Temporal Order Research Articles

Spatial attention in mental arithmetic: A literature review and meta-analysis.

We review the evidence for the conceptual association between arithmetic and space and quantify the effect size in meta-analyses. We focus on three effects: (a) the operational momentum effect (OME), which has been defined as participants' tendency to overestimate results of addition problems and underestimate results of subtraction problems; (b) the arithmetic cueing effect, in which arithmetic problems serve as spatial cues in target detection or temporal order judgment tasks; and (c) the associations between arithmetic and space observed with eye- and hand-tracking studies. The OME was consistently found in paradigms that provided the participants with numerical response alternatives. The OME shows a large effect size, driven by an underestimation during subtraction while addition was unbiased. In contrast, paradigms in which participants indicated their estimate by transcoding their final estimate to a spatial reference frame revealed no consistent OME. Arithmetic cueing studies show a reliable small to medium effect size, driven by a rightward bias for addition. Finally, eye- and hand-tracking studies point to replicable associations between arithmetic and eye or hand movements. To account for the complexity of the observed pattern, we introduce the Adaptive Pathways in Mental Arithmetic (APiMA) framework. The model accommodates central notions of numerical and arithmetic processing and helps identifying which pathway a given paradigm operates on. It proposes that the divergence between OME and arithmetic cueing studies comes from the predominant use of non-symbolic versus symbolic stimuli, respectively. Overall, our review and findings clearly support an association between arithmetic and spatial processing.

Memory for space and time in 2-year-olds

Although the capacity to remember spatial and temporal information may develop at different rates throughout childhood, its early development has rarely been examined within the same participants, using the same task, and across different time delays elucidating retention of different aspects of early episodic memories. We used a novel tablet game to investigate memory for objects’ spatial locations and temporal order in a sample of toddlers ranging in age from 2 years to 2;8 years (M = 2;4 years, SD = 2 months; N = 73). We examined performance both immediately after an initial and an additional demonstration, following a 20-minute delay, and 1 week after learning; performance was also assessed following a new demonstration after the 1-week delay test. Using a linear mixed model, we found that toddlers remembered spatial locations better than temporal order, and temporal memory decayed more quickly and did not benefit from reminders compared to spatial memory, underscoring that early memory fragility may depend on the type of information being retained.

Temporal Ordering of Biomarkers in Dutch-Type Hereditary Cerebral Amyloid Angiopathy.

The temporal ordering of biomarkers for cerebral amyloid angiopathy (CAA) is important for their use in trials and for the understanding of the pathological cascade of CAA. We investigated the presence and abnormality of the most common biomarkers in the largest (pre)symptomatic Dutch-type hereditary CAA (D-CAA) cohort to date. We included cross-sectional data from participants with (pre)symptomatic D-CAA and controls without CAA. We investigated CAA-related cerebral small vessel disease markers on 3T-MRI, cerebrovascular reactivity with functional 7T-MRI (fMRI) and amyloid-β40 and amyloid-β42 levels in cerebrospinal fluid. We calculated frequencies and plotted biomarker abnormality according to age to form scatterplots. We included 68 participants with D-CAA (59% presymptomatic, mean age, 50 [range, 26-75] years; 53% women), 53 controls (mean age, 51 years; 42% women) for cerebrospinal fluid analysis and 36 controls (mean age, 53 years; 100% women) for fMRI analysis. Decreased cerebrospinal fluid amyloid-β40 and amyloid-β42 levels were the earliest biomarkers present: all D-CAA participants had lower levels of amyloid-β40 and amyloid-β42 compared with controls (youngest participant 30 years). Markers of nonhemorrhagic injury (>20 enlarged perivascular spaces in the centrum semiovale and white matter hyperintensities Fazekas score, ≥2, present in 83% [n=54]) and markers of impaired cerebrovascular reactivity (abnormal BOLD amplitude, time to peak and time to baseline, present in 56% [n=38]) were present from the age of 30 years. Finally, markers of hemorrhagic injury were present in 64% (n=41) and only appeared after the age of 41 years (first microbleeds and macrobleeds followed by cortical superficial siderosis). Our results suggest that amyloid biomarkers in cerebrospinal fluid are the first to become abnormal in CAA, followed by MRI biomarkers for cerebrovascular reactivity and nonhemorrhagic injury and lastly hemorrhagic injury. This temporal ordering probably reflects the pathological stages of CAA and should be taken into account when future therapeutic trials targeting specific stages are designed.

