Modeling mutual feedback between users and recommender systems

Recommender systems daily influence our decisions on the Internet. While considerable attention has been given to issues such as recommendation accuracy and user privacy, the long-term mutual feedback between a recommender system and the decisions of its users has been neglected so far. We propose here a model of network evolution which allows us to study the complex dynamics induced by this feedback, including the hysteresis effect which is typical for systems with non-linear dynamics. Despite the popular belief that recommendation helps users to discover new things, we find that the long-term use of recommendation can contribute to the rise of extremely popular items and thus ultimately narrow the user choice. These results are supported by measurements of the time evolution of item popularity inequality in real systems. We show that this adverse effect of recommendation can be tamed by sacrificing part of short-term recommendation accuracy

A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence , especially for China where most population has not been infected and most Omicron transmissions are silent. This paper serves as the first study to reveal the complete silent transmission dynamics of COVID-19 overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, retailing, catering and hotel staff are more likely to get infected than other professions. Unlike all other age groups and professions, elderly and retirees are more likely to get infected at home than outside home.Comment: 39 pages, 5+9 figure

The reinforcing influence of recommendations on global diversification

Recommender systems are promising ways to filter the overabundant information in modern society. Their algorithms help individuals to explore decent items, but it is unclear how they allocate popularity among items. In this paper, we simulate successive recommendations and measure their influence on the dispersion of item popularity by Gini coefficient. Our result indicates that local diffusion and collaborative filtering reinforce the popularity of hot items, widening the popularity dispersion. On the other hand, the heat conduction algorithm increases the popularity of the niche items and generates smaller dispersion of item popularity. Simulations are compared to mean-field predictions. Our results suggest that recommender systems have reinforcing influence on global diversification.Comment: 6 pages, 6 figure

CSIndicators: Get tailored climate indicators for applications in your sector

CSIndicators is an R package that gathers generalised methods for the flexible computation of climate-related indicators. Each method represents a specific mathematical approach which is combined with the possibility of selecting a flexible time period to define the indicator. This enables a wide range of possibilities for tailoring indicators to sectorial climate service applications. This package is intended for sub-seasonal, seasonal and decadal climate predictions, but its methods are also applicable to other time scales. Additionally, this package is compatible with the CSTools R package for climate forecast post-processing.This package was developed in the context of H2020 MED-GOLD (776467), S2S4E (776787), VITIGEOSS (869565) projects and Horizon Europe ASPECT project (101081460).Peer ReviewedPostprint (published version

New primers for methylation-specific polymerase chain reaction enhance specificity of detecting STAT1 methylation

AbstractObjectiveSignal transducer and activator of transcription (STAT)1 is a key tumor suppressor, which is always methylated in a variety of human cancers. However, nonspecific primers for the detection of specific promoter hypermethylation of STAT1 gene can lead to false-positive or false-negative results for gene methylation.Materials and MethodsWe designed new primers for the detection of STAT1 methylation and compared the sensitivities and specificities of these new primers with prior published primers by methylation-specific polymerase chain reaction (PCR) from ovarian clear cell carcinomas. The mRNA expression levels of STAT1 in these cancerous tissues were also evaluated by reverse-transcriptase PCR and correlated with the results of promoter methylation of STAT1 gene.ResultsNine (39%) of the 23 samples detected by the new primers and 13 samples (56%) detected by prior published primers showed STAT1 methylation. A direct DNA sequencing test revealed that four of the 13 samples (30.8%) showed false positivity for STAT1 methylation using the prior published primers. In contrast, none of the nine samples was false-positive for the detection of STAT1 methylation using the new primers. The new primers for the detection of STAT1 methylation showed 100% specificity and 100% sensitivity without false positivity.ConclusionSpecific primers for methylation-specific PCR are mandatory for the accurate detection of STAT1 gene methylation. Besides, specific primers can generate correct interpretation of STAT1 gene methylation, and its correlation with the clinicopathological characteristics and outcome of cancer patients

A brain-targeting lipidated peptide for neutralizing RNA-mediated toxicity in Polyglutamine Diseases

Abstract Polyglutamine (PolyQ) diseases are progressive neurodegenerative disorders caused by both protein- and RNA-mediated toxicities. We previously showed that a peptidyl inhibitor, P3, which binds directly to expanded CAG RNA can inhibit RNA-induced nucleolar stress and suppress RNA-induced neurotoxicity. Here we report a N-acetylated and C-amidated derivative of P3, P3V8, that showed a more than 20-fold increase in its affinity for expanded CAG RNA. The P3V8 peptide also more potently alleviated expanded RNA-induced cytotoxicity in vitro, and suppressed polyQ neurodegeneration in Drosophila with no observed toxic effects. Further N-palmitoylation of P3V8 (L1P3V8) not only significantly improved its cellular uptake and stability, but also facilitated its systemic exposure and brain uptake in rats via intranasal administration. Our findings demonstrate that concomitant N-acetylation, C-amidation and palmitoylation of P3 significantly improve both its bioactivity and pharmacological profile. L1P3V8 possesses drug/lead-like properties that can be further developed into a lead inhibitor for the treatment of polyQ diseases