Shift In State Research Articles

State shifts in the deep Critical Zone drive landscape evolution in volcanic terrains

Volcanic provinces are among the most active but least well understood landscapes on Earth. Here, we show that the central Cascade arc, USA, exhibits systematic spatial covariation of topography and hydrology that are linked to aging volcanic bedrock, suggesting systematic controls on landscape evolution. At the Cascade crest, a locus of Quaternary volcanism, water circulates deeply through the upper [Formula: see text]1 km of crust but transitions to shallow and dominantly horizontal flow as rocks age away from the arc front. We argue that this spatial pattern reflects a temporal state shift in the deep Critical Zone. Chemical weathering at depth, surface particulate deposition, and tectonic forcing drive landscapes away from an initial state with minimal topographic dissection, large vertical hydraulic conductivity, abundant lakes, and muted hydrographs toward a state of deep fluvial dissection, small vertical hydraulic conductivity, few lakes, and flashy hydrographs. This state shift has major implications for regional water resources. Drill hole temperature profiles imply at least [Formula: see text] km[Formula: see text] of active groundwater currently stored at the Cascade Range crest, with discharge variability a strong function of bedrock age. Deeply circulating groundwater also impacts volcanism, and Holocene High Cascades eruptions reflect explosive magma-water interactions that increase regional volcanic hazard potential. We propose that a Critical Zone state shift drives volcanic landscape evolution in wet climates and represents a framework for understanding interconnected solid earth dynamics and climate in these terrains.

Global trends and regime state shifts of lacustrine aquatic vegetation

Global trends and regime state shifts of lacustrine aquatic vegetation

A shift towards super-critical brain dynamics predicts Alzheimer’s disease progression

Alzheimer’s disease (AD) is the most common form of dementia with continuum of disease progression of increasing severity from subjective cognitive decline (SCD) to mild cognitive impairment (MCI), and lastly to AD. The transition from MCI to AD has been linked to brain hyper-synchronization, but the underlying mechanisms leading to this are unknown. Here, we hypothesized that excessive excitation in AD disease progression would shift brain dynamics towards supercriticality across an extended regime of critical-like dynamics. In this framework, healthy brain activity during aging preserves operation at near the critical phase transition at balanced excitation-inhibition (E/I). To test this hypothesis, we used source-reconstructed resting-state MEG data from a cross-sectional cohort (N=343) of individuals with SCD, MCI and healthy controls (HC) as well as from a longitudinal cohort (N=45) of MCI patients. We then assessed brain criticality by quantifying long-range temporal correlations (LRTCs) and functional EI (fE/I) of neuronal oscillations. LRTCs were attenuated in SCD in spectrally and anatomically constrained regions while this breakdown was progressively more widespread in MC. In parallel, fE/I was increased in the MCI but not in the SC cohort. Both observations also predicted the disease progression in the longitudinal cohort. Finally, using machine learning trained on functional (LRTCs, fE/I) and structural (MTL volumes) features, we show that LRTCs and f/EI are the most informative features for accurate classification of individuals with SCD while structural changes accurate classify the individuals with MCI. These findings establish that a shift towards super-critical brain dynamics reflects early AD disease progression.Significance StatementThe neuronal mechanisms underlying progression of Alzheimer’s disease (AD) are not well understood. One characteristic feature of AD is brain hyper-synchronization, but the mechanisms leading to this hyper-synchronization have remained unclear. We investigated whether AD dementia progression can be explained in the framework of brain criticality by the shift in individual brain states along an extended regime of critical-like dynamics. We analysed brain criticality with measures of long-range temporal correlations and functional excitation-inhibition balance from source-reconstructed resting-state magnetoencephalography (MEG) data. We show that AD dementia progression is associated with a gradual increase in excitability and a progressive shift towards super-critical brain dynamics.

