gebhart/patrepeval huggingface dataset:
A Systematic Review of Carotenoids in the Management of Diabetic Retinopathy (2021)

Diabetic retinopathy, which was primarily regarded as a microvascular disease, is the leading cause of irreversible blindness worldwide. With obesity at epidemic proportions, diabetes-related ocular problems are exponentially increasing in the developed world. Oxidative stress due to hyperglycemic states and its associated inflammation is one of the pathological mechanisms which leads to depletion of endogenous antioxidants in retina in a diabetic patient. This contributes to a cascade of events that finally leads to retinal neurodegeneration and irreversible vision loss. The xanthophylls lutein and zeaxanthin are known to promote retinal health, improve visual function in retinal diseases such as age-related macular degeneration that has oxidative damage central in its etiopathogenesis. Thus, it can be hypothesized that dietary supplements with xanthophylls that are potent antioxidants may regenerate the compromised antioxidant capacity as a consequence of the diabetic state, therefore ultimately promoting retinal health and visual improvement. We performed a comprehensive literature review of the National Library of Medicine and Web of Science databases, resulting in 341 publications meeting search criteria, of which, 18 were found eligible for inclusion in this review. Lutein and zeaxanthin demonstrated significant protection against capillary cell degeneration and hyperglycemia-induced changes in retinal vasculature. Observational studies indicate that depletion of xanthophyll carotenoids in the macula may represent a novel feature of DR, specifically in patients with type 2 or poorly managed type 1 diabetes. Meanwhile, early interventional trials with dietary carotenoid supplementation show promise in improving their levels in serum and macular pigments concomitant with benefits in visual performance. These findings provide a strong molecular basis and a line of evidence that suggests carotenoid vitamin therapy may offer enhanced neuroprotective effects with therapeutic potential to function as an adjunct nutraceutical strategy for management of diabetic retinopathy.
Equine leukocyte elastase inhibitor. Primary structure and identification as a thymosin-binding protein. (1992)

The primary structure of an elastase inhibitor located in the cytoplasm of leukocytes obtained from the equine species has been determined. By sequence comparison, the protein was found to be a member of the serpin family with strong identity to plasminogen activator inhibitor-2. In contrast to other serpins this protein contained no carbohydrate and had a blocked amino terminus. Preliminary evidence indicates that the inhibitor has the additional feature of being a thymosin beta 4-binding protein, since this polypeptide was always located in purified preparations of the protein. This suggests a physiological role for cytoplasmic elastase inhibitors in the thymosin beta 4-regulated rearrangement of the cytoskeleton of leukocytes.
Stereoselective Reduction of Benzils:  A New Convenient Route to Enantiomerically Pure 1,2-Diarylethanediols (1996)

ADVERTISEMENT RETURN TO ISSUEPREVNoteNEXTStereoselective Reduction of Benzils: A New Convenient Route to Enantiomerically Pure 1,2-DiarylethanediolsK. R. K. Prasad and N. N. JoshiView Author Information Division of Organic Chemistry (Synthesis), National Chemical Laboratory, Pune - 411 008, IndiaCite this: J. Org. Chem. 1996, 61, 11, 3888–3889Publication Date (Web):May 31, 1996Publication History Received14 November 1995Published online31 May 1996Published inissue 1 January 1996https://pubs.acs.org/doi/10.1021/jo952017jhttps://doi.org/10.1021/jo952017jbrief-reportACS PublicationsCopyright © 1996 American Chemical SocietyRequest reuse permissionsArticle Views1300Altmetric-Citations63LEARN 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 InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Catalysts,Molecular structure,Redox reactions,Stereoselectivity,Transfer reactions Get e-Alerts
Detection of live breast cancer cells in brightfield microscopy images containing white blood cells by image analysis and deep learning (2021)

Abstract Significance Circulating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug testing and molecular characterization. Aim The goal is to develop and test deep learning (DL) approaches to detect unstained breast cancer cells in bright field microscopy images that contain white blood cells (WBCs). Approach We tested two convolutional neural network (CNN) approaches. The first approach allows investigation of the prominent features extracted by CNN to discriminate cancer cells from WBCs. The second approach is based on Faster Region-based Convolutional Neural Network (Faster R-CNN). Results Both approaches detected cancer cells with high sensitivity and specificity with the Faster R-CNN being more efficient and suitable for deployment. The distinctive feature used by the CNN used to discriminate is cell size, however in the absence of size difference, the CNN was found to be capable of learning other features. The Faster R-CNN was found to be robust with respect to intensity and contrast image transformations. Conclusions CNN-based deep learning approaches could be potentially applied to detect patient-derived CTCs from images of blood samples.
Microstructured optical fibers embedded with semiconductors and metals: a potential route to fiberized metamaterials (2008)

