George Mason University

We present an individual-centric model for COVID-19 spread in an urban setting. We first analyze patient and route data of infected patients from January 20, 2020, to May 31, 2020, collected by the Korean Center for Disease Control & Prevention (KCDC) and discover how infection clusters develop as a function of time. This analysis offers a statistical characterization of mobility habits and patterns of individuals at the beginning of the pandemic. While the KCDC data offer a wealth of information, they are also by their nature limited. To compensate for their limitations, we use detailed mobility data from Berlin, Germany after observing that mobility of individuals is surprisingly similar in both Berlin and Seoul. Using information from the Berlin mobility data, we cross-fertilize the KCDC Seoul data set and use it to parameterize an agent-based simulation that models the spread of the disease in an urban environment. After validating the simulation predictions with ground truth infection spread in Seoul, we study the importance of each input parameter on the prediction accuracy, compare the performance of our model to state-of-the-art approaches, and show how to use the proposed model to evaluate different what-if counter-measure scenarios.

Learning the directed acyclic graph (DAG) among causal variables is a fundamental pre-task in causal discovery. Available DAG learning solutions canonically focus on homogeneous nodes with multiple variables and assume i.i.d. samples, how to learn DAG on typical attributed heterogeneous network (AHN) composed with different types of inter-dependent nodes and diverse attributes is a practical but more difficult task. In this paper, we propose HetDAG to identify DAG among nodes from heterogeneous network. HetDAG first embeds different types of node attributes and aggregates these embeddings as the node's raw representation. Then it uses contrastive learning with prior network structure to explore latent relationships between nodes and update the representation. Next, HetDAG introduces an attention-based DAG learning module that takes node representations as input to search DAG and orient edges between nodes. To the best of our knowledge, HetDAG is the first study to learn DAG on heterogeneous networks. Extensive experiments on both semi-synthetic and real data show that HetDAG can learn DAG in an efficacy way and outperforms the state-of-the-art approaches. The results on real biological networks confirm that HetDAG can find out the causal relations between lncRNAs and miRNAs.

Privy counselors proclaimed Mary Tudor queen on July 18, 1553. Two days later Lady Jane Grey, queen for nine days, went from throne to Tower. Jane’s father-in-law, the Duke of Northumberland, awaited execution a month after that. Mary had defeated these usurpers, a feat “of Herculean rather than womanly daring.” The need to construct images of power, piety, and magnificence was imminent to reinforce control of her realm. In establishing herself as England’s first female monarch, Mary took on “an unprecedented position in a deeply patriarchal society,” as historian Anna Whitelock writes. To whom might Mary have turned for guidance at this pivotal moment in her life? Few historians note that at Mary’s ascension, the legacy of her grandmother, Isabella of Castile, was well established; Mary’s mother, Catherine of Aragon, had undoubtedly emphasized to her daughter the exploits of this fierce Castilian mujer. This chapter argues that Mary, in her effort to convey legitimacy, authority, and privilege under the aegis of female agency, knew that a strategic use of images would be pivotal, and that it was to her grandmother’s prototypes, in addition to her Tudor forebears’, to which she turned.

The critically endangered black rhinoceros (Diceros bicornis; black rhino) experiences extinction threats from poaching in-situ. The ex-situ population, which serves as a genetic reservoir against impending extinction threats, experiences its own threats to survival related to several disease syndromes not typically observed among their wild counterparts. We performed an untargeted metabolomic analysis of serum from 30 ex-situ housed black rhinos (Eastern black rhino, EBR, n = 14 animals; Southern black rhino, SBR, n = 16 animals) and analyzed differences in metabolite profiles between subspecies, sex, and health status (healthy n = 13 vs. diseased n = 14). Of the 636 metabolites detected, several were differentially (fold change > 1.5; p < 0.05) expressed between EBR vs. SBR (40 metabolites), female vs. male (36 metabolites), and healthy vs. diseased (22 metabolites). Results suggest dysregulation of propanoate, amino acid metabolism, and bile acid biosynthesis in the subspecies and sex comparisons. Assessment of healthy versus diseased rhinos indicates involvement of arachidonic acid metabolism, bile acid biosynthesis, and the pentose phosphate pathway in animals exhibiting inflammatory disease syndromes. This study represents the first systematic characterization of the circulating serum metabolome in the black rhinoceros. Findings further implicate mitochondrial and immune dysfunction as key contributors for the diverse disease syndromes reported in ex-situ managed black rhinos.

