gebhart/patrepeval huggingface dataset:
A REVIEW OF DRIVER PERFORMANCE WITH HEAD-UP DISPLAYS (1996)

Since 1988, passenger car manufacturers have introduced automobiles which can present visual information to the driver through the windshield by way of a Head-Up Display (HUD). The HUD allows the driver the ability to access visually displayed information in closer proximity to forward scene events relative to a conventional head-down (HD), instrument panel display. In this first generation of production HUD vehicles, the head-up (HU) information has included digital speed, turn signals, high beam indicator, and master and/or specific telltale warnings. The next generation of HUDs may include information which would not be redundantly displayed at HD locations, provided technological advances can be made to ensure HUD image visibility under a range of conditions comparable to HD displays. Assuming this technological challenge can be overcome, automotive HUDs have increased potential to improve the driver-vehicle interface, present information which could not be effectively communicated via a HD display, and increase display space and interface design flexibility. In addition, future HUDs may include more advanced driver content, including navigation/route guidance, headway aiding, intelligent cruise control/forward collision warning, lane maintenance aiding, infrared night vision displays, and roadway-to-vehicle communication information. These relatively unexplored content areas may be better suited for taking advantage of the unique strengths of the HUD technology and yield the greatest potential benefit of a HUD to the driver. The purpose of the first part of this review is to provide an evaluation of questions which have been raised about HUDs against existing research examining production HUD content. This part of the review is intended to provide a point of departure and foundation for understanding the effects of displaying other types of HUD information on driver performance. Overall, this growing body of research suggests current production HUDs will improve driver performance. To the extent to which accidents are caused by allocating visual attention to HD displays, these results also suggest HUDs may improve traffic safety. The purpose of the second part of this review is to briefly discuss automotive research which has examined more advanced driver HUD content.
White organic light-emitting devices incorporating nanoparticles of II–VI semiconductors (2007)

A blue–green fluorescent organic dye and red-emitting nanoparticles, based on II–VI semiconductors, have been used together in the fabrication of white organic light-emitting devices. In this work, the materials were combined in two different ways: in the form of a blend, and as separate layers deposited on the opposite sides of the substrate. The blended-layer structure provided purer white emission. However, this device also exhibited a number of disadvantages, namely a high drive voltage, a low efficiency and some colour instability. These problems could be avoided by using a device structure that was fabricated using separate dye and nanoparticle layers.
DrugTargetInspector: An assistance tool for patient treatment stratification (2015)

Cancer is a large class of diseases that are characterized by a common set of features, known as the Hallmarks of cancer. One of these hallmarks is the acquisition of genome instability and mutations. This, combined with high proliferation rates and failure of repair mechanisms, leads to clonal evolution as well as a high genotypic and phenotypic diversity within the tumor. As a consequence, treatment and therapy of malignant tumors is still a grand challenge. Moreover, under selective pressure, e.g., caused by chemotherapy, resistant subpopulations can emerge that then may lead to relapse. In order to minimize the risk of developing multidrug-resistant tumor cell populations, optimal (combination) therapies have to be determined on the basis of an in-depth characterization of the tumor's genetic and phenotypic makeup, a process that is an important aspect of stratified medicine and precision medicine. We present DrugTargetInspector (DTI), an interactive assistance tool for treatment stratification. DTI analyzes genomic, transcriptomic, and proteomic datasets and provides information on deregulated drug targets, enriched biological pathways, and deregulated subnetworks, as well as mutations and their potential effects on putative drug targets and genes of interest. To demonstrate DTI's broad scope of applicability, we present case studies on several cancer types and different types of input -omics data. DTI's integrative approach allows users to characterize the tumor under investigation based on various -omics datasets and to elucidate putative treatment options based on clinical decision guidelines, but also proposing additional points of intervention that might be neglected otherwise. DTI can be freely accessed at http://dti.bioinf.uni-sb.de.
3P1-L05 Robotization of Rubber Hose Insertion Using a Dual-arm Robot(Robots for Works (1)) (2014)

