Researchers, enterprises, and governments have made great efforts to detect misinformation promptly and accurately. Traditional solutions either examine complicated hand-crafted features or rely... Show moreResearchers, enterprises, and governments have made great efforts to detect misinformation promptly and accurately. Traditional solutions either examine complicated hand-crafted features or rely heavily on the constructed credibility networks to extract useful indicators for discerning false information. However, such approaches require insightful domain expert knowledge and intensive feature engineering that are often non-generalizable. Recent advances in deep learning techniques have spurred learning high-level representations from textual and image content and discovering diffusion patterns with various neural networks. Despite the progress made by these methods, they still face the problem of overdependence on the content features and fail to discriminate against the influence of each user involved in the process of rumor spreading. Different user-aspect information plays different roles in various stages of rumor diffusion, effectively extract features from each aspect, and aggregate the learned features into a unique representation, which has not been well investigated. To address these limitations, we propose a novel model, UMLARD (User-aspect Multi-view Learning with Attention for Rumor Detection), to effectively learn the representation of different views of the users who engaged in spreading the tweet, and fuse the learned features through the distinguishable fusion mechanism. Finally, we concatenate the learned user-aspect features with content features to form a unique representation and feed it into a fully connected layer to predict the label of rumors. Our experiments conducted on real-world datasets demonstrate that UMLARD significantly improves the rumor detection performance compared to state-of-the-art baselines. It also allows explainability of the model behavior and the predicted results. Show less
Capelôa, L.; Yazdi, M.; Zhang, H.; Chen, X.; Nie, Y.; Wagner, E.; ... ; Barz, M. 2021
ABC-type triblock copolymers are a rising platform especially for oligonucleotide delivery as they offer an additional functionality besides the anyhow needed functions of shielding and... Show moreABC-type triblock copolymers are a rising platform especially for oligonucleotide delivery as they offer an additional functionality besides the anyhow needed functions of shielding and complexation. The authors present a polypept(o)ide-based triblock copolymer synthesized by amine-initiated ring-opening polymerization (ROP) of N-carboxyanhydrides (NCAs), comprising a shielding block A of polysarcosine (pSar), a poly(S-ethylsulfonyl-l-cystein) (pCys(SO2 Et)) block B for bioreversible and chemo-selective cross-linking and a poly(l-lysine) (pLys) block C for complexation to construct polyion complex (PIC) micelles as vehicle for small interfering RNA (siRNA) delivery. The self-assembly behavior of ABC-type triblocks is investigated to derive correlations between block lengths of the polymer and PIC micelle structure, showing an enormous effect of the β-sheet forming pCys(SO2 Et) block. Moreover, the block enables the introduction of disulfide cross-links by reaction with multifunctional thiols to increase stability against dilution. The right content of the additional block leads to well-defined cross-linked 50-60 nm PIC micelles purified from production impurities and determinable siRNA loading. These PIC micelles can deliver functional siRNA into Neuro2A and KB cells evaluated by cellular uptake and specific gene knockdown assays. Show less
Information cascade size prediction is one of the primary challenges for understanding the diffusion of information. Traditional feature-based methods heavily rely on the quality of handcrafted... Show moreInformation cascade size prediction is one of the primary challenges for understanding the diffusion of information. Traditional feature-based methods heavily rely on the quality of handcrafted features, requiring extensive domain knowledge and hard to generalize to new domains. Recently, inspired by the success of deep learning in computer vision and natural language processing, researchers have developed neural network-based approaches for tackling this problem. However, existing deep learning-based methods either focused on modeling the temporal characteristics of cascades but ignored the structural information or failed to take the order-scale and position-scale into consideration in modeling structures of information propagation. This paper proposed a novel graph neural network-based model, called MUCas, to learn the latent representations of cascade graphs from a multi-scale perspective, which can make full use of the direction-scale, high-order-scale, position-scale, and dynamic-scale of cascades via a newly designed MUlti-scale Graph Capsule Network (MUG-Caps) and the influence-attention mechanism. Extensive experiments conducted on two real-world data sets demonstrate that our MUCas significantly outperforms the state-of-the-art approaches. Show less
