Mechanism of S-Oxygenation by a Cysteine Dioxygenase Model Complex

Devesh Kumar; G. Narahari Sastry; David P. Goldberg; Sam P. de Visser

doi:10.1021/jp208230g

The Character of Low-Lying Excited States of Mixed-Ligand Metal Carbonyls. TD-DFT and CASSCF/CASPT2 Study of [W(CO)4L] (L = ethylenediamine, N,N'-dialkyl-1,4-diazabutadiene) and [W(CO)5L] (L = pyridine, 4-cyanopyridine)

Collection of Czechoslovak Chemical Communications ◽

10.1135/cccc20030089 ◽

2003 ◽

Vol 68 (1) ◽

pp. 89-104 ◽

Cited By ~ 6

Author(s):

Stanislav Záliš ◽

Antonín Vlček ◽

Chantal Daniel

Keyword(s):

Excited States ◽

Substitution Effect ◽

Electronic Transitions ◽

Ligand Field ◽

Metal Carbonyls ◽

Electron Density Redistribution ◽

Time Resolved ◽

Model Complex ◽

Td Dft ◽

Time Resolved Infrared Spectroscopy

This contribution presents the results of the TD-DFT and CASSCF/CASPT2 calculations on [W(CO)4(MeDAB)] (MeDAB = N,N'-dimethyl-1,4-diazabutadiene), [W(CO)4(en)] (en = ethylenediamine), [W(CO)5(py)] (py = pyridine) and [W(CO)5(CNpy)] (CNpy = 4-cyanopyridine) complexes. Contrary to the textbook interpretation, calculations on the model complex [W(CO)4(MeDAB)] and [W(CO)5(CNpy)] show that the lowest W→MeDAB and W→CNpy MLCT excited states are immediately followed in energy by several W→CO MLCT states, instead of ligand-field (LF) states. The lowest-lying excited states of [W(CO)4(en)] system were characterized as W(COeq)2→COax CT excitations, which involve a remarkable electron density redistribution between axial and equatorial CO ligands. [W(CO)5(py)] possesses closely-lying W→CO and W→py MLCT excited states. The calculated energies of these states are sensitive to the computational methodology used and can be easily influenced by a substitution effect. The calculated shifts of [W(CO)4(en)] stretching CO frequencies due to excitation are in agreement with picosecond time-resolved infrared spectroscopy experiments and confirm the occurrence of low-lying M→CO MLCT transitions. No LF electronic transitions were found for either of the complexes studied in the region up to 4 eV.

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Integrative Data Analysis from a Unifying Research Synthesis Perspective

10.1093/oso/9780190676001.003.0020 ◽

2018 ◽

Author(s):

Eun-Young Mun ◽

Anne E. Ray

Keyword(s):

Data Analysis ◽

Large Scale ◽

Research Synthesis ◽

Alcohol Intervention ◽

Data Set ◽

Integrative Data Analysis ◽

Level Data ◽

Model Complex ◽

Wide Range ◽

Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

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Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine

Applied Sciences ◽

10.3390/app11093867 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3867

Author(s):

Zhewei Liu ◽

Zijia Zhang ◽

Yaoming Cai ◽

Yilin Miao ◽

Zhikun Chen

Keyword(s):

Extreme Learning Machine ◽

Supervised Classification ◽

Structured Data ◽

Model Complex ◽

Euclidean Domain ◽

Noise Data ◽

Data Points ◽

Learning Machine ◽

Random Hypergraph ◽

Special Case

Extreme Learning Machine (ELM) is characterized by simplicity, generalization ability, and computational efficiency. However, previous ELMs fail to consider the inherent high-order relationship among data points, resulting in being powerless on structured data and poor robustness on noise data. This paper presents a novel semi-supervised ELM, termed Hypergraph Convolutional ELM (HGCELM), based on using hypergraph convolution to extend ELM into the non-Euclidean domain. The method inherits all the advantages from ELM, and consists of a random hypergraph convolutional layer followed by a hypergraph convolutional regression layer, enabling it to model complex intraclass variations. We show that the traditional ELM is a special case of the HGCELM model in the regular Euclidean domain. Extensive experimental results show that HGCELM remarkably outperforms eight competitive methods on 26 classification benchmarks.

