aberrant intraepithelial T cell clones and an increasing risk of evolution into enteropathy-associated T cell lymphoma (EATL). There is debate in the literature whether these cases are actually a smoldering lymphoma from the outset. The mechanism inducing T cell proliferation and prognosis remains unknown. Recently, alemtuzumab has been proposed as a promising new approach to treat these patients. Only few single cases have been tested presently, nevertheless, in all of them a clinical improvement has been observed, while intraepithelial lymphocytes (IELs) effectively targeted by alemtuzumab are still a debated issue. Using 2D-DIGE, we found hyperexpressed proteins specifically associated with aberrant T cell in a patient with CD by comparing the protein expression with that of patients with CD and polyclonal T cell or with that of control subjects (patients with polyclonal T cell and no CD). Proteins with a higher expression in duodenal biopsy of the patient with aberrant T cell were identified as IgM, apolipoprotein C-III, and Charcot-Leyden crystal proteins. These preliminary data allow hypothesizing different clinical effects of alemtuzumab in patients with CD, since besides the probable effect of alemtuzumab on T cell, it could effect inflammatory-associated CD52(+) IgM(+)B cell and eosinophils cells, known to produce IgM and Charcot-Leyden crystal proteins, which we demonstrated to be altered in this patient. Results also emphasize the possible association of apolipoprotein with aberrant T cell proliferation.
Parametric-Rate Inference for One-Sided Differentiable Parameters (2016)

Suppose one has a collection of parameters indexed by a (possibly infinite dimensional) set. Given data generated from some distribution, the objective is to estimate the maximal parameter in this collection evaluated at this distribution. This estimation problem is typically non-regular when the maximizing parameter is non-unique, and as a result standard asymptotic techniques generally fail in this case. We present a technique for developing parametric-rate confidence intervals for the quantity of interest in these non-regular settings. We show that our estimator is asymptotically efficient when the maximizing parameter is unique so that regular estimation is possible. We apply our technique to a recent example from the literature in which one wishes to report the maximal absolute correlation between a prespecified outcome and one of p predictors. The simplicity of our technique enables an analysis of the previously open case where p grows with sample size. Specifically, we only require that log(p) grows slower than the square root of n, where n is the sample size. We show that, unlike earlier approaches, our method scales to massive data sets: the point estimate and confidence intervals can be constructed in O(np) time.
Studies on the Mechanism of 1,2-Dihydropyrazin-2-one Ring Formation from Dipeptidyl Chloromethyl Ketone and Its Chemical Properties: Immediate Deamination during Catalytic Hydrogenation (2005)

1,2-Dihydropyrazin-2-one derivatives, which have two aminoalkyl groups at the positions 3 and 6, were found to be efficient tools for the construction of potent, selective and long-acting opioid mimetics. During the course of preparation, we found that the catalytic hydrogenation of 3,6-bis(benzyloxycarbonylaminomethyl)-5-methyl-1,2-dihydropyrazin-2-one to remove the benzyloxycarbonyl groups resulted in a side reaction. By MS and NMR studies and by preparation of additional 1,2-dihydropyrazin-2-one derivatives, the structure of the by-product was identified as 3-aminomethyl-5,6-dimethyl-1,2-dihydropyrazin-2-one. Preparation of additional compounds substituted with deuterium provided us with sufficient information to confirm the structure of the product and to support a cyclization mechanism in its formation.
Edge-Gated CNNs for Volumetric Semantic Segmentation of Medical Images (2020)

