Machine Learning Prediction of Quantum Yields and Wavelengths of Aggregation-Induced Emission Molecules.

The aggregation-induced emission (AIE) effect exhibits a significant influence on the development of luminescent materials and has made remarkable progress over the past decades. The advancement of high-performance AIE materials requires fast and accurate predictions of their photophysical properties, which is impeded by the inherent limitations of quantum chemical calculations. In this work, we present an accurate machine learning approach for the fast predictions of quantum yields and wavelengths to screen out AIE molecules. A database of about 563 organic luminescent molecules with quantum yields and wavelengths in the monomeric/aggregated states was established. Individual/combined molecular fingerprints were selected and compared elaborately to attain appropriate molecular descriptors. Different machine learning algorithms combined with favorable molecular fingerprints were further screened to achieve more accurate prediction models. The simulation results indicate that combined molecular fingerprints yield more accurate predictions in the aggregated states, and random forest and gradient boosting regression algorithms show the best predictions in quantum yields and wavelengths, respectively. Given the successful applications of machine learning in quantum yields and wavelengths, it is reasonable to anticipate that machine learning can serve as a complementary strategy to traditional experimental/theoretical methods in the investigation of aggregation-induced luminescent molecules to facilitate the discovery of luminescent materials.

The Neural Separability of Emotion Reactivity and Regulation.

One foundational distinction in affective science is between emotion reactivity and regulation. This conceptual distinction has long been assumed to be instantiated in spatially separable brain systems (a typical example: amygdala/insula for reactivity and frontoparietal areas for regulation). In this research, we begin by reviewing previous findings that support and contradict the neural separability hypothesis concerning emotional reactivity and regulation. Further, we conduct a direct test of this hypothesis with empirical data. In five studies involving healthy and clinical samples (total n = 336), we assessed neural responses using fMRI while participants were asked to either react naturally or regulate their emotions (using reappraisal) while viewing emotionally evocative stimuli. Across five studies, we failed to find support for the neural separability hypothesis. In univariate analyses, both presumptive "reactivity" and "regulation" brain regions demonstrated equal or greater activation for the reactivity contrast than for the regulation contrast. In multivariate pattern analyses (MVPA), classifiers decoded reactivity (vs. neutral) trials more accurately than regulation (vs. reactivity) trials using multivoxel data in both presumptive "reactivity" and "regulation" regions. These findings suggest that emotion reactivity and regulation-as measured via fMRI-may not be as spatially separable in the brain as previously assumed. Our secondary whole-brain analyses revealed largely consistent results. We discuss the two theoretical possibilities regarding the neural separability hypothesis and offer thoughts for future research. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-023-00227-9.

The Role of Emotion Regulation, Affect, and Sleep in Individuals With Sleep Bruxism and Those Without: Protocol for a Remote Longitudinal Observational Study.

BACKGROUND: Sleep bruxism (SB) is an oral behavior characterized by high levels of repetitive jaw muscle activity during sleep, leading to teeth grinding and clenching, and may develop into a disorder. Despite its prevalence and negative outcomes on oral health and quality of life, there is currently no cure for SB. The etiology of SB remains poorly understood, but recent research suggests a potential role of negative emotions and maladaptive emotion regulation (ER). OBJECTIVE: This study's primary aim investigates whether ER is impaired in individuals with SB, while controlling for affective and sleep disturbances. The secondary aim tests for the presence of cross-sectional and longitudinal mediation pathways in the bidirectional relationships among SB, ER, affect, and sleep. METHODS: The study used a nonrandomized repeated-measures observational design and was conducted remotely. Participants aged 18-49 years underwent a 14-day ambulatory assessment. Data collection was carried out using electronic platforms. We assessed trait and state SB and ER alongside affect and sleep variables. We measured SB using self-reported trait questionnaires, ecological momentary assessment (EMA) for real-time reports of SB behavior, and portable electromyography for multinight assessment of rhythmic masticatory muscle activity. We assessed ER through self-reported trait questionnaires, EMA for real-time reports of ER strategies, and heart rate variability derived from an electrocardiography wireless physiological sensor as an objective physiological measure. Participants' trait affect and real-time emotional experiences were obtained using self-reported trait questionnaires and EMA. Sleep patterns and quality were evaluated using self-reported trait questionnaires and sleep diaries, as well as actigraphy as a physiological measure. For the primary objective, analyses will test for maladaptive ER in terms of strategy use frequency and effectiveness as a function of SB using targeted contrasts in the general linear model. Control analyses will be conducted to examine the persistence of the SB-ER relationship after adjusting for affective and sleep measures, as well as demographic variables. For the secondary objective, cross-sectional and longitudinal mediation analyses will test various competing models of directional effects among self-reported and physiological measures of SB, ER, affect, and sleep. RESULTS: This research received funding in April 2017. Data collection took place from August 2020 to March 2022. In all, 237 participants were eligible and completed the study. Data analysis has not yet started. CONCLUSIONS: We hope that the effort to thoroughly measure SB and ER using gold standard methods and cutting-edge technology will advance the knowledge of SB. The findings of this study may contribute to a better understanding of the relationship among SB, ER, affect, and sleep disturbances. By identifying the role of ER in SB, the results may pave the way for the development of targeted interventions for SB management to alleviate the pain and distress of those affected. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/41719.

