Time-resolved Signals Research Articles

Excited State Dynamics of Geometrical Evolution of α-Substituted Dibenzoylmethanatoboron Difluoride Complex with Aggregation-Induced Emission Property.

Organic molecules with an aggregation-induced emission (AIE) property have been attracting much attention from the viewpoint of application to solid state emissive materials. For the AIE mechanism, quantum mechanical studies proposed the restriction of the intramolecular motion (RIM) model with the contribution of the conical intersection (CI) and deduced the importance of the restricted access to a conical intersection (RACI) in the potential energy surface (PES). Although these theoretical studies have contributed to the elucidation of AIE phenomena, direct detection of the reaction dynamics is indispensable to clarify the actual PES and the deactivation mechanism. Along this line, we investigated excited state dynamics of the AIE molecule with dibenzoylmethanatoboron difluoride complexes using time-resolved absorption spectroscopies in both visible and infrared (IR) regions. While the reference system of 1,3-bis(4-methoxyphenyl)methanatoboron difluoride (2aBF2) showed strong emission in solution, the methyl-substituted derivative at the α-position of the dioxaborine ring (2amBF2) led to the very weak fluorescence in solution but strong emission in the solid state. Time-resolved visible absorption measurements revealed a peak shift and broadening of the stimulated emission in the solution of 2amBF2, owing to the rapid change of the molecular geometry. With the temporal evolution of time-resolved IR absorption signals and density functional theory (DFT) calculation of these systems, it was deduced that 2amBF2 has two stable geometries, namely, planar and bending, in the S1 state and the bending geometry in the S1 state led to rapid conversion to the S0 state. These results support the RACI model in the aggregated states, leading to the AIE properties.

Cavity-enhanced Faraday rotation spectroscopy for interference-free measurement of OH radical at 2.8 μm

An instrument based on cavity-enhanced Faraday rotation spectroscopy (CE-FRS) operating at 2.8 μm has been developed for interference-free measurement of OH radicals in the laboratory. By off-axis coupling of a continuous-wave laser into a high finesse optical cavity, FRS signal is obtained from balanced detection of time-integrated light intensity leaking out of the cavity in the presence of magnetic field. Radio-frequency white noise (5–520 MHz) was injected into laser current which reduced intensity fluctuations in cavity transmission, thus improved the signal-to-noise ratio of the spectroscopic signal by a factor of 2. The setup provides a simple and robust spectroscopic instrument for in-situ and highly-selective detection of paramagnetic species. We demonstrated the instrument’s capabilities using OH radical with concentration in the range of 1012 molecule.cm−3, generated by microwave discharge of water vapor at low pressure. The CE-FRS instrument exhibited a limit of detection of ∼ 1010 molecule.cm−3 in an integration time of 20 s, which is enhanced by a factor of 2.5 compared to cavity-enhanced wavelength modulation spectroscopy involving an off-axis integrated cavity output spectroscopy approach. A time-resolved FRS signal was recorded in a pulsed microwave discharge regime, giving a millisecond time resolution for the measurement of OH concentration profile. The developed instrument provides a potential analytical tool for the measurement of OH concentration for chemical kinetic study in reactor cells.

Time-resolved ARPES signal in pumped semiconductors within the dynamical projective operatorial approach

Time-resolved ARPES signal in pumped semiconductors within the dynamical projective operatorial approach

Newly discovered Early Cretaceous Au mineralization overprinting Late Jurassic Pb-Zn-Ag mineralization in the Qin-Hang metallogenic belt (South China)

