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Coupling between molecules and vacuum photon fields inside an optical cavity has proven to be an effective way to engineer molecular properties, in particular reactivity. To ease the rationalization of cavity induced effects we introduce an ab initio method leading to the first fully consistent molecular orbital theory for quantum electrodynamics environments. Our framework is non-perturbative and explains modifications of the electronic structure due to the interaction with the photon field. In this work, we show that the newly developed orbital theory can be used to predict cavity induced modifications of molecular reactivity and pinpoint classes of systems with significant cavity effects. We also investigate electronic cavity-induced modifications of reaction mechanisms in vibrational strong coupling regimes. Theoretical description of light-matter coupling in the strong-coupling regime is challenging. Here the authors introduce a fully consistent ab-initio method of molecular orbital theory applicable to material systems in quantum electrodynamics environments.

We present an ab initio correlated approach to study molecules that interact strongly with quantum fields in an optical cavity. Quantum electrodynamics coupled cluster theory provides a nonperturbative description of cavity-induced effects in ground and excited states. Using this theory, we show how quantum fields can be used to manipulate charge transfer and photochemical properties of molecules. We propose a strategy to lift electronic degeneracies and induce modifications in the ground-state potential energy surface close to a conical intersection.

Background Mammographic density is known to decrease over time in postmenopausal women. Longitudinal changes in mammographic density prior to breast cancer diagnosis have been widely discussed and less density reduction has been observed for breast developing versus not developing cancer. We aimed to verify these findings among participants of BreastScreen Norway. Methods In this retrospective cohort study, data from 78,182 women aged 50–69 years who attended three consecutive screening rounds between 2007 and 2020 were included. Among those women, 970 were diagnosed with screen-detected and 308 with interval cancer. Mammographic density data was obtained from an automated software and included absolute (cm 3 ) and percent (%) dense volume for each breast and for each woman, per examination. A linear mixed-effects regression model estimating differences in density between the breast developing and not developing cancer was applied to evaluate longitudinal changes, separately for absolute and percent dense volume. The model was adjusted for age at first screening examination, breast volume, follow-up time, history of benign breast disease, body mass index, family history, hormone therapy, use of alcohol and smoking. Results were presented as linear regression coefficient estimates with 95% confidence intervals (CI). Results Mean age at the third screening examination for women without breast cancer was 62.5 (standard deviation, SD: 5.1) years, while mean age at diagnosis was 62.3 (SD: 4.4) years for women with screen-detected cancer and 61.9 (SD: 4.8) years for women with interval cancer. In our model, absolute and percent dense volume decreased with follow-up time, estimate=-0.010 (95%CI -0.010; -0.009) and estimate=-0.013 (95%CI -0.014; -0.013), respectively, indicating the overall negative effect of time on mammographic density. The interaction between time and development of breast cancer was positive for absolute and percent dense volume, estimate = 0.009 (95%CI 0.004; 0.014) for both, which implied that mammographic density in breasts developing cancer was stable or slightly decreasing. Conclusions Less reduction in longitudinally assessed mammographic density was observed for breasts developing versus not developing cancer in our study. This difference might be used for more precise 4–6 years breast cancer risk prediction and screening personalization.

Accurate modeling of conical intersections is crucial in nonadiabatic molecular dynamics, as these features govern processes such as radiationless transitions and photochemical reactions. Conventional electronic structure methods, including Hartree–Fock, density functional theory, and their time-dependent extensions, struggle in this regime. Due to their single reference nature and separate treatment of ground and excited states, they fail to capture ground state intersections. Multiconfigurational approaches overcome these limitations, but at a prohibitive computational cost. In this work, we propose a modified Hartree–Fock framework, referred to as Convex Hartree–Fock, that optimizes the reference within a tailored subspace by removing projections along selected Hessian eigenvectors. The ground and excited states are then obtained through subsequent Hamiltonian diagonalization. We validate the approach across several test cases and benchmark its performance against time-dependent Hartree–Fock within the Tamm-Dancoff approximation. Accurate modeling of conical intersections is essential for describing the nonadiabatic molecular dynamics behind photoinduced processes, but conventional single-reference electronic structure methods fail to capture the associated ground state intersections. In this work, the authors propose a modified Hartree–Fock framework, referred to as Convex Hartree–Fock, that optimizes the reference within a tailored subspace by removing projections along selected Hessian eigenvectors, offering an alternative to more expensive multiconfigurational approaches.

