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PURPOSE: Merkel cell carcinoma (MCC) is an aggressive skin cancer with a 40% recurrence rate, lacking effective prognostic biomarkers and surveillance methods. This prospective, multicenter, observational study aimed to evaluate circulating tumor DNA (ctDNA) as a biomarker for detecting MCC recurrence. METHODS: Plasma samples, clinical data, and imaging results were collected from 319 patients. A tumor-informed ctDNA assay was used for analysis. Patients were divided into discovery (167 patients) and validation (152 patients) cohorts. Diagnostic performance, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), was assessed. RESULTS: ctDNA showed high sensitivity, 95% (discovery; 95% CI, 87 to 99) and 94% (validation; 95% CI, 85 to 98), for detecting disease at enrollment, with corresponding specificities of 90% (95% CI, 82 to 95) and 86% (95% CI, 77 to 93). A positive ctDNA during surveillance indicated increased recurrence risk, with hazard ratios (HRs) of 6.8 (discovery; 95% CI, 2.9 to 16) and 20 (validation; 95% CI, 8.3 to 50). The PPV for clinical recurrence at 1 year after a positive ctDNA test was 69% (discovery; 95% CI, 32 to 91) and 94% (validation; 95% CI, 71 to 100), respectively. The NPV at 135 days after a negative ctDNA test was 94% (discovery; 95% CI, 90 to 97) and 93% (validation; 95% CI, 89 to 97), respectively. Patients positive for ctDNA within 4 months after treatment had higher rates of recurrence, with 1-year rates of 74% versus 21% (adjusted HR, 7.4 [95% CI, 2.7 to 20]). CONCLUSION: ctDNA testing exhibited high prognostic accuracy in detecting MCC recurrence, suggesting its potential to reduce frequent surveillance imaging. ctDNA also identifies high-risk patients who need more frequent imaging and may be best suited for adjuvant therapy trials.

While recent and significant progress made in natural language processing and artificial intelligence (AI) has the potential to drastically influence the field of language education, many language educators and administrators remain unfamiliar with these recent technological advances and their pedagogical implications. The primary purpose of this paper is to raise the awareness of language educators regarding ethical and pedagogical issues stemming from student, teacher, and administrator use of generative AI tools such as large language models (LLMs) and AI chatbots. These issues include ethical ways of teaching with AI, questions of ownership, writing skills development, the accuracy and reliability of generated output, the potential to widen the educational divide, and AI bias. We conclude by offering suggestions for language educators and calling for further discussion.

Environmentally persistent free radicals (EPFRs) play an important role in aerosol effects on air quality and public health, but their atmospheric abundance and sources are poorly understood. We measured EPFRs contained in PM2.5 collected in Fairbanks, Alaska, in winter 2022. We find that EPFR concentrations were enhanced during surface-based inversion and correlate strongly with incomplete combustion markers, including carbon monoxide and elemental carbon (R2 > 0.75). EPFRs exhibit moderately good correlations with PAHs, biomass burning organic aerosols, and potassium (R2 > 0.4). We also observe strong correlations of EPFRs with hydrocarbon-like organic aerosols, Fe and Ti (R2 > 0.6), and single-particle mass spectrometry measurements reveal internal mixing of PAHs, with potassium and iron. These results suggest that residential wood burning and vehicle tailpipes are major sources of EPFRs and nontailpipe emissions, such as brake wear and road dust, may contribute to the stabilization of EPFRs. Exposure to the observed EPFR concentrations (18 ± 12 pmol m-3) would be equivalent to smoking ∼0.4-1 cigarette daily. Very strong correlations (R2 > 0.8) of EPFR with hydroxyl radical formation in surrogate lung fluid indicate that exposure to EPFRs may induce oxidative stress in the human respiratory tract.

