Processing Framework Research Articles

Sensory processing and child appetitive traits: findings from the ROLO longitudinal birth cohort study

BackgroundOral sensory hypersensitivity has been linked with fussy eating predominantly in non-typically developing children. We hypothesized that child ‘Oral’ (touch, smell and taste in the mouth) and ‘Social-Emotional’ (response to social expectations) sensory processing are associated with child appetitive traits in typically developing preteen children. Additionally, we explored relationships between maternal sensory profiles and their offspring’s sensory profile.MethodsThis is secondary analysis of 130 mother-child dyads from the 9–11-year-old follow-up of the ROLO longitudinal birth cohort study. The Dunn Sensory Profile (Adolescent/Adult) and the Dunn Child Sensory Profile 2 were used to assess maternal and child sensory profiles, respectively. The Children’s Eating Behaviour Questionnaire was used to assess child appetitive traits. Multiple linear regression examined associations between ‘Oral’ and ‘Social-Emotional’ segments of the child sensory profile and child appetitive traits. Associations between maternal and child sensory profiles were examined using sensory profile quadrants (Dunn’s sensory processing framework). These refer to four distinct patterns of sensory processing that indicate how an individual responds to sensory input.ResultsIn total, 130 mother-child dyads were included. In adjusted analysis child ‘Oral’ sensory processing was associated with higher mean scores in the appetitive traits ‘Desire to Drink’ (B = 0.044, 95% CI = 0.025,0.062) and ‘Food Fussiness’ (B = 0.080, 95% CI = 0.059,0.101) and with lower mean scores in ‘Enjoyment of Food’ (B=-0.038, 95% CI -0.055,-0.022). Child ‘Social-Emotional’ responses were associated with higher mean scores in ‘Desire to Drink’ and ‘Food Fussiness’. Higher scores in the maternal sensory profile quadrants of ‘Sensitivity’, ‘Avoiding’ and ‘Registration’ were positively associated with higher scores in the corresponding child sensory profile quadrants.ConclusionOur findings suggest that in typically developing children presenting with fussy eating, oral sensory hypersensitivity and higher scores in social-emotional responses to sensory processing may be an underlying determinant. The relationship observed between ‘Oral’ and ‘Social-Emotional’ sensory processing segments and ‘Desire to Drink’ requires further research to ascertain the type of drinks being consumed and how this impacts appetite. Further research is also required to explore the influence of maternal sensory profile on their offsprings response to sensory input.

Co-designing postpartum contraceptive services with and for immigrant women in Sweden: lessons learned from the IMPROVE-it project

Background and aimImmigrant women in many high-income countries including Sweden, report unmet need of sexual and reproductive health and rights, and face worse pregnancy outcomes and higher risk of unintended pregnancies. Postpartum contraceptive services are often inadequate to meet their needs. Co-design has shown to reduce health inequities, yet little is known about using this method for postpartum contraceptive service development and even less in collaborating with immigrant populations. The aim of this paper is to describe the co-design process and the strategies that were developed to help develop tailored and acceptable postpartum contraceptive services for immigrant women in Sweden.MethodsThe paper describes a co-design process that took place during 2022–2023, including the cyclical digital consultations with Arabic and Somali speaking immigrant women, midwives and researchers, as well as the outputs from the process. The theoretical framework for the co-design process was the ‘Double Diamond’ Design Process Model. Data analysis included qualitative content analysis.ResultsThe co-design process led to the joint development of intervention materials and strategies to improve postpartum contraceptive services. Specifically, the process revealed ideas on how to improve contraceptive counseling within three pre-identified areas of change: improve physical access to contraceptive services; improved communication strategies using visual aids and information charts; and empowerment strategies that focus on reflective practice without assumptions about what a group of women might expect. We found that participants contributed actively to the process with ideas and suggestions, and that the co-design process facilitated positive reflections on ongoing counseling practices.ConclusionThe co-design process resulted in the successful and participative development of innovative tools and activities to improve contraceptive counseling services. This approach is original because it involves both immigrant women, often left behind, and midwives delivering the services. Whilst this interplay allowed for careful refinement of services and tools by using an iterative process, it also facilitated reflective midwifery practice.

