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We consider efficient implementations of the generalized lasso dual path algorithm given by Tibshirani and Taylor in 2011 . We first describe a generic approach that covers any penalty matrix D and any (full column rank) matrix X of predictor variables. We then describe fast implementations for the special cases of trend filtering problems, fused lasso problems, and sparse fused lasso problems, both with X = I and a general matrix X. These specialized implementations offer a considerable improvement over the generic implementation, both in terms of numerical stability and efficiency of the solution path computation. These algorithms are all available for use in the genlasso R package, which can be found in the CRAN repository.

Neonatal septic arthritis is a rare but potentially destructive condition requiring prompt diagnosis and intervention to prevent long‐term sequelae. We reported a premature neonate who presented on day of life 20 with refusal to move the left hip and was diagnosed with septic arthritis caused by Staphylococcus epidermidis, an uncommon pathogen in neonates without indwelling devices. Diagnostic imaging confirmed left hip septic arthritis, and surgical irrigation and drainage were performed. Operative cultures grew S. epidermidis, and the patient completed targeted intravenous antimicrobial therapy with full clinical recovery and preserved joint function. This case underscores the importance of maintaining a high index of suspicion for septic arthritis in premature infants and recognizing that coagulase‐negative staphylococci may represent true pathogens in the appropriate clinical context. Key Clinical Message Neonatal septic arthritis may present with subtle musculoskeletal findings. Although Staphylococcus epidermidis is often considered a contaminant, it can rarely cause true septic arthritis in premature infants without indwelling devices. Early recognition, surgical drainage, and targeted antimicrobial therapy are essential to prevent permanent joint damage.

In this paper, we examine whether confidence scores produced by the C++ re-implementation of Whisper (Radford et al., in: International conference on machine learning, 2023) can be used to score L2 learners of English and classify them. We test whether the language prediction and its probability can be used to classify French learners of English using a specifically collected dataset for read speech and a graded corpus, the ANGLISH corpus (Tortel and Hirst, in: Proceedings of speech prosody 2010 , 2010. https://doi.org/10.21437/SpeechProsody.2010-49 ). We show that probability scores associated with the Whisper subtokens can be used to classify learners into levels using the knn algorithm. We show the limitations of the language detection probability beyond an initial threshold where the native language L1 of the learner can actually be predicted by the speaker. We have also used the ISLE corpus (Menzel et al., in: Proceedings of LREC 2000: Language resources and evaluation conference, European Language Resources Association, 2000) to test the prediction of the levels of Italian and German learners of English (Atwell et al., in: ICAME Jurnal, 27:5–18, 2003). We show how language detection for Whisper’s multilingual larger models can be used to detect less advanced learners’ first language but cannot be used for learner level classification with advanced learners. Using a greedy alignment algorithm, we also discuss the confidence score assigned to Whisper output subtokens and how this may be used for speaker scoring, prediction of learner levels, and learner feedback. We show that low confidence scores and alternative transcriptions can be used as potential cues for learner pronunciation errors.

A C-N cross-coupling approach involving oxidative amidations of aromatic aldehydes in the presence of an amide-based nickel(II) pincer catalyst (2) is demonstrated. Upon optimization, quick reaction times (15 min) and an ideal temperature (25 °C) were established and implemented for the conversion of 33 different amide products using only 0.2 mol% of catalyst. Moderate to good turnover numbers (TONs) were obtained for secondary benzamide products, and moderate TONs were obtained for tertiary benzamide products, with the highest turnover number calculated for the 4-chloro-N-(3-phenylpropyl)benzamide product (4i, 309). Gas chromatographic–mass spectrometric (GC–MS) analysis also indicates the formation of alcohols in different reactions, indicating an oxidative amidation process. Kinetic studies were performed by varying the amount of catalyst, aldehyde, LiHMDS base, and amine substrate to determine the order of reaction for each component. Benzaldehyde and benzaldehyde-d6 were reacted with benzylamine, and the kH/kD ratio was determined to understand the rate-determining step. Isotope labeling further revealed that deuterium was being transferred to both the alcohol side product and the target amide product. With the help of kinetic data and UV–visible spectra, a mechanism for the amidation process via the catalyst (2) is proposed through a Ni(I)–Ni(III) pathway.

Background Opioid use remains a major public health issue, especially among young adults. Despite investment in harm reduction and supply-side strategies such as reducing overprescribing and safe medication disposal, little is known about demand-side issues, such as reasons for use and pathways to opioid use. Adolescents and young adults who struggle with opioid use disorder (OUD) are multifaceted individuals with varied individual histories, experiences, challenges, skills, relationships, and lives. Methods To inform the development of prevention strategies that hold promise for addressing opioid use, this study employs brief structured surveys and semi-structured in-depth interviews with 30 young adults (ages 18–29; 19 female, 23 White, 16 from Suburban areas) in recovery from OUD. For survey data, we used descriptive statistics to summarize the means and variance of retrospectively reported risk and protective factors associated with opioid use. For in-depth interview data, we used a combination of thematic analysis and codebook approaches to generate common themes and experiences shared by participants. Results Surveys revealed that the most endorsed risk factors pertained to emotions (emotional neglect and emotional abuse) followed by sexual abuse, physical abuse, and physical neglect. Themes generated from qualitative analyses reveal challenging experiences during adolescence, such as unaddressed mental health, social, and emotional needs , which were often reported as reasons for opioid initiation and use. Through surveys and interviews, we also identified positive assets, such as skills and social relationship s that were present for many participants during adolescence. Conclusion Implications include the need for universal prevention strategies that include emotion-focused interventions and supports alongside current harm reduction and environmental strategies to regulate prescriptions; the potential utility of more emotion-focused items being included on screening tools; and more voices of young people in recovery.