Aims The HERMES (HEart failure Molecular Epidemiology for Therapeutic targets) consortium aims to identify the genomic and molecular basis of heart failure.Methods and results The consortium... Show moreAims The HERMES (HEart failure Molecular Epidemiology for Therapeutic targets) consortium aims to identify the genomic and molecular basis of heart failure.Methods and results The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome-wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow-up following heart failure diagnosis ranged from 2 to 116 months. Forty-nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34-90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of >1.10 for common variants (allele frequency > 0.05) and >1.20 for low-frequency variants (allele frequency 0.01-0.05) at P < 5 x 10(-8) under an additive genetic model.Conclusions HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction. Show less
Researchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering. They require lots of human actions and... Show moreResearchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering. They require lots of human actions and are difficult to generalize. Deep learning solutions come to help. However, they usually fail to capture the underlying structure of the rumor propagation and the influence of all participants involved in the spreading chain. In this study, we propose a novel participant level rumor detection framework. It explicitly models and integrates various fine-grained user representations (i.e., user influence, susceptibility, and temporal information) of all participants from the propagation threads via deep representation learning. Experiments conducted on real world datasets demonstrate a significant accuracy improvement of our approach. Theoretically, we contribute to the effective usage of data science and analytics for social information diffusion design, particularly rumor detection. Practically, our results can be used to improve the quality of rumor detection services for social platforms. Show less
Chen, X.; Jin, R.; Jiang, Q.; Bi, Q.; He, T.; Song, X.; ... ; Nie, Y. 2021
The tumor hypoxic microenvironment not only induces genetic and epigenetic changes in tumor cells, immature vessels formation for oxygen demand, but also compromises the efficiency of therapeutic... Show moreThe tumor hypoxic microenvironment not only induces genetic and epigenetic changes in tumor cells, immature vessels formation for oxygen demand, but also compromises the efficiency of therapeutic interventions. On the other hand, conventional therapeutic approaches which kill tumor cells or destroy tumor blood vessels to block nutrition and oxygen supply usually facilitate even harsher microenvironment. Thus, simultaneously relieving the strained response of tumor cells and blood vessels represents a promising strategy to reverse the adverse tumor hypoxic microenvironment. In the present study, an integrated amphiphilic system (RSCD) is designed based on Angiotensin II receptor blocker candesartan for siRNA delivery against the hypoxia-inducible factor-1 alpha (HIF-1α), aiming at both vascular and cellular "relaxation" to reconstruct a tumor normoxic microenvironment. Both in vitro and in vivo studies have confirmed that the hypoxia-inducible HIF-1α expression is down-regulated by 70% and vascular growth is inhibited by 60%. The "relaxation" therapy enables neovascularization with more complete and organized structures to obviously increase the oxygen level inside tumor, which results in a 50% growth inhibition. Moreover, reconstruction of tumor microenvironment enhances tumor-targeted drug delivery, and significantly improves the chemotherapeutic and photodynamic anticancer treatments. Show less
Researchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering, which requires extensive manual... Show moreResearchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering, which requires extensive manual efforts and are difficult to generalize to different domains. Recently, deep learning solutions have emerged as the de facto methods which detect online rumors in an end-to-end manner. However, they still fail to fully capture the dissemination patterns of rumors. In this study, we propose a novel diffusion-based rumor detection model, called Macroscopic and Microscopic-aware Rumor Detection, to explore the full-scale diffusion patterns of information. It leverages graph neural networks to learn the macroscopic diffusion of rumor propagation and capture microscopic diffusion patterns using bidirectional recurrent neural networks while taking into account the user-time series. Moreover, it leverages knowledge distillation technique to create a more informative student model and further improve the model performance. Experiments conducted on two real-world data sets demonstrate that our method achieves significant accuracy improvements over the state-of-the-art baseline models on rumor detection. Show less
