Trade or Trick?

The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Beyond centralized exchanges (CEXs), decentralized exchanges (DEXs) are introduced to allow users to trade cryptocurrency without transferring the custody of their digital assets to the middlemen, thus eliminating the security and privacy issues of traditional CEX. Uniswap, as the most prominent cryptocurrency DEX, is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first collect all the transactions related to Uniswap V2 exchange and investigate the landscape of cryptocurrency trading on Uniswap from different perspectives. Then, we propose an accurate approach for flagging scam tokens on Uniswap based on a guilt-by-association heuristic and a machine-learning powered technique. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens and liquidity pools are created specialized for the "rug pull" scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams in league with the scam token/pool creators. The scammers have gained a profit of at least $16 million from 39,762 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem, and our approach can act as a whistleblower that identifies scam tokens at their early stages.

Guilt by association: Restricting humanitarian assistance in the name of counterterrorism

In certain contexts associated with counterterrorism, some governments and military forces have stigmatized civilians, not because of the acts they perform but rather from loose associations with groups perceived as “terrorists”, based on geographical proximity or common social, ethnic and religious backgrounds. Access to humanitarian assistance has been affected by this stigmatization, and in specific geographical areas it has been blocked, restricted, made conditional or undermined. This article draws on recent literature and examples to argue that certain counterterrorism frameworks and practices have inhibited the impartial delivery of aid to all affected populations.

Identification and Functional Annotation of Long Intergenic Non-coding RNAs in the Brassicaceae

Long intergenic noncoding RNAs (lincRNAs) are a large yet enigmatic class of eukaryotic transcripts with critical biological functions. Despite the wealth of RNA-seq data available, lincRNA identification lags in the plant lineage. In addition, there is a need for a harmonized identification and annotation effort to enable cross-species functional and genomic comparisons. In this study we processed >24 Tbp of RNA-seq data from >16,000 experiments to identify ~130,000 lincRNAs in four Brassicaceae: Arabidopsis thaliana, Camelina sativa, Brassica rapa, and Eutrema salsugineum. We used Nanopore RNA-seq, transcriptome-wide structural information, peptide data, and epigenomic data to characterize these lincRNAs and identify functional motifs. We then used comparative genomic and transcriptomic approaches to highlight lincRNAs in our dataset with sequence or transcriptional evolutionary conservation, including lincRNAs transcribed adjacent to orthologous genes that display little sequence similarity and likely function as transcriptional regulators. Finally, we used guilt-by-association techniques to further classify these lincRNAs according to putative function. LincRNAs with Brassicaceae-conserved putative miRNA binding motifs, short ORFs, and whose expression is modulated by abiotic stress are a few of the annotations that will prioritize and guide future functional analyses.

Digital Dystopia

Autocratic regimes, democratic majorities, private platforms, and religious or professional organizations can achieve social control by managing the flow of information about individuals’ behavior. Bundling the agents’ political, organizational, or religious attitudes with information about their prosocial conduct makes them care about behaviors that they otherwise would not. The incorporation of the individuals’ social graph in their social score further promotes soft control but destroys the social fabric. Both bundling and guilt by association are most effective in a society that has weak ties and is politically docile. (JEL D64, D72, D83, D91, K38, Z13)

Convergent and distributed effects of the 3q29 deletion on the human neural transcriptome

The 3q29 deletion (3q29Del) confers high risk for schizophrenia and other neurodevelopmental and psychiatric disorders. However, no single gene in this interval is definitively associated with disease, prompting the hypothesis that neuropsychiatric sequelae emerge upon loss of multiple functionally-connected genes. 3q29 genes are unevenly annotated and the impact of 3q29Del on the human neural transcriptome is unknown. To systematically formulate unbiased hypotheses about molecular mechanisms linking 3q29Del to neuropsychiatric illness, we conducted a systems-level network analysis of the non-pathological adult human cortical transcriptome and generated evidence-based predictions that relate 3q29 genes to novel functions and disease associations. The 21 protein-coding genes located in the interval segregated into seven clusters of highly co-expressed genes, demonstrating both convergent and distributed effects of 3q29Del across the interrogated transcriptomic landscape. Pathway analysis of these clusters indicated involvement in nervous-system functions, including synaptic signaling and organization, as well as core cellular functions, including transcriptional regulation, posttranslational modifications, chromatin remodeling, and mitochondrial metabolism. Top network-neighbors of 3q29 genes showed significant overlap with known schizophrenia, autism, and intellectual disability-risk genes, suggesting that 3q29Del biology is relevant to idiopathic disease. Leveraging “guilt by association”, we propose nine 3q29 genes, including one hub gene, as prioritized drivers of neuropsychiatric risk. These results provide testable hypotheses for experimental analysis on causal drivers and mechanisms of the largest known genetic risk factor for schizophrenia and highlight the study of normal function in non-pathological postmortem tissue to further our understanding of psychiatric genetics, especially for rare syndromes like 3q29Del, where access to neural tissue from carriers is unavailable or limited.

