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Toxoplasma gondii is a useful model for intracellular parasitism given its ease of culture in the laboratory and genomic resources. However, as for many other eukaryotes, the T. gondii genome contains hundreds of sequence gaps owing to repetitive and/or unclonable sequences that disrupt the assembly process. Here, we use the Oxford Nanopore Minion platform to generate near-complete de novo genome assemblies for multiple strains of T. gondii and its near relative, N. caninum We significantly improved T. gondii genome contiguity (average N50 of ∼6.6 Mb) and added ∼2 Mb of newly assembled sequence. For all of the T. gondii strains that we sequenced (RH, ME49, CTG, II×III progeny clones CL13, S27, S21, S26, and D3X1), the largest contig ranged in size between 11.9 and 12.1 Mb in size, which is larger than any previously reported T. gondii chromosome, and found to be due to a consistent fusion of Chromosomes VIIb and VIII. These data were validated by mapping existing T. gondii ME49 Hi-C data to our assembly, providing parallel lines of evidence that the T. gondii karyotype consists of 13, rather than 14, chromosomes. By using this technology, we also resolved hundreds of tandem repeats of varying lengths, including in well-known host-targeting effector loci like rhoptry protein 5 (ROP5) and ROP38 Finally, when we compared T. gondii with N. caninum, we found that although the 13-chromosome karyotype was conserved, extensive, previously unappreciated chromosome-scale rearrangements had occurred in T. gondii and N. caninum since their most recent common ancestry.

Background: Previous research, although scarce, has indicated that the general public is still relatively unaware of developmental language disorder (DLD), one of the most common (neuro)developmental disorders. Raising awareness would increase timely involvement in intervention procedures. Aims: To examine public awareness of DLD in the neighbouring countries of Croatia, Italy and Slovenia, as well as to assess the influence of age, gender and education level on that awareness. Also, to investigate public knowledge about the professionals who recognize DLD and to compare the awareness of DLD with that of other (neuro)developmental disorders in childhood. Methods & Procedures: A convenience sample of adults living in the countries of the Adriatic region--Croatia (N = 92), Italy (N = 105) and Slovenia (N = 90)--were asked to fill out a paper-and-pencil questionnaire (public survey) developed within the Working Group 3 of the COST Action IS1406. Responses were analysed quantitatively as a function of age, gender, education level and country using the t-test and analysis of variance (ANOVA). Outcomes & Results: Public awareness of DLD is still unsatisfactory in all three countries. Around 70% of respondents reported having heard of DLD; however, only around 20% of Croatian, 40% of Italian and 5% of Slovenian respondents provided an adequate definition of DLD. Differences in research and clinical traditions may explain the observed variations amongst the three countries. Education level was the only variable that was significantly associated with an awareness of DLD in Croatia and Italy: there, more educated people showed a higher awareness and more correct knowledge, which was not found in the Slovenian sample. Respondents generally perceived speech and language pathologists (SLPs) as the professionals responsible for recognizing DLD. Finally, people possess the highest awareness of autism spectrum disorder (ASD), while the awareness of DLD and other (neuro)developmental disorders is equally low. Conclusions & Implications: Public awareness of DLD varies substantially among the three countries, but there is space for improvement in each of them. The findings of this study build on the existing data from the international group of collaborators, and argue for well-planned, systematic awareness-raising activities in the region.

The use of reward systems is common in education, particularly at the primary school level. Indeed, there are very few classes in primary schools in which such systems are not implemented. Token economy are used to encourage students to adopt appropriate target social and academic behaviors. However, consensus on their effectiveness is lacking. It is beyond the scope of this article to determine whether token economy systems actually achieve their intended purpose. This article does provide important insights into their origins and offers an up-to-date overview of the research on their effects, which serve as a basis for recommendations to educators and administrators who may need to take a position on their use.

Individuals concerned with subgroup differences on standardized tests suggest replacing these tests with holistic evaluations of unstructured application materials, such as letters of recommendation (LORs), which they posit show less bias. We empirically investigate this proposition that LORs are bias-free, and argue that LORs might actually invite systematic, race and gender subgroup differences in the content and evaluation of LORs. We text analyzed over 37,000 LORs submitted on behalf of over 10,000 graduate school applicants. Results showed that LOR content does differ across applicants. Furthermore, we see some systematic gender, race, and gender-race intersection differences in LOR content. Content of LORs also systematically differed between degree programs (S.T.E.M. vs. non-S.T.E.M.) and degree sought (doctoral vs. masters). Finally, LOR content alone did not predict an appreciable amount of variance in offers of admission (the first barrier to increasing diversity and inclusion in graduate programs). Our results, combined with past research on LOR content bias, highlight concerns that LORs can be biased against marginalized groups. We conclude with suggestions for reducing potential bias in LOR and for increasing diversity in graduate programs.

The purpose of the experiment reported here was to investigate whether mind mapping adds any value as a studying technique to that of testing. We compared the effects of mind mapping on acquisition on graders (N = 68) in a secondary school in two separate consecutive periods of learning called series. In series 1, half the students used mind mapping plus testing to learn the content of the course, and other half used testing alone. In series 2, the organization was reversed, in a repeated measure design. The results revealed that students' performances improved during the two series, but that they did not progress more when they used mind mapping. These results indicate that the popularity of mind mapping in learning practices does not reflect its effectiveness. We discuss our results in relation with the need for teaching practices to be based on theories of evidence-based education rather than on intuition only.

