Early Stop Research Articles

Mice lacking α4 nicotinic acetylcholine receptors are protected against alcohol-associated liver injury.

Chronic heavy alcohol consumption is a major risk factor for the development of liver steatosis, fibrosis, and cirrhosis, but the mechanisms by which alcohol causes liver damage remain incompletely elucidated. This group has reported that α4 nicotinic acetylcholine receptors (α4 nAChRs) act as sensors for alcohol in lung cells. This study tested the hypothesis that α4 nAChRs mediate the effects of alcohol in the liver. Expression of acetylcholine receptor subunits in mouse liver was determined by RNA sequencing (RNA-seq). α4 nAChR knockout (α4 KO) mice were generated in C57BL/6J mice by introducing a mutation encoding an early stop codon in exon 4 of Chrna4, the gene encoding the α4 subunit of the nAChR. The presence of the inactivating mutation was established by polymerase chain reaction and genomic sequencing, and the lack of α4 nAChR function was confirmed in primary fibroblasts isolated from the α4 KO mice. Wild-type (WT) and α4 KO mice were fed the Lieber-DeCarli diet (with 36% of calories from alcohol) or pair fed an isocaloric maltose-dextrin control diet for a 6-week period that included a ramping up phase of increasing dietary alcohol. Chrna4 was the most abundantly expressed nAChR subunit gene in mouse livers. After 6 weeks of alcohol exposure, WT mice had elevated serum transaminases and their livers showed increased fat accumulation, decreased Sirt1 protein levels, and accumulation of markers of oxidative stress and inflammation including Cyp2E1, Nos2, Sod1, Slc7a11, TNFα, and PAI1. All these responses to alcohol were either absent or significantly attenuated in α4 KO animals. Together, these observations support the conclusion that activation of α4 nAChRs by alcohol or one of its metabolites is one of the initial events promoting the accumulation of excess fat and expression of inflammatory mediators. Thus, α4 nAChRs may represent viable targets for intervention in chronic alcohol-related liver disease.

A Continuous Robot Vision Approach for Predicting Shapes and Visually Perceived Weights of Garments

We present a continuous perception approach that learns geometric and physical similarities between garments by continuously observing a garment while a robot picks it up from a table. The aim is to capture and encode geometric and physical characteristics of a garment into a manifold where a decision can be carried out, such as predicting the garment’s shape class and its visually perceived weight. Our approach features an early stop strategy, which means that a robot does not need to observe a full video sequence of a garment being picked up from a crumpled to a hanging state to make a prediction, taking 8 seconds in average to classify garment shapes. In our experiments, we find that our approach achieves prediction accuracies of 93% for shape classification and 98.5% for predicting weights and advances state-of-art approaches in similar robotic perception tasks by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\,22\%$</tex-math></inline-formula> for shape classification.

Regularizing End-to-End Speech Translation with Triangular Decomposition Agreement

End-to-end speech-to-text translation (E2E-ST) is becoming increasingly popular due to the potential of its less error propagation, lower latency, and fewer parameters. Given the triplet training corpus〈speech, transcription, translation〉, the conventional high-quality E2E-ST system leverages the〈speech, transcription〉pair to pre-train the model and then utilizes the〈speech, translation〉pair to optimize it further. However, this process only involves two-tuple data at each stage, and this loose coupling fails to fully exploit the association between triplet data. In this paper, we attempt to model the joint probability of transcription and translation based on the speech input to directly leverage such triplet data. Based on that, we propose a novel regularization method for model training to improve the agreement of dual-path decomposition within triplet data, which should be equal in theory. To achieve this goal, we introduce two Kullback-Leibler divergence regularization terms into the model training objective to reduce the mismatch between output probabilities of dual-path. Then the well-trained model can be naturally transformed as the E2E-ST models by a pre-defined early stop tag. Experiments on the MuST-C benchmark demonstrate that our proposed approach significantly outperforms state-of-the-art E2E-ST baselines on all 8 language pairs while achieving better performance in the automatic speech recognition task.

