Adaptive Regulation Research Articles

P-759 Safety and quality of MAR (medically assisted reproduction) provisions in Spain in response to adaptative regulation during a public health emergency: a national registry analysis

Abstract Study question Did the level of quality and the safety of MAR treatments change in response to the restrictive medical care regulations during the COVID emergency? Summary answer All assessed markers of PMA treatment quality and safety were maintained, indicating a robust quality structure of the Spanish PMA medical system. What is known already Acute public health stressors can affect the provision of effective and safe care in specific healthcare areas, as operational constrains are put in place and resource allocation is shifted to emerging areas of need. During 2020, Spain was heavily affected by the COVID pandemic, and the provision of PMA was constrained both in time and scope for several months. Here, we examine the results of these constrains on the quality and safety of PMA treatments performed in the country through the comparative analysis of pre- and post- emergency national data. Study design, size, duration This retrospective study includes aggregate registry data from 316 PMA centers in Spain, representing 100% of reported IVF/ICSI treatments in the country. Data refer to treatments performed in 2019, 2020 and 2021.Treatment data were compared on an intention to treat basis (ITT) whenever possible. Participants/materials, setting, methods A total of 48.123, 40.453 and 49.734 IVF/ICSI treatments with own oocytes were initiated in 2019, 2020 and 2021 respectively. Oocyte donor stimulation were 15.807, 12.001, and 13.287, respectively. Safety markers analyzed were: oocytes collected (donation), multiple pregnancies and births, complications of treatment requiring hospitalization. Indicators of efficacy were pregnancy and live birth rates, with and without PGT-A, on an ITT basis. Data were compared by ANOVA or t-test. A p < 0.05 was considered significant. Main results and the role of chance The ratio between transfers and clinical results in IVF/ICSI cycles with own eggs, according to treatment intention, has remained stable in both pregnancies [34.4-34.8%] and deliveries [24.7-25.7%] between 2019, 2020 and 2021 Births with multiple fetuses were 11.3%, 9.4% and 8.7% respectively. Live births from PGT-A cycles were 22.4% in 2019 and 23.0% in 2020. The results of cycles with donor eggs remained stable in both pregnancies [56.7-58.7%] and deliveries [43.3-44.2%] between 2019, 2020 and 2021. Births with multiple fetuses were 10.7%, 9.8% and 8.2% respectively. Births of higher order than twins were anecdotal. Complications requiring hospital admission have remained at 0.20% - 0.026% of total treatments, with ovarian hyperstimulation and hemorrhages being the most frequent. Limitations, reasons for caution The aggregate nature of data did not allow for a more in depth associative analysis. Wider implications of the findings The analyzed indicators have remained within the expected ranges, suggesting a low impact of the COVID measures on the operational quality and safety of the TRA units. Trial registration number NA

Emotion regulation in emotionally focused therapists working with high-conflict couples.

Guided by the Person-of-the Therapist Training (POTT) Model, the current qualitative study explores emotional experiences and emotion regulation strategies of emotionally focused trained therapists who work with high-conflict couples in Turkey. Twenty-one therapists who completed at least the externship in emotionally focused couple therapy (EFCT) and had prior or current clinical experience working with high-conflict couple(s) were recruited through various social media platforms and professional organizations' listservs. Semistructured individual interviews were conducted, audio-recorded, and transcribed verbatim. Thematic analysis of the qualitative data revealed five main themes: (1) Different Compelling Emotional Experiences of the Therapists, (2) Sun After Storm, (3) Triggers of Therapists' Emotions, (4) Perceived Adaptive Emotion Regulation Strategies, and (5) Positive Impact of the Therapist's Regulation Strategies on the Therapy Process. Overall, the findings supported the three phases of the POTT model: namely, knowledge of self, access to self, and use of self. Our study demonstrates the need for integrating self-of-the-therapist work into the clinical practice, training, and supervision of therapists working with distressed couples.

Cognitive reappraisal and affective response to physical activity: associations with physical activity behavior.