Carbonate apatite (CA) is a synthetic derivative of hydroxyapatite, which we have been exploring as a drug delivery nanocarrier in the context of cancer in vitro and in vivo. This nanocarrier showed great potential delivering anti-cancer drugs, plasmids containing tumour suppressor genes and siRNAs against oncogenes in pre-clinical models. We compared here two formulations of CA—the low-Ca²⁺ CA (made with 4 mM Ca²⁺) used for in vitro studies in cell lines and the high-Ca²⁺ CA (made with 40 mM Ca²⁺) used in mouse models—in terms of protein corona formed with different concentrations of serum in vivo and in vitro. The 10-fold more Ca²⁺ in high-Ca²⁺ CA helped produce enough particles in an injectable volume for in vivo delivery of therapeutics. Both formulations made particles of similar size, but their composition differed slightly in terms of Na and Mg content. In serum-containing media, the size of the particles was dependent on the serum concentration. The protein corona around both formulations was almost similar and included albumin, fetuin, haemoglobin, and immunoglobulins. CA was not cytotoxic, and instead an increased expression of ribosomal machinery and glycolytic and cytoskeletal proteins was observed, which promoted translation, growth, and proliferation in cancer cells.

Both human and non-human simian adenoviruses (HAdVs and SAdVs, respectively) have been used as gene therapy and vaccine vectors. The high prevalence of HAdVs and the neutralizing antibodies associated with prior infection, may limit HAdV-based vector use in human subjects. To overcome this drawback, a vector derived from a newly isolated and characterized macaque adenovirus was constructed. SAdVs (33.9%) were screened from 115 SAdV fecal samples collected at a zoological park. One novel SAdV was isolated and the whole genome was sequenced and analyzed. The pre-existing neutralizing antibody levels were very low against this isolate (10%). Interestingly, SAdV vector constructs that lack E3 region could not produce infectious progeny in HEK293 cells, suggesting that the E3 region is necessary for SAdV replication. The absence of E3 region could be compensated for by replacement with HAdV-5 E4orf6; the resultant construct could replicate well in HEK293 cells. The enhanced Green Fluorescent Protein (eGFP) was inserted into SAdV E3 region and expressed at high level. One-step growth curve showed that the replication of the SAdVs with HAdV-5 E4orf6 substitution and E1/E3 deletion was similar to that of wild-type SAdVs in HEK293 cells, but the modified SAdVs were replication-deficient in A549 cells which lack HAdV-5 E1A and E1B. Finally, we demonstrated that GZ3-12 could infect cells expressing hCAR or hDSG2 receptors. The successful isolation, characterization, and modification of novel SAdVs provide a potentially important vaccine and gene therapy candidate and a new strategy for the rapid acquisition and development of non-HAdV-based alternative vectors for human health applications. IMPORTANCE Adenoviruses are widely used in gene therapy and vaccine delivery. Due to the high prevalence of human adenoviruses (HAdVs), the pre-existing immunity against HAdVs in humans is common, which limits the wide and repetitive use of HAdV vectors. In contrast, the pre-existing immunity against simian adenoviruses (SAdVs) is low in humans. Therefore, we performed epidemiological investigations of SAdVs in simians and found that the SAdV prevalence was as high as 33.9%. The whole-genome sequencing and sequence analysis showed SAdV diversity and possible cross species transmission. One isolate with low level of pre-existing neutralizing antibodies in humans was used to construct replication-deficient SAdV vectors with E4orf6 substitution and E1/E3 deletion. Interestingly, we found that the E3 region plays a critical role in its replication in human cells, but the absence of this region could be compensated for by the E4orf6 from HAdV-5 and the E1 expression intrinsic to HEK293 cells.

