Events In Process Research Articles

Shared parameter modeling of longitudinal data allowing for possibly informative visiting process and terminal event.

Joint modeling of longitudinal and time-to-event data, particularly through shared parameter models (SPMs), is a common approach for handling longitudinal marker data with an informative terminal event. A critical but often neglected assumption in this context is that the visiting/observation process is noninformative, depending solely on past marker values and visit times. When this assumption fails, the visiting process becomes informative, resulting potentially to biased SPM estimates. Existing methods generally rely on a conditional independence assumption, positing that the marker model, visiting process, and time-to-event model are independent given shared or correlated random effects. Moreover, they are typically built on an intensity-based visiting process using calendar time. This study introduces a unified approach for jointly modeling a normally distributed marker, the visiting process, and time-to-event data in the form of competing risks. Our model conditions on the history of observed marker values, prior visit times, the marker's random effects, and possibly a frailty term independent of the random effects. While our approach aligns with the shared-parameter framework, it does not presume conditional independence between the processes. Additionally, the visiting process can be defined on either a gap time scale, via proportional hazard models, or a calendar time scale, via proportional intensity models. Through extensive simulation studies, we assess the performance of our proposed methodology. We demonstrate that disregarding an informative visiting process can yield significantly biased marker estimates. However, misspecification of the visiting process can also lead to biased estimates. The gap time formulation exhibits greater robustness compared to the intensity-based model when the visiting process is misspecified. In general, enriching the visiting process with prior visit history enhances performance. We further apply our methodology to real longitudinal data from HIV, where visit frequency varies substantially among individuals.

Dynamics of Vortex Matter in 2D Gapless Superconducting Materials with Impurities.

The recent interest in using ultrafast single-photon detectors in research and commercial applications has garnered significant attention from the scientific community. The dynamic event in the detection process consists of a photon causing local destruction of the order parameter, and then the applied current dissipates heat, bringing the material even more out of the superconducting state and then spiking a voltage peak in a measurement device. We investigated the role of superconducting and thermal parameters of the Generalized Time-Dependent Ginzburg-Landau (GTDGL) theory within the event of the first vortex penetration and the thermal dissipation in superconductors near the critical temperature. Moreover, in the vicinity of the Bogomolny point of the static Ginzburg-Landau theory, where κ ≈ 1/√2 and T → Tc, it has been found severely different vortex profiles. We point out that within the GTDGL theory, the presence of impurities (usual or paramagnetic) influences flux penetration and enhances the dissipation of heat, causing anomalous vortex configurations.

Causal Learning: Monitoring Business Processes Based on Causal Structures.

Conventional methods for process monitoring often fail to capture the causal relationships that drive outcomes, making hard to distinguish causal anomalies from mere correlations in activity flows. Hence, there is a need for approaches that allow causal interpretation of atypical scenarios (anomalies), allowing to identify the influence of operational variables on these anomalies. This article introduces (CaProM), an innovative technique based on causality techniques, applied during the planning phase in business process environments. The technique combines two causal perspectives: anomaly attribution and distribution change attribution. It has three stages: (1) process events are collected and recorded, identifying flow instances; (2) causal learning of process activities, building a directed acyclic graphs (DAGs) represent dependencies among variables; and (3) use of DAGs to monitor the process, detecting anomalies and critical nodes. The technique was validated with a industry dataset from the banking sector, comprising 562 activity flow plans. The study monitored causal structures during the planning and execution stages, and allowed to identify the main factor behind a major deviation from planned values. This work contributes to business process monitoring by introducing a causal approach that enhances both the interpretability and explainability of anomalies. The technique allows to understand which specific variables have caused an atypical scenario, providing a clear view of the causal relationships within processes and ensuring greater accuracy in decision-making. This causal analysis employs cross-sectional data, avoiding the need to average multiple time instances and reducing potential biases, and unlike time series methods, it preserves the relationships among variables.

Signal detection

Acoustic emission (AE) occurring in concrete fracture process is a combination of concrete fracture at local material level and the propagation of induced elastic waves at structural level. Simulation of the fracture-induced AE in concrete needs a model that is able to address the fracture behavior and the wave propagation at the same time. However, to date, no available modelling tools can simulate the whole fracture-induced AE process. Among others, the lattice models have the potential to achieve the simulation of the whole fracture-induced AE process. However, the lattice models are currently restricted to statistical analysis of AE events in fracture process. This paper investigates the feasibility and limitation of lattice model for the simulation of elastic wave propagation process. Several simulation cases are performed considering the propagation of both plane waves and point-source waves. The simulation results, in terms of wave velocity and geometric spreading loss of wave amplitudes, agree well with the analytical solutions. This study represents the first step for the simulation of complete fracture-induced AE waves in concrete.

