Temporal Order Research Articles

The Impact of Adverse Childhood Experiences on Cognitive Control Across the Lifespan: A Systematic Review and Meta-analysis of Prospective Studies.

Adverse childhood experiences (ACEs) are strongly associated with impaired cognitive control, yet research on ACEs' effects across cognitive control domains-working memory, cognitive flexibility, and inhibitory control-remains sparse. This systematic review and meta-analysis evaluated the overall impact of ACEs on each of these cognitive control domains and explored moderating factors such as age, gender, cognitive control paradigms, and ACEs subtypes based on the dimensional model of adversity and psychopathology. A database search was conducted in SCOPUS, MedLine, PsycINFO, and Web of Science. Only prospective studies were included to ensure temporal order inferences, with at least two data collection points, assessing ACEs at baseline (T1) and cognitive control during follow-up (T2). Thirty-two studies (N = 26,863) producing 124 effect sizes were analyzed. Three-level meta-analyses revealed small-to-medium negative associations between ACEs and overall cognitive control (g = -0.32), and in each domain: working memory (g = -0.28), cognitive flexibility (g = -0.28), and inhibitory control (g = -0.32). The negative associations between ACEs and cognitive control were consistent across age, gender, and cognitive control paradigms. ACEs subtypes moderated the association with cognitive flexibility (p = .04) but not working memory or inhibitory control. Specifically, the deprivation subtype exhibited a stronger negative association with cognitive flexibility compared to threat and threat-and-deprivation subtypes. These findings highlight the pervasive negative impact of ACEs on cognitive control across ages and emphasize the need for targeted interventions. Implications, current gaps, limitations in research, and future study recommendations are discussed.

A preserved neural code for temporal order between memory formation and recall in the human medial temporal lobe.

Temporal memory enables us to remember the temporal order of events happening in our life. The human medial temporal lobe (MTL) appears to contain neural representations supporting temporal memory formation, but the cellular mechanisms that preserve temporal order information for recall are largely unknown. Here, we examined whether human MTL neuronal activity represents the temporal position of events during memory formation and recall, using invasive single and multi-unit recordings in human epilepsy patients (n = 19). Participants freely navigated a virtual environment in order to explore and remember locations and temporal positions of objects. During each exploration period, they sequentially encountered two or three different objects, placed in different locations. This allowed us to examine single- and multi-unit neuronal firing rates (FR) as a function of the temporal position the objects were presented in. We found that a significant number of multi-units and single-units in various MTL regions including the hippocampus showed selectivity to the temporal position of objects during the exploration period. During recall, patients were asked to indicate which one of two objects from the same trial was found latter. Neural firing rates during recall showed a selectivity supporting recall of temporal positions. Interestingly, most of the selective single-units that stayed selective during encoding and recall preserved their temporal position preference. Our results thus suggest that neuronal activity in the human MTL contains a preserved neural code for temporal order in memory formation and recall.

Comparative Effectiveness of Antipsychotics in Patients With Schizophrenia Spectrum Disorder