Abstract Textures and edges contribute different information to image recognition. Edges and boundaries encode shape information, while textures manifest the appearance of regions. Despite the success of Convolutional Neural Networks (CNNs) in computer vision and medical image analysis applications, predominantly only texture abstractions are learned, which often leads to imprecise boundary delineations. In medical imaging, expert manual segmentation often relies on organ boundaries; for example, to manually segment a liver, a medical practitioner usually identifies edges first and subsequently fills in the segmentation mask. Motivated by these observations, we propose a plug-and-play module, dubbed Edge-Gated CNNs (EG-CNNs), that can be used with existing encoder-decoder architectures to process both edge and texture information. The EG-CNN learns to emphasize the edges in the encoder, to predict crisp boundaries by an auxiliary edge supervision, and to fuse its output with the original CNN output. We evaluate the effectiveness of the EG-CNN with various mainstream CNNs on two publicly available datasets, BraTS 19 and KiTS 19 for brain tumor and kidney semantic segmentation. We demonstrate how the addition of EG-CNN consistently improves segmentation accuracy and generalization performance.
Intron retention facilitates splice variant diversity in calcium-activated big potassium channel populations (2010)

We report that the stress axis-regulated exon (STREX)-containing calcium-activated big potassium (BKCa) channel splice variant expression and physiology are regulated in part by cytoplasmic splicing and intron retention. NextGen sequencing of the mRNA complement of pooled hippocampal dendrite samples found intron 17a (i17a), the intron immediately preceding STREX, in the BKCa mRNA. Further molecular analyses of i17a revealed that the majority of i17a-containing BKCa channel mRNAs associate with STREX. i17a siRNA treatment followed by STREX protein immunocytochemistry demonstrated both reduced levels and altered subcellular distribution of STREX-containing BKCa channel protein. Selective reduction of i17a-BKCa or STREX-BKCa mRNAs induced similar changes in the burst firing properties of hippocampal neurons. Collectively, these data show that STREX splice variant regulation via cytoplasmic splicing and intron retention helps generate STREX-dependent BKCa current diversity in hippocampal neurons.
Calibration-free absolute quantification of particle concentration by statistical analyses of photoacoustic signalsin vivo (2014)

Currently, laser fluence calibration is typically required for quantitative measurement of particle concentration in photoacoustic imaging. Here, we present a calibration-free method to quantify the absolute particle concentration by statistically analyzing photoacoustic signals. The proposed method is based on the fact that Brownian motion induces particle count fluctuation in the detection volume. If the count of particles in the detection volume is assumed to follow the Poisson distribution, its expected value can be calculated by the photoacoustic signal mean and variance. We first derived a theoretical model for photoacoustic signals. Then, we applied our method to quantitative measurement of different concentrations of various particles, including red blood cells. Finally, we performed in vivo experiments to demonstrate the potential of our method in biological applications. The experimental results agreed well with the predictions from the theoretical model suggesting that our method can be used for noninvasive measurement of absolute particle concentrations in deep tissue without fluence calibration.
Determination and Comparison of the Baseline Proteomes of the Versatile MicrobeRhodopseudomonaspalustrisunder Its Major Metabolic States (2006)

Rhodopseudomonas palustris is a purple nonsulfur anoxygenic phototrophic bacterium that is ubiquitous in soil and water. R. palustris is metabolically versatile with respect to energy generation and carbon and nitrogen metabolism. We have characterized and compared the baseline proteome of a R. palustris wild-type strain grown under six metabolic conditions. The methodology for proteome analysis involved protein fractionation by centrifugation, subsequent digestion with trypsin, and analysis of peptides by liquid chromatography coupled with tandem mass spectrometry. Using these methods, we identified 1664 proteins out of 4836 predicted proteins with conservative filtering constraints. A total of 107 novel hypothetical proteins and 218 conserved hypothetical proteins were detected. Qualitative analyses revealed over 311 proteins exhibiting marked differences between conditions, many of these being hypothetical or conserved hypothetical proteins showing strong correlations with different metabolic modes. For example, five proteins encoded by genes from a novel operon appeared only after anaerobic growth with no evidence of these proteins in extracts of aerobically grown cells. Proteins known to be associated with specialized growth states such as nitrogen fixation, photoautotrophic, or growth on benzoate, were observed to be up-regulated under those states.
Coding for x-ray scatter imaging (2013)

We present coding strategies for x-ray scatter imaging, with focus on pencil and fan beam geometries. Coded apertures spatially modulate the scatter signal prior to measurement, and appropriate reconstruction algorithms recover the tomographic images.
24th National Medicinal Chemistry Symposium (1994)

