gebhart/patrepeval huggingface dataset:
The Crystal Structure of BRAF in Complex with an Organoruthenium Inhibitor Reveals a Mechanism for Inhibition of an Active Form of BRAF Kinase (2009)

Substitution mutations in the BRAF serine/threonine kinase are found in a variety of human cancers. Such mutations occur in approximately 70% of human malignant melanomas, and a single hyperactivating V600E mutation is found in the activation segment of the kinase domain and accounts for more than 90% of these mutations. Given this correlation, the molecular mechanism for BRAF regulation as well as oncogenic activation has attracted considerable interest, and activated forms of BRAF, such as BRAF(V600E), have become attractive targets for small molecule inhibition. Here we report on the identification and subsequent optimization of a potent BRAF inhibitor, CS292, based on an organometallic kinase inhibitor scaffold. A cocrystal structure of CS292 in complex with the BRAF kinase domain reveals that CS292 binds to the ATP binding pocket of the kinase and is an ATP competitive inhibitor. The structure of the kinase-inhibitor complex also demonstrates that CS292 binds to BRAF in an active conformation and suggests a mechanism for regulation of BRAF by phosphorylation and BRAF(V600E) oncogene-induced activation. The structure of CS292 bound to the active form of the BRAF kinase also provides a novel scaffold for the design of BRAF(V600E) oncogene selective BRAF inhibitors for therapeutic application.
Mobile Edge Computing Network Control: Tradeoff between Delay and Cost (2020)

As mobile edge computing (MEC) finds widespread use for relieving the computational burden of compute- and interaction-intensive applications on end user devices, understanding the resulting delay and cost performance is drawing significant attention. While most existing works focus on single-task offloading in single-hop MEC networks, next generation applications (e.g., industrial automation, augmented/virtual reality) require advance models and algorithms for dynamic configuration of multi-task services over multi-hop MEC networks. In this work, we leverage recent advances in dynamic cloud network control to provide a comprehensive study of the performance of multi-hop MEC networks, addressing the key problems of multi-task offloading, timely packet scheduling, and joint computation and communication resource allocation. We present a fully distributed algorithm based on Lyapunov control theory that achieves throughput-optimal performance with delay and cost guarantees. Simulation results validate our theoretical analysis and provide insightful guidelines on the interplay between communication and computation resources in MEC networks.
Eumelanin for flexible, bio-micro-supercapacitors (2016)

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Knockout of Kir6.2 negates ischemic preconditioning-induced protection of myocardial energetics (2003)

Although ischemic preconditioning induces bioenergetic tolerance and thereby remodels energy metabolism that is crucial for postischemic recovery of the heart, the molecular components associated with preservation of cellular energy production, transfer, and utilization are not fully understood. Here myocardial bioenergetic dynamics were assessed by (18)O-assisted (31)P-NMR spectroscopy in control or preconditioned hearts from wild-type (WT) or Kir6.2-knockout (Kir6.2-KO) mice that lack metabolism-sensing sarcolemmal ATP-sensitive K(+) (K(ATP)) channels. In WT vs. Kir6.2-KO hearts, preconditioning induced a significantly higher total ATP turnover (232 +/- 20 vs. 155 +/- 15 nmol x mg protein(-1) x min(-1)), ATP synthesis rate (58 +/- 3 vs. 46 +/- 3% (18)O labeling of gamma-ATP), and ATP consumption rate (51 +/- 4 vs. 31 +/- 4% (18)O labeling of P(i)) after ischemia-reperfusion. Moreover, preconditioning preserved cardiac creatine kinase-catalyzed phosphotransfer in WT (234 +/- 26 nmol x mg protein(-1) x min(-1)) but not Kir6.2-KO (133 +/- 18 nmol x mg protein(-1) x min(-1)) hearts. In contrast with WT hearts, preconditioning failed to preserve contractile recovery in Kir6.2-KO hearts, as tight coupling between postischemic performance and high-energy phosphoryl transfer was compromised in the K(ATP)-channel-deficient myocardium. Thus intact K(ATP) channels are integral in ischemic preconditioning-induced protection of cellular energetic dynamics and associated cardiac performance.
Semi-supervised classification and clustering analysis for variable stars (2022)