Saprophytic: Decomposition and Tropical Environmental Time in Caribbean Literature

Abstract: During the period of Caribbean decolonization (1950–65), a subset of Caribbean authors reimagined the temporal role of the continental Caribbean's tropical interior: rather than a space outside history that colonists or residents could use as a resource to construct historical progress, the tropical hinterlands became a historical agent that possessed and assimilated people into an alternative temporal order through a saprophytic process. This essay focuses on three novels—Wilson Harris' The Palace of the Peacock (1960), Edgar Mittelholzer's My Bones and My Flute (1955), and Alejo Carpentier's The Lost Steps (1953)—that enact what I term a "saprophytic temporality": a form of time in which the past is constantly recycled, decomposed, and transformed into new forms. This process of continual regeneration also causes Caribbean residents to realize their involvement in ongoing imperial violence against the interior and its Indigenous inhabitants. In effect, this key subset of Caribbean novels of the 1950s and 1960s imply that the alternative to colonial development is not the independent Caribbean nation but a stranger and more unfathomable form of existence defined by the temporality of the Caribbean environment.

Reduced Visuospatial Attention in Personal Space is Not Limited to the Affected Limb in Complex Regional Pain Syndrome.

Alterations in spatial attention have been reported in people with chronic pain and may be relevant to understanding its cortical mechanisms and developing novel treatments. There is conflicting evidence as to whether people with Complex Regional Pain Syndrome (CRPS) have reduced visuospatial attention to their affected limb and/or its surrounding space, with some evidence that these deficits may be greater in personal (bodily) space. We aimed to test the competing hypotheses of whether the visuospatial attentional bias is specific to the personal space of the affected limb or generalizes to the personal space of other parts of the affected side of the body. Using visual Temporal Order Judgement tasks, we measured spatial attention in the personal space of the hands and feet of patients with upper (n=14) or lower (n=14) limb CRPS and pain-free controls (n=17). Participants judged the order of two light flashes presented at different temporal offsets on each of their hands or feet. Slower processing of the flash on one side relative to the other reflects reduced attention to that side of space. Controls prioritized stimuli on the non-dominant (left) relative to dominant side, consistent with the well-documented normal leftward bias of attention (ie "pseudoneglect"). Regardless of the location (upper or lower limb) of the pain or visual stimuli, people with CRPS showed no such asymmetry, representing reduced attention to the affected side (compared to the greater attention of controls to their non-dominant side). More severe CRPS symptoms were associated with a greater tendency to deprioritize stimuli on the affected side. Our findings suggest that relative visuospatial bias in CRPS is generalized to the personal space of the affected side of the body, rather than being specific to the personal space of the CRPS-affected limb.

Long-Term Forecasting Using MAMTF: A Matrix Attention Model Based on the Time and Frequency Domains

There are many time series forecasting methods, but there are few research methods for long-term multivariate time series forecasting, which are mainly dominated by a series of forecasting models developed on the basis of a transformer. The aim of this study is to perform forecasting for multivariate time series data and to improve the forecasting accuracy of the model. In the recent past, it has appeared that the prediction effect of linear models surpasses that of the family of self-attention mechanism models, which encourages us to look for new methods to solve the problem of long-term multivariate time series forecasting. In order to overcome the problem that the temporal order of information is easily broken in the self-attention family and that it is difficult to capture information on long-distance data using recurrent neural network models, we propose a matrix attention mechanism, which is able to weight each previous data point equally without breaking the temporal order of the data, so that the overall data information can be fully utilized. We used the matrix attention mechanism as the basic module to construct the frequency domain block and time domain block. Since complex and variable seasonal component features are difficult to capture in the time domain, mapping them to the frequency domain reduces the complexity of the seasonal components themselves and facilitates data feature extraction. Therefore, we use the frequency domain block to extract the seasonal information with high randomness and poor regularity to help the model capture the local dynamics. The time domain block is used to extract the smooth floating trend component information to help the model capture long-term change patterns. This also improves the overall prediction performance of the model. It is experimentally demonstrated that our model achieves the best prediction results on three public datasets and one private dataset.