Antipsychotics are the cornerstone of maintenance treatment in schizophrenia spectrum disorders, but it is unclear which agents should be prioritized by prescribers. To investigate the clinical effectiveness of antipsychotics, including recent market entries, in comparison with oral olanzapine in relapse and treatment failure prevention among individuals with schizophrenia spectrum disorder. This comparative effectiveness research study with a within-individual analysis included data from Swedish health care registers of inpatient and specialized outpatient care, sickness absence, and disability pensions among all individuals aged 16 to 65 years who were diagnosed with schizophrenia spectrum disorder from January 1, 2006, to December 31, 2021, including an incident cohort and a prevalent cohort. Specific antipsychotics. The risks for psychosis relapse hospitalization and treatment failure (psychiatric hospitalization, death, or change in an antipsychotic medication) were adjusted for the temporal order of treatments, time since cohort entry, and concomitant drugs. Comparisons of all antipsychotics with oral olanzapine, the most commonly used antipsychotic, were investigated. Among the full cohort of 131 476 individuals, the mean (SD) age of the study cohort was 45.7 (16.2) years (70 054 men [53.3%]). During a median follow-up of 12.0 years [IQR, 5.2-16.0 years], 48.5% of patients (N = 63 730) experienced relapse and 71.1% (N = 93 464) underwent treatment failure at least once. Compared with oral olanzapine, paliperidone 3-month long-acting injectable (LAI) was associated with the lowest adjusted hazard ratio (AHR) in the prevention of relapses (AHR, 0.66; 95% CI, 0.51-0.86), followed by aripiprazole LAI (AHR, 0.77 [95% CI, 0.70-0.84]), olanzapine LAI (AHR, 0.79 [95% CI, 0.73-0.86]), and clozapine (AHR, 0.82 [95% CI, 0.79-0.86]). Quetiapine was associated with the highest risk of relapse (AHR, 1.44 [95% CI, 1.38-1.51]). For prevention of treatment failure, paliperidone 3-month LAI was associated with the lowest AHR (AHR, 0.36 [95% CI, 0.31-0.42]), followed by aripiprazole LAI (AHR, 0.60 [95% CI, 0.57-0.63]), olanzapine LAI (AHR, 0.67 [95% CI, 0.63-0.72]), and paliperidone 1-month LAI (AHR, 0.71 [95% CI, 0.68-0.74]). This comparative effectiveness research study demonstrated large differences in the risk of relapse and treatment failure among specific antipsychotic treatments. The findings contradict the widely held conception that all antipsychotics are equally effective in relapse prevention.

Random fractional kinematic wave equations of overland flow: The HPM solutions and applications

This paper presents new findings from analyses of a random fractional kinematic wave equation (rfKWE) for overland flow. The rfKWE is featured with orders of temporal and spatial fractional derivatives and with the roughness parameter, the effective rainfall intensity and infiltration rate as random variables. The new solutions are derived with the aid of a numerical method named the homotopy perturbation method (HPM) and approximate solutions are presented for different situations. The solutions are evaluated with data from overland flow flumes with simulated rainfall in the laboratory. The results suggest that on an infiltrating surface the temporal nonlocality of overland flow represented by the temporal order of fractional derivatives diminishes over time while the spatial nonlocality manifested by the spatial order of fractional derivatives continue if there is overland flow. It shows that the widely used unit discharge-height relationship is a special case of the solution of the rfKWE. Procedures are demonstrated for determining the fractional roughness coefficient, nf, the order of spatial fractional derivative, ρ, and the steady-state infiltration rate during the overland flow, As. The analyses of the data show that the mean spatial order of fractional derivative is ρ=1.25, the mean flow pattern parameter m=1.50, and the mean fractional roughness coefficient is nf=0.002 which is smaller than the conventional roughness coefficient, n=0.108. With these average values of the parameters and their standard deviations, simulations were performed to demonstrate the use of the methods, which is also a comparison of the classic KWE and rfKWE models.

Microtubule choreography: spindle self-organization during cell division

AbstractDuring cell division, the network of microtubules undergoes massive rearrangement to self-organize into the spindle, a bipolar structure essential for accurate chromosome segregation. This structure ensures the stable transmission of the genome from the mother cell to two daughter cells, yet the process by which the ordered architecture emerges from a collection of protein “parts” remains a mystery. In this review, we focus on several key spindle proteins, describing how they move, crosslink, and grow microtubules in vitro and contribute to the spindle’s structural organization. We categorize these proteins into groups, such as transporters, bundlers, and nucleators, to highlight their functional roles. We also present an advanced perspective on the spindle’s complex polymer architecture and its temporal assembly order in cellular contexts. This in situ level information should guide the minimal reconstitution of the spindle, helping to elucidate the biophysical principles underlying essential cytoskeletal self-organization.