Development of Worksheets for Immunomodulator Shared Decision-Making to Facilitate Patient-Clinician Communication: A Quality Improvement Project Employing Design Thinking Principles.

Shared decision-making (SDM) is a principle of humanistic, patient-centered health care within the field of rheumatology. However, clear communication between patients and their clinicians regarding the benefits and risks of immunomodulators may be challenging in a clinical setting. The design-thinking process is a human-centered approach to quality improvement that can help to identify insights to uphold high-quality communication. The development process adhered to the Stanford design thinking process framework, encompassing 5 stages: (1) empathize, (2) define, (3) ideate, (4) prototype, and (5) test. During the empathy stage, quality improvement members spent 4 hours immersed in the clinical setting observing how patients and clinicians engage in SDM conversations. These observations were augmented by unstructured debriefing sessions to better understand the needs and drivers of high-quality SDM. Following this, a rapid ideation workshop was convened to generate creative solutions. These led to rapid prototyping and testing, yielding a final product. The iterative design process identified 4 critical needs: (1) ensuring comprehensibility of materials, (2) upholding accuracy of information, (3) balancing standardization with individualization, and (4) promoting retention of knowledge. During the rapid ideation workshop, the concept of a Worksheet for Immunomodulator Shared Decision-Making (WISDM) was introduced and selected for further elaboration. This led to the creation of 5 prototypes for methotrexate, which were subsequently tested. These were reconciled and modified to make a final product. The WISDM template contains 7 elements that support SDM. Forty-five WISDMs were created for 23 immunomodulators. Further investigation will focus on how WISDMs exactly impact SDM.

Comprehensive Utilization of Avocado in Biorefinery: A Bibliometric and Co-Occurrence Approach 2003–2023

In recent years, the consumption of avocado, both fresh and processed, has experienced a significant worldwide increase due to its recognized nutritional value and beneficial health effects. However, this industrial processing generates a substantial amount of underutilized byproducts, primarily the peel and seed, leading to significant environmental and economic challenges. Fortunately, these residues are rich in bioactive phytochemicals, making their recovery an excellent opportunity to enhance the sustainability and profitability of the modern avocado industry. This bibliometric analysis utilizes data from the Scopus platform to explore the comprehensive utilization of avocado waste. By employing a biorefinery approach and computational tools, the study aims to identify and extract value-added compounds with potential applications in the food, pharmaceutical, chemical, and cosmetic industries. The results highlight that the most relevant research topics are currently focused on sustainable and comprehensive biotransformation of avocado byproducts. Additionally, there is a growing emphasis on methods for extracting valuable products, characterizing their properties, and identifying potentially exploitable active compounds. Furthermore, research is increasingly exploring the environmental and economic factors associated with new research advancements, such as emerging environmental regulations, certifications, substitutes, and technological applications. One key gap identified in recent research advancements is the lack of a sustainable diagnostic framework for avocado utilization processes in a cascade system (multiple high-value consumer products and by-products such as bioplastic). This suggests a crucial area for future research efforts.

RDoC Framework Through the Lens of Predictive Processing: Focusing on Cognitive Systems Domain.

In response to shortcomings of the current classification system in translating discoveries from basic science to clinical applications, NIMH offers a new framework for studying mental health disorders called Research Domain Criteria (RDoC). This framework holds a multidimensional outlook on psychopathologies focusing on functional domains of behavior and their implementing neural circuits. In parallel, the Predictive Processing (PP) framework stands as a leading theory of human brain function, offering a unified explanation for various types of information processing in the brain. While both frameworks share an interest in studying psychopathologies based on pathophysiology, their integration still needs to be explored. Here, we argued in favor of the explanatory power of PP to be a groundwork for the RDoC matrix in validating its constructs and creating testable hypotheses about mechanistic interactions between molecular biomarkers and clinical traits. Together, predictive processing may serve as a foundation for achieving the goals of the RDoC framework.