ABSTRACT The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning approaches have become commonplace. However, this needs a considerable collection of expert-labelled light curves to achieve adequate performance, which is costly to construct. To solve this problem, we introduce two approaches. First, a semi-supervised hierarchical method, which requires substantially less trained data than supervised methods. Second, a clustering analysis procedure that finds groups that may correspond to classes or subclasses of variable stars. Both methods are primarily supported by dimensionality reduction of the data for visualization and to avoid the curse of dimensionality. We tested our methods with catalogues collected from the Optical Gravitational Lensing Experiment (OGLE), the Catalina Sky Survey (CSS), and the Gaia survey. The semi-supervised method reaches a performance of around 90 per cent for all of our three selected catalogues of variable stars using only $5{{\ \rm per\ cent}}$ of the data in the training. This method is suitable for classifying the main classes of variable stars when there is only a small amount of training data. Our clustering analysis confirms that most of the clusters found have a purity over 90 per cent with respect to classes and 80 per cent with respect to subclasses, suggesting that this type of analysis can be used in large-scale variability surveys as an initial step to identify which classes or subclasses of variable stars are present in the data and/or to build training sets, among many other possible applications.
Layered Silicate‐Induced Enhancement of Fracture Toughness of Epoxy Molding Compounds over a Wide Temperature Range (2005)

Abstract Summary: The fracture toughness of EMC was dramatically increased over a wide temperature range by the addition of a very low volume fraction of layered silicates to EMC filled with micro‐silica particles. Layered silicate‐EMC nanocomposites containing intercalated and the exfoliated silicates were fabricated by using o ‐cresol and biphenyl type epoxy resins, respectively. It was found that exfoliated silicates were more effective than intercalated silicates at toughening EMC at temperatures above T g of the epoxy resin. Enhanced fracture toughness of EMC over a wide temperature range, from ambient to 230 °C has been attributed to the presence of layered silicates, which induces macroscopic crack deflection and severe plastic deformation in front of the crack tip. magnified image
Controllable Ag nanostructure patterning in a microfluidic channel for real-time SERS systems (2014)

We present a microfluidic patterning system for fabricating nanostructured Ag thin films via a polyol method. The fabricated Ag thin films can be used immediately in a real-time SERS sensing system. The Ag thin films are formed on the inner surfaces of a microfluidic channel so that a Ag-patterned Si wafer and a Ag-patterned PDMS channel are produced by the fabrication. The optimum sensing region and fabrication duration for effective SERS detection were determined. As SERS active substrates, the patterned Ag thin films exhibit an enhancement factor (EF) of 4.25 × 10(10). The Ag-patterned polymer channel was attached to a glass substrate and used as a microfluidic sensing system for the real-time monitoring of biomolecule concentrations. This microfluidic patterning system provides a low-cost process for the fabrication of materials that are useful in medical and pharmaceutical detection and can be employed in mass production.
Accelerating Dijkstra's Algorithm Using Multiresolution Priority Queues (2018)

Multiresolution priority queues are recently introduced data structures that can trade-off a small bounded error for faster performance. When used to implement the frontier set in label setting algorithms, they provide a new mathematical approach to classic graph problems such as the computation of shortest paths or minimum spanning trees. To understand how they work, this paper presents a mathematical study of multiresolution label setting algorithms. The theory is general in that the classic mathematical results respond to the particular case where the problem's resolution is infinitely small. The concept of multiresolution helps break the information theoretic barriers of the problem by achieving lower time complexity at the cost of introducing a bounded error. Such error is proven independent of the graph size, a relevant feature in uniform-cost search algorithms where graphs can be infinitely large. Properly tuned, Dijkstra's multiresolution algorithm delivers an amortized cost of O(|V|+|E|), where V and E are the set of vertices and edges, respectively. Benchmarks show speedups of 53% and 26% when applied to Dijkstra's and A* algorithms on a graph of US roads with 87,575 vertices and 121,961 edges.
The Supreme Biodegradable Polymer DES in Acute and Chronic Coronary Syndromes: A PIONEER III Substudy (2023)

Compared with the DP-EES, the Supreme DES seemed safe and effective with a similar TLF at 12 months in both patients with ACS and those with CCS.
Does hydrogen change the fullerene like structure in CNx thin films (2009)

The authors have reported the structure of the nanoclusters in carbon nitride thin films before (Ref-1). In this work, effects of addition of hydrogen in the deposition gas-mixture on the structures of carbon nitride thin films prepared by magnetron sputtering were investigated using Raman spectroscopy. Raman measurements showed that the structures of carbon nanoclusters remained unaffected by addition of hydrogen in the carbon nitride films. On the other hand, the structures of amorphous thin films were affected by addition of hydrogen in the deposition gas-mixture. These are explained in terms of changes in the ratios of the D peak to the G peak intensities and shifts in the G peak centres.
Adhesion Characteristics between Chemical Vapor Deposited Cu and TiN Films: Aspects of Process Integration (2002)