This paper presents a combination of R packages—user contributed toolkits written in a common core programming language—to facilitate the humanistic investigation of digitised, text-based corpora. Our survey of text analysis packages includes those of our own creation (cleanNLP and fasttextM) as well as packages built by other research groups (stringi, readtext, hyphenatr, quanteda, and hunspell). By operating on generic object types, these packages unite research innovations in corpus linguistics, natural language processing, machine learning, statistics, and digital humanities. We begin by extrapolating on the theoretical benefits of R as an elaborate gluing language for bringing together several areas of expertise and compare it to linguistic concordances and other tool-based approaches to text analysis in the digital humanities. We then showcase the practical benefits of an ecosystem by illustrating how R packages have been integrated into a digital humanities project. Throughout, the focus is on moving beyond the bag-ofwords, lexical frequency model by incorporating linguistically-driven analyses in research.

Children as young as 10 years old are hired to work on farms in the United States (U.S.). These children are largely Latinx. Using interview data collected from 202 North Carolina Latinx child farmworkers in 2017, this analysis documents the heath characteristics and occupational injuries of Latinx child farmworkers and delineates characteristics associated with their health and occupational injuries. Latinx child farmworkers include girls (37.6%) and boys (62.4%), aged 10 to 17 years, with 17.8% being migrant farmworkers. Three-quarters reported receiving medical and dental care in the past year. Respiratory (15.8%) and vision (20.3%) problems were prevalent. Girls more than boys, and younger more than older children had greater health service utilization. Occupational injuries were common, with 26.2% reporting a traumatic injury, 44.1% a dermatological injury, 42.6% a musculoskeletal injury, and 45.5% heat-related illness in the past year. Age increased the odds of reporting work injuries and heat-related illness, and being a non-migrant reduced the odds of reporting work injuries. These results emphasize the need for greater documentation of child farmworker occupational health and safety. They underscore the need to change occupational safety policy to ensure that children working in agriculture have the same protections as those working in all other U.S. industries.

Microworlds are simulations in computational environments where the student can manipulate objects and learn from those manipulations. Since their creation, they have been used in a wide range of academic areas to improve students learning from elementary school to college. However, their effectiveness is unclear since many studies do not measure the acquired knowledge after the use of microworlds but instead they focus on self-evaluation. Furthermore, it has not been clear whether its effect on learning is related to the teaching method. In this study, we perform a meta-analysis to ascertain the impact of microworlds combined with different teaching methods on students’ knowledge acquisition. We applied a selection criterion to a collection of 668 studies and were left with 10 microworld applications relevant to our learning context. These studies were then assessed through a meta-analysis using effect size with Cohen’s d and p-value. Our analysis shows that the cognitive methods combined with microworlds have a great impact on the knowledge acquisition (d = 1.03; p < 0.001) but failed to show a significant effect (d = 0.21) for expository methods.

Our special issue explores audio and visual (AV) data as form, method, and practice in the digital humanities. Spurred by recent advances in computing alongside disciplinary expansions of what counts as evidence, audio and visual ways of knowing are enjoying a more prominent place in the field. Whether the creation, analysis, and sharing of audiovisual data or audiovisual ways of communicating scholarly knowledge, scholars are building compelling avenues of inquiry that are changing how we know, what we know, and why we know in the digital humanities (DH). These epistemological shifts not only challenge existing methodological and theoretical pathways within the field of audiovisual studies, but most importantly defy existing knowledge hierarchies within the entire field of DH.

A substantial amount of historical data is now available in the form of raw, digitized text. Common examples include letters, newspaper articles, personal notes, diary entries, legal documents and transcribed speeches. While some stand-alone software applications provide tools for analyzing text data, a programming language offers increased flexibility to analyze a corpus of text documents. In this tutorial we guide users through the basics of text analysis within the R programming language. The approach we take involves only using a tokenizer that parses text into elements such as words, phrases and sentences. By the end of the lesson users will be able to: - employ exploratory analyses to check for errors and detect high-level patterns; - apply basic stylometric methods over time and across authors; - approach document summarization to provide a high-level description of the elements in a corpus. All of these will be demonstrated on a dataset from the text of United States Presidential State of the Union Addresses.1 We assume that users have only a very basic understanding of the R programming language. The ‘R Basics with Tabular Data’ lesson by Taryn Dewar2 is an excellent guide that covers all of the R knowledge assumed here, such as installing and starting R, installing and loading packages, importing data and working with basic R data. Users can download R for their operating system from The Comprehensive R Archive Network. Though not required, we also recommend that new users download RStudio, an open source development environment for writing and executing R programs. All of the code in this lesson was tested in R version 3.3.2, though we expect it to run properly on any future version of the software.