Sequential Model Research Articles

Exploring the interdependence of calcium and chloride activation of O2 evolution in photosystem II.

Calcium and chloride are activators of oxygen evolution in photosystem II (PSII), the light-absorbing water oxidase of higher plants, algae, and cyanobacteria. Calcium is an essential part of the catalytic Mn4CaO5 cluster that carries out water oxidation and chloride has two nearby binding sites, one of which is associated with a major water channel. The co-activation of oxygen evolution by the two ions is examined in higher plant PSII lacking the extrinsic PsbP and PsbQ subunits using a bisubstrate enzyme kinetics approach. Analysis of three different preparations at pH 6.3 indicates that the Michaelis constant, KM, for each ion is less than the dissociation constant, KS, and that the affinity of PSII for Ca2+ is about ten-fold greater than for Cl-, in agreement with previous studies. Results are consistent with a sequential binding model in which either ion can bind first and each promotes the activation by the second ion. At pH 5.5, similar results are found, except with a higher affinity for Cl- and lower affinity for Ca2+. Observation of the slow-decaying Tyr Z radical, YZ•, at 77 K and the coupled S2YZ• radical at 10 K, which are both associated with Ca2+ depletion, shows that Cl- is necessary for their observation. Given the order of electron and proton transfer events, this indicates that chloride is required to reach the S3 state preceding Ca2+ loss and possibly for stabilization of YZ• after it forms. Interdependence through hydrogen bonding is considered in the context of the water environment that intervenes between Cl- at the Cl-1 site and the Ca2+/Tyr Z region.

Enhancing Power Lines Detection Using Deep Learning and Feature-Level Fusion of Infrared and Visible Light Images

AbstractThe detection of power lines is critical for flight safety, especially for drones and low-flying aircraft. Power line detection models help prevent collisions, reducing potential damage and preserving lives, while also safeguarding critical infrastructure. This has led to significant research efforts to develop accurate detection models. In this study, we employ paired infrared–visible power line datasets to train three distinct deep learning models. The first two models are sequential deep learning models based on VGG16 and AlexNet networks. They are tailored for detection in visible images, while they were optimized again for infrared images. For the third model, we introduce an innovative deep learning architecture utilizing Functional Application Programming Interface, affording us the flexibility to construct a multi-input model with shared layers. Our proposed model accepts paired images (visible and infrared) as inputs. Then, a feature-level fusion process is applied to merge the extracted features from both inputs and generate an enriched feature map. This approach amalgamates the advantages of visible images, which boast high resolution and rich texture features, with infrared images, which excel in high contrast and clear vision under adverse environmental conditions. Comparing the outcomes of the three models, our proposed model emerges as the front runner, boasting an impressive accuracy rate of 99.37%. Moreover, real-time processing was adopted by conducting ablation experiments to optimize the model and reduce the number of trainable parameters, resulting in an inference speed of 2.7 milliseconds per frame.

Abstract PO4-07-03: Machine learning breast cancer risk prediction using sequential past mammograms - a pilot study

Abstract Background and Aim Some studies have reported the use of mammogram AI deep learning algorithms to accurately predict the risk of future breast cancer development in women. In these studies, random and unrelated mammograms were independently used for training of the AI model. We propose a novel deep learning system using sequential past mammograms instead of random studies and aimed to determine if it can further improve risk prediction in a pilot study. Methods Pre-training of the backbone of a Swin-transformer machine learning model was performed by applying region-of-interest lesional masks of benign and malignant on a training set of 1644 mammograms derived from the open-access Curated Breast Imaging Subset of Digital Database for Screening Mammography dataset. To further develop the model, deep learning training and validation were performed on the Cohort of Screen-age Women (CSAW) open-access screening mammogram set which was derived from the long term data of 873 women who developed breast cancer for the first time and 7850 non-cancer women. We then compared the diagnostic accuracy of a model using sequential past mammograms for training against the standard, non-sequential model. For the sequential model, imaging segmentation of the potential cancer site on mammography was performed on all past, pre-cancer mammograms obtained within 60 to 2555 days of cancer diagnosis in the pre-processing stage for breast cancer cases. AI training was then carried out by combining the pre-cancer mammograms belonging to the same woman in a temporal sequence using a transformer encoder. The learning rate started from 1e-6 and decayed 10 times every 10 epochs. For the non-sequential model, we removed the temporal encoder and used random, unrelated mammogram studies for training. Validation was performed on a dataset consisting of 75 pre-cancer mammograms from breast cancer cases and 69 mammograms from non-cancer cases. Results Sensitivity, specificity and overall accuracy on the validation set were 77.3% (58/75), 68.1% (47/69) and (105/144) 72.9% respectively for the sequential model compared to 69.3% (52/75), 69.6% (48/69) and 69.4% (100/144) respectively for the non-sequential model. Conclusion The study showed that deep learning breast cancer risk prediction can be further improved by using sequential past mammograms instead of random mammograms for AI model training. This can also potentially enhance other AI risk prediction models that employ combined mammogram and traditional breast cancer clinical risk factors. A larger validation study will be useful. Contact information: email Lester Leong at lester.leong.c.h@singhealth.com.sg ROC curve for breast cancer risk prediction using AI deep learning training with sequential past mammograms. Citation Format: Lester Leong, Mingjie Xu, Weimin Huang, Erli Zhang, Engracia Loh, Sze Yiun Teo, Geok Hoon Lim, Veronique Kiak Mien Tan, Yirong Sim, Fredrik Strand, Ryan Tan. Machine learning breast cancer risk prediction using sequential past mammograms - a pilot study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-03.