Functional optoelectronic materials such as bulk crystalline semiconductors and plasmonic materials such as metals inside optical waveguides could lead to fiber devices with radically new electronic and photonic degrees of freedom. The growth of such materials inside microstructured optical fiber air-silica templates using our microfluidic ultrahigh pressure chemical fluid technique will be discussed, along with the performance of initial demonstrator devices and their putative metamaterials applications.
Physical Properties of Polypropylene Foams – how to Predict and Control Them (2004)

Low density extruded PP foams are increasingly being used in food packaging and automotive applications and their use is also being extended into other applications, such as insulation and protective packaging. This commercial success has been made possible by the development of HMS-PP, the unique physical property profile of PP itself and the ability modify the foam properties over a wide range by appropriate selection of a blend partner. It is the last factor that is the focus of the present paper. We will describe foaming studies using HMS-PP with different classes of polyolefin blend partner and different blowing agents. By combining this extensive experimental data with theoretical models, we are able to provide a predictive tool for selecting material combinations, which will enable the desired foam properties to be achieved by foam producers and end-users.
Macrophage calcium reporter mice reveal immune cell communication in vitro and in vivo (2021)

Cell communication underlies emergent functions in diverse cell types and tissues. Recent evidence suggests that macrophages are organized in communicating networks, but new tools are needed to quantitatively characterize the resulting cellular conversations. Here, we infer cell communication from spatiotemporal correlations of intracellular calcium dynamics that are non-destructively imaged across cell populations expressing genetically encoded calcium indicators. We describe a hematopoietic calcium reporter mouse (Csf1r<sup>Cre</sup>GCaMP5<sup>fl</sup>) and a computational analysis pipeline for inferring communication between reporter cells based on "excess synchrony." We observed signals suggestive of cell communication in macrophages treated with immune-stimulatory DNA <i>in vitro</i> and tumor-associated immune cells imaged in a dorsal window chamber model <i>in vivo</i>. Together, the methods described here expand the toolkit for discovery of cell communication events in macrophages and other immune cells.
Mobile robot control based on hybrid neuro-fuzzy value gradient reinforcement learning (2017)

This paper uses value gradient learning (VGL) to track a reference trajectory under uncertainties, by computing the optimal left and right torque values for a nonholonomic mobile robot. VGL is a high-performance algorithm in adaptive dynamic programming (ADP). Here, it is used as a critic function after fitting a first-order Sugeno fuzzy neural network (FNN) structure to critic and actor networks. Moreover, this work handles the impacts of unmodeled bounded disturbances with various friction values. The simulation is introduced to compare two approaches. The first uses an actor network that confirms the ability of the mobile robot dynamic model to follow a desired trajectory. This approach demonstrates a significant enhancement of the robot's capability to absorb unstructured disturbance signals and friction effects. The second type of results use a critic-optimal-control approach, calculating the optimal control signal for the affine dynamic model of the robot. This completely removes the actor network to exploit reduced computational complexity with faster responses. The simulation is introduced to compare both cases.
Nanocarriers for Vascular Delivery of Anti-Inflammatory Agents (2014)

There is a need for improved treatment of acute vascular inflammation in conditions such as ischemia-reperfusion injury, acute lung injury, sepsis, and stroke. The vascular endothelium represents an important therapeutic target in these conditions. Furthermore, some anti-inflammatory agents (AIAs) (e.g., biotherapeutics) require precise delivery into subcellular compartments. In theory, optimized delivery to the desired site of action may improve the effects and enable new mechanisms of action of these AIAs. Diverse nanocarriers (NCs) and strategies for targeting them to endothelial cells have been designed and explored for this purpose. Studies in animal models suggest that delivery of AIAs using NCs may provide potent and specific molecular interventions in inflammatory pathways. However, the industrial development and clinical translation of complex NC-AIA formulations are challenging. Rigorous analysis of therapeutic/side effect and benefit/cost ratios is necessary to identify and optimize the approaches that may find clinical utility in the management of acute inflammation.
MIDAS: An Impact Scale for DDoS attacks (2007)

We usually have well-defined classification scales to estimate the intensity and impact of natural disasters. Prominent examples are the Richter and the Fujita scales for measuring earthquakes and tornadoes respectively. In this paper, we apply similar ideas to estimate the impact of distributed denial of service (DDoS) attacks from the perspective of network operators. Devising such a classification scale improves our understanding of DDoS attacks by assessing the actual damage incurred from an ISP's perspective, and allows comparison of various mitigation strategies. We have designed MIDAS, a DDoS impact scale, based on the economic impact of a DDoS attack, calculated using economic and network data. We then present an approximation of the MIDAS scale that relies only on network measurements for ease of computation. To demonstrate the usefulness of the scale, we perform sensitivity analysis to qualitatively validate the magnitude of the scale value for diverse attacks.
Monitoring the Condition of a Valve and Linear Actuator in Hydraulic Systems (2006)