Cognitive reappraisal (CR), as an adaptive emotion regulation strategy, may play a role in transforming affect in a positive direction during or after exercise, thereby supporting physical activity (PA) adherence. The present study aimed to test the associations among PA, CR frequency, and affective response to PA, and further to examine the role of CR on PA behavior through affective response. A cross-sectional study was conducted with a sample of 105 adults, 74 of whom were women, with a mean age of 25.91. Self-report scales were used to measure PA, CR, and affective response to PA. Along with scales, demographic questions on age, sex, and education level were included. Data was collected via an online questionnaire. The frequency of CR use was positively associated with affective response, and affective response with PA behavior. Mediation analysis revealed that affective response mediated the relationship between CR and PA. Results were in the expected direction demonstrating the mediating role of affective response between CR and PA which implies that PA adherence might be facilitated by CR engagement. PA intervention programs should consider implementing CR ability and use frequency improving techniques.

Control of ecological networks: Abundance control or ecological regulation?

Complex ecosystems often exhibit a tipping point around which a small perturbation can lead to the loss of the basic functionality of ecosystems. It is challenging to develop a control strategy to bring ecosystems to the desired stable states. Typically, two methods are employed to restore the functionality of ecosystems: abundance control and ecological regulation. Abundance control involves directly managing species abundance through methods such as trapping, shooting, or poisoning. On the other hand, ecological regulation is a strategy for ecosystems to self-regulate through environment improvement. To enhance the effectiveness of ecosystem recovery, we propose adaptive regulation by combining the two control strategies from mathematical and network science perspectives. Criteria for controlling ecosystems to reach equilibrium with or without noise perturbation are established. The time and energy costs of restoring an ecosystem to equilibrium often determine the choice of control strategy, thus, we estimate the control costs. Furthermore, we observe that the regulation parameter in adaptive regulation affects both time and energy costs, with a trade-off existing between them. By optimizing the regulation parameter based on a performance index with fixed weights for time and energy costs, we can minimize the total cost. Moreover, we discuss the impact of the complexity of ecological networks on control costs, where the more complex the networks, the higher the costs. We provide corresponding theoretical analyses for random networks, predator-prey networks, and mixture networks.

TRIM2 promotes metabolic adaptation to glutamine deprivation via enhancement of CPT1A activity.

Cancer cells undergo metabolic adaptation to promote their survival and growth under energy stress conditions, yet the underlying mechanisms remain largely unclear. Here, we report that tripartite motif-containing protein 2 (TRIM2) is upregulated in response to glutamine deprivation by the transcription factor cyclic AMP-dependent transcription factor (ATF4).TRIM2 is shown to specifically interact with carnitine O-palmitoyltransferase 1 (CPT1A), a rate-limiting enzyme of fatty acid oxidation. Via this interaction, TRIM2 enhances the enzymatic activity of CPT1A, thereby regulating intracellular lipid levels and protecting cells from glutamine deprivation-induced apoptosis. Furthermore, TRIM2 is able to promote both in vitro cell proliferation and in vivo xenograft tumor growth via CPT1A. Together, these findings establish TRIM2 as an important regulator of the metabolic adaptation of cancer cells to glutamine deprivation and implicate TRIM2 as a potential therapeutic target for cancer.

Robust Adaptive Output Regulation for EV Dynamic Wireless Charging System With Sinusoidal Disturbance of Unknown Frequency

Robust Adaptive Output Regulation for EV Dynamic Wireless Charging System With Sinusoidal Disturbance of Unknown Frequency

Research on load-resistant enhancement control optimization of composite energy power supply system based on deep reinforcement learning

Abstract To solve the problem of insufficient inertia in the power electronics components under the development trend of power electronics in the transmission system of heavy-duty electric vehicles, combined with the composite energy power supply mode and control method, voltage-based feedback channels and virtual capacitor branches are designed for both engine-generator sets and DCDC control loops. These innovations compensate for system inertia and introduce a TD3 deep reinforcement learning-based adaptive regulation component to manage electrical systems in response to DC voltage fluctuations. This approach enhances the stability of the entire system in the face of load disturbances. Strategy validations demonstrate that this method ensures stable system operation, significantly mitigates DC voltage fluctuations during sudden load changes and is highly adaptable without relying on system models.