A sequential three-way risk sorting model with the cautionary principle under probabilistic linguistic environment

Risk assessment plays a crucial role in managing risks associated with systems, products and services. In practical risk assessment, the cautionary principle is widely adopted by individuals to prevent accidents, and the degree of caution varies with the risk level of potential hazardous activities (PHAs). Therefore, it is necessary to explore a sequential risk assessment model integrating the cautionary principle. To this end, we design an improved risk assessment model based on sequential three-way decision (STWD) model and cautionary principle. Firstly, a risk distance measure under probabilistic linguistic environment is proposed to effectively reflect varying degrees of caution when individuals analyzed PHAs of different risk levels. Then, a comprehensive risk weight optimization model is constructed on the consideration of the importance ranking and correlations of risk attributes. Subsequently, STWD and the as low as reasonably practicable (ALARP) principle are integrated to build the risk sorting model, and both the risk prioritization and classification of PHAs are obtained from the sequential risk sorting process. Finally, we verify the reliability of our proposed model through an example of a floating offshore wind turbine system. The findings validate the crucial role of cautionary principle in risk assessment.

Tuning the Topography of Non‐Wetting Surfaces to Reduce Short‐Term Microbial Contamination Within Hospitals

AbstractMicrobial contamination of hospital surfaces is a major contributor to infectious disease transmission. This work demonstrates that superhydrophobic (Cassie‐Baxter) micro post topographies can significantly reduce cell attachment compared to flat controls. For ordered micro post arrays (post diameters 0.3 to 150 µm), the attachment of four pathogens (Pseudomonas aeruginosa, Staphylococcus aureus, Escherichia coli, and Candida albicans) from discrete contaminant droplets upon short‐term contact (15 s to 30 min) are assessed. There is a 3‐4‐log decrease in microbial attachment when reducing the micro posts diameters from 150 to 0.3 µm for all strains, with large posts (>20 µm) exhibiting similar attachment rates to flat controls. The critical, maximum feature size to prevent attachment can be tuned depending on the ratio of the cell size to post diameter. Two potential mechanisms are discussed for this size effect. First, application of the random sequential adsorption model shows that this relative post/cell size effect may be due to a reduced probability of attachment, which is theorized to be the dominant mechanism. Alternatively, a physical model is suggested for bacterial cell “pull‐off” due to surface tension forces during droplet dewetting. This work may be important for the design of non‐wetting antimicrobial surfaces within healthcare environments.

A vector-based coastline shape classification approach using sequential deep learning model

Coastlines play a crucial role in coastal dynamics, and classifying their shape is an essential requirement for coastal analysis. With the development of Coastal Management Systems (CMS), structured and high-resolution vector-format coastlines have become increasingly available compared to remote sensing image coastlines. However, due to the challenges of accurate description and ambiguous classification rules, automatic classification of vector coastlines has been a difficult but urgent problem to solve. In this paper, we propose a data-driven approach for classifying the shape of vector coastlines, according to their morphological characteristics. The method utilizes a sequence-based deep learning algorithm to model and classify coastline segments. We construct a dataset including five representative types of vector coastlines, train and evaluate the model using this dataset. The evaluation results show that the proposed method outperforms all baselines, achieving a classification accuracy of 93.20%. This method can be integrated into existing Coastal Management Systems to enhance their morphological analysis functions, making a valuable contribution to the applications of Artificial Intelligence (AI) in coastal management.

