Ligand Optimization of Exchange Interaction in Co(II) Dimer Single Molecule Magnet by Machine Learning.

Designing single-molecule magnets (SMMs) for potential applications in quantum computing and high-density data storage requires tuning their magnetic properties, especially the strength of the magnetic interaction. These properties can be characterized by first-principles calculations based on density functional theory (DFT). In this work, we study the experimentally synthesized Co(II) dimer (Co2(C5NH5)4(µ-PO2(CH2C6H5)2)3) SMM with the goal to control the exchange energy, ΔEJ, between the Co atoms through tuning of the capping ligands. The experimentally synthesized Co(II) dimer molecule has a very small ΔEJ < 1 meV. We assemble a DFT data set of 1081 ligand substitutions for the Co(II) dimer. The ligand exchange provides a broad range of exchange energies, ΔEJ, from +50 to -200 meV, with 80% of the ligands yielding a small ΔEJ < 10 meV. We identify descriptors for the classification and regression of ΔEJ using gradient boosting machine learning models. We compare one-hot encoded, structure-based, and chemical descriptors consisting of the HOMO/LUMO energies of the individual ligands and the maximum electronegativity difference and bond order for the ligand atom connecting to Co. We observe a similar overall performance with the chemical descriptors outperforming the other descriptors. We show that the exchange coupling, ΔEJ, is correlated to the difference in the average bridging angle between the ferromagnetic and antiferromagnetic states, similar to the Goodenough-Kanamori rules.

Preictal variability of high-frequency oscillation rates in refractory epilepsy.

OBJECTIVE: High-frequency oscillations (HFOs) have shown promising utility in the spatial localization of the seizure onset zone for patients with focal refractory epilepsy. Comparatively few studies have addressed potential temporal variations in HFOs, or their role in the preictal period. Here, we introduce a novel evaluation of the instantaneous HFO rate through interictal and peri-ictal epochs to assess their usefulness in identifying imminent seizure onset. METHODS: Utilizing an automated HFO detector, we analyzed intracranial electroencephalographic data from 30 patients with refractory epilepsy undergoing long-term presurgical evaluation. We evaluated HFO rates both as a 30-minute average and as a continuous function of time and used nonparametric statistical methods to compare individual and population-level differences in rate during peri-ictal and interictal periods. RESULTS: Mean HFO rate was significantly higher for all epochs in seizure onset zone channels versus other channels. Across the 30 patients of our cohort, we found no statistically significant differences in mean HFO rate during preictal and interictal epochs. For continuous HFO rates in seizure onset zone channels, however, we found significant population-wide increases in preictal trends relative to interictal periods. Using a data-driven analysis, we identified a subset of 11 patients in whom either preictal HFO rates or their continuous trends were significantly increased relative to those of interictal baseline and the rest of the population. SIGNIFICANCE: These results corroborate existing findings that HFO rates within epileptic tissue are higher during interictal periods. We show this finding is also present in preictal, ictal, and postictal data, and identify a novel biomarker of preictal state: an upward trend in HFO rate leading into seizures in some patients. Overall, our findings provide preliminary evidence that HFOs can function as a temporal biomarker of seizure onset.

Multi-state extrapolation of UV/Vis absorption spectra with QM/QM hybrid methods.

In this work, we present a simple approach to simulate absorption spectra from hybrid QM/QM calculations. The goal is to obtain reliable spectra for compounds that are too large to be treated efficiently at a high level of theory. The present approach is based on the extrapolation of the entire absorption spectrum obtained by individual subcalculations. Our program locates the main spectral features in each subcalculation, e.g., band peaks and shoulders, and fits them to Gaussian functions. Each Gaussian is then extrapolated with a formula similar to that of ONIOM (Our own N-layered Integrated molecular Orbital molecular Mechanics). However, information about individual excitations is not necessary so that difficult state-matching across subcalculations is avoided. This multi-state extrapolation thus requires relatively low implementation effort while affording maximum flexibility in the choice of methods to be combined in the hybrid approach. The test calculations show the efficacy and robustness of this methodology in reproducing the spectrum computed for the entire molecule at a high level of theory.

Computational prediction of associations between long non-coding RNAs and proteins.

BACKGROUND: Though most of the transcripts are long non-coding RNAs (lncRNAs), little is known about their functions. lncRNAs usually function through interactions with proteins, which implies the importance of identifying the binding proteins of lncRNAs in understanding the molecular mechanisms underlying the functions of lncRNAs. Only a few approaches are available for predicting interactions between lncRNAs and proteins. In this study, we introduce a new method lncPro. RESULTS: By encoding RNA and protein sequences into numeric vectors, we used matrix multiplication to score each RNA-protein pair. This score can be used to measure the interactions between an RNA-protein pair. This method effectively discriminates interacting and non-interacting RNA-protein pairs and predicts RNA-protein interactions within a given complex. Applying this method on all human proteins, we found that the long non-coding RNAs we collected tend to interact with nuclear proteins and RNA-binding proteins. CONCLUSIONS: Compared with the existing approaches, our method shortens the time for training matrix and obtains optimal results based on the model being used. The ability of predicting the associations between lncRNAs and proteins has also been enhanced. Our method provides an idea on how to integrate different information into the prediction process.