This paper reports on research that extends knowledge about higher education students' perceptions of online engagement. In particular, the study aimed to identify what students thought engagement was and how they experienced it. Understanding students' views about online engagement will provide a more comprehensive understanding of the topic and should assist instructional designers to support academic staff to develop online courses that are more likely to engage their students. Using a mixed-methods approach, the study found that students felt most engaged with learning when doing practical, hands-on activities. Additional findings from the qualitative and quantitative data are highlighted, with some differences between the students' perceptions in the different types of data, particularly concerning social engagement. This suggests that further research is warranted. The paper offers several practical implications for student learning.

The indoor air quality of a residential home during winter in Fairbanks, Alaska, was investigated and contrasted with outdoor levels. Twenty-four-hour average indoor and outdoor filter samples were collected from January 17 to February 25, 2022, in a residential area with high outdoor PM2.5 concentrations. The oxidative potential of PM2.5 was determined using the dithiothreitol-depletion assay (OPDTT). For the unoccupied house, the background indoor-to-outdoor (I/O) ratio of mass-normalized OP (OPmDTT), a measure of the intrinsic health-relevant properties of the aerosol, was less than 1 (0.53 ± 0.37), implying a loss of aerosol toxicity as air was transported indoors. This may result from transport and volatility losses driven by the large gradients in temperature (average outdoor temperature of -19°C/average indoor temperature of 21 °C) or relative humidity (average outdoor RH of 78%/average indoor RH of 11%), or both. Various indoor activities, including pellet stove use, simple cooking experiments, incense burning, and mixtures of these activities, were conducted. The experiments produced PM2.5 with a highly variable OPmDTT. PM2.5 from cooking emissions had the lowest OP values, while pellet stove PM2.5 had the highest. Correlations between volume-normalized OPDTT (OPvDTT), relevant to exposure, and indoor PM2.5 mass concentration during experiments were much lower compared to those in outdoor environments. This suggests that mass concentration alone can be a poor indicator of possible adverse effects of various indoor emissions. These findings highlight the importance of considering both the quantity of particles and sources (chemical composition), as health metrics for indoor air quality.

The oxidative potential (OP) of outdoor PM2.5 in wintertime Fairbanks, Alaska, is investigated and compared to those in wintertime Atlanta and Los Angeles. Approximately 40 filter samples collected in January-February 2022 at a Fairbanks residential site were analyzed for OP utilizing dithiothreitol-depletion (OPDTT) and hydroxyl-generation (OPOH) assays. The study-average PM2.5 mass concentration was 12.8 μg/m3, with a 1 h average maximum of 89.0 μg/m3. Regression analysis, correlations with source tracers, and contrast between cold and warmer events indicated that OPDTT was mainly sensitive to copper, elemental carbon, and organic aerosol from residential wood burning, and OPOH to iron and organic aerosol from vehicles. Despite low photochemically-driven oxidation rates, the water-soluble fraction of OPDTT was unusually high at 77%, mainly from wood burning emissions. In contrast to other locations, the Fairbanks average PM2.5 mass concentration was higher than Atlanta and Los Angeles, whereas OPDTT in Fairbanks and Atlanta were similar, and Los Angeles had the highest OPDTT and OPOH. Site differences were observed in OP when normalized by both the volume of air sampled and the particle mass concentration, corresponding to exposure and the intrinsic health-related properties of PM2.5, respectively. The sensitivity of OP assays to specific aerosol components and sources can provide insights beyond the PM2.5 mass concentration when assessing air quality.