Corruption in the Local Government System of the Russian State (According to Investigative Cases of the 1590–1630s)

Introduction. The purpose of the article is to study how corruption was understood in the Moscow state and how the fight against it was carried out in the system of local government to show the vector of institutional shift in the Russian state of the late 16th – first third of the 17th century. Methods and materials. The subject of the analysis is three investigative cases of corruption revealed during the 1590–1630s in three regions of Russia: Veliky Novgorod, Pskov, and Sviyazhsk. The investigation of cases was carried out using searches, which consist of questions from detectives and consolidated answers from people being interviewed, sealed with their assault. Analysis. During the study, a reconstruction of the corruption networks created by the clerk and governors was carried out, the methods and forms of obtaining illegal profits were studied, and an assessment was made of its volume and the scale of corruption in the Russian state. Administrators integrated into corrupt networks were punished with confiscation of their movable property and money savings and deprivation of office; sometimes the abuses they committed were not considered grounds for holding them liable. Results. Estimated calculations of corrupt profits do not convince that widespread ideas about the total corruption of the Russian administrative apparatus are justified enough. Corruption income of officials during the performance of their official duties and at the expense of the governed was illegal; by producing these laws, the state apparatus lost its patrimonial features and acquired a bureaucratic character, and the state became absolutist.

Microbial functional diversity and redundancy: moving forward.

Microbial functional ecology is expanding as we can now measure the traits of wild microbes that affect ecosystem functioning. Here, we review techniques and advances that could be the bedrock for a unified framework to study microbial functions. These include our newfound access to environmental microbial genomes, collections of microbial traits, but also our ability to study microbes' distribution and expression. We then explore the technical, ecological, and evolutionary processes that could explain environmental patterns of microbial functional diversity and redundancy. Next, we suggest reconciling microbiology with biodiversity-ecosystem-functioning studies by experimentally testing the significance of microbial functional diversity and redundancy for the efficiency, resistance, and resilience of ecosystem processes. Such advances will aid in identifying state shifts and tipping points in microbiomes, enhancing our understanding of how and where will microbiomes guide Earth's biomes in the context of a changing planet.

Topological states in finite graphene nanoribbons tuned by electric fields

In this comprehensive study, we conduct a theoretical investigation into the Stark shift of topological states (TSs) in finite armchair graphene nanoribbons (AGNRs) and heterostructures under transverse electric fields. Our focus centers on the multiple end zigzag edge states of AGNRs and the interface states of9--7--9AGNR heterostructures. For the formal TSs, we observe a distinctive blue Stark shift in energy levels relative to the electric field within a range where the energy levels of TSs do not merge into the energy levels of bulk states. Conversely, for the latter TSs, we identify an oscillatory Stark shift in energy levels around the Fermi level. Simultaneously, we reveal the impact of the Stark effect on the transmission coefficients for both types of TSs. Notably, we uncover intriguing spectra in the multiple end zigzag edge states. In the case of finite9--7--9AGNR heterostructures, the spectra of transmission coefficient reveal that the coupling strength between the topological interface states can be well controlled by the transverse electric fields. The outcomes of this research not only contribute to a deeper understanding of the electronic property in graphene-based materials but also pave the way for innovations in next-generation electronic devices and quantum technologies.

Research on mood monitoring and intervention for anxiety disorder patients based on deep learning wearable devices

Background Anxiety disorders are common mental health issues that have a significant effect on people's quality of life. Conventional techniques for tracking emotional states frequently lack the accuracy and sensitivity needed for successful intervention. Objectives This project aims to create a sophisticated monitoring system that uses deep learning methods to evaluate physiological data from wearables, emphasizing heart rate variability (HRV), to forecast patients’ emotional states who suffer from anxiety disorders. Methods Wearable equipment monitors physiological characteristics, which we used to obtain patient HRV data. We processed the data using a Bidirectional Long-Short-Term Memory (Bi-LSTM) network to evaluate time-dependent variables and enhance the precision of emotional state predictions. The physiological signals were used to teach the model to recognize different emotional states, such as neutral, happy, and sad. Results Outperforming conventional machine learning models, the Bi-LSTM model showed a high accuracy rate of up to 97% in predicting emotional states. The findings suggest that ongoing HRV monitoring can accurately track shifts in emotional states and enable prompt responses. Conclusion This work emphasizes the possibility of real-time emotional state monitoring in patients with anxiety disorders with wearable technology and deep learning. The results point to the potential benefits of this strategy for improving emotional regulation and improving anxiety sufferers’ quality of life, opening new avenues for investigation and advancement in the field of mental health therapies.