The influence of subjective socioeconomic status on inclination to offline collective action: a sequential mediation framework of group relative deprivation and online collective behavioral intentions

This study investigates the influence of subjective socioeconomic status (SES) on the inclination towards collective action among college students while exploring the mediating roles of group relative deprivation and intentions of online collective behavior. A sample of 250 undergraduate students from diverse Chinese universities participated in a survey, anonymously completing measures of subjective SES, group relative deprivation, intention of online collective behavior, and intention of offline collective action. Utilizing Model 6 of the SPSS PROCESS macro, the data was analyzed to examine sequential mediation effects. Results indicate a significant negative predictive relationship between SES and offline collective behavior intention (β=-0.843, p<0.001). Mediation analysis reveals a sequential mediating mechanism, with SES exerting an indirect effect on offline collective behavior dispositions through group relative deprivation (β=-0.423, p<0.001), further influencing the intention of online collective behavior (β=0.657, p<0.01). The sequential mediation model explains 21.9% of the total effect. This study contributes to understanding the complex mechanisms underlying the impact of subjective SES on collective action intentions, emphasizing the importance of considering subjective perceptions in predicting collective behavior. These findings have implications for theoretical frameworks and practical interventions aimed at fostering collective action in various societal contexts.

SCRIPTS OF A COMPLEX AFFECTIVE FUNCTIONS REGULATION

The current study presents preliminary results from an extensive study called "Potential to be well", conducted with different quotas taking in account gender, people with or without psy­chiatric diagnosis and cohorts of different ages. The aim of the re­search is to test some scales for self-assessment developed by positive psychology, implementing the multi-method ap­proach, but also to analyze the data for existing patterns’ differences in complex affective func­tions and dysfunctions. The “resilience”, “subjective life-satisfaction”, “emotional expressive­ness”, “expressive ambivalence”, “suppression or reappraisal in emotion regulation” invento­ries are reliable and valid measures, which reflect complex subjective functions from the emo­tional sphere. It is noted that these indices follow a bi-directional logic – some patterns express negative trends in experiences, and others reflect a positive psychological transformation of primary functions helping to overcome emotional difficulties and turning adversity into ad­vantage. It was also found that three scripts of complex mediatory sequential models are demonstrated, which combined, predict how two intuitive routes sustain and effect deterioration or improvement in the context of an individual case interpretation.

Advance Sequential Models for Asset Trading: Leveraging LSTM & RNN with Stochastic RSI and MACD Indicators

Abstract: The goal of stock market prediction is to forecast a company's financial stocks' future worth. The application of machine learning, which creates predictions based on the values of the current stock market indices by training on their prior values, is a recent trend in stock market prediction technologies. Machine learning utilises many models to facilitate accurate and reliable prediction. The study focuses on stock value prediction using regression and LSTM-based machine learning. Considered factors are volume, low, high, open, and closed. We also employ algorithmic trading, which is a technique for automatically executing orders based on pre-programmed trading instructions that take volume, price, and time into account. This kind of trading aims to take advantage of a computer's speed and computational power in comparison to human traders. Algorithmic trading has become more popular in the twenty-first century, prompting traders to respond. According to a 2019 research, 92% of traders use algorithms rather than people. The moving average convergence & divergence and the relative strength index are our primary tools. One technical tool employed in financial market analysis is the relative strength index, or RSI. Charting a stock's historical and present strength or weakness with retail and institutional traders is the goal. Investment banks, mutual funds, pension funds, and hedge funds all utilise it extensively when they need to perform trades. based on the closing prices of a recent trading period, too fast for a human or the market. It is important to distinguish the indicator from relative strength. The RSI measures the speed and size of price changes and is categorised as a momentum oscillator. The rate at which prices rise or fall is known as momentum. The ratio of higher closes to lower closes, where closures are defined as averages of absolute values of price changes, is the relative strength, or RS. The RSI determines momentum by dividing higher closes by overall closes, or stocks with more or stronger positive moves. The RSI is most commonly employed during a 14-day period and is scaled from 0 to 100, with 70 and 30 representing the high and low readings, respectively. Alternately shorter or longer outlooks are associated with shorter or longer time frames. Although they happen less frequently, high and low levels— 80 and 20, or 90 and 10—indicate increased momentum. J. Welles Wilder created the relative strength index, which was first published in the June 1978 issue of commodities magazine and in his 1978 book New concepts in technical trading systems, and in commodities magazine in the June 1978 issue.