Novel homozygous variant in the PDZD7 gene in a family with nonsyndromic sensorineural hearing loss

Hearing loss is the most common sensory neural disorder in humans, and according to a WHO estimation, 5.5% (466 million) of people worldwide have disabling hearing loss. In this study, a Chinese family with prelingual sensorineural hearing loss was investigated. The affected individuals showed moderately severe hearing loss at all frequencies. Using target genome enrichment and high-throughput sequencing, the homozygous variant c.2372del; p.(Ser791fs) was identified in PDZD7. This variant lies in exon 15 of PDZD7 and results in a frame shift followed by an early stop codon. It is classified as pathogenic according to the ACMG/AMP guidelines and ClinGen specifications. Our study expands the pathogenic variant spectrum of PDZD7 and strengthens the clinical importance of this gene in patients with moderately severe hearing loss.

FtsZ at mid-cell is essential in Escherichia coli until the late stage of constriction.

There has been recent debate as to the source of constriction force during cell division. FtsZ can generate a constriction force on tubular membranes in vitro, suggesting it may generate the constriction force in vivo. However, another study showed that mutants of FtsZ did not affect the rate of constriction, whereas mutants of the PG assembly did, suggesting that PG assembly may push the constriction from the outside. Supporting this model, two groups found that cells that have initiated constriction can complete septation while the Z ring is poisoned with the FtsZ targeting antibiotic PC190723. PC19 arrests treadmilling but leaves FtsZ in place. We sought to determine if a fully assembled Z ring is necessary during constriction. To do this, we used a temperature-sensitive FtsZ mutant, FtsZ84. FtsZ84 supports cell division at 30 °C, but it disassembles from the Z ring within 1 min upon a temperature jump to 42 °C. Following the temperature jump we found that cells in early constriction stop constricting. Cells that had progressed to the later stage of division finished constriction without a Z ring. These results show that in Escherichia coli, an assembled Z ring is essential for constriction except in the final stage, contradicting the simplest interpretation of previous studies using PC19.

Brain Tumor Modelling Using the CRISPR-Cas9 Base Editing Technology

Introduction: In common solid tumors, a plethora of genetic alterations can act as disease-driver mutation. Approximately, 95% of these variants are single-base substitutions, being cytidine-to-thymidine (C-to-T) conversion the most common among them. Cytosine base editors (CBEs) enable efficient C-to-T substitutions at targeted loci without double-stranded breaks, overcoming one of the main limitations in classical CRISPR-Cas9 technology. A major application of base editing technology is the study or treatment of disease-associated point mutations. In this project, we will focus on the most common point mutations in different genes across paediatric and adult brain tumors: e.g. CTNNB1 and TP53, frequently altered in the medulloblastoma, and PIK3CA and TP53, commonly associated with adult gliomas. Material and methods: To properly recapitulate some of the genetic alterations identified in brain tumors, we have combined the RCAS-TVA model and the CBE system for somatic genome editing. We have generated an animal model expressing TVA receptor under the control of specific promoters (GFAP or Nestin) together with an inducible CBE in order to exploit the base editing technology in the neural stem cells compartment in vivo. By intracranial injection of these mice with sgRNAs for specific genetic alterations, we will be able to recapitulate these point mutations in vivo. Results: We have successfully generated a novel mouse model for generation of brain tumors driven by point mutations in several genes that are known to drive tumorigenesis in humans. Characterization of these tumors allowed the detection by Sanger sequencing of C-to-T conversion in all samples, as well as the activation of downstream signaling derived from each mutations. We observed a complete loss of TP53 expression upon Tp53Q97*, which translates into an early stop codon in Tp53 sequence. On the other hand, we also validated the overexpression of TP53 as a consequence of Tp53R270C mutations with no major changes in downstream effectors, as it has been previously described in the literature. Conclusion: By integrating the RCAS-TVA system together with BE we have developed a more precise and flexible tool to better recapitulate in a more efficient way many point mutations identified in different brain tumor types. Using the RCAS-TVA-BE model we were able to model common point mutations in brain cancer and confirm their contribution in tumor formation.

The Effectiveness of Hypnobreast feeding Massage on Anxiety and Breast Milk Production in Postpartum Mothers

Low milk production on the first day after delivery causes the mother to experience anxiety, which increases the hormone cortisol. When the hormone cortisol level is high, milk production will be hampered, resulting in an early stop of the breastfeeding process. This study aimed to analyze the effectiveness of Hypno-breastfeeding massage on anxiety and milk production in postpartum mothers at the Public Health Center.