Dual perspective denoising model for session-based recommendation

Session-based recommendation predicts the next interaction option for an anonymous user based on his/her short-term behavior records. A key challenge faced by session-based recommendation is the processing of noise information. That is, the precise identification of noise items and noise connections. Existing research typically follows two assumptions: (1) the latest interaction item is directly treated as the user’s real-time preference; (2) the temporal order relationship between items is directly treated as the user’s true interaction intention conversion pattern. These strict assumptions ignore two facts: (1) the items that the user accidentally clicked on also exist in the interaction session. (2) the connection edges caused by noise items or user interaction intention drift are also fused into the explicit sequential structure. To address the above two issues, we propose a new Dual Perspective Denoising Model for session-based recommendation called DPDM. Specifically, DPDM mainly consists of two parts: Noise Item Recognition Network and Noise Structure Reconstruction Network. The Noise Item Recognition Network removes noise items from the session through a sparse attention mechanism and virtual target representation. The Noise Structure Reconstruction Network composed of Sequence Module and Co-occurrence Module is used to mine users’ real interaction intention conversion patterns. The Sequence Module captures sequence relationships after removing noise items. The Co-occurrence Module eliminates noise connections by optimizing explicit interaction sequences with L0 activation regularization. Extensive experiments were conducted on three representative real-world datasets, and DPDM achieved positive results, demonstrating the effectiveness of the components in the model.

Emotional and temporal order effects – a comparison between word-cued and important autobiographical memories recall orders

ABSTRACT The retrieval mechanisms associated with the recall of autobiographical memories (AMs) may differ according to the cueing method used to elicit AMs. In the present study, we provide a systematic comparison between word-cued and important AMs in terms of two recall order effects, namely a temporal and an emotional order effect. The sample comprised 104 adults aged between 59 and 90 years. On two measurement occasions, participants recalled up to 20 word-cued and 15 important AMs. For each memory, participants provided their age at the time when the event occurred (age-at-event) and rated its emotionality. Order effects were analysed based on multivariate multilevel model with autoregressive effects for emotionality and age-at-event, respectively. Whereas word-cued AMs showed a stronger emotional order effect, important AMs were recalled temporarily ordered. Individuals differed in the extent to which they ordered AMs along a temporal or emotional dimension. These differences could partly be explained by personality traits. For instance, higher scores on neuroticism were associated with a stronger emotional order effect in both memory types. Findings are discussed in terms of the retrieval mechanisms that are involved in the recall of word-cued and important memory recall and how they may be affected by personality.

Audio Generation with Multiple Conditional Diffusion Model

Text-based audio generation models have limitations as they cannot encompass all the information in audio, leading to restricted controllability when relying solely on text. To address this issue, we propose a novel model that enhances the controllability of existing pre-trained text-to-audio models by incorporating additional conditions including content (timestamp) and style (pitch contour and energy contour) as supplements to the text. This approach achieves fine-grained control over the temporal order, pitch, and energy of generated audio. To preserve the diversity of generation, we employ a trainable control condition encoder that is enhanced by a large language model and a trainable Fusion-Net to encode and fuse the additional conditions while keeping the weights of the pre-trained text-to-audio model frozen. Due to the lack of suitable datasets and evaluation metrics, we consolidate existing datasets into a new dataset comprising the audio and corresponding conditions and use a series of evaluation metrics to evaluate the controllability performance. Experimental results demonstrate that our model successfully achieves fine-grained control to accomplish controllable audio generation.

