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Quantum simulation of alignment dependent differential cross sections in co-propagating molecular beams at cold collision energies
Cold collisions can be achieved experimentally by co-propagating colliding partners of similar mass within a single molecular beam. This technique, combined with Stark-induced adiabatic Raman passage (SARP), makes it possible to measure the angular distributions for different molecular axis alignments of the incoming molecules, thus probing the stereodynamics of the collisions at very low energies. Reproducing SARP experiments is a very stringent test for theory, even though the systems under study involve closed-shell atoms and molecules with very few electrons for which exact quantum scattering calculations on highly accurate potential energy surfaces are computationally feasible. While simulations of some experimental results using first-principles theoretical calculations have been satisfactory, theory has been unable to reproduce the experimental angular distributions for He + D2 inelastic collisions. Furthermore, an ℓ = 1 partial-wave resonance predicted by theory came at variance with the ℓ = 2 resonance obtained by fitting the experimental results. Here, we demonstrate how theory and experiment can be reconciled by the explicit consideration of the divergence of the molecular beam. While the effect of the divergence is almost irrelevant for collision energies higher than 0.5 K, at lower collision energies, it can produce significantly different results. Our simulations show that signatures of an ℓ = 2 resonance obtained through fitting of the experimental angular distribution ignoring beam divergence is, in fact, an ℓ = 1 resonance observed in the scattering calculations. These results indicate that the combination of theory and experiment is necessary for the analysis and interpretation of complex molecular beam experiments.
A nucleic acid labeling chemistry reveals surface DNA on exosomes
Chemical labeling of nucleic acids is essential to pinpoint the structure, localization, and function of RNA and DNA. Yet, reversible sequence-independent chemistries that can label native RNA and DNA remain poorly developed. Here, we describe Reversible Uridine Nitrilium-mediated Addition (RUNA), a reversible covalent chemistry that selectively modifies uridine and thymidine residues via N3 deprotonation and reaction with a nitrilium ion intermediate generated from an aldehyde and an isonitrile. The reaction forms a stable N3 adduct that can be quantitatively reversed by hydrolysis. By using reagents that are either membrane permeable or impermeable, we demonstrate the localization and function of DNA on exosomes. Although exosomes harbor nucleic acids, whether the latter are encapsulated in the exosome lumen or are surface-adhered is unknown. RUNA revealed that exosomes display DNA on their outer surface. The abundance of such surface DNA increases upon DNA-damage accumulation in cancer cells that are treated with a PARP inhibitor. This surface DNA drives exosome uptake by M2-polarized macrophages through scavenger receptors and triggers a shift toward an M1-like proinflammatory state. The selective labeling of surface DNA revealed an unexpected mechanism by which exosomes engage innate immune cells. RUNA is a versatile tool to analyze the nucleic acid content and functionality of extracellular vesicles in health and disease.
Caffeine treatment modulates neuronal density and oxidative stress pathway to preserve mood state and memory function in sleep-deprived rats
Anharmonic phonons via quantum thermal bath simulations
Lattice vibrations within crystalline solids, or phonons, provide information on a variety of important material characteristics, from thermal qualities to optical properties and phase transition behavior. When the material contains light ions or is subjected to sufficiently low temperatures and/or high pressures, anharmonic and nuclear quantum effects (NQEs) may significantly alter its phonon characteristics. An accurate method, combining the quantum correlator approach with path integral molecular dynamics, was recently proposed to capture these effects [Morresi et al., J. Chem. Phys. 154(22), 224108 (2021)]. Unfortunately, this scheme may incur a substantial computational cost due to the increased number of degrees of freedom introduced by path integrals. In this work, we present an alternative that promises to mitigate this problem by accounting for NQEs via the quantum thermal bath (QTB) method. This is the first full exploration of the use of QTB for the calculation of phonon dispersion relations. We demonstrate the noteworthy efficiency and accuracy of the scheme and analyze its upsides and drawbacks by first considering 1-dimensional systems and then the physically interesting case of solid neon.
