Miracle-dz/UniFAR-FTUs huggingface dataset:
cAMP-Dependent Signaling Restores AP Firing in Dormant SA Node Cells via Enhancement of Surface Membrane Currents and Calcium Coupling

Action potential (AP) firing rate and rhythm of sinoatrial nodal cells (SANC) are controlled by synergy between intracellular rhythmic local Ca2+ releases (LCRs) (“Ca2+ clock”) and sarcolemmal electrogenic mechanisms (“membrane clock”). However, some SANC do not fire APs (dormant SANC). Prior studies have shown that β-adrenoceptor stimulation can restore AP firing in these cells. Here we tested whether this relates to improvement of synchronization of clock coupling. We characterized membrane potential, ion currents, Ca2+ dynamics, and phospholamban (PLB) phosphorylation, regulating Ca2+ pump in enzymatically isolated single guinea pig SANC prior to, during, and following β-adrenoceptor stimulation (isoproterenol) or application of cell-permeant cAMP (CPT-cAMP). Phosphorylation of PLB (Serine 16) was quantified in the same cells following Ca2+ measurement. In dormant SANC LCRs were small and disorganized at baseline, membrane potential was depolarized (−38 ± 1 mV, n = 46), and ICaL, If, and IK densities were smaller vs SANC firing APs. β-adrenoceptor stimulation or application of CPT-cAMP led to de novo spontaneous AP generation in 44 and 46% of dormant SANC, respectively. The initial response was an increase in size, rhythmicity and synchronization of LCRs, paralleled with membrane hyperpolarization and small amplitude APs (rate ∼1 Hz). During the transition to steady-state AP firing, LCR size further increased, while LCR period shortened. LCRs became more synchronized resulting in the growth of an ensemble LCR signal peaked in late diastole, culminating in AP ignition; the rate of diastolic depolarization, AP amplitude, and AP firing rate increased. ICaL, IK, and If amplitudes in dormant SANC increased in response to β-adrenoceptor stimulation. During washout, all changes reversed in order. Total PLB was higher, but the ratio of phosphorylated PLB (Serine 16) to total PLB was lower in dormant SANC. β-adrenoceptor stimulation increased this ratio in AP-firing cells. Thus, transition of dormant SANC to AP firing is linked to the increased functional coupling of membrane and Ca2+ clock proteins. The transition occurs via (i) an increase in cAMP-mediated phosphorylation of PLB accelerating Ca2+ pumping, (ii) increased spatiotemporal LCR synchronization, yielding a larger diastolic LCR ensemble signal resulting in an earlier increase in diastolic INCX; and (iii) increased current densities of If, ICaL, and IK.
Characterization of Glycoside Hydrolase Families 13 and 31 Reveals Expansion and Diversification of α-Amylase Genes in the Phlebotomine Lutzomyia longipalpis and Modulation of Sandfly Glycosidase Activities by Leishmania Infection

Sugar-rich food sources are essential for sandflies to meet their energy demands, achieving more prolonged survival. The digestion of carbohydrates from food is mainly realized by glycoside hydrolases (GH). To identify genes coding for α-glycosidases and α-amylases belonging to Glycoside Hydrolase Family 13 (GH13) and Glycoside Hydrolase Family 31 (GH31) in Lutzomyia longipalpis, we performed an HMMER search against its genome using known sequences from other dipteran species. The sequences retrieved were classified based on BLASTP best hit, analysis of conserved regions by alignment with sequences of proteins with known structure, and phylogenetic analysis comparing with orthologous proteins from other dipteran species. Using RT-PCR analysis, we evaluated the expression of GH13 and GH31 genes, in the gut and rest of the body of females, in four different conditions: non-fed, sugar-fed, blood-fed, and Leishmania mexicana infected females. L. longipalpis has GH13/31 genes that code for enzymes involved in various aspects of sugar metabolism, as carbohydrate digestion, storage, and mobilization of glycogen reserves, proteins involved in transport, control of N-glycosylation quality, as well as others with a putative function in the regulation of myogenesis. These proteins are representatives of GH13 and GH31 families, and their roles seem to be conserved. Most of the enzymes seem to be active with conserved consense sequences, including the expected catalytic residues. α-amylases also demonstrated the presence of calcium and chloride binding sites. L. longipalpis genome shows an expansion in the α-amylase gene family, with two clusters. In contrast, a retraction in the number of α-glucosidases occurred. The expansion of α-amylases is probably related to the specialization of these proteins for different substrates or inhibitors, which might correlate with the higher diversity of plant foods available in the natural habitat of L. longipalpis. The expression of α-glucosidase genes is higher in blood-fed females, suggesting their role in blood digestion. Besides that, in blood-fed females infected with the parasite Leishmania mexicana, these genes were also modulated. Glycoside Hydrolases from families 13 and 31 are essential for the metabolism of L. longipalpis, and GH13 enzymes seem to be involved in the interaction between sandflies and Leishmania.
Computed Tomography Radiomics Kinetics as Early Imaging Correlates of Osteoradionecrosis in Oropharyngeal Cancer Patients