Disentangling the Li-Ion transport and Boundary Phase Transition Processes in Li10 GeP2 S12 Electrolyte by In-Operando High-Pressure and High-Resolution NMR Spectroscopy.

Li-ion transport and phase transition of solid electrolytes are critical and fundamental issues governing the rate and cycling performances of solid-state batteries. In this work, in-operando high-pressure nuclear magnetic resonance (NMR) spectroscopy for the solid-state battery is developed and applied, in combination with 6 Li-tracer NMR and high-resolution NMR spectroscopy, to investigate the Li10 GeP2 S12 electrolyte under true-to-life operation conditions. The results reveal that the Li10 GeP2 S12 phase may become more disordered and a large amount of conductive metastable ß-Li3 PS4 as the glassy matrix in the electrolyte transforms into less conductive phases, mainly γ-Li3 PS4 , when high current densities (e.g., ≥0.5 mA cm-2 ) are applied to the electrolyte. The overall Li-transport also varies and shows a tendency of boundary phases and Li10 GeP2 S12 synergistic dominant conduction at high currents. Accordingly, a mechanism of structural change induced by stress variation due to the drastic morphological change during Li-In alloying at high currents, and the local Li+ diffusion coefficient discrepancy is proposed. These new findings of Li-ion transport and boundary phase transition in Li10 GeP2 S12 solid electrolyte under high-pressure and high current density are first reported and will help provide previously lacking insights into the relationship of structure and performance of Li10 GeP2 S12 .

Daytime affect and sleep EEG activity: A data-driven exploration.

It has long been thought that links between affect and sleep are bidirectional. However, few studies have directly assessed the relationships between: (1) pre-sleep affect and sleep electroencephalogram (EEG) activity; and (2) sleep EEG activity and post-sleep affect. This study aims to systematically explore the correlations between pre-/post-sleep affect and EEG activity during sleep. In a community sample of adults (n = 51), we measured participants' positive and negative affect in the evening before sleep and in the next morning after sleep. Participants slept at their residence for 1 night of EEG recording. Using Fourier transforms, the EEG power at each channel was estimated during rapid eye movement sleep and non-rapid eye movement sleep for the full range of sleep EEG frequencies. We first present heatmaps of the raw correlations between pre-/post-sleep affect and EEG power during rapid eye movement and non-rapid eye movement sleep. We then thresholded the raw correlations with a medium effect size |r| ≥ 0.3. Using a cluster-based permutation test, we identified a significant cluster indicating a negative correlation between pre-sleep positive affect and EEG power in the alpha frequency range during rapid eye movement sleep. This result suggests that more positive affect during the daytime may be associated with less fragmented rapid eye movement sleep that night. Overall, our exploratory results lay the foundation for confirmatory research on the relationship between daytime affect and sleep EEG activity.

Emotion regulation and choice of bilateral mastectomy for the treatment of unilateral breast cancer.