Magmatic and mineralizing events in South China were traditionally thought to be limited between 150 Ma and 130 Ma despite the numerous Mesozoic-aged magmatic-hydrothermal deposits in the region. This viewpoint is being reevaluated in light of new age data. Kangjiawan is a representative Pb-Zn-Ag-Au deposit in the Hengyang Basin in the Qin-Hang metallogenic belt of South China that contains 1.96 Mt of Pb-Zn resource with 4.70% Zn, 6.02% Pb; 2280.5 t of Ag resource with 108.23 g/t of Ag; and 58 t of Au resource with 3.25 g/t of Au. Its mineralization can be divided into four stages: (I) pre-ore quartz−pyrite, (II) quartz−pyrite−galena−sphalerite, (III) quartz−galena−sphalerite, and (IV) calcite−pyrite. Multiple generations of pyrite were identified at Kangjiawan. Generation I pyrite (Py1, formed in Stage I) is coarse-grained, texturally homogeneous, and contains abundant silicate inclusions (e.g., K-feldspar and titanite). Generation II pyrite (Py2, formed in Stage II) is generally fine-grained and exhibits a core-rim texture, with the porous, sphalerite−galena inclusion-bearing core (Py2a) having replaced Py1 and the rim (Py2b) being homogeneous. Generation III pyrite (Py3, formed in Stage IV) is subdivided into Py3a and Py3b, with the homogeneous Py3a often replaced by the porous Py3b. The low Co/Ni ratio of Py1, along with the abundant silicate inclusions, is suggestive of intense fluid-rock interaction during Stage I. Py2a is As-Cu-Pb-Ag-Au−rich, Co-depleted, and has elevated Ag/Co (average 3.24) and As/Co (average 10,217.14) ratios, which are suggestive of extensive fluid boiling during the formation of Py2a. This is also supported by the ubiquitous Py2-cemented hydrothermal breccia. In contrast, Py2b is depleted in As-Cu-Pb-Ag-Au but enriched in Co, which is indicative of non-boiling conditions. Subsequent sealing of fractures by veins may have caused the formation of coarse-grained sphalerite and galena in Stage III. The similar 207Pb/206Pb values among Py1 (average 0.849), Stage II galena (average 0.849), Stage III galena (average 0.849), and the nearby Shuikoushan granodiorite stock (corrected average 0.848) suggests that mineralization in stages I−III may represent distal skarn-type Pb-Zn-Ag mineralization genetically related to the Shuikoushan stock. This interpretation is supported by the similar ages of mineralization Stage I (apatite U-Pb: 159.4 ± 1.0 Ma) and Stage II (fuchsite 40Ar-39Ar: 158.1 ± 0.4 Ma) to the age of Shuikoushan stock (zircon U-Pb age: ca. 158 Ma). As the primary Au-hosting mineral at Kangjiawan, the As- and Au-rich Py3a occurs in sharp contact with porous Co-Ni-Se-Bi-Mo-As−poor Py3b, which commonly contains abundant fine-grained galena and sphalerite inclusions. This suggests that Py3b formed via coupled dissolution−re-precipitation of Py3a. This coupled dissolution−re-precipitation reaction favors the remobilization of Au from within the Py3a structure and its re-enrichment as Au inclusions in Py3b; this is supported by the anomalous Au peaks in time-resolved signal profiles from laser ablation−inductively coupled plasma−mass spectrometry (LA-ICP-MS) and the abundant outliers for Au concentrations in Py3b. The markedly distinct trace-element geochemistry of Py3 compared to those of Py1 and Py2, and the U-Pb age of Stage IV calcite (ca. 138 Ma), are indicative of an Early Cretaceous Au mineralization event. Published calcite C-O isotope data, coupled with the narrow range of δ34S values (0.6‰−2.2‰) of Py3, suggest that the Kangjiawan Au mineralization may be associated with a concealed Early Cretaceous pluton. We propose that the Kangjiawan deposit formed via the overprinting of Late Jurassic distal skarn Pb-Zn-Ag mineralization by Early Cretaceous magmatic-hydrothermal Au mineralization, which demonstrates that 150−135 Ma may also be an important period for the formation of the magmatic-hydrothermal mineralization belt.