Root Cause Analysis in Microservice Using Neural Granger Causal Discovery

In recent years, microservices have gained widespread adoption in IT operations due to their scalability, maintenance, and flexibility. However, it becomes challenging for site reliability engineers (SREs) to pinpoint the root cause due to the complex relationship in microservices when facing system malfunctions. Previous research employed structure learning methods (e.g., PC-algorithm) to establish causal relationships and derive root causes from causal graphs. Nevertheless, they ignored the temporal order of time series data and failed to leverage the rich information inherent in the temporal relationships. For instance, in cases where there is a sudden spike in CPU utilization, it can lead to an increase in latency for other microservices. However, in this scenario, the anomaly in CPU utilization occurs before the latency increases, rather than simultaneously. As a result, the PC-algorithm fails to capture such characteristics. To address these challenges, we propose RUN, a novel approach for root cause analysis using neural Granger causal discovery with contrastive learning. RUN enhances the backbone encoder by integrating contextual information from time series and leverages a time series forecasting model to conduct neural Granger causal discovery. In addition, RUN incorporates Pagerank with a personalization vector to efficiently recommend the top-k root causes. Extensive experiments conducted on the synthetic and real-world microservice-based datasets demonstrate that RUN noticeably outperforms the state-of-the-art root cause analysis methods. Moreover, we provide an analysis scenario for the sock-shop case to showcase the practicality and efficacy of RUN in microservice-based applications. Our code is publicly available at https://github.com/zmlin1998/RUN.

Acute Administration of HIV-1 Tat Protein Drives Glutamatergic Alterations in a Rodent Model of HIV-Associated Neurocognitive Disorders

HIV-1-associated neurocognitive disorders (HAND) are a major comorbidity of HIV-1 infection, marked by impairment of executive function varying in severity. HAND affects nearly half of people living with HIV (PLWH), with mild forms predominating since the use of anti-retroviral therapies (ART). The HIV-1 transactivator of transcription (Tat) protein is found in the cerebrospinal fluid of patients adherent to ART, and its administration or expression in animals causes cognitive symptoms. Studies of Tat interaction with the N-methyl-D-aspartate receptor (NMDAR) suggest that glutamate toxicity contributes to Tat-induced impairments. To identify changes in regional glutamatergic circuitry underlying cognitive impairment, we injected recombinant Tat86 or saline to medial prefrontal cortex (mPFC) of male Sprague–Dawley rats. Rats were assessed with behavioral tasks that involve intact functioning of mPFC including the novel object recognition (NOR), spatial object recognition (SOR), and temporal order (TO) tasks at 1 and 2 postoperative weeks. Following testing, mPFC tissue was collected and analyzed by RT-PCR. Results showed Tat86 in mPFC-induced impairment in SOR, and upregulation of Grin1 and Grin2a transcripts. To further understand the mechanism of Tat toxicity, we assessed the effects of full-length Tat101 on gene expression in mPFC by RNA sequencing. The results of RNAseq suggest that glutamatergic effects of Tat86 are maintained with Tat101, as Grin2a was upregulated in Tat101-injected tissue, among other differentially expressed genes. Spatial learning and memory impairment and Grin2a upregulation suggest that exposure to Tat protein drives adaptation in mPFC, altering the function of circuitry supporting spatial learning and memory.

Signals that control MAIT cell function in healthy and inflamed human tissues.

Mucosal-associated invariant T (MAIT) cells have a semi-invariant T-cell receptor that allows recognition of antigen in the context of the MHC class I-related (MR1) protein. Metabolic intermediates of the riboflavin synthesis pathway have been identified as MR1-restricted antigens with agonist properties. As riboflavin synthesis occurs in many bacterial species, but not human cells, it has been proposed that the main purpose of MAIT cells is antibacterial surveillance and protection. The majority of human MAIT cells secrete interferon-gamma (IFNg) upon activation, while some MAIT cells in tissues can also express IL-17. Given that MAIT cells are present in human barrier tissues colonized by a microbiome, MAIT cells must somehow be able to distinguish colonization from infection to ensure effector functions are only elicited when necessary. Importantly, MAIT cells have additional functional properties, including the potential to contribute to restoring tissue homeostasis by expression of CTLA-4 and secretion of the cytokine IL-22. A recent study provided compelling data indicating that the range of human MAIT cell functional properties is explained by plasticity rather than distinct lineages. This further underscores the necessity to better understand how different signals regulate MAIT cell function. In this review, we highlight what is known in regards to activating and inhibitory signals for MAIT cells with a specific focus on signals relevant to healthy and inflamed tissues. We consider the quantity, quality, and the temporal order of these signals on MAIT cell function and discuss the current limitations of computational tools to extrapolate which signals are received by MAIT cells in human tissues. Using lessons learned from conventional CD8 T cells, we also discuss how TCR signals may integrate with cytokine signals in MAIT cells to elicit distinct functional states.