Aircraft human‐machine interaction assistant design: A novel multimodal data processing and application framework

AbstractDuring aircraft operations, pilots rely on human‐machine interaction platforms to access essential information services. However, the development of a highly usable aerial assistant necessitates the incorporation of two interaction modes: active‐command and passive‐response modes, along with three input modes: voice inputs, situation inputs, and plan inputs. This research focuses on the design of an aircraft human‐machine interaction assistant (AHMIA), which serves as a multimodal data processing and application framework for human‐to‐machine interaction in a fully voice‐controlled manner. For the voice mode, a finetuned FunASR model is employed, leveraging private aeronautical datasets to enable specific aeronautical speech recognition. For the situation mode, a hierarchical situation events extraction model is proposed, facilitating the utilization of high‐level situational features. For the plan mode, a multi‐formations double‐code network plan diagram with a timeline is utilized to effectively represent plan information. Notably, to bridge the gap between human language and machine language, a hierarchical knowledge engine named process‐event‐condition‐order‐skill (PECOS) is introduced. PECOS provides three distinct products: the PECOS model, the PECOS state chart, and the PECOS knowledge description. Simulation results within the air confrontation scenario demonstrate that AHMIA enables active‐command and passive‐response interactions with pilots, thereby enhancing the overall interaction modality.

Building more entrepreneurial organizations through external innovation contests

AbstractFirms are increasingly adopting innovation contests to obtain ideas for new products and services from external parties, but many firms may not be sufficiently entrepreneurial to benefit from those ideas. Using an inductive longitudinal case study of three financial service firms, we explore the value of external innovation contests for less entrepreneurial and stagnant firms. Our findings indicate that stagnant firms indeed struggle to benefit from ideas generated through external innovation contests. However, we also show that firms undergo a structural change process toward higher entrepreneurial orientation through such contests. In particular, they become aware of an organizational readiness gap and act on it by (i) developing entrepreneurial skills, (ii) collaborating with external partners, and (iii) adapting organizational design and governance. Based on our findings, we propose an original framework for a corporate entrepreneurial learning process triggered by the innovation contest experience.

An Efficient Framework for Secure Dynamic Skyline Query Processing in the Cloud

AbstractThis study introduces an innovative framework named scale for processing dynamic skyline queries securely in cloud environments. Unlike previous approaches that require complex operations on encrypted data, scale simplifies dynamic skyline domination to mere comparisons, significantly improving query efficiency. Through empirical evaluations over four datasets, we show that scale accelerates query processing nearly 1000-fold compared to existing state-of-the-art methods. Specifically, scale shows significant efficiency improvements by simplifying query interactions to a single round between the user and the cloud, which is validated through empirical studies on multiple datasets. Moreover, we introduce two distributed versions of scale, dist-scale-s and dist-scale-e, which further optimize performance by facilitating parallel processing. This adaptation showcases a substantial reduction in response times and computational overhead, underpinning the scalability and effectiveness of our framework in handling large-scale, secure cloud-based queries.

Response to 'Is the Brain Mostly a Modular System or Do We Need Distributed Processing Frameworks?': Contemplating the Questions We Ask in Cognitive Neuroscience

Response to 'Is the Brain Mostly a Modular System or Do We Need Distributed Processing Frameworks?': Contemplating the Questions We Ask in Cognitive Neuroscience

A Gaussian process model for stellar activity in 2D line profile time-series

ABSTRACT Stellar active regions like spots and faculae can distort the shapes of spectral lines, inducing variations in the radial velocities that are often orders of magnitude larger than the signals from Earth-like planets. Efforts to mitigate these activity signals have hitherto focused on either the time or the velocity (wavelength) domains. We present a physics-driven Gaussian process (GP) framework to model activity signals directly in time series of line profiles or cross-correlation functions (CCFs). Unlike existing methods that correct activity signals in line profile time series, our approach exploits the time correlation between velocity (wavelength) bins in the line profile variations, and is based on a simplified but physically motivated model for the origin of these variations. When tested on both synthetic and real data sets with signal-to-noise ratios down to ∼100, our method was able to separate the planetary signal from the activity signal, even when their periods were identical. We also conducted injection/recovery tests using two years of realistically sampled HARPS-N solar data, demonstrating the ability of the method to accurately recover a signal induced by a 1.5-Earth mass planet with a semi-amplitude of 0.3 m s−1 and a period of 33 d during high solar activity.