Weaving Our Kuwentos (Stories) toward Ginhawa (Aliveness): Pilipinx American Social Work MotherScholars Enacting Praxes of Survival and Thrivance in the Academy

Five Pilipina American (PA) social work MotherScholars, from a doctoral student to an interim dean, used kuwentuhan (Pilipinx methodology) to amplify their survivance and thrivance despite attempted exclusion, reduction, and distortion as Pilipinos by coloniality/modernity. Grounded in decolonial feminism (the view that oppressions such as sexism and racism co-constitute coloniality and that unsettling oppressions disrupts hegemony) and Pinayism (an integrated framework revaluing the labor, intellect, and nurturance of mothering through a cultural lens), the authors work coalitionally across their PA diversity to re-center ginhawa (aliveness or sense of ease and wellness). Together, they embarked on an iterative self-study process of data generation and analysis that included presenting, recording, and transcribing two panel presentations at a premier social work conference, writing reflections and hay(na)ku poems about their experiences and processes, reading and rereading the data, and meeting and discussing the data, their process, and past and current events pertinent to the content. The stories highlight how the authors are living and enlivening decoloniality, and that, in so doing, they continue a lineage of those who have resisted coloniality/modernity and promoted thrivance. Collectively, these kuwentos (stories), reflections, hay(na)ku, and their weaving together, are memory, resistance, counter-storytelling, and healing.

More or Less Likeable: The Role of Behavior-Independent Events in the Disposition Formation Process

ABSTRACT Affective disposition theory (ADT) and its extensions posit that character liking/disliking is based on viewer perceptions of character behaviors or the activation of character schemas that imply moral/immoral behaviors. This paper systematically examines whether events that happen to a character independent of their behavior also contribute to the development of character liking. Based on the modified affective disposition model (MADM), two studies were conducted where participants read a story introduction that manipulated whether a positive or negative event (described as egoistic need satisfaction/thwarting by the MADM) had befallen a character. In Study 1, the character was liked more when a negative economic event had befallen them and liked less when a positive economic event had befallen them as compared to a control condition. In Study 2, these effects were replicated. However, in Study 2 we also examined whether health-related events would elicit similar effects. Participants liked the character more regardless of whether a positive or negative health event befell her. These findings suggest unique contextual effects associated with which specific egoistic intuitions are satisfied or thwarted. As such they provide novel directions for research designed to test and specify the predictions of the MADM.

Timed Genetic Process Mining for Robust Tracking of Processes under Incomplete Event Log Conditions

In process mining, an event log is a structured collection of recorded events that describes the execution of processes within an organization. The completeness of event logs is crucial for ensuring accurate and reliable process models. Incomplete event logs, which can result from system errors, manual data entry mistakes, or irregular operational patterns, undermine the integrity of these models. Addressing this issue is essential for constructing accurate models. This research aims to enhance process model performance and robustness by transforming incomplete event logs into complete ones using a process discovery algorithm. Genetic process mining, a type of process discovery algorithm, is chosen for its ability to evaluate multiple candidate solutions concurrently, effectively recovering missing events and improving log completeness. However, the original form of the genetic process mining algorithm is not optimized for handling incomplete logs, which can result in incorrect models being discovered. To address this limitation, this research proposes a modified approach that incorporates timing information to better manage incomplete logs. By leveraging timing data, the algorithm can infer missing events, leading to process tracking and reconstruction which is more accurate. Experimental results validate the effectiveness of the modified algorithm, showing higher fitness and precision scores, improved process model comparisons, and a good level of coverage without errors. Additionally, several advanced metrics for conformance checking are presented to further validate the process models and event logs discovered by both algorithms.

Summer heat wave in 2022 led to rapid warming of permafrost in the central Qinghai-Tibet Plateau

Extreme events with increasing frequency and intensity are significantly affecting the permafrost environment. Analysis using the ERA5-Land reanalysis data revealed that the permafrost region of the central Qinghai-Tibet Plateau (QTP) experienced the summer heat wave in 2022. Four active layer sites experienced maximum active layer thicknesses (ALT) in 2022 (mean: 207.7 cm), which was 20% higher than the mean ALT during 2000–2021 (mean: 175.9 cm). The mean annual ground temperature (MAGT) observed in 2022 was also the highest, exceeding the average of the previous years by 10%. The contribution fraction of heat wave to the seasonal thaw depth of active layer was quantified using Stefan model with ranging from 6.6% to 13.6%, and the maximum contribution fraction occurs in 2022. These findings are helpful to better understand the impact processes of extreme events on the active layer and permafrost.

