gebhart/patrepeval huggingface dataset:
Internal Referencing for 13C Position-Specific Isotope Analysis Measured by NMR Spectrometry (2015)

The intramolecular (13)C composition of a molecule retains evidence relevant to its (bio)synthetic history and can provide valuable information in numerous fields ranging from biochemistry to environmental sciences. Isotope ratio monitoring by (13)C NMR spectrometry (irm-(13)C NMR) is a generic method that offers the potential to conduct (13)C position-specific isotope analysis with a precision better than 1‰. Until now, determining absolute values also required measurement of the global (or bulk) (13)C composition (δ(13)Cg) by mass spectrometry. In a radical new approach, it is shown that an internal isotopic chemical reference for irm-(13)C NMR can be used instead. The strategy uses (1)H NMR to quantify both the number of moles of the reference and of the studied compound present in the NMR tube. Thus, the sample preparation protocol is greatly simplified, bypassing the previous requirement for precise purity and mass determination. The key to accurate results is suppressing the effect of radiation damping in (1)H NMR which produces signal distortion and alters quantification. The methodology, applied to vanillin with dimethylsulfone as an internal standard, has an equivalent accuracy (<1‰) to that of the conventional approach. Hence, it was possible to clearly identify vanillin from different origins based on the (13)C isotopic profiles.
Natural Killer Cell Tolerance: Control by Self or Self-Control? (2012)

A major challenge for the immune system is to control pathogens and stressed cells, such as infected or tumors cells, while sparing healthy self-cells. To achieve this tolerance to self, immune cells must recognize and differentiate "self" versus "nonself" and "self" versus "altered self." In the absence of self-tolerance, cells of the adaptive immune system attack healthy cells and cause autoimmune diseases such as lupus, psoriasis, and type I diabetes. Mechanisms at work to ensure tolerance in the innate immune system are still poorly understood. Natural killer cells are innate immune lymphocytes, which have the capacity to kill cellular targets and produce cytokines without prior specific sensitization. Because of these intrinsic effector capacities, tolerance mechanisms must exist to prevent autoreactivity. Herein, we will review the present knowledge on NK cell tolerance.
Older adults with Alzheimer’s disease exhibit an altered relationship between plasma sTREM2 and inflammatory markers (2021)

Abstract Background Alzheimer’s disease (AD) is the 6th leading cause of death in the US, and currently lacks a blood‐based biomarker for early detection. Triggering receptor expressed on myeloid cells 2 (TREM2) is an immune regulator shown to be protective against AD; a soluble form (sTREM2) circulates in peripheral blood and is a potential indicator of disease progression. Genetic variants of TREM2 are risk factors for AD and other neurodegenerative diseases. Multiple studies also have reported an altered peripheral immune response in AD. However, little is known about the relationship between plasma sTREM2 and the altered peripheral immune response in AD. Our objective was to examine the relationship between sTREM2 and inflammatory markers in a spectrum of older adults including cognitively normal (CN) controls, amnestic mild cognitive impairment (MCI) and AD. The hypothesis of this exploratory study was that TREM2‐related inflammatory activity is a stage specific (MCI or AD dementia) biomarker of AD. Method Stages of AD progression were characterized based on symptoms, designated in our study as CN controls, MCI, and AD. Alternatively, CSF amyloid beta, phosphorylated tau and total tau were measured, as described by the NIA‐AA Research Framework “ATN continuum”. Our study aims were to (1) define the relationship between sTREM2‐related inflammatory activity and AD clinical diagnosis, and (2) characterize sTREM2‐related inflammatory activity within the ATN continuum. Plasma sTREM2 and 38 cytokines were measured using the Luminex platform. Result We investigated this hypothesis in an exploratory cohort of CN, MCI, and AD subjects. We observed differential plasma sTREM2 and cytokines levels, correlations, and patterns across both AD groups defined by symptoms and the ATN continuum. This suggests the existence of alteration in normal peripheral sTREM2‐related inflammatory activity along AD and ATN stages. Conclusion These data provide important support to the hypothesis that TREM2‐related inflammatory activity is a stage specific biomarker of AD progression. Our results provides the groundwork for future functional investigation and therapeutic strategy research.
Raman Amplification Properties of Ultralow Loss Photonic Crystal Fibers (2006)