MDAR: A Multiscale Features-Based Network for Remotely Measuring Human Heart Rate Utilizing Dual-Branch Architecture and Alternating Frame Shifts in Facial Videos

Remote photoplethysmography (rPPG) refers to a non-contact technique that measures heart rate through analyzing the subtle signal changes of facial blood flow captured by video sensors. It is widely used in contactless medical monitoring, remote health management, and activity monitoring, providing a more convenient and non-invasive way to monitor heart health. However, factors such as ambient light variations, facial movements, and differences in light absorption and reflection pose challenges to deep learning-based methods. To solve these difficulties, we put forward a measurement network of heart rate based on multiscale features. In this study, we designed and implemented a dual-branch signal processing framework that combines static and dynamic features, proposing a novel and efficient method for feature fusion, enhancing the robustness and reliability of the signal. Furthermore, we proposed an alternate time-shift module to enhance the model’s temporal depth. To integrate the features extracted at different scales, we utilized a multiscale feature fusion method, enabling the model to accurately capture subtle changes in blood flow. We conducted cross-validation on three public datasets: UBFC-rPPG, PURE, and MMPD. The results demonstrate that MDAR not only ensures fast inference speed but also significantly improves performance. The two main indicators, MAE and MAPE, achieved improvements of at least 30.6% and 30.2%, respectively, surpassing state-of-the-art methods. These conclusions highlight the potential advantages of MDAR for practical applications.

Localization of underground pipeline intrusion sources using cross-correlation CNN: application in pile-driving model test

Preserving the structural integrity of critical infrastructure systems necessitates a heightened focus on fortifying the protection of underground pipelines. To this end, this paper presents an innovative approach, namely the Multi-Sample Joint Localization Method (MSJLM) utilizing Cross-Correlation Convolutional Neural Networks (CC-CNN), aimed at precisely localizing intrusion sources in the vicinity of underground pipelines. Traditional techniques for detecting and pinpointing pipeline intrusions primarily rely on a single sensor monitoring point, which is susceptible to inherent errors and constraints. In contrast, the MSJLM proposed in this study leverages data from multiple samples, integrating diverse data sources through correlation analyses to elevate precision and reliability. The utilization of the CC-CNN framework for processing aggregated data has proven highly successful in extracting spatial features and identifying patterns. Furthermore, the effectiveness of this method is corroborated through validation via a pile-driving model test.

A Time Series Synthetic Control Causal Evaluation of the UK’s Mini-Budget Policy on Stock Market

In this paper, we propose a modified synthetic control causal analysis for time series data with volatility in terms of absolute value of return outcomes taken into account in constructing the prediction of potential outcomes for time series causal analysis. The consistency property of the synthetic weight parameter estimators is developed theoretically under a time series data-generating process framework. The application to evaluate the UK’s mini-budget policy, announced by the then Chancellor on 23 September 2022, which had significant implications for the stock market, is examined and analysed. Comparisons with traditional synthetic control and synthetic difference in difference (DID) methods for evaluation of the effect of the mini-budget policy on the UK’s stock market are also discussed.

A New Tool for Extracting Static and Dynamic Parameters from [18F]F-DOPA PET/CT in Pediatric Gliomas

Background/Objectives: PET imaging with [18F]F-DOPA has demonstrated high potential for the evaluation and management of pediatric brain gliomas. Manual extraction of PET parameters is time-consuming, lacks reproducibility, and varies with operator experience. Methods: In this study, we tested whether a semi-automated image processing framework could overcome these limitations. Pediatric patients with available static and/or dynamic [18F]F-DOPA PET studies were evaluated retrospectively. We developed a Python software to automate clinical index calculations, including preprocessing to delineate tumor volumes from structural MRI, accounting for lesions with low [18F]F-DOPA uptake. A total of 73 subjects with treatment-naïve low- and high-grade gliomas, who underwent brain MRI within two weeks of [18F]F-DOPA PET, were included and analyzed. Static analysis was conducted on all subjects, while dynamic analysis was performed on 32 patients. Results: For 68 subjects, the Intraclass Correlation Coefficient for T/S between manual and ground truth segmentation was 0.91. Using our tool, ICC improved to 0.94. Our method demonstrated good reproducibility in extracting static tumor-to-striatum ratio (p = 0.357); however, significant differences were observed in tumor slope (p < 0.05). No significant differences were found in time-to-peak (p = 0.167) and striatum slope (p = 0.36). Conclusions: Our framework aids in analyzing [18F]F-DOPA PET images of pediatric brain tumors by automating clinical score extraction, simplifying segmentation and Time Activity Curve extraction, reducing user variability, and enhancing reproducibility.