Secure and robust randomness with sequential quantum measurements

Quantum correlations between measurements of separated observers are crucial for applications like randomness generation and key distribution. Although device-independent security can be certified with minimal assumptions, current protocols have limited performance. Here, we exploit sequential measurements, defined with a precise temporal order, to enhance performance by reusing quantum states. We provide a geometric perspective and a general mathematical framework, analytically proving a Tsirelson-like boundary for sequential quantum correlations, which represents a trade-off in nonlocality shared by sequential users. This boundary is advantageous for secure quantum randomness generation, certifying maximum bits per state with one remote and two sequential parties, even if one sequential user shares no nonlocality. Our simple qubit protocol reaches this boundary, and numerical analysis shows improved robustness under realistic noise. A photonic implementation confirms feasibility and robustness. This study advances the understanding of sequential quantum correlations and offers insights for efficient device-independent protocols.

Clustering-based compression for raster time series

Abstract A raster time series is a sequence of independent rasters arranged chronologically covering the same geographical area. These are commonly used to depict the temporal evolution of represented variables. The $T$-$k^{2}$-raster is a compact data structure that performs very well in practice for compact representations for raster time series. This structure classifies each raster as a snapshot or a log and encodes logs concerning their reference snapshots, which are the immediately preceding selected snapshots. An enhanced version of the $T$-$k^{2}$-raster, called Heuristic $T$-$k^{2}$-raster, incorporates a heuristic for automating the selection of snapshots. In this study, we investigate the optimality of the heuristic employed in Heuristic $T$-$k^{2}$-raster by comparing it with a dynamic programming (DP) approach. Our experimental evaluation demonstrates that Heuristic $T$-$k^{2}$-raster is a near-optimal solution, achieving compression performance almost identical to the DP method. These results indicate that variations of the structure that maintain the temporal order of the rasters are unlikely to significantly improve compression. Consequently, we explore an alternative approach based on clustering, where rasters are grouped according to their similarity, regardless of their temporal order. Our experimental evaluation reveals that this clustering-based strategy can enhance compression in scenarios characterized by cyclic behaviour.

Emotional state dynamics impacts temporal memory

ABSTRACT Emotional fluctuations are ubiquitous in everyday life, but precisely how they sculpt the temporal organisation of memories remains unclear. Here, we designed a novel task – the Emotion Boundary Task – wherein participants viewed sequences of negative and neutral images surrounded by a colour border. We manipulated perceptual context (border colour), emotional-picture valence, as well as the direction of emotional-valence shifts (i.e., shifts from neutral-to-negative and negative-to-neutral events) to create events with a shared perceptual and/or emotional context. We measured memory for temporal order and temporal distances for images processed within and across events. Negative images processed within events were remembered as closer in time compared to neutral ones. In contrast, temporal distances were remembered as longer for images spanning neutral-to-negative shifts – suggesting temporal dilation in memory with the onset of a negative event following a previously-neutral state. The extent of negative-picture induced temporal dilation in memory correlated with dispositional negativity across individuals. Lastly, temporal order memory was enhanced for recently-presented negative (versus neutral) images. These findings suggest that emotional-state dynamics matters when considering emotion-temporal memory interactions: While persistent negative events may compress subjectively remembered time, dynamic shifts from neutral-to-negative events produce temporal dilation in memory, with implications for adaptive emotional functioning.

The Development of the Mental Timeline is Related to Temporal Memory

ABSTRACT Although we cannot see or touch time, across many cultures, we use spatial representations to think about this abstract concept. Spatial representations of time are thought to support temporal concepts that might otherwise be difficult to represent and reason about, such as the temporal component of episodic memory. One common form of spatially representing time is the mental timeline, which is a linear projection of time onto space. Adults and older children from many cultures spontaneously activate the mental timeline when remembering the order of events. However, the mental timeline develops slowly throughout early childhood as children gain increasing experience with formal schooling and cultural artifacts. Here, we explored how individual differences in the development of American children’s mental timelines relate to their memory for temporal order (N = 96, M age = 6.22 years). We first tested children’s memory for the order and location of events through a memory task involving short videos. We then tested children’s spontaneous spatial representations of temporal order using an open-ended timeline construction task. The linearity of children’s timeline arrangements predicted their memory for temporal order but not memory for locations: children who spontaneously represented time linearly had significantly better temporal memory performance than children who represented time nonlinearly, but there was no difference in location memory performance. These data provide preliminary evidence that the development of the mental timeline may support the development of temporal memory.