In the manufacturing factories, there are many working processes dealing with deformable objects, and automation of such working processes is strongly required. However, since manipulating deformable objects by a robot is difficult in general, the introduction of the robot has not progressed much. As an example of such works, inserting rubber hose is present. In this paper, the end-effectors for robotization of rubber hose insertion is developed, and manipulation planning for robot arms is proposed. The effectiveness of proposed method is experimentally shown.
Flow cyto metric analysis of the cytotoxic effects of Actinobacillus actinomycetemcomitans leu kotoxi n on human natural killer cells (2005)

The goal of this investigation was to determine if human natural killer (NK) cells were susceptible to the cytolytic effects of the Actinobacillus actinomycetemcomitans leukotoxin (LTX). Following treatment with LTX (0-200 ng/ml), NK cell activation by interleukin-2 (IL-2) was evaluated. LTX inhibited the IL-2-induced expression of both CD69 and the IL-2 receptor. Furthermore, the upregulation of CDS6 was also impaired. To determine whether the observed functional deficits were the result of cell death, NK cell viability was evaluated by flow cytometry. Changes in forward and side light scatter patterns consistent with cell death were observed within 60 mm. Direct analysis of cell viability by measuring propidium iodide exclusion, however, indicated little change in the viability of LTX-treated NK cells. Electron microscopic analysis of NK cells exposed to LTX revealed early nuclear alterations characterized by hyperchromaticity, nuclear fragmentation, and condensation of nucleoplasm. However, no change in membrane integrity was initially noted. Finally, LTX caused a rapid and sustained elevation in the intracellular levels of Ca2 . These morphological and biochemical changes are consistent with the notion of programmed cell death. J. Leukoc. Biol. 55: 153-160; 1994.
Supplementary Methods, Figures 1 - 7, and Tables 1 - 6 from Radiation-Enhanced Lung Cancer Progression in a Transgenic Mouse Model of Lung Cancer Is Predictive of Outcomes in Human Lung and Breast Cancer (2023)

&lt;p&gt;PDF file - 1617KB, Figure S1. Malignant Characteristics of Invasive Adenocarcinoma in K-rasLA1 Mice; Figure S2. Radiation Effects on the Incidence of Various Endpoints in K-rasLA1 Mice; Figure S3. Comparative Genomic Analyses and Classifier Isolation from Irradiated Versus Unirradiated Control K-rasLA1 Mice; Figure S4. Comparative Genomic Analysis of Whole Lungs Reveals Unique Gene Classifiers Capable of Specifying Individual Experimental Cohorts; Figure S5. Only "Fractionated" Classifier Demonstrates Clinical Relevance for Lung Cancer Patient Survival; Figure S6. "Fractionated" Classifier Capable of Predicting Overall Survival in Patients with Breast, but not Lung Squamous Cell Cancer; Supplementary Figure S7. Cox Regression Analysis Exposes 6 Genes Within "Fractionated" Classifier Which Retain Predictive Capacity; Table S1. Logistic Regression Analysis of Unirradiated K-rasLA1 Mice for Gender and Strain Effects on Various Endpoints; Table S2. Logistic Regression Analysis for Gender and Strain Effects on Various Endpoints Controlling for Experiment; Table S3. Logistic Regression Analysis of Radiation Effects on the Incidence of Invasive Adenocarcinoma Controlling for Gender and Strain; Table S4. Multivariate Cox Analysis for Gender and Strain Effects on Overall Survival Controlling for Experiment; Table S5. Multivariate Cox Analysis of Radiation Effects on Overall Survival Controlling for Gender and Strain Effects; Table S6. IPA Network Annotations Associated with Corresponding Gene Lists.&lt;/p&gt;
Systems analysis in the study of the motor-control system: Control theory alone is insufficient (1982)

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
560 Differential activation of natriuretic peptides in human heart failure and relationship to cardiac function: evidence for peptide deficiency (2003)

