gebhart/patrepeval huggingface dataset:
Crystal Structure of Type II Peptide Deformylase from Staphylococcus aureus (2002)

The first crystal structure of Class II peptide deformylase has been determined. The enzyme from Staphylococcus aureus has been overexpressed and purified in Escherichia coli and the structure determined by x-ray crystallography to 1.9 A resolution. The purified iron-enriched form of S. aureus peptide deformylase enzyme retained high activity over many months. In contrast, the iron-enriched form of the E. coli enzyme is very labile. Comparison of the two structures details many differences; however, there is no structural explanation for the dramatic activity differences we observed. The protein structure of the S. aureus enzyme reveals a fold similar, but not identical to, the well characterized E. coli enzyme. The most striking deviation of the S. aureus from the E. coli structure is the unique conformation of the C-terminal amino acids. The distinctive C-terminal helix of the latter is replaced by a strand in S. aureus which wraps around the enzyme, terminating near the active site. Although there are no differences at the amino acid level near the active site metal ion, significant changes are noted in the peptide binding cleft which may play a role in the design of general peptide deformylase inhibitors.
Novel Isoflavone, Cinnamic Acid, and Triterpenoid Glycosides in Soybean Molasses (1999)

Seven known isoflavones, genistein (4), daidzein (5), glycitein (6), formononetin (7), genistin (8), daidzin (9), and glycitein 7-O-beta-D-(6' '-O-acetylglucopyranoside) (10), ferulic acid, and two known saponin glycosides, soysaponin I (14) and soysaponin A2 (15), were isolated from soybean molasses. Several new compounds were also isolated and identified, including three isoflavones (1-3), two cinnamic acid ester glycosides (11) and (12), and a new saponin hexaglycoside (13). The structures of the new compounds were established on the basis of spectral data interpretation.
Compressive beamforming with co-prime arrays (2014)

The results of compressive beamforming using arrays formed by Nyquist, co-prime samplers, Wichmann rulers, and Golomb rulers are shown along with forms of array gain, resolution and latency as measures of performance. Results will be shown for the idea case of few sources with Gaussian amplitudes in spatially white Gaussian white noise. Results will also be shown for data taken on the Five Octave Research Array (FORA). [This work was supported by ONR.]
Adversarial Speaker Adaptation (2019)

We propose a novel adversarial speaker adaptation (ASA) scheme, in which adversarial learning is applied to regularize the distribution of deep hidden features in a speaker-dependent (SD) deep neural network (DNN) acoustic model to be close to that of a fixed speaker-independent (SI) DNN acoustic model during adaptation. An additional discriminator network is introduced to distinguish the deep features generated by the SD model from those produced by the SI model. In ASA, with a fixed SI model as the reference, an SD model is jointly optimized with the discriminator network to minimize the senone classification loss, and simultaneously to mini-maximize the SI/SD discrimination loss on the adaptation data. With ASA, a senone-discriminative deep feature is learned in the SD model with a similar distribution to that of the SI model. With such a regularized and adapted deep feature, the SD model can perform improved automatic speech recognition on the target speaker's speech. Evaluated on the Microsoft short message dictation dataset, ASA achieves 14.4% and 7.9% relative word error rate improvements for supervised and unsupervised adaptation, respectively, over an SI model trained from 2600 hours data, with 200 adaptation utterances per speaker.
Experimental Observation of the Quantum Anomalous Hall Effect in a Magnetic Topological Insulator (2013)

Quantized and Anomalous The Hall effect, an electromagnetic phenomenon with a straightforward explanation, has many exotic counterparts, including a quantized version occurring independently of the presence of external magnetic fields. Inspired by a theoretical prediction of the quantum anomalous Hall (QAH) effect in magnetically doped topological insulator thin films, Chang et al. (p. 167 , published online 14 March; see the Perspective by Oh ) prepared thin films of the compound Cr0.15(Bi0.1Sb0.9)1.85Te3, with Cr as the magnetic dopant. They observed a plateau in the Hall resistance as a function of the gating voltage without any applied magnetic fields, signifying the achievement of the QAH state.
Generation of Pedal Operation Patterns of Individual Drivers in Car-Following for Personalized Cruise Control (2007)