The RNA binding domain abundant in apicomplexans (RAP) is a protein domain identified in a diverse group of proteins, called RAP proteins, many of which have been shown to be involved in RNA binding. To understand the expansion and potential function of the RAP proteins, we conducted a hidden Markov model based screen among the proteomes of 54 eukaryotes, 17 bacteria and 12 archaea. We demonstrated that the domain is present in closely and distantly related organisms with particular expansions in Alveolata and Chlorophyta, and are not unique to Apicomplexa as previously believed. All RAP proteins identified can be decomposed into two parts. In the N-terminal region, the presence of variable helical repeats seems to participate in the specific targeting of diverse RNAs, while the RAP domain is mostly identified in the C-terminal region and is highly conserved across the different phylogenetic groups studied. Several conserved residues defining the signature motif could be crucial to ensure the function(s) of the RAP proteins. Modelling of RAP domains in apicomplexan parasites confirmed an ⍺/β structure of a restriction endonuclease-like fold. The phylogenetic trees generated from multiple alignment of RAP domains and full-length proteins from various distantly related eukaryotes indicated a complex evolutionary history of this family. We further discuss these results to assess the potential function of this protein family in apicomplexan parasites.

ABSTRACT IMPACT: This study provides public health and K-12 school districts with a pragmatic, flexible, adaptable model showing COVID-19 transmission dynamics, using local data and program elements that are modifiable and with an online model for easy use, to enable safe and equitable re-opening and maintenance of in-person learning. OBJECTIVES/GOALS: School closures resulting from the COVID-19 pandemic disrupt student education and health and exacerbate inequities. Public health agencies and school districts currently lack pragmatic models to assess the effects of potential strategies for resuming and maintaining in-person learning on outcomes such as transmission and attendance. METHODS/STUDY POPULATION: This study explored how various combinations of transmission-mitigating interventions affect health and learning outcomes in a range of underlying epidemiological conditions. The CTSA science team developed a conceptual framework and an agent-based simulation model with parameters including prevalence, transmission, testing, preventive and responsive actions, infection control, population behavior and awareness, and the potential impact of vaccine adoption and exemption policies. The team partnered with a large school district to ensure relevance of the program components to decision-making. RESULTS/ANTICIPATED RESULTS: The model shows that no single program element or condition ensures safety. Combining interventions can result in synergy in the mitigation efforts. Even without testing, an efficient health screening process with forthcoming risk reporting, combined with on-campus infection control, can reduce on-campus transmission. The resulting model is accessible online to enable exploration of likely scenarios. It is adaptable as COVID-19 science evolves, including for testing and vaccines. DISCUSSION/SIGNIFICANCE OF FINDINGS: This research provides public health agencies and school districts with a model that couples local conditions with programmatic elements to help inform the local COVID-19 response, recognizing that decisions about the school community are often complex politically, technically, and operationally when it comes to addressing a health crisis.

ABSTRACT IMPACT: The mobilization of a CTSA-sponsored team with multi-disciplinary translational science expertise enabled the university to provide a range of T1-T4 expertise to a large, complex school district that resulted in permanent learning and data science infrastructure. OBJECTIVES/GOALS: The Clinical Translational Science Institute (CTSI) formed a multidisciplinary science team to provide expertise in support of the re-opening of in-person learning in the second-largest U.S. school district during the COVID-19 pandemic. METHODS/STUDY POPULATION: The assembled interdisciplinary science team provided expertise in epidemiology, machine learning, causal inference and agent-based modeling, data and improvement science, biostatistics, clinical and laboratory medicine, health education, community engagement, and experience in outbreak investigation and management. The team included TL1 pre and postdoctoral fellows and mobilized scientists from multiple professional schools and T1-T4 stages of translational research. RESULTS/ANTICIPATED RESULTS: Tangible outcomes achieved using this team approach included the development of practical metrics for use in the school community, a learning process, the integration of preventive design elements into a testing and tracing program, and targeted and data-driven health education. The team, for example, generated new data displays for community engagement and collaborated with the school district in their use to visualize, learn from, and act on variation across a 700 square mile region. DISCUSSION/SIGNIFICANCE OF FINDINGS: Novel translational methods can be used to establish a learning environment and data science infrastructure that complements efforts of public health agencies to aid schools in the COVID-19 pandemic. These new capabilities apply to COVID-19 testing and vaccines and can be mobilized for future population health challenges faced by school districts.

The recent Coronavirus Disease 2019 pandemic has once again reminded us the importance of understanding infectious diseases. One important but understudied area in infectious disease research is the role of nuclear architecture or the physical arrangement of the genome in the nucleus in controlling gene regulation and pathogenicity. Recent advances in research methods, such as Genome-wide chromosome conformation capture using high-throughput sequencing (Hi-C), have allowed for easier analysis of nuclear architecture and chromosomal reorganization in both the infectious disease agents themselves as well as in their host cells. This review will discuss broadly on what is known about nuclear architecture in infectious disease, with an emphasis on chromosomal reorganization, and briefly discuss what steps are required next in the field.

Multiple hosts and various life cycle stages prompt the human malaria parasite, Plasmodium falciparum, to acquire sophisticated molecular mechanisms to ensure its survival, spread, and transmission to its next host. To face these environmental challenges, increasing evidence suggests that the parasite has developed complex and complementary layers of regulatory mechanisms controlling gene expression. Here, we discuss the recent developments in the discovery of molecular components that contribute to cell replication and differentiation and highlight the major contributions of epigenetics, transcription factors, and nuclear architecture in controlling gene regulation and life cycle progression in Plasmodium spp.

This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the LHC. In this paper, a multihead attention message passing network is used to classify the relationship between detector hits by labelling graph edges, determining whether hits were produced by the same underlying particle, and if so, the particle type. The trained model is 84% accurate overall, and performs best on the EM shower and muon track classes. The model’s strengths and weaknesses are discussed, and plans for developing this technique further are summarised.