Front-End Model of Wireless Network Combined with Artificial Intelligence in Computer Information Management System

To address the security problem of computer information management, an artificial intelligence- (AI-) based information intrusion detection model is built in combination with wireless network. Firstly, the background and characteristics of wireless local area network (WLAN) technology are analyzed, and the relationship between AI technology and deep learning is introduced. Secondly, an intrusion detection model on account of long short-term memory (LSTM) neural network and gated recursive unit (GRU) is constructed after analysis of different neural network models. The L2 weight attenuation and dropout regularization strategies are combined with the neural network model. Finally, an intrusion detection front-end model combining wireless network and AI is established. From the comparison of intrusion detection experiments, the generalization ability of the model can be improved by using L2 weight attenuation and dropout regularization strategies. Nevertheless, the performance improvement is only slight, so the early stop method is adopted instead of the regularization strategy. Compared with the existing classification models, the overall performance of LSTM and GRU models is improved by about 17%. The performance of GRU model is not much different from that of LSTM model, but the amount of computation is reduced. Therefore, GRU model is the optimal choice to construct intrusion detection system. The intrusion detection models in WLAN and GRU can improve the security performance of computer information management system. To sum up, this work provides reference for the development of computer management system.

An evaluation of the effectiveness of countermeasures for improving the safety of dilemma zones: A driving simulator study

The main objective of this driving simulator study is to analyze the behavior of the driver at the start of the yellow signal of a signalized rural intersection and identify the most effective countermeasures for tackling the dilemma zone, namely an area on the intersection approach where vehicles at the start of the yellow phase can neither safely stop before the stop line nor cross the intersection. The following countermeasures were tested in the study on a sample of 48 drivers: green signal countdown timers, GSCT (C1); a new pattern of vertical and horizontal warning signs (C2); and an advanced on-board driver assistance system based on augmented reality (AR) and connected vehicle technologies (C3). These countermeasures were tested and compared to a baseline condition (B) where no countermeasures were applied. Based on the results of this study, the C2 and C3 countermeasures have proven to be valid tools for reducing driver indecision when approaching signalized intersections at the start of the yellow signal. In fact, using C2 and C3, the length of the dilemma zone was equal to 30 m and 36 m, respectively, with a reduction of about 50%, as compared to the baseline condition (B). Moreover, a reduced number of false behaviors was recorded, as well as a greater consistency in driver decision-making behaviors. Conversely, the C1 countermeasure did not lead to a significant improvement in the dilemma zone: an unnecessary increase in early stop rates was recorded, resulting in reduced intersection efficiency and operations.

Differential Regulation of an OsIspH1, the Functional 4-Hydroxy-3-Methylbut-2-Enyl Diphosphate Reductase, for Photosynthetic Pigment Biosynthesis in Rice Leaves and Seeds.

The methylerythritol 4-phosphate (MEP) pathway is responsible for providing common precursors for the biosynthesis of diverse plastidial terpenoids, including chlorophylls, carotenoids, and phytohormones, in plants. In rice (Oryza sativa), the last-step genes encoding 4-hydroxy-3-methylbut-2-enyl diphosphate reductase [HDR/isoprenoid synthesis H (IspH)] have been annotated in two genes (OsIspH1 and OsIspH2) in the rice genome. The spatial transcript levels indicated that OsIspH1 is highly expressed in all tissues at different developmental stages, whereas OsIspH2 is barely expressed due to an early stop in exon 1 caused by splicing error. OsIspH1 localized into plastids and osisph1, a T-DNA inserted knockout mutant, showed an albino phenotype, indicating that OsIspH1 is the only functional gene. To elucidate the role of OsIspH1 in the MEP pathway, we created two single (H145P and K407R) and double (H145P/K407R) mutations and performed complementation tests in two hdr mutants, including Escherichia coli DLYT1 strains and osisph1 rice plants. The results showed that every single mutation retained HDR function, but a double mutation lost it, proposing that the complementary relations of two residues might be important for enzyme activity but not each residue. When overexpressed in rice plants, the double-mutated gene, OsIspH1MUT, reduced chlorophyll and carotenoid biosynthesis in the leaves and seeds. It confirmed the crucial role of OsIspH1 in plastidic terpenoid biosynthesis, revealing organ-specific differential regulation of OsIspH1 in rice plants.