This paper presents a method to generate car-following patterns for individual drivers. We assume that driving is a recursive process. A driver recognizes a road environment such as velocity and following distance and adjusts gas and brake pedal positions. A vehicle status changes according to the driver's operation and the road environment changes according to the vehicle status. Driving patterns of each driver are modeled with a Gaussian mixture model (GMM), which is trained as a joint probability distribution of following distance, velocity, pedal position signals and their dynamics. Gas and brake pedal operation patterns are generated from the GMMs in a maximum likelihood criterion so that the conditional probability is maximized for a given environment i.e., following distance and velocity. Experimental results for a driving simulator show that car-following patterns generated from GMMs for three different drivers maintain their individual driving characteristics.
Lung Tumor Diagnosis and Subtype Discovery by Gene Expression Profiling (2006)

The optimal treatment of patients with complex diseases, such as cancers, depends on the accurate diagnosis by using a combination of clinical and histopathological data. In many scenarios, it becomes tremendously difficult because of the limitations in clinical presentation and histopathology. To accurate diagnose complex diseases, the molecular classification based on gene or protein expression profiles are indispensable for modern medicine. Moreover, many heterogeneous diseases consist of various potential subtypes in molecular basis and differ remarkably in their response to therapies. It is critical to accurate predict subgroup on disease gene expression profiles. More fundamental knowledge of the molecular basis and classification of disease could aid in the prediction of patient outcome, the informed selection of therapies, and identification of novel molecular targets for therapy. In this paper, we propose a new disease diagnostic method, probabilistic boosting tree (PB tree) method, on gene expression profiles of lung tumors. It enables accurate disease classification and subtype discovery in disease. It automatically constructs a tree in which each node combines a number of weak classifiers into a strong classifier. Also, subtype discovery is naturally embedded in the learning process. Our algorithm achieves excellent diagnostic performance, and meanwhile it is capable of detecting the disease subtype based on gene expression profile.
10-step Synthesis of 20-nor-Salvinorin A by Dynamic Strategic Bond Analysis (2017)

Salvinorin A (SalA) is a plant metabolite that agonizes the human kappa-opioid receptor (κ-OR) with high affinity and high selectivity over mu- and delta-opioid receptors. Its therapeutic potential has stimulated extensive semi-synthetic studies and total synthesis campaigns. However, structural modification of SalA has been complicated by its instability, and efficient total synthesis has been frustrated by its dense, complex architecture. Treatment of strategic bonds in SalA as dynamic and dependent on structural perturbation enabled the identification of an efficient retrosynthetic pathway. Here we show that deletion of C20 simultaneously stabilizes the SalA skeleton, simplifies its synthesis and retains its high affinity and selectivity for the κ-OR. The resulting 10-step synthesis now opens the SalA scaffold to deep-seated property modification.
Using honey in wound care (2006)

Honey is primarily a herbal product with some modifications that are made by the bees that process the nectar or sap collected from the plants to store as honey. The types of phytochemicals present in a honey depend on the plant source of the honey. The hydrogen peroxide that is formed in honey by an enzyme the bees add, and sometimes also particular phytochemicals from the nectar or sap, give honey antibacterial activity that is sufficient to be effective in clearing infection from wounds. The phytochemicals also give honey its antioxidant activity which is also important in wound care, acting to decrease inflammation. Honey has been used in wound care since ancient times, but was displaced from use by the advent of antibiotics. With the widespread resistance to antibiotics developing in bacteria, it is now being &apos;rediscovered&apos;, and in many cases is proving to give better results than modern wound-care products. It has the advantage of providing moist healing conditions without the risk of bacterial growth, preventing adhesion of dressings to wound tissues, giving rapid removal of pus, dead tissue and debris from wounds, decreasing inflammation and thus decreasing swelling, pain and exudation of serum and preventing scarring, and speeding up the growth of tissues to repair wounds.
Model Checking a Self-Stabilizing Distributed Clock Synchronization Protocol for Arbitrary Digraphs (2013)