Hydramethylnon induces mitochondria-mediated apoptosis in BEAS-2B human bronchial epithelial cells

Typically used household chemicals comprise numerous compounds. Determining mixture toxicity, as observed when using household chemicals containing multiple substances, is of considerable importance from a regulatory perspective. Upon examining the toxic effects of household chemical mixtures, we observed that hydramethylnon combined with tetramethrin resulted in synergistic toxicity. To determine the unknown toxicity mechanism of hydramethylnon, which carries the risk of inhalation exposure when using household chemicals, we conducted a further investigation using BEAS-2B cells, a human bronchial epithelial cell line. Hydramethylnon-induced cytotoxicity was determined following 24 and 48 h of exposure using the water-soluble tetrazolium 1 and lactate dehydrogenase assays. To elucidate the toxicity mechanism, we utilized flow cytometry and measured the levels of apoptosis-related proteins and caspase activities. Given that hydramethylnon, as an insecticide, disrupts the mitochondrial electron transfer chain, we analyzed the relevant mechanisms, including mitochondrial superoxide levels as well as the mitochondrial membrane potential (MMP). Hydramethylnon dose-dependently induced BEAS-2B cell apoptosis via the intrinsic pathway. Furthermore, it significantly increased mitochondrial superoxide levels and disrupted the MMP. Pre-treatment with a caspase inhibitor (Z-DEVD-FMK) confirmed that hydramethylnon induced caspase-dependent apoptosis. Apoptosis, a key event in the toxicological process of chemicals, can lead to lung diseases, including fibrosis and cancer. The results of the present study suggest a mechanism of toxicity of hydramethrylnon, an organofluorine biocide whose toxicity has been little studied, to the lung epithelium. Considering the potential risks associated with inhalation exposure, these results highlight the need for careful management and regulation of hydramethylnon.

KF-PEV: a causal Kalman filter-based particle event velocimetry

Event-based pixel sensors asynchronously report changes in log-intensity in microsecond-order resolution. Its exceptional speed, cost effectiveness, and sparse event stream make it an attractive imaging modality for particle tracking velocimetry. In this work, we propose a causal Kalman filter-based particle event velocimetry (KF-PEV). Using the Kalman filter model to track the events generated by the particles seeded in the flow medium, KF-PEV yields the linear least squares estimate of the particle track velocities corresponding to the flow vector field. KF-PEV processes events in a computationally efficient and streaming manner (i.e., causal and iteratively updating). Our simulation-based benchmarking study with synthetic particle event data confirms that the proposed KF-PEV outperforms the conventional frame-based particle image/tracking velocimetry as well as the state-of-the-art event-based particle velocimetry methods. In a real-world water tunnel event-based sensor data experiment performed on what we believe to be the widest field view ever reported, KF-PEV accurately predicted the expected flow field of the SD7003 wing, including details such as the lower velocity in the wake and the flow separation around the underside of an angled wing.

The posterior intralaminar thalamic nucleus promotes nose-to-nose contacts leading to prosocial reception in the sequence of mouse social interaction.

Efficient social interaction is essential for an adaptive life and consists of sequential processes of multisensory events with social counterparts. Social touch/contact is a unique component that promotes a sequence of social behaviours initiated by detection and approach to assess a social stimulus and subsequent touch/contact interaction to form prosocial relationships. We hypothesized that the thalamic sensory relay circuit from the posterior intralaminar nucleus of the thalamus (pIL) to the paraventricular nucleus of the hypothalamus (PVN) and the medial amygdala (MeA) plays a key role in the social contact-mediated sequence of events. We found that neurons in the pIL along with the PVN and MeA were activated by social encounters and that pIL activity was more abundant in a direct physical encounter, whereas MeA activity was dominant in an indirect through grid encounter. Chemogenetic inhibition of pIL neurons selectively decreased the investigatory approach and sniffing of a same-sex, but not an opposite-sex, stimulus mouse in an indirect encounter situation and decreased the facial/snout contact ratio in a direct encounter setting. Furthermore, chemogenetic pIL inhibition had no impact on anxiety-like behaviours or body coordinative motor behaviours, but it impaired whisker-related and plantar touch tactile sense. We propose that the pIL circuit can relay social tactile sensations and mediate the sequence of nonsexual prosocial interactions through an investigatory approach to tactile contact and thus plays a significant role in establishing prosocial relationships in mouse models.