Precisely regulation of photochromism via absorption-induced photoreaction attenuation and light intensity dependence for time-resolved asymmetric encryption

With the growing need for secure information storage, encryption materials are required to be designed with high information capacity, easy to prepare, and hard to decode. Here, donor–acceptor Stenhouse adducts (DASAs), known for their straightforward synthesis, are used to develop a new time-resolved asymmetric encryption strategy, enhancing information capacity. The photoswitching property of DASAs is regulated by the intensity of initial absorbance and the intensity of light irritation. Besides, we recognize the relationship between the intensity of initial absorbance and photoswitching rate constant is caused by the absorption-induced photoreaction attenuation (AIPA). By rationally regulating the photo-bleching time of DASAs, the information can be sequentially revealed (colored state) or concealed (bleached state) in the desired temporal order. As a result, a quick response (QR) code label for practical application and a multi-level encoding scheme for sophisticated asymmetric encryption have been proposed. Specifically, the advanced asymmetric encryption technique employs layer sequence and light intensity as private keys, diverging from traditional public key systems. Information is sequentially displayed over time. The accurate information can only be revealed at the correct temporal dimension (layer sequence) and the appropriate decoding parameter (light intensity), both of which are precisely controlled by AIPA. This study not only unveils an approach to time-resolved asymmetric encryption using DASAs, enhancing the security and capacity of information storage, but also paves the way for the development of high-security encryption materials that are both highly efficient and resistant to unauthorized decoding efforts.

Temporal Sequencing of Documents

We outline an unsupervised method for temporal rank ordering of sets of historical documents, namely American State of the Union Addresses and DEEDS, a corpus of medieval English property transfer documents. Our method relies upon effectively capturing the gradual change in word usage via a bandwidth estimate for the non-parametric Generalized Linear Models (Fan, Heckman, and Wand, 1995). The number of possible rank orders needed to search through for cost functions related to the bandwidth can be quite large, even for a small set of documents. We tackle this problem of combinatorial optimization using the Simulated Annealing algorithm, which allows us to obtain the optimal document temporal orders. Our rank ordering method significantly improved the temporal sequencing of both corpora compared to a randomly sequenced baseline. This unsupervised approach should enable the temporal ordering of undated document sets.

Regional deep atrophy: Using temporal information to automatically identify regions associated with Alzheimer’s disease progression from longitudinal MRI

Abstract Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer’s disease (AD). Estimating brain progression patterns can be applied to understanding the therapeutic effects of amyloid-clearing drugs in research and detecting the earliest sign of accelerated atrophy in clinical settings. However, most state-of-the-art measurements calculate changes directly by segmentation and/or deformable registration of MRI images, and may misreport head motion or MRI artifacts as neurodegeneration, impacting their accuracy. In our previous study, we developed a deep learning method DeepAtrophy that uses a convolutional neural network to quantify differences between longitudinal MRI scan pairs that are associated with time. DeepAtrophy has high accuracy in inferring temporal information from longitudinal MRI scans, such as temporal order or relative interscan interval. DeepAtrophy also provides an overall atrophy score that was shown to perform well as a potential biomarker of disease progression and treatment efficacy. However, DeepAtrophy is not interpretable, and it is unclear what changes in the MRI contribute to progression measurements. In this paper, we propose Regional Deep Atrophy (RDA), which combines the temporal inference approach from DeepAtrophy with a deformable registration neural network and attention mechanism that highlights regions in the MRI image where longitudinal changes are contributing to temporal inference. RDA has similar prediction accuracy as DeepAtrophy, but its additional interpretability makes it more acceptable for use in clinical settings, and may lead to more sensitive biomarkers for disease monitoring and progression understanding in preclinical AD.

