FibVID: Comprehensive fake news diffusion dataset during the COVID-19 period.

As the SARS-CoV-2 (COVID-19) pandemic has run rampant worldwide, the dissemination of misinformation has sown confusion on a global scale. Thus, understanding the propagation of fake news and implementing countermeasures has become exceedingly important to the well-being of society. To assist this cause, we produce a valuable dataset called FibVID (Fake news information-broadcasting dataset of COVID-19), which addresses COVID-19 and non-COVID news from three key angles. First, we provide truth and falsehood (T/F) indicators of news items, as labeled and validated by several fact-checking platforms (e.g., Snopes and Politifact). Second, we collect spurious-claim-related tweets and retweets from Twitter, one of the world's largest social networks. Third, we provide basic user information, including the terms and characteristics of "heavy fake news" user to present a better understanding of T/F claims in consideration of COVID-19. FibVID provides several significant contributions. It helps to uncover propagation patterns of news items and themes related to identifying their authenticity. It further helps catalog and identify the traits of users who engage in fake news diffusion. We also provide suggestions for future applications of FibVID with a few exploratory analyses to examine the effectiveness of the approaches used.

Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities.

Importance: Suicide is a leading cause of death in the US. However, official national statistics on suicide rates are delayed by 1 to 2 years, hampering evidence-based public health planning and decision-making. Objective: To estimate weekly suicide fatalities in the US in near real time. Design, Setting, and Participants: This cross-sectional national study used a machine learning pipeline to combine signals from several streams of real-time information to estimate weekly suicide fatalities in the US in near real time. This 2-phase approach first fits optimal machine learning models to each individual data stream and subsequently combines predictions made from each data stream via an artificial neural network. National-level US administrative data on suicide deaths, health services, and economic, meteorological, and online data were variously obtained from 2014 to 2017. Data were analyzed from January 1, 2014, to December 31, 2017. Exposures: Longitudinal data on suicide-related exposures were obtained from multiple, heterogeneous streams: emergency department visits for suicide ideation and attempts collected via the National Syndromic Surveillance Program (2015-2017); calls to the National Suicide Prevention Lifeline (2014-2017); calls to US poison control centers for intentional self-harm (2014-2017); consumer price index and seasonality-adjusted unemployment rate, hourly earnings, home price index, and 3-month and 10-year yield curves from the Federal Reserve Economic Data (2014-2017); weekly daylight hours (2014-2017); Google and YouTube search trends related to suicide (2014-2017); and public posts on suicide on Reddit (2 314 533 posts), Twitter (9 327 472 tweets; 2015-2017), and Tumblr (1 670 378 posts; 2014-2017). Main Outcomes and Measures: Weekly estimates of suicide fatalities in the US were obtained through a machine learning pipeline that integrated the above data sources. Estimates were compared statistically with actual fatalities recorded by the National Vital Statistics System. Results: Combining information from multiple data streams, the machine learning method yielded estimates of weekly suicide deaths with high correlation to actual counts and trends (Pearson correlation, 0.811; P < .001), while estimating annual suicide rates with low error (0.55%). Conclusions and Relevance: The proposed ensemble machine learning framework reduces the error for annual suicide rate estimation to less than one-tenth of that of current forecasting approaches that use only historical information on suicide deaths. These findings establish a novel approach for tracking suicide fatalities in near real time and provide the potential for an effective public health response such as supporting budgetary decisions or deploying interventions.

Universal inherent fluctuations in statistical counting of large particles in slurry used for semiconductor manufacturing.

In the chemical mechanical polishing process of semiconductor manufacturing, the concentration of 'large' particles ([Formula: see text]0.5 µm) in the slurry, which is considerably larger in size than the main abrasives ([Formula: see text] 0.1 µm), is a critical parameter that strongly influences manufacturing defects, yields, and reliabilities of large-scale-integrated circuits. Various instruments, so-called particle counters, based on light scattering, light extinction, and holography techniques have been developed to measure and monitor the large particle concentration in semiconductor fabs in real time. However, sizeable fluctuation in the measured particle concentration complicates the statistical process control in the fabs worldwide. Here, we show that an inherent fluctuation exists in the counting of large particles, which is universal, independent of instrument type, and quantitatively determined by the instrument's operation parameters. We analytically derive a statistical theory of the fluctuation based on Poisson statistics and validate the theory through experiments and Monte-Carlo simulation. Furthermore, we provide a strategy to enhance the measurement accuracy by statistically adjusting the instrumental parameters commonly involved in the particle counters. The present results and analyses could be useful for statistical process control in semiconductor fabs to prevent large particle-induced defects such as micro-scratches and pits on wafers.