Two decades of climate tipping points research: Progress and outlook

The potential for nonlinear ‘surprises’ in the climate system has been recognised since the 1960s. One such nonlinearity is a tipping point: when change in a system becomes self-sustaining once forced passed a threshold, triggering a state shift that is often abrupt and irreversible. Research on climate tipping points has flourished since 2005, recently culminating in the release of the Global Tipping Points Report at COP28 and calls for targeted assessment by the Intergovernmental Panel on Climate Change. Here I summarise progress in our growing understanding of climate tipping points and reflect on their implications for climate action and the outlook for future research.

A statistical approach for systematic identification of transition cells from scRNA-seq data.

Decoding cellular state transitions is crucial for understanding complex biological processes in development and disease. While recent advancements in single-cell RNA sequencing (scRNA-seq) offer insights into cellular trajectories, existing tools primarily study expressional rather than regulatory state shifts. We present CellTran, a statistical approach utilizing paired-gene expression correlations to detect transition cells from scRNA-seq data without explicitly resolving gene regulatory networks. Applying our approach to various contexts, including tissue regeneration, embryonic development, preinvasive lesions, and humoral responses post-vaccination, reveals transition cells and their distinct gene expression profiles. Our study sheds light on the underlying molecular mechanisms driving cellular state transitions, enhancing our ability to identify therapeutic targets for disease interventions.

Spin Regulation of Nickel Single Atom Catalyst via Axial Phosphor‐Coordination Achieves Near Unity CO Selectivity in Electrochemical CO2 Reduction

AbstractThe engineering of the spin state and axial coordination of the metal center of single‐atom catalyst (SAC) represents an effective strategy for regulating the catalytic activity, selectivity, and stability toward electrocatalytic reduction of CO2 (ECO2R). However, rational design and deliberate fabrication of SACs with axial coordination of specified atoms remain challenging. Herein, Ni single atoms with axial coordination of phosphorus (NiP−N4−C) and four planar nitrogen atoms are fabricated, which induces reorientation of the 3d orbitals of the Ni atom and shift of the spin state from low (S = 0) to high (S = 2). The enhanced d−p orbital coupling between the Ni and the adsorbents enhances CO2 activation and reduces energy barrier for formation of *COOH, a key intermediate in the ECO2R to produce CO, enabling the high activity and near unity selectivity of CO in ECO2R in a broad potential range of 600 mV (−0.4–−1.0 V vs reversible hydrogen electrode vs RHE), achieving a turnover frequency of 37.2 s−1 at −1.0 V versus RHE. As a bifunctional cathode electrocatalyst, NiP−N4−C demonstrates a peak power density of 18.5 mW cm−2 and maintains cycling durability over 70 h in rechargeable Zn−CO2 batteries.

Optimizing nitrogen management for pollution control in Lake Baiyangdian following water replenishment

Eutrophication is an ecological process showing the state shift of a lake. This shift could be triggered when the external nitrogen (N) loads exceed N thresholds. Meanwhile, external water inputs and the resulting changes in lake water depth could affect N thresholds. Thus, the shift towards eutrophication may occur more quickly when the N thresholds decrease. Lake Baiyangdian is located in the North China Plain and plays an essential role in ecosystem service provision. However, this lake may have seen a decrease in the N threshold decrease due to frequent water replenishment since 2015. In this study, we compared the external N loads to Lake Baiyangdian with the N thresholds from 2012 to 2017. For this, we considered the effects of water replenishment by linking the MARINA-Lakes and the PCLake + models. Then, we assessed how N thresholds could be met by external N loads from sub-basins of Lake Baiyangdian under 2017 and different N management cases, including improved crop yield and efficiency (S1), improved sewage treatment (S2), improved manure management (S3), and combined options (S4). Results indicate that a 45% reduction in river export of N to Lake Baiyangdian was found from 2012 to 2017. Agricultural sources (fertilizer and manure) accounted for 59% of river exports of N in 2017. River N exports to the lake are projected to be reduced by 13–67% under the four cases. In 2017, the N-load response curve exhibited hysteresis with a 56–87% decrease in N thresholds compared to 2012. Measures in S4 can help to reduce external N exports to Lake Baiyangdian below the N thresholds. Our study emphasizes the importance of combined N management strategies to mitigate the eutrophication risk of the lake. These results offer valuable insights for N management in lake basins experiencing increasing water depth resulting from water replenishment.