The Relationship between Adolescents’ Covert Narcissism and School Adjustment: Testing the Mediating Effects of Emotional Clarity and Adaptive Cognitive Emotion Regulation Strategies

The Relationship between Adolescents’ Covert Narcissism and School Adjustment: Testing the Mediating Effects of Emotional Clarity and Adaptive Cognitive Emotion Regulation Strategies

MKDAT: Multi-Level Knowledge Distillation with Adaptive Temperature for Distantly Supervised Relation Extraction

Distantly supervised relation extraction (DSRE), first used to address the limitations of manually annotated data via automatically annotating the data with triplet facts, is prone to issues such as mislabeled annotations due to the interference of noisy annotations. To address the interference of noisy annotations, we leveraged a novel knowledge distillation (KD) method which was different from the conventional models on DSRE. More specifically, we proposed a model-agnostic KD method, Multi-Level Knowledge Distillation with Adaptive Temperature (MKDAT), which mainly involves two modules: Adaptive Temperature Regulation (ATR) and Multi-Level Knowledge Distilling (MKD). ATR allocates adaptive entropy-based distillation temperatures to different training instances for providing a moderate softening supervision to the student, in which label hardening is possible for instances with great entropy. MKD combines the bag-level and instance-level knowledge of the teacher as supervisions of the student, and trains the teacher and student at the bag and instance levels, respectively, which aims at mitigating the effects of noisy annotation and improving the sentence-level prediction performance. In addition, we implemented three MKDAT models based on the CNN, PCNN, and ATT-BiLSTM neural networks, respectively, and the experimental results show that our distillation models outperform the baseline models on bag-level and instance-level evaluations.

THE USE OF DIGITAL TECHNOLOGIES FOR INTELLIGENT TRAFFIC MANAGEMENT

The purpose of the study is to develop, with the help of artificial intelligence, an innovative digital microcontroller network traffic light object capable of adaptively responding to traffic congestion and changing the time phases of enabling the green signal in interaction with neighboring traffic light objects. The study uses a method of system analysis using computer simulation of existing analog and hybrid traffic light controllers. As a result of the study, an assessment of their effectiveness in the adaptive regulation of modern traffic flows in densely populated cities of the country is given. The results of simulation modeling in the AnyLogic PLE environment were used for calculations. The experimental data of complex entrance/exit road junctions of Almaty and Astana cities were taken as a basis, taking into account the time cyclograms of traffic flow characteristics of highways. An assessment was also given of the existing methods of regulating complex road transport hubs to streamline negative congestion in the center of Almaty and its expressways.

Tail connectedness of DeFi and CeFi with accessible banking pillars: Unveiling novel insights through wavelet and quantile cross-spectral coherence analyses

This study evaluates the interplay between decentralized finance (DeFi) and centralized finance (CeFi) within the realm of inclusive banking by examining their foundational components, namely, alternative finance industry, distributed ledger platforms, and future payment technologies. The alternative finance industry leverages innovative technologies, such as direct lending, crowdfunding, automated wealth management, on-demand insurance, and digital currencies. This feature aligns well with the DeFi principles, and it is also beneficial to CeFi. Distributed ledger platforms offer transparency and security for both DeFi and CeFi, whereas future payment technologies provide user-friendly solutions. This study applies novel wavelet and quantile cross-spectral coherence analyses to obtain nuanced relationship insights. This methodology categorizes crypto finance into the DeFi and broader cryptocurrency ecosystems to facilitate robust comparison with traditional banking. It further delineates democratized banking into alternative financing mechanisms, distributed ledger technologies, and future payment capabilities. Wavelet coherence analysis reveals time–frequency associations, whereas quantile cross-spectral coherence unveils extreme connections under different market conditions. The findings suggest the existence of dynamic relationships between inclusive banking components and various financial infrastructures. These relationships vary across return distributions and time scales. In regular markets, positive associations are seen in yearly and monthly frequency cycles with alternative finance and centralized services exhibiting the strongest linkage. During periods of extremely high returns, CeFi and centralized-decentralized (hybrid) infrastructures exhibit limited interconnectivity with democratized banking. Moreover, DeFi's association with distributed ledger technology is negligible. In bear markets, the strongest positive associations are observed between inclusive access and conventional, hybrid, and decentralized financial systems. These insights provide new perspectives on the dynamics between financial systems during extreme conditions. This study emphasizes the need for adaptive regulation, international cooperation, and a balance between innovation and stability to protect and empower users. Collaboration between academia and policymakers is essential to navigate this evolving landscape and advance democratized banking.