BACKGROUND: There has been steadily increasing use of bilateral mastectomy (BMX) in the treatment of primary breast cancer (BC). In this study, we utilized functional magnetic resonance imaging (fMRI) to examine the influence of emotion regulation on the decision of newly diagnosed BC patients to choose BMX rather than non-BMX treatments. METHODS: We recruited 123 women with unilateral BC, 61 of whom received BMX and 62 of whom received non-BMX treatments, and 39 healthy controls. While participants were in the fMRI scanner, we showed them BC-related and non-BC-negative images. In one condition, they were instructed to watch the images naturally. In another, they were instructed to regulate their negative emotion. We compared the fMRI signal during these conditions throughout the brain. RESULTS: With non-BC-negative images as the baseline, BC patients showed greater self-reported reactivity and neural reactivity to BC-related images in brain regions associated with self-reflection than did controls. Among the BC patients, the BMX group showed weaker activation in prefrontal emotion regulation brain regions during emotion regulation than did the non-BMX group. CONCLUSIONS: BC patients are understandably emotionally hyper-reactive to BC-related stimuli and those who ultimately received BMX experience more difficulty in regulating BC-related negative emotion than non-BMX BC patients. These findings offer neuropsychological evidence that difficulty in managing anxiety related to the possibility of cancer recurrence is a factor in surgical treatment decision-making and may be an intervention target with the goal of strengthening the management of cancer-related anxiety by nonsurgical means. TRIAL REGISTRATION: NCT03050463.

Pre-sleep affect predicts subsequent REM frontal theta in nonlinear fashion.

Pre-sleep affect is thought to influence sleep, but associations with both sleep architecture and the electroencephalographic (EEG) power spectrum are mixed. In this pre-registered study, we assessed negative valence and arousal 1 h pre-sleep in 52 adults drawn from the community, then recorded one night of polysomnography (PSG) in participants' own homes. Pre-sleep affect was not associated with nonrapid eye movement (NREM) or rapid eye movement (REM) sleep architecture parameters, but we did observe inverted U-shaped relationships between both negative valence and arousal and REM frontal theta power, such that theta power was highest at moderate negative valence and arousal, and lowest at either affective extreme. When entered into a model together, both valence and arousal accounted for independent variance. Secondary analyses revealed a similar quadratic association with pre-sleep positive valence, suggesting a nonspecific effect of pre-sleep valence on REM frontal theta. Robustness checks confirmed that effects were not explained by homeostatic sleep pressure or sleep timing. Our results suggest that mixed findings in the literature may reflect different ends of a quadratic function, underscoring the importance of assessing how different components of pre-sleep affect relate to sleep.

Negative dream affect is associated with next-day affect level, but not with affect reactivity or affect regulation.

There is increasing evidence that sleep plays an important role in affective processing. However, it is unclear whether dreaming-the subjective experiences we have during sleep-also serves an affect regulation function. Here, we investigated the within-person relationship between negative affect experienced in dreams and next-day waking affect level, affect reactivity, and affect regulation. For 5 days, 40 participants reported their dreams and rated their dream affect and post-sleep waking affect level upon morning awakening. Thereafter, they performed an affect reactivity and regulation task which involved viewing neutral and negative pictures with the instruction either to simply view the pictures or to down-regulate the affect evoked by these pictures. Multilevel regression analyses showed that the more negative affect people experienced in their dreams at night, the more negative affect and the less positive affect they reported the next morning. However, negative dream affect was associated neither with affect reactivity to the pictures nor with the ability to down-regulate negative affect in response to these pictures. In fact, Bayesian analyses favored the null hypotheses. These findings fail to provide support for the affect regulation function of dreaming and, instead, speak for affective continuity between dreaming and post-sleep wakefulness.

Theoretical and Experimental Studies of Gallate Melilite Electrides from Topotactic Reduction of Interstitial Oxide Ion Conductors.

A nonstoichiometric La1.5Sr0.5Ga3O7.25 melilite oxide ion conductor features active interstitial oxygen defects in its pentagonal rings with high mobility. In this study, electron localization function calculated by density functional theory indicated that the interstitial oxide ions located in the pentagonal rings of gallate melilites may be removed and replaced by electron anions that are confined within the pentagonal rings, which would therefore convert the melilite interstitial oxide ion conductor into a zero-dimensional (0D) electride. The more active interstitial oxide ions, compared to the framework oxide ions, make the La1.5Sr0.5Ga3O7.25 melilite structure more reducible by CaH2 using topotactic reduction, in contrast to the hardly reducible nature of parent LaSrGa3O7. The topotactic reduction enhances the bulk electronic conduction (σ ∼ 0.003 S/cm at 400 °C) by ∼ 1 order of magnitude for La1.5Sr0.5Ga3O7.25. The oxygen loss in the melilite structure was verified and most likely took place on the active interstitial oxide ions. The identified confinement space for electronic anions in melilite interstitial oxide ion conductors presented here provides a strategy to access inorganic electrides from interstitial oxide ion conductor electrolytes.

