gebhart/patrepeval huggingface dataset:
Biochemical Studies on Buckwheat<i>α</i>-Glucosidase (1969)

Journal Article Biochemical Studies on Buckwheat α-Glucosidase: Part III. Transglucosylation Action of the Enzyme and Isolation of the Reaction Products Get access Miho Takahashi, Miho Takahashi Department of Agricultural Chemistry, Faculty of Agriculture, Hokkaido University, Sapporo Search for other works by this author on: Oxford Academic Google Scholar Tokuji Shimomura, Tokuji Shimomura Department of Agricultural Chemistry, Faculty of Agriculture, Hokkaido University, Sapporo Search for other works by this author on: Oxford Academic Google Scholar Seiya Chiba Seiya Chiba Department of Agricultural Chemistry, Faculty of Agriculture, Hokkaido University, Sapporo Search for other works by this author on: Oxford Academic Google Scholar Agricultural and Biological Chemistry, Volume 33, Issue 10, 1 October 1969, Pages 1399–1410, https://doi.org/10.1080/00021369.1969.10859481 Published: 01 October 1969 Article history Received: 07 January 1969 Published: 01 October 1969
A comparative study with a 755 nm picosecond Alexandrite laser with a diffractive lens array and a 532 nm/1064 nm Nd:YAG with a holographic optic (2017)

This study demonstrates that both fractional picosecond devices produce vacuoles in the skin, which are most likely due to areas of laser induced optical breakdown (LIOB). In the patients (skin type II-IV) we observed scatter areas of hemorrhage in the skin, due to vascular damage with the 532 nm and 1064 nm, but not with 755 nm wavelengths. Lasers Surg. Med. 50:37-44, 2018. © 2017 Wiley Periodicals, Inc.
Negative surface ionization of hydrogen atoms and molecules (1996)

The total yield of H− ions, Y(Ein), produced in backscattering of low-energy H+ and H+2 ions from polycrystalline gold, tungsten, and molybdenum converter surfaces was measured at normal incidence in the energy range Ein=2–30 eV per nucleus. The yield per nucleus is independent of the ion mass. This indicates that the molecular ions are dissociated before colliding with the converter surface. A universal expression for Y(Ein) was developed by combining the electron tunneling theory with atomic scattering theory. This expression agrees well with measurements. The yield is completely characterized by two parameters, Eth/RE and RNη0, which can be determined experimentally: Y=0 for Ein=Eth/RE, and Y approaches the maximum yield Rη0 as Ein increases. These parameters were determined from measured H− yields in ion beam backscattering experiments, as well as for backscattering of thermal distributions of hydrogen atoms. For beam experiments, the maximum yield of 0.3 per nucleus was obtained for Mo/Cs converters with 1.5 eV work function. A higher maximum yield of 0.42 was obtained from experiments on backscattering thermal distributions of H atoms. This is attributed to high extraction fields. The universal yield formula made it possible to compare the results of the two different types of experiments.
A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering (2021)

Chatbots are envisioned to dramatically change the future of Software Engineering, allowing practitioners to chat and inquire about their software projects and interact with different services using natural language. At the heart of every chatbot is a Natural Language Understanding (NLU) component that enables the chatbot to understand natural language input. Recently, many NLU platforms were provided to serve as an off-the-shelf NLU component for chatbots, however, selecting the best NLU for Software Engineering chatbots remains an open challenge. Therefore, in this paper, we evaluate four of the most commonly used NLUs, namely IBM Watson, Google Dialogflow, Rasa, and Microsoft LUIS to shed light on which NLU should be used in Software Engineering based chatbots. Specifically, we examine the NLUs’ performance in classifying intents, confidence scores stability, and extracting entities. To evaluate the NLUs, we use two datasets that reflect two common tasks performed by Software Engineering practitioners, 1) the task of chatting with the chatbot to ask questions about software repositories 2) the task of asking development questions on Q&A forums (e.g., Stack Overflow). According to our findings, IBM Watson is the best performing NLU when considering the three aspects (intents classification, confidence scores, and entity extraction). However, the results from each individual aspect show that, in intents classification, IBM Watson performs the best with an F1-measure <inline-formula><tex-math notation="LaTeX">$>$</tex-math></inline-formula> 84%, but in confidence scores, Rasa comes on top with a median confidence score higher than 0.91. Our results also show that all NLUs, except for Dialogflow, generally provide trustable confidence scores. For entity extraction, Microsoft LUIS and IBM Watson outperform other NLUs in the two SE tasks. Our results provide guidance to software engineering practitioners when deciding which NLU to use in their chatbots.
3D surface reconstruction based on Kinect (2013)

