Comparison