This paper examines the properties of Cu films deposited on different TiN substrates. The TiN substrates were prepared using TiCl4/NH3 chemistry based chemical vapor deposited (CVD) TiN process by varying the preparation methods and temperatures. Properties examined include morphology and crystallinity as well as adhesion characteristics of the Cu films on the TiN substrates. It was found that the Cu film properties were strongly affected by the overall stress in the multi-layer structure and the amount of residual chlorine (Cl) in the TiN film. When the stress conditions of the Cu and TiN films are the same, good adhesion occurred. Higher Cl concentrations caused adhesion enhancement of the Cu film; however, surface morphology was degraded. Angle resolved X-ray photoelectron spectroscopy (XPS) results and depth analysis implied that fast Cu diffusion occurred in the near-interface region. It is suggested that Cl enhances the diffusion of Cu, resulting in the formation of a diffusion interface that improves adhesion.
A METHOD FOR PROFILING THE DISTRIBUTION OF EIGENVALUES USING THE AS METHOD (2010)

This paper is concerned with solving large-scale eigenvalue problems by algebraic sub-structuring and contour integral. We combine Algebraic Sub-structuring (AS) method and the Contour Integral Rayleigh-Ritz (CIRR) method. The AS method calculates approximate eigenpairs fast and has been shown to be efficient for vibration and acoustic analysis. However, the application areas of this method have been limited because its accuracy is usually lower than other methods. On the other hand, if the appropriate domains are chosen, the CIRR method produces accurate solutions. However, it is difficult to choose these domains without the information of eigenvalue distribution. We propose a combination of AS and CIRR such as the AS method is used as a method for profiling a distribution of eigenvalues, and the accurate solutions are produced by the CIRR method using the information of eigenvalue distribution provided by AS. We show our method is effective from the result of applying this method to the molecular orbital calculations.
Predictive Model for Particle Residence Time Distributions in Riser Reactors. Part 1: Model Development and Validation (2017)

In this computational study, we model the mixing of biomass pyrolysis vapor with a solid catalyst in circulating riser reactors with a focus on the determination of solid catalyst residence time distributions (RTDs). A comprehensive set of 2D and 3D simulations were conducted for a pilot-scale riser using the Eulerian–Eulerian two-fluid modeling framework with and without subgrid-scale models for the gas–solid interaction. A validation test case was also simulated and compared to experiments, showing agreement in the pressure gradient and RTD mean and spread. For simulation cases, it was found that for accurate RTD prediction, the Johnson and Jackson partial slip solid boundary condition was required for all models, and a subgrid model is useful so that ultra high resolutions grids, which are very computationally intensive, are not required. We discovered a 2/3 scaling relation for the RTD mean and spread when comparing resolved 2D simulations to validated unresolved 3D subgrid-scale model simulations.
Supplementary Methods from Metabolic Reprogramming by Dual AKT/ERK Inhibition through Imipridones Elicits Unique Vulnerabilities in Glioblastoma (2023)

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Manufacturing of a PET Filament from Recycled Material for Material Extrusion (MEX) (2022)

Due to its low cost and easy use, the use of material extrusion (MEX) as an additive manufacturing (AM) technology has increased rapidly in recent years. However, this process mainly involves the processing of new plastics. Combining the MEX process with polyethylene terephthalate (PET), which offers a high potential for mechanical and chemical recyclability, opens up a broad spectrum of reutilization possibilities. Turning used PET bottles into printable filament for MEX is not only a recycling option, but also an attractive upcycling scenario that can lead to the production of complex, functional parts. This work analyzes the process of extruding recycled PET bottle flakes into a filament, taking different extrusion screws and extrusion parameters into account. The filament is subsequently processed with MEX into tensile tests. An accompanying thermal, rheological and mechanical characterization of the recycled resin is performed to offer a comparison to the virgin material and a commercially available glycol modified polyethylene terephthalate (PETG) filament. The results show the importance of adequate drying parameters prior to the extrusion and the sensitivity of the material to moisture, leading to degradation. The recycled material is more prone to degradation and presents lower viscosities. Mechanical tests display a higher tensile strength of the recycled and virgin resin in comparison to the PETG. The extrusion of the used PET into a filament and the subsequent printing with the MEX process offers a viable recycling process for the discarded material.
Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network (2020)

We propose a recurrent neural network for a "model-free" simulation of a dynamical system with unknown parameters without prior knowledge. The deep learning model aims to jointly learn the nonlinear time marching operator and the effects of the unknown parameters from a time series dataset. We assume that the time series data set consists of an ensemble of trajectories for a range of the parameters. The learning task is formulated as a statistical inference problem by considering the unknown parameters as random variables. A latent variable is introduced to model the effects of the unknown parameters, and a variational inference method is employed to simultaneously train probabilistic models for the time marching operator and an approximate posterior distribution for the latent variable. Unlike the classical variational inference, where a factorized distribution is used to approximate the posterior, we employ a feedforward neural network supplemented by an encoder recurrent neural network to develop a more flexible probabilistic model. The approximate posterior distribution makes an inference on a trajectory to identify the effects of the unknown parameters. The time marching operator is approximated by a recurrent neural network, which takes a latent state sampled from the approximate posterior distribution as one of the input variables, to compute the time evolution of the probability distribution conditioned on the latent variable. In the numerical experiments, it is shown that the proposed variational inference model makes a more accurate simulation compared to the standard recurrent neural networks. It is found that the proposed deep learning model is capable of correctly identifying the dimensions of the random parameters and learning a representation of complex time series data.
Tunable color correction for noisy images (2020)