MiMeNet: Exploring microbiome-metabolome relationships using neural networks

The advance in microbiome and metabolome studies has generated rich omics data revealing the involvement of the microbial community in host disease pathogenesis through interactions with their host at a metabolic level. However, the computational tools to uncover these relationships are just emerging. Here, we present MiMeNet, a neural network framework for modeling microbe-metabolite relationships. Using ten iterations of 10-fold cross-validation on three paired microbiome-metabolome datasets, we show that MiMeNet more accurately predicts metabolite abundances (mean Spearman correlation coefficients increase from 0.108 to 0.309, 0.276 to 0.457, and -0.272 to 0.264) and identifies more well-predicted metabolites (increase in the number of well-predicted metabolites from 198 to 366, 104 to 143, and 4 to 29) compared to state-of-art linear models for individual metabolite predictions. Additionally, we demonstrate that MiMeNet can group microbes and metabolites with similar interaction patterns and functions to illuminate the underlying structure of the microbe-metabolite interaction network, which could potentially shed light on uncharacterized metabolites through “Guilt by Association”. Our results demonstrated that MiMeNet is a powerful tool to provide insights into the causes of metabolic dysregulation in disease, facilitating future hypothesis generation at the interface of the microbiome and metabolomics.

Identification and characterization of proteins of unknown function (PUFs) in Clostridium thermocellum DSM 1313 strains as potential genetic engineering targets

Background Mass spectrometry-based proteomics can identify and quantify thousands of proteins from individual microbial species, but a significant percentage of these proteins are unannotated and hence classified as proteins of unknown function (PUFs). Due to the difficulty in extracting meaningful metabolic information, PUFs are often overlooked or discarded during data analysis, even though they might be critically important in functional activities, in particular for metabolic engineering research. Results We optimized and employed a pipeline integrating various “guilt-by-association” (GBA) metrics, including differential expression and co-expression analyses of high-throughput mass spectrometry proteome data and phylogenetic coevolution analysis, and sequence homology-based approaches to determine putative functions for PUFs in Clostridium thermocellum. Our various analyses provided putative functional information for over 95% of the PUFs detected by mass spectrometry in a wild-type and/or an engineered strain of C. thermocellum. In particular, we validated a predicted acyltransferase PUF (WP_003519433.1) with functional activity towards 2-phenylethyl alcohol, consistent with our GBA and sequence homology-based predictions. Conclusions This work demonstrates the value of leveraging sequence homology-based annotations with empirical evidence based on the concept of GBA to broadly predict putative functions for PUFs, opening avenues to further interrogation via targeted experiments.

How Inductive and Deductive Generalization Shape the Guilt-by-Association Phenomenon Among Firms: Theory and Evidence

This study advances and tests the notion that the phenomenon of guilt by association-- whereby innocent organizations are penalized due to their similarity to offending organizations-- is shaped by two distinct forms of generalization. We analyze how and why evaluators’ interpretative process following instances of corporate misconduct will likely include not only inductive generalization (rooted in similarity judgments and prototype-based categorization) but also deductive generalizing (rooted in evaluators’ theories and causal-based categorization). We highlight the role and relevance of this neglected distinction by extending guilt-by-association predictions to include two unique predictions based on deductive generalization. First, we posit a recipient effect: if an innocent organization falls under a negative stereotype that causally links the innocent firm with corporate misconduct, then that innocent firm will suffer a greater negative spillover effect, irrespective of its similarity to the offending firm. Second, we also posit a transmission effect: if the offending firm falls under the same negative stereotype, then the negative spillover effect to other similar firms will be lessened. We also analyze how media discourse can foster negative stereotypes, and thus amplify these two effects. We find support for our hypotheses in an analysis of stock market reactions to corporate misconduct for all U.S. and international firms using reverse mergers to gain publicly traded status in the United States. We discuss the implications of our theoretical perspective and empirical findings for research on corporate misconduct, guilt by association, and stock market prejudice.