Explore the vast range of titles available.

Distrust in scientific expertise 1 – 14 is dangerous. Opposition to vaccination with a future vaccine against SARS-CoV-2, the causal agent of COVID-19, for example, could amplify outbreaks 2 – 4 , as happened for measles in 2019 5 , 6 . Homemade remedies 7 , 8 and falsehoods are being shared widely on the Internet, as well as dismissals of expert advice 9 – 11 . There is a lack of understanding about how this distrust evolves at the system level 13 , 14 . Here we provide a map of the contention surrounding vaccines that has emerged from the global pool of around three billion Facebook users. Its core reveals a multi-sided landscape of unprecedented intricacy that involves nearly 100 million individuals partitioned into highly dynamic, interconnected clusters across cities, countries, continents and languages. Although smaller in overall size, anti-vaccination clusters manage to become highly entangled with undecided clusters in the main online network, whereas pro-vaccination clusters are more peripheral. Our theoretical framework reproduces the recent explosive growth in anti-vaccination views, and predicts that these views will dominate in a decade. Insights provided by this framework can inform new policies and approaches to interrupt this shift to negative views. Our results challenge the conventional thinking about undecided individuals in issues of contention surrounding health, shed light on other issues of contention such as climate change 11 , and highlight the key role of network cluster dynamics in multi-species ecologies 15 . Insights into the interactions between pro- and anti-vaccination clusters on Facebook can enable policies and approaches that attempt to interrupt the shift to anti-vaccination views and persuade undecided individuals to adopt a pro-vaccination stance.

Influence operations are large-scale efforts to manipulate public opinion. The rapid detection and disruption of these operations is critical for healthy public discourse. Emergent AI technologies may enable novel operations that evade detection and influence public discourse on social media with greater scale, reach, and specificity. New methods of detection with inductive learning capacity will be needed to identify novel operations before they indelibly alter public opinion and events. To this end, we develop an inductive learning framework that: (1) determines content- and graph-based indicators that are not specific to any operation; (2) uses graph learning to encode abstract signatures of coordinated manipulation; and (3) evaluates generalization capacity by training and testing models across operations originating from Russia, China, and Iran. We find that this framework enables strong cross-operation generalization while also revealing salient indicators-illustrating a generic approach which directly complements transductive methodologies, thereby enhancing detection coverage.

Statistical methods to test for differential expression traditionally assume that each gene's expression summaries are independent across arrays. When certain preprocessing methods are used to obtain those summaries, this assumption is not necessarily true. In general, the erroneous assumption of dependence results in a loss of statistical power. We introduce a diagnostic measure of numerical dependence for gene expression summaries from any preprocessing method and discuss the relative performance of several common preprocessing methods with respect to this measure. Some common preprocessing methods introduce non-trivial levels of numerical dependence. The issue of (between-array) dependence has received little if any attention in the literature, and researchers working with gene expression data should not take such properties for granted, or they risk unnecessarily losing statistical power.

Purpose Major orthopedic surgeries of the lower extremities, which heavily injure the metaphyseal region, are strongly associated with the risk of developing deep vein thrombosis (DVT). This study aims to investigate the role of metaphyseal trauma as an independent risk factor for DVT. Methods Patients undergoing major orthopedic surgery of the hip and knee had their existing DVT risk factors recorded. Metaphyseal trauma was defined by the extent of bone injury during these surgeries. The samples were categorized into three surgery groups: total arthroplasty group (TA), hemiarthroplasty group (HA), and the open reduction internal fixation group (ORIF). Logistic regression test between significant existing risk factors and surgery groups determines the independent association between risk factors and DVT. Result The study found a 24.8% incidence of asymptomatic DVT in patients undergoing major orthopedic surgeries, with the highest prevalence (37.2%) in TA, which had the largest extent of metaphyseal trauma and the least existing DVT risk factors. TA showed 6.2 OR and 95% CI ( p  = 0.036) compared to the other existing risk factor in relation to DVT incidence. Conclusion Metaphyseal bone trauma in the hip and knee major orthopedic surgery is an independent risk factor for deep vein thrombosis.

While deductive methods have been remarkably successful in physics, such methods face major difficulties in describing many-body complex systems such as brain networks and social systems. These systems exhibit emergent properties that cannot be fully explained by mechanistic understanding of their individual components, thereby requiring effective theories at the scale(s) of observation. Data-driven discovery of effective dynamics is an appealing approach to learning such theories, but without incorporation of strong physical priors, black-box neural solvers offer little interpretability and may fail to generalize due to lack of physical realism. Moreover, learning effective dynamics for many-body complex systems is challenging due to factors such as non-equilibrium behavior, long-range interactions, and large system size. To this end, we present a machine learning framework, ROMA (Renormalized Operators with Multiscale Attention), that introduces several novel and complementary innovations aimed specifically at learning effective dynamics of complex systems. This includes several composable neural modules: (1) coarse-graining operators inspired by geometric and laplacian renormalization groups; (2) a multiscale attention mechanism that learns multiscale interactions; and (3) a conditioning mechanism that incorporates multiscale interactions for enhanced forecasting and discovery of effective dynamics. The main advances of this work are: (I) defining a neural renormalization procedure for complex systems; (II) adapting the attention mechanism to learn multiscale interactions; and (III) extending operator learning methods to solve coupled many-body systems. We apply this framework in challenging conditions: large systems of more than 1M nodes, long-range interactions, and noisy input-output data for two contrasting examples: Kuramoto oscillators and Burgers-like social dynamics. We demonstrate that the ROMA framework improves scalability and positive transfer between forecasting and effective dynamics tasks compared to state-of-the-art operator learning techniques, while also giving insight into multiscale interactions. Additionally, we investigate power law scaling in the number of model parameters, and demonstrate a departure from typical power law exponents in the presence of hierarchical and multiscale interactions.