Intrapulse Spectral Evolution in Photospheric Gamma-Ray Bursts

Photons that decouple from a relativistic jet do so over a range of radii, leading to a spreading in arrival times at the observer. Therefore, changes to the comoving photon distribution across the decoupling zone are encoded in the emitted signal. In this paper, we study such spectral evolution occurring across a pulse. We track the radiation from the deep subphotospheric regions all the way to the observed time-resolved signal, accounting for emission at various angles and radii. We assume a simple power-law photon spectrum injection over a range of optical depths and let the photons interact with the local plasma. At high optical depths, we find that the radiation exists in one of three characteristic regimes, two of which exhibit a high-energy power law. Depending on the nature of the injection, this power law can persist to low optical depths and manifest itself during the rise time of the pulse with a spectral index β ≈ α − 1, where α is the low-energy spectral index. The results are given in the context of a gamma-ray burst jet, but are general to optically thick, relativistic outflows.

On-the-Fly Simulation of Two-Dimensional Fluorescence-Excitation Spectra.

Two-dimensional (2D) fluorescence-excitation (2D-FLEX) spectroscopy is a recently proposed nonlinear femtosecond technique for the detection of photoinduced dynamics. The method records a time-resolved fluorescence signal in its excitation- and detection-frequency dependence and hence combines the exclusive detection of excited state dynamics (fluorescence) with signals resolved in both excitation and emission frequencies (2D electronic spectroscopy). In this work, we develop an on-the-fly protocol for the simulation of 2D-FLEX spectra of molecular systems, which is based on interfacing the classical doorway-window representation of spectroscopic responses with trajectory surface hopping simulations. Applying this methodology to gas-phase pyrazine, we show that femtosecond 2D-FLEX spectra can deliver detailed information that is otherwise obtainable via attosecond spectroscopy.

Real-time structural characterization of protein response to a caged compound by fast detector readout and high-brilliance synchrotron radiation

Protein dynamics are essential to biological function, and methods to determine such structural rearrangements constitute a frontier in structural biology. Synchrotron radiation can track real-time protein dynamics, but accessibility to dedicated high-flux single X-ray pulse time-resolved beamlines is scarce and protein targets amendable to such characterization are limited. These limitations can be alleviated by triggering the reaction by laser-induced activation of a caged compound and probing the structural dynamics by fast-readout detectors. In this work, we established time-resolved X-ray solution scattering (TR-XSS) at the CoSAXS beamline at the MAX IV Laboratory synchrotron. Laser-induced activation of caged ATP initiated phosphoryl transfer in the adenylate kinase (AdK) enzyme, and the reaction was monitored up to 50ms with a 2-ms temporal resolution achieved by the detector readout. The time-resolved structural signal of the protein showed minimal radiation damage effects and excellent agreement to data collected by a single X-ray pulse approach.

Dynamic representation of multidimensional object properties in the human brain.

Our visual world consists of an immense number of unique objects and yet, we are easily able to identify, distinguish, interact, and reason about the things we see within a few hundred milliseconds. This requires that we integrate and focus on a wide array of object properties to support specific behavioral goals. In the current study, we examined how these rich object representations unfold in the human brain by modelling time-resolved MEG signals evoked by viewing single presentations of tens of thousands of object images. Based on millions of behavioral judgments, the object space can be captured in 66 dimensions that we use to guide our understanding of the neural representation of this space. We find that all dimensions are reflected in the time course of response with distinct temporal profiles for different object dimensions. These profiles fell into two broad types, with either a distinct and early peak (~125 ms) or a slow rise to a late peak (~300 ms). Further, early effects were stable across participants, in contrast to later effects which showed more variability, suggesting that early peaks may carry stimulus-specific and later peaks more participant-specific information. Dimensions with early peaks appeared to be primarily visual dimensions and those with later peaks more conceptual, suggesting that conceptual representations are more variable across people. Together, these data provide a comprehensive account of how behaviorally-relevant object properties unfold in the human brain and contribute to the rich nature of object vision.