Osteoradionecrosis (ORN) is a major side-effect of radiation therapy in oropharyngeal cancer (OPC) patients. In this study, we demonstrate that early prediction of ORN is possible by analyzing the temporal evolution of mandibular subvolumes receiving radiation. For our analysis, we use computed tomography (CT) scans from 21 OPC patients treated with Intensity Modulated Radiation Therapy (IMRT) with subsequent radiographically-proven ≥ grade II ORN, at three different time points: pre-IMRT, 2-months, and 6-months post-IMRT. For each patient, radiomic features were extracted from a mandibular subvolume that developed ORN and a control subvolume that received the same dose but did not develop ORN. We used a Multivariate Functional Principal Component Analysis (MFPCA) approach to characterize the temporal trajectories of these features. The proposed MFPCA model performs the best at classifying ORN vs. Control subvolumes with an area under curve (AUC) = 0.74 [95% confidence interval (C.I.): 0.61–0.90], significantly outperforming existing approaches such as a pre-IMRT features model or a delta model based on changes at intermediate time points, i.e., at 2- and 6-month follow-up. This suggests that temporal trajectories of radiomics features derived from sequential pre- and post-RT CT scans can provide markers that are correlates of RT-induced mandibular injury, and consequently aid in earlier management of ORN.
The Asymmetric Effect of Panic Index on Cryptocurrencies

The Covid 19 pandemic is the first major crisis facing cryptocurrencies. Therefore, the reaction of the cryptocurrency markets is important. News about epidemics affects investors' decisions. Panic index (PIndex) is an index created from news about the Covid 19 outbreak. In the study, it is used to measure the impact of decisions on the crypto money market. As cryptocurrencies, Bitcoin (BTC), Etherium (ETH), and Ripple (XRP), which have the highest transaction volume in the crypto money market, are included in the analysis. The relationship between Panic Index and the three major cryptocurrencies with the largest share in the cryptocurrency market was investigated by Ardl and Hatemi-J asymmetric causality test. Traditional causality tests acknowledge that the effects of positive and negative changes are the same. However, there may be asymmetric information and different investor behaviors in financial markets. In the study, Hatemi-J [1] Asymmetric Causality Test was conducted to examine the asymmetric relationship and symmetric relationship between Pindex and cryptocurrencies by separating them into positive and negative shocks. According to the results of the Hatemi-J causality analysis, positive shocks in the panic index are the cause of negative shocks for all cryptocurrencies. In other words, increases in the panic index are caused to fall the value of Bitcoin, Ethereum, and Ripple cryptocurrencies decrease. The results show that cryptocurrencies were not a safe haven for the investor during the Covid 19 period, as they acted similarly to other financial assets.
Expression, Prognosis and Gene Regulation Network of NFAT Transcription Factors in Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide. The nuclear factor of activated T cells (NFAT) family is implicated in tumorigenesis and progression in various types of cancer. However, little is known about their expression patterns, distinct prognostic values, and potential regulatory networks in NSCLC. In this study, we comprehensively analyzed the distinct expression and prognostic value of NFATs in NSCLC through various large databases, including the Oncomine, UCSC Xena Browser, UALCAN databases, Kaplan–Meier Plotter, cBioPortal, and Enrichr. In lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), NFAT1/2/4/5 mRNA expression levels were significantly decreased and NFAT3 mRNA expression level was significantly increased. The cBioPortal database analysis showed that the mRNA dysregulation was one of the single most important factors for NFAT alteration in LUAD and LUSC and that both LUAD and LUSC cases with the alterations in the mRNA expression of NFATs had significantly better overall survival (OS). High expression levels of NFAT1/2/4/5 were significantly associated with better OS in LUAD, whereas high NFAT3 expression led to a worse OS. Overexpression of NFAT1/2 predicted better OS in LUSC, whereas high NFAT5 expression led to a worse OS. The networks for NFATs and the 50 most frequently altered neighbor genes in LUAD and LUSC were also constructed. NFATs and genes significantly associated with NFAT mRNA expression in LUAD and LUSC were significantly enriched in the cGMP-dependent protein kinase and Wnt signaling pathways. These results showed that the NFAT family members displayed varying degrees of abnormal expressions, suggesting that NFATs may be therapeutic targets for patients with NSCLC. Aberrant expression of NFATs was found to be associated with OS in the patients with NSCLC; among NFATs, NFAT3/4 may be new biomarkers for the prognosis of LUAD. However, further studies are required to validate our findings.
Deep Learning-Based Estimation of Axial Length and Subfoveal Choroidal Thickness From Color Fundus Photographs