In this paper, a simple and efficient 3D surface reconstruction algorithm which can be implemented on mid or low-end computers is proposed. In this algorithm, the 3D images are obtained by a depth motion sensing device named Kinect. Data from region of interests are obtained by segmenting images whose k-neighbor relationship is established with a k-dimensional tree (KD-tree). After patching the point cloud holes with a bicubic spline function and a triangular mesh, the 3D surface of the object is reconstructed. Furthermore, the neighborhoods defined differently are used to connect regions since several non-connected regions exist in the point cloud. Experimental results show that the proposed algorithm based on the Kinect platform can be used to reconstruct complete and smooth surface even if the point cloud is non-connected in space and some of the captured data are lost.
Traffic signal control by distributed Reinforcement Learning with min-sum communication (2017)

Reinforcement learning (RL) is a promising technique for adaptive signal control based on real-time traffic data. However, the implementation of RL in large-scale traffic signal network remains an open challenge due to the extremely high computational cost. In this paper, we integrate a distributed communication technique into RL so that the learning and searching cost of large-scale multi-agent system can be significantly reduced. In particular, we first decompose the global Q-function into local Q-functions, so each local signal agent can compute its own optimal policy based on local observations. We then apply the max-sum message passing algorithm to share information among agents, for finding a stable and optimal global policy that is accepted by all agents. Due to this information sharing characteristic, our approach is more optimal than decentralized RL, and it is much more efficient than centralized RL. Further, we perform realistic simulation to demonstrate that this distributed RL approach outperforms decentralized RL algorithm, as well as some typical heuristic decentralized control methods.
Error statistics and construction of error correcting codes for differentially encoded CPSK channels (Corresp.) (1978)

A simple Markov chain model for differentially encoded CPSK Channels faciliates the calculation of the probability of the occurrence of several most probable error patterns in a given codeword. Several error correcting codes which can efficiently correct several most-probable error patterns are proposed and their error performances are analyzed.
[Deposition from Alice S. Nicholas by District Attorney Henry Wade, January 20, 1964] (1964)

Deposition from Alice S. Nichols taken by District Attorney Henry Wade regarding her relationship with Jack Ruby.
A review of rapid serial visual presentation-based brain–computer interfaces (2017)

Rapid serial visual presentation (RSVP) combined with the detection of event-related brain responses facilitates the selection of relevant information contained in a stream of images presented rapidly to a human. Event related potentials (ERPs) measured non-invasively with electroencephalography (EEG) can be associated with infrequent targets amongst a stream of images. Human-machine symbiosis may be augmented by enabling human interaction with a computer, without overt movement, and/or enable optimization of image/information sorting processes involving humans. Features of the human visual system impact on the success of the RSVP paradigm, but pre-attentive processing supports the identification of target information post presentation of the information by assessing the co-occurrence or time-locked EEG potentials. This paper presents a comprehensive review and evaluation of the limited, but significant, literature on research in RSVP-based brain-computer interfaces (BCIs). Applications that use RSVP-based BCIs are categorized based on display mode and protocol design, whilst a range of factors influencing ERP evocation and detection are analyzed. Guidelines for using the RSVP-based BCI paradigms are recommended, with a view to further standardizing methods and enhancing the inter-relatability of experimental design to support future research and the use of RSVP-based BCIs in practice.
The impact of teacher–student relationships on the development of talent: Connections with Gagné’s DMGT 2.0 (2016)

Teacher–student relationships are a critical component in education, influencing development across emotional, behavioural, academic and social domains. While there is considerable research on teacher–student relationships in the everyday classroom, little has been identified on the impact of teacher– student relationships within the gifted and talented population. This review draws together research on teacher–student relationships and Gagné’s gifted and talented developmental theory. The paper firstly discusses teacher–student relationships in a broad sense and defines the gifted and talented developmental process in line with Gagné’s theory. The paper then combines the two bodies of research to explore and discuss the potential impacts that teacher–student relationships may have on the development of talent. In particular, the paper will discuss the effects that teacher–student relationships have on the identification processes needed to acknowledge those who are gifted and talented. Additionally, the paper will explore how motivation in regards to academic achievement can be fuelled by negative teacher–student relationships. A discussion will follow on how relationships formed with peers can affect those who are gifted and talented and how the teacher–student relationship can have a significant role in how peer relationships develop.
Neo‐kyotorphin, an analgesic peptide isolated from human lung carcinoma (1986)

A pentapeptide with analgesic activity has been isolated from human lung squamous cell carcinoma and from three other types of propagated tumors of human lung small-cell carcinoma (SCC), adenoma (AD) and large-cell carcinoma (LCC) in nude mice. The amino acid sequence of the peptide has been revealed to be H-Thr-Ser-Lys-Tyr-Arg-OH, which is exactly the same as that of neo-kyotorphin, an analgesic peptide originally isolated from bovine brain [(1982) Life Sci. 31, 1733]. No neo-kyotorphin could be isolated from normal lung tissue using the same procedures as those used for carcinomas. The results suggest that the presence of neo-kyotorphin in the lung carcinoma may represent the ectopic expression of peptide hormone. Our findings constitute the first example of a human lung carcinoma producing analgesic peptide.
NF-kappaB Signaling Pathways in Neurological Inflammation: A Mini Review (2015)