Contraction-relaxation cycle characteristics and sensitivity to cholinergic stimulation suggest involvement of the Gs-adenylyl cyclase-pathway. The concentration response curves to 5-HT were shifted to higher concentrations by 0.5 nM of the 5-HT4 selective antagonist GR113808, with pEC50-values of 7.62 ± 0.06 (SEM, n = 6) and 7.32 ± 0.06 (SEM, n = 6, p < 0.005), respectively, and the derived pKb value of 9.5 for GR113808 documents involvement of 5-HT4 receptors. Quantitative RT-PCR revealed a 4-fold increase of 5-HT4(b) mRNA expression in ventricles from CHF rat hearts compared to SHAM rat hearts.
Deep Algorithm Unrolling for Blind Image Deblurring (2019)

Blind image deblurring remains a topic of enduring interest. Learning based approaches, especially those that employ neural networks have emerged to complement traditional model based methods and in many cases achieve vastly enhanced performance. That said, neural network approaches are generally empirically designed and the underlying structures are difficult to interpret. In recent years, a promising technique called algorithm unrolling has been developed that has helped connect iterative algorithms such as those for sparse coding to neural network architectures. However, such connections have not been made yet for blind image deblurring. In this paper, we propose a neural network architecture based on this idea. We first present an iterative algorithm that may be considered as a generalization of the traditional total-variation regularization method in the gradient domain. We then unroll the algorithm to construct a neural network for image deblurring which we refer to as Deep Unrolling for Blind Deblurring (DUBLID). Key algorithm parameters are learned with the help of training images. Our proposed deep network DUBLID achieves significant practical performance gains while enjoying interpretability at the same time. Extensive experimental results show that DUBLID outperforms many state-of-the-art methods and in addition is computationally faster.
Supplementary Figure S4 from Small-Molecule ONC201/TIC10 Targets Chemotherapy-Resistant Colorectal Cancer Stem–like Cells in an Akt/Foxo3a/TRAIL–Dependent Manner (2023)

&lt;p&gt;ONC201/TIC10-mediated CSC depletion involves inhibition of Akt, ERK and activation of Foxo3a.&lt;/p&gt;
Histone γ-H2AX as a pharmacodynamic biomarker for the indenoisoquinoline topoisomerase I inhibitors selected for preclinical development. (2006)

Proc Amer Assoc Cancer Res, Volume 47, 2006

5534 

Camptothecin derivatives are effective anticancer drugs, especially against solid tumors. Although they are known to selectively target DNA topoisomerase I (Top1) their action cannot be predicted based on Top1 levels. However, phosphorylated histone H2AX (γ-H2AX) formation can be detected by immunofluorescence microscopy following camptothecin treatment. Histone H2AX is rapidly phosphorylated in the chromatin flanking camptothecin-induced replication DNA double-strand break (DSB) (Furuta et al., J Biol Chem. 2003 May 30;278(22):20303-12). As camptothecins are chemically unstable and have clinical limitations, we have synthesized indenoisoquinolines as novel Top1 inhibitors. Of the 300 derivatives tested, based on potent Top1 inhibition, selectivity for Top1-overexpressing cells and potent antiproliferative activity, we presently report 4 novel indenoisoquinoline derivatives (NSC D-724998, NSC D-725776, NSC D-726972 and NSC D-727357) that are under consideration for therapeutic development. To help evaluate the efficacy of these drugs prior to clinical trials, our goal was to determine a biomarker that would validate the activity of the indenoisoquinolines in tumor samples. Dose and time dependent γ-H2AX foci formation was readily observed in cells treated with the indenoisoquinolines, indicative of DSBs formed as a consequence of a replication forks colliding with the drug-mediated Top1-DNA single-strand breaks. These γ-H2AX foci were detectable at pharmacologically relevant dose and thus could be used as biomarker for Top1 inhibitors. Work is in progress to evaluate if γ-H2AX foci can be detected in animal models.
Minimum Variance Estimation of a Sparse Vector within the Linear\n Gaussian Model: An RKHS Approach (2013)