Problematic social media use mediatesthe effect of cyberbullying victimisation on psychosomatic complaints in adolescents.

Adolescent psychosomatic complaints remain a public health issue globally. Studies suggestthat cyberbullying victimisation, particularly on social media, could heighten the risk of psychosomatic complaints. However, the mechanisms underlying theassociationsbetween cyberbullying victimisationandpsychosomatic complaints remain unclear. This cross-cultural study examines the mediating effect of problematic social media use (PSMU) on the association between cyberbullying victimisation and psychosomatic complaints among adolescents in high income countries. We analysed data on adolescents aged 11-16.5years (weighted N = 142,298) in 35 countries participating in the 2018 Health Behaviour in School-aged Children(HBSC) study. Path analysis using bootstrapping technique tested the hypothesised mediating role of PSMU. Results from the sequential binary mixed effects logit models showed that adolescents who were victims of cyberbullying were 2.39 times significantly morelikely to report psychosomatic complaints than those who never experienced cyberbullying (AOR = 2.39; 95%CI = 2.29, 2.49). PSMU partially mediated the association between cyberbullying victimisation and psychosomatic complaints accounting for 12% ( =0.01162, 95%CI = 0.0110, 0.0120) of the total effect. Additional analysis revealed a moderation effect of PSMU on the association between cyberbullying victimisation and psychosomatic complaints. Our findings suggest that while cyberbullying victimisation substantially influences psychosomatic complaints, the association is partially explained by PSMU. Policy and public health interventions for cyberbullying-related psychosomatic complaints in adolescents should target safe social media use.

Effects of perceived stress on turnover intention of female healthcare staff: a serial multiple mediation model

BackgroundHealthcare staff in China, especially females, work in a high-pressure, high-load, and high-risk environment, which affects the physical and mental health, the efficiency and quality of work, and increases turnover intention. The present study investigated the relationship between perceived stress and turnover intention in female healthcare staff, and the effects of future-oriented coping and work-family balance on this relationship.MethodsFour hundred thirty-five female medical workers were recruited to perform a perceived stress scale, future-oriented coping inventory, work-family balance scale and turnover intention scale. Meanwhile, serial multiple mediation analysis was performed using PROCESS.Results1) Perceived stress positively predicted the level of turnover intention in female healthcare staff; 2) Preventive coping and proactive coping showed mediation effects on the relationship between perceived stress and turnover intention, and preventive coping positively related to proactive coping; 3) The work-family balance also showed mediation effects on the relationship between perceived stress and turnover intention; 4) Preventive coping, proactive coping and work-family balance showed a serial multiple mediation on the relationship between perceived stress and turnover intention in female healthcare workers.ConclusionsPerceived stress affects the level of turnover intention in female healthcare staff through preventive coping, proactive coping, and work-family balance. In addition, the sequential model of future-oriented coping was validated among female healthcare staff.

Joint modeling of causal phrases-sentiments-aspects using Hierarchical Pitman Yor Process

Traditional sentiment-aware topic models assume that topic or sentiment transition occurs from either a sentence to the next sentence or from a word to the next word. Such models cannot capture a topic or sentiment transition at phrase boundaries. Further, most of the models adopt a sentiment lexicon to initialize sentiment priors and this approach induces coverage problems. To overcome the above-cited limitations, we have proposed a topic model that extracts aspects, sentiments, and causal phrases simultaneously by leveraging Hierarchical Pitman Yor Process (HPYP) that is modified using a sentiment component, a word-tagger to guide the causal phrase generation and a sentiment prior initialized through a sequential model to address coverage problems. We have evaluated our model on six datasets and found that the proposed model outperforms the baselines in terms of perplexity by 14%, topical coherence by 20%, topic diversity by 5%, sentiment classification task’s accuracy by 4% and, precision, recall and F1 score by 2%. Ablation studies assert that sequence model based sentiment prior initialization results in increasing the accuracy of sentiment classification by 2%.