X-ray absorption (XA) spectroscopy is an essential experimental tool to investigate the local structure of liquid water. Interpretation of the experiment poses a significant challenge and requires a quantitative theoretical description. High-quality theoretical XA spectra require reliable molecular dynamics simulations and accurate electronic structure calculations. Here, we present the first successful application of coupled cluster theory to model the XA spectrum of liquid water. We overcome the computational limitations on system size by employing a multilevel coupled cluster framework for large molecular systems. Excellent agreement with the experimental spectrum is achieved by including triple excitations in the wave function and using molecular structures from state-of-the-art path-integral molecular dynamics. We demonstrate that an accurate description of the electronic structure within the first solvation shell is sufficient to successfully model the XA spectrum of liquid water within the multilevel framework. Furthermore, we present a rigorous charge transfer analysis of the XA spectrum, which is reliable due to the accuracy and robustness of the electronic structure methodology. This analysis aligns with previous studies regarding the character of the prominent features of the XA spectrum of liquid water. Accurate modeling of X-ray absorption (XA) is necessary to interpret experimental spectra. Here, the authors use coupled cluster models and path-integral ab initio molecular dynamics to provide insights into the XA spectrum of liquid water.

The fate of thymine upon excitation by ultraviolet radiation has been the subject of intense debate. Today, it is widely believed that its ultrafast excited state gas phase decay stems from a radiationless transition from the bright π π * state to a dark n π * state. However, conflicting theoretical predictions have made the experimental data difficult to interpret. Here we simulate the early gas phase ultrafast dynamics in thymine at the highest level of theory to date. This is made possible by performing wavepacket dynamics with a recently developed coupled cluster method. Our simulation confirms an ultrafast π π * to n π * transition ( τ  = 41 ± 14 fs). Furthermore, the predicted oxygen-edge X-ray absorption spectra agree quantitatively with experiment. We also predict an as-yet uncharacterized π σ * channel that leads to hydrogen dissociation at one of the two N-H bonds. Similar behavior has been identified in other heteroaromatic compounds, including adenine, and several authors have speculated that a similar pathway may exist in thymine. However, this was never confirmed theoretically or experimentally. This prediction calls for renewed efforts to experimentally identify or exclude the presence of this channel. The photophysics of thymine in the gas phase are still under debate. Here the authors perform coupled-cluster-based dynamics simulations to predict time-resolved X-ray absorption spectra and reveal a hydrogen dissociation channel.

Objectives To compare results of selected performance measures in mammographic screening for an artificial intelligence (AI) system versus independent double reading by radiologists. Methods In this retrospective study, we analyzed data from 949 screen-detected breast cancers, 305 interval cancers, and 13,646 negative examinations performed in BreastScreen Norway during the period from 2010 to 2018. An AI system scored the examinations from 1 to 10, based on the risk of malignancy. Results from the AI system were compared to screening results after independent double reading. AI score 10 was set as the threshold. The results were stratified by mammographic density. Results A total of 92.7% of the screen-detected and 40.0% of the interval cancers had an AI score of 10. Among women with a negative screening outcome, 9.1% had an AI score of 10. For women with the highest breast density, the AI system scored 100% of the screen-detected cancers and 48.6% of the interval cancers with an AI score of 10, which resulted in a sensitivity of 80.9% for women with the highest breast density for the AI system, compared to 62.8% for independent double reading. For women with screen-detected cancers who had prior mammograms available, 41.9% had an AI score of 10 at the prior screening round. Conclusions The high proportion of cancers with an AI score of 10 indicates a promising performance of the AI system, particularly for women with dense breasts. Results on prior mammograms with AI score 10 illustrate the potential for earlier detection of breast cancers by using AI in screen-reading. Key Points • The AI system scored 93% of the screen-detected cancers and 40% of the interval cancers with AI score 10. • The AI system scored all screen-detected cancers and almost 50% of interval cancers among women with the highest breast density with AI score 10. • About 40% of the screen-detected cancers had an AI score of 10 on the prior mammograms, indicating a potential for earlier detection by using AI in screen-reading.