Synthesis of Nanostructured (Ni0.7-W0.3)1-XRux Electrocatalysts for Hydrogen Evolution Reaction in PEM Water Electrolysis

The development of cost-effective and efficient electrocatalysts for the hydrogen evolution reaction (HER) is crucial for advancing renewable energy applications. Recently, the authors identified the Ni0.7-W0.3 sample as a promising alternative electrocatalyst candidate for HER in acidic media. However, further enhancement of the electrocatalytic activity of this material is needed. Therefore, to improve the activity towards hydrogen evolution in acidic media, the influence of adding small amounts (≤10 mol.%) of Ru to the Ni0.7-W0.3 sample was studied. Ruthenium is known for its highly efficient performance in the HER and is considered a promising alternative to Pt due to its relatively low price and similar hydrogen bonding strength. Low-Ru-loading hybrid electrocatalysts have great potential as efficient materials for HER, achieving a balance of low cost and high activity.Herein, ternary (Ni0.7-W0.3)1-xRux (x = 0, 2, 4, 6, 8, 10) nanocomposite electrocatalysts with different proportions of Ru were synthesized using the solution combustion synthesis (SCS) method, followed by annealing under a reductive atmosphere (10 vol.% H2/Ar flow) for application as HER electrocatalysts in acidic electrolysis. Morphological characteristics of the nanocomposite materials were examined using scanning electron microscopy (SEM) and transmission electron microscopy (TEM), along with energy-dispersive X-ray spectroscopy (EDX). Microscopy techniques revealed a porous, coral-like morphology, a distinctive feature commonly observed in combustion products synthesized using the SCS methodology. EDX elemental mapping analyses confirmed the uniform dispersion of Ru, Ni, and W elements throughout the sample, suggesting a close interconnection of the ternary species within the nanostructure. N2 adsorption/desorption measurements showed a high specific surface area and mesoporous structures in the as-prepared products. X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) analyses revealed that the (Ni0.7-W0.3)1-xRux nanocomposites consist of Ru and Ni0.85W0.15 metallic phases, along with oxygen-deficient WO2, with their composition varying depending on the Ru:Ni:W molar ratio in the precursor solution.Electrochemical assessments in an acidic medium (0.5 M H2SO4) using linear sweep voltammetry (LSV) and electrochemical impedance spectroscopy (EIS) unveiled that the incorporation of Ru into the Ni0.7-W0.3 matrix yielded a synergetic effect, resulting in a significant improvement of the catalyst’s intrinsic electrocatalytic activity relative to both Ni0.7-W0.3 and the commercial 5 wt.% Ru/C. This improvement was attributed to the synergistic effects arising from the change in the electronic structure of the catalyst induced by the interaction between different electrocatalyst components (Ru, Ni, and W), which effectively accelerated the kinetics of the HER, enhancing the catalytic activity. The change in the electronic structure was evident in the spectroscopy techniques, which revealed the shift in the oxidation state of Ru, inducing a downshift of the d-band center of Ru nanoparticles, resulting in reduced binding strength to reactive intermediates and improved HER kinetics on the Ru surface. Furthermore, its three-dimensional structure, stemming from the nanoparticle morphology and mesoporous structure, contributes to the high exposure of active sites throughout the nanocomposite material. This work has demonstrated that engineering the electronic interactions between ternary composite systems is a promising route for constructing high-performance electrocatalysts. Although inferior to the state-of-the-art 20 wt.% Pt/C, the ternary Ru-Ni-W nanocomposite emerges as a promising and cost-effective alternative cathode material for HER in acidic media. This research contributes to the ongoing efforts to develop efficient and economically viable electrocatalysts for renewable energy technologies.

Operando Spectroelectrochemical and Computational Techniques to Describe the Mars-Van Krevelen Mechanism on Nitride Mxenes