Sound and space are intimately and intricately interconnected, but it remains unclear in what ways sound and music call forth in listeners—cognitively and emotionally— sensations of space. This thesis approaches sonic evocations of space from two perspectives that, despite their commonalities, are often considered separately. The first is lived space, the types of expanses we experience it in daily life, the second is outer or extraplanetary space. Existing scholarship on the relationships between sound and space, including that of Edward S. Casey and Steven Feld (1982), Steve Larson (2012), Georgina Born (2013), and Gascia Ouzonian (2020), reach beyond the physical conceptions of sound and into the metaphorical and the cultural, creating a rich discourse about place, orientation, and perception. Informed by the seminal theories of Victor Zuckerkandl (1973), George Lakoff (1980), and Mark Johnson (1980, 2007), among others, this thesis explores embodied musical metaphor using an empirical cognition study in which twenty-six voluntary participants were asked to respond to successions of pitches drawn from popular science fiction film scores and to note any kind of motion and spatiality they felt. The results of this experiment demonstrate that melodic figures are tied to ideas of physical motion and that sizes of intervallic relationships matter. In all, this thesis shows that listeners’ sense of space is greatly informed by specific cues from their sonic environment, and that sound can be used to challenge or manipulate spatial perception, especially in multimedia applications.

The COVID-19 pandemic of 2020 was a devastating crisis with widespread public health and economic ramifications. During this downturn, marginalized communities faced particularly high levels of economic instability. These same communities have also been among the last to regain their footing as the economy recovered. Amidst these challenges that faced American workers, a body of research reveals that labor unions may have helped millions maintain their jobs, income, and a semblance of economic stability. This is consistent with the economic benefits that have been associated with labor unions in the United States. However, no studies have assessed whether unions also preserved employment and earnings among immigrant workers. At nearly 20 percent of the workforce, immigrants constitute a crucial part of the U.S. economy. This study helps understand the role of unions in preserving the economic well-being of immigrant workers amidst the pandemic. Using Current Population Survey data, I find that immigrants covered by unions were 11.7 percent less likely to be unemployed due to the pandemic and native-born union workers were 7.3 percent less likely. I also find that immigrants covered by labor unions earned $76.73 more per week than their non-union counterparts and unionized native-born workers earned $133.35 more. These findings contribute to existing literature by revealing the extent to which unions can protect the economic stability of immigrant workers in times of crisis. This study also provides evidence to lawmakers showing that enabling immigrant workers to more easily join unions would better support their well-being and that of the American economy.

Purpose: To identify criterion-referenced cut-points in cardiorespiratory fitness (CRF) associated with metabolic syndrome (MetS) in a nationally-representative sample of young- and middle-aged American adults. Methods: The analytic sample comprised 3302 Americans aged 20–49 years who participated in the 1999–2000, 2001–2002, and 2003–2004 cycles of the National Health and Nutrition Examination Survey. CRF was assessed by a submaximal run/walk test on a treadmill. MetS was determined using American Heart Association criteria, measured as the presence of three or more risk factors (high waist circumference, high blood pressure, high fasting triglycerides, high fasting glucose, and low high-density lipoprotein cholesterol). Receiver operating characteristic (ROC) curves were used to identify gender- and age group-specific cut-points for CRF associated with increased MetS. Effect sizes of 0.56, 0.64, and 0.71 were used as thresholds for low, moderate and high, respectively. Results: ROC analysis demonstrated high discriminatory ability of CRF to detect MetS for men aged 20–29 years (AUC = 0.77, 95% confidence interval [CI] = 0.65, 0.89), with low discriminatory ability for women aged 20–29 years (AUC = 0.59, 95%CI = 0.46, 0.72) and 40–49 years (AUC = 0.59, 95%CI = 0.49, 0.70). There was negligible discriminatory ability for all other gender and age groups (i.e., AUC <0.56). Conclusion: This study identified the first criterion-referenced cut-points in CRF associated with MetS in a nationally-representative sample of young- and middle-aged American adults. It shows that CRF was inconsistently associated with MetS, with high discriminatory ability for men aged 20–29 years and negligible to low discriminatory ability for all other gender and age groups. CRF, therefore, shows poor utility as a screening tool for MetS except for young men.

B(C6F5)3 and P(MeNCH2CH2)3N form a classical Lewis adduct, (C6F5)3BP(MeNCH2CH2)3N. Although the adduct does not exhibit spectroscopic evidence of dissociation into its constituent Lewis acid and base, products of frustrated Lewis pair (FLP) addition reactions are observed with PhNCO, PhCH2N3, PhNSO, and CO2. Recently, a bench-stable trityl cation and P(MeNCH2CH2)3N were also found to form a classical Lewis adduct that does not exhibit spectroscopic evidence of dissociation. This adduct was able to carry out the heterolytic splitting of H2. Interestingly, when this and other newly synthesized bench-stable trityl cations were combined with phosphine bases such as P(t-Bu)3 and P(o-tol)3, FLPs capable of heterolytic splitting of H2 were generated and, in at least one case, reversible activation of H2 was observed. These recent findings open avenues towards the application of bench-stable carbon-based Lewis acids for FLP mediated hydrogenation.