This study aimed to develop an automated computer-based algorithm to estimate axial length and subfoveal choroidal thickness (SFCT) based on color fundus photographs. In the population-based Beijing Eye Study 2011, we took fundus photographs and measured SFCT by optical coherence tomography (OCT) and axial length by optical low-coherence reflectometry. Using 6394 color fundus images taken from 3468 participants, we trained and evaluated a deep-learning-based algorithm for estimation of axial length and SFCT. The algorithm had a mean absolute error (MAE) for estimating axial length and SFCT of 0.56 mm [95% confidence interval (CI): 0.53,0.61] and 49.20 μm (95% CI: 45.83,52.54), respectively. Estimated values and measured data showed coefficients of determination of r2 = 0.59 (95% CI: 0.50,0.65) for axial length and r2 = 0.62 (95% CI: 0.57,0.67) for SFCT. Bland–Altman plots revealed a mean difference in axial length and SFCT of −0.16 mm (95% CI: −1.60,1.27 mm) and of −4.40 μm (95% CI, −131.8,122.9 μm), respectively. For the estimation of axial length, heat map analysis showed that signals predominantly from overall of the macular region, the foveal region, and the extrafoveal region were used in the eyes with an axial length of < 22 mm, 22–26 mm, and > 26 mm, respectively. For the estimation of SFCT, the convolutional neural network (CNN) used mostly the central part of the macular region, the fovea or perifovea, independently of the SFCT. Our study shows that deep-learning-based algorithms may be helpful in estimating axial length and SFCT based on conventional color fundus images. They may be a further step in the semiautomatic assessment of the eye.
Effects of Hydroxylated Mephedrone Metabolites on Monoamine Transporter Activity in vitro

Mephedrone is a largely abused psychostimulant. It elicits the release of monoamines via the high affinity transporters for dopamine (DAT), norepinephrine (NET) and serotonin (SERT). Stereoselective metabolic reactions are involved in the inactivation and the elimination of its chemical structure. However, during these processes, several structures are generated and some of them have been reported to be still pharmacologically active. In this study 1) we have newly synthetized several putative mephedrone metabolites, 2) compared their activity at monoamine transporters, 3) generated quantitative structure activity relationships, and 4) exploited the chemical structure of the putative metabolites to screen a urine sample from a drug user and dissect mephedrone metabolism. We have found that most of the tested metabolites are weak inhibitors of monoamine transporters and that all of them are more potent at DAT and NET in comparison to SERT. The only exception is represented by the COOH-metabolite which shows no pharmacological activity at all three monoamine transporters. The enantioselectivity of mephedrone and its metabolites is present mainly at SERT, with only minor effects at DAT and NET being introduced when the β-keto group is reduced to an OH-group. Importantly, while at DAT the putative metabolites did not show changes in inhibitory potencies, but rather changes in their substrate/blocker profile, at SERT they showed mainly changes in inhibitory potencies. Molecular modeling suggests that the hydrophobic nature of a specific SERT subpocket may be involved in such loss of affinity. Finally, the assessment of the putative metabolites in one urine sample of mephedrone user displayed two previously uncharacterized metabolites, 4-COOH-nor-mephedrone (4-COOH-MC) and dihydro-4- nor-mephedrone (dihydro-4-MC). These results confirm and expand previous studies highlighting the importance of the stereochemistry in the pharmacodynamics of phase-1 metabolites of mephedrone, established their structure-activity relationships at DAT, NET and SERT and pave the way for a systematic dissection of mephedrone metabolic routes. Given the number of structures found having residual and modified pharmacological profiles, these findings may help in understanding the complex subjective effects of administered mephedrone. Moreover, the dissection of mephedrone metabolic routes may help in developing new therapies for treating psychostimulants acute intoxications.
Fault-Tolerant Distributed Transactions on Blockchain