RETURN TO ISSUEPREVMeeting NewsNEXT24th National Medicinal Chemistry SymposiumCite this: Chem. Eng. News 1994, 72, 14, 54Publication Date (Print):April 4, 1994Publication History Published online11 November 2010Published inissue 4 April 1994https://doi.org/10.1021/cen-v072n014.p054Copyright © 1994 American Chemical SocietyArticle Views9Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (91 KB) SUBJECTS:Medicinal chemistry Get e-Alerts
Locked Intramedullary Total Wrist Arthrodesis (2012)

Total wrist arthrodesis is commonly performed using fixation plates, which can produce soft tissue irritation, often require removal, and limit the ability to position the hand in space. The Skeletal Dynamics IMPLATE is an intramedullary total wrist fusion device designed to provide stable fixation while avoiding the problems associated with plates. Radial and metacarpal locked intramedullary nails are inserted and joined by a connector. Desired hand placement is achieved by selecting the proper connector length and angle, then orienting it appropriately. Fusion mass compression is obtained by virtue of longitudinal threads on the radial nail that allow for length adjustment. Seven wrists in three men and four women were treated with this device and followed for a minimum of 24 weeks. In all cases, local cancellous bone graft was used and the third carpometacarpal (CMC) joint incorporated into the fusion. The median age was 49 (range, 28-71) years. Indications for fusion were two posttraumatic arthritides, three rheumatoid arthritides, one spastic deformity, and one infection. Patients were evaluated before surgery and at final follow-up using the Fernandez pain score and grip strength measurements using a hand-held dynamometer. All patients improved their grip strength and decreased their pain scores. All fusions united, and none of the patients presented dorsal soft tissue problems or required implant removal. One rheumatoid patient required secondary surgery for removal of a retained palmar osteophyte. This device delivers stable fixation, facilitates hand placement, and does not require removal.
Supplementary Table S1 from Bispecific Antibodies and Antibody–Drug Conjugates (ADCs) Bridging HER2 and Prolactin Receptor Improve Efficacy of HER2 ADCs (2023)

&lt;p&gt;Surface Plasmon Resonance (SRP)-derived binding affinities and kinetic constants of HER2(T)xPRLR and its parental antibodies: HER2 antibody and H1H7672P2.&lt;/p&gt;
UWB antenna on chip packaging for BAN applications (2014)

A new ultrawideband (UWB) antenna for body area network (BAN) applications is proposed in this paper. For compactness reasons, the antenna is studied in order to be integrated on a chip packaging. In addition, a special attention has been given to design an antenna with main polarization normal to the human head. This antenna provides broadband characteristics and omnidirectional radiation patterns over the whole 7-9 GHz frequency band.
Fluorimetric determination of phytic acid based on the activation of the oxidation of 2,2′-dipyridyl ketone hydrazone catalysed by Cu(II) (1999)

Phytic acid exerts an activation effect on the oxidation of 2,2'-dipyridyl ketone hydrazone catalysed by Cu(II) ion and the oxidation product is highly fluorescent. A fixed time method for the fluorimetric determination of phytic acid based on this effect is described. The calibration graph is linear over the range 0.05-0.6 mg l-1 phytic acid, resulting in a limit of detection of 0.03 mg l-1 phytic acid. The relative standard deviation is in the range 1.4-1.8%, depending on the sample analysed. The method was successfully applied to the determination of phytic acid in human urine (20 samples) and food samples (nine different products). The results obtained for urine samples ranged from 0.31 to 3.6 mg l-1 phytic acid and for food samples from 3.8 to 22 mg g-1 phytic acid. This is the first procedure to be reported for the determination of phytic acid based on fluorimetric measurements.
Energy-Conservation Clustering Protocol Based on Heat Conductivity for Wireless Sensor Networks (2005)