Abstract 1200: A novel algorithm for deconvolving cancer allele-specific clone copy number and copy number evolution

Abstract Both single nucleotide variants (SNVs) and somatic copy number alterations (SCNAs) accumulate in cancer cells during tumor development, fuelling clonal evolution. However, accurate inference of their coevolution from bulk DNA sequencing is challenging. We present ALPACA (ALlele-specific Phylogenetic Analysis of clone Copy number Alterations), a novel algorithm to allow the practical inference of SNV and SCNA coevolution. ALPACA is framed as an optimisation problem, that takes as input bulk tumor sample copy number mixtures and the tumor phylogenetic tree constructed from SNVs, and deconvolves the optimal, allele-specific integer copy number profiles for every distinct clone present in the tumor. To circumvent the challenge of joint SNV/SCNA inference, we postulate that in high mutation burden tumors all relevant SCNA clones are identifiable by their unique SNVs, hence ALPACA leverages phylogenetic trees constructed from bulk tumor sequencing data using SNVs. Secondly, to restrict the clone-specific copy number search space, ALPACA leverages constraints imposed by multisample sequencing, specifically in cases where clones are present across multiple samples or clones from different samples are phylogenetically related. Finally, ALPACA relies on parsimony and biologically informed constraints to further guide the deconvolution process. We demonstrate that ALPACA infers the copy number evolution of complex simulated tumors with higher accuracy than current state-of-the-art methods. We apply ALPACA to the large, multisample non-small cell lung cancer (NSCLC) cohort from the recent longitudinal, prospective TRACERx421 study, and show that ALPACA uncovers loss of heterozygosity and amplification events in minor subclones that were previously missed using standard approaches. ALPACA's assignment of SCNA driver events to branches of the phylogenetic tree reveals the evolutionary ordering of SNVs and SCNAs during NSCLC tumor evolution, such as late focal amplifications affecting the TERT gene locus occurring in subclones which expand and dominate a tumor sample. ALPACA enables an in-depth analysis of the coevolution of mutations and SCNAs in the tumor’s evolutionary history, revealing distinct patterns of copy number evolution. Notably, ALPACA identifies common patterns of copy number changes across the genome characterizing metastatic seeding clones, revealing that they harbor an increased number of SCNAs compared to clones that do not metastasize. Additionally, ALPACA uncovers subclonal enrichment for CCND1 amplification in primary tumor subclones that seed metastasis, and an overall increase of SCNA events occurring in both tumors with polyclonal seeding and extrathoracic metastases. Clone-level results obtained with ALPACA can offer new clinical insights and enable new types of analysis, e.g. copy number signature analysis including temporal order of SCNA acquisition. Citation Format: Piotr Pawlik, Kristiana K. Grigoriadis, Abigail Bunkum, Simone Zaccaria, Nicholas McGranahan, Charles Swanton. A novel algorithm for deconvolving cancer allele-specific clone copy number and copy number evolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1200.

Analysis and Preliminary Design of Variable Flux Reluctance Machines: A Perspective from Working Field Harmonics

Variable flux reluctance machines (VFRMs) are increasingly attracting research interest due to their magnetless and robust brushless structure. Under the modulation effect of the airgap permeance, the VFRM operates with a series of field harmonics, distinguishing it from conventional AC synchronous machines. This paper deals with the analysis and preliminary design of the VFRM from the perspective of multiple working airgap field harmonics. Firstly, the spatial and temporal order of the working field harmonics are defined. The systematic winding theory, including the unified star of slots and winding factor calculation method, is established to consider all these working harmonics. Then, an average torque model is built and simplified. The key role of 1st-order rotor permeance, 1st- and 3rd-order polarized stator permeance is deduced. The relationship between key parameters and average torque is computed, providing a guideline for the preliminary design of the VFRM.