A personalized deep learning denoising strategy for low-count PET images.

Objective. Deep learning denoising networks are typically trained with images that are representative of the testing data. Due to the large variability of the noise levels in positron emission tomography (PET) images, it is challenging to develop a proper training set for general clinical use. Our work aims to develop a personalized denoising strategy for the low-count PET images at various noise levels.Approach.We first investigated the impact of the noise level in the training images on the model performance. Five 3D U-Net models were trained on five groups of images at different noise levels, and a one-size-fits-all model was trained on images covering a wider range of noise levels. We then developed a personalized weighting method by linearly blending the results from two models trained on 20%-count level images and 60%-count level images to balance the trade-off between noise reduction and spatial blurring. By adjusting the weighting factor, denoising can be conducted in a personalized and task-dependent way.Main results.The evaluation results of the six models showed that models trained on noisier images had better performance in denoising but introduced more spatial blurriness, and the one-size-fits-all model did not generalize well when deployed for testing images with a wide range of noise levels. The personalized denoising results showed that noisier images require higher weights on noise reduction to maximize the structural similarity and mean squared error. And model trained on 20%-count level images can produce the best liver lesion detectability.Significance.Our study demonstrated that in deep learning-based low dose PET denoising, noise levels in the training input images have a substantial impact on the model performance. The proposed personalized denoising strategy utilized two training sets to overcome the drawbacks introduced by each individual network and provided a series of denoised results for clinical reading.

Virtual high-count PET image generation using a deep learning method.

PURPOSE: Recently, deep learning-based methods have been established to denoise the low-count positron emission tomography (PET) images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan time, and improve image quality for equivalent lesion detectability and clinical diagnosis. In clinical settings, the majority scans are still acquired using standard injection dose with standard scan time. In this work, we applied a 3D U-Net network to reduce the noise of standard-count PET images to obtain the virtual-high-count (VHC) PET images for identifying the potential benefits of the obtained VHC PET images. METHODS: The training datasets, including down-sampled standard-count PET images as the network input and high-count images as the desired network output, were derived from 27 whole-body PET datasets, which were acquired using 90-min dynamic scan. The down-sampled standard-count PET images were rebinned with matched noise level of 195 clinical static PET datasets, by matching the normalized standard derivation (NSTD) inside 3D liver region of interests (ROIs). Cross-validation was performed on 27 PET datasets. Normalized mean square error (NMSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and standard uptake value (SUV) bias of lesions were used for evaluation on standard-count and VHC PET images, with real-high-count PET image of 90 min as the gold standard. In addition, the network trained with 27 dynamic PET datasets was applied to 195 clinical static datasets to obtain VHC PET images. The NSTD and mean/max SUV of hypermetabolic lesions in standard-count and VHC PET images were evaluated. Three experienced nuclear medicine physicians evaluated the overall image quality of randomly selected 50 out of 195 patients' standard-count and VHC images and conducted 5-score ranking. A Wilcoxon signed-rank test was used to compare differences in the grading of standard-count and VHC images. RESULTS: The cross-validation results showed that VHC PET images had improved quantitative metrics scores than the standard-count PET images. The mean/max SUVs of 35 lesions in the standard-count and true-high-count PET images did not show significantly statistical difference. Similarly, the mean/max SUVs of VHC and true-high-count PET images did not show significantly statistical difference. For the 195 clinical data, the VHC PET images had a significantly lower NSTD than the standard-count images. The mean/max SUVs of 215 hypermetabolic lesions in the VHC and standard-count images showed no statistically significant difference. In the image quality evaluation by three experienced nuclear medicine physicians, standard-count images and VHC images received scores with mean and standard deviation of 3.34±0.80 and 4.26 ± 0.72 from Physician 1, 3.02 ± 0.87 and 3.96 ± 0.73 from Physician 2, and 3.74 ± 1.10 and 4.58 ± 0.57 from Physician 3, respectively. The VHC images were consistently ranked higher than the standard-count images. The Wilcoxon signed-rank test also indicated that the image quality evaluation between standard-count and VHC images had significant difference. CONCLUSIONS: A DL method was proposed to convert the standard-count images to the VHC images. The VHC images had reduced noise level. No significant difference in mean/max SUV to the standard-count images was observed. VHC images improved image quality for better lesion detectability and clinical diagnosis.

Multistate QM/QM Extrapolation of UV/Vis Absorption Spectra with Point Charge Embedding.