The electrochemical nitrogen reduction reaction (NRR) provides an opportunity to convert nitrogen (N2) to ammonia (NH3) at ambient conditions. Protons (H+), which stem from water oxidation, are necessary for this process, but result in the hydrogen evolution reaction (HER) reducing overall selectivity. Another cause for low selectivity in the NRR is the high energy required to activate the N≡N bond during typical associative/dissociative mechanisms. An avenue to mitigate these challenges altogether is available on transition metal nitride materials through the Mars-van Krevelen (MvK) mechanism, where sub-lattice nitrogen atoms are protonated to form NH3, which then desorb to form nitrogen vacancies that require less energy to weaken the N≡N. Recently, we have provided preliminary evidence that the Ti2NT x MXene operates through this MvK mechanism. To further corroborate this mechanism, a series of electrochemical and operando spectroscopic techniques, including X-ray absorption spectroscopy (XAS) and Fourier-transform infrared (FTIR) spectroscopy, were utilized. We use XAS to track the Ti oxidation state shift as a function of applied potential to understand the role of the Ti in the MvK mechanism and to understand the dynamic change in the Ti-N-Ti bond network. Furthermore, we use FTIR to elucidate in real-time the adsorption of NRR reactive species and intermediates on the MXene surface, especially with regards to the periodicity of the -NH2 and N-N spectroscopic intensities, which allows for the MvK mechanism to be differentiated from the typical associative/dissociative mechanisms. Finally, we use DFT and AIMD to corroborate the experimental work and further enhance our understanding of the mechanisms of NRR. These findings serve as the foundation to produce green NH3 using earth abundant resources.

Enhancing learning process modeling for session-aware knowledge tracing

Session-aware knowledge tracing tries to predict learners’ performance, by splitting learners’ sequences into sessions and modeling their learning within and between sessions. However, there still is a lack of comprehensive understanding of the learning processes and session-form learning patterns. Moreover, the knowledge state shifts between sessions at the knowledge concept level remain unexplored. To this end, we conduct in-depth data analysis to understand learners’ learning processes and session-form learning patterns. Then, we perform an empirical study validating knowledge state shifts at the knowledge concept level in real-world educational datasets. Subsequently, a method of Enhancing Learning Process Modeling for Session-aware Knowledge Tracing, ELPKT, is proposed to capture the knowledge state shifts at the knowledge concept level and track knowledge state across sessions. Specifically, the ELPKT models learners’ learning process as intra-sessions and inter-sessions from the knowledge concept level. In intra-sessions, fine-grained behaviors are used to capture learners’ short-term knowledge states accurately. In inter-sessions, learners’ knowledge retentions and decays are modeled to capture the knowledge state shift between sessions. Extensive experiments on four real-world datasets demonstrate that ELPKT outperforms the existing methods in learners’ performance prediction. Additionally, ELPKT shows its ability to capture the knowledge state shifts between sessions and provide interpretability for the predicted results.

Towards ecosystem-based techniques for tipping point detection.

An ecosystem shifts to an alternative stable state when a threshold of accumulated pressure (i.e. direct impact of environmental change or human activities) is exceeded. Detecting this threshold in empirical data remains a challenge because ecosystems are governed by complex interlinkages and feedback loops between their components and pressures. In addition, multiple feedback mechanisms exist that can make an ecosystem resilient to state shifts. Therefore, unless a broad ecological perspective is used to detect state shifts, it remains questionable to what extent current detection methods really capture ecosystem state shifts and whether inferences made from smaller scale analyses can be implemented into ecosystem management. We reviewed the techniques currently used for retrospective detection of state shifts detection from empirical data. We show that most techniques are not suitable for taking a broad ecosystem perspective because approximately 85% do not combine intervariable non-linear relationships and high-dimensional data from multiple ecosystem variables, but rather tend to focus on one subsystem of the ecosystem. Thus, our perception of state shifts may be limited by methods that are often used on smaller data sets, unrepresentative of whole ecosystems. By reviewing the characteristics, advantages, and limitations of the current techniques, we identify methods that provide the potential to incorporate a broad ecosystem-based approach. We therefore provide perspectives into developing techniques better suited for detecting ecosystem state shifts that incorporate intervariable interactions and high-dimensionality data.

Maternal and zygotic contributions to H3K4me1 chromatin marking during germ layer formation

An early step in triploblastic embryo differentiation is the formation of the three germ layers. Maternal pioneer transcription factors (TFs) bind to embryonic enhancers before zygotic genome activation, initiating germ layer specification. While maternal TFs' role in establishing epigenetic marks is known, how early pluripotent cells gain spatially restricted epigenetic identities remains unclear. We show that by the early gastrula stage, H3K4me1-marked regions become distinct in each germ layer, with certain chromatin regions forming high density H3K4me1 marked regions (HDRs). Genes associated with these HDRs are more robustly expressed compared to those associated with low density H3K4me1 marked regions (LDRs) in the genome. This process is driven by the sequential actions of maternal and zygotic factors. Knockdown of key maternal endodermal TFs (Otx1, Vegt and Foxh1) leads to a loss of endodermal H3K4me1 marks in endoderm, with a concurrent emergence of ectodermal and mesodermal marks, indicating a shift in chromatin state. This work highlights the importance of coordinated activities of maternal and zygotic TFs in defining the regionally-resolved and dynamic process of chromatin modification conferred by H3K4me1 in the early Xenopus embryo.