Frontoparietal and Default Mode Network Contributions to Self-Referential Processing in Social Anxiety Disorder.

Social anxiety disorder (SAD) is characterized by negative self-referential processing, which triggers excessive emotional reactivity. In healthy individuals, positive self-views typically predominate and are supported by regions of the default mode network (DMN) that represent self-related information and regions of the frontoparietal control network (FPCN) that contribute to metacognitive awareness and emotion regulation. The current study used functional magnetic resonance imaging (fMRI) to examine patterns of DMN and FPCN activation during positive and negative self-referential judgments in SAD patients (N = 97) and controls (N = 34). As expected, SAD patients demonstrated a striking difference in self-beliefs compared with non-anxious healthy controls, endorsing fewer positive traits and more negative traits. However, SAD patients and controls demonstrated largely similar patterns of DMN and FPCN recruitment during self-referential judgements. No significant group differences were observed. However, equivalence testing identified numerous regions demonstrating effect sizes that were not small enough to conclude that they were practically equivalent to zero, despite the nonsignificant null hypothesis test. These regions may be key targets to investigate in future studies using larger samples.

Individual differences in perceived sleep quality do not predict negative affect reactivity or regulation.

Do people who have low-quality sleep tend to have more negative affect? This question is of great public interest, and many would assume the answer is "yes." However, previous findings have been mixed, possibly due to differing measures of sleep and affect, or to a failure to separately examine negative affect reactivity and regulation. Across two studies, we assessed adults' perceived sleep quality for at least two weeks and tested their negative affect reactivity and regulation in response to unpleasant pictures (Study 1) or painful thermal stimulation (Study 2) using both self-report and physiological measures. The relationships between perceived sleep quality, on the one hand, and negative affect reactivity and regulation, on the other, were non-significant. Furthermore, a Bayesian approach unanimously favored the null hypothesis. These results suggest that individual differences in perceived sleep quality may not predict negative affect reactivity or regulation across adult individuals.

Serum Anti-Müllerian Hormone Levels Were Negatively Associated With Body Fat Percentage in PCOS Patients.

Background: Obesity is a state of excess body fat accumulation, and appears to be closely associated with polycystic ovary syndrome (PCOS). Notably, plausible biological pathways through which obesity can regulate anti-Müllerian hormone (AMH) production have been proposed, and women with PCOS characteristically have an increased AMH level. Body fat accumulation can be described by body fat percentage (BFP). However, the relationship between BFP and AMH still remains unclear. Materials and Methods: A total of 87 controls and 156 PCOS patients were divided into lean and overweight/obese groups, and the PCOS patients were further divided into hyper-AMH and normal-AMH subgroups. Univariate regression was used to assess the unadjusted relationship between AMH and outcome variables, multivariable regression analysis was performed to test whether and how serum AMH levels were associated with BFP after adjusting for other co-variables. Receiver-operating characteristic (ROC) curve analyses were used to test the utility of BFP for the diagnosis of PCOS. Results: BFP was higher in PCOS patients compared with controls, regardless of obesity. Serum AMH levels were negatively associated with BFP in the PCOS group (r = -0.371; P < 0.001) but not in the control group (r = -0.095; P = 0.385). Multivariable logistic regression analysis showed that elevated BFP was associated with a high risk of PCOS (odds ratio, 1.290; 95% confidence interval, 1.084-1.534, P = 0.004). Furthermore, the combination of BFP and serum AMH into a multivariate model gave an improved area under the curve (AUC) of 88.5%, with a sensitivity of 72.4% and specificity of 87.4%; the positive and negative predictive values were 91.2% and 63.9%, respectively. One limitation of this study is all the conclusion reported was based on small sample size. Conclusions: Herein, we described the negative correlation between BFP and serum AMH levels for the first time, and the present results highlight the importance of further investigation into the role of BFP, especially in body fat-related AMH change as it relates to the underlying pathogenesis of PCOS.