The Alaskan Layered Pollution And Chemical Analysis (ALPACA) field experiment was a collaborative study designed to improve understanding of pollution sources and chemical processes during winter (cold climate and low-photochemical activity), to investigate indoor pollution, and to study dispersion of pollution as affected by frequent temperature inversions. A number of the research goals were motivated by questions raised by residents of Fairbanks, Alaska, where the study was held. This paper describes the measurement strategies and the conditions encountered during the January and February 2022 field experiment, and reports early examples of how the measurements addressed research goals, particularly those of interest to the residents. Outdoor air measurements showed high concentrations of particulate matter and pollutant gases including volatile organic carbon species. During pollution events, low winds and extremely stable atmospheric conditions trapped pollution below 73 m, an extremely shallow vertical scale. Tethered-balloon-based measurements intercepted plumes aloft, which were associated with power plant point sources through transport modeling. Because cold climate residents spend much of their time indoors, the study included an indoor air quality component, where measurements were made inside and outside a house to study infiltration and indoor sources. In the absence of indoor activities such as cooking and/or heating with a pellet stove, indoor particulate matter concentrations were lower than outdoors; however, cooking and pellet stove burns often caused higher indoor particulate matter concentrations than outdoors. The mass-normalized particulate matter oxidative potential, a health-relevant property measured here by the reactivity with dithiothreiol, of indoor particles varied by source, with cooking particles having less oxidative potential per mass than pellet stove particles.

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With recent public access to large language models via chatbots, the field of language education is seeing unprecedented levels of interest in how AI will affect language learning and teaching. As attention is primarily focused on student misuse of the technology, the potential affordances of generative AI tools may often be overlooked. In this paper we highlight potential affordances of AI generative tools, such as ChatGPT, for creating language learning and teaching materials and assessments. We offer suggestions on crafting prompts as well as provide example prompts and the materials and assessments generated by ChatGPT in response. We also present a framework for evaluation that combines both of Hubbard's (1988) framework for CALL evaluation and The Douglas Fir Group's (2016) transdisciplinary framework that may assist language educators in determining when it is appropriate to leverage generative AI tools. We then briefly illustrate how the framework has been used in our own evaluations. Finally, we suggest language educators consider how the concerns and opinions of using AI-based tools held by individuals and institutions within their own micro, meso, and macro sociocultural levels align or conflict with those of the individuals and institutions of their students' micro, meso, and macro sociocultural levels.

This study investigates the correlates of statistics anxiety. Considering that statistics anxiety and spatial anxiety have been separately correlated with related constructs (e.g., mathematics anxiety, academic performance, etc.), the possibility that spatial anxiety plays a role in statistics anxiety is explored. When facing statistics or mathematics operations, people may imagine or visualize the task operations they must do to obtain the result. To examine this hypothesis, 778 students in a Social or Health Sciences program, enrolled in a--often mandatory--statistics course from Canadian, French and Belgian universities completed an online survey. The results show moderate to strong positive correlations between all three types of anxiety (spatial, mathematics, and statistics). In addition, a mediation analysis reveals the intermediate role played by mathematics anxiety in the relationship between spatial and statistics anxieties. Nonetheless, the direct link from spatial anxiety to statistics anxiety is non-negligible in the model. Finally, the results also indicate that women report higher levels of statistics anxiety, which may be partly explained by their higher level of spatial anxiety.

Comparison shopping is good financial practice, but situations involving numbers and computations are challenging for consumers with math anxiety. We asked North Americans (N = 256) to select the better deal between two products differing in volume and price. As predicted, math anxiety was negatively related to performance on this Price Comparison Task. We then explored the mechanism underlying this relation by testing math competency, price calculation ability, need for cognition, and cognitive reflection as potential mediators. The results from a competing mediator analysis indicated that all factors, apart from need for cognition, served as significant independent mediators between math anxiety and performance on our Price Comparison Task. This study has important implications for how--and why--math anxiety relates to a person's ability to accurately compare product prices. These data suggest that consumers higher in math anxiety may represent a financially vulnerable population, particularly in the context of financial tasks that are inherently mathematical.

The transition to online instruction during the COVID-19 pandemic was unprecedented and forced many universities to quickly embrace online distance learning. This context created new challenges, particularly around assessment strategies. Empirical research has demonstrated that formative assessment fosters more active learning in online classrooms. However, formative assessment strategies are not always adapted well to online platforms based on the nature of the subject matter and the size of the class. This qualitative case study sought to understand instructors' experiences and strategies for conducting assessment remotely, specifically for large-size undergraduate courses. The investigation relied on data from semi-structured interviews with University of Maryland, College Park instructors who received a Teaching Innovation Grant from the Provost's Office in Summer 2020 intended to fund sustainable online delivery beyond the emergency response teaching phase. For this analysis, we analyzed the transcripts of 13 interviews, representing a diverse range of programs, schools, and faculty seniority levels at the university. Findings show instructors experienced several successes during course retooling, including significant increases in student performance. Most instructors also indicated that they would continue to keep new online assessment strategies for the future, regardless of whether that future includes online, blended, or in-person delivery. Despite the anticipation that the pandemic would fuel more opportunities for cheating, there was only one experience of academic dishonesty.