Hive mind: Microbial communities and the making of memory
Research on rural co-governance model under “micro-regeneration of community spaces” - a case study of Shanghai Pujiang country park
Photodynamics of amino acids under UV excitation: Extraterrestrial amino acids
The detection of amino acids in extraterrestrial environments has important implications for astrobiology and prebiotic chemistry, yet the pathways to their syntheses and their photostability under such conditions remain unclear. In this study, we employ femtosecond UV-pump visible-probe spectroscopy to investigate the ultrafast relaxation dynamics of non-aromatic amino acids in aqueous environments, representative of those found in Murchison-type meteorites and pristine Bennu samples. Using excitation wavelengths around 210 nm and probing in the visible range, two distinct decay lifetimes are identified, revealing the coexistence of internal conversion and fluorescence decay mechanisms. Internal conversion lifetimes range from 25 to 32 ps, showing efficient non-radiative relaxation processes, while fluorescence decays have a slower timescale of about 1 ns. These findings can help clarify the excited-state relaxation pathways that influence the photostability and survival of these amino acids.
1D domino-like phase transformation enables material programming in 2D MoTe <sub>2</sub>
Phase transformation is a fundamental phenomenon in nature, vital for both the scientific understanding and industrial applications of materials. The emergence of two-dimensional (2D) materials introduces new physical attributes that challenge traditional phase transformation theories due to their reduced dimensionality. In monolayer transition metal dichalcogenides (TMDCs), phase transformation is typically described as a martensitic process characterized by concerted atomic displacements. Nevertheless, the large energy barrier in 2D TMDCs makes such transformations difficult to realize, posing a substantial challenge to the experimental research on the microscopic mechanism, and hindering the precise regulation of material properties. To address this, we investigate the phase transformation in monolayer MoTe 2 through advanced molecular dynamics simulations accelerated by deep learning potential. Our results uncover that the phase transformation proceeds in a one-dimensional (1D), domino-like manner, exhibiting features of both martensitic and reconstructive transformations. This unique mechanism provides tunability over the process, enabling remarkably enhanced nonlinear optical responses and rapid electrical switching. This work advances current phase transformation understanding and provides perspectives for the phase engineering in other 2D materials.
Therapeutic potential of Citrus medica L. (cv. ‘Liscia’ and cv. ‘Rugosa’) phytocompounds targeting biofilm formation, quorum sensing, and antioxidant defense mechanisms
Non-additive ion effects on the coil–globule equilibrium of a generic polymer in aqueous salt solutions
Mixtures of weakly and strongly hydrated anions induce non-additive changes in the LCST of thermoresponsive polymers, such as PNIPAM and PEO. Large-scale atomistic simulations of PNIPAM–NaI–Na2SO4 mixtures have shown that these effects arise from the interplay between favorable PNIPAM–iodide interactions and depletion of strongly hydrated sulfate ions. Here, we investigate whether chemically specific polymer–anion interactions are necessary to reproduce such behavior. To this end, we study the coil–globule transition of a generic uncharged linear polymer with non-specific polymer–water and polymer–ion van der Waals interactions in atomistic aqueous solutions of single and mixed salts. Simulations are performed at fixed concentrations of the strongly hydrated salt, Na2SO4, and increasing concentrations of the weakly hydrated salts, NaSCN and NaI. The generic polymer qualitatively reproduces experimentally observed trends in pure NaSCN and Na2SO4 solutions, as well as the non-additive behavior in mixed salt solutions. In particular, the model captures the mutually reinforcing preferential accumulation of weakly hydrated SCN− ions and the depletion of strongly hydrated SO42− ions near the polymer that underlies the non-additive behavior. This mutual enhancement correlates with the partitioning of sodium ions from the counterion cloud of SCN− ions to that of SO42− ions and is consistent with atomistic simulations of PNIPAM solutions. The model also reproduces the effects of background salt concentration and weakly hydrated anion identity on the non-additive behavior. These results demonstrate that non-specific polymer–ion and polymer–water interactions are sufficient to reproduce non-additive salt effects, suggesting a dominant role of bulk ion–ion and ion–water interactions.