We consider minimum variance estimation within the sparse linear Gaussian\nmodel (SLGM). A sparse vector is to be estimated from a linearly transformed\nversion embedded in Gaussian noise. Our analysis is based on the theory of\nreproducing kernel Hilbert spaces (RKHS). After a characterization of the RKHS\nassociated with the SLGM, we derive novel lower bounds on the minimum variance\nachievable by estimators with a prescribed bias function. This includes the\nimportant case of unbiased estimation. The variance bounds are obtained via an\northogonal projection of the prescribed mean function onto a subspace of the\nRKHS associated with the SLGM. Furthermore, we specialize our bounds to\ncompressed sensing measurement matrices and express them in terms of the\nrestricted isometry and coherence parameters. For the special case of the SLGM\ngiven by the sparse signal in noise model (SSNM), we derive closed-form\nexpressions of the minimum achievable variance (Barankin bound) and the\ncorresponding locally minimum variance estimator. We also analyze the effects\nof exact and approximate sparsity information and show that the minimum\nachievable variance for exact sparsity is not a limiting case of that for\napproximate sparsity. Finally, we compare our bounds with the variance of three\nwell-known estimators, namely, the maximum-likelihood estimator, the\nhard-thresholding estimator, and compressive reconstruction using the\northogonal matching pursuit.\n
A New Simple UWB Monocycle Pulse Generator (2006)

A new simple ultra-wideband (UWB) monocycle pulse generator based on step recovery diode is presented. This pulse generator uses nonlinear components with a single power supply and microstrip transmission lines. It is made up of three parts : a Gaussian pulse generator, an impulse-shaping network, and a monocycle-pulse forming circuit. The proposed circuit allows to generate ultra-short Gaussian monocycles of 270-ps pulse duration with a bandwidth of 9-GHz and a pulse repetition rate of 100-MHz.
Recent advances in cognitive communications (2007)

This article describes recent advances in cognitive communications. We combine the concepts of signal processing, communications, pattern classification, and machine learning to make dynamic use of the spectrum, such that the emanated signals do not interfere with the existing ones. Unlike other programs such as neXt Generation communications of the Defense Advanced Research Projects Agency, where radio scene analysis is performed to find the spectrum holes or the white space, we make use of the white, as well as the gray space for non- interfering signal transmission. We examine the possibility of employing machine perception and autonomous machine learning technologies to the autonomous design and analysis of air interfaces. The underlying premise is that a learning module will facilitate adaptation in the standard classification process so that the presence of new types of waveforms can be detected, features that best facilitate classification of the previously and newly identified signals can be determined, and waveforms can be generated by using the basis-set orthogonal to the ones present in the environment. Incremental learning and prediction allows knowledge enhancement as more snapshots of data are processed, resulting in improved decisions. Some of the contributions of this project include technological advances in signal detection, feature identification, signal classification, sub-space tracking, adaptive waveform design, machine learning, and prediction.
Circulating Levels of Calcitonin Gene-Related Peptide in Patients with Medullary Thyroid Carcinoma (1989)

Calcitonin gene-related peptide, originally found in rat medullary thyroid carcinoma cells, was measured by radioimmunoassay in plasma samples with added aprotinin from 17 normal volunteers and 21 patients with medullary thyroid carcinoma. The concentrations were below 75 pmol/l in all 17 normal subjects with a mean of 33 +/- 19 pmol/l plasma. In the patients, the plasma calcitonin gene-related peptide concentrations ranged from below 5 pmol/l to 236 pmol/l, although the mean (36 +/- 48 pmol/l) was not significantly different from the mean normal level. Increase in the plasma calcitonin gene-related peptide level was observed in 2 of 5 patients in response to infusion of 4.3 mg/kg of calcium for 10 minutes and in 3 of 5 patients in response to infusion of 4 micrograms/kg of tetragastrin for 5 minutes. These observations suggest that measurement of calcitonin gene-related peptide level may be helpful in determination of diseases in which the level of calcitonin is of less diagnostic value.
Operationally simple electrochemical method for the conversion of acetone into high-specification jet fuel (2022)

Highly branched alkanes and cycloalkanes are produced via electrochemical processing of the renewable platform molecule mesityl oxide, which is a dimer of biogenic acetone. The final product is a sustainable, drop-in jet fuel.
Antibiotic prophylaxis after variceal hemorrhage reduces incidence of early rebleeding. (2004)