Carbon price forecasting based on CEEMDAN and LSTM

After signing the Paris Agreement and piloting carbon trading for many years, China has taken a significant step toward carbon neutrality. Carbon price forecasting is helpful to construct an effective and stable carbon pricing mechanism and provide practical guidance for production, operation, and investment. This paper builds multiple one-step-ahead predictors to analyze and forecast carbon prices, based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Long Short-Term Memory (LSTM) recurrent neural network, with the close prices of Guangzhou Emission Trading Scheme (Guangzhou ETS) from March 14, 2014, to August 31, 2021. After an initial period of unreasonable pricing and market adapting, the carbon price of the Guangzhou ETS began to recover gradually. According to previous literature, this paper summarizes two fundamental CEEMDAN–LSTM frameworks and proposes a hybrid one combined with Variational Modal Decomposition (VMD). With the help of adaptive reducing learning rate Adam optimizer and the early stop mechanism, the forecast turns out stable and reliable results, with a best average coefficient of determination (R2) of 0.982 and Mean Absolute Percentage Error (MAPE) of 0.555%, which shows that Sample Entropy integration and re-decomposition methods are conducive to carbon price forecasting. Validations of four ETS and different timesteps also verify the effectiveness of the hybrid VMD LSTM method, but it still needs to be optimized for practice.

Ankk1 Loss of Function Disrupts Dopaminergic Pathways in Zebrafish.

Ankyrin repeat and kinase domain containing 1 (ANKK1) is a member of the receptor-interacting protein serine/threonine kinase family, known to be involved in cell proliferation, differentiation and activation of transcription factors. Genetic variation within the ANKK1 locus is suggested to play a role in vulnerability to addictions. However, ANKK1 mechanism of action is still poorly understood. It has been suggested that ANKK1 may affect the development and/or functioning of dopaminergic pathways. To test this hypothesis, we generated a CRISPR-Cas9 loss of function ankk1 zebrafish line causing a 27 bp insertion that disrupts the ankk1 sequence introducing an early stop codon. We found that ankk1 transcript levels were significantly lower in ankk1 mutant (ankk127ins) fish compared to their wild type (ankk1+/+) siblings. In ankk1+/+ adult zebrafish brain, ankk1 protein was detected in isocortex, hippocampus, basolateral amygdala, mesencephalon, and cerebellum, resembling the mammalian distribution pattern. In contrast, ankk1 protein was reduced in the brain of ankk127ins/27ins fish. Quantitative polymerase chain reaction analysis revealed an increase in expression of drd2b mRNA in ankk127ins at both larval and adult stages. In ankk1+/+ adult zebrafish brain, drd2 protein was detected in cerebral cortex, cerebellum, hippocampus, and caudate homolog regions, resembling the pattern in humans. In contrast, drd2 expression was reduced in cortical regions of ankk127ins/27ins being predominantly found in the hindbrain. No differences in the number of cell bodies or axonal projections detected by anti-tyrosine hydroxylase immunostaining on 3 days post fertilization (dpf) larvae were found. Behavioral analysis revealed altered sensitivity to effects of both amisulpride and apomorphine on locomotion and startle habituation, consistent with a broad loss of both pre and post synaptic receptors. Ankk127ins mutants showed reduced sensitivity to the effect of the selective dopamine receptor antagonist amisulpride on locomotor responses to acoustic startle and were differentially sensitive to the effects of the non-selective dopamine agonist apomorphine on both locomotion and habituation. Taken together, our findings strengthen the hypothesis of a functional relationship between ANKK1 and DRD2, supporting a role for ANKK1 in the maintenance and/or functioning of dopaminergic pathways. Further work is needed to disentangle ANKK1’s role at different developmental stages.

Circulating Cytokines in Metastatic Breast Cancer Patients Select Different Prognostic Groups and Patients Who Might Benefit from Treatment beyond Progression.