The simulation of UV/vis absorption spectra of large chromophores is prohibitively expensive with accurate quantum mechanical (QM) methods. Thus, hybrid methods, which treat the core chromophoric region at a high level of theory while the substituent effects are treated with a more computationally efficient method, may provide the best compromise between cost and accuracy. The ONIOM (Our own N-layered Integrated molecular Orbital molecular Mechanics) method has proved successful at describing ground-state processes. However, for excited states, it suffers from difficulties in matching the correct excited states among the different levels of theory. We devised an approach, based on the ONIOM extrapolation formula, to combine two QM levels of theory to extrapolate entire excitation bands rather than individual states. In this contribution, we extend the same QM/QM hybrid scheme to include polarization effects on the core region through point charge embedding. The charges are computed to reproduce the electrostatic potential of the entire chromophore at the low level of theory, with proper constraints to avoid overpolarization issues at the boundary between layers. We test this approach on a variety of model compounds that show how the multistate QM/QM-embedding scheme is able to accurately reproduce the spectrum of the entire system at the high level of theory better than (i) the bare QM/QM hybrid scheme, (ii) the low-level calculation on the entire system, and (iii) the high-level calculation on the core region.

Redaction of false high frequency oscillations due to muscle artifact improves specificity to epileptic tissue.

OBJECTIVE: High Frequency Oscillations (HFOs) are a promising biomarker of epilepsy. HFOs are typically acquired on intracranial electrodes, but contamination from muscle artifacts is still problematic in HFO analysis. This paper evaluates the effect of myogenic artifacts on intracranial HFO detection and how to remove them. METHODS: Intracranial EEG was recorded in 31 patients. HFOs were detected for the entire recording using an automated algorithm. When available, simultaneous scalp EEG was used to identify periods of muscle artifact. Those markings were used to train an automated scalp EMG detector and an intracranial EMG detector. Specificity to epileptic tissue was evaluated by comparison with seizure onset zone and resected volume in patients with good outcome. RESULTS: EMG artifacts are frequent and produce large numbers of false HFOs, especially in the anterior temporal lobe. The scalp and intracranial EMG detectors both had high accuracy. Removing false HFOs improved specificity to epileptic tissue. CONCLUSIONS: Evaluation of HFOs requires accounting for the effect of muscle artifact. We present two tools that effectively mitigate the effect of muscle artifact on HFOs. SIGNIFICANCE: Removing muscle artifacts improves the specificity of HFOs to epileptic tissue. Future HFO work should account for this effect, especially when using automated algorithms or when scalp electrodes are not present.

Coupled Cluster Theory with Induced Dipole Polarizable Embedding for Ground and Excited States.

In this work, we present the theory and implementation of the coupled cluster single and double excitations (CCSD) method combined with a classical polarizable molecular mechanics force field (MMPol) based on the induced dipole model. The method is developed to compute electronic excitation energies within the state specific (SS) and linear response (LR) formalisms for the interaction of the quantum mechanical and classical regions. Furthermore, we consider an approximate expression of the correlation energy, originally developed for CCSD with implicit solvation models, where the interaction term is linear in the coupled cluster density. This approximation allows us to include the explicit contribution of the environment to the CC equations without increasing the computational effort. The test calculations on microsolvated systems, where the CCSD/MMPol method is compared to full CCSD calculations, demonstrates the reliability of this computational protocol for all interaction schemes (errors < 2%). We also show that it is important to include induced dipoles on all atom centers of the classical region and that too diffuse functions in the basis set may be problematic due to too strong interaction with the environment.

An EOM-CCSD-PCM Benchmark for Electronic Excitation Energies of Solvated Molecules.

In this work, we benchmark the equation of motion coupled cluster with single and double excitations (EOM-CCSD) method combined with the polarizable continuum model (PCM) for the calculation of electronic excitation energies of solvated molecules. EOM-CCSD is one of the most accurate methods for computing one-electron excitation energies, and accounting for the solvent effect on this property is a key challenge. PCM is one of the most widely employed solvation models due to its adaptability to virtually any solute and its efficient implementation with density functional theory methods (DFT). Our goal in this work is to evaluate the reliability of EOM-CCSD-PCM, especially compared to time-dependent DFT-PCM (TDDFT-PCM). Comparisons between calculated and experimental excitation energies show that EOM-CCSD-PCM consistently overestimates experimental results by 0.4-0.5 eV, which is larger than the expected EOM-CCSD error in vacuo. We attribute this decrease in accuracy to the approximated solvation model. Thus, we investigate a particularly important source of error: the lack of H-bonding interactions in PCM. We show that this issue can be addressed by computing an energy shift, ΔHB, from bare-PCM to microsolvation + PCM at DFT level. Our results show that such a shift is independent of the functional used, contrary to the absolute value of the excitation energy. Hence, we suggest an efficient protocol where the EOM-CCSD-PCM transition energy is corrected by ΔHB(DFT), which consistently improves the agreement with the experimental measurements.