Emotion Regulation Mediates the Associations of Loneliness and Empathy with Death Anxiety in the Elderly

Introduction: In old age, diseases and frailty can be minimized through proper care and understanding, paving the way for healthy and normative aging. The present study aimed to investigate the mediating role of emotion regulation in the relationship between loneliness and empathy with death anxiety in the elderly. Methods: This study utilized structural equation modeling. The statistical population included all elderly residents of Ahvaz, Iran, in 2023. Convenient sampling was employed to select 108 elderly individuals. The research instruments included the Death Anxiety Scale, UCLA Loneliness Scale, Interpersonal Reactivity Index, and Cognitive Emotion Regulation Questionnaire. Pearson correlation coefficient and structural equation modeling were adopted for data analysis. Results: There was a direct relationship between loneliness and adaptive emotion regulation and maladaptive emotion regulation in the elderly. Additionally, there was a direct relationship between empathy and death anxiety, adaptive emotion regulation, and maladaptive emotion regulation. Moreover, a positive relationship was observed between maladaptive emotion regulation and death anxiety, while a negative relationship existed between adaptive emotion regulation and death anxiety in the elderly (p < 0.001). The results revealed an indirect relationship between loneliness and death anxiety mediated by emotion regulation. There was also an indirect relationship between empathy and death anxiety mediated by emotion regulation (p < 0.001). Conclusion: The proposed model exhibited a good fit. Therefore, raising awareness and implementing measures to empower the elderly in emotion regulation concerning the relationship between loneliness and empathy can play a key role in reducing their death anxiety.

Polyvictimization and emotion regulation in daily life: An ecological momentary assessment study of young adults.

Individuals who experience polyvictimization, or victimization across multiple life contexts, show particularly severe outcomes across a range of mental health diagnoses, potentially related to difficulties in regulating emotion. However, prior research has been limited by reliance on cross-sectional designs and retrospective trait measures of emotion regulation. The present study used ecological momentary assessment to test associations between polyvictimization and emotion regulation in daily life. After completing a baseline survey assessing trauma exposure, mental health symptoms, and trait emotion regulation, undergraduates (N = 122) completed smartphone surveys assessing state emotion regulation four times per day for 14 days. Low correspondence was observed between trait and state measures of emotion regulation. After accounting for baseline mental health symptoms, polyvictimization independently predicted lower scores on an aggregative measure of putatively adaptive strategies (i.e., reappraisal, acceptance, problem solving) in daily life. However, polyvictimization did not predict aggregate scores of putatively maladaptive strategies (i.e., cognitive and expressive suppression, rumination, impulsive behavior, avoidance). Baseline depression and posttraumatic stress predicted higher use of putatively maladaptive strategies. Results suggest polyvictimization may be associated with less use of adaptive emotion regulation in daily life, whereas mental health symptoms may be associated with more use of maladaptive strategies. These findings highlight the utility of experience sampling approaches for increasing insight into emotion regulation difficulties among trauma survivors. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Role of Mitochondrial Sirtuins (SIRT3, SIRT4 and SIRT5) in Renal Cell Metabolism: Implication for Kidney Diseases.

Kidney diseases, including chronic kidney disease (CKD), diabetic nephropathy, and acute kidney injury (AKI), represent a significant global health burden. The kidneys are metabolically very active organs demanding a large amount of ATP. They are composed of highly specialized cell types in the glomerulus and subsequent tubular compartments which fine-tune metabolism to meet their numerous and diverse functions. Defective renal cell metabolism, including altered fatty acid oxidation or glycolysis, has been linked to both AKI and CKD. Mitochondria play a vital role in renal metabolism, and emerging research has identified mitochondrial sirtuins (SIRT3, SIRT4 and SIRT5) as key regulators of renal cell metabolic adaptation, especially SIRT3. Sirtuins belong to an evolutionarily conserved family of mainly NAD+-dependent deacetylases, deacylases, and ADP-ribosyl transferases. Their dependence on NAD+, used as a co-substrate, directly links their enzymatic activity to the metabolic status of the cell. In the kidney, SIRT3 has been described to play crucial roles in the regulation of mitochondrial function, and the antioxidative and antifibrotic response. SIRT3 has been found to be constantly downregulated in renal diseases. Genetic or pharmacologic upregulation of SIRT3 has also been associated with beneficial renal outcomes. Importantly, experimental pieces of evidence suggest that SIRT3 may act as an important energy sensor in renal cells by regulating the activity of key enzymes involved in metabolic adaptation. Activation of SIRT3 may thus represent an interesting strategy to ameliorate renal cell energetics. In this review, we discuss the roles of SIRT3 in lipid and glucose metabolism and in mediating a metabolic switch in a physiological and pathological context. Moreover, we highlight the emerging significance of other mitochondrial sirtuins, SIRT4 and SIRT5, in renal metabolism. Understanding the role of mitochondrial sirtuins in kidney diseases may also open new avenues for innovative and efficient therapeutic interventions and ultimately improve the management of renal injuries.

