bluuebunny/crossref_metadata_2025_split huggingface dataset:
The reverse mode of the Na+/Ca2+ exchanger contributes to the pacemaker mechanism in rabbit sinus node cells
Authors: Tóth Noémi , Loewe Axel , Szlovák Jozefina , Kohajda Zsófia , Bitay Gergő , Levijoki Jouko , Papp Julius Gy. , Varró András , Nagy Norbert 
Published: December 2022

AbstractSinus node (SN) pacemaking is based on a coupling between surface membrane ion-channels and intracellular Ca2+-handling. The fundamental role of the inward Na+/Ca2+ exchanger (NCX) is firmly established. However, little is known about the reverse mode exchange. A simulation study attributed important role to reverse NCX activity, however experimental evidence is still missing. Whole-cell and perforated patch-clamp experiments were performed on rabbit SN cells supplemented with fluorescent Ca2+-tracking. We established 2 and 8 mM pipette NaCl groups to suppress and enable reverse NCX. NCX was assessed by specific block with 1 μM ORM-10962. Mechanistic simulations were performed by Maltsev–Lakatta minimal computational SN model. Active reverse NCX resulted in larger Ca2+-transient amplitude with larger SR Ca2+-content. Spontaneous action potential (AP) frequency increased with 8 mM NaCl. When reverse NCX was facilitated by 1 μM strophantin the Ca2+i and spontaneous rate increased. ORM-10962 applied prior to strophantin prevented Ca2+i and AP cycle change. Computational simulations indicated gradually increasing reverse NCX current, Ca2+i and heart rate with increasing Na+i. Our results provide further evidence for the role of reverse NCX in SN pacemaking. The reverse NCX activity may provide additional Ca2+-influx that could increase SR Ca2+-content, which consequently leads to enhanced pacemaking activity.
A versatile approach to evaluate the occurrence of microfibers in mussels Mytilus galloprovincialis
Authors: Volgare Michela , Santonicola Serena , Cocca Mariacristina , Avolio Roberto , Castaldo Rachele , Errico Maria Emanuela , Gentile Gennaro , Raimo Gennaro , Gasperi Maurizio , Colavita Giampaolo 
Published: December 2022

AbstractMicroplastics of fibrous shape are esteemed to be the most abundant micro-debris form present in the environment. Despite the occurrence of microfibers in fish may pose a risk to human health, the literature is scarce regarding studies on the contamination in commercial marine fish mostly due to methodological issues. In this study, a versatile approach, able to discriminate among natural and synthetic microfibers according to the evaluation of specific morphological features, is proposed in farmed mussels (Mytilus galloprovincialis). The approach was useful to determine that microfibers were present in 74% of mussel samples, with a mean number of 14.57 microfibers/individual, corresponding to 3.13 microfibers/g w.w. A negative correlation between the size of analysed mussels and the amount of microfibers/g w.w. was detected, showing that smaller specimens contained more microfibers than the larger ones. This work paves the way to further studies aimed to adequately assess the risk that microfibers may pose to marine biota, also considering the commercial value as seafood items of many species of the Mytilus genus and the potential implication for human exposure.
Associative memory of structured knowledge
Authors: Steinberg Julia , Sompolinsky Haim 
Published: December 2022

AbstractA long standing challenge in biological and artificial intelligence is to understand how new knowledge can be constructed from known building blocks in a way that is amenable for computation by neuronal circuits. Here we focus on the task of storage and recall of structured knowledge in long-term memory. Specifically, we ask how recurrent neuronal networks can store and retrieve multiple knowledge structures. We model each structure as a set of binary relations between events and attributes (attributes may represent e.g., temporal order, spatial location, role in semantic structure), and map each structure to a distributed neuronal activity pattern using a vector symbolic architecture scheme.We then use associative memory plasticity rules to store the binarized patterns as fixed points in a recurrent network. By a combination of signal-to-noise analysis and numerical simulations, we demonstrate that our model allows for efficient storage of these knowledge structures, such that the memorized structures as well as their individual building blocks (e.g., events and attributes) can be subsequently retrieved from partial retrieving cues. We show that long-term memory of structured knowledge relies on a new principle of computation beyond the memory basins. Finally, we show that our model can be extended to store sequences of memories as single attractors.
Deep-learning-based AI for evaluating estimated nonperfusion areas requiring further examination in ultra-widefield fundus images
Authors: Inoda Satoru , Takahashi Hidenori , Yamagata Hitoshi , Hisadome Yoichiro , Kondo Yusuke , Tampo Hironobu , Sakamoto Shinichi , Katada Yusaku , Kurihara Toshihide , Kawashima Hidetoshi , Yanagi Yasuo 
Published: December 2022