3D Bioprinting is revolutionizing the fields of personalized and precision medicine by enabling the manufacturing of bioartificial implants that recapitulate the structural and functional characteristics of native tissues. However, the lack of quantitative and noninvasive techniques to longitudinally track the function of implants has hampered clinical applications of bioprinted scaffolds. In this study, multimaterial 3D bioprinting, engineered nanoparticles (NPs), and spectral photon‐counting computed tomography (PCCT) technologies were integrated for the aim of developing a new precision medicine approach to custom‐engineer scaffolds with traceability. Multiple CT‐visible hydrogel‐based bioinks, containing distinct molecular (iodine and gadolinium) and NP (iodine‐loaded liposome, gold, methacrylated gold (AuMA), and Gd2O3) contrast agents, were used to bioprint scaffolds with varying geometries at adequate fidelity levels. In vitro release studies, together with printing fidelity, mechanical, and biocompatibility tests identified AuMA and Gd2O3 NPs as optimal reagents to track bioprinted constructs. Spectral PCCT imaging of scaffolds in vitro and subcutaneous implants in mice enabled noninvasive material discrimination and contrast agent quantification. Together, these results establish a novel theranostic platform with high precision, tunability, throughput, and reproducibility and open new prospects for a broad range of applications in the field of precision and personalized regenerative medicine. This article is protected by copyright. All rights reserved

Background Go Nisha Go™ (GNG), is a mobile game combining behavioural science, human-centric design, game-based learning, and interactive storytelling. The model uses a direct-to-consumer (DTC) approach to deliver information, products, services, interactive learning, and agency-building experiences directly to girls. The game’s five episodes focus on issues of menstrual health management, fertility awareness, consent, contraception, and negotiation for delay of marriage and career. The game’s effectiveness on indicators linked to these issues will be measured using an encouragement design in a randomized controlled trial (RCT). Methods A two-arm RCT will be conducted in three cities in India: Patna, Jaipur, and Delhi-NCR. The first arm is the treatment (encouragement) arm (n = 975) where the participants will be encouraged to download and play the game, and the second arm (n = 975) where the participants will not receive any nudges/encouragement to play the game. They may or may not have access to the game. After the baseline recruitment, participants will be randomly assigned to these two arms across the three locations. Participants of the treatment/encouragement arm will receive continuous support as part of the encouragement design to adopt, install the game from the Google Play Store at no cost, and play all levels on their Android devices. The encouragement activity will continue for ten weeks, during which participants will receive creative messages via weekly phone calls and WhatsApp messages. We will conduct the follow-up survey with all the participants (n = 1950) from the baseline survey after ten weeks of exposure. We will conduct 60 in-depth qualitative interviews (20 at each location) with a sub-sample of the participants from the encouragement arm to augment the quantitative surveys. Discussion Following pre-testing of survey tools for feasibility of methodologies, we will recruit participants, randomize, collect baseline data, execute the encouragement design, and conduct the follow-up survey with eligible adolescents as written in the study protocol. Our study will add insights for the implementation of an encouragement design in RCTs with adolescent girls in the spectrum of game-based learning on sexual and reproductive health in India. Our study will provide evidence to support the outcome evaluation of the digital mobile game app, GNG. To our knowledge this is the first ever outcome evaluation study for a game-based application, and this study is expected to facilitate scalability of a direct-to-consumer approach to improve adolescent sexual and reproductive health outcomes in India. Trial registration number: ctri.nic.in: CTRI/2023/03/050447.