A gaussian process framework for solving forward and inverse problems involving nonlinear partial differential equations

A gaussian process framework for solving forward and inverse problems involving nonlinear partial differential equations

Forest Change Mapping using Multi-Source Satellite SAR, Optical, and LiDAR Remote Sensing Data

Abstract. This study highlights the efficacy of leveraging multi-source satellite remote sensing for precise and dependable forest change mapping. Forests play a crucial role as carbon reservoirs and are indispensable components of the global carbon and water cycle, providing essential ecosystem services. Despite their significance, forests face deforestation, diseases, and climate change threats. Recent satellite remote sensing technology advancements have facilitated accurate, persistent, and large-scale forest dynamics monitoring. New generation satellite LiDAR systems, such as GEDI and ICESat-2, offer frequent and global height information at high spatial resolutions. This research presents a processing framework for mapping forest changes by integrating SAR and optical features from Sentinel-1 and Sentinel-2 imagery with canopy heights derived from GEDI and ICESat-2 datasets. Multiple experiments and analyses were conducted in two study areas. The findings underscore the significant impact of incorporating canopy height information in enhancing the accuracy of forest change mapping, resulting in a 15% improvement in precision and a 13% enhancement in F1-score in the experimental setups. Furthermore, the developed model exhibits increased reliability and confidence in identifying correctly changed and unchanged areas while being less confident in incorrect predictions.

Disturbances of Interoception in Schizophrenia – Focus on Neuroimaging

Abstract: The current paper presents the most recent theoretical understanding of interoception (INTC) in light of the predictive processing framework and the Embodied Predictive Interoceptive Coding model and reviews recent experimental data on different interoceptive disturbances in schizophrenia (SCZ) such as lower interoceptive accuracy, nociceptive dysfunction reflected by reduced pain sensitivity, disturbed thermoregulation, impaired gastrointestinal regulation, cardiovascular and respiratory dysfunction, and immune disturbances. The immense clinical implications of these disturbances are discussed in the context of the high rates of morbidity and mortality in SCZ related to suicide and cardiac death. Next, the potential of INTC-related treatments is reviewed, and conclusions are made on the evidence for the use of vagus nerve stimulation, mindfulness-based interventions, and exercise therapy. The neuroimaging perspective is given in a brief account of the INTC-related findings in healthy individuals suggesting the right anterior insula and anterior cingulate cortex as key brain areas for interoceptive awareness, accuracy, and sensibility, by supporting higher-order representation and integration of visceral, and somatosensory interoception with exteroceptive information. In SCZ, on the other hand, neuroimaging research on specific interoceptive abilities is lacking but indirect evidence points to disrupted activation of major INTC network hubs. There is converging evidence of changes in these hubs in terms of both structure and function (volume reductions, aberrant activation, and connectivity). This narrative review identifies several gaps in contemporary INTC-related research in SCZ that need to be addressed in future experimental and clinical studies to elucidate the exact nature of the disturbances of interoceptive abilities in modalities other than cardioception; the interactions between different interoceptive modalities; the association between interoceptive abilities and other mental functions (such as emotion recognition and regulation, decision making, learning, memory, etc.); the efficacy of nonpharmacological and pharmacological INTC-based interventions; and the relation of disturbed interoceptive abilities with brain structure and function.

Structure and process of Parkinson's disease rehabilitation in the southern sector of Ghana: A cross-sectional survey.