The topic of condition monitoring has been a growing area of research in both academia and industry for much of the last two decades. Condition monitoring of fluid power equipment has been no exception to this trend. Much of the research work associated with monitoring the condition of fluid power equipment has centered on pump and motor components due to their relatively high cost and complexity. The work in this paper focuses on the lesser expensive, but more common components of valves and linear actuators. The primary focus of the work presented here pertains to assessing the independent component condition of a valve-controlled linear actuator circuit. The paper first presents simulation studies to establish techniques for proper data collection, neural network training and output interpretation. The neural network approach is then applied to a valve and linear actuator of a John Deere 410E Backhoe Loader. The results indicate that the concept can be applied to a commercial system and is feasible for implementation.
Genetic links in angioimmunoblastic T-cell lymphoma (AITL), clonal hematopoiesis and concomitant hematologic malignancies provide insights into the cell of origin, etiology and biomarker discovery for AITL (2020)

Abstract We generated and compared the mutation profiles through targeted sequencing of the primary tumors and matched bone marrow/peripheral blood samples in 25 patients with angioimmunoblastic T-cell lymphoma (AITL) and 2 with peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS). Our results provided strong evidence that AITL/PTCL-NOS, clonal hematopoiesis (CH) as well as other concomitant myeloid and even B-cell hematologic neoplasms (CHN), frequently arose from common mutated hematopoietic stem cell clones. Aberrant AID/APOBEC activity-associated substitutions and tobacco smoking-associated substitutions were enriched in the early CH-associated mutations and late non-CH associated mutations during AITL/PTCL-NOS development, respectively. Moreover, survival analysis showed that the presence of CH harboring ≥ 2 pathogenic TET2 variants with ≥ 15% of allele burden conferred higher risk for CHN (P = 0.0034, hazard ratio = 10.81). These findings provide insights into the cell origin and etiology of AITL, and provide a novel stratification biomarker for CHN risk in AITL/PTCL-NOS patients.
Direct digital additive manufacturing technologies: Path towards hybrid integration (2012)

In the past decade, additive manufacturing and printed electronics technologies have expanded rapidly on a global scale. As the additive manufacturing techniques have become more capable and affordable, and able to work with a broader range of materials, the machines are increasingly being used to make advanced products at significantly lower costs and risks. The additive manufacturing industry is populated by a broad family of technologies, and the present paper provides an overview of key additive manufacturing technologies and their impact on materials processing, device applications, and future markets. Our R&D efforts on the development of core technologies for the realization of flexible electronics, and 3D microscale structures are also highlighted.
Power Delivery Network Modeling and Benchmarking for Emerging Heterogeneous Integration Technologies (2019)

In this article, a power delivery network (PDN) modeling framework for heterogeneous integration platforms is presented, which includes both IR-drop and transient analysis. The model is validated using IBM power grid benchmarks, and maximum relative errors are less than 7.3%. To evaluate interposer and bridge-chip-based integration platforms, we assume a field-programmable gate array (FPGA)-CPU 2.5-D integration platform, of which the FPGA consumes 45 W and the CPU consumes 75 W. The simulation results show that an interposer with dense power/ground grid and microbumps can suppress power supply noise (PSN) by a small margin with the requirement of high-density through-silicon vias (TSVs). For bridge-chip-based integration, under the assumption that the active dice above the bridge-chips are not connected to the package power/ground planes, some PDN challenges are evaluated. Using multiple bridge-chips and smaller overlap areas between the bridge-chips and the active dice, the worst-case PSN in bridge-chip-based integration is minimized. Next, we perform parametric studies for these 2.5-D integration platforms as a function of C4 bump pitch and metal layers. Lastly, we propose to use TSVs in the bridgechip to reduce PSN. With nine bundled TSVs of 8 μm diameter, the IR-drop of bridge-chip-based integration technologies is only 1% higher than the standalone case.
<title>Rare-earth-doped polymer optical amplifiers</title> (2000)