Momentary Minority Stress, Nicotine Use, and Craving: Moderation by Nicotine-Use Motives Among Sexual Minority Youth

ABSTRACT Objective This pre-registered analysis aimed to examine the moderating role of nicotine-use motives on the association between minority stress and nicotine use and craving among sexual minority youth. Method Data stem from a 30-day ecological momentary assessment study conducted among 83 sexual minority youth ages 15–19 years old (M age = 17.96, SD = 1.10; 56.63% cisgender women; 73.5% non-Hispanic White). Participants were instructed to complete at least four assessments per day on wireless devices. Two types of multilevel models were estimated: models predicting day-level nicotine use and models predicting momentary nicotine use craving. Results Experiencing minority stressors was not associated with day-level nicotine use, but it was associated with greater momentary nicotine craving. Nicotine use-motives did not moderate the association between minority stress and nicotine use. In contrast, stress-reduction motives, assessed as a person-level trait, moderated the association between minority stress and nicotine craving, such that nicotine craving after experiencing a minority stressor was consistently higher relative to when minority stress had not been reported. Sensitivity analyses that examined associations between minority stress and nicotine use on a given day, regardless of temporal order, showed that minority stress was associated with higher odds of nicotine use on that day. Conclusions Experiencing minority stressors did not predict day-level nicotine use but does contribute to greater momentary nicotine craving, informing minority stress theory. Consistency of the minority stress and nicotine craving relation, largely regardless of trait-level motives, highlights the potential context dependence of nicotine craving among sexual minority youth. Preregistration: This study was preregistered at osf.io/w5sz9.

Object Stability, Attention, and Temporal Order Judgments

Object Stability, Attention, and Temporal Order Judgments

Living and Dying in Shadow Times

Normative time frameworks obscure the ontological ambiguities of lived time, particularly in relation to illness and death. These forms of ambiguous lived time constitute what we call ‘shadow time’ – the unnoticed and the unknowable in our dominant temporal order. We examine what we call ‘diagnostic time’, ‘prognostic time’, ‘terminal time’ and ‘mourning time’ as shades of shadow time that elude the ordering of time that orders life. A critical stance to temporality is necessary, we argue, in order to recognise the regulatory imperatives of normative time, to mark the slippages between the demands of normative time and how time is often lived (out) and, importantly, to do justice to those living in the complicated ontological ambiguities of lived shadow time – where we see the intricate folding of life and death

Anchoring MnWO4 Nanorods on LaTiO2N Nanoplates for Boosted Visible Light-Driven Overall CO2 Reduction.

The photocatalytic conversion of CO2 into hydrocarbon fuel holds immense potential for achieving a carbon closed loop and carbon neutrality. Developing efficient photocatalysts plays a pivotal role in enabling the widespread application of photocatalytic CO2 reduction on a large scale. Herein, a novel S-scheme MnWO4/LaTiO2N heterojunction composite is successfully synthesized by a hydrothermal method. This composite catalyst demonstrates excellent photocatalytic activity in the reduction of CO2 to CO and CH4 using water molecules as electron donors under visible light irradiation, and the optimized 30% MnWO4/LaTiO2N composite displays significantly enhanced CO and CH4 yields of 3.94 and 0.81 μmol g-1 h-1, respectively, and the corresponding utilized photoelectron number reaches 14.7 μmol g-1 h-1, which is approximately 7.7 and 12.9 times that of LaTiO2N and MnWO4. The enhancement in photocatalytic activity of the composites can be ascribed to the construction of an S-scheme heterojunction, which exhibits improved charge transfer dynamics, retains the strongest redox capacity, and effectively suppresses back reactions. In situ Fourier-transform infrared imaging provides evidence, to a certain extent, for the existence of a temporal gradient order in the generation of multiple products during the photocatalytic reduction of CO2.

The Hippocampus Represents Information about Movements in Their Temporal Position in a Learned Motor Sequence.