Cancer induces immune suppression to overcome its recognition and eradication by the immune system. Cytokines are messengers able to modulate immune response or suppression. There is great interest in the evaluation of their changes during treatment in order to identify their relationship with clinical outcome. We evaluated 18 cytokines in breast cancer patients treated with eribulin before starting treatment (T0) and after four courses of therapy (T1). Longitudinal modifications were considered and cytokine clusters through PCA and HCPC correlated to patients’ outcomes were identified. Forty-one metastatic breast cancer patients and fifteen healthy volunteers were included. After clustering, we identified at T0 six patient clusters with different risk of relapse and death. At T1, only four clusters were identified, and three of them accounted for thirty-eight of forty-one patients, suggesting a possible role of treatment in reducing heterogeneity. The cluster with the best survival at T1 was characterized by low levels of IL-4, IL-6, IL-8, IL-10, CCL-2, CCL-4, and TGF-β. The cluster showing the worst survival encompassed high levels of IL-4, IL-6, IL-8, IL-10, CCL-2, and IFN-γ. A subgroup of patients with short progression-free survival (PFS) and long overall survival (OS) was comprised in the cluster characterized by low levels of CCL-2, IL-6, IL-8, IL-10, and IL-12 at T0. Our data support the prognostic significance of longitudinal serum cytokine analysis. This approach may help identify patients for whom early treatment stop avoids needless toxicity or might justify treatment beyond early progression. Further investigations are required to validate this hypothesis.

Alteration of Hematological and Biochemical Parameters to Predict Severity in SARS-CoV-2 Infection

Introduction: SARS- CoV-2 global pandemic has fired the world into a notable health catastrophe, with over 187,348,181 cases and 4,044,898 deaths as of July 10 2021. Many studies have been conducted to determine the clinical, biological, and radiological characteristics of COVID-19 for better identification of infected people at an early stage and stop disease development to an advanced state. Objectives: The aim &amp; objective of our study was to take advantage of the biochemical and hematological parameters in pre-dicting the prognosis and mortality in disease severity COVID-19 patients. Methods: This single-center retrospective, observational study was supervised to have all the admitted patients (n = 90) possessing COVID-19 Polymerase chain reaction (PCR) positive, and assessed those for prognosis and disease outcome by taking advantage of several biochemical and hematological markers. Results: Out of 90 patients, 53(58.89%) had mild to moderate disease that were included in the non-severe group; 37(41.11%) had severe to critical disease, of which 17 died and 20 survived. Advanced age, presence of comorbidities and infection were risk factors for advancement to severe disease. The existence of statistically significant abnormalities in the following parameters were strongly related with the advancement to severe disease: white blood cells (WBC, p=0.002), neutrophils (p=0.001), lymphocytes (p=0.001), C-reactive protein (CRP, P&lt;0.001), D-dimer (P&lt;0.001), Lactate dehydrogenase (LDH, p&lt;0.001), Ferritin (P&lt;0.001), creatinine (p=0.001), aspartate aminotransferase (AST, P=0.012), procalcitonin (P&lt;0.001) and alanine aminotransferase (ALT, P=0.001) during both admission and hospitalization. Conclusion: The inflammatory markers, biochemical parameters and hematological indices are a good guide for predicting the severity and disease outcome of coronavirus disease.

Large-Area Land-Cover Changes Monitoring With Time-Series Remote Sensing Images Using Transferable Deep Models

Dense time-series remote sensing images have transformed the traditional bitemporal land-cover change detection to continuous monitoring. Previous work mostly employs linear fitting, prediction, or decomposition methods, and the detection accuracy is not high. The latest progress of deep learning (DL) shows its advantages in time-series change monitoring. However, DL models are computationally expensive and require lots of labeled samples, resulting in often employed prediction-threshold-based unsupervised change detection method. However, the determination of a reasonable threshold has always been a big problem. Therefore, we proposed the similarity-measurement-based deep transfer learning for time-series adaptive change detection (SDTL-TSACD) model. First, a standard dynamic time warping (SDTW) distance was proposed and used to cluster large-scale time series into multiple subcategories with high time-series similarity. Second, a time convolutional network (TCN) was used for nonlinear time-series fitting and prediction, and an early stop strategy was used to prevent overfitting. Then, the trained TCN model would be transferred and performed pixel-by-pixel time-series prediction within the same category, and the SDTW was also used to evaluate the prediction accuracy. Finally, the Otsu adaptive threshold was used to detect change points, and the spatial neighbor relationship was used to eliminate the pseudo-change points. Change detection results using 132 benchmark datasets showed that the SDTL-TSACD performed well in both accuracy and efficiency. In addition, the MOD13Q1-EVI images from 2001 to 2020 were used to study the land-cover change of the Loess Plateau, and the SDTL-TSACD also showed a good ability to solve practical problems.