Neodymium (Nd) doped polymer based optical amplifiers have been studied. NdCl<SUB>3</SUB>.6H<SUB>2</SUB>O and Nd(III) hexafluoracetylacetonate were used as dopants. Photoluminescence at 0.89 micrometers , 1.06 micrometers and 1.33 micrometers from Nd ions in polyimide was detected. Phase separation with possible formation of liquid crystal polymer was observed in NdCl<SUB>3</SUB>.6H<SUB>2</SUB>O doped polyimide. Thin film waveguides and multimode channel waveguides were fabricated using a novel planarizing technique to reduce scattering losses. Optical gain of about 5.4 dB at 1.06 micrometers was demonstrated in a 5 cm long NdCl<SUB>3</SUB>.6H<SUB>2</SUB>O doped polyimide channel waveguide.
Thermodynamic interactions and correlations in mixtures of two homopolymers and a block copolymer by small angle neutron scattering (1993)

Thermodynamic interactions in mixtures of two homopolymers and a block copolymer were obtained from small-angle neutron scattering (SANS) measurements. Experimental SANS profiles from homogeneous, ternary mixtures of model polyolefins—poly(ethyl butylene)/poly(methyl butylene)/poly(methyl butylene)-b-poly(ethyl butylene)—were compared with theoretical predictions based on the multicomponent random phase approximation (RPA). The polymers were nearly monodisperse and were synthesized by saturating the double bonds in anionically synthesized polydienes with H2 and D2, thus yielding polyolefins with neutron scattering contrast. The theoretical scattering profiles depend on 16 structural parameters and six Flory–Huggins interaction parameters χ, all of which were obtained independently. The χ parameters were obtained from SANS measurements on binary poly(ethyl butylene)/poly(methyl butylene) blends. The SANS profiles obtained from the ternary blends were in quantitative agreement with theoretical predictions over the observable q range (0.01–0.08 Å−1) and over a wide range of block copolymer concentrations (from 10 to 80 vol. % copolymer). This suggests that the measured χ parameters are consistent with the original ideas of Flory and Huggins that χ is a measure of monomer–monomer interactions, and is thus independent of block copolymer concentration. Partial structure factors, related to correlations in the concentration of individual components, were examined by selective labeling and were also found to be in quantitative agreement with the predictions of the multicomponent RPA.
Metamagnetics with rainbow colors (2007)

A family of coupled nanostrips with varying dimensions is demonstrated exhibiting optical magnetic responses across the whole visible spectrum, from red to blue. We refer to such a phenomenon as rainbow magnetism. The experimental and analytical studies of such structures provide us with a universal building block and a general recipe for producing controllable optical magnetism for various practical implementations.
Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles (2020)

In this paper, we propose a novel form of the loss function to increase the performance of LiDAR-based 3d object detection and obtain more explainable and convincing uncertainty for the prediction. The loss function was designed using corner transformation and uncertainty modeling. With the new loss function, the performance of our method on the val split of KITTI dataset shows up to a 15% increase in terms of Average Precision (AP) comparing with the baseline using simple L1 Loss. In the study of the characteristics of predicted uncertainties, we find that generally more accurate prediction of the bounding box is usually accompanied by lower uncertainty. The distribution of corner uncertainties agrees on the distribution of the point cloud in the bounding box, which means the corner with denser observed points has lower uncertainty. Moreover, our method also learns the constraint from the cuboid geometry of the bounding box in uncertainty prediction. Finally, we propose an efficient Bayesian updating method to recover the uncertainty for the original parameters of the bounding boxes which can help to provide probabilistic results for the planning module.
Assessing the "Physical Cliff": Detailed Quantification of Age-Related Differences in Daily Patterns of Physical Activity (2013)

The use of accelerometers to characterize minute-by-minute intensity, cumulative physical activity counts, and daily activity patterns provides detailed data not gathered by traditional subjective methods, particularly at low levels of activity. The findings of a 1.3% decrease per year in activity from mid-to-late life, and the corresponding drop in afternoon and evening activity, provide new information that may be useful when targeting future interventions. Further, this methodology addresses essential gaps in understanding activity patterns and trends in more sedentary sectors of the population.
An experimental assessment of transmit opportunities in packet based networks (2011)

In this paper we propose a novel suite of transmit opportunity detection methods that effectively exploit the gray space present in high traffic packet networks. Our method adopts a holistic view of the primary network and proactively decides the maximum safe transmit power that does not affect any of the nodes in the primary network. This is achieved via an implicit feedback mechanism from the Primary system to the Secondary system. This is the first time that such a PU-SU feedback link has been shown to naturally arise in the framework of Dynamic Spectrum Access. A novel use of primary packet interarrival duration is developed to rapidly and robustly perform change detection in the primary network's Quality of Service. We develop and evaluate the performance of a battery of tests for such a network state change detection and provide guidelines for transitioning from one test to another in a sequential manner. We thoroughly validate the efficacy of the methods through exhaustive field testing on real world large scale IEEE 802.11 WLAN deployments.