Since the introduction of Bitcoin—the first widespread application driven by blockchain— the interest of the public and private sectors in blockchain has skyrocketed. In recent years, blockchain-based fabrics have been used to address challenges in diverse fields such as trade, food production, property rights, identity-management, aid delivery, health care, and fraud prevention. This widespread interest follows from fundamental concepts on which blockchains are built that together embed the notion of trust, upon which blockchains are built. 1. Blockchains provide data transparancy. Data in a blockchain is stored in the form of a ledger, which contains an ordered history of all the transactions. This facilitates oversight and auditing. 2. Blockchains ensure data integrity by using strong cryptographic primitives. This guarantees that transactions accepted by the blockchain are authenticated by its issuer, are immutable, and cannot be repudiated by the issuer. This ensures accountability. 3. Blockchains are decentralized, democratic, and resilient. They use consensus-based replication to decentralize the ledger among many independent participants. Thus, it can operate completely decentralized and does not require trust in a single authority. Additions to the chain are performed by consensus, in which all participants have a democratic voice in maintaining the integrity of the blockchain. Due to the usage of replication and consensus, blockchains are also highly resilient to malicious attacks even when a significant portion of the participants are malicious. It further increases the opportunity for fairness and equity through democratization. These fundamental concepts and the technologies behind them—a generic ledger-based data model, cryptographically ensured data integrity, and consensus-based replication—prove to be a powerful and inspiring combination, a catalyst to promote computational trust. In this book, we present an in-depth study of blockchain, unraveling its revolutionary promise to instill computational trust in society, all carefully tailored to a broad audience including students, researchers, and practitioners. We offer a comprehensive overview of theoretical limitations and practical usability of consensus protocols while examining the diverse landscape of how blockchains are manifested in their permissioned and permissionless forms.
Where Does Theory Have It Right? A Comparison of Theory-Driven and Empirical Agent Based Models

: Computational social science has witnessed a shift from pure theoretical to empirical agent-based models (ABMs) grounded in data-driven correlations between behavioral factors defining agents’ decisions. There is a strong urge to go beyond theoretical ABMs with behavioral theories setting stylized rules that guide agents’ actions, especially when it concerns policy-related simulations. However, it remains unclear to what extent theory-driven ABMs mislead, if at all, a choice of a policy when compared to the outcomes of models with empirical micro-foundations. This is especially relevant for pro-environmental policies that increasingly rely on quantifying cumulative effects of individual behavioral changes, where ABMs are so helpful. We propose a comparison framework to address this methodological dilemma, which quantitatively explores the gap in predictions between theory- and data-driven ABMs. Inspired by the existing theory-driven model, ORVin-T, which studies the individual choice between organic and conventional products, we design a survey to collect data on individual preferences and purchasing decisions. We then use this extensive empirical microdata to build an empirical twin, ORVin-E, replacing the theoretical assumptions and secondary aggregated data used to parametrize agents’ decision strategies with our empirical survey data. We compare the models in terms of key outputs, perform sensitivity analysis, and explore three policy scenarios. We observe that the theory-driven model predicts the shifts to organic consumption as accurately as the ABM with empirical micro-foundations at both aggregated and individual scales. There are slight differences ( ± 5% ) between the estimations of the two models with regard to different behavioral change scenarios: increasing conventional tax, launching organic social-informational campaigns, and their combination. Our findings highlight the goodness of fit and usefulness of theoretical modeling efforts, at least in the case of incremental behavioral change. It sheds light on the conditions when theory-driven and data-driven models are aligned and on the value of empirical data for studying systemic changes.
A 24‐hour operational single‐phase quasi‐Z‐source photovoltaic power system