Investigation of Botulinum Neurotoxin Types from Clostridium botulinum Causing A Recent Outbreak in Vietnam

Clostridium botulinum is one of the causes of undiagnosed sudden deaths in humans due to the lethal botulinum neurotoxins (BoNTs). Foodborne botulism rarely occurs in developed countries because of being closely monitored, in opposite to developing countries including Vietnam. In the August 2020 food poisoning outbreak in Vietnam, presence of Clostridium botulinum and BoNTs was identified by culture and mouse bioassay, however, information regarding the possible toxin types was unclear. To examine the types of toxin, we designed primers for specific amplification of gene regions encoding the light chain (LC) domains for both BoNT/A and BoNT/B. After optimization, the expected PCR products were sent for sequencing. The results showed that the sequence of gene encoding BoNT/A LC was 99.2% identical to the CB-27 strain. The sequence of gene encoding BoNT/B LC was approximately 98.8% identical to reference strains. Additionally, we analyzed the sequences of the inferred proteins and identified a substitution that resulted in an early stop codon as previously found in a defective form of BoNT/B. Collectively, we provided the first evidence for C. botulinum strain possessing A(b) type in this studied outbreak. Further enzyme activity and neutralization assays are necessary to validate this preliminary toxin typing.

Restrained expression of canine glucocorticoid receptor splice variants \u03b1 and P prognosticates fatal disease outcome in SIRS

Glucocorticoids play a central role in the inflammatory response and alleviate the symptoms in critically ill patients. The glucocorticoid action relies on the glucocorticoid receptor (GR) which translocates into the nucleus upon ligand-binding and regulates transcription of a battery of genes. Although the GR is encoded by a single gene, dozens of its splice variants have been described in diverse species. The GRα isoform encodes the full, functionally active protein that is composed of a transactivation, a DNA-binding, and a C-terminal ligand-binding domain. The second most highly expressed receptor variant, the GR-P, is formed by an intron retention that introduces an early stop codon and results in a probably dysfunctional protein with truncated ligand-binding domain. We described the canine ortholog of GR-P and showed that this splice variant is highly abundant in the peripheral blood of dogs. The level of cGRα and cGR-P transcripts are elevated in patients of SIRS and the survival rate is increased with elevated cGRα and cGR-P expression. The ratio of cGRα and cGR-P mRNA did not differ between the survivor and non-survivor patients; thus, the total GR expression is more pertinent than the relative expression of GR isoforms in assessment of the disease outcome.

Avoiding collaborative paradox in multi‐agent reinforcement learning

The collaboration productively interacting between multi-agents has become an emerging issue in real-world applications. In reinforcement learning, multi-agent environments present challenges beyond tractable issues in single-agent settings. This collaborative environment has the following highly complex attributes: sparse rewards for task completion, limited communications between each other, and only partial observations. In particular, adjustments in an agent's action policy result in a nonstationary environment from the other agent's perspective, which causes high variance in the learned policies and prevents the direct use of reinforcement learning approaches. Unexpected social loafing caused by high dispersion makes it difficult for all agents to succeed in collaborative tasks. Therefore, we address a paradox caused by the social loafing to significantly reduce total returns after a certain timestep of multi-agent reinforcement learning. We further demonstrate that the collaborative paradox in multi-agent environments can be avoided by our proposed effective early stop method leveraging a metric for social loafing.

Abstract 13952: A Novel Genetic Mini-Pig Model of Cardiac Myosin-Binding Protein C (MYBPC3) Dysfunction: Evidence for Altered Sarcomere Structure and Systolic/Diastolic Functional Reserve in vivo