A time two-grid algorithm for two-dimensional nonlinear time-fractional partial integro-differential equations

In this paper, a temporal second order two-grid difference scheme is proposed for the two-dimensional nonlinear time-fractional partial integro-differential equations with a weakly singular kernel. The first-order backward difference and L1 formula are used in the temporal direction to estimate the first level of time, the L2−1σ formula and L1-type formula are used in the temporal direction for later time steps, and the central difference formula is used in the spatial directions. To improve the computational efficiency of nonlinear system, an efficient time two-grid algorithm is proposed. This algorithm firstly solves a nonlinear system on the coarse grid, and then the Lagrangian linear interpolation is applied on the coarse grid to estimate the function values on the fine grid. The stability and convergence of the two-grid difference scheme are analyzed by the energy method. The convergence order of the two-grid difference scheme is O(τF2+τC4+hx2+hy2), where τF and τC are the time step sizes of fine grid and coarse grid respectively, while hx and hy are the space step sizes. Numerical experiments show that the accuracy of the theoretical analysis and the efficiency of the two-grid algorithm.

Order effects in stimulus discrimination challenge established models of comparative judgement: A meta-analytic review of the Type B effect.

This paper provides a comprehensive review of the TypeB effect (TBE), a phenomenon reflected in the observation that discrimination sensitivity varies with the order of stimuli in comparative judgment tasks, such as the two-alternative forced-choice (2AFC) paradigm. Specifically, when the difference threshold is lower (higher) with the constant standard preceding rather than following the variable comparison, one speaks of a negative (positive) TBE. Importantly, prominent psychophysical difference models such as signal detection theory (Green & Swets, 1966) cannot easily account for the TBE, and are hence challenged by it. The present meta-analysis provides substantial evidence for the TBE across various stimulus attributes, suggesting that the TBE is a general feature of discrimination experiments when standard and comparison are presented successively. Thus, inconsistent with psychophysical difference models, subjective differences between stimuli are not merely a function of their physical differences but rather also depend on their temporal order. From the literature, we identify four classes of potential candidate theories explaining the origin of the TBE, namely (1) differential weighting of the stimulus magnitudes at the two positions (e.g., Hellström, Psychological Research, 39, 345-388 1977), (2) internal reference formation (e.g., Dyjas, Bausenhart, & Ulrich, Attention, Perception, & Psychophysics, 74, 1819-1841 2012), (3) Bayesian updating (e.g., de Jong, Akyürek, & van Rijn, Psychonomic Bulletin and Review, 28, 1183-1190 2021), and (4) biased threshold estimation (García-Pérez & Alcalá-Quintana, Attention, Perception & Psychophysics, 72, 1155-1178 2010). As these models, to some extent, make differential predictions about the direction of the TBE, investigating the respective boundary conditions of positive and negative TBEs might be a valuable perspective for diagnostic future research.

Error modulates categorization of subsecond durations in multitasking contexts.

Monitoring errors consumes limited cognitive resources and can disrupt subsequent task performance in multitasking scenarios. However, there is a dearth of empirical evidence concerning this interference with prospective estimation of time. In this study, we sought to investigate this issue through a serial multitasking experiment, employing a temporal bisection task as the primary task. We introduced two task contexts by implementing two different concurrent tasks. In one context, participants were tasked with discriminating the size difference between two visual items, while in the other context, they were required to judge the temporal order of similar visual items. The primary task remained the same for the entire experiment. Psychophysical metrics, including subjective bias (determined by the bisection point) and temporal sensitivity (measured by the Weber ratio), in addition to reaction time, remained unaltered in the primary task regardless of the perceptual context exerted by the concurrent tasks. However, commission of error in the concurrent tasks (i.e., non-specific errors) led to a right-ward shift in the bisection point, indicating underestimation of time after errors. Applying a drift-diffusion framework for temporal decision making, we observed alterations in the starting point and drift rate parameters, supporting the error-induced underestimation of time. The error-induced effects were all diminished with increasing a delay between the primary and concurrent task, indicating an adaptive response to errors at a trial level. Furthermore, the error-induced shift in the bisection point was diminished in the second half of the experiment, probably because of a decline in error significance and subsequent monitoring response. These findings indicate that non-specific errors impact the prospective estimation of time in multitasking scenarios, yet their effects can be alleviated through both local and global reallocation of cognitive resources from error processing to time processing.