This report presents the mechanical verification of a self-stabilizing distributed clock synchronization protocol for arbitrary digraphs in the absence of faults. This protocol does not rely on assumptions about the initial state of the system, other than the presence of at least one node, and no central clock or a centrally generated signal, pulse, or message is used. The system under study is an arbitrary, non-partitioned digraph ranging from fully connected to 1-connected networks of nodes while allowing for differences in the network elements. Nodes are anonymous, i.e., they do not have unique identities. There is no theoretical limit on the maximum number of participating nodes. The only constraint on the behavior of the node is that the interactions with other nodes are restricted to defined links and interfaces. This protocol deterministically converges within a time bound that is a linear function of the self-stabilization period.
Alternate novolak resin fractionation (1999)

Novolak resins fractionated using a unique method, were compared to resins fractionated with conventional methods. The potential for improving the fractionation/separation process and for making improved or more consistent resist with the resins was identified. Several experimental designs were run to determine optimum conditions needed to achieve better separation. Isolated resins were used to make experimental i-line photoresists which were tested against resists made with the conventional processes.
Cover Picture: Discovery and Target Identification of an Antiproliferative Agent in Live Cells Using Fluorescence Difference in Two‐Dimensional Gel Electrophoresis (Angew. Chem. Int. Ed. 22/2012) (2012)

Target identification is the crown jewel of chemical biology. In their Communication on page 5447 ff., S. B. Park et al. have developed a new target identification method, fluorescence difference in two-dimensional gel electrophoresis (FITGE), to observe the interactions between proteins and small molecules in an intact cellular environment. FITGE successfully identifies protein differentiation between specific and nonspecific binding of photoaffinity probes.
Succinct Representation of Static Packet Classifiers (2009)

We develop algorithms for the compact representation of the 1- and 2-dimensional tries that are used for Internet packet classification. Our compact representations are experimentally compared with competing compact representations for 1- and multi-dimensional packet classifiers and found to simultaneously reduce the number of memory accesses required for a lookup as well as the memory required to store the classifier.
Crystalline organic semiconductor thin-film optical waveguides (1990)

Recently, crystalline organic semiconductors have been of interest because of their excellent electronic and optical properties. One crystalline organic semiconductor, 3,4,9,10-perylenetetracarboxylic dianhydride (PTCDA), is of particular importance owing to its usefulness in optoelectronic integrated circuits based on PTCDA-inorganic semiconductor heterojunction structures.
Efficient Verifiable Secret Sharing with Share Recovery in BFT Protocols (2019)

Byzantine fault tolerant state machine replication (SMR) provides powerful integrity guarantees, but fails to provide any privacy guarantee whatsoever. A natural way to add such privacy guarantees is to secret-share state instead of fully replicating it. Such a com- bination would enable simple solutions to difficult problems, such as a fair exchange or a distributed certification authority. However, incorporating secret shared state into traditional Byzantine fault tolerant (BFT) SMR protocols presents unique challenges. BFT protocols often use a network model that has some degree of asynchrony, making verifiable secret sharing (VSS) unsuitable. However, full asynchronous VSS (AVSS) is unnecessary as well since the BFT algorithm provides a broadcast channel. We first present the VSS with share recovery problem, which is the subproblem of AVSS required to incorporate secret shared state into a BFT engine. Then, we provide the first VSS with share recovery solution, KZG-VSSR, in which a failure-free sharing incurs only a constant number of cryptographic operations per replica. Finally, we show how to efficiently integrate any instantiation of VSSR into a BFT replication protocol while incurring only constant overhead. Instantiating VSSR with prior AVSS protocols would require a quadratic communication cost for a single shared value and incur a linear overhead when incorporated into BFT replication. We demonstrate our end-to-end solution via a a private key-value store built using BFT replication and two instantiations of VSSR, KZG-VSSR and Ped-VSSR, and present its evaluation.
The Development of a Lean-Premixed Trapped Vortex Combustor (2003)