Metformin-restricted motility of an NRF2-activated lung cancer cell line involves NAD+ depletion, rather than AMPK- or BACH1 signaling
Abstract NRF2 is a redox-sensitive and cytoprotective transcription factor. Cancer cells exploit mutations in the NRF2-inhibitor KEAP1 and subsequent NRF2 overactivation for increased therapy resistance and malignancy. AMP-activated protein kinase (AMPK) leads to NRF2 phosphorylation and enhanced βTrCP-mediated degradation in KEAP1-deficient contexts. This study examined whether pharmacological activation of AMPK by metformin decreased levels of NRF2 and stress resilience in KEAP1-deficient A549 lung adenocarcinoma cells. Metformin treatment did not alter NRF2 half-life, expression of selected canonical NRF2 target genes, or sensitivity to cisplatin or taxol. Instead, metformin reduced abundance of BACH1, a transcription factor not only competing with NRF2 but also favoring migration of cancer cells. Testing for a potential AMPK-BACH1 motility axis in A549 cells, we observed reduced cell migration in wound closure and transwell assays upon treatment with metformin. However, depletion or overexpression of BACH1, or AMPK knockdown negated that metformin impaired cell migration primarily via BACH1 or AMPK. Metformin, as mitochondrial complex I inhibitor, increased the cellular NADH/NAD+ ratio. Supplementation with nicotinamide mononucleotide fueling NAD+ synthesis restored motility in metformin-treated cells. Thus, metformin administration does not seem to alleviate NRF2 activity but suppresses migration in KEAP1-deficient A549 cells. The latter is linked to NAD+ depletion, rather than AMPK activation or BACH1 downregulation.
Time reversal breaking of colloidal particles in cells
We investigate signatures of broken time reversal symmetry in stochastic trajectory data, employing the previously introduced three point correlation called mean back relaxation. We specifically investigate data from a simple driven model as well as from colloidal particles within living or passivated biological cells. Both in the model and in cell data, mean back relaxation (MBR) detects broken time reversal symmetry and, furthermore, allows determining relevant time and length scales of activity. For the cells, by applying various drugs, we show that it is predominantly the presence of microtubules which is needed for a time reversal symmetry breaking. We employ a bound for entropy production and find that it is in striking qualitative agreement with previously determined active energies that quantify violation of the fluctuation dissipation theorem.
Training humans to detect AI-generated faces
As AI-generated faces become indistinguishable from real ones, deepfake technology poses escalating threats to information integrity and security. While algorithms can detect deepfakes, they suffer from opacity and critical vulnerabilities—and training humans to identify specific visual artifacts has proven largely ineffective. Here, we introduce a fundamentally different approach that harnesses people’s global impressions of faces. Building on findings that AI and human faces evoke systematically different perceptual impressions (Miller et al., 2023), we trained participants to attend to these distinguishing qualities without explicit instruction on how to use them. Using a rigorous pre–post design with untrained test faces, we demonstrate that all participants ( N = 45) improved, with mean accuracy nearly doubling. High performers achieved near-perfect detection, and participants developed metacognitive insight, showing appropriate confidence calibration only after training. A test–retest control study ruled out practice effects as an explanation for training gains, and an online replication demonstrates the scalability of our approach. As the technology advances, targeting systematic biases that are inherent to generative AI, and which manifest in global features, may offer a more durable defense than approaches reliant on image artifacts alone.
A machine learning–based optimal charging strategy for PV-assisted electric vehicle systems incorporating second-life batteries under degradation constraints
Abstract The rapid expansion of electric vehicles (EVs) and residential photovoltaic (PV) systems has created new challenges in battery charging management, particularly due to the variability of renewable energy, grid limitations, and battery aging effects. In this work, we present a machine learning–based Energy Management System (EMS) designed for PV-assisted smart charging of EVs and second-life batteries. The proposed system estimates the Optimal Charging Duration Class (OCDC) using an XGBoost model trained on real-time operating variables such as state of charge (SOC), battery temperature, available PV surplus, and degradation-related indicators. Rather than relying on conventional continuous power control, the proposed approach adopts discrete charging modes that dynamically adjust to operating conditions, aiming to improve both energy utilization and battery health. Degradation considerations are incorporated in a practical, control-oriented manner by avoiding operating regions associated with accelerated aging, instead of explicitly modeling electrochemical processes. The system is evaluated within a simulation framework that includes realistic PV generation profiles, load demand, and thermal behavior. Although hardware implementation is not yet included, it is identified as an important direction for future validation. The results show that the proposed EMS increases PV self-consumption by around 22% while reducing exposure to high-temperature operation, high state-of-charge conditions, and unnecessary cycling. These outcomes indicate a potential reduction in degradation risk, especially for second-life battery applications. Overall, the study demonstrates that integrating machine learning with real-time energy management can provide an efficient and health-aware solution for smart charging in residential and hybrid renewable energy systems.