Spatio-temporal evolution law of anisotropic shear damage on rock mass joint surface affected by joint morphology

The spatial–temporal evolution law of shear damage on the joint surface is closely related to its morphological characteristics. Understanding the heterogeneous failure behavior of the joint surface is essential for revealing the controlling role of the joint morphology. The diorite from the Guanshan Tunnel is investigated through anisotropic direct shear tests in this study. Based on shear damage quantification and acoustic emission (AE) localization techniques, the heterogeneous damage evolution law of joint surfaces under different joint compression strength (JCS), normal stress (σn), and morphology parameter (M) is studied. The results indicate that the joint surface morphology, shear damage, and AE parameters exhibit similar anisotropic characteristics in different shear directions. An increase in the morphology parameter leads to an increase in shear failure, with shear fracture energy and the number of fracture events showing an increasing trend. Meanwhile, the occurrence of a large number of fracture points shifts from the pre-peak nonlinear period to the post-peak period. However, the evolution process of fracture events exhibits certain similarities, though the morphology parameters are different in two opposite shear directions. Additionally, shear damage volume and fracture energy demonstrate consistency in spatial distribution and quantitative characteristics, while the linear scale factor between cumulative shear fracture energy and damage volume decreases with increasing joint surface strength. These findings assist in a better understanding of the anti-sliding property of joint surfaces and provide crucial clues for further exploration of joint surface failure mechanisms.

A latent profile analysis and network analysis of anxiety and depression symptoms in Chinese widowed elderly

BackgroundWidowhood, as a traumatic event in the aging process, may lead to adverse psychological consequences such as anxiety and depression. However, the heterogeneity of anxiety and depression comorbidity patterns in widowed elderly and the interrelationships between symptoms have not been adequately studied. Method10,239 elderly aged 65 years and older were screened from the Chinese Longitudinal Healthy Longevity Survey (CLHLS 2017–2018), to assess depression and anxiety using the Center for Epidemiologic Studies Depression Scale (CESD) and the 7-item Generalized Anxiety Disorder Questionnaire (GAD). The subgroups of widowed elderly with similar patterns of symptoms were identified by latent profile analysis (LPA). The structure of anxiety-depressive comorbidity network was characterized using “bridge expected influence” as centrality indices. Network stability was tested using a case drop bootstrap program. A network comparison test (NCT) was performed to examine the differences in network characteristics across LPA subgroups. ResultLPA identified dichotomous profiles: low comorbid (n = 4457) and high comorbid (n = 692). NCT revealed a significant difference in the global strength between networks (S = 0.631, p < 0.001). GAD1 (Nervousness or anxiety) is the common bridging symptom for both networks, while the bridging symptom for the high comorbidity network also includes GAD3 (Generalized worry). LimitationsCross-sectional methods are unable to verify causal relationships, and further randomized controlled trials are warranted. ConclusionAnxiety-depressive pattern in Chinese widowed elderly can be categorized into a low comorbid or a high comorbid group. GAD3 (Generalized worry) can be used as the core intervention target during intervention.

Integrative in silico approaches to analyse microRNA-mediated responses in human diseases.

Advancements in sequencing technologies have facilitated omics level information generation for various diseases in human. High-throughput technologies have become a powerful tool to understand differential expression studies and transcriptional network analysis. An understanding of complex transcriptional networks in human diseases requires integration of datasets representing different RNA species including microRNA (miRNA) and messenger RNA (mRNA). This review emphasises on conceptual explanation of generalized workflow and methodologies to the miRNA mediated responses in human diseases by using different in silico analysis. Although,there have been many prior explorations in miRNA-mediated responses in human diseases, the advantages, limitations and overcoming the limitation through different statistical techniques have not yet been discussed. This review focuses on miRNAs as important gene regulators in human diseases, methodologies for miRNA-target gene prediction and data driven methods for enrichment and network analysis for miRnome-targetome interactions. Additionally, it proposes an integrative workflow to analyse structural components of networks obtained from high-throughput data. This review explains how to apply the existing methods to analyse miRNA-mediated responses in human diseases. It addresses unique characteristics of different analysis, its limitations and its statistical solutions influencing the choice of methods for the analysis through a workflow. Moreover, it provides an overview of promising common integrative approaches to comprehend miRNA-mediated gene regulatory events in biological processes in humans. The proposed methodologies and workflow shall help in the analysis of multi-source data to identify molecular signatures of various human diseases.