For the first time our data indicate a role for new onset infections in initiating early rebleedings. Immediate prophylactic antibiotic treatment for patients at high risk of infection might be effective in lowering both, the risk of acquiring infections and early rebleeding.
ChemInform Abstract: Enantiopure 4‐ and 5‐Aminopiperidin‐2‐ones: Regiocontrolled Synthesis and Conformational Characterization as Bioactive β‐Turn Mimetics. (2001)

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.
Synthesis and Reactions of Fluorous Carbobenzyloxy (FCbz) Derivatives of α‐Amino Acids. (2003)

Abstract For Abstract see ChemInform Abstract in Full Text.
What Controls the Melting Properties of DNA-Linked Gold Nanoparticle Assemblies?* (2020)

In this chapter, the author reports a series of experiments and a theoretical model designed to systematically define and evaluates the relative importance of nanoparticle, oligonucleotide, and environmental variables that contribute to the observed sharp melting transitions associated with DNA-linked nanoparticle structures. These variables include the size of the nanoparticles, the surface density of the oligonucleotides on the nanoparticles, the dielectric constant of the surrounding medium, target concentration, and the position of the nanoparticles with respect to one another within the aggregate. Understanding the fundamental origins of the melting properties of DNA-linked nanoparticle aggregates (or monolayers) is of paramount importance because these properties directly impact one’s ability to formulate high sensitivity and selectivity DNA detection systems and construct materials from these novel nanoparticle materials. Nanoparticles heavily functionalized with oligonucleotides have been used as probes in a variety of DNA detection methods and as elemental building blocks in materials synthesis schemes based upon the sequence-specific hybridization properties of DNA.
Small-molecule CSF1R kinase inhibitors; review of patents 2015-present (2020)

The field has developed rapidly from 2014 to the present, with many different chemotypes proving to be potent inhibitors. The range of potential utilities of CSF1R inhibitors has also expanded to include dementia, ulcerative colitis/Crohn's disease, rheumatoid arthritis inflammation, and fibrosis.
Optimization of Operating Conditions for 112-Gb/s Polarization-Multiplexed 16-QAM Optical Modulation (2011)

In this letter, we analyze a 16-ary quadrature amplitude modulation (16-QAM) optical signal generated by applying four-level electrical signals to a single in-phase/quadrature (IQ)-modulator. The amplitude of optical output of the Mach-Zehnder modulator (MZM) in the IQ-modulator needs to be carefully adjusted, and the limited extinction ratio of MZM results in unwanted phase offset. Required optical signal-to-noise ratio is estimated in the 112-Gb/s polarization-division-multiplexed (PDM) 16-QAM signal by numerical simulations with various amplitudes of the electrical modulation signals. Optimal operating conditions are found to guarantee the best performance of the received PDM 16-QAM signal.
Real-Time Reinforcement Learning Optimized Energy Management for a 48V Mild Hybrid Electric Vehicle (2019)

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Energy management of hybrid vehicle has been a widely researched area. Strategies like dynamic programming (DP), equivalent consumption minimization strategy (ECMS), Pontryagin’s minimum principle (PMP) are well analyzed in literatures. However, the adaptive optimization work is still lacking, especially for reinforcement learning (RL). In this paper, Q-learning, as one of the model-free reinforcement learning method, is implemented in a mid-size 48V mild parallel hybrid electric vehicle (HEV) framework to optimize the fuel economy. Different from other RL work in HEV, this paper only considers vehicle speed and vehicle torque demand as the Q-learning states. SOC is not included for the reduction of state dimension. This paper focuses on showing that the EMS with non-SOC state vectors are capable of controlling the vehicle and outputting satisfactory results. Electric motor torque demand is chosen as action. In the reward function definition, the fuel consumption contains engine fuel consumption and equivalent battery fuel consumption. The Q-learning table is executed over Worldwide Harmonized Light Vehicle driving cycle. The learning process shows fast convergence and fuel economy improvement of 5%. The Q-learning strategy is compared with baseline thermostatic rule-based EMS and ECMS. The Q-learning result shows 8.89% fuel economy improvement than the rule-based EMS and 0.88% than the ECMS.&lt;/div&gt;&lt;/div&gt;