Many photoinduced processes including photosynthesis and human vision happen in organic molecules and involve coupled femtosecond dynamics of nuclei and electrons. Organic molecules with heteroatoms often possess an important excited-state relaxation channel from an optically allowed ππ * to a dark nπ * state. The ππ */ nπ * internal conversion is difficult to investigate, as most spectroscopic methods are not exclusively sensitive to changes in the excited-state electronic structure. Here, we report achieving the required sensitivity by exploiting the element and site specificity of near-edge soft X-ray absorption spectroscopy. As a hole forms in the n orbital during ππ */ nπ * internal conversion, the absorption spectrum at the heteroatom K-edge exhibits an additional resonance. We demonstrate the concept using the nucleobase thymine at the oxygen K-edge, and unambiguously show that ππ */ nπ * internal conversion takes place within (60 ± 30) fs. High-level-coupled cluster calculations confirm the method’s impressive electronic structure sensitivity for excited-state investigations. Many photo-induced processes such as photosynthesis occur in organic molecules, but their femtosecond excited-state dynamics are difficult to track. Here, the authors exploit the element and site selectivity of soft X-ray absorption to sensitively follow the ultrafast ππ */ nπ * electronic relaxation of hetero-organic molecules.

Objectives To compare the location of AI markings on screening mammograms with cancer location on diagnostic mammograms, and to classify interval cancers with high AI score as false negative, minimal sign, or true negative. Methods In a retrospective study from 2022, we compared the performance of an AI system with independent double reading according to cancer detection. We found 93% (880/949) of the screen-detected cancers, and 40% (122/305) of the interval cancers to have the highest AI risk score (AI score of 10). In this study, four breast radiologists reviewed mammograms from 126 randomly selected screen-detected cancers and all 120 interval cancers with an AI score of 10. The location of the AI marking was stated as correct/not correct in craniocaudal and mediolateral oblique view. Interval cancers with an AI score of 10 were classified as false negative, minimal sign significant/non-specific, or true negative. Results All screen-detected cancers and 78% (93/120) of the interval cancers with an AI score of 10 were correctly located by the AI system. The AI markings matched in both views for 79% (100/126) of the screen-detected cancers and 22% (26/120) of the interval cancers. For interval cancers with an AI score of 10, 11% (13/120) were correctly located and classified as false negative, 10% (12/120) as minimal sign significant, 26% (31/120) as minimal sign non-specific, and 31% (37/120) as true negative. Conclusion AI markings corresponded to cancer location for all screen-detected cancers and 78% of the interval cancers with high AI score, indicating a potential for reducing the number of interval cancers. However, it is uncertain whether interval cancers with subtle findings in only one view are actionable for recall in a true screening setting. Clinical relevance statement In this study, AI markings corresponded to the location of the cancer in a high percentage of cases, indicating that the AI system accurately identifies the cancer location in mammograms with a high AI score. Key Points • All screen-detected and 78% of the interval cancers with high AI risk score (AI score of 10) had AI markings in one or two views corresponding to the location of the cancer on diagnostic images. • Among all 120 interval cancers with an AI score of 10, 21% (25/120) were classified as a false negative or minimal sign significant and had AI markings matching the cancer location, suggesting they may be visible on prior screening. • Most of the correctly located interval cancers matched only in one view, and the majority were classified as either true negative or minimal sign non-specific, indicating low potential for being detected earlier in a real screening setting.

A dynamical rearrangement in the electronic structure of a molecule can be driven by different phenomena, including nuclear motion, electronic coherence or electron correlation. Recording such electronic dynamics and identifying its fate in an aqueous solution has remained a challenge. Here, we reveal the electronic dynamics induced by electronic relaxation through conical intersections in both isolated and solvated pyrazine molecules using X-ray spectroscopy. We show that the ensuing created dynamics corresponds to a cyclic rearrangement of the electronic structure around the aromatic ring. Furthermore, we found that such electronic dynamics were entirely suppressed when pyrazine was dissolved in water. Our observations confirm that conical intersections can create electronic dynamics that are not directly excited by the pump pulse and that aqueous solvation can dephase them in less than 40 fs. These results have implications for the investigation of electronic dynamics created during light-induced molecular dynamics and shed light on their susceptibility to aqueous solvation. Tracking ultrafast electronic changes in molecules is challenging, especially in liquids. An X-ray spectroscopy study in pyrazine now shows electronic dynamics created at conical intersections that are rapidly suppressed when the molecule is in water.