Probing skin photoallergens in reconstructed human epidermis: An EPR spin trapping investigation.

Photoallergic contact dermatitis is a skin disease caused by combined exposure to photoreactive chemicals and sunlight. Exposure to allergens and the risk of skin sensitization is an essential regulatory issue within the industry. Yet, only few non-validated assays for photoallergy assessment exist as the pathogenesis is not fully deciphered. Improving such assays and/or developing new ones require an understanding of the chemical mechanisms involved. The first key event in the photosensitization process, namely chemical binding of the photoallergen to endogenous proteins, is thought to proceed via photo-mediated radicals arising from the photoallergen. Moreover, the mechanism of action of these radicals if formed in the epidermis is not known and far from being unraveled. We present here an original proof-of-concept methodology to probe radical generation from allergens in contact with photoexposed skin, using electron paramagnetic resonance and spin trapping in a reconstructed human epidermis model mimicking real-life exposure scenarios.

Nature as Event: A Study on John Dewey’s Naturalism

John Dewey’s naturalism requires viewing nature and experience from the perspective of holism, emphasising the continuity between these two. To Dewey, nature is not a fixed entity, but an event in an ongoing process of unfolding. The temporality of an event can meet Dewey’s requirement of constructing a philosophical notion about change and development. The event has a relatively stable structure. The continuity between living things and non-living things becomes possible because of the characteristic transactions of events, and experience thus becomes something emergent in nature and actively intervenes in its unfolding. The emergence of human intelligence and the application of language has lifted nature to a controllable and operable plane, making experience a crucial guide for the unfolding of nature.

Two-episode mineralization in the Haerdaban Pb–Zn deposit, NW China: Insights from sulfide trace elements, in situ S–Pb isotopes, and Rb–Sr geochronology

The Haerdaban Pb–Zn deposit, located in the eastern West Tianshan Orogen, hosts stratiform and vein-type mineralization within Precambrian carbonate rocks. However, there has been limited research on the distinctions and relationships between these two mineralization styles. In this study, we compared trace element distributions, fluid conditions, material sources, and mineralization ages between stratiform and vein-type mineralization and reconstructed a detailed genetic model. Two episodes and four stages of mineralization were identified, including five generations of pyrite and two generations of sphalerite. The sedimentary exhalative episode represents stratiform mineralization including Stage I pyrite–pyrrhotite layers (Py-1, Py-2a, Py-2b, and Py-3) and Stage II sphalerite–galena layers (Sph-1). The magmatic–hydrothermal episode represents vein-type mineralization including Stage III pyrite–quartz–calcite veins (Py-4) and Stage IV sphalerite–galena–quartz–calcite veins (Sph-2). LA–ICP–MS analysis of Se–Co–Ni–As–Ag–Sb–Bi contents in pyrite suggests that Py-1 to Py-3 formed under relatively low temperatures (280 ± 8 °C) and high ƒO2 sedimentary conditions. Py-4 formed under relatively high temperatures (339 ± 18 °C) and low ƒO2 hydrothermal conditions. Variations in Fe–Mn–In–Ga–Ge–Cd–Cu contents in sphalerite indicate that Sph-1 formed under relatively low temperature (211 ± 7 °C), intermediate ƒS2 (logƒS2 = −11.1 ± 0.5), and moderate pH sedimentary exhalative conditions. Sph-2 formed under relatively high temperature (292 ± 5 °C), high ƒS2 (logƒS2 = −7.4 ± 0.2), and low pH hydrothermal conditions. In situ analysis of sulfide S–Pb suggests that ore-forming materials for stratiform mineralization were primarily derived from marine sediments, while those for vein-type mineralization primarily originated from magmatic sources. Sph-1 from stratiform mineralization yielded an Rb–Sr isochron age of 719 ± 16 Ma, while Sph-2 from vein-type mineralization exhibited an Rb–Sr isochron age of 380.3 ± 7.7 Ma. Random Forest classification of trace elements in pyrite and sphalerite predicted that stratiform mineralization is of sedimentary genesis, whereas vein-type mineralization is of magmatic–hydrothermal genesis. Based on our results, we identified a two-episode mineralization history for the Haerdaban Pb–Zn deposit: Neoproterozoic syngenetic sedimentary exhalative mineralization overprinted by Devonian epigenetic magmatic–hydrothermal mineralization. The results of this study highlight the importance of considering multiple geological events in ore-forming processes and will facilitate the exploration of similar deposits in the West Tianshan Orogen.