Microfluidic paper-based chemiluminescence sensing platform based on functionalized CaCO3 for time-resolved multiplex detection of avian influenza virus biomarkers

Multiplex detection can enhance diagnostic precision and improve diagnostic efficiency, providing important assistance for epidemiological investigation and epidemic prevention. There is a great need for multi-detection sensing platforms to accurately diagnose diseases. Herein, we reported a μPAD-based chemiluminescence (CL) assay for ultrasensitive multiplex detection of AIV biomarkers, based on three DNAzyme/Lum/PEI/CaCO3. Three time-resolved CL signals were sequentially generated with detection limits of 0.32, 0.34, and 0.29 pM for H1N1, H7N9, and H5N1, respectively, and with excellent selectivity against interfering DNA. The recovery test in human serum displayed satisfactory analysis capabilities for complex biological samples. The μPAD-based CL assay achieved multiplex detection within 70 s, with a high time resolution of 20 s. The proposed strategy has the advantages of low cost, high sensitivity, good selectivity, and wide time resolution, the μPAD-based CL assay has shown great potential in the early and accurate diagnosis of diseases.

GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals.

Toward the development of effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning (DL) framework based on the graph convolutional neural networks (GCNs) is presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson's matrix of overall signals, the graph Laplacian of EEG electrodes is built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected (FC) softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and groupwise predictions. It has achieved the highest averaged accuracy, 93.06% and 88.57% (PhysioNet dataset), 96.24% and 80.89% (high gamma dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance is stably reproducible among repetitive experiments for cross-validation. The excellent performance of our method has shown that it is an important step toward better BCI approaches. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain MI. A DL library for EEG task classification including the code for this study is open source at https://github.com/SuperBruceJia/ EEG-DL for scientific research.

A MASH simulation of the photoexcited dynamics of cyclobutanone.

In response to a community prediction challenge, we simulate the nonadiabatic dynamics of cyclobutanone using the mapping approach to surface hopping (MASH). We consider the first 500fs of relaxation following photoexcitation to the S2 state and predict the corresponding time-resolved electron-diffraction signal that will be measured by the planned experiment. 397 abinitio trajectories were obtained on the fly with state-averaged complete active space self-consistent field using a (12,11) active space. To obtain an estimate of the potential systematic error, 198 of the trajectories were calculated using an aug-cc-pVDZ basis set and 199 with a 6-31+G* basis set. MASH is a recently proposed independent trajectory method for simulating nonadiabatic dynamics, originally derived for two-state problems. As there are three relevant electronic states in this system, we used a newly developed multi-state generalization of MASH for the simulation: the uncoupled spheres multi-state MASH method (unSMASH). This study, therefore, serves both as an investigation of the photodissociation dynamics of cyclobutanone, and also as a demonstration of the applicability of unSMASH to abinitio simulations. In line with previous experimental studies, we observe that the simulated dynamics is dominated by three sets of dissociation products, C3H6 + CO, C2H4 + C2H2O, and C2H4 + CH2 + CO, and we interpret our predicted electron-diffraction signal in terms of the key features of the associated dissociation pathways.

Terahertz deep learning fusion computed tomography

Terahertz (THz) tomographic imaging based on time-resolved THz signals has raised significant attention due to its non-invasive, non-destructive, non-ionizing, material-classification, and ultrafast-frame-rate nature for object exploration and inspection. However, the material and geometric information of the tested objects is inherently embedded in the highly distorted THz time-domain signals, leading to substantial computational complexity and the necessity for intricate multi-physics models to extract the desired information. To address this challenge, we present a THz multi-dimensional tomographic framework and multi-scale spatio-spectral fusion Unet (MS3-Unet), capable of fusing and collaborating the THz signals across diverse signal domains. MS3-Unet employs multi-scale branches to extract spatio-spectral features, which are subsequently processed through element-wise adaptive filters and fused to achieve high-quality THz image restoration. Evaluated by geometry-variant objects, MS3-Unet outperforms other peer methods in PSNR and SSIM. In addition to the superior performance, the proposed framework additionally provides high scalable, adjustable, and accessible interface to collaborate with different user-defined models or methods.