A full-vectorial finite element method is used to investigate Raman amplification performances of ultralow loss photonic crystal fibers. More than 10 dB of net Raman gain can be achieved regardless large scattering losses.
Non-culprit coronary artery percutaneous coronary intervention during acute ST-segment elevation myocardial infarction: insights from the APEX-AMI trial (2010)

Non-culprit coronary interventions were performed at the time of primary PCI in 10% of MVD patients and were significantly associated with increased mortality. Our data support current guideline recommendations discouraging the performance of such procedures in stable primary PCI patients. Prospective randomized study of this issue may be warranted.
A control algorithm of power converters in smart-grids for providing uninterruptible ancillary services (2010)

This paper deals with the use of grid-connected photovoltaic (PV) power plants for supplying the ancillary service of reactive power compensation to the distribution grid. A control strategy has been developed for controlling both real and reactive power exchanged with the distribution network. The paper suggests how the real and reactive power references should be selected in order to maximize the electrical energy generated by PV arrays and to exploit the apparent power of the inverter close to its rated value. The choice suggested for reference signals allows reactive power compensation service also when the solar irradiation is negligible. The control strategy is then applied to a sample grid-connected PV unit, which has been simulated in Simulink environment. Numerical simulations show that, using an adaptive Perturb & Observe algorithm, the maximum power point tracking is achieved in about 2 seconds, both at starting and when the irradiation changes. The inverter is also capable of giving without interruptions the reactive power support to the grid, reducing grid currents and voltage drops along the line respect to the classical configuration where no reactive compensation is present.
Multilayer perceptron-based DFE with lattice structure (2001)

The severely distorting channels limit the use of linear equalizers and the use of the nonlinear equalizers then becomes justifiable. Neural-network-based equalizers, especially the multilayer perceptron (MLP)-based equalizers, are computationally efficient alternative to currently used nonlinear filter realizations, e.g., the Volterra type. The drawback of the MLP-based equalizers is, however, their slow rate of convergence, which limit their use in practical systems. In this work, the effect of whitening the input data in a multilayer perceptron-based decision feedback equalizer (DFE) is evaluated. It is shown from computer simulations that whitening the received data employing adaptive lattice channel equalization algorithms improves the convergence rate and bit error rate performances of multilayer perceptron-based DFE. The adaptive lattice algorithm is a modification to the one developed by Ling and Proakis (1985). The consistency in performance is observed in both time-invariant and time-varying channels. Finally, it is found in this work that, for time-invariant channels, the MLP DFE outperforms the least mean squares (LMS)-based DFE. However, for time-varying channels comparable performance is obtained for the two configurations.
Color constancy and non-uniform illumination: Can existing algorithms work? (2011)

The color and distribution of illuminants can significantly alter the appearance of a scene. The goal of color constancy (CC) is to remove the color bias introduced by the illuminants. Most existing CC algorithms assume a uniformly illuminated scene. However, more often than not, this assumption is an insufficient approximation of real-world illumination conditions (multiple light sources, shadows, interreflections, etc.). Thus, illumination should be locally determined, taking under consideration that multiple illuminants may be present. In this paper we investigate the suitability of adapting 5 state-of-the-art color constancy methods so that they can be used for local illuminant estimation. Given an arbitrary image, we segment it into superpixels of approximately similar color. Each of the methods is applied independently on every superpixel. For improved accuracy, these independent estimates are combined into a single illuminant-color value per superpixel. We evaluated different fusion methodologies. Our experiments indicate that the best performance is obtained by fusion strategies that combine the outputs of the estimators using regression.
Applying data technologies to combat AMR: current status, challenges, and opportunities on the way forward (2022)