A Machine-Learning Protocol for Ultraviolet Protein-Backbone Absorption Spectroscopy under Environmental Fluctuations.

Ultraviolet (UV) absorption spectra are commonly used for characterizing the global structure of proteins. However, the theoretical interpretation of UV spectra is hindered by the large number of required expensive ab initio calculations of excited states spanning a huge conformation space. We present a machine-learning (ML) protocol for far-UV (FUV) spectra of proteins, which can predict FUV spectra of proteins with comparable accuracy to density functional theory (DFT) calculations but with 3-4 orders of magnitude reduced computational cost. It further shows excellent predictive power and transferability that can be used to probe structural mutations and protein folding pathways.

RabbitMash: accelerating hash-based genome analysis on modern multi-core architectures.

MOTIVATION: Mash is a popular hash-based genome analysis toolkit with applications to important downstream analyses tasks such as clustering and assembly. However, Mash is currently not able to fully exploit the capabilities of modern multi-core architectures, which in turn leads to high runtimes for large-scale genomic datasets. RESULTS: We present RabbitMash, an efficient highly optimized implementation of Mash which can take full advantage of modern hardware including multi-threading, vectorization and fast I/O. We show that our approach achieves speedups of at least 1.3, 9.8, 8.5 and 4.4 compared to Mash for the operations sketch, dist, triangle and screen, respectively. Furthermore, RabbitMash is able to compute the all-versus-all distances of 100 321 genomes in <5 min on a 40-core workstation while Mash requires over 40 min. AVAILABILITY AND IMPLEMENTATION: RabbitMash is available at https://github.com/ZekunYin/RabbitMash. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Accurate Machine Learning Prediction of Protein Circular Dichroism Spectra with Embedded Density Descriptors.

A data-driven approach to simulate circular dichroism (CD) spectra is appealing for fast protein secondary structure determination, yet the challenge of predicting electric and magnetic transition dipole moments poses a substantial barrier for the goal. To address this problem, we designed a new machine learning (ML) protocol in which ordinary pure geometry-based descriptors are replaced with alternative embedded density descriptors and electric and magnetic transition dipole moments are successfully predicted with an accuracy comparable to first-principle calculation. The ML model is able to not only simulate protein CD spectra nearly 4 orders of magnitude faster than conventional first-principle simulation but also obtain CD spectra in good agreement with experiments. Finally, we predicted a series of CD spectra of the Trp-cage protein associated with continuous changes of protein configuration along its folding path, showing the potential of our ML model for supporting real-time CD spectroscopy study of protein dynamics.

Azopyrazole-Based Photoswitchable Anion Receptor for Dihydrogen Phosphate Transport.

Small-molecule anion carriers are potential reagents used in the treatment of diseases caused by dysregulated anion transport. Photoswitchable anion receptors, which can be reversibly switched between isomers by light and thereby cause reversible changes in anion binding affinity, have been receiving enormous interest. Here, based on the well-known photoswitch 1-N-methyl-3-phenylazopyrazole (3pzH), we designed a novel tetramethylamide-3pzH (3pzH_TA) photoswitchable receptor that achieves highly efficient and durable anion transportation. It enables high photoisomerization ratios of E → Z (>98%) and Z → E (97%) with a thermal half-life two times longer than that of 3pzH. We further demonstrated the high sensitivity of 3pzH_TA toward H2PO4- anion and revealed the key role of hydrogen bonds between H2PO4- and Z isomer in the strength of anion binding. Our findings open up a new strategy for the rational design and understanding of new types of photoswitchable anion receptors.

Micro-encapsulated essential oils and organic acids combination improves intestinal barrier function, inflammatory responses and microbiota of weaned piglets challenged with enterotoxigenic Escherichia coli F4 (K88+).