Introduction: Mutations in MYPBC3, the gene encoding cardiac Myosin-Binding Protein C (cMyBP-C), a regulatory sarcomeric protein, are known to play a pathogenic role in hypertrophic cardiomyopathy (HCM). However, the molecular bases for this pathophysiology remain poorly understood. Here, a novel large-animal MYPBC3-mutant model was developed and characterized to determine the association between myofilament abnormalities induced by cMyBP-C dysfunction and the resulting in vivo phenotype. Methods: A missense (early stop) E330X MYPBC3 mutation was introduced in Yucatan mini-pigs. Young heterozygous mutants (C3) and age-matched wildtype (WT) herd-mates were studied. In vivo imaging (MRI) and invasive hemodynamics were performed along with ex vivo measurements of tension dynamics, sarcomere structure (x-ray diffraction), and ATP turnover rates, including the population of myosin heads in the super-relaxed state (SRX) in LV fibers/tissues. Load-independent systolic and diastolic function (via LV pressure-volume relationships), and β-adrenergic receptor (β-AR) cardiac reserve (dobutamine 5 ug/kg/min IV) were assessed in vivo. Results: Relaxed C3 fibers showed reduced ordering of the myosin heads around the thick filament and a sensitizing shift of myosin-heads towards actin. However, C3 pigs showed normal Ca2+ sensitivity ex vivo with mildly decreased systolic function at rest in vivo (LVEF: 49 ± 1 vs. 56 ± 2%; vmax: 31 ± 1 vs. 38 ± 5 1/s; Ees: 8.5 ± 1.0 vs. 10.2 ± 1.5 mmHg/mL; all P &lt; 0.05 vs. CTRL). Upon β-AR stimulation, C3 pigs became hypercontractile (Ees: 18.0 ± 3.1 vs. 14.3 ± 2.8 mmHg/mL in CTRL, P &lt;0.05) showing markedly increased (P&lt;0.05) end-diastolic stiffness (Eed: 1.7 ± 0.4 vs. 1.3 ± 0.3 mmHg/mL in CTRL) and pressures; C3 fibers showed SRX dysregulation dependent on the phosphorylation state. Conclusions: MYPBC3 mutant mini-pigs showed a complex phenotype, characterized by myofilament abnormalities ex vivo and β-AR dependent hypercontractility and diastolic dysfunction in vivo . Taken together, these data generated in a translationally-relevant novel large-animal model further support the role of cMyBP-C as a key regulator of contraction and relaxation, but also can provide insights into the pathophysiology of HCM.

Abstract 12381: Clinical Implications of SCN5A and SCN10A Loss of Function Variants in 169,610 Exomes Representing the General Population

Background: Genetic variation in the genes SCN5A and SCN10A has previously been associated with cardiac disease and conduction velocity through genome-wide association studies (GWAS) and rare variant analyses. The two neighboring genes constitute the much-investigated SCN5A-SCN10A locus, and it has been challenging to determine the region’s function as GWAS often identifies variants in non-coding regions. The cardiac function of SCN5A is well established whereas the role of SCN10A in cardiac conduction and disease remains disputed. The study focuses on SCN10A while SCN5A was included for comparative analyses. Methods and Results: We accessed the UK Biobank database, which contains data on &gt;500,000 individuals. The recent exome release provides whole-exome sequencing data on 169,610 unrelated European individuals while resting ECGs are available on 44,987 individuals. We identified 668 individuals harboring LOF-variants (leading to early stop codon, frameshift or splice site disruption) in SCN10A and SCN5A with a MAF &lt;1%. Among the 668 individuals, 543 individuals carried SCN10A variants, whereas 125 individuals held a variant in SCN5A . There were 88 and 37 different variants in SCN10A and SCN5A , respectively. From these individuals, we obtained 69 ECGs in sinus rhythm and 55 of these were recorded on individuals with a SCN10A LOF-variant and 14 ECGs were recorded on individuals with a SCN5A LOF-variant. Linear regression analyses established an association between SCN5A LOF-variants and prolongation of the PR-interval as carrying a variant corresponds to an average PR prolongation of 25.1 milliseconds (P = 0.001). There were no statistically significant alterations among other ECG parameters. Furthermore, we could not determine correlations of ECG parameters for SCN10A LOF-variants. A burden test on SCN10A and SCN5A LOF-variants could not establish a relationship with atrial fibrillation (P = 0.26 and P = 0.41, respectively) and heart failure (P = 0.92 and P = 0.17, respectively). Remaining possible disease associations could not be properly established due to low number of cases. Conclusion: These findings confirm the significance of SCN5A in cardiac conduction while questioning the function of SCN10A in human cardiac conduction and disease.