A lean-premixed trapped vortex combustor (TVC) has been developed and tested. The TVC was fired on methane and tested at the General Applied Sciences Laboratory (GASL). Additionally, for baseline data, a simple bluff body combustor was tested. All testing was performed at elevated pressures and inlet temperatures and at lean fuel-air ratios representative of power generation gas turbine engines. Both bluff body and TVC data showed competitive oxides of nitrogen (NOx) emissions of <25 ppm (corrected to 15% oxygen dry condition), which served as a basis for future optimization. Combustion efficiency was routinely above 99.5%. An optimized version of the TVC incorporating flame stabilizing features displayed promising emissions: NOx/CO/UHC levels were optimized to as low as 9/9/0ppm (corrected to 15% O2 dry), with corresponding combustion efficiency above 99.9%. Because of this configuration’s robust and straightforward design, it has the potential for successful integration into a prototype engine. This paper describes the combustors, their testing and the evaluation of the test results.
Blockchain-Assisted Spectrum Trading between Elastic Virtual Optical Networks (2019)

In communication networks, network virtualization can usually provide better capacity utilization and quality of service (QoS) than what can be achieved otherwise. However, conventional resource allocation for virtualized networks would still follow a fixed pattern based on the predicted capacity needs of the users, even though, in reality, the actual traffic demand of a user will always tend to fluctuate. The mismatch between the fixed capacity allocation and the actual fluctuating traffic would lead to degradation of provisioned network services and inefficiency in the assigned network capacity. To overcome this, we propose a new spectrum trading (ST) scheme between virtual optical networks (VONs) in the context of an elastic optical network (EON). The key idea here is to allow different VONs to trade their spectrum resources according to the actual capacity they need at different time instants. A VON with unused spectra can then trade away its unused spectra to other VONs that are short of spectrum resources at that time. In exchange, it is rewarded with a certain amount of credit for its contribution to the ST community, which it can then use later to get extra bandwidth, if needed. The trust-worthiness of the trading records between the VONs is ensured in a distributed fashion through a blockchain-assisted account book that is updated whenever a new trade occurs. For this, we develop a software-defined control plane to enable spectrum trading in an EON. The performance of the ST scheme is evaluated and compared with a scenario without such trading. Our results show that the proposed ST scheme is effective in improving the QoS of each VON and significantly improves the overall network capacity utilization.
Circulating Cardiac Troponin‐I Autoantibodies in Human Plasma and Serum (2009)

We identified IgG reactive with human cardiac troponin-I (cTnI) in plasma and serum samples (N = 1930) from normal blood donors, and in sample cohorts characterized on the basis of clinical biomarkers associated with cardiac, infectious, and autoimmune diseases. cTnI and brain natriuretic peptide were the biomarkers chosen to reflect myocyte damage or left ventricular dysfunction, respectively. The infectious disease cohorts were serologically positive for antibodies to hepatitis B (natural infection), hepatitis C virus, and Chagas (i.e., T.cruzi). The autoimmune cohorts were represented by samples from diagnosed systemic lupus erythematosus (biomarker: dsDNA) and rheumatoid arthritis (biomarker: rheumatoid factor) subjects. The prevalence of IgG autoantibodies reactive with cTnI was high in the normal donor cohort (95/750, 12.7%). The prevalence in the other sample cohorts was not significantly different from that in the normal blood donors, with the exception of a slight increase in the rheumatoid factor cohort (28/137, 20.4%). The presence of anti-cTnI IgG in highly reactive samples was confirmed by inhibition with the antigen and further by screening with a library of peptides derived from the human cTnI amino acid sequence. Our data suggest that these autoantibodies are polyspecific, encompassing epitopes across the entire cTnI sequence, including the cardiac-specific amino terminal region.
Experience leads to accurate design of NIR gasoline analysis systems (1994)

Careful design of a near-infrared (NIR) analysis system can optimize reformulated gasoline blending. NIR has gained a great deal of attention recently as an on-line process analysis technique. When properly installed, calibrated, and serviced, NIR can offer attractive returns on investment for refiners. The major operating advantages of NIR include faster analysis time for key blending properties and increased accuracy and precision, which can allow the refiner to blend closer to specification. And NIR analyzers can determine several analytical test results simultaneously. The method and data analysis are described.
<i>N</i><sup>6</sup>‐Substituted 9‐methyladenines: A new class of adenosine receptor antagonists (1987)