AbstractWe herein propose a PraNet-based deep-learning model for estimating the size of non-perfusion area (NPA) in pseudo-color fundus photos from an ultra-wide-field (UWF) image. We trained the model with focal loss and weighted binary cross-entropy loss to deal with the class-imbalanced dataset, and optimized hyperparameters in order to minimize validation loss. As expected, the resultant PraNet-based deep-learning model outperformed previously published methods. For verification, we used UWF fundus images with NPA and used Bland–Altman plots to compare estimated NPA with the ground truth in FA, which demonstrated that bias between the eNPA and ground truth was smaller than 10% of the confidence limits zone and that the number of outliers was less than 10% of observed paired images. The accuracy of the model was also tested on an external dataset from another institution, which confirmed the generalization of the model. For validation, we employed a contingency table for ROC analysis to judge the sensitivity and specificity of the estimated-NPA (eNPA). The results demonstrated that the sensitivity and specificity ranged from 83.3–87.0% and 79.3–85.7%, respectively. In conclusion, we developed an AI model capable of estimating NPA size from only an UWF image without angiography using PraNet-based deep learning. This is a potentially useful tool in monitoring eyes with ischemic retinal diseases.
Topological network features determine convergence rate of distributed average algorithms
Authors: Sirocchi Christel , Bogliolo Alessandro 
Published: December 2022

AbstractGossip algorithms are message-passing schemes designed to compute averages and other global functions over networks through asynchronous and randomised pairwise interactions. Gossip-based protocols have drawn much attention for achieving robust and fault-tolerant communication while maintaining simplicity and scalability. However, the frequent propagation of redundant information makes them inefficient and resource-intensive. Most previous works have been devoted to deriving performance bounds and developing faster algorithms tailored to specific structures. In contrast, this study focuses on characterising the effect of topological network features on performance so that faster convergence can be engineered by acting on the underlying network rather than the gossip algorithm. The numerical experiments identify the topological limiting factors, the most predictive graph metrics, and the most efficient algorithms for each graph family and for all graphs, providing guidelines for designing and maintaining resource-efficient networks. Regression analyses confirm the explanatory power of structural features and demonstrate the validity of the topological approach in performance estimation. Finally, the high predictive capabilities of local metrics and the possibility of computing them in a distributed manner and at a low computational cost inform the design and implementation of a novel distributed approach for predicting performance from the network topology.
Compensability of an enhanced incidence of spermatozoa with cytoplasmic droplets in boar semen for use in artificial insemination: a single cell approach
Authors: Henning Heiko , Luther Anne-Marie , Höfner-Schmiing Lisa , Waberski Dagmar 
Published: December 2022

AbstractThis single cell study aimed to clarify whether an elevated incidence of sperm with a retained cytoplasmic droplet (CD) can be compensated by a higher sperm number in boar semen doses to maintain fertility. Cluster analysis of motile spermatozoa (ten boars) revealed that spermatozoa with a CD are underrepresented in the fast, linearly moving sperm cohort compared to morphologically normal sperm. Nonetheless, the response to the motility stimulator procaine was barely affected in spermatozoa with distal CD (Cramer’s V = 0.14), but moderately affected in sperm with proximal CD (V = 0.28). Viability was lower in sperm with distal CD (p < 0.05) but not with proximal CD compared to normal sperm during 168 h storage of extended semen samples (n = 11) and subsequent thermic stress. Morphologically normal sperm from normospermic samples (n = 10) or samples with a high incidence (≥ 15%) of sperm with CD (n = 9) had similar motility patterns and responses to procaine. The origin of morphologically normal sperm had no effect on sperm viability (p > 0.05; n = 26). In conclusion, a moderately enhanced prevalence of sperm with CD seems to be compensable by an increase in sperm numbers in boar semen doses.
Optical monitoring of the plant growth status using polarimetry
Authors: Kim Jongyoon , Shin Yu Kyeong , Nam Yunsu , Lee Jun Gu , Lee Ji-Hoon 
Published: December 2022