Predicting the photodynamics of cyclobutanone triggered by a laser pulse at 200nm and its MeV-UED signals-A trajectory surface hopping and XMS-CASPT2 perspective.

This work is part of a prediction challenge that invited theoretical/computational chemists to predict the photochemistry of cyclobutanone in the gas phase, excited at 200nm by a laser pulse, and the expected signal that will be recorded during a time-resolved megaelectronvolt ultrafast electron diffraction (MeV-UED). We present here our theoretical predictions based on a combination of trajectory surface hopping with XMS-CASPT2 (for the nonadiabatic molecular dynamics) and Born-Oppenheimer molecular dynamics with MP2 (for the athermal ground-state dynamics following internal conversion), coined (NA+BO)MD. The initial conditions were sampled from Born-Oppenheimer molecular dynamics coupled to a quantum thermostat. Our simulations indicate that the main photoproducts after 2ps of dynamics are CO + cyclopropane (50%), CO + propene (10%), and ethene and ketene (34%). The photoexcited cyclobutanone in its second excited electronic state S2 can follow two pathways for its nonradiative decay: (i) a ring-opening in S2 and a subsequent rapid decay to the ground electronic state, where the photoproducts are formed, or (ii) a transfer through a closed-ring conical intersection to S1, where cyclobutanone ring opens and then funnels to the ground state. Lifetimes for the photoproduct and electronic populations were determined. We calculated a stationary MeV-UED signal [difference pair distribution function-ΔPDF(r)] for each (interpolated) pathway as well as a time-resolved signal [ΔPDF(r,t) and ΔI/I(s,t)] for the full swarm of (NA+BO)MD trajectories. Furthermore, our analysis provides time-independent basis functions that can be used to fit the time-dependent experimental UED signals [both ΔPDF(r,t) and ΔI/I(s,t)] and potentially recover the population of photoproducts. We also offer a detailed analysis of the limitations of our model and their potential impact on the predicted experimental signals.

Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks

In order to efficiently and accurately monitor blood glucose concentration (BGC) synthetically influenced by various factors, quantitative blood glucose in vitro detection was studied using photoacoustic temporal spectroscopy (PTS) combined with a fusion deep neural network (fDNN). Meanwhile, a photoacoustic detection system influenced by five factors was set up, and 625 time-resolved photoacoustic signals of rabbit blood were collected under different influencing factors.In view of the sequence property for temporal signals, a dimension convolutional neural network (1DCNN) was established to extract features containing BGC. Through the parameters optimization and adjusting, the mean square error (MSE) of BGC was 0.51001 mmol/L for 125 testing sets. Then, due to the long-term dependence on temporal signals, a long short-term memory (LSTM) module was connected to enhance the prediction accuracy of BGC. With the optimal LSTM layers, the MSE of BGC decreased to 0.32104 mmol/L. To further improve prediction accuracy, a self-attention mechanism (SAM) module was coupled into and formed an fDNN model, i.e., 1DCNN-SAM-LSTM. The fDNN model not only combines the advantages of temporal expansion of 1DCNN and data long-term memory of LSTM, but also focuses on the learning of more important features of BGC. Comparison results show that the fDNN model outperforms the other six models. The determination coefficient of BGC for the testing set was 0.990, and the MSE reached 0.1432 mmol/L. Results demonstrate that PTS combined with 1DCNN-SAM-LSTM ensures higher accuracy of BGC under the synthetical influence of various factors, as well as greatly enhances the detection efficiency.

Evidence of Excited-State Vibrational Mode Governing the Photorelaxation of a Charge-Transfer Complex.