Rumor Propagation is Amplified by Echo Chambers in Social Media.

Spreading rumors on the Internet has become increasingly pervasive due to the proliferation of online social media. This paper investigates how rumors are amplified by a group of users who share similar interests or views, dubbed as an echo chamber. To this end, we identify and analyze 'rumor' echo chambers, each of which is a group of users who have participated in propagating common rumors. By collecting and analyzing 125 recent rumors from six popular fact-checking sites, and their associated 289,202 tweets/retweets generated by 176,362 users, we find that the rumors that are spread by rumor echo chamber members tend to be more viral and quickly propagated than those that are not spread by echo chamber members. We propose the notion of an echo chamber network that represents relations among rumor echo chambers. By identifying the hub rumor echo chambers (in terms of connectivity to other rumor echo chambers) in the echo chamber network, we show that the top 10% of hub rumor echo chambers contribute to propagation of 24% rumors by eliciting more than 36% of retweets, implying that core rumor echo chambers significantly contribute to rumor spreads.

Notch reporter activity in breast cancer cell lines identifies a subset of cells with stem cell activity.

Developmental pathways such as Notch play a pivotal role in tissue-specific stem cell self-renewal as well as in tumor development. However, the role of Notch signaling in breast cancer stem cells (CSC) remains to be determined. We utilized a lentiviral Notch reporter system to identify a subset of cells with a higher Notch activity (Notch(+)) or reduced activity (Notch(-)) in multiple breast cancer cell lines. Using in vitro and mouse xenotransplantation assays, we investigated the role of the Notch pathway in breast CSC regulation. Breast cancer cells with increased Notch activity displayed increased sphere formation as well as expression of breast CSC markers. Interestingly Notch(+) cells displayed higher Notch4 expression in both basal and luminal breast cancer cell lines. Moreover, Notch(+) cells demonstrated tumor initiation capacity at serial dilutions in mouse xenografts, whereas Notch(-) cells failed to generate tumors. γ-Secretase inhibitor (GSI), a Notch blocker but not a chemotherapeutic agent, effectively targets these Notch(+) cells in vitro and in mouse xenografts. Furthermore, elevated Notch4 and Hey1 expression in primary patient samples correlated with poor patient survival. Our study revealed a molecular mechanism for the role of Notch-mediated regulation of breast CSCs and provided a compelling rationale for CSC-targeted therapeutics.

Degradation characteristics of metoprolol during UV/chlorination reaction and a factorial design optimization.

Metoprolol (MTP), a hypertension depressor, has been increasingly detected even after conventional water treatment processes. In this study, the removal of MTP was compared using chlorination (Cl2), UV-C photolysis, and UV/chlorination (Cl2/UV) reactions. The results showed that the UV/chlorination reaction was most effective for MTP removal. MTP removal during UV/chlorination reaction was optimized under various conditions of UV intensity (1.1-4.4 mW/cm(2)), chlorine dose (1-5 mg/L as Cl2), pH (2-9), and dissolved organic matter (DOM, 1-4 mgC/L) using a two-level factorial design with 16 experimental combinations of the four factors. Among the factors examined, DOM scavenging by OH radicals was the most dominant in terms of MTP removal during UV/chlorination reaction. The established model fit well with the experimental results using to various water samples including surface waters, filtered and tap water samples. The optimized conditions (UV intensity=4.4 mW/cm(2), [Cl2]=5 mg/L, pH 7, and [DOM]=0.8-1.1 mgC/L) of the model removed more than 78.9% of MTP for filtered water samples during UV/chlorination reaction. Using LC-MS/MS, five byproducts of MTP (molecular weight: 171, 211, 309, 313, and 341, respectively) were identified during UV/chlorination reaction. Based on this information, the MTP transformation mechanism during UV/chlorination was suggested. Our results imply that applying UV/chlorination process after filtration stage in the water treatment plant (WTP) would be the most appropriate for effective removal of MTP.

Adsorption characteristics of selected hydrophilic and hydrophobic micropollutants in water using activated carbon.