AbstractPolarimetry is a powerful characterization technique that uses a wealth of information from electromagnetic waves, including polarization. Using the rich information provided by polarimetry, it is being actively studied in biomedical fields such as cancer and tumor diagnosis. Despite its importance and potential in agriculture, polarimetry for living plants has not been well studied. A Stokes polarimetric imaging system was built to determine the correlation between the polarization states of the light passing through the leaf and the growth states of lettuce. The Stokes parameter s3 associated with circular polarization increased over time and was strongly correlated with the growth of lettuce seedlings. In the statistical analysis, the distribution of s3 followed the generalized extreme value (GEV) probability density function. Salt stress retarded plant growth, and the concentration of treated sodium chloride (NaCl) showed a negative correlation with the location parameter μ of GEV. The clear correlation reported here will open the possibility of polarization measurements on living plants, enabling real-time monitoring of plant health.
Residual ST-segment elevation to predict long-term clinical and CMR-derived outcomes in STEMI
Authors: Merenciano-González Héctor , Marcos-Garcés Víctor , Gavara Jose , Pedro-Tudela Ana , Lopez-Lereu Maria P. , Monmeneu Jose V. , Perez Nerea , Rios-Navarro Cesar , de Dios Elena , Gabaldón-Pérez Ana , Albiach Cristina , Racugno Paolo , Bonanad Clara , Canoves Joaquim , Chorro Francisco J. , Bodi Vicente 
Published: December 2022

AbstractResidual ST-segment elevation after ST-segment elevation myocardial infarction (STEMI) has traditionally been considered a predictor of left ventricular (LV) dysfunction and ventricular aneurism. However, the implications in terms of long-term prognosis and cardiac magnetic resonance (CMR)-derived structural consequences are unclear. A total of 488 reperfused STEMI patients were prospectively included. The number of Q wave leads with residual ST-segment elevation > 1 mm (Q-STE) at pre-discharge ECG was assessed. LV ejection fraction (LVEF, %) and infarct size (IS, % of LV mass) were quantified in 319 patients at 6-month CMR. Major adverse cardiac events (MACE) were defined as all-cause death and/or re-admission for acute heart failure (HF), whichever occurred first. During a mean follow-up of 6.1 years, 92 MACE (18.9%), 39 deaths and 53 HF were recorded. After adjustment for baseline characteristics, Q-STE (per lead with > 1 mm) was independently associated with a higher risk of long-term MACE (HR 1.24 [1.07–1.44] per lead, p = 0.004), reduced (< 40%) LVEF (HR 1.36 [1.02–1.82] per lead, p = 0.04) and large (> 30% of LV mass) IS (HR 1.43 [1.11–1.85] per lead, p = 0.006) at 6-month CMR. Patients with Q-STE ≥ 2 leads (n = 172, 35.2%) displayed lower MACE-free survival, more depressed LVEF, and larger IS at 6-month CMR (p < 0.001 for all comparisons). Residual ST-segment elevation after STEMI represents a universally available tool that predicts worse long-term clinical and CMR-derived structural outcomes.
Mechanism underlying vascular remodeling in relation to circulating CD34-positive cells among older Japanese men
Authors: Shimizu Yuji 
Published: December 2022