Modern, nonlinear, time-resolved spectroscopic techniques have opened new doors for investigating the intriguing but complex world of photoinduced ultrafast out-of-equilibrium phenomena and charge dynamics. The interaction between light and matter introduces an additional dimension, where the complex interplay between electronic and vibrational dynamics needs the most advanced theoretical-computational protocols to be fully understood on the molecular scale. In this study, we showcase the capabilities of ab initio molecular dynamics simulation integrated with a multiresolution wavelet protocol to carefully investigate the excited-state relaxation dynamics in a noncovalent complex involving tetramethylbenzene (TMB) and tetracyanoquinodimethane (TCNQ) undergoing charge transfer (CT) upon photoexcitation. Our protocol provides an accurate description that facilitates a direct comparison between transient vibrational analysis and time-resolved spectroscopic signals. This molecular level perspective enhances our understanding of photorelaxation processes confined in the adiabatic regime and offers an improved interpretation of vibrational spectra. Furthermore, it enables the quantification of anharmonic vibrational couplings between high- and low-frequency modes, specifically the TCNQ "rocking" and "bending" modes. Additionally, it identifies the primary vibrational mode that governs the adiabaticity between the ground state and the CT state. This comprehensive understanding of photorelaxation processes holds significant importance in the rational design and precise control of more efficient photovoltaic and sensor devices.

Simultaneous Inversion of Particle Size Distribution, Thermal Accommodation Coefficient, and Temperature of In-Flame Soot Aggregates Using Laser-Induced Incandescence.

Measuring the size distribution and temperature of high-temperature dispersed particles, particularly in-flame soot, holds paramount importance across various industries. Laser-induced incandescence (LII) stands out as a potent non-contact diagnostic technology for in-flame soot, although its effectiveness is hindered by uncertainties associated with pre-determined thermal properties. To tackle this challenge, our study proposes a multi-parameter inversion strategy-simultaneous inversion of particle size distribution, thermal accommodation coefficient, and initial temperature of in-flame soot aggregates using time-resolved LII signals. Analyzing the responses of different heat transfer sub-models to temperature rise demonstrates the necessity of incorporating sublimation and thermionic emission for accurately reproducing LII signals of high-temperature dispersed particles. Consequently, we selected a particular LII model for the multi-parameter inversion strategy. Our research reveals that LII-based particle sizing is sensitive to biases in the initial temperature of particles (equivalent to the flame temperature), underscoring the need for the proposed multi-parameter inversion strategy. Numerical results obtained at two typical flame temperatures, 1100 K and 1700 K, illustrate that selecting an appropriate laser fluence enables the simultaneous inversion of particle size distribution, thermal accommodation coefficient, and initial particle temperatures of soot aggregates with high accuracy and confidence using the LII technique.

Multi-channel feature extraction for virtual histological staining of photon absorption remote sensing images

Accurate and fast histological staining is crucial in histopathology, impacting diagnostic precision and reliability. Traditional staining methods are time-consuming and subjective, causing delays in diagnosis. Digital pathology plays a vital role in advancing and optimizing histology processes to improve efficiency and reduce turnaround times. This study introduces a novel deep learning-based framework for virtual histological staining using photon absorption remote sensing (PARS) images. By extracting features from PARS time-resolved signals using a variant of the K-means method, valuable multi-modal information is captured. The proposed multi-channel cycleGAN model expands on the traditional cycleGAN framework, allowing the inclusion of additional features. Experimental results reveal that specific combinations of features outperform the conventional channels by improving the labeling of tissue structures prior to model training. Applied to human skin and mouse brain tissue, the results underscore the significance of choosing the optimal combination of features, as it reveals a substantial visual and quantitative concurrence between the virtually stained and the gold standard chemically stained hematoxylin and eosin images, surpassing the performance of other feature combinations. Accurate virtual staining is valuable for reliable diagnostic information, aiding pathologists in disease classification, grading, and treatment planning. This study aims to advance label-free histological imaging and opens doors for intraoperative microscopy applications.