The NF-κB (nuclear factor κ-light-chain-enhancer of activated B cells) transcription factor family is a pleiotropic regulator of many cellular signaling pathways, providing a mechanism for the cells in response to a wide variety of stimuli linking to inflammation. The stimulated cells will be regulated by not only the canonical but also non-canonical NF-κB pathways. To initiate both of these pathways, IκB-degradation triggers NF-κB release and the nuclear translocated-heterodimer (or homodimer) can associate with the κB sites of promoter to regulate the gene transcriptions. NF-κB ubiquitously expresses in neurons and the constitutive NF-κB activation is associated with processing of neuronal information. NF-κB can regulate the transcription of genes such as chemokines, cytokines, proinflammatory enzymes, adhesion molecules, proinflammatory transcription factors, and other factors to modulate the neuronal survival. In neuronal insult, NF-κB constitutively active in neuron cell bodies can protect neurons against different injuries and regulate the neuronal inflammatory reactions. Besides neurons, NF-κB transcription factors are abundant in glial cells and cerebral blood vessels and the diverse functions of NF-κB also regulate the inflammatory reaction around the neuronal environment. NF-κB transcription factors are abundant in the brain and exhibit diverse functions. Several central nerve system (CNS) diseases are linked to NF-κB activated by inflammatory mediators. The RelA and c-Rel expression produce opposite effects on neuronal survival. Importantly, c-Rel expression in CNS plays a critical role in anti-apoptosis and reduces the age-related behaviors. Moreover, the different subunits of NF-κB dimer formation can modulate the neuroninflammation, neuronal protection, or neurotoxicity. The diverse functions of NF-κB depend on the subunits of the NF-κB dimer-formation which enable us to develop a therapeutic approach to neuroinflammation based on a new concept of inflammation as a strategic tool in neuronal cells. However, the detail role of NF-κB in neuroinflammation, remains to be clarified. In the present article, we provide an updated review of the current state of our knowledge about relationship between NF-κB and neuroinflammation.
Evaluation of the Heat Inactivation of <i>Escherichia coli</i> and <i>Lactobacillus plantarum</i> by Differential Scanning Calorimetry (2002)

Differential scanning calorimetry (DSC) is used to evaluate the thermal stability and reversibility after heat treatment of transitions associated with various cellular components of Escherichia coli and Lactobacillus plantarum. The reversibility and the change in the thermal stability of individual transitions are evaluated by a second temperature scan after preheating in the DSC to various temperatures between 40 and 130 degrees C. The viability of bacteria after a heat treatment between 55 and 70 degrees C in the DSC is determined by both plate count and calorimetric data. The fractional viability values based on calorimetric and plate count data show a linear relationship. Viability loss and the irreversible change in DSC thermograms of pretreated whole cells are highly correlated between 55 and 70 degrees C. Comparison of DSC scans for isolated ribosomes shows that the thermal stability of E. coli ribosomes is greater than that of L. plantarum ribosomes, consistent with the greater thermal tolerance of E. coli observed from viability loss and DSC scans of whole cells.
Isolation and characterization of a ‘quinone-trapping’ substance from a crude <i>Carica papaya</i> protein preparation (1998)

Abstract The oxidation of different phenols [4-methylcatechol, chlorogenic acid, (−)-epicatechin and (+)-catechin] by endive polyphenoloxidase (PPO) was investigated in the presence of an extract from Carica papaya. The occurrence of cysteine and another ‘quinone-trapping’ substance in the extract was demonstrated. The unknown substance was purified, as a 4-methylcatechol conjugate form, by a combination of Bio-gel Pz chromatography and semipreparative high-performance liquid chromatography (HPLC). Use of liquid chromatography/tandem mass spectroscopy (LC–MS/MS) equipment and an amino acid analyser allowed us to identify this agent as a dipeptide cysteine-glutamic acid, commercially available as γ-Glu-Cys. γ-Glu-Cys formed one adduct compound with 4-methylcatechol and chlorogenic acid, and two with the flavan-3-ols. The thiol adducts were not substrates for endive PPO but, in the case of the 4-methylcatechol conjugate, they acted as competitive PPO inhibitors.
Ein neues Diterpen aus Salvia officinalis L. und eine Notiz zur Konstitution von Pikrosalvin (1964)

