Can classical mechanics sense conical intersection?

Conical intersection (CI) leads to fast electronic energy transfer. However, Hamm and Stock [Phys. Rev. Lett. 109, 173201 (2012)] showed the existence of a vibrational CI and its role in vibrational energy relaxation. In this paper, we further investigate the vibrational energy relaxation using an isolated model Hamiltonian system of four vibrational modes with two distinctively different timescales (two fast modes and two slow modes). We show that the excitation of the slow modes plays a crucial role in the energy relaxation mechanism. We also analyze the system from a mixed quantum-classical (surface hopping method) and a completely classical point of view. Notably, surface hopping and even classical simulations also capture fast energy relaxation, which is a signature of CI's existence.

A room-temperature gas sensor based on 2D Ni-Co-Zn ternary oxide nanoflakes for selective and sensitive ammonia detection.

While most of the reports on NH3 gas sensors are either based on metal oxide composites with other 2D materials, polymers or noble metals or involve multi-step-based synthesis routes, this work is the first report on a pristine ternary metal oxide, 2D NiCo2ZnO4 nanoflake based room-temperature (RT) NH3 gas sensor. The 2D NiCo2ZnO4 nanoflakes were prepared by a one-step hydrothermal method. FESEM and TEM images displayed micro-flower like morphologies, containing vertically aligned interwoven porous 2D nanoflakes, whereas XPS and XRD data confirmed the successful growth of this ternary metal-oxide. This sensor revealed a good response, repeatability, linearity (R2 = 0.9976), a low detection limit of 3.024 ppb, and a response time of 74.84 s with excellent selectivity towards NH3 over six other VOCs. This improved performance of the sensor is ascribed to its large specific surface area (127.647 m2 g-1) resulting from the 2D nanoflake like structure, good electronic conductivity, variable valence states and abundant surface-active oxygen of NiCo2ZnO4. Thus, this highly selective 2D NiCo2ZnO4 based RT NH3 gas sensor can be an attractive solution for the fabrication of next-generation NH3 gas sensors.

The Effect of Illumination on Positive Fusional Vergence.

Background: Positive fusional vergence (PFV) is vital in maintaining fusion in critical and continuous near tasks such as reading or performing digital screen tasks. This study investigated how PFV changed under various lighting conditions. Methods: This cross-sectional study recruited 34 participants aged between 21 and 25 years, with best corrected visual acuity (BCVA) 0.0 logMAR and insignificant refractive error. Three different illuminations-low illumination (50 lux), medium lighting (100 lux), and high illumination (150 lux)-were used to examine the ocular parameters PFV (blur, break, and recovery points), contrast sensitivity and pupil diameter. Results: Pupil diameter changed significantly in different room illuminations (p = 0.00). There was no significant difference in contrast sensitivity across the three levels of room illumination (p = 0.368). Mean PFV (SD) (blur) was 14.5 (2.5) in 50 lux, 10.2 (2.2) in 100 lux, and 8.2 (2.1) in 150 lux. Under 50, 100 and 150 lux, respectively, the mean PFV (SD) (break) values were 16.7 (2.4), 13.4 (1.8), and 10.8 (2.2), and the mean PFV (SD) (recovery) values were 13.3 (2.1), 10.7 (2.1), and 7.5 (2.7). With increased illumination levels, PFV blur, break, and recovery values were significantly lower (p < 0.001). Conclusions: PFV values were significantly higher in lower illumination. Clinicians should be aware that room illumination affected the PFV values measured.

Graph-BERT and language model-based framework for protein-protein interaction identification.

Identification of protein-protein interactions (PPI) is among the critical problems in the domain of bioinformatics. Previous studies have utilized different AI-based models for PPI classification with advances in artificial intelligence (AI) techniques. The input to these models is the features extracted from different sources of protein information, mainly sequence-derived features. In this work, we present an AI-based PPI identification model utilizing a PPI network and protein sequences. The PPI network is represented as a graph where each node is a protein pair, and an edge is defined between two nodes if there exists a common protein between these nodes. Each node in a graph has a feature vector. In this work, we have used the language model to extract feature vectors directly from protein sequences. The feature vectors for protein in pairs are concatenated and used as a node feature vector of a PPI network graph. Finally, we have used the Graph-BERT model to encode the PPI network graph with sequence-based features and learn the hidden representation of the feature vector for each node. The next step involves feeding the learned representations of nodes to the fully connected layer, the output of which is fed into the softmax layer to classify the protein interactions. To assess the efficacy of the proposed PPI model, we have performed experiments on several PPI datasets. The experimental results demonstrate that the proposed approach surpasses the existing PPI works and designed baselines in classifying PPI.

Prediction of Protein-Protein Interactions Using Vision Transformer and Language Model.

The knowledge of protein-protein interaction (PPI) helps us to understand proteins' functions, the causes and growth of several diseases, and can aid in designing new drugs. The majority of existing PPI research has relied mainly on sequence-based approaches. With the availability of multi-omics datasets (sequence, 3D structure) and advancements in deep learning techniques, it is feasible to develop a deep multi-modal framework that fuses the features learned from different sources of information to predict PPI. In this work, we propose a multi-modal approach utilizing protein sequence and 3D structure. To extract features from the 3D structure of proteins, we use a pre-trained vision transformer model that has been fine-tuned on the structural representation of proteins. The protein sequence is encoded into a feature vector using a pre-trained language model. The feature vectors extracted from the two modalities are fused and then fed to the neural network classifier to predict the protein interactions. To showcase the effectiveness of the proposed methodology, we conduct experiments on two popular PPI datasets, namely, the human dataset and the S. cerevisiae dataset. Our approach outperforms the existing methodologies to predict PPI, including multi-modal approaches. We also evaluate the contributions of each modality by designing uni-modal baselines. We perform experiments with three modalities as well, having gene ontology as the third modality.