Abstract B084: Overcoming drug resistance of ‘tri-complex’ pan-RAS inhibitors: Inhibition of mTOR signaling

Abstract Background: RAS mutations occur in ~20% of all cancers that drive tumor progression, and therapy targeting most predominant RAS mutations remains unavailable. Mutant-selective RAS inhibitors (RASi), such as FDA-approved KRAS G12C inhibitors, have demonstrated initial clinical responses but drug resistance frequently emerges that minimize the drug efficacy, mechanistically mediated by RAS signaling reactivation. Methods and Results: To address this challenge, we characterized a class of novel pan-RASi that universally inhibit all RAS mutations, which forms a ‘tri-complex’ with the chaperone protein (cyclophilin A) that specifically binds to RAS, including wild-type RAS (KRAS, NRAS, HRAS). Mutant-selective RASi induced RAS signaling rebound intensively, whereas pan-RASi enabling to inhibit wild-type RAS minimized this rebound, which was surmountable in a concentration-dependent manner. Importantly, pan-RASi abrogated cell viability in patient-derived KRAS-mutant cancer organoids rather than in normal patient-derived wild-type RAS organoids, implicating drug selectivity and manageable toxicity. Interestingly, intrinsic resistance to pan-RASi was rare in RAS-mutant cancer cell screening but was observed only in a handful of pancreatic and lung cancer cell lines. Our time-resolved signaling analyses of pan-RASi treatment showed the re-activation of mTOR signaling and a contrast correlation that mTOR signaling decreased in sensitive lines but remained in resistant lines, underscoring the critical contribution of mTOR pathways to RASi resistance. Mechanistically, we further identified mTOR signaling that drives intrinsic resistance to pan-RASi, and combined pan-RASi with mTOR inhibitors (mTORi) suppressed mTOR signaling in resistant lines, thereby improving efficacy. Conclusion: Pan-RASi hold promise in treating RAS-mutant cancer clinically and a combination of pan-RASi with mTORi can improve drug efficacy. Citation Format: Qingxiang (Nick) Lin. Overcoming drug resistance of ‘tri-complex’ pan-RAS inhibitors: Inhibition of mTOR signaling [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr B084.

Impact of supporting nanometric membranes on the thermo-optical dynamics of individual plasmonic nanodisks.

The thermal dynamics and transient optical response of individual gold nanodisks supported on thin silicon nitride membranes were investigated using optical time-resolved pump-probe spectroscopy and finite-element modeling. The effect of reducing the membrane thickness from 50 nm to 15 nm on the nanodisk thermal dynamics was explored. A significant deceleration of the nanodisk cooling kinetics was observed, and linked to a quasi-two-dimensional heat diffusion process within the 15 nm thick membrane, without detectable modification of its thermal conductivity. Systematic measurements involving different optical probe wavelengths additionally revealed the contribution of indirect membrane heating to the measured time-resolved signals, an effect particularly pronounced in the spectral range where direct optical heating of the nanodisk induces minimal ultrafast modifications of its extinction cross-section.

Non-Gaussian Signal Statistics’ Impact on LIBS Analysis

A detailed study has been carried out to reveal signal statistics’ impact on analysis sensitivity in laser-induced breakdown spectroscopy (LIBS) measurements. For several signals measured simultaneously, it was demonstrated that space-, spectra- and time-integrated plasma emission followed a normal distribution while the spectra- and time-resolved LIBS signal (atomic line intensity, plasma background emissions) distribution functions were biased compared to a Gaussian distribution function. For the first time in LIBS, the impact of a non-Gaussian distribution function on the limit of detection (LOD)’s determination has been studied in detail for single-shot spectra as well as for averaged spectra. Here, we demonstrated that the non-symmetrical distribution of the LIBS signals influenced the estimated LODs, so knowledge of a LIBS signal’s distribution function provides more reliable results, and the analysis sensitivity can be wrongly estimated if Gaussian distribution is presumed.