A series of 15 N6-substituted 9-methyladenines have been assessed as antagonists of A2-adenosine receptor-mediated stimulation of adenylate cyclase in membranes of human platelets and rat PC12 cells and of A1-adenosine receptor-mediated inhibition of adenylate cyclases in membranes of rat fat cells and as inhibitors of binding of N6-R-[3H]phenylisopropyladenosine to A1-adenosine receptors in rat brain membranes. N6 substitution can markedly increase the potency of 9-methyladenine at A1 receptors, while having lesser effects or even decreasing potency at A2 receptors. Effects of N6 substituents on adenosine receptor activity of the 9-methyladenines are reminiscent of effects of N6 substituents on activity of adenosine, suggesting that N6 substituted 9-methyladenines bind to adenosine receptors in the same orientation as do N6-substituted adenosines. N6-Cyclopentyl-9-methyladenine with Ki values at the A1 receptors of 1.3 microM (fat cells) and 0.5 microM (brain) is at least 100-fold more potent than 9-methyladenine (Ki 100 microM, both receptors), while at the A2 receptors KB values of 5 microM (platelets) and 25 microM (PC12 cells) make it 5-fold more potent and equipotent, respectively, compared to 9-methyladenine (KB 24 microM, both receptors). N6-Cyclopentyl and several other N6-alkyl and N6-cycloalkyl analogs are selective for A1 receptors while 9-methyladenine is the most A2 receptor selective antagonist. The N6-R- and N6-S-(1-phenyl-2-propyl)-9-methyladenines, analogous to N6-R- and N6-S-phenylisopropyladenosines, exhibit stereoselectivity at both A1 and A2 receptors. Marked differences in potency of certain N6-substituted 9-methyladenines at the A2 receptors of human platelets and rat PC12 cells provide evidence that these are not identical receptors.
A Low-Energy Machine-Learning Classifier Based on Clocked Comparators for Direct Inference on Analog Sensors (2017)

This paper presents a system, where clocked comparators consuming only CV <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> energy directly derive classification decisions from analog sensor signals, thereby replacing instrumentation amplifiers, ADCs, and digital MACs, as typically required. A machine-learning algorithm for training the classifier is presented, which enables circuit non-idealities as well as severe energy/area scaling in analog circuits to be overcome. Furthermore, a noise model of the system is presented and experimentally verified, providing a means to predict and optimize classification error probability in a given application. The noise model shows that superior noise efficiency is achieved by the comparator-based system compared with a system based on linear low-noise amplifiers. A prototype in 130-nm CMOS performs image recognition of handwritten numerical digits, by taking raw analog pixels as the inputs. Due to pin limitations on the chip, the images with 28 × 28 = 784 pixels are resized and downsampled to give 47 pixel features, yielding an accuracy of 90% for an ideal ten-way classification system (MATLAB simulated). The prototype comparator-based system achieves equivalent performance with a total energy of 543 pJ per ten-way classification at a rate up to 1.3 M images per second, representing 33× lower energy than an ADC/digital-MAC system.
Optimizing refresh of a set of materialized views (2005)

In many data warehousing environments, it is common to have materialized views (MVs) at different levels of aggregation of one or more dimensions. The extreme case of this is relational OLAP environments, where, for performance reasons, nearly all levels of aggregation across all dimensions may be computed and stored in MVs. Furthermore, base tables and MVs are usually partitioned for ease and speed of maintenance. In these scenarios, updates to the base table are done using Bulk or Partition operations like add, exchange, truncate and drop partition. If changes to base tables can be tracked at the partition level, join dependencies. functional dependencies and query rewrite can be used to optimize refresh of an individual MV. The refresh optimizer, in the presence of partitioned tables and MVs, may recognize dependencies between base table and the MV partitions leading to the generation of very efficient refresh expressions. Additionally, in the presence of multiple MVs, the refresh subsytem can come up with an optimal refresh schedule such that MVs can be refreshed using query rewrite against previously refreshed MVs. This makes the database server more manageable and user friendly since a single function call can optimally refresh all the MVs in the system.