This study evaluated the effects of micro-encapsulated (protected) organic acids (OA) and essential oils (EO) combination, P(OA + EO), and effects of a regular blend of free acids (FA) on the growth, immune responses, intestinal barrier and microbiota of weaned piglets challenged with enterotoxigenic Escherichia coli (ETEC) F4 (K88+). A total of 30 crossbred (Duroc × Landrace × Large White) weaned barrows (7.41 ± 0.06 kg, 28 d old) were assigned randomly to 5 treatments: 1) non-challenged positive control (PC), 2) ETEC F4 (K88+)-challenged negative control (NC), 3) NC + kitasamycin at 50 mg/kg + olaquindox at 100 mg/kg + free acidifier (FA) at 5 g/kg, 4) NC + kitasamycin at 50 mg/kg + olaquindox at 100 mg/kg + P(OA + EO) at 1 g/kg (P1), 5) NC + kitasamycin at 50 mg/kg + olaquindox at 100 mg/kg + P(OA + EO) at 2 g/kg (P2). Each dietary treatment had 6 replicates of one piglet each and the study lasted for 3 wk. On d 7, pigs in NC, FA, P1 and P2 were orally dosed with 10 mL of ETEC F4 (K88+) culture (1 × 109 CFU/mL). From d 7 to 14 after the ETEC F4 (K88+) challenge, P1 increased gain-to-feed ratio (G:F) significantly (P < 0.05) compared with NC and FA groups. From d 14 to 21, P2 increased the average daily gain of pigs (P < 0.05) compared with NC and FA groups. Compared with NC, P2 reduced tumor necrosis factor-α (TNF-α), interleukin (IL)-6 and IL-10 concentrations (P < 0.05) in sera collected at 4 h later after ETEC F4 (K88+) challenge. On d 21, P1 increased occludin and zonula occludens-1 protein expression in ileum compared with NC (P < 0.05). After this 3-wk experiment, alpha diversity of gut microbiota was decreased by P2 compared with PC, and P1 increased the relative abundance of Lactobacillus in ileum, cecum and colon (P < 0.05). In conclusion, dietary P(OA + EO) additive at 2 g/kg combined with antibiotics could improve piglet performance and attenuate inflammation, and P(OA + EO) additive at 1 g/kg combined with antibiotics improved intestinal barrier and increased beneficial microbiota composition after an F4 (K88+) challenge.

A Machine Learning Protocol for Predicting Protein Infrared Spectra.

Infrared (IR) absorption provides important chemical fingerprints of biomolecules. Protein secondary structure determination from IR spectra is tedious since its theoretical interpretation requires repeated expensive quantum-mechanical calculations in a fluctuating environment. Herein we present a novel machine learning protocol that uses a few key structural descriptors to rapidly predict amide I IR spectra of various proteins and agrees well with experiment. Its transferability enabled us to distinguish protein secondary structures, probe atomic structure variations with temperature, and monitor protein folding. This approach offers a cost-effective tool to model the relationship between protein spectra and their biological/chemical properties.

Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties.

Machine learning has revolutionized the high-dimensional representations for molecular properties such as potential energy. However, there are scarce machine learning models targeting tensorial properties, which are rotationally covariant. Here, we propose tensorial neural network (NN) models to learn both tensorial response and transition properties in which atomic coordinate vectors are multiplied with scalar NN outputs or their derivatives to preserve the rotationally covariant symmetry. This strategy keeps structural descriptors symmetry invariant so that the resulting tensorial NN models are as efficient as their scalar counterparts. We validate the performance and universality of this approach by learning response properties of water oligomers and liquid water and transition dipole moment of a model structural unit of proteins. Machine-learned tensorial models have enabled efficient simulations of vibrational spectra of liquid water and ultraviolet spectra of realistic proteins, promising feasible and accurate spectroscopic simulations for biomolecules and materials.

Using Machine Learning to Predict the Dissociation Energy of Organic Carbonyls.

Bond dissociation energy (BDE), an indicator of the strength of chemical bonds, exhibits great potential for evaluating and screening high-performance materials and catalysts, which are of critical importance in industrial applications. However, the measurement or computation of BDE via conventional experimental or theoretical methods is usually costly and involved, substantially preventing the BDE from being applied to large-scale and high-throughput studies. Therefore, a potentially more efficient approach for estimating BDE is highly desirable. To this end, we combined first-principles calculations and machine learning techniques, including neural networks and random forest, to explore the inner relationships between carbonyl structure and its BDE. Results show that machine learning can not only effectively reproduce the computed BDEs of carbonyls but also in turn serve as guidance for the rational design of carbonyl structure aimed at optimizing performance.