Orbital-aware approximations to high-order RDM calculations
In this work, we analyze the structure of higher-order reduced density matrices (RDMs) from the perspective of correlated electron transition processes. We introduce the orbital overlap degree (OOD) as a quantitative measure of the overlap between creation and annihilation orbital indices in individual RDM elements. Systematic analysis of representative strongly correlated systems reveals a strong correlation between the OOD and the magnitude of RDM elements. Motivated by this observation, we develop an orbital-aware (OA) approximation for calculating higher-order RDMs, i.e., oa4-RDMs, that selectively approximates the evaluation of fourth order RDM elements. The oa4-RDMs can be integrated with RDM-based multi-reference approaches, e.g., the density-matrix renormalized group n-electron valence second-order perturbation theory with oa4-RDMs (DMRG-oa4-NEVPT2). Numerical results for Cr2 and 1,2-dioxetanone show that the OA approximation significantly reduces computational cost while preserving the dominant contributions in strongly correlated calculations, and orbital-type analysis of trans-polyacetylene oligomers demonstrates the general applicability of OOD as a descriptor across different orbital representations.
Multiscale characterization of the human claustrum from histology to MRI
The claustrum is a thin, bilateral structure embedded deep within the human brain. Its widespread cortical connectivity has motivated perhaps the broadest range of functional hypotheses of any subcortical structure. Yet its complex, sheet-like morphology has hindered investigation in living humans, leaving a small in vivo MRI literature marked by large and often implausible discrepancies. Here, we construct a three-dimensional histological “gold standard” model of the human claustrum and systematically evaluate three ultra-high field 7-Tesla MRI datasets against this reference and its downsampled derivatives. We show that apparent discrepancies in MRI-based claustrum morphology arise primarily from resolution-dependent effects rather than contrast limitations, which transform the claustrum’s intricate sheet into an artifactually thickened ribbon. Despite this, submillimeter MRI reliably captures a dorsal “core” containing most claustral volume and cell density and encompassing major corticoclaustral connectivity, and at the highest acquired resolution (0.5 mm isotropic), the ventral claustrum’s extension into the temporal lobe is partially recovered, with uncertainty reflecting boundary imprecision rather than anatomical absence. Together, these findings overturn the view that the human claustrum is inaccessible to MRI and establish a foundation for future functional and clinical investigation in the living human brain.
Fuzzy cognitive explainable AI framework integrating ResNet-50 and causal clinical concept reasoning for skin lesion detection
Chlorine–carbon bond cleavage in chloroacetyl chloride induced by low-energy (&lt; 8 eV) electrons: Theoretical and experimental studies
Despite their toxicity and environmental impact, chlorinated compounds remain indispensable to modern industry. Although their complete replacement is improbable, innovation in processes, e.g., activation of carbon–chlorine (C–Cl) bonds, could redefine how they may be used. Chloroacetyl chloride (CAC), in particular, serves as a versatile building block in numerous chemical syntheses. Here, we investigate the interaction of CAC with low-energy (&lt;8 eV) electrons. At these energies, resonant processes are responsible for the molecular fragmentation leading to the C–Cl bond cleavage as the predominant dissociation channel. The agreement between density functional theory (DFT) calculations, earlier results [Hacaloglu et al., J. Phys. Chem. 94, 4412–4415 (1990)], and the revisited experimental observations confirms the reliability of the theoretical approach. The gained information may contribute to the development of electron-based methods for chemical synthesis.