Antimicrobial resistance (AMR) is a growing public health threat, estimated to cause over 10 million deaths per year and cost the global economy 100 trillion USD by 2050 under status quo projections. These losses would mainly result from an increase in the morbidity and mortality from treatment failure, AMR infections during medical procedures, and a loss of quality of life attributed to AMR. Numerous interventions have been proposed to control the development of AMR and mitigate the risks posed by its spread. This paper reviews key aspects of bacterial AMR management and control which make essential use of data technologies such as artificial intelligence, machine learning, and mathematical and statistical modelling, fields that have seen rapid developments in this century. Although data technologies have become an integral part of biomedical research, their impact on AMR management has remained modest. We outline the use of data technologies to combat AMR, detailing recent advancements in four complementary categories: surveillance, prevention, diagnosis, and treatment. We provide an overview on current AMR control approaches using data technologies within biomedical research, clinical practice, and in the "One Health" context. We discuss the potential impact and challenges wider implementation of data technologies is facing in high-income as well as in low- and middle-income countries, and recommend concrete actions needed to allow these technologies to be more readily integrated within the healthcare and public health sectors.
Simulations at a newly derived reference model of the human lung (2004)

Based on an in-vitro preparation of an adult human lung combined with high-resolution tomography we developed a realistic graph representation of the bronchial tree of a particular human lung. The graph contains topological information about spatial coordinates, connectivities, diameters and branching angles of 1453 bronchi up to the 17th Horsfield order, and is characterized by asymmetric and multifractal properties. This geometrical model was the basis for the development of an unstructured, multiphase CFD model of the trachea and upper airways. This is directly relevant to research in that intricate anatomical system geometries are employed. Based on medical imaging data CFD modeling associated with complex moving geometries, multi-phase/multi-species physics, and turbulence is incorporated. We contrast this approach with the use of mass-transport equations that describe the gas transport axially. Results show that many of the transport processes within the airways depend quite sensitively on the geometry of the bronchial bifurcations and the structure of the boundaries.
On time-domain coexistence of unlicensed and licensed spectrum users (2012)

This paper aims to exploit spectrum white spaces in time-domain via the Dynamic Spectrum Access (DSA) technology. DSA relies on opportunistic access of licensed spectrum by unlicensed devices. Any “unlicensed-with-licensed” coexistence must ensure “safe” (i.e., un-interfered) communications for the incumbents, while achieving high spectrum-use efficiency for the secondary users. We propose a novel and comprehensive metric called the Coexistence Goodness Factor (CGF) to accurately model the inherent tradeoff between incumbent safety and unlicensed access efficiency for time-domain DSA-based coexistence. To optimize the coexistence performance, we propose a generic, online, dual-mode DSA coexistence protocol. The unlicensed devices attempt to estimate incumbent behavior patterns, and enter the Aggressive Mode (AM) once such a pattern is found, while they stay in the Safe Mode (SM) otherwise. For low-overhead and reliable estimation of incumbent spectrum-usage patterns, we propose algorithms based on Approximate Entropy (ApEn). Further, we design Spectrum-Conscious WiFi (SpeCWiFi), which provisions the proposed DSA coexistence scheme to the base 802.11 MAC. We conduct an extensive experimental evaluation of SpeCWiFi using a MadWifi-based prototype implementation in conjunction with 802.11 wireless cards. The evaluation shows that SpeCWiFi achieves excellent time-domain DSA coexistence in the presence of different types of licensed spectrum, including fast-varying channels that feature short-duration white spaces.
Role of Caspases in Cytokine-Induced Barrier Breakdown in Human Brain Endothelial Cells (2012)

