On the Mechanics of NFT Valuation: AI Ethics and Social Media

As CryptoPunks pioneers the innovation of non-fungible tokens (NFTs) in AI and art, the valuation mechanics of NFTs has become a trending topic. Earlier research identifies the impact of ethics and society on the price prediction of CryptoPunks. Since the booming year of the NFT market in 2021, the discussion of CryptoPunks has propagated on social media. Still, existing literature hasn't considered the social sentiment factors after the historical turning point on NFT valuation. In this paper, we study how sentiments in social media, together with gender and skin tone, contribute to NFT valuations by an empirical analysis of social media, blockchain, and crypto exchange data. We evidence social sentiments as a significant contributor to the price prediction of CryptoPunks. Furthermore, we document structure changes in the valuation mechanics before and after 2021. Although people's attitudes towards Cryptopunks are primarily positive, our findings reflect imbalances in transaction activities and pricing based on gender and skin tone. Our result is consistent and robust, controlling for the rarity of an NFT based on the set of human-readable attributes, including gender and skin tone. Our research contributes to the interdisciplinary study at the intersection of AI, Ethics, and Society, focusing on the ecosystem of decentralized AI or blockchain. We provide our data and code for replicability as open access on GitHub.Comment: Presented at ChainScience Conference, 2003 (arXiv:2307.03277v2 [cs.DC] 11 Jul 2023

Decoding Social Sentiment in DAO: A Comparative Analysis of Blockchain Governance Communities

Blockchain technology is leading a revolutionary transformation across diverse industries, with effective governance standing as a critical determinant for the success and sustainability of blockchain projects. Community forums, pivotal in engaging decentralized autonomous organizations (DAOs), wield a substantial impact on blockchain governance decisions. Concurrently, Natural Language Processing (NLP), particularly sentiment analysis, provides powerful insights from textual data. While prior research has explored the potential of NLP tools in social media sentiment analysis, a gap persists in understanding the sentiment landscape of blockchain governance communities. The evolving discourse and sentiment dynamics on the forums of top DAOs remain largely unknown. This paper delves deep into the evolving discourse and sentiment dynamics on the public forums of leading DeFi projects -- Aave, Uniswap, Curve Dao, Aragon, Yearn.finance, Merit Circle, and Balancer -- placing a primary focus on discussions related to governance issues. Despite differing activity patterns, participants across these decentralized communities consistently express positive sentiments in their Discord discussions, indicating optimism towards governance decisions. Additionally, our research suggests a potential interplay between discussion intensity and sentiment dynamics, indicating that higher discussion volumes may contribute to more stable and positive emotions. The insights gained from this study are valuable for decision-makers in blockchain governance, underscoring the pivotal role of sentiment analysis in interpreting community emotions and its evolving impact on the landscape of blockchain governance. This research significantly contributes to the interdisciplinary exploration of the intersection of blockchain and society, with a specific emphasis on the decentralized blockchain governance ecosystem

Visual Contrast Modulates Operant Learning Responses in Larval Zebrafish

The larval zebrafish is a promising vertebrate model organism to study neural mechanisms underlying learning and memory due to its small brain and rich behavioral repertoire. Here, we report on a high-throughput operant conditioning system for zebrafish larvae, which can simultaneously train 12 fish to associate a visual conditioned pattern with electroshocks. We find that the learning responses can be enhanced by the visual contrast, not the spatial features of the conditioned patterns, highlighted by several behavioral metrics. By further characterizing the learning curves as well as memory extinction, we demonstrate that the percentage of learners and the memory length increase as the conditioned pattern becomes darker. Finally, little difference in operant learning responses was found between AB wild-type fish and elavl3:H2B-GCaMP6f transgenic fish

Sedimentation of a single soluble particle at low Reynolds and high P\'eclet numbers

We investigate experimentally the dissolution of an almost spherical butyramide particle during its sedimentation, in the low Reynolds high P\'eclet regime. The particle sediments in a quiescent aqueous solution, and its shape and position are measured simultaneously by a camera attached to a translation stage. The particle is tracked in real time, and the translation stage moves accordingly to keep the particle in the field of the camera. The measurements from the particle image show that the radius shrinking rate is constant with time, and independent of the initial radius of the particle. We explain this with a simple model, based on the sedimentation law in the Stokes' regime and the mass transfer rate at low Reynolds and high P\'eclet numbers. The theoretical and experimental results are consistent within $20\%$. We introduce two correction factors to take into account the non-sphericity of the particle and the inclusions of air bubbles inside the particle, and reach quantitative agreement. With these corrections, the indirect measurement of the radius shrinking rate deduced from the position measurement is also in agreement with the model. We discuss other correction factors, and explain why there are negligible in the present experiment. We also compute the effective Sherwood number as a function of an effective P\'eclet number

RNA topoisomerase is prevalent in all domains of life and associates with polyribosomes in animals

