scholarly journals Catalytic mechanism of the colistin resistance protein MCR-1

2021 ◽  
Author(s):  
Reynier Suardíaz ◽  
Emily Lythell ◽  
Philip Hinchliffe ◽  
Marc van der Kamp ◽  
James Spencer ◽  
...  
Keyword(s):  
Reaction Mechanism ◽  
Antimicrobial Resistance ◽  
Dft Calculations ◽  
Catalytic Reaction ◽  
Catalytic Mechanism ◽  
Cluster Models ◽  
Colistin Resistance ◽  
Resistance Protein ◽  
Catalytic Reaction Mechanism

Elucidation of the catalytic reaction mechanism of MCR-1 enzyme, responsible for the antimicrobial resistance to colistin, using DFT calculations on cluster models.

Cluster models for the interaction of HC1 with non-polar surfaces of γ-Al2O3

1993 ◽  
Vol 286 (3) ◽  
pp. 333-345 ◽  
Author(s):  
Marina Lindblad ◽  
Pakkanen Tapani A.
Keyword(s):  
Cluster Models ◽  
Polar Surfaces

scholarly journals Massive Black Holes in Star Clusters. II. Realistic Cluster Models

2004 ◽  
Vol 613 (2) ◽  
pp. 1143-1156 ◽  
Author(s):  
Holger Baumgardt ◽  
Junichiro Makino ◽  
Toshikazu Ebisuzaki
Keyword(s):  
Black Holes ◽  
Star Clusters ◽  
Cluster Models ◽  
Massive Black Holes

Wavenumber spectra of oscillations of open star cluster models

2015 ◽  
Vol 70 (1) ◽  
pp. 71-82 ◽  
Author(s):  
V. M. Danilov ◽  
S. I. Putkov
Keyword(s):  
Star Cluster ◽  
Cluster Models ◽  
Open Star ◽  
Open Star Cluster

scholarly journals Chemical-shift tensors of heavy nuclei in network solids: a DFT/ZORA investigation of 207Pb chemical-shift tensors using the bond-valence method

2015 ◽  
Vol 17 (38) ◽  
pp. 25014-25026 ◽  
Author(s):  
Fahri Alkan ◽  
C. Dybowski
Keyword(s):  
Chemical Shift ◽  
Quantum Chemistry ◽  
Principal Components ◽  
Bond Valence ◽  
Cluster Models ◽  
Magnetic Shielding ◽  
Heavy Nuclei ◽  
Chemical Shift Tensors ◽  
Accurate Computation ◽  
Bond Valence Model

Accurate computation of 207Pb magnetic shielding principal components is within the reach of quantum chemistry methods by employing relativistic ZORA/DFT and cluster models adapted from the bond valence model.

Routine Clustering of Mobile Sensor Data Facilitates Psychotic Relapse Prediction in Schizophrenia Patients (Preprint)

2021 ◽  
Author(s):  
Joanne Zhou ◽  
Bishal Lamichhane ◽  
Dror Ben-Zeev ◽  
Andrew Campbell ◽  
Akane Sano
Keyword(s):  
Prediction Models ◽  
Gaussian Mixture ◽  
Mobile Sensing ◽  
Cluster Models ◽  
Sensor Data ◽  
Sensing Data ◽  
Model Based ◽  
Clustering Model ◽  
Relapse Prediction ◽  
Lower Variability

BACKGROUND Behavioral representations obtained from mobile sensing data could be helpful for the prediction of an oncoming psychotic relapse in schizophrenia patients and delivery of timely interventions to mitigate such relapse. OBJECTIVE In this work, we aim to develop clustering models to obtain behavioral representations from continuous multimodal mobile sensing data towards relapse prediction tasks. The identified clusters could represent different routine behavioral trends related to daily living of patients as well as atypical behavioral trends associated with impending relapse. METHODS We used the mobile sensing data obtained in the CrossCheck project for our analysis. Continuous data from six different mobile sensing-based modalities (e.g. ambient light, sound/conversation, acceleration etc.) obtained from a total of 63 schizophrenia patients, each monitored for up to a year, were used for the clustering models and relapse prediction evaluation. Two clustering models, Gaussian Mixture Model (GMM) and Partition Around Medoids (PAM), were used to obtain behavioral representations from the mobile sensing data. These models have different notions of similarity between behaviors as represented by the mobile sensing data and thus provide differing behavioral characterizations. The features obtained from the clustering models were used to train and evaluate a personalized relapse prediction model using Balanced Random Forest. The personalization was done by identifying optimal features for a given patient based on a personalization subset consisting of other patients who are of similar age. RESULTS The clusters identified using the GMM and PAM models were found to represent different behavioral patterns (such as clusters representing sedentary days, active but with low communications days, etc.). While GMM based models better characterized routine behaviors by discovering dense clusters with low cluster spread, some other identified clusters had a larger cluster spread likely indicating heterogeneous behavioral characterizations. PAM model based clusters on the other hand had lower variability of cluster spread, indicating more homogeneous behavioral characterization in the obtained clusters. Significant changes near the relapse periods were seen in the obtained behavioral representation features from the clustering models. The clustering model based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0.24 for the relapse prediction task in a leave-one-patient-out evaluation setting. This obtained F2 score is significantly higher than a random classification baseline with an average F2 score of 0.042. CONCLUSIONS Mobile sensing can capture behavioral trends using different sensing modalities. Clustering of the daily mobile sensing data may help discover routine as well as atypical behavioral trends. In this work, we used GMM and PAM-based cluster models to obtain behavioral trends in schizophrenia patients. The features derived from the cluster models were found to be predictive for detecting an oncoming psychotic relapse. Such relapse prediction models can be helpful to enable timely interventions.

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