Our repertoire of motor skills is filled with sequential movements that need to be performed in a specific order. Here, we used functional magnetic resonance imaging to investigate whether the human hippocampus, a region known to support temporal order in non-motor memory, represents information about the order of sequential motor actions in human participants (both sexes). We also examined such representations in other regions of the motor network (i.e., the premotor cortex, supplementary motor area, anterior superior parietal lobule, and striatum) already known for their critical role in motor sequence learning. Results showed that the hippocampus represents information about movements in their learned temporal position in the sequence, but not about movements or temporal positions in random movement patterns. Other regions of the motor network coded for movements in their learned temporal position, as well as movements and positions in random movement patterns. Importantly, movement coding contributed to sequence learning patterns in primary, supplementary, and premotor cortices but not in striatal and parietal regions. Our findings deepen our understanding of how striatal and cortical regions contribute to motor sequence learning and point to the capacity of the hippocampus to represent movements in their temporal context, an ability possibly explaining its contribution to motor learning.

Language-guided temporal primitive modeling for skeleton-based action recognition

Human actions describe the complex body dynamics, which are defined by an ordered set of sub-action primitives. Existing methods in skeleton-based action recognition primarily focus on designing various networks to learn the entire action representation, in alignment with the action label. However, training the heterogeneous action as a whole increases the burden on the network and is not conducive to learning the temporal structure of the action. In this paper, the traditional action label is extended into the ordered primitive language description by employing a large language model, which not only unveils the temporal structure of the action but also enriches its connotation. Based on this, we propose a language-guided skeleton representation learning network (LGS-Net) that leverages the language description to guide the skeleton feature learning from the ordered primitive and global action levels. Especially, the primitive guidance aligns the features of skeleton clips with those of the ordered primitive descriptions while preserving temporal order, which promotes the network to model the temporal primitive and capture the internal structure within the action from skeletons. To enhance the cross-modal alignment performance, we develop an innovative temporal alignment loss supplemented with diversity and sparsity regularization terms to generate the discriminative multi-modal representation. Evaluated on three benchmark datasets, NTU-60, NTU-120 and N-UCLA, the proposed LGS-Net achieves comparable results to state-of-the-art methods, which proves the effectiveness of the language-guided learning mechanism.

Analyzing the Sequence of Phases Leading to the Formation of the Active Region 13664, with Potential Carrington-like Characteristics

Abstract Several recurrent X-class flares from Active Region (AR) 13664 triggered a severe G5-class geomagnetic storm between 2024 May 10 and 11. The morphology and compactness of this AR closely resemble the AR responsible for the famous Carrington Event of 1859. Although the induced geomagnetic currents produced a value of the Dst index, probably 1 order of magnitude weaker than that of the Carrington Event, the characteristics of AR 13664 warrant special attention. Understanding the mechanisms of magnetic field emergence and transformation in the solar atmosphere that lead to the formation of such an extensive, compact, and complex AR is crucial. Our analysis of the emerging flux and horizontal motions of the magnetic structures observed in the photosphere reveals the fundamental role of a sequence of emerging bipoles at the same latitude and longitude, followed by converging and shear motions. This temporal order of processes frequently invoked in magnetohydrodynamic models—emergence, converging motions, and shear motions—is critical for the storage of magnetic energy preceding strong solar eruptions that, under the right timing, location, and direction conditions, can trigger severe space weather events on Earth.

Integrating age modeling into a hierarchical Bayesian framework for inferring the pattern and rate of past sea-level changes from uncertainty-prone proxy data

Inferring the pattern and rate of past sea-level changes from uncertainty-prone proxy records requires formal statistical analyses, preferably in a hierarchical framework. The commonly used error-in-variables method treats the relative sea level as a collection of random variables drawn from the multivariate Gaussian distribution. However, this method does not make any use of prior information about the sea-level index points as constraints in the inferential process, thereby leading to anomalously large uncertainties for the time periods when observational data are absent. Here, a hierarchical Bayesian model of past sea-level changes is presented. Specifically, the stochastically varying relative sea level is modeled as a piecewise linear process with an additive independent Brownian increment arriving in a Gaussian fashion. The treatment of temporal uncertainties associated with the sea-level index points in the partially observed proxy records also differs from the existing methods. Instead of calibrating the radiocarbon ages individually, the corresponding calendar ages are treated as random variables and inferred recursively according to their temporal order. Illustrative studies using synthetic and real-world data demonstrate the promise of this model.