Autophagy regulates the maturation of hematopoietic precursors in the embryo

An understanding of the mechanisms regulating embryonic hematopoietic stem cell (HSC) development would facilitate their regeneration. The aorta-gonad-mesonephros region is the site for HSC production from hemogenic endothelial cells (HEC). While several distinct regulators are involved in this process, it is not yet known whether macroautophagy (autophagy) plays a role in hematopoiesis in the pre-liver stage. Here, we show that different states of autophagy exist in hematopoietic precursors and correlate with hematopoietic potential based on the LC3-RFP-EGFP mouse model. Deficiency of autophagy-related gene 5 (Atg5) specifically in endothelial cells disrupts endothelial to hematopoietic transition (EHT), by blocking the autophagic process. Using combined approaches, including single-cell RNA-sequencing (scRNA-seq), we have confirmed that Atg5 deletion interrupts developmental temporal order of EHT to further affect the pre-HSC I maturation, and that autophagy influences hemogenic potential of HEC and the formation of pre-HSC I likely via the nucleolin pathway. These findings demonstrate a role for autophagy in the formation/maturation of hematopoietic precursors.

Spatiotemporal Dynamics of Successive Activations across the Human Brain during Simple Arithmetic Processing.

Previous neuroimaging studies have offered unique insights about the spatial organization of activations and deactivations across the brain; however, these were not powered to explore the exact timing of events at the subsecond scale combined with a precise anatomical source of information at the level of individual brains. As a result, we know little about the order of engagement across different brain regions during a given cognitive task. Using experimental arithmetic tasks as a prototype for human-unique symbolic processing, we recorded directly across 10,076 brain sites in 85 human subjects (52% female) using the intracranial electroencephalography. Our data revealed a remarkably distributed change of activity in almost half of the sampled sites. In each activated brain region, we found juxtaposed neuronal populations preferentially responsive to either the target or control conditions, arranged in an anatomically orderly manner. Notably, an orderly successive activation of a set of brain regions-anatomically consistent across subjects-was observed in individual brains. The temporal order of activations across these sites was replicable across subjects and trials. Moreover, the degree of functional connectivity between the sites decreased as a function of temporal distance between regions, suggesting that the information is partially leaked or transformed along the processing chain. Our study complements prior imaging studies by providing hitherto unknown information about the timing of events in the brain during arithmetic processing. Such findings can be a basis for developing mechanistic computational models of human-specific cognitive symbolic systems.

Differential contributions of body form, motion, and temporal information to subjective action understanding in naturalistic stimuli.

We investigated the factors underlying naturalistic action recognition and understanding, as well as the errors occurring during recognition failures. Participants saw full-light stimuli of ten different whole-body actions presented in three different conditions: as normal videos, as videos with the temporal order of the frames scrambled, and as single static representative frames. After each stimulus presentation participants completed one of two tasks-a forced choice task where they were given the ten potential action labels as options, or a free description task, where they could describe the action performed in each stimulus in their own words. While generally, a combination of form, motion, and temporal information led to the highest action understanding, for some actions form information was sufficient and adding motion and temporal information did not increase recognition accuracy. We also analyzed errors in action recognition and found primarily two different types. One type of error was on the semantic level, while the other consisted of reverting to the kinematic level of body part processing without any attribution of semantics. We elaborate on these results in the context of naturalistic action perception.