AbstractDevelopment of structural atherosclerosis, an established cardiovascular risk factor, requires hematopoietic stem cells known as CD34-positive cells. However, an inverse association between circulating CD34-positive cell count and cardiovascular disease has been reported. These studies evoke a contradiction: characteristics associated with a low risk of developing structural atherosclerosis are also associated with a high risk of cardiovascular disease. To clarify the mechanisms underlying vascular remodeling, we conducted several epidemiological studies of Japanese men aged 60 to 69 years who participated in annual health check-ups. The present study summarizes those epidemiological studies and adds some discussion. From the perspective of endothelial repair activity, there are significant differences between functional versus structural atherosclerosis. Aggressive endothelial repair increases both functional and structural atherosclerosis. Deficient endothelial repair related to a shortage of CD34-positive cells due to consumption furthers functional atherosclerosis but not structural atherosclerosis. Therefore, the lack of structural atherosclerosis does not always reflect a favorable condition for the endothelium. Although further investigation is necessary, the present study suggests that higher endothelial repair activity that leads to structural atherosclerosis might have a beneficial effect on vascular health among older men.
Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance
Authors: Kang Baeki E. , Park Aron , Yang Hyekyung , Jo Yunju , Oh Tae Gyu , Jeong Seung Min , Ji Yosep , Kim Hyung‐Lae , Kim Han‐Na , Auwerx Johan , Nam Seungyoon , Park Cheol-Young , Ryu Dongryeol 
Published: December 2022

AbstractA simple predictive biomarker for fatty liver disease is required for individuals with insulin resistance. Here, we developed a supervised machine learning-based classifier for fatty liver disease using fecal 16S rDNA sequencing data. Based on the Kangbuk Samsung Hospital cohort (n = 777), we generated a random forest classifier to predict fatty liver diseases in individuals with or without insulin resistance (n = 166 and n = 611, respectively). The model performance was evaluated based on metrics, including accuracy, area under receiver operating curve (AUROC), kappa, and F1-score. The developed classifier for fatty liver diseases performed better in individuals with insulin resistance (AUROC = 0.77). We further optimized the classifiers using genetic algorithm. The improved classifier for insulin resistance, consisting of ten microbial genera, presented an advanced classification (AUROC = 0.93), whereas the improved classifier for insulin-sensitive individuals failed to distinguish participants with fatty liver diseases from the healthy. The classifier for individuals with insulin resistance was comparable or superior to previous methods predicting fatty liver diseases (accuracy = 0.83, kappa = 0.50, F1-score = 0.89), such as the fatty liver index. We identified the ten genera as a core set from the human gut microbiome, which could be a diagnostic biomarker of fatty liver diseases for insulin resistant individuals. Collectively, these findings indicate that the machine learning classifier for fatty liver diseases in the presence of insulin resistance is comparable or superior to commonly used methods.
The effects for inflammatory responses by CPP with different colloidal properties in hemodialysis patients
Authors: Mukai Hideyuki , Miura Yutaka , Kotani Kazuhiko , Kotoda Atsushi , Kurosu Hiroshi , Yamada Toshiyuki , Kuro-o Makoto , Iwazu Yoshitaka 
Published: December 2022

AbstractCalciprotein particles (CPPs) are colloids composed of solid-phase calcium-phosphate and serum protein fetuin-A. CPPs form a polydispersed system with different particle size and density. CPPs with specific physical properties can induce calcification and innate immune responses in cultured cells. In hemodialysis patients, blood CPP levels were reported to correlate with vascular calcification and inflammation. However, little is known about relation between these disorders and physical properties of CPPs. Here, we show that the association between physical properties of plasma CPPs and serum levels of inflammatory cytokines/chemokines in 78 hemodialysis out-patients by cross-sectional study. Patients with cardiovascular disease (CVD) had significantly higher high density CPP (H-CPP) levels than patients without CVD but not low density CPP (L-CPP). Seven cytokines/chemokines (EGF, eotaxin, IL-8, IP-10, MCP-1, MIP-1, MIP-1β and TNFα) were detectable in the serum samples from > 95% of the patients. In multivariate regression analysis, H-CPP was positively associated with eotaxin after adjusting for age, gender, smoking, serum phosphate and FGF23. L-CPP was negatively associated with IL-8 after adjusting for age, gender, serum albumin, phosphate and FGF23. High H-CPP levels were associated with pro-inflammatory response, whereas L-CPPs were associated with anti-inflammatory response. CPPs with different physical properties may impact differently on pathophysiology in HD patients.
Effects of diversity on thermal niche variation in bird communities under climate change
Authors: Marjakangas Emma-Liina , Santangeli Andrea , Johnston Alison , Michel Nicole L. , Princé Karine , Lehikoinen Aleksi 
Published: December 2022