During neuroinflammation, cytokines such as TNF-α and IFN-γ secreted by activated leukocytes and/or CNS resident cells have been shown to alter the phenotype and function of brain endothelial cells (BECs) leading to blood-brain barrier breakdown. In this study, we show that the human BEC line hCMEC/D3 expresses the receptors for TNF-α, TNF receptor 1 and TNF receptor 2, and for IFN-γ. BEC activation with TNF-α alone or in combination with IFN-γ induced endothelial leakage of paracellular tracers. At high cytokine concentrations (10 and 100 ng/ml), this effect was associated with caspase-3/7 activation and apoptotic cell death as evidenced by annexin V staining and DNA fragmentation (TUNEL) assays. In addition, inhibition of JNK and protein kinase C activation at these doses partially prevented activation of caspase-3/7, although only JNK inhibition was partially able to prevent the increase in BEC paracellular permeability induced by cytokines. By contrast, lower cytokine concentrations (1 ng/ml) also led to effector caspase activation, increased paracellular flux, and redistribution of zonula occludens-1 and VE-cadherin but failed to induce apoptosis. Under these conditions, specific caspase-3 and caspase-9, but not caspase-8, inhibitors partially blocked cytokine-induced disruption of tight and adherens junctions and BEC paracellular permeability. Our results suggest that the concentration of cytokines in the CNS endothelial microenvironment determines the extent of caspase-mediated barrier permeability changes, which may be generalized as a result of apoptosis or more subtle as a result of alterations in the organization of junctional complex molecules.
Faculty Opinions recommendation of Enzyme-inhibitor-like tuning of Ca(2+) channel connectivity with calmodulin. (2010)

Ca 2+ channels and calmodulin are two prominent signaling hubs 1 that synergistically impact functions as diverse as cardiac excitability 2 , synaptic plasticity 3 , and gene transcription 4 . It is thereby fitting that these hubs are in some sense coordinated, as the opening of Ca V 1-2 Ca 2+ channels are regulated by a single calmodulin (CaM) constitutively complexed with channels 5 . The Ca 2+ -free form of CaM (apoCaM) is already preassociated with the IQ domain on the channel carboxy terminus, and subsequent Ca 2+ binding to this 'resident' CaM drives conformational changes that then trigger regulation of channel opening 6 . Another potential avenue for channel-CaM coordination could arise from the absence of Ca 2+ regulation in channels lacking a preassociated CaM 6,7 . Natural fluctuations in CaM levels might then influence the fraction of regulatable channels, and thereby the overall strength of Ca 2+ feedback. However, the prevailing view has been that the ultra-strong affinity of channels for apoCaM ensures their saturation with CaM 8 , yielding a significant form of concentration independence between Ca 2+ channels and CaM. Here, we reveal significant exceptions to this autonomy, by combining electrophysiology to characterize channel regulation, with optical FRET sensor determination of free apoCaM concentration in live cells 9 . This approach translates quantitative CaM biochemistry from the traditional test-tube context, into the realm of functioning holochannels within intact cells. From this perspective, we find that long splice forms of Ca V 1.3 and Ca V 1.4 channels include a distal carboxy tail Given the ubiquity of these channels Strategies like ours promise key advances for the in situ analysis of signaling molecules resistant to in vitro reconstitution, such as Ca 2+ channels.
Near-Optimal Microprocessor and Accelerators Codesign with Latency and Throughput Constraints (2013)

A systematic methodology for near-optimal software/hardware codesign mapping onto an FPGA platform with microprocessor and HW accelerators is proposed. The mapping steps deal with the inter-organization, the foreground memory management, and the datapath mapping. A step is described by parameters and equations combined in a scalable template. Mapping decisions are propagated as design constraints to prune suboptimal options in next steps. Several performance-area Pareto points are produced by instantiating the parameters. To evaluate our methodology we map a real-time bio-imaging application and loop-dominated benchmarks.
A biosensing soft robot: Autonomous parsing of chemical signals through integrated organic and inorganic interfaces (2019)