Importance Breast cancer mortality is complex and traditional approaches that seek to identify determinants of mortality assume that their effects on mortality are stationary across geographic space and scales. Objective To identify geographic variation in the associations of population demographics, environmental, lifestyle, and health care access with breast cancer mortality at the US county-level. Design, Setting, and Participants This geospatial cross-sectional study used data from the Surveillance, Epidemiology, and End Results (SEER) database on adult female patients with breast cancer. Statistical and spatial analysis was completed using adjusted mortality rates from 2015 to 2019 for 2176 counties in the US. Data were analyzed July 2022. Exposures County-level population demographics, environmental, lifestyle, and health care access variables were obtained from open data sources. Main Outcomes and Measures Model coefficients describing the association between 18 variables and age-adjusted breast cancer mortality rate. Compared with a multivariable linear regression (OLS), multiscale geographically weighted regression (MGWR) relaxed the assumption of spatial stationarity and allowed for the magnitude, direction, and significance of coefficients to change across geographic space. Results Both OLS and MGWR models agreed that county-level age-adjusted breast cancer mortality rates were significantly positively associated with obesity (OLS: β, 1.21; 95% CI, 0.88 to 1.54; mean [SD] MGWR: β, 0.72 [0.02]) and negatively associated with proportion of adults screened via mammograms (OLS: β, −1.27; 95% CI, −1.70 to −0.84; mean [SD] MGWR: β, −1.07 [0.16]). Furthermore, the MGWR model revealed that these 2 determinants were associated with a stationary effect on mortality across the US. However, the MGWR model provided important insights on other county-level factors differentially associated with breast cancer mortality across the US. Both models agreed that smoking (OLS: β, −0.65; 95% CI, −0.98 to −0.32; mean [SD] MGWR: β, −0.75 [0.92]), food environment index (OLS: β, −1.35; 95% CI, −1.72 to −0.98; mean [SD] MGWR: β, −1.69 [0.70]), exercise opportunities (OLS: β, −0.56; 95% CI, −0.91 to −0.21; mean [SD] MGWR: β, −0.59 [0.81]), racial segregation (OLS: β, −0.60; 95% CI, −0.89 to −0.31; mean [SD] MGWR: β, −0.47 [0.41]), mental health care physician ratio (OLS: β, −0.93; 95% CI, −1.44 to −0.42; mean [SD] MGWR: β, −0.48 [0.92]), and primary care physician ratio (OLS: β, −1.46; 95% CI, −2.13 to −0.79; mean [SD] MGWR: β, −1.06 [0.57]) were negatively associated with breast cancer mortality, and that light pollution was positively associated (OLS: β, 0.48; 95% CI, 0.24 to 0.72; mean [SD] MGWR: β, 0.27 [0.04]). But in the MGWR model, the magnitude of effect sizes and significance varied across geographical regions. Inversely, the OLS model found that disability was not a significant variable for breast cancer mortality, yet the MGWR model found that it was significantly positively associated in some geographical locations. Conclusions and Relevance This cross-sectional study found that not all social determinants associated with breast cancer mortality are spatially stationary and provides spatially explicit insights for public health practitioners to guide geographically targeted interventions.

Solar geoengineering (SG) may have the potential to reduce extreme climate damages worldwide. Yet, international coordination will make the difference between success and failure in leveraging it. Using a simple game-theoretic framework, we investigate whether the stability of an efficient, self-enforcing international agreement on SG is attainable. We demonstrate that side payments from countries less vulnerable to climate change to those more vulnerable can guarantee the stability of an efficient agreement. The size of the side payments will vary within a zone of possible agreement, which will change depending on certain key assumptions. For example, assuming stronger mitigation reduces the necessary payments. Alternatively, asymmetry in national damages from SG over-provision vs. under-provision justifies larger payments; here, the welfare-optimal strategy may be deployment that makes no one worse off. We also show that an agreement may be stable without side payments if deployment costs are substantial and counter-SG is available, while a moratorium may be socially optimal if SG brings substantial global non-excludable fixed costs.

