gebhart/patrepeval huggingface dataset:
Forecasting Hotspots—A Predictive Analytics Approach (2010)

Current visual analytics systems provide users with the means to explore trends in their data. Linked views and interactive displays provide insight into correlations among people, events, and places in space and time. Analysts search for events of interest through statistical tools linked to visual displays, drill down into the data, and form hypotheses based upon the available information. However, current systems stop short of predicting events. In spatiotemporal data, analysts are searching for regions of space and time with unusually high incidences of events (hotspots). In the cases where hotspots are found, analysts would like to predict how these regions may grow in order to plan resource allocation and preventative measures. Furthermore, analysts would also like to predict where future hotspots may occur. To facilitate such forecasting, we have created a predictive visual analytics toolkit that provides analysts with linked spatiotemporal and statistical analytic views. Our system models spatiotemporal events through the combination of kernel density estimation for event distribution and seasonal trend decomposition by loess smoothing for temporal predictions. We provide analysts with estimates of error in our modeling, along with spatial and temporal alerts to indicate the occurrence of statistically significant hotspots. Spatial data are distributed based on a modeling of previous event locations, thereby maintaining a temporal coherence with past events. Such tools allow analysts to perform real-time hypothesis testing, plan intervention strategies, and allocate resources to correspond to perceived threats.
Planning a safe drilling path for cochlear implantation surgery using image registration techniques (2007)

The procedure currently used for cochlear implementation requires wide surgical exposure to identify anatomic landmarks. At our institution a minimally invasive technique is being developed that will permit to perform the procedure with a small burr hole. This technique does, however, require identifying pre-operatively a surgical path that reaches the cochlea without touching sensitive structures. This path can be found interactively by localizing a point in the facial recess and another point in the basal turn of the cochlea in the pre-operative CT images. Unfortunately, this is a difficult task because these structures are small and difficult to visualize. As an alternative, outlines of those two structures can be drawn first by a qualified surgeon in one image volume selected as an atlas. This atlas can then be registered to other image volumes to permit automatic localization. In this work a 12 parameter affine registration is performed first using mutual information as a similarity measure. After that a non rigid registration algorithm is applied to register the ear in the atlas to the patient's ear. The structures outlined in the atlas are deformed using the computed transformations and the resulting intensity centroids are used to draw the required safe path. The developed algorithm has been tested on eleven ears. In every instance, the path was deemed acceptable.
Identification of 6-amino-1<i>H</i>-pyrazolo[3,4-<i>d</i>]pyrimidines with <i>in vivo</i> efficacy against visceral leishmaniasis (2020)

Visceral leishmaniasis (VL) affects millions of people across the world, largely in developing nations. It is fatal if left untreated and the current treatments are inadequate. As such, there is an urgent need for new, improved medicines. In this paper, we describe the identification of a 6-amino-<i>N</i>-(piperidin-4-yl)-1<i>H</i>-pyrazolo[3,4-<i>d</i>]pyrimidine scaffold and its optimization to give compounds which showed efficacy when orally dosed in a mouse model of VL.
Near-Optimum Blind Decision Feedback Equalization for ATSC Digital Television Receivers (2004)

This paper presents a near-optimum blind decision feedback equalizer (DFE) for the receivers of Advanced Television Systems Committee (ATSC) digital television. By adopting a modified trellis decoder (MTD) with a trace- back depth of 1 for the decision device in the DFE, we obtain a hardware-efficient, blind DFE approaching the performance of an optimum DFE which has no error propagation. In the MTD, the absolute distance is used rather than the squared Euclidean distance for the computation of the branch metrics. This results in a reduction of the computational complexity over the original trellis decoding scheme. Compared to the conventional slicer, the MTD shows an outstanding performance improvement in decision error probability and is comparable to the original trellis decoder using the Euclidean distance. Reducing error propagation by use of the MTD in the DFE leads to the improvement of convergence performance in terms of convergence speed and residual error. Simulation results show that the proposed blind DFE performs much better than the blind DFE with the slicer, and the difference is prominent at the trellis decoder following the blind DFE.
Artificial Intelligence AI Assisted Thermography to Detect Corrosion Under Insulation CUI (2021)

Abstract Corrosion under insulation (CUI) is a critical challenge that affects the integrity of assets where the oil and gas industry is not immune. Its severity arises due to its hidden nature as it can often times go unnoticed. CUI is stimulated, in principle, by moisture ingress through the insulation layers to the surface of the pipeline. This Artificial Intelligence (AI)-powered detection technology stemmed from an urgent need to detect the presence of these corrosion types. The new approach is based on a Cyber Physical (CP) system that maximizes the potential of thermographic imaging by using a Machine Learning application of Artificial Intelligence. In this work, we describe how common image processing techniques from infra-red images of assets can be enhanced using a machine learning approach allowing the detection of locations highly vulnerable to corrosion through pinpointing locations of CUI anomalies and areas of concern. The machine learning is examining the progression of thermal images, captured over time, corrosion and factors that cause this degradation are predicted by extracting thermal anomaly features and correlating them with corrosion and irregularities in the structural integrity of assets verified visually during the initial learning phase of the ML algorithm. The ML classifier has shown outstanding results in predicting CUI anomalies with a predictive accuracy in the range of 85 – 90% projected from 185 real field assets. Also, IR imaging by itself is subjective and operator dependent, however with this cyber physical transfer learning approach, such dependency has been eliminated. The results and conclusions of this work on real field assets in operation demonstrate the feasibility of this technique to predict and detect thermal anomalies directly correlated to CUI. This innovative work has led to the development of a cyber-physical that meets the demands of inspection units across the oil and gas industry, providing a real-time system and online assessment tool to monitor the presence of CUI enhancing the output from thermography technologies, using Artificial Intelligence (AI) and machine learning technology. Additional benefits of this approach include safety enhancement through non-contact online inspection and cost savings by reducing the associated scaffolding and downtime.
6DOF flight control of fixed-wing aircraft by Trajectory Linearization (2011)

In this paper, Trajectory Linearization Control (TLC) is used to design a six degree-of-freedom, autonomous, trajectory-tracking controller for a fixed-wing vehicle dynamics model. TLC combines an open-loop dynamic inverse of the plant dynamics with a closed-loop tracking-error regulator that accounts for model mismatch, disturbances, and excitation of internal dynamics. Feedback gains are obtained symbolically as a function of the nominal trajectory, thus avoiding the use of gain scheduling, and enabling operation across the full flight-envelope without the need for mode switching. The design method is presented, and trajectory tracking simulation results are given for a climbing, bank-to turn maneuver.
Analysis of false-positive microcalcification clusters identified by a mammographic computer-aided detection scheme (1994)

The accuracy of computer-aided detection (CAD) schemes involves a tradeoff between high sensitivity and low false-positive rate. In an on-going study, we are analyzing our CAD scheme for the detection of clustered microcalcifications in digital mammograms to determine the causes of false-negative and false-positive clusters. Two different limitations that lead to false-negatives and false-positives have been identified. The first limitation is imposed by the quality of the digital mammogram, whereas the second is a consequence of the similarities of radiographic features between true and false clusters. In this paper, we examine the effects of image quality, particularly image noise, on the performance of our CAD scheme. Preliminary results indicate that the performance of our scheme is limited by anatomic noise and x-ray quantum noise. Almost all the false positives detected in clinical images by our CAD scheme are caused by a combination of these two forms of noise.
Polyphosphazenes with Novel Architectures:  Influence on Physical Properties and Behavior as Solid Polymer Electrolytes (1998)

Three types of polyphosphazenes with different architectures have been synthesized and characterized. The influence of the polymer architecture on solid ionic conductivity was of particular interest. The first type includes linear oligo- and polyphosphazenes with the general formula [NP(OCH2CH2OCH2CH2OCH3)2]n (MEEP) with different chain lengths. The second type consists of a series of triarmed star-branched polyphosphazenes with the general formula N{CH2CH2NH(CF3CH2O)2P[NP(OCH2CH2OCH2CH2OCH3)2]n}3 with different arm lengths. These were synthesized via the reaction of the tridentate initiator [N{CH2CH2NH(CF3CH2O)2PN−PCl3+}3][PCl6-]3 with the phosphoranimine Cl3PNSiMe3 in CH2Cl2 followed by halogen replacement with sodium (methoxyethoxy)ethoxide. The molecular weights in this system were carefully controlled by variation of the monomer-to-initiator ratios, and the effect of polymer molecular weight on solid ionic conductivity was examined. The third polymer system was designed to examine the effect of complex branching on ionic conductivity. Thus, a highly branched polymer containing five branches from a cyclotriphosphazene pendent side group (with 26 ethyleneoxy units per repeat unit) was synthesized. The conductivity of this polymer in the presence of three different salts has been measured and compared to the behavior of MEEP with a corresponding molecular weight. The mechanism of ion transport in these systems is discussed.
Immunoglobulins stimulate cultured Schwann cell maturation and promote their potential to induce axonal outgrowth (2015)

We conclude that IVIG can positively influence the Schwann cell differentiation process and that it enhances their regenerative potential.
Templated polymers enable selective capture and release of lysophosphatidic acid in human plasma via optimization of non-covalent binding to functional monomers (2015)

The first solid phase extraction materials for selective lysophosphatidic acid (LPA) enrichment from human plasma are described. Molecularly imprinted polymers were designed, synthesized and evaluated as cartridge fillings. They enabled a relatively rapid and simple extraction protocol for LPA without any need for multiple liquid-liquid extraction steps. The five major subspecies of lysophosphatidic acid are readily separated from all other native plasma phospholipids, including those well-known to interfere with LPA quantitation, such as phosphatidylcholine and lysophosphatidylcholine. Outstanding LPA purity is obtained via these solid phase materials in a tandem extraction setup.
Eliminating the capsule-like layer to promote glucose uptake for hyaluronan production by engineered Corynebacterium glutamicum (2020)

Hyaluronan is widely used in cosmetics and pharmaceutics. Development of robust and safe cell factories and cultivation approaches to efficiently produce hyaluronan is of many interests. Here, we describe the metabolic engineering of Corynebacterium glutamicum and application of a fermentation strategy to manufacture hyaluronan with different molecular weights. C. glutamicum is engineered by combinatorial overexpression of type I hyaluronan synthase, enzymes of intermediate metabolic pathways and attenuation of extracellular polysaccharide biosynthesis. The engineered strain produces 34.2 g L<sup>-1</sup> hyaluronan in fed-batch cultures. We find secreted hyaluronan encapsulates C. glutamicum, changes its cell morphology and inhibits metabolism. Disruption of the encapsulation with leech hyaluronidase restores metabolism and leads to hyper hyaluronan productions of 74.1 g L<sup>-1</sup>. Meanwhile, the molecular weight of hyaluronan is also highly tunable. These results demonstrate combinatorial optimization of cell factories and the extracellular environment is efficacious and likely applicable for the production of other biopolymers.
Recommendations for improved data processing from expired gas analysis indirect calorimetry (2010)

\n\t\t\t\t\tThere is currently no universally recommended and accepted method of data processing within the science of indirect calorimetry for either mixing chamber or breath-by-breath systems of expired gas analysis. Exercise physiologists were first surveyed to determine methods used to process oxygen consumption ([OV0312]O 2) data, and current attitudes to data processing within the science of indirect calorimetry. Breath-by-breath datasets obtained from indirect calorimetry during incremental exercise were then used to demonstrate the consequences of commonly used time, breath and digital filter post-acquisition data processing strategies. Assessment of the variability in breath-by-breath data was determined using multiple regression based on the independent variables ventilation (VE), and the expired gas fractions for oxygen and carbon dioxide, FEO 2 and FECO2, respectively. Based on the results of explanation of variance of the breath-by-breath [OV0312]O2 data, methods of processing to remove variability were proposed for time-averaged, breath-averaged and digital filter applications. Among exercise physiologists, the strategy used to remove the variability in sequential [OV0312]O2 measurements varied widely, and consisted of time averages (30 sec [38%], 60 sec [18%], 20 sec [11%], 15 sec [8%]), a moving average of five to 11 breaths (10%), and the middle five of seven breaths (7%). Most respondents indicated that they used multiple criteria to establish maximum [OV0312]O 2 ([OV0312]O2max) including: the attainment of age-predicted maximum heart rate (HRmax) [53%], respiratory exchange ratio (RER) &gt;1.10 (49%) or RER &gt;1.15 (27%) and a rating of perceived exertion (RPE) of &gt;17, 18 or 19 (20%). The reasons stated for these strategies included their own beliefs (32%), what they were taught (26%), what they read in research articles (22%), tradition (13%) and the influence of their colleagues (7%). The combination of VE, FEO 2 and FECO2 removed 96-98% of [OV0312]O2 breath-by-breath variability in incremental and steady-state exercise [OV0312]O2 data sets, respectively. Correction of residual error in [OV0312]O2 datasets to 10% of the raw variability results from application of a 30-second time average, 15-breath running average, or a 0.04 Hz low cut-off digital filter. Thus, we recommend that once these data processing strategies are used, the peak or maximal value becomes the highest processed datapoint. Exercise physiologists need to agree on, and continually refine through empirical research, a consistent process for analysing data from indirect calorimetry.\n\t\t\t\t
Development of a dual phantom technique for measuring the fast neutron component of dose in boron neutron capture therapy (2015)

The dual phantom technique using the combination of a pure water phantom and a 10%-6LiOH phantom developed in this work provides an effective method for dose estimation of the fast neutron component in BNCT. Improvement in the accuracy achieved with the proposed technique results in improved RBE estimation for biological experiments and clinical practice.
Web-Based Visualization of Large Hierarchical Graphs Using Invisible Links in a Hyperbolic Space (2000)

Many real-world data warehouse applications involve navigation of large highly connected hierarchies e.g. web pages, product catalogs, and document hot topics hierarchies. Quite often users are confused and overwhelmed with many complex displays. This paper discusses a new invisible link technique for linking a large highly connected graph in a hyperbolic space without cluttering the display. Only the primary links are shown to the user. All other cross-links are hidden in the property of each node and invisible to the user. These invisible links only appear when the user focuses on the node. “Invisible link” allows a user to freely focus on the hierarchy of interest.
Stable-Isotope Trapping and High-Throughput Screenings of Reactive Metabolites Using the Isotope MS Signature (2004)

A highly efficient method has been developed to detect and identify reactive metabolites, using stable-isotope trapping combined with ESI-MS/MS neutral loss scanning. A mixture of glutathione (GSH, gamma-glutamylcysteinylglycine) and the stable-isotope labeled compound (GSX, gamma-glutamylcysteinylglycine-(13)C(2)-(15)N) was used at an equal molar ratio to trap reactive metabolites generated in microsomal incubations. Samples resulting from incubations were cleaned and concentrated by SPE, followed by LC-MS/MS analyses using constant neutral loss scanning for 129 Da (the gamma-glutamyl moiety) to detect formed GSH conjugates. Unambiguous identification of glutathione adducts was greatly facilitated by the presence of a unique MS signature of a prominent isotopic doublet that differs in mass by 3 Da. Further structural characterization of conjugates was achieved with high confidence by subsequently acquiring MS/MS spectra that were featured by neutral losses of 75 and 129 Da for GSH adducts and 78 and 129 Da for isotopic GSX adducts. The reliability of this method was vigorously validated using a number of compounds known to form reactive metabolites. Superior sensitivity was demonstrated by the capability of the current approach to identify reactive metabolites at low abundance. Because of the unique isotopic MS signature, ultrafast analyses of reactive metabolites were accomplished by direct injection of cleaned samples into mass spectrometers for neutral loss scanning. More importantly, this study has demonstrated the feasibility of the current method for completely automated detection of reactive metabolites via computer-assisted pattern recognition.
Mammalian Sir2 Homolog SIRT3 Regulates Global Mitochondrial Lysine Acetylation (2007)

Homologs of the Saccharomyces cerevisiae Sir2 protein, sirtuins, promote longevity in many organisms. Studies of the sirtuin SIRT3 have so far been limited to cell culture systems. Here, we investigate the localization and function of SIRT3 in vivo. We show that endogenous mouse SIRT3 is a soluble mitochondrial protein. To address the function and relevance of SIRT3 in the regulation of energy metabolism, we generated and phenotypically characterized SIRT3 knockout mice. SIRT3-deficient animals exhibit striking mitochondrial protein hyperacetylation, suggesting that SIRT3 is a major mitochondrial deacetylase. In contrast, no mitochondrial hyperacetylation was detectable in mice lacking the two other mitochondrial sirtuins, SIRT4 and SIRT5. Surprisingly, despite this biochemical phenotype, SIRT3-deficient mice are metabolically unremarkable under basal conditions and show normal adaptive thermogenesis, a process previously suggested to involve SIRT3. Overall, our results extend the recent finding of lysine acetylation of mitochondrial proteins and demonstrate that SIRT3 has evolved to control reversible lysine acetylation in this organelle.
Experimental and Numerical Investigation of the Argon Power Cycle (2018)

Carbon capture has been deemed crucial by the Intergovernmental Panel on Climate Change if the world is to achieve the ambitious goals stated in the Paris agreement. A deeper integration of renewable energy sources is also needed if we are to mitigate the large amount of greenhouse gas emitted as a result of increasing world fossil fuel energy consumption. These new power technologies bring an increased need for distributed fast dispatch power and energy storage that counteract their intermittent nature. A novel technological approach to provide fast dispatch emission free power is the use of the Argon Power Cycle, a technology that makes carbon capture an integral part of its functioning principle. The core concept behind this technology is a closed loop internal combustion engine cycle working with a monoatomic gas in concert with a membrane gas separation unit. By replacing the working fluid of internal combustion engines with a synthetic mixture of monoatomic gases and oxygen, the theoretical thermal efficiency can be increased up to 80%, more than 20% over conventional air cycles. Furthermore, the absence of nitrogen in the system prevents formation of nitrogen oxides, eliminating the need for expensive exhaust gas after-treatment and allowing for efficient use of renewable generated hydrogen fuel. In the case of hydrocarbon fuels, the closed loop nature of the cycle affords to boost the pressure and concentration of gases in the exhaust stream at no penalty to the cycle, providing the driving force to cost effective gas membrane separation of carbon dioxide. In this work we investigated the potential benefits of the Argon Power Cycle to improve upon current stationary power generation systems regarding efficiency, air pollutants and greenhouse gas emissions. A cooperative fuel research engine was used to carry out experiments and evaluate engine performance in relation to its air breathing counterpart. A 30% efficiency improvement was achieved and results showed a reduction on engine heat losses and an overall increase on the indicated mean effective pressure, despite the lesser oxygen content present in the working fluid. Greenhouse gas emissions were reduced as expected due to a substantial increase in efficiency and nitric oxides were eliminated as it was expected. Numerical simulation were carried out to predict the performance and energy penalty of a membrane separation unit. Energy penalties as low as 2% were obtained capturing 100% of the carbon dioxide generated.
CAKE: Code-based Algorithm for Key Encapsulation. (2017)

Current widely-used key exchange (KE) mechanisms will be vulnerable to quantum attacks when sufficiently strong quantum computers become available. Therefore, devising quantum-resistant replacements that combine efficiency with solid security guarantees is an important and challenging task. This paper proposes several contributions towards this goal. First, we introduce “CAKE”, a key encapsulation algorithm based on the QC-MDPC McEliece encryption scheme, with two major improvements: (a) the use of ephemeral keys that defeats a recent reaction attack against MDPC decoding of the corresponding encryption scheme and (b) a highly efficient key generation procedure for QC-MDPC-based cryptosystems. Then, we present an authenticated key exchange protocol based on CAKE, which is suitable for the Internet Key Exchange (IKE) standard. We prove that CAKE is IND-CPA secure, that the protocol is SK-Secure, and suggest practical parameters. Compared to other post-quantum schemes, we believe that CAKE is a promising candidate for post-quantum key exchange standardization.