The integration of synthetic biology and soft robotics can fundamentally advance sensory, diagnostic, and therapeutic functionality of bioinspired machines. However, such integration is currently impeded by the lack of soft-matter architectures that interface synthetic cells with electronics and actuators for controlled stimulation and response during robotic operation. Here, we synthesized a soft gripper that uses engineered bacteria for detecting chemicals in the environment, a flexible light-emitting diode (LED) circuit for converting biological to electronic signals, and soft pneu-net actuators for converting the electronic signals to movement of the gripper. We show that the hybrid bio-LED-actuator module enabled the gripper to detect chemical signals by applying pressure and releasing the contents of a chemical-infused hydrogel. The biohybrid gripper used chemical sensing and feedback to make actionable decisions during a pick-and-place operation. This work opens previously unidentified avenues in soft materials, synthetic biology, and integrated interfacial robotic systems.
<title>Evaluation of a new method for stenosis quantification from 3D x-ray angiography images</title> (2001)

A new method for stenosis quantification from 3D X-ray angiography images has been evaluated on both phantom and clinical data. On phantoms, for the parts larger or equal to 3 mm, the standard deviation of the measurement error has always found to be less or equal to 0.4 mm, and the maximum measurement error less than 0.17 mm. No clear relationship has been observed between the performances of the quantification method and the acquisition FoV. On clinical data, the 3D quantification method proved to be more robust to vessel bifurcations than its 3D equivalent. On a total of 15 clinical cases, the differences between 2D and 3D quantification were always less than 0.7 mm. The conclusion is that stenosis quantification from 3D X-4ay angiography images is an attractive alternative to quantification from 2D X-ray images.
Multi-Level Policy and Reward-Based Deep Reinforcement Learning Framework for Image Captioning (2019)

Image captioning is one of the most challenging tasks in AI because it requires an understanding of both complex visuals and natural language. Because image captioning is essentially a sequential prediction task, recent advances in image captioning have used reinforcement learning (RL) to better explore the dynamics of word-by-word generation. However, the existing RL-based image captioning methods rely primarily on a single policy network and reward function-an approach that is not well matched to the multi-level (word and sentence) and multi-modal (vision and language) nature of the task. To solve this problem, we propose a novel multi-level policy and reward RL framework for image captioning that can be easily integrated with RNN-based captioning models, language metrics, or visual-semantic functions for optimization. Specifically, the proposed framework includes two modules: 1) a multi-level policy network that jointly updates the word- and sentence-level policies for word generation; and 2) a multi-level reward function that collaboratively leverages both a vision-language reward and a language-language reward to guide the policy. Furthermore, we propose a guidance term to bridge the policy and the reward for RL optimization. The extensive experiments on the MSCOCO and Flickr30k datasets and the analyses show that the proposed framework achieves competitive performances on a variety of evaluation metrics. In addition, we conduct ablation studies on multiple variants of the proposed framework and explore several representative image captioning models and metrics for the word-level policy network and the language-language reward function to evaluate the generalization ability of the proposed framework.
Identification of GATA3 as a Breast Cancer Prognostic Marker by Global Gene Expression Meta-analysis (2005)

GATA binding protein 3 (GATA3) is a transcriptional activator highly expressed by the luminal epithelial cells in the breast. Here we did a meta-analysis of the available breast cancer cDNA data sets on a cohort of 305 patients and found that GATA3 was one of the top genes with low expression in invasive carcinomas with poor clinical outcome. To validate its prognostic utility, we did a tissue microarray analysis on a cohort of 139 consecutive invasive carcinomas (n = 417 tissue samples) immunostained with a monoclonal antibody against GATA3. Low GATA3 expression was associated with higher histologic grade (P < 0.001), positive nodes (P = 0.002), larger tumor size (P = 0.03), negative estrogen receptor and progesterone receptor (P < 0.001 for both), and HER2-neu overexpression (P = 0.03). Patients whose tumors expressed low GATA3 had significantly shorter overall and disease-free survival when compared with those whose tumors had high GATA3 levels. The hazard ratio of metastasis or recurrence according to the GATA3 status was 0.31 (95% confidence interval, 0.13-0.74; P = 0.009). Cox multivariate analysis showed that GATA3 had independent prognostic significance above and beyond conventional variables. Our data suggest that immunohistochemical analysis of GATA3 may be the basis for a new clinically applicable test to predict tumor recurrence early in the progression of breast cancer.