Bullying is a relevant concept in education, composed by different dimensions and it is significantly associated to other variables. Therefore, there is a need for an instrument which can analyze the level of bullying as a single dimension, as the Cisneros Self-test on Bullying (Oñate & Piñuel, 2005). This instrument analyzes the intensity of bullying and to value its relevance on the different variables mentioned above. Our main goal was to validate this instrument in 531 Dominican secondary students. To validate it, we utilized two instruments: the Emotional Instability Scale (Caprara & Pastorelli, 1993) and the Prosocial Conduct Questionnaire (Caprara & Pastorelli, 1993). Several competitive structural models were estimated, and results supported the reliability and validity of this instrument. Therefore, we can conclude that the Cisneros's scale is a good instrument to measure global bullying. We highlight its influence on future interventions considering the relevance that bullying has on many other variables as aggressiveness, prosocial behavior or suicide ideation.

Children's knowledge of the ordinal relations among number symbols is related to their mathematical learning. Ordinal knowledge has been measured using judgment (i.e., decide whether a sequence of three digits is in order) and ordering tasks (i.e., order three digits from smallest to largest). However, the question remains whether performance on these two ordinal tasks tap into similar cognitive processes. Canadian children (N = 87; Age M = 8.7 years, Grade 3) completed symbolic number tasks (i.e., number comparison, ordering, and order judgment) and measures of arithmetic fluency (i.e., addition and subtraction) and working memory (i.e., digit span backward). For both ordinal tasks, there was a reverse distance effect for ordered sequences such that children responded faster to adjacent than to non-adjacent sequences (e.g., 2 3 4 vs. 4 7 9) and a canonical distance effect for unordered sequences such that children responded faster to non-adjacent than to adjacent sequences (e.g., 4 2 3 vs. 4 9 7). Working memory and number comparison each predicted unique variance in the ordinal measures (ordering, order judgment, and a latent ordinal factor based on the two measures). Furthermore, ordinal skills superseded the role of number comparison as the key predictor of arithmetic, controlling for children's gender and working memory skills. In summary, although both ordering and order judgment tasks index ordinal knowledge, a latent factor that excludes task-specific error may be a better index than either task separately.

Driven by the increased availability of Learning Management System data, this study explored its value and sought understanding of student behaviour through the information contained in activity level log data. Specifically, this study examined analytics data to understand students' engagement with online videos. Learning analytics data from the Moodle[superscript TM] and Vimeo® platforms were compared. The research also examined the impact of video length on engagement, and how engagement with videos changed over the course of a semester when multiple video resources were used in a course. The comparison in platform learning analytics showed differences in metrics thus offering a caution to users relying on unidimensional metrics. While the results support the notion that log data do provide educators with an opportunity for review, the time and expertise in extracting, handling, and using the data may stifle its widespread adoption.

Chronic skin ulcers in patients with suspected pyoderma gangrenosum can, on closer inspection and further workup, have a different cause. Recognition of key features on clinical examination such as the presence of atrophie blanche is key to avoid misdiagnosis of pyoderma gangrenosum and its subsequent treatment with high-dose corticosteroids and other immunosuppressive medications.