Wearable Perovskite Films for On‐Line Monitoring of Radiotracers in Nuclear Medicine

AbstractOn‐line in situ monitoring of radiation emitted by radiotracers during intravenous injection is of crucial importance for the safety of patients and personnel involved in nuclear medicine treatments and analysis. The aim of this work is to assess wearable perovskite ionizing radiation detectors for such application. With this purpose pixelated direct detector based on thin films of 2D layered perovskite PEA2PbBr4 are tested for the monitoring of gamma‐rays emitted by 18F radiopharmaceutical under different experimental conditions. The wearable detector is here demonstrated being able to detect in real‐time the gamma‐rays emitted by annihilation of 18F radiotracer. Its reliability is assessed by 18F half‐life measurement with a maximum accuracy deviation within 8%. The detector also successes in its validation for the foreseen application, demonstrating able to monitor in real‐time the whole injection process and extravasation event.

ProcessCarbonAgent: A large language models-empowered autonomous agent for decision-making in manufacturing carbon emission management

Knowledge-intensive production represents a primary trend in industrial manufacturing, which heavily relies on the production logs of large-scale, historically similar orders for enhancing production efficiency and process quality. These logs are essential for predicting resource allocation and identifying bottlenecks in throughput. As a result, root cause analysis of the production process state is crucial for supporting decision-making in these settings. However, current methodologies heavily depend on expert knowledge, making the analysis time-consuming and inefficient for large-scale, multivariable processes. Although the development of large language models and autonomous agents presents a potential solution, these models are limited in their direct interaction with event logs due to inadequate data representation, token constraints, and insufficient accuracy. Therefore, enabling the interactive capabilities of large language models to overcome these specific limitations in process event data and industrial domain illusions poses a significant challenge. To address these issues, this paper introduces the ProcessCarbonAgent framework, an autonomous agent empowered by large language models, designed to enhance decision-making within industrial processes. Initially, a process data agent combines predefined semantic text representation methods with process template prompting strategies to improve interaction capabilities. Subsequently, an intention agent utilizing self-information and large language models is developed to address context length limitations by identifying and eliminating redundancies. Finally, a two-stage confidence estimation method is implemented to refine the precision of decision-making assistance, thereby improving the accuracy of decisions supported by large language models. Experiments with textile industry carbon emission data reveal that the assisted decision-making scores employing a compression ratio of 0.5, closely align with scores from manually labeled evaluations, with a 98% overlap across scoring intervals. Moreover, in contrast to relying solely on the original evaluation method, the two-stage confidence estimation method has led to a 20% increase in accuracy performance. The ProcessCarbonAgent achieved scores of 16.64, 55.13, 26.32, and 34.17 on METEOR, BERTScore, NUBIA, and BLEURT, respectively. The results demonstrate that the ProcessCarbonAgent framework significantly enhances the decision-making process for high-carbon emission states in industrial production, providing technical support for the low-carbon transformation and intelligent upgrading of these processes.

Translatome analysis in acute ischemic stroke: Astrocytes and microglia exhibit differences in poststroke alternative splicing of expressed transcripts.

Astrocytes and microglia undergo dynamic and complex morphological and functional changes following ischemic stroke, which are instrumental in both inflammatory responses and neural repair. While gene expression alterations poststroke have been extensively studied, investigations into posttranscriptional regulatory mechanisms, specifically alternative splicing (AS), remain limited. Utilizing previously reported Ribo-Tag-seq data, this study analyzed AS alterations in poststroke astrocytes and microglia from young adult male and female mice. Our findings reveal that in astrocytes, compared to the sham group, 109 differential alternative splicing (DAS) events were observed at 4 h poststroke, which increased to 320 at day 3. In microglia, these numbers were 316 and 266, respectively. Interestingly, the disparity between DAS genes and differentially expressed genes is substantial, with fewer than 10 genes shared at both poststroke time points in astrocytes and microglia. Gene ontology enrichment analysis revealed the involvement of these DAS genes in diverse functions, encompassing immune response (Adam8, Ccr1), metabolism (Acsl6, Pcyt2, Myo5a), and developmental cell growth (App), among others. Selective DAS events were further validated by semiquantitative RT-PCR. Overall, this study comprehensively describes the AS alterations in astrocytes and microglia during the hyperacute and acute phases of ischemic stroke and underscores the significance of certain hub DAS events in neuroinflammatory processes.