Objective: To assess knowledge, attitudes, and practices (KAP) related to antimicrobial resistance (AMR) among college students. Participants: Undergraduate students at a large public university in the United States. Methods: Anonymous online questionnaire completed in early 2020. Results: While 82% of participants knew that resistant pathogens can spread between people, 38% believed that antibiotics weaken the immune system and 32% believed that AMR is only a problem for people who take antibiotics often. Many undergraduates have or would stop taking antibiotics before completing a full course because of side effects (44%) or feeling better (38%), and some would take (23%) or share (13%) antibiotics that had not been prescribed to the recipient. Only 57% are worried about AMR, compared to 88% who are worried about global climate change. Conclusions: Health education about antimicrobial stewardship and other global health issues must improve knowledge, perceptions, health behaviors, self-efficacy, and social norms.

Intellectual property (IP) can internalize positive externalities associated with the creation and discovery of ideas, thereby increasing investment in efforts to create and discover ideas. However, IP law also causes negative externalities. Strict IP rights raise the transaction costs associated with consuming and building on existing ideas. This causes a tragedy of the anticommons, in which valuable resources are underused and underdeveloped. By disincentivizing creative projects that build on existing ideas, IP protection, even if it increases original innovation, can inadvertently reduce the rate of iterative innovation. The net effect of IP law on innovation and welfare depends on the relative magnitude of these positive and negative externalities. We argue that the current regime probably suffers from excessive, and excessively rigid, IP protection. This motivates the search for institutional alternatives and complements. We suggest that a monocentric IP rights regime may not be the only, or the most efficient, way to internalize the positive externalities of innovation. The knowledge economy supports the emergence of diverse, polycentric forms of bottom-up self-governance, both market and community led, that entail the citizen coproduction of the norms and practices of intellectual creation and discovery.

This review highlights Learning Engineering Toolkit: Evidenced-based Practices from The Learning Sciences, Instructional Design, and Beyond edited by Jim Goodell and Janet Kolodner. Learning Engineering Toolkit serves as a detailed field guide to the collaborative and interdisciplinary aspects of learning engineering. Using an e-Learning and instructional design perspective, this review critically examines the core of the book's message; a foundational definition for learning engineering; and associated themes, strategies, and tools used to illuminate this promising field.

As one of the fastest growing populations in the K-12 public school system, multilingual learners (MLs), particularly those from Hispanic and/or Latinx backgrounds, represent the future workforce of the nation [1, 2, 10, 12]. Yet, they are drastically underrepresented in STEM, including computer science (CS) fields and little is known about effective ways to teach computational skills to MLs at the elementary school level [1, 3, 5, 9]. This three-year collaborative project, funded by the National Science Foundation, aims to develop linguistically inclusive integrated computer science (CS) curricula using educational robotics for elementary students in grades 3-5. More specifically, in this project, we integrate CS with mathematics, science, and English language arts to extend all elementary students’ exposure to meaningful and relevant CS experiences [4, 8, 11]. The integrated units incorporate a range of linguistically inclusive pedagogical strategies and language scaffolds to engage MLs in language-rich CS experiences, provide them with equitable learning opportunities, and support their development of computational thinking skills. The units are designed using Predict-Run-Investigate-Modify-Make (PRIMM) and TIPP pedagogical frameworks [6, 7] to scaffold students’ learning of CT concepts and promote CS learning. The project will utilize a design-based research framework gathering classroom-based data, assessment data, and interviews with teachers and students. The central research questions explore how participation in the project influences elementary teachers’ CS teaching efficacy beliefs and identity positionings as teachers of CS and MLs. The research questions related to students include how the participation in the integrated units impacts students’ CS skills, views of computer scientists, and computer scientist identity. We are in the process of providing professional development programs for teachers. At the beginning and end of the PD program, we will gather data from participant teachers. In the following academic semester, the participant teachers will be expected to implement the curricular materials in their own classrooms. Prior and subsequent to the class implementations, the data will be collected from students to examine the effect of curricular units.