AdaFN-AG: Enhancing multimodal interaction with Adaptive Feature Normalization for multimodal sentiment analysis

In multimodal sentiment analysis, achieving effective fusion among text, acoustic, and visual modalities for enhanced sentiment prediction is a crucial research topic. Recent studies typically employ tensor-based or attention-based mechanisms for multimodal fusion. However, the former fails to achieve satisfactory prediction performance, and the latter complicates the computation of fusion between non-textual modalities. Therefore, this paper proposes the multimodal sentiment analysis model based on Adaptive Feature Normalization and Attention Gating mechanism (AdaFN-AG). Firstly, facing highly synchronized non-textual modalities, we design the Adaptive Feature Normalization (AdaFN) method, which focuses more on sentiment features interaction rather than timing features association. In AdaFN, acoustic and visual modality features achieve cross-modal interaction through normalization, inverse normalization, and mix-up operations, with weights utilized for adaptive strength regulation of the cross-modal interaction. Meanwhile, we design the Attention Gating mechanism that facilitates cross-modal interactions between textual and non-textual modalities through cross-attention and captures timing associations, while the gating module concurrently regulates the intensity of these interactions. Additionally, we employ self-attention to capture the intrinsic correlations within single-modal features. Subsequently, we conduct experiments on three benchmark datasets for multimodal sentiment analysis, with the results indicating that AdaFN-AG outperforms the baselines across the majority of evaluation metrics. Through research and experiments, we validate that AdaFN-AG not only enhances performance by adopting appropriate methods for different types of cross-modal interactions while conserving computational resources but also verifies the generalization capability of the AdaFN method.

Basis of gene-specific transcription regulation by the Integrator complex

The Integrator complex attenuates gene expression via the premature termination of RNA polymerase II (RNAP2) at promoter-proximal pausing sites. It is required for stimulus response, cell differentiation, and neurodevelopment, but how gene-specific and adaptive regulation by Integrator is achieved remains unclear. Here, we identify two sites on human Integrator subunits 13/14 that serve as binding hubs for sequence-specific transcription factors (TFs) and other transcription effector complexes. When Integrator is attached to paused RNAP2, these hubs are positioned upstream of the transcription bubble, consistent with simultaneous TF-promoter tethering. The TFs co-localize with Integrator genome-wide, increase Integrator abundance on target genes, and co-regulate responsive transcriptional programs. For instance, sensory cilia formation induced by glucose starvation depends on Integrator-TF contacts. Our data suggest TF-mediated promoter recruitment of Integrator as a widespread mechanism for targeted transcription regulation.

Protein Phosphorylation Nexus of Cyanobacterial Adaptation and Metabolism

Protein phosphorylation serves as a fundamental regulatory mechanism to modulate cellular responses to environmental stimuli and plays a crucial role in orchestrating adaptation and metabolic homeostasis in various diverse organisms. In cyanobacteria, an ancient phylum of significant ecological and biotechnological relevance, protein phosphorylation emerges as a central regulatory axis mediating adaptive responses that are essential for survival and growth. This exhaustive review thoroughly explores the complex terrain of protein phosphorylation in cyanobacterial adaptation and metabolism, illustrating its diverse forms and functional implications. Commencing with an overview of cyanobacterial physiology and the historical trajectory of protein phosphorylation research in prokaryotes, this review navigates through the complex mechanisms of two-component sensory systems and their interplay with protein phosphorylation. Furthermore, it investigates the different feeding modes of cyanobacteria and highlights the complex interplay between photoautotrophy, environmental variables, and susceptibility to photo-inhibition. The significant elucidation of the regulatory role of protein phosphorylation in coordinating light harvesting with the acquisition of inorganic nutrients underscores its fundamental importance in the cyanobacterial physiology. This review highlights its novelty by synthesizing existing knowledge and proposing future research trajectories, thereby contributing to the deeper elucidation of cyanobacterial adaptation and metabolic regulation through protein phosphorylation.