CNSC-25. CONCURRENT IMMUNE CHECKPOINT BLOCKADE ENHANCES THE SURVIVAL BENEFIT OF CLINICALLY RELEVANT MAPK PATHWAY INHIBITORS IN A T CELL-DEPENDENT MANNER

Abstract BACKGROUND Despite the availability of targeted therapies, gliomas carrying the BRAFV600E mutation remain challenging to treat. Combined pharmacologic BRAFV600E and MEK inhibition is clinically used in patients with BRAFV600E mutant gliomas, but primary and acquired resistance is reported in ~70% of high-grade glioma patients. OBJECTIVES To gain mechanistic insights into therapy adaptation and escape for developing combination therapies that enhance clinical effects of combined BRAFV600E inhibition with Dabrafenib and MEK inhibition with Trametinib (BRAFi+MEKi) in high-grade gliomas. APPROACH Patients’ pre- and post-treatment glioma tissues, patient-derived and mouse cell lines, and two novel immunocompetent mouse models for BRAFV600E mutant high-grade gliomas were analyzed for the effect of BRAFi+MEKi using a range of techniques including bulk and single-cell RNA sequencing, immunofluorescence staining, flow cytometry, and mass cytometry. RESULTS We present new data showing that the BRAFi+MEKi combination significantly reduced glioma cell viability while inducing glial differentiation programs, potentially associated with programmed death receptor (PD-1) signaling. This suggests a profound shift in the glioma cell differentiation state that is linked to an inclination toward T cell inhibition. This prompted us to combine BRAFi+MEKi with immune checkpoint inhibitors targeting PD-L1 and CTLA-4, which significantly decreased T cell inhibition. Furthermore, from mouse studies, this combination therapy conferred a survival benefit over single BRAFi+MEKi combination therapy alone. Additionally, BRAFi+MEKi treatment triggered an interferon-gamma response, indicative of cytotoxicity, and enhanced the expression of MHC molecules associated with antigen presentation, only in drug-sensitive but not drug-resistant gliomas. The lack of improved therapeutic efficacy of immune checkpoint blockade in athymic mice, which lack T cells, emphasized the critical role of T cell activity in driving the success of the combination treatment. CLINICAL IMPACT Our preclinical findings highlight the potential of concurrent BRAFi+MEKi treatment and immune checkpoint blockade for overcoming therapy resistance in BRAF mutant high-grade glioma patients.

Electronic structure, elasticity, magnetism of Mn2X In (X = Fe, Co) full Heusler compounds under biaxial strain: First-principles calculations

Abstract The electronic structure, elasticity, and magnetic properties of the Mn2 X In (X = Fe, Co) full-Heusler compounds are comprehensively investigated via first-principles calculations. The calculated elastic constants indicate that both Mn2FeIn and Mn2CoIn possess ductility. At the optimal lattice constants, the magnetic moments are found to be 1.40 μ B/f.u. for Mn2FeIn and 1.69 μ B/f.u. for Mn2CoIn. Under the biaxial strain ranging from −2% to 5%, Mn2FeIn demonstrates a remarkable variation in the spin polarization, spanning from −2% to 74%, positioning it as a promising candidate for applications in spintronic devices. Analysis of the electronic structure reveals that the change in spin polarization under strain is due to the shift of the spin-down states at the Fermi surface. Additionally, under biaxial strain, the magnetic anisotropy of Mn2FeIn undergoes a transition of easy-axis direction. Utilizing second-order perturbation theory and electronic structure analysis, the variation in magnetic anisotropy with strain can be attributed to changes of d-orbital states near the Fermi surface.

Goal Shifts Structure Memories and Prioritize Event-defining Information in Memory.