Accommodative spasm and its different treatment approaches: A systematic review.

This article is about the accommodation spasm. The primary rule for near vision is ciliary muscle constriction, synchronised convergence of both eyes, and pupil constriction. Any weaknesses in these components could result in an accommodative spasm. Variable retinoscopic reflex, unstable refractive error, and lead of accommodation in near retinoscopy are common causes of spasm. We conducted a thorough literature search in the PubMed and Google Scholar databases for published journals prior to June 2022, with no data limitations. This review contains twenty-eight case reports, six cohort studies, four book references, four review articles, and two comparative studies after applying the inclusion and exclusion criteria. The majority of studies looked at accommodative spasm, near reflex spasm, and pseudomyopia. The most common causes of accommodative spasm are excessive close work, emotional distress, head injury, and strabismus. Despite side effects or an insufficient regimen, cycloplegic drops are effective in diagnosing accommodation spasm. The modified optical fogging technique is also effective and may be an option for treating accommodative spasm symptoms. Bifocals for near work, manifest refraction, base-in prisms, and vision therapy are some of the other management options. As a result, it requires a comprehensive clinical treatment strategy. This review aims to investigate the various aetiology and treatments responsible for accommodative spasm and proposes widely implementing the modified optical fogging method and vision therapy in clinics as comprehensive management to reduce the future upward trend of accommodative spasm.

Intramolecular vibrational energy redistribution and the quantum ergodicity transition: a phase space perspective.

Intramolecular vibrational energy redistribution (IVR) impacts the dynamics of reactions in a profound way. Theoretical and experimental studies are increasingly indicating that accounting for the finite rate of energy flow is critical for uncovering the correct reaction mechanisms and calculating accurate rates. This requires an explicit understanding of the influence and interplay of the various anharmonic (Fermi) resonances that lead to the coupling of the vibrational modes. In this regard, the local random matrix theory (LRMT) and the related Bose-statistics triangle rule (BSTR) model have emerged as powerful and predictive quantum theories for IVR. In this Perspective we highlight the close correspondence between LRMT and the classical phase space perspective on IVR, primarily using model Hamiltonians with three degrees of freedom. Our purpose for this is threefold. First, this clearly brings out the extent to which IVR pathways are essentially classical, and hence crucial towards attempts to control IVR. Second, given that LRMT and BSTR are designed to be applicable for large molecules, the exquisite correspondence observed even for small molecules allows for insights into the quantum ergodicity transition. Third, we showcase the power of modern nonlinear dynamics methods in analysing high dimensional phase spaces, thereby extending the deep insights into IVR that were earlier gained for systems with effectively two degrees of freedom. We begin with a brief overview of recent examples where IVR plays an important role and conclude by mentioning the outstanding problems and the potential connections to issues of interest in other fields.

Stable chaos and delayed onset of statisticality in unimolecular dissociation reactions.

Statistical models provide a powerful and useful class of approximations for calculating reaction rates by bypassing the need for detailed, and often difficult, dynamical considerations. Such approaches invariably invoke specific assumptions about the extent of intramolecular vibrational energy flow in the system. However, the nature of the transition to the statistical regime as a function of the molecular parameters is far from being completely understood. Here, we use tools from nonlinear dynamics to study the transition to statisticality in a model unimolecular reaction by explicitly visualizing the high dimensional classical phase space. We identify generic features in the phase space involving the intersection of two or more independent anharmonic resonances and show that the presence of correlated, but chaotic, intramolecular dynamics near such junctions leads to nonstatisticality. Interestingly, akin to the stability of asteroids in the Solar System, molecules can stay protected from dissociation at the junctions for several picoseconds due to the phenomenon of stable chaos.

Relevance of the Resonance Junctions on the Arnold Web to Dynamical Tunneling and Eigenstate Delocalization.

We study the competition and correspondence between the classical and quantum routes to intramolecular vibrational energy redistribution (IVR) in a three degrees of freedom model effective Hamiltonian. Specifically, we focus on the classical and the quantum dynamics near the resonance junctions on the Arnold web that are formed by an intersection of independent resonances. The regime of interest models the IVR dynamics from highly excited initial states near dissociation thresholds of molecular systems wherein both classical and purely quantum, involving dynamical tunneling, routes to IVR coexist. In the vicinity of a resonance junction, classical chaos is inevitably present, and hence one expects the quantum IVR pathways to have a strong classical component as well. We show that with increasing resonant coupling strengths the classical component of IVR leads to a transition from coherent dynamical tunneling to incoherent dynamical tunneling. Furthermore, we establish that the quantum IVR dynamics can be predicted based on the structures on the classical Arnold web. In addition, we investigate the nature of the highly excited eigenstates to identify the quantum signatures of the multiplicity-2 junctions. For the parameter regimes studies herein, by projecting the eigenstates onto the Arnold web, we find that eigenstates in the vicinity of the junctions are primarily delocalized due to dynamical tunneling.