Battery energy stored single‐phase quasi‐Z‐source inverter photovoltaic (BES‐qZSI‐PV) power system shows many advantages in comparison with other tri‐port inverter‐based PV power systems. However, the existing BES‐qZSI‐PV power system does not work at night or cloudy day because of its circuit limitation. This paper proposes a 24‐h operational BES‐qZS‐PV system by the following approaches: (1) the main power circuit is proposed for the day and night operation; (2) the detailed circuit models including the DC model and the second‐harmonics ripple model are built by considering scenarios of day and night operations; (3) the design method of quasi‐Z‐source network parameters is proposed to meet the requirement of day and night operations; and (4) the control strategy is proposed to achieve the high‐performance system operation all the time and seamless transition between day and night operation. After theoretical modeling and analysis, the built models are verified by comparing their calculation results with the circuit‐based simulation and experimental results. The proposed single‐phase BES‐qZSI‐PV power system is tested; experimental and simulation results are captured when the system operates in day, night, and transition to validate the proposed 24‐h operational BES‐qZSI‐PV system.
Intention or Request: The Impact of Message Structures

This paper investigates how different message structures impact communication strategy as well as sender and receiver behavior. Specifically, we focus on comparing communication games with messages stating an intention versus a request. Our experimental results show that when a game includes self-signaling or self-committing messages, the two message structures yield negligibly different results. However, when the messages of the game are neither self-signaling nor self-committing, we find that more subjects send messages suggesting cooperation with request than intention. Interestingly, subjects also deviate from their suggested actions more frequently with request than intention. We surmise lying aversion plays a prominent role in contributing to the differences in games where messages lack the self-committing property.
PATTERN RECOGNITION AND IMAGE ANALYSIS MILIEU Analysis of the Dynamical Biological Objects of Optical Microscopy

We generalize the experience of the applied and theoretical investigations performed in the last 7‒8 years, including the generated algorithms and procedures for analyzing the behavior of the cells’ population as a system of dynamical objects by using a concept of the integral optical f low. We determine the main types of motion which make it possible to separate the key moments of the motion of cells in the population and describe the stages of the cells’ development and their interaction with each other. In order to describe the motion based on the integral optical f low, we generate maps which make possible to determine the moment at which the state starts to change and classify the main types of motion in the cells’ population: directed motion, aggregation (cells motion towards each other), dispersion (cells’ motion in different directions with respect to the common center), division (formation of several new cells in the place where the old one was), and apoptosis (cell destruction). We develop the algorithms for analyzing cells’ motion by using the integral optical f low. The generated procedure is intended for nondestructively checking the development of the cells’ population and it can be used in automatic systems of cell engineering. The procedure based on integral optical f low analysis and motion maps makes it possible to monitor the development of the cells’ population at the new level by considering the interaction of cells.
PATTERN RECOGNITION AND IMAGE ANALYSIS MILIEU Analysis of Digital Images in Order to Control the Manufacturing of Integrated Circuits

This paper offers a brief overview of the main theoretical and practical results in digital image processing in the manufacturing of integrated circuits (ICs), obtained by the authors and staff of the Laboratory for Systems Identification of the United Institute for Informatics Problems of the National Academy of Sciences of Belarus. The developed methods, algorithms, and computer analysis technology combine conventional digital processing methods and neural networks. This ensures robust recognition and optical inspection of the objects of layouts of ICs with the desired accuracy. The obtained results are used at the leading enterprises of electronic engineering of the Republic of Belarus in VLSI designing and manufacturing, as well as in the fabrication of optical inspection units for VLSI semiconductor wafers.