One of the most critical issues in wireless sensor networks is energy efficiency because of the limited energy network nodes can store in batteries. Therefore, these networks require robust wireless communication protocols that are designed to be energy-efficient. In this paper, we introduce a routing protocol based on an energy-temperature transformation principle, called RETT. Our objectives are to maximize the lifespan of the entire network rather than maximizing the life span for individual nodes. We do this to avoid sections of the network becoming unreachable when critical nodes along a routing path run out of power. In RETT, which is a cluster based protocol, the head of cluster is able to select the optimal route for sending or relaying the data from the source towards the base station. RETT is based on a thermo dynamic analogy where the expected life spans are transformed into temperatures and the routing algorithm is searching for the hottest path between the source and the destination.
Turbulence Characteristics in a Rushton Stirring Vessel Measured via Time Resolved DPIV (2006)

Stirred vessels are devices that find extensive industrial applications particularly in mineral and chemical industries. Interactions of solid particles and/or bubbles and particles depend on the characteristics of turbulent flow. In many analytical models, the rate of collision is a function of turbulence dissipation. It has been known that dissipation levels are much higher in the neighborhood of the agitating mechanism, in our case the Rushton impeller. In this paper we use time-resolved DPIV to measure the velocity field with a spatial resolution down to 100 μm, and a frequency resolution of 500 Hz. The range of Reynolds numbers investigated varied from 20,000 to 50,000, with the smallest Kolmogorov length scale of about 15 μm. The flow in the impeller stream of a Rushton impeller can be best summarized as a radial jet with a pair of convecting tip vortices. The turbulence quantities were found by removing the periodic component from the blade passing, which is the dominant part of the measured velocities. Dissipation was calculated from the velocity gradients, and assuming isotropy. We provide further evidence that larger dissipation values in the vicinity of the impeller are consistent with the dynamic motion generated by the blade passage. This is somewhat anti-intuitive, because energy is dissipated at the smallest eddy scales, and the immediate vicinity of the impeller contains large vortical structures and provides little space or time for such structures to break down. The maximum and mean normalized dissipation in the impeller stream showed decreasing trends with the Reynolds number. Other normalized turbulence quantities, namely Vrms and in plane vorticity are presented. Our experiments agree very well with other experimental studies. Estimates of turbulence characteristics and in particular distributions of turbulent energy dissipation determined in this study will be used in estimating rates of collisions of bubbles and particles in stirred vessels.
Combinatorial screening of thin film materials: An overview (2015)

Over the past several decades, technological advancement has grown increasingly dependent on new and advanced materials. Accelerating the pace of new material discovery is thus critical to tackling global challenges in areas of energy, health, and security, for example. There is a pressing need to develop and utilize high throughput screening technologies for the development of new materials, as material discovery has fallen behind the product design cycles in many sectors of industry. This article describes techniques of high throughput combinatorial thin film material growth and characterization developed over the past several years. Although being adopted in selected industries, combinatorial screening technologies for thin film materials are still in their infancy. Caution must be exercised in selecting relevant combinatorial libraries and extrapolating from small-scale deposition techniques to industrially relevant processes. There are tremendous opportunities in the field of combinatorial discovery of thin film materials, as it enters its golden age along with the Materials Genome Initiative, which aims to change the pathway of materials discovery.
Nickel-titanium-phosphate cathodes (2023)

Cathode materials having an improved electronic conductivity allowing for faster kinetics in the electrochemical reaction, as well as higher conductivity to meet the power requirements for many consumer applications, especially at low temperatures. The cathode material comprises a compound from the family of compounds where the basic unit is generally represented by Li.sub.xNi.sub.0.5TiOPO.sub.4. The structure of Li.sub.xNi.sub.0.5TiOPO.sub.4 includes corner sharing octahedra [TiO.sub.6] running along the C-axis. The structure is such that nearly three Li atoms are being inserted in Li.sub.xNi.sub.0.5TiOPO.sub.4. A cell in accordance with the principles of the present invention is rechargable and demonstrates a high capacity of lithium intercalation and fast kinetics.