Despite the widespread adoption of public-facing large language models (LLMs) over the past several months, we still know little about the complexities of machine-generated language in comparison to human-generated language. To better understand how lexical complexity differs between human- and LLM-produced texts, we elicited responses from four commercially-available LLMs (ChatGPT 3.5, ChatGPT 4.0, Claude, and Bard), and compared them to writing from humans from different backgrounds (i.e., L1 and L2 English users) and education levels.We also investigated whether the LLMs demonstrated consistent style across targeted prompts, as compared to the human participants. Through an analysis of six dimensions of lexical diversity (volume, abundance, variety-repetition, evenness, disparity, dispersion), preliminary results suggest that LLM-generated text differs from human-generated with regards to lexical diversity, and texts created by LLMs demonstrate less variation than human-written text. We will discuss the implications of these differences for future research and education in applied linguistics.

We have captured an 8.7 A conformational change that takes place in the cleavage site of the hammerhead ribozyme during self-cleavage, using X-ray crystallography combined with physical and chemical trapping techniques. This rearrangement brings the hammerhead ribozyme from the ground state into a conformation that is poised to form the transition state geometry required for hammerhead RNA self-cleavage. Use of a 5’-C-methylated ribose adjacent to the cleavage site permits this ordinarily transient conformational change to be kinetically trapped and observed crystallographically after initiating the hammerhead ribozyme reaction in the crystal. Cleavage of the corresponding unmodified hammerhead ribozyme in the crystal under otherwise identical conditions is faster than in solution, indicating that we have indeed trapped a catalytically relevant intermediate form of this RNA enzyme

Abstract The double-cone geometry is a saddle of the gravitational path integral, which explains the chaotic statistics of the spectrum of black hole microstates. This geometry is the usual AdS-Schwarzschild black hole, but with a periodic identification of the time coordinate; the resulting singularity at the black hole horizon is regulated by making the geometry slightly complex. Here, we consider generalizations of the double-cone geometry which include the Lorentzian cosmology that sits between the event horizon and the black hole singularity. We analyze this in two and three dimensions, where the cosmology has compact spatial sections and big bang/crunch singularities. These singularities are regulated in the same way by slightly complexifying the metric. We show that this is possible while satisfying the Kontsevich-Segal criterion, implying that these geometries can be interpreted as perturbatively stable saddle points in general relativity. This procedure leads to a novel description of the cosmology in terms of standard observables in the dual boundary CFT. In three dimensions, the cosmological solution gives a new contribution to the two-point function of the density of states in the boundary CFT. Unlike the usual double cone, it describes correlations between black hole microstates with different masses, and in a limit describes correlations between the statistics of heavy states and states near the BTZ threshold.

Abstract The ongoing increase in wild boar populations across Europe has fostered human–wildlife conflicts, including the transmission of emerging pathogens with zoonotic importance. Blastocystis is a ubiquitous, faecal-oral transmitted protist that can cause gastrointestinal illnesses and is observed in humans and animals worldwide. The role of wildlife in the epidemiology of Blastocystis is insufficiently understood. Thus, we investigated the occurrence and subtype diversity of Blastocystis in free-ranging wild boars from the Iberian Peninsula using conventional PCR and next-generation amplicon sequencing of a fragment of the ssu RNA gene. A total of 459 wild boar faecal samples were collected across Spain (n = 360) and Portugal (n = 99) between 2014 and 2021. Blastocystis was present in 15.3% (70/459; 95% CI 12.1–18.9) of the wild boars analysed, and its occurrence was significantly higher in Portugal (34.3%, 34/99; 95% CI 25.1–44.6) than in Spain (10.0%, 36/360; 95% CI 7.1–13.6). Seven Blastocystis subtypes (ST5, ST10b, ST13–ST15, ST24b, and ST43) were detected among the surveyed wild boar populations, with greater variability detected in Portuguese samples. ST5 was identified in all the Blastocystis-positive animals, whereas 14.3% of them harboured ST mixed colonisations. Our results demonstrate that Blastocystis ST5 is particularly adapted to infect wild boars. The additional identification of zoonotic STs reinforces the role of wild boars as spreaders of zoonotic infections with public health significance.

Background: We investigated the efficacy and safety of dupilumab in Japanese patients aged ≥6 months to