This thesis consists of five chapters on how to construct prediction sets for different types of data and models in a parametric or nonparametric Bayesian paradigm. The motivation of the models... Show moreThis thesis consists of five chapters on how to construct prediction sets for different types of data and models in a parametric or nonparametric Bayesian paradigm. The motivation of the models comes from applications in pharmaceutical manufacturing and quality control. Show less
Over the past decades, China's rice production area has experienced a substantial change in spatial distribution that has exacerbated national freshwater scarcity. To support the development of... Show moreOver the past decades, China's rice production area has experienced a substantial change in spatial distribution that has exacerbated national freshwater scarcity. To support the development of guidelines for sustainable water use in rice cropping, this study explores the potential for achieving a downscaled freshwater use boundary with high spatial resolution while maintaining China's current production levels. We found that, to operate within the boundary, which was defined using a water scarcity index, national irrigation water use for rice cropping should reduce by 10% in water-scarce regions, implying a 10% loss in national rice production without further intervention. However, using scenario analysis, we found that the production losses can be reduced to approximately 7% by closing yield gaps, and fully compensated by closing harvest area gaps in water-rich regions. The closing of both the yield and harvest area gaps allows an increase of 6.9 million metric tons of rice (3% of the national production). The water-rich regions which are suitable for double-rice systems show a high potential to increase rice production. The spatial redistribution of rice production under these scenarios resulted in a reduction in the national water-scarcity footprint related to rice cropping of 52-55%. These results demonstrate that, to reach the downscaled water use boundary, national redistribution of rice production is necessary and urgent. Our study provides detailed spatial information to support water and land use decisions. Show less
Jong, T. A. de; Benschop, T.; Chen, X.; Krasovskii, E.E.; Dood, M.J.A. de; Tromp, R.M.; ... ; Molen, S.J. van der 2021
In twisted bilayer graphene (TBG) a moiré pattern forms that introduces a new length scale to the material. At the 'magic' twist angle of 1.1°, this causes a flat band to form, yielding emergent... Show moreIn twisted bilayer graphene (TBG) a moiré pattern forms that introduces a new length scale to the material. At the 'magic' twist angle of 1.1°, this causes a flat band to form, yielding emergent properties such as correlated insulator behavior and superconductivity [1-4]. In general, the moiré structure in TBG varies spatially, influencing the local electronic properties [5-9] and hence the outcome of macroscopic charge transport experiments. In particular, to understand the wide variety observed in the phase diagrams and critical temperatures, a more detailed understanding of the local moiré variation is needed [10]. Here, we study spatial and temporal variations of the moiré pattern in TBG using aberration-corrected Low Energy Electron Microscopy (AC-LEEM) [11,12]. The spatial variation we find is lower than reported previously. At 500°C, we observe thermal fluctuations of the moiré lattice, corresponding to collective atomic displacements of less than 70pm on a time scale of seconds [13], homogenizing the sample. Despite previous concerns, no untwisting of the layers is found, even at temperatures as high as 600°C [14,15]. From these observations, we conclude that thermal annealing can be used to decrease the local disorder in TBG samples. Finally, we report the existence of individual edge dislocations in the atomic and moiré lattice. These topological defects break translation symmetry and are anticipated to exhibit unique local electronic properties. Show less