Every day, we encounter far more information than we could possibly remember. Thus, our memory systems must organize and prioritize the details from an experience that can adaptively guide the storage and retrieval of specific episodic events. Prior work has shown that shifts in internal goal states can function as event boundaries, chunking experiences into distinct and memorable episodes. In addition, at short delays, memory for contextual information at boundaries has been shown to be enhanced compared with items within each event. However, it remains unclear if these memory enhancements are limited to features that signal a meaningful transition between events. To determine how changes in dynamic goal states influence the organization and content of long-term memory, we designed a 2-day experiment in which participants viewed a series of black-and-white objects surrounded by a color border on a two-by-two grid. The location of the object on the grid determined which of two tasks participants performed on a given trial. To examine if distinct types of goal shifts modulate the effects of event segmentation, we changed the border color, the task, or both after every four items in a sequence. We found that goal shifts influenced temporal memory in a manner consistent with the formation of distinct events. However, for subjective memory representations in particular, these effects differed by the type of event boundary. Furthermore, to examine if goal shifts lead to the prioritization of goal-relevant features in longer lasting memories, we tested source memory for each object's color and grid location both immediately and after a 24-hr delay. On the immediate test, boundaries enhanced the memory for all concurrent source features compared with nonboundary items, but only if those boundaries involved a goal shift. In contrast, after a delay, the source memory was selectively enhanced for the feature relevant to the goal shift. These findings suggest that goals can adaptively structure memories by prioritizing contextual features that define a unique episode in memory.

Nonlinear optical properties of La3+-Doped tellurite-zinc glasses: Impact of chemical bonding and physical properties via Raman and XPS

Understanding the connection between the local environments of lanthanum ions in tellurite glasses and their nonlinear optical (NLO) properties is essential for various technological applications. Rare earth ions can serve as probes for the local environments of the host material if the structure-property correlations are well understood. In this study, we investigated glasses with compositions 70TeO2 - (30-x)ZnO - (x)La2O3 (mol%) (x = 0, 5, 7, 9) fabricated by the melt-quenching technique. The capabilities of combined Raman and X-ray photoemission (XPS) spectroscopy were used to identify these glasses and define spectral deconvolution for distinguishing them in the formation of bridging oxygens (BO) and non-bridging oxygens (NBO). NLO properties were evaluated using open-aperture (OA) and closed-aperture (CA) Z-scan measurements with femtosecond (fs) laser pulses (∼220 fs, 750 Hz) at wavelengths ranging from 600 to 1500 nm. Raman spectra revealed a shift to higher frequencies from 731 to 746 cm−1, with increasing of La3+ ions, indicating the conversion of TeO4 polyhedra to TeO3 structural units, affecting the Te-O- Zn2+ or Te=O bonds and resulting in a variation of the polarizability. Besides the conventional analysis of oxidation state and chemical shifts of the core-level peaks, XPS spectroscopy included a careful analysis of the type oxygen bridges and metal-oxygen bond length to distinguish otherwise similar spectral contributions of different TZL glasses. Glasses with a high fraction of Zn2⁺ ions bonded with oxygen form NBO and modify the local structure of the glass network, while La³⁺ ions coordinate with oxygen atoms or interact with the lone pairs of tellurium atoms. By analyzing the correlation in different concentrations of La3+ ions, the Te-O bonds and NBO/(BO + NBO) ratio were clearly demonstrated. These findings suggest charge compensation between the Zn2⁺ and La³⁺ ions, for maintaining structural stability within the glass network. This study also reveals an enhancement of NLO properties as a function of La3+ ions concentration in TZL glasses under fs laser excitation due to resonant nonlinearity. The third-order optical nonlinearities showed an increase in the two-photon absorption coefficient (β) from 0.26 × 10−2 cm GW−1 (TZL5) to 0.62 × 10−2 cm GW−1 (TZL9), along with an increase in the nonlinear refractive index (n2) from 0.32 × 10−18 m2 W−1 to 0.65 × 10−18 m2 W−1. This enhancement is attributed to the increased number of NBOs and the high polarizability of La3+ ions. For all-optical switching (AOS) applications, two figures of merit were applied using the n2, α0, and β coefficients. The results suggest that the TZL glasses studied are potential candidates for AOS devices. Thus, this work not only elucidates the factors underlying the NLO properties but also highlights the promising potential of these glasses for use in AOS devices.