DNA Topoisomerases are essential to resolve topological problems during DNA metabolism in all species. However, the prevalence and function of RNA topoisomerases remain uncertain. Here, we show that RNA topoisomerase activity is prevalent in Type IA topoisomerases from bacteria, archaea, and eukarya. Moreover, this activity always requires the conserved Type IA core domains and the same catalytic residue used in DNA topoisomerase reaction; however, it does not absolutely require the non-conserved carboxyl-terminal domain (CTD), which is necessary for relaxation reactions of supercoiled DNA. The RNA topoisomerase activity of human Top3β differs from that of Escherichia coli topoisomerase I in that the former but not the latter requires the CTD, indicating that topoisomerases have developed distinct mechanisms during evolution to catalyze RNA topoisomerase reactions. Notably, Top3β proteins from several animals associate with polyribosomes, which are units of mRNA translation, whereas the Top3 homologs from E. coli and yeast lack the association. The Top3β-polyribosome association requires TDRD3, which directly interacts with Top3β and is present in animals but not bacteria or yeast. We propose that RNA topoisomerases arose in the early RNA world, and that they are retained through all domains of DNA-based life, where they mediate mRNA translation as part of polyribosomes in animals

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Evaluation of a Clustering Approach to Define Distinct Subgroups of Patients With Migraine to Select Electroacupuncture Treatments

Background and ObjectivesThe objective of this study was to propose a clustering approach to identify migraine subgroups and test the clinical usefulness of the approach by providing prognostic information for electroacupuncture treatment selection.MethodsParticipants with migraine without aura (MWoA) were asked to complete a daily headache diary, self-rating depression and anxiety, and quality-of-life questionnaires. Whole-brain functional connectivities (FCs) were assessed on resting-state functional MRI (fMRI). By integrating clinical measurements and fMRI data, partial least squares correlation and hierarchical clustering analysis were used to cluster participants with MWoA. Multivariate pattern analysis was applied to validate the proposed subgrouping strategy. Some participants had an 8-week electroacupuncture treatment, and the response rate was compared between different MWoA subgroups.ResultsIn study 1, a total of 97 participants (age of 28.2 & PLUSMN; 1.0 years, 70 female participants) with MWoA and 77 healthy controls (HCs) (age of 26.8 & PLUSMN; 0.1 years, 61 female participants) were enrolled (dataset 1), and 2 MWoA subgroups were defined. The participants in subgroup 1 had a significantly lower headache frequency (times/month of 4.4 & PLUSMN; 1.1) and significantly higher self-ratings of depression (depression score of 49.5 & PLUSMN; 2.3) when compared with participants in subgroup 2 (times/month of 7.0 & PLUSMN; 0.6 and depression score of 43.4 & PLUSMN; 1.2). The between-group differences of FCs were predominantly related to the amygdala, thalamus, hippocampus, and parahippocampal area. In study 2, 33 participants with MWoA (age of 30.9 & PLUSMN; 2.0 years, 28 female participants) and 23 HCs (age of 29.8 & PLUSMN; 1.1 years, 13 female participants) were enrolled as an independent dataset (dataset 2). The classification analysis validated the effectiveness of the 2-cluster solution of participants with MWoA in datasets 1 and 2. In study 3, 58 participants with MWoA were willing to receive electroacupuncture treatment and were assigned to different subgroups. Participants in different subgroups exhibited different response rates (p = 0.03, OR CI 0.086-0.93) to electroacupuncture treatment (18% and 44% for subgroups 1 and 2, respectively).DiscussionOur study proposed a novel clustering approach to define distinct MWoA subgroups, which could be useful for refining the diagnosis of participants with MWoA and guiding individualized strategies for pain prophylaxis and analgesia

Efficient computation of motif discovery on Intel Many Integrated Core (MIC) Architecture

Abstract Background Novel sequence motifs detection is becoming increasingly essential in computational biology. However, the high computational cost greatly constrains the efficiency of most motif discovery algorithms. Results In this paper, we accelerate MEME algorithm targeted on Intel Many Integrated Core (MIC) Architecture and present a parallel implementation of MEME called MIC-MEME base on hybrid CPU/MIC computing framework. Our method focuses on parallelizing the starting point searching method and improving iteration updating strategy of the algorithm. MIC-MEME has achieved significant speedups of 26.6 for ZOOPS model and 30.2 for OOPS model on average for the overall runtime when benchmarked on the experimental platform with two Xeon Phi 3120 coprocessors. Conclusions Furthermore, MIC-MEME has been compared with state-of-arts methods and it shows good scalability with respect to dataset size and the number of MICs. Source code: https://github.com/hkwkevin28/MIC-MEME

Focusing on RISC assembly in mammalian cells

RISC (RNA-induced silencing complex) is a central protein complex in RNAi, into which a siRNA strand is assembled to become effective in gene silencing. By using an in vitro RNAi reaction based on Drosophila embryo extract, an asymmetric model was recently proposed for RISC assembly of siRNA strands, suggesting that the strand that is more loosely paired at its 5′ end is selectively assembled into RISC and results in target gene silencing. However, in the present study, we were unable to establish such a correlation in cell-based RNAi assays, as well as in large-scale RNAi data analyses. This suggests that the thermodynamic stability of siRNA is not a major determinant of gene silencing in mammalian cells. Further studies on fork siRNAs showed that mismatch at the 5′ end of the siRNA sense strand decreased RISC assembly of the antisense strand, but surprisingly did not increase RISC assembly of the sense strand. More interestingly, measurements of melting temperature showed that the terminal stability of fork siRNAs correlated with the positions of the mismatches, but not gene silencing efficacy. In summary, our data demonstrate that there is no definite correlation between siRNA stability and gene silencing in mammalian cells, which suggests that instead of thermodynamic stability, other features of the siRNA duplex contribute to RISC assembly in RNAi