Specific adsorption of anions is an important aspect in surface electrochemistry for its influence on reaction kinetics in either a promoted or inhibited fashion. Perchloric acid is typically... Show moreSpecific adsorption of anions is an important aspect in surface electrochemistry for its influence on reaction kinetics in either a promoted or inhibited fashion. Perchloric acid is typically considered as an ideal electrolyte for investigating electrocatalytic reactions due to the lack of specific adsorption of the perchlorate anion on several metal electrodes. In this work, cyclic voltammetry and computational methods are combined to investigate the interfacial processes on a Pd monolayer deposited on Pt(111) single crystal electrode in perchloric acid solution. The "hydrogen region" of this PdMLPt(111) surface exhibits two voltammetric peaks: the first "hydrogen peak" at 0.246 V-RHE actually involves the replacement of hydrogen by hydroxyl, and the second "hydrogen peak" H-II at 0.306 V-RHE appears to be the replacement of adsorbed hydroxyl by specific perchlorate adsorption. The two peaks merge into a single peak when a more strongly adsorbed anion, such as sulfate, is involved. Our density functional theory calculations qualitatively support the peak assignment and show that anions generally bind more strongly to the PdMLPt(111) surface than to Pt(111). Show less
Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association... Show moreHeart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies. Show less
The focus throughout this thesis will be on gathering fundamental studies of the detailed structure and composition of the electrode/electrolyte interface effect on the rate and mechanism of key... Show moreThe focus throughout this thesis will be on gathering fundamental studies of the detailed structure and composition of the electrode/electrolyte interface effect on the rate and mechanism of key electrocatalytic reactions. The first part (Chapter 2 and 3) of this PhD thesis is about the studies of the non-Nernstian dependence on pH of the step-related voltammetric peak on platinum surface. The combined experimental and computational studies prove the existence of the co-adsorbed alkaline metal cation (Li, Na, K, and Cs) and hydroxyl at step sites of a platinum electrode. The co-adsorbed alkaline metal cation weakens the hydroxyl adsorption which yielding the anomalous non-Nernstian dependence on pH of the step-related “hydrogen peaks”. The second part starts from Chapter 4 changes first to the study of adsorption processes on a Pd monolayer-modified Pt(111) surface. Chapter 5 deals with the mechanism of electrocatalytic oxidation of formic acid and reduction of carbon dioxide on this Pd monolayer-modified Pt(111) electrode. The work in Chapter 6 explores the effects of electrolyte composition and catalysts surface structure on formic acid oxidation reaction. Show less
Anxiety disorders arise from disruptions among the highly interconnected circuits that normally serve to process the streams of potentially threatening stimuli. The resulting imbalance among these... Show moreAnxiety disorders arise from disruptions among the highly interconnected circuits that normally serve to process the streams of potentially threatening stimuli. The resulting imbalance among these circuits can cause a fundamental misinterpretation of neural sensory information as threatening and can lead to the inappropriate emotional and behavioral responses observed in anxiety disorders. There is considerable preclinical evidence that the GABAergic system, in general, and its alpha 2-and/or alpha 5-subunitcontaining GABA(A) receptor subtypes, in particular, are involved in the pathophysiology of anxiety disorders. However, the clinical efficacy of GABA-A alpha 2-selective agonists for the treatment of anxiety disorders has not been unequivocally demonstrated. In this review, we present several human pharmacological studies that have been performed with the aim of identifying the pharmacologically active doses/exposure levels of several GABA-A subtype-selective novel compounds with potential anxiolytic effects. The pharmacological selectivity of novel alpha 2-subtype-selective GABA(A) receptor partial agonists has been demonstrated by their distinct effect profiles on the neurophysiological and neuropsychological measurements that reflect the functions of multiple CNS domains compared with those of benzodiazepines, which are nonselective, full GABA(A) agonists. Normalizing the undesired pharmacodynamic side effects against the desired on-target effects on the saccadic peak velocity is a useful approach for presenting the pharmacological features of GABA(A)-ergic modulators. Moreover, combining the anxiogenic symptom provocation paradigm with validated neurophysiological and neuropsychological biomarkers may provide further construct validity for the clinical effects of novel anxiolytic agents. In addition, the observed drug effects on serum prolactin levels support the use of serum prolactin levels as a complementary neuroendocrine biomarker to further validate the pharmacodynamic measurements used during the clinical pharmacological study of novel anxiolytic agents. Show less