AbstractClimate change alters ecological communities by affecting individual species and interactions between species. However, the impacts of climate change may be buffered by community diversity: diverse communities may be more resistant to climate-driven perturbations than simple communities. Here, we assess how diversity influences long-term thermal niche variation in communities under climate change. We use 50-year continental-scale data on bird communities during breeding and non-breeding seasons to quantify the communities’ thermal variability. Thermal variability is measured as the temporal change in the community’s average thermal niche and it indicates community’s response to climate change. Then, we study how the thermal variability varies as a function of taxonomic, functional, and evolutionary diversity using linear models. We find that communities with low thermal niche variation have higher functional diversity, with this pattern being measurable in the non-breeding but not in the breeding season. Given the expected increase in seasonal variation in the future climate, the differences in bird communities’ thermal variability between breeding and non-breeding seasons may grow wider. Importantly, our results suggest that functionally diverse wildlife communities can mitigate effects of climate change by hindering changes in thermal niche variability, which underscores the importance of addressing the climate and biodiversity crises together.
Effect of using 5A’s model for lifestyle counseling on psychological symptoms in women with polycystic ovary syndrome: a randomized field trial
Authors: ZareMobini Fatemeh , Farajzadegan Ziba , Kazemi Ashraf , Salehi Mehrdad 
Published: December 2022

AbstractLifestyle modification in women with polycystic ovary syndrome (PCOS) could be associated with increased psychological symptoms. This study aimed to evaluate the effect of lifestyle modification counseling using 5A’s model on the psychological symptoms of women with PCOS. This double-blind, randomized field trial was performed on 70 women with PCOS in two groups of lifestyle modification counseling based on 5A’s model and counseling without using the model. The intervention was performed based on five stages of the 5A’s model (Assess, Advise, Agree, Assist, Arrange) during a week, and psychological symptoms were assessed using Symptom Checklist-90-R before the intervention and one and three months after the intervention. The results showed that one and three months after the intervention, the level of psychological symptoms, except obsessive–compulsive level, were significantly lower in the intervention group than in the control group (p < 0.05). Moreover, the level of these symptoms decreased over time in the intervention group (p < 0.0001). Using the 5A’s model in lifestyle modification counseling is associated with the promotion of psychological health in women with PCOS, and this model is recommended for lifestyle counseling in women with polycystic ovary syndrome.
A longitudinal investigation on problematic Facebook use, psychological distress and well-being during the second wave of COVID-19 pandemic
Authors: Bonfanti Rubinia Celeste , Salerno Laura , Brugnera Agostino , Lo Coco Gianluca 
Published: December 2022

AbstractThe social isolation and the subsequent, increased use of Social Networking Sites due to the COVID-19 pandemic have had an impact on subjective well-being around the world. The present longitudinal study examined whether changes in psychological distress and well-being during the Italian second wave of the pandemic differ among people with different levels of Problematic Facebook Use (PFU). A total of 493 participants (Mage = 24.55 ± 7.25; 80.3% females) completed measures of passive use of Facebook, social comparison orientation on Facebook, fear of missing out, psychological distress (depressive symptoms and fear of COVID-19 pandemic) and well-being across three waves. Latent class analysis (LCA) categorized participants into three groups with different PFU levels: healthy users, moderate PFU users, and high PFU users. Hierarchical linear modeling (HLM) showed that the between-person level (class membership) accounted for most of the variability in psychological distress and well-being. No significant changes were found in psychological distress and well-being over time, but the High PFU users showed greater levels of psychological distress and lower levels of well-being at each time point. The findings of this study suggest that the relationship between PFU, psychological distress and well-being may reflect trait-like time-invariant differences between individuals rather than state-like changes.