The structure, process and outcome (SPO) framework has been used to establish that improved structures of care influences an enhanced process of stroke care resulting in improved outcome. Although, both stroke and Parkinson's disease (PD) are neurological conditions that require prolonged period of care and rehabilitation, the SPO framework has not yet been used to describe the existing structures and processes of PD care and rehabilitation in Ghana. To describe the current structures and processes of PD rehabilitation across the three healthcare levels (primary, secondary and tertiary) in the southern sector of Ghana. An online cross-sectional descriptive survey design was used via REDCap. Fifty-two therapists, including department heads, physiotherapists, occupational therapists and speech and language therapists from seven healthcare facilities were purposefully sampled and invited to complete the survey on structures and processes of PD rehabilitation. Descriptive statistical analysis was performed with SPSS v29.0. Comparative analysis was performed using Pearson's chi-square and Kruskal-Wallis test (H-test) as appropriate. Statistical significance was set at p < 0.05. Forty-nine (94.2%) responses were received and four incomplete responses were excluded from analysis. Structure of rehabilitation: Most of the participants were physiotherapists (n = 36/45; 80.0%) and tertiary level professionals (n = 31/45; 68.9%). Only physiotherapy service was available across all the healthcare levels. Persons with PD were mostly referred for rehabilitation at Hoehn and Yahr stage III as reported by 55.6% (n = 20/36) of the therapists. Process of rehabilitation: Most therapists treated persons with PD two times weekly (n = 19/33; 57.6%) and on individual basis (n = 29/33; 87.9%). Only 9 (27.2%) and 7 (21.2%) therapists often used clinical guidelines or outcome measures respectively in managing PD and 93.9% of the therapists had no local protocols. The structures and processes needed for PD rehabilitation exist but differ across the healthcare levels and among therapists. Physiotherapy was the most common rehabilitation service available at all levels of healthcare.

Concurrent perception of competing predictions: A "split-stimulus effect".

Visual illusions are systematic misperceptions that can help us glean the heuristics with which the brain constructs visual experience. In a recently discovered visual illusion (the "frame effect"), it has been shown that flashing a stimulus inside of a moving frame produces a large misperception of that stimulus's position. Across two experiments, we investigated a novel illusion (the "split stimulus effect") where the symmetrical motion of two overlaid frames produces two simultaneous positional misperceptions of a single stimulus. That is, one stimulus is presented but two are perceived. In both experiments, a single red dot was flashed when the moving frames reversed direction, and participants were asked to report how many dots they saw. Naïve participants sometimes reported seeing two dots when only one was presented, indicating spontaneous perception of the illusion. A Bayesian analysis of the population prevalence of this effect was conducted. The dependence of this effect on the frames' speed, the dot's opacity, spatial attention, as the presence/absence of pre-flash motion ("postdiction") was also investigated, and the features of this illusion were compared to similar motion position illusions within a predictive processing framework. In demonstrating this illusory "splitting" effect, this study is the first to show that it is possible to be simultaneously aware of two opposing perceptual predictions about a single object and provides evidence of the hyperpriors that limit and inform the structure of visual experience.

Improving learning experience through implementing standardized team-based learning process in undergraduate medical education

IntroductionTeam-Based Learning (TBL) is a highly structured active learning methodology shown to have positive learning outcomes in undergraduate medical education (UME). While many medical schools have increasingly adopted TBL, there is considerable variation reported in the literature in the implementation of the standard sequence and key components of TBL. The extent to which this variation affects the benefits of TBL is not well understood. This study aims to assess the impact of implementing the standardized framework of the TBL process on student perception of the benefits of TBL.MethodsFaculty at our institute were surveyed to determine the frequency of implementation of TBL standards, as described in the literature. Second-year osteopathic medical students (OMS II) were surveyed for their perception of the experience of TBL at the beginning of the academic year. Curricular interventions targeting the enforcement of the standard steps and key components of TBL were implemented in a particular 6-week system course. The students were re-surveyed at the end of the course. Descriptive and thematic analyses were performed on quantitative and qualitative data.ResultsTwenty four of 31 (77.4%) faculty participated in the faculty survey, 53 of 105 OMS II students (50.4%) participated in the pre-intervention survey, and 72 of 104 OMS II students (69.2%) participated in the post-intervention survey. Faculty survey results indicated inconsistent implementation of key steps and components of the TBL process. Comparisons of student pre- and post-survey means of the perceived value of various aspects of TBL showed significant improvement in multiple aspects of the TBL process, including readiness assurance tests, immediate feedback, and application activities. Both before and after the intervention, students highly rated the value of teamwork and opportunities for practical application of concepts within TBL.ConclusionWhile medical students value teamwork in UME and professional development, their perception of TBL can be less than positive if not conducted deliberatively. Assessment of the implementation of the standard TBL steps and components and development of corresponding targeted interventions may improve the TBL experience of medical students. Our process could be generalized to help educators interested in improving TBL in their home institutes.