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Complex Uncertainty of Surface Data Modeling via the Type-2 Fuzzy B-Spline Model

Mathematics ◽

10.3390/math9091054 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1054

Author(s):

Rozaimi Zakaria ◽

Abd. Fatah Wahab ◽

Isfarita Ismail ◽

Mohammad Izat Emir Zulkifly

Keyword(s):

Complex Surface ◽

Complex Data ◽

Fuzzy Data ◽

Control Points ◽

B Spline ◽

Spline Surface ◽

Data Control ◽

Surface Data ◽

Model Complex

This paper discusses the construction of a type-2 fuzzy B-spline model to model complex uncertainty of surface data. To construct this model, the type-2 fuzzy set theory, which includes type-2 fuzzy number concepts and type-2 fuzzy relation, is used to define the complex uncertainty of surface data in type-2 fuzzy data/control points. These type-2 fuzzy data/control points are blended with the B-spline surface function to produce the proposed model, which can be visualized and analyzed further. Various processes, namely fuzzification, type-reduction and defuzzification are defined to achieve a crisp, type-2 fuzzy B-spline surface, representing uncertainty complex surface data. This paper ends with a numerical example of terrain modeling, which shows the effectiveness of handling the uncertainty complex data.

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Hospital length of stay for COVID-19 patients: Data-driven methods for forward planning

BMC Infectious Diseases ◽

10.1186/s12879-021-06371-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Bindu Vekaria ◽

Christopher Overton ◽

Arkadiusz Wiśniowski ◽

Shazaad Ahmad ◽

Andrea Aparicio-Castro ◽

...

Keyword(s):

Length Of Stay ◽

Hospital Admission ◽

Failure Time ◽

National Level ◽

Hospital Length ◽

Survival Model ◽

Data Driven ◽

Hospital Length Of Stay ◽

Similar Data ◽

Model Complex

Abstract Background Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data. Method On a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy. Results All methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 8.0 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 18.9 days. Conclusions Given the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases, instead correcting for truncation in the data, but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.

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Stabilization of [4Fe-4S] Ferredoxin Model Complex by a Combination of Hydrophobic Cholyl Group and the Specific NH···S Hydrogen Bond in Aqueous Micellar Solution

Polymer Journal ◽

10.1295/polymj.29.949 ◽

1997 ◽

Vol 29 (11) ◽

pp. 949-951 ◽

Cited By ~ 2

Author(s):

Norikazu Ueyama ◽

Masahiro Inohara ◽

Takafumi Ueno ◽

Taka-aki Okamura ◽

Akira Nakamura

Keyword(s):

Hydrogen Bond ◽

Micellar Solution ◽

Aqueous Micellar Solution ◽

Model Complex

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Big Data: New Tricks for Econometrics

The Journal of Economic Perspectives ◽

10.1257/jep.28.2.3 ◽

2014 ◽

Vol 28 (2) ◽

pp. 3-28 ◽

Cited By ~ 443

Author(s):

Hal R. Varian

Keyword(s):

Big Data ◽

Linear Models ◽

Neural Nets ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Techniques ◽

Model Complex ◽

Vector Machines ◽

Sheer Size ◽

Complex Relationships

Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by economists.

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Unsupervised Anomaly Detection with Distillated Teacher-Student Network Ensemble

Entropy ◽

10.3390/e23020201 ◽

2021 ◽

Vol 23 (2) ◽

pp. 201

Author(s):

Qinfeng Xiao ◽

Jing Wang ◽

Youfang Lin ◽

Wenbo Gongsa ◽

Ganghui Hu ◽

...

Keyword(s):

Anomaly Detection ◽

Multivariate Data ◽

Failure Detection ◽

Superior Performance ◽

Detection Algorithms ◽

Teacher Student ◽

Model Complex ◽

Unsupervised Anomaly Detection ◽

Real World Datasets ◽

Complex Features

We address the problem of unsupervised anomaly detection for multivariate data. Traditional machine learning based anomaly detection algorithms rely on specific assumptions of normal patterns and fail to model complex feature interactions and relations. Recently, existing deep learning based methods are promising for extracting representations from complex features. These methods train an auxiliary task, e.g., reconstruction and prediction, on normal samples. They further assume that anomalies fail to perform well on the auxiliary task since they are never trained during the model optimization. However, the assumption does not always hold in practice. Deep models may also perform the auxiliary task well on anomalous samples, leading to the failure detection of anomalies. To effectively detect anomalies for multivariate data, this paper introduces a teacher-student distillation based framework Distillated Teacher-Student Network Ensemble (DTSNE). The paradigm of the teacher-student distillation is able to deal with high-dimensional complex features. In addition, an ensemble of student networks provides a better capability to avoid generalizing the auxiliary task performance on anomalous samples. To validate the effectiveness of our model, we conduct extensive experiments on real-world datasets. Experimental results show superior performance of DTSNE over competing methods. Analysis and discussion towards the behavior of our model are also provided in the experiment section.

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