Predictability‐Aware Subsequence Modeling for Sequential Recommendation

Sequential recommendation frames the recommendation task as a next‐item prediction problem, where the model is trained to predict the next item given a user behavior sequence. While recent research has made significant progress in developing advanced models for this task, there exists a notable gap in the exploration of subsequences and the predictability inherent in user behavior sequences. This oversight can lead models to recall inconsequential sequential patterns, adversely affecting recommendation quality. In this paper, we introduce a novel approach to augmenting sequential recommendation by integrating predictability awareness into subsequence modeling. Our method begins by discerning the predictability of target items; those easily predicted often align with the preceding subsequence, while those that are hard to predict typically indicate transitions to other subsequences. Leveraging this predictability information, we enhance the discovery of meaningful subsequences within individual user behavior sequences. Evaluation of four benchmark data sets using various state‐of‐the‐art sequential models illustrates the efficacy of our approach in enhancing recommendation performance. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Representational and sequential feature learning model for fault classification in power system

ABSTRACT The classification of three-phase faults in electrical power systems is essential. The vast majority of fault classification techniques are focused on machine learning framework that requires pre-processing algorithms and rely highly on feature engineering. In this paper, a hybrid Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) deep learning model is designed and employed for fault classification. With this combination, both representational and sequential features are automatically extracted and effectively learned using lesser trainable parameters from the raw temporal data. The raw three-phase current and voltage data are used as input for the proposed model. The performance of the proposed model is tested using a 10-fold cross-validation technique on large simulated data produced from a standard IEEE 30-bus system. The efficacy of the proposed model is confirmed by comparing it with different standards and existing models from the literature. The impact of noisy data and load variation conditions are also studied and analysed on the model’s performance.

Multimodal workflows optimally predict response to repetitive transcranial magnetic stimulation in patients with schizophrenia: a multisite machine learning analysis

The response variability to repetitive transcranial magnetic stimulation (rTMS) challenges the effective use of this treatment option in patients with schizophrenia. This variability may be deciphered by leveraging predictive information in structural MRI, clinical, sociodemographic, and genetic data using artificial intelligence. We developed and cross-validated rTMS response prediction models in patients with schizophrenia drawn from the multisite RESIS trial. The models incorporated pre-treatment sMRI, clinical, sociodemographic, and polygenic risk score (PRS) data. Patients were randomly assigned to receive active (N = 45) or sham (N = 47) rTMS treatment. The prediction target was individual response, defined as ≥20% reduction in pre-treatment negative symptom sum scores of the Positive and Negative Syndrome Scale. Our multimodal sequential prediction workflow achieved a balanced accuracy (BAC) of 94% (non-responders: 92%, responders: 95%) in the active-treated group and 50% in the sham-treated group. The clinical, clinical + PRS, and sMRI-based classifiers yielded BACs of 65%, 76%, and 80%, respectively. Apparent sadness, inability to feel, educational attainment PRS, and unemployment were most predictive of non-response in the clinical + PRS model, while grey matter density reductions in the default mode, limbic networks, and the cerebellum were most predictive in the sMRI model. Our sequential modelling approach provided superior predictive performance while minimising the diagnostic burden in the clinical setting. Predictive patterns suggest that rTMS responders may have higher levels of brain grey matter in the default mode and salience networks which increases their likelihood of profiting from plasticity-inducing brain stimulation methods, such as rTMS. The future clinical implementation of our models requires findings to be replicated at the international scale using stratified clinical trial designs.

An intelligent error correction model for English grammar with hybrid attention mechanism and RNN algorithm

Abstract This article proposes an English grammar intelligent error correction model based on the attention mechanism and Recurrent Neural Network (RNN) algorithm. It aims to improve the accuracy and effectiveness of error correction by combining the powerful context-capturing ability of the attention mechanism with the sequential modeling ability of RNN. First, based on the improvement of recurrent neural networks, a bidirectional gated recurrent network is added to form a dual encoder structure. The encoder is responsible for reading and understanding the input text, while the decoder is responsible for generating the corrected text. Second, the attention mechanism is introduced into the decoder to convert the output of the encoder into the attention probability distribution for integration. This allows the model to focus on the relevant input word as it generates each corrected word. The results of the study showed that the model was 2.35% points higher than statistical machine translation–neural machine translation in the CoNLL-2014 test set, and only 1.24 points lower than the human assessment score, almost close to the human assessment level. The model proposed in this study not only created a new way of English grammar error correction based on the attention mechanism and RNN algorithm in theory but also effectively improved the accuracy and efficiency of English grammar error correction in practice. It further provides English learners with higher-quality intelligent error correction tools, which can help them learn and improve their English level more effectively.