In this study, we investigated adsorption characteristics of nine selected micropollutants (six pharmaceuticals, two pesticides, and one endocrine disruptor) in water using an activated carbon. The effects of carbon dosage, contact time, pH, DOM (dissolved organic matter), and temperature on the adsorption removal of micropollutants were examined. Increasing carbon dosage and contact time enhanced the removal of micropollutants. Sorption coefficients of hydrophilic compounds (caffeine, acetaminophen, sulfamethoxazole, and sulfamethazine) fit a linear isotherm and hydrophobic compounds (naproxen, diclofenac, 2, 4-D, triclocarban, and atrazine) fit a Freundlich isotherm. The removal of hydrophobic pollutants and caffeine were independent of pH changes, but acetaminophen, sulfamethazine, and sulfamethoxazole were adsorbed by mainly electrostatic interaction with activated carbon and so were affected by pH. The decrease in adsorption removal in surface water samples was observed and this decrease was more significant for hydrophobic than hydrophilic compounds. The decline in the adsorption capacity in surface water samples is caused by the competitive inhibition of DOM with micropollutants onto activated carbon. Low temperature (5°C) also decreased the adsorption removal of micropollutants, and affected hydrophobic compounds more than hydrophilic compounds. The results obtained in this study can be applied to optimize the adsorption capacities of micropollutants using activated carbon in water treatment process.

Dye sensitization of four low index TiO2 single crystal photoelectrodes with a series of dicarboxylated cyanine dyes.

Four dicarboxylated cyanine dyes were used to sensitize single-crystal anatase (001), anatase (101), rutile (001), and rutile (100) surfaces. Incident photon to current efficiencies (IPCE) spectra and isotherms were gathered for the different combination of dyes and surfaces. The maximum coverage of the surface-bound dyes on the TiO2 crystal surfaces was determined by photochronocoulometric measurements. The IPCE spectra of the surface-bound dyes revealed that both the dye monomers and H-aggregates were both present and generated photocurrent. The relative abundance of dye monomers and H-aggregates was found to be strongly dependent on the crystallographic face used as the substrate for sensitization. The ratio of dye monomer to H-aggregate was quantified by fitting the IPCE spectra with a sum of the dye monomer and H-aggregate solution spectra. The trends in surface coverage were explained using a simple "lattice matching" model where the distance between the coordinatively unsaturated Ti binding sites on the various TiO2 crystallographic surfaces was compared with the distance between the carboxylate groups on the dyes. The rutile (100) surface had the highest coverage for all the dyes in agreement with the predictions of the lattice-matching model. Absorbed photon-to-current-efficiencies (APCEs) were calculated from the incident photon current efficiencies, the extinction coefficients and the measured surface coverages. The factors that affect the APCE values such as the relative injection yield for monomers and aggregate, the relative surface coverage values for monomers and aggregates, and semiconductor doping levels are discussed.

Influence of surface chemistry on the binding and electronic coupling of CdSe quantum dots to single crystal TiO2 surfaces.

Sensitization of mesoporous nanocrystalline TiO(2) solar cells with quantum confined semiconductor nanocrystals (QDs) has some advantages over organic dyes or inorganic complex sensitizers, yet the reported efficiencies of laboratory devices are not currently competitive with those of dye sensitized cells. Several methods previously utilized to bind CdSe QDs to mesoporous TiO(2) films were investigated using low index faces of both anatase and rutile TiO(2) polytypes as model systems. The in situ ligand exchange method, where 3-mercaptopropionic acid (MPA) covered TiO(2) crystal surfaces are treated with trioctylphosphine (TOP)/trioctylphosphine oxide (TOPO) (TOP/TOPO)-capped CdSe QDs, resulted in very irreproducible and usually low sensitized photocurrents. The ex situ ligand exchange method, whereby MPA-capped QDs are synthesized and directly adsorbed onto bare TiO(2) single crystals, resulted in both reproducible sensitized photocurrents and surface coverages that are verified with atomic force microscopy (AFM). Purification of the nanocrystals and adjustment of the pH of the sensitization solution to >10.2 was found to prevent QD agglomeration and takes advantage of the dual chemical functionality of MPA to directly link the QDs to the TiO(2) surface. The spectral response of the incident photon to current efficiencies of CdSe QDs was directly compared to the commonly used sensitizer cis-di(thiocyanato)-bis(4,4;-dicarboxy-2,2'-bipyridine)ruthenium(II) (N3) on the same single crystals.