Experimental studies on controlling dispersion and fluid flow in paper membranes/assays using hydrogel

Paper membranes find applications in various fields, including Point-of-Care (POC) diagnostics, separation, filtration, and fluid transport. Still, they are frequently plagued by dispersion-related issues that lead to uneven flow, further affecting sensitivity and specificity, especially in detection assays. Hence, flow control in paper membranes becomes essential to achieving an effective paper-based platform. The present work develops a novel methodology based on adaptive flow regulation to optimize dispersion and flow control. This approach leverages real-time data to measure the controlled flow rates and prevent dispersion using hydrogel across paper-based membranes. Effects of three major parameters, namely mixing speed (100 rpm – 400 rpm), hydrogel concentration (6.45 mg/ml–12.9 mg/ml) for preparing hydrogel, and types of filter paper membranes are investigated in detail. Imaging techniques are applied to capture high-resolution images of fluid behavior on the paper strip surface at various time intervals, and the results are demonstrated in terms of mean intensity as a quantifiable indicator (0−255). It was found that the hydrogel prepared using 400 rpm mixing speed at a specific concentration exhibited significant resistance to the fluid flow with minimal dispersion. Furthermore, the Filter-paper 441 membrane is compared with other filter-paper membranes used in this experiment. Notably, a 43 %-79 % reduction in dispersion was observed for the hydrogel-laden paper membranes. Additionally, the dispersion was largely minimized even when lower concentrations of sodium hydroxide were detected on hydrogel-based paper assays, enhancing its sensitivity. Overall, this research offers a multifaceted solution to reduce dispersion and optimize flow control in paper-based membranes using hydrogel to improve the performance of the paper assays.

An improved genetic algorithm for robot path planning

The conventional genetic algorithm (GA) for path planning exists several drawbacks, such as uncertainty in the direction of robot movement, circuitous routes, low convergence rates, and prolonged search time. To solve these problems, this study introduces an improved GA-based path-planning algorithm that adopts adaptive regulation of crossover and mutation probabilities. This algorithm uses a hybrid selection strategy that merges elite, tournament, and roulette wheel selection methods. An adaptive approach is implemented to control the speed of population evolution through crossover and mutation. Combining with a local search operation enhances the optimization capability of the algorithm. The proposed algorithm was compared with the traditional GA through simulations, demonstrating shorter path lengths and reduced search times.

Emotion regulation styles and the tendency to learn from academic failures.

Research on learners' reactions to failure finds negative emotions may present an obstacle for learning; a painful experience of failure may result in disengagement and avoidance. However, research on styles of emotion regulation and learning from failure is scarce. Self-determination theory's (SDT) conception of adaptive and maladaptive emotion regulation differentiates among three styles of regulation: integrative emotion regulation (IER), suppressive emotion regulation (SER) and amotivated emotion regulation. Two studies were conducted, one cross-sectional and one longitudinal, to test the associations between IER and learning from failure among adolescents. Study 1 comprised 184 adolescents (mean age = 16.55; SD = 1.2). Study 2 comprised 565 adolescents (8-12 graders). The main analysis was based on 192 adolescents' perceptions of failing math grades. Study 1 surveyed adolescents on their emotion regulation styles, adaptive and maladaptive coping practices when dealing with failure and tendency to learn from failure. Study 2 was longitudinal and focused on failure in math. We approached participants twice, before and after the math test. In both studies, IER was related to adaptive coping practices and the tendency to learn from failure. In Study 2, adaptive coping practices mediated relations between IER and learning from failure in math and learning from failure mediated relations between IER and future engagement. These findings suggest that styles of emotion regulation play an important role in learning from failure.