Discretionary HR practices and happiness at work (HAW): a sequential mediation model of perceived organizational support and meaning of work

PurposeThis paper aims to explore the mechanisms by which employees’ happiness at work (HAW) can be promoted. Drawing on the social exchange theory (SET), this study examined the relationships among discretionary human resource (HR) practices, perceived organizational support (POS), meaning of work (MOW) and HAW.Design/methodology/approachA three-path mediation model was developed to test the proposed relationships. The data were collected from Pakistani business professionals (n = 361), and hypotheses were tested using the PROCESS macro for SPSS .FindingsThe results suggest that POS mediates the relationship between discretionary HR practices and HAW. Also, MOW mediated the relationship between discretionary HR practices and HAW. Hence, both POS and MOW were found to be independent mediators. Further, the data provided support for the serial mediation of POS and MOW in the relationship between discretionary HR practices and HAW.Practical implicationsThis research provides insights to organizations and their management on how discretionary HR practices can enhance employees’ POS, MOW and HAW.Originality/valueThe findings show that discretionary HR practices are associated with employees’ HAW. In addition, two mediators (POS and MOW) were found to serially mediate the aforesaid relationships. These findings are novel, as no prior research has used this nascent methodological approach to deepen our understanding by examining the associations between discretionary HR practices, POS, MOW and employees’ HAW.

Different states of synaptic vesicle priming explain target cell type–dependent differences in neurotransmitter release

Pronounced differences in neurotransmitter release from a given presynaptic neuron, depending on the synaptic target, are among the most intriguing features of cortical networks. Hippocampal pyramidal cells (PCs) release glutamate with low probability to somatostatin expressing oriens-lacunosum-moleculare (O-LM) interneurons (INs), and the postsynaptic responses show robust short-term facilitation, whereas the release from the same presynaptic axons onto fast-spiking INs (FSINs) is ~10-fold higher and the excitatory postsynaptic currents (EPSCs) display depression. The mechanisms underlying these vastly different synaptic behaviors have not been conclusively identified. Here, we applied a combined functional, pharmacological, and modeling approach to address whether the main difference lies in the action potential-evoked fusion or else in upstream priming processes of synaptic vesicles (SVs). A sequential two-step SV priming model was fitted to the peak amplitudes of unitary EPSCs recorded in response to complex trains of presynaptic stimuli in acute hippocampal slices of adult mice. At PC-FSIN connections, the fusion probability (Pfusion) of well-primed SVs is 0.6, and 44% of docked SVs are in a fusion-competent state. At PC-O-LM synapses, Pfusion is only 40% lower (0.36), whereas the fraction of well-primed SVs is 6.5-fold smaller. Pharmacological enhancement of fusion by 4-AP and priming by PDBU was recaptured by the model with a selective increase of Pfusion and the fraction of well-primed SVs, respectively. Our results demonstrate that the low fidelity of transmission at PC-O-LM synapses can be explained by a low occupancy of the release sites by well-primed SVs.

Calcium ions promote migrasome formation via Synaptotagmin-1.

Migrasomes, organelles crucial for cell communication, undergo distinct stages of nucleation, maturation, and expansion. The regulatory mechanisms of migrasome formation, particularly through biological cues, remain largely unexplored. This study reveals that calcium is essential for migrasome formation. Furthermore, we identify that Synaptotagmin-1 (Syt1), a well-known calcium sensor, is not only enriched in migrasomes but also indispensable for their formation. The calcium-binding ability of Syt1 is key to initiating migrasome formation. The recruitment of Syt1 to migrasome formation sites (MFS) triggers the swelling of MFS into unstable precursors, which are subsequently stabilized through the sequential recruitment of tetraspanins. Our findings reveal how calcium regulates migrasome formation and propose a sequential interaction model involving Syt1 and Tetraspanins in the formation and stabilization of migrasomes.

Tackling Cyclicity in Causal Models with Cross-Sectional Data Using a Partial Least Squares Approach: Implications for the Sequential Model of Internet Appropriation

Tackling Cyclicity in Causal Models with Cross-Sectional Data Using a Partial Least Squares Approach: Implications for the Sequential Model of Internet Appropriation