Color correction is one of the most essential camera imaging operations that transforms a camera-specific RGB color space to a standard color space, typically the XYZ or the sRGB color space. Linear color correction (LCC) and polynomial color correction (PCC) are two widely used methods; they perform the color space transformation using a color correction matrix. Owing to the use of high-order terms, PCC generally achieves lower colorimetric errors than LCC. However, PCC amplifies noise more severely than LCC. Consequently, for noisy images, there exists a trade-off between LCC and PCC regarding color fidelity and noise amplification. We propose a color correction framework called tunable color correction (TCC) that enables us to tune the color correction matrix between the LCC and the PCC models. We also derive a mean squared error calculation model of PCC that enables us to select the best trade-off balance in the TCC framework. We experimentally demonstrate that TCC effectively balances the trade-off for noisy images and outperforms LCC and PCC. We also generalize TCC to multispectral cases and demonstrate its effectiveness by taking the color correction for an RGB-near-infrared sensor as an example.
Fluorogel Elastomers with Tunable Transparency, Elasticity, Shape‐Memory, and Antifouling Properties (2014)

Abstract Omniphobic fluorogel elastomers were prepared by photocuring perfluorinated acrylates and a perfluoropolyether crosslinker. By tuning either the chemical composition or the temperature that control the crystallinity of the resulting polymer chains, a broad range of optical and mechanical properties of the fluorogel can be achieved. After infusing with fluorinated lubricants, the fluorogels showed excellent resistance to wetting by various liquids and anti‐biofouling behavior, while maintaining cytocompatiblity.
Subspace clustering of high-dimensional data: a predictive approach (2012)

In several application domains, high-dimensional observations are collected and then analysed in search for naturally occurring data clusters which might provide further insights about the nature of the problem. In this paper we describe a new approach for partitioning such high-dimensional data. Our assumption is that, within each cluster, the data can be approximated well by a linear subspace estimated by means of a principal component analysis (PCA). The proposed algorithm, Predictive Subspace Clustering (PSC) partitions the data into clusters while simultaneously estimating cluster-wise PCA parameters. The algorithm minimises an objective function that depends upon a new measure of influence for PCA models. A penalised version of the algorithm is also described for carrying our simultaneous subspace clustering and variable selection. The convergence of PSC is discussed in detail, and extensive simulation results and comparisons to competing methods are presented. The comparative performance of PSC has been assessed on six real gene expression data sets for which PSC often provides state-of-art results.
Sustained delivery and efficacy of polymeric nanoparticles containing osteopontin and bone sialoprotein antisenses in rats with breast cancer bone metastasis (2009)

Poor prognosis in mammary carcinoma is associated with a certain expression profile of a defined set of genes including osteopontin and bone sialoprotein. Efficient and specific delivery of antisenses (AS) and a protection of the sequences from degradation are the crucial conditions for AS therapeutic efficiency. We hypothesized that effective and safe AS delivery direceted against these genes could be achieved by polymeric nanoparticles (NP) fabricated from a biocompatible polymer. Due to their nano-size range and small negative charge, AS-NP can overcome the absorption barrier offering increased resistance to nuclease degradation, sustained duration of AS administration, and consequently, prolonged antisense action. The ASs designed against OPN and BSP-II were successfully encapsulated in NP composed of the biodegradable and biocompatible polylactide-co-glycolide polymer (PLGA), exhibiting sustained release and stability of the ASs. The therapeutic efficacy of the AS-NP delivery system was examined in vitro, and in a breast cancer bone metastasis animal model of MDA-MB-231 human breast cancer cells in nude rats. Treatment with OPN-AS or BSP-AS loaded NP in comparison with osmotic mini-pumps (locoregional injection and SC implants, respectively) resulted in a significant decrease in both, tumor bone metastasis incidence and in the size of the lesions in rats with metastases. Despite its smaller dose, AS-NP exhibited a better therapeutic efficacy than osmotic mini-pumps in terms of lesion ratio at later time periods (8-12 weeks). It may be concluded that AS delivery by NP is a promising therapeutic modality providing stability of the encapsulated AS and a sustained release.