Abstract Aus den Blättern von Salvia officinalis L. konnte eine neue Diterpen‐diphenol‐carbonsäure (äls Diacetylverbindung) isoliert und in ihrer Konstitution aufgeklärt werden. Durch eine relativ einfache und übersichtlich verlaufende Reaktionsfolge liess sich eine Verknüpfung rnit dem in derselben Pflanzenart von B RIESKORN et al. aufgefundenen Pikrosalvin erzielen, womit die von diesen Autoren postulierte Konstitution XIII (vgl. [6]) für Pikrosalvin im Sinne der Formel XIV modifiziert werden sollte. Pikrosalvin selbst liess sich in dem untersuchten Drogenmuster nicht auffinden.
Automatic SLA Matching and Provider Selection in Grid and Cloud Computing Markets (2012)

Cloud computing is a novel computing paradigm that offers data, software, and hardware services in a manner similar to traditional utilities such as water, electricity, and telephony. Usually, in Cloud and Grid computing, contracts between traders are established using Service Level Agreements (SLAs), which include objectives of service usage. However, due to the rapidly growing number of service offerings and the lack of a standard for their specification, manual service selection is a costly task, preventing the successful implementation of ubiquitous computing on demand. In order to counteract these issues, automatic methods for matching SLAs are necessary. In this paper, we introduce a method for finding semantically equal SLA elements from differing SLAs by utilizing several machine learning algorithms. Moreover, we utilize this method to enable automatic selection of optimal service offerings for Cloud and Grid users. Finally, we introduce a framework for automatic SLA management, present a simulation-based evaluation, and demonstrate several significant benefits of our approach for Cloud and Grid users.
Scents and Sensibility: A Molecular Logic of Olfactory Perception (Nobel Lecture) (2005)

Abstract For Abstract see ChemInform Abstract in Full Text.
Image registration by maximization of combined mutual information and gradient information (2000)

Mutual information has developed into an accurate measure for rigid and affine monomodality and multimodality image registration. The robustness of the measure is questionable, however. A possible reason for this is the absence of spatial information in the measure. The present paper proposes to include spatial information by combining mutual information with a term based on the image gradient of the images to be registered. The gradient term not only seeks to align locations of high gradient magnitude, but also aims for a similar orientation of the gradients at these locations. Results of combining both standard mutual information as well as a normalized measure are presented for rigid registration of three-dimensional clinical images [magnetic resonance (MR), computed tomography (CT), and positron emission tomography (PET)]. The results indicate that the combined measures yield a better registration function does mutual information or normalized mutual information per se. The registration functions are less sensitive to low sampling resolution, do not contain incorrect global maxima that are sometimes found in the mutual information function, and interpolation-induced local minima can be reduced. These characteristics yield the promise of more robust registration measures. The accuracy of the combined measures is similar to that of mutual information-based methods.
Three-dimensional clustered speckle fields: theory, simulations and experimental verification (2012)

Clustered speckle patterns are a particular type of speckles that appear when a coherently illuminated diffuser is imaged through a multiple aperture pupil mask attached to a lens. The cluster formation is the result of the complex speckle modulations of the multiple interferences produced by the apertures. In this paper, a three-dimensional analytical approach to simulate cluster speckles everywhere after the lens is presented. This approach has the possibility of including multiple aperture masks at the lens and at the diffuser, in contrast to previous works which were also limited to the description of the patterns only at the image plane. This model contributes to the development of tailor made speckle patterns that can be used in diverse optical applications, including those lying in the focus region. The approach is validated under different conditions by comparing experimental results with simulations on a statistical basis. Some aspects of possible uses of these clusters are briefly revised, such as optical trapping, manipulation and metrology.
Deep Learning Based Regression and Multi-class Models for Acute Oral Toxicity Prediction (2017)

For quantitative structure-property relationship (QSPR) studies in chemoinformatics, it is important to get interpretable relationship between chemical properties and chemical features. However, the predictive power and interpretability of QSPR models are usually two different objectives that are difficult to achieve simultaneously. A deep learning architecture using molecular graph encoding convolutional neural networks (MGE-CNN) provided a universal strategy to construct interpretable QSPR models with high predictive power. Instead of using application-specific preset molecular descriptors or fingerprints, the models can be resolved using raw and pertinent features without manual intervention or selection. In this study, we developed acute oral toxicity (AOT) models of compounds using the MGE-CNN architecture as a case study. Three types of high-level predictive models: regression model (deepAOT-R), multi-classification model (deepAOT-C) and multi-task model (deepAOT-CR) for AOT evaluation were constructed. These models highly outperformed previously reported models. For the two external datasets containing 1673 (test set I) and 375 (test set II) compounds, the R2 and mean absolute error (MAE) of deepAOT-R on the test set I were 0.864 and 0.195, and the prediction accuracy of deepAOT-C was 95.5% and 96.3% on the test set I and II, respectively. The two external prediction accuracy of deepAOT-CR is 95.0% and 94.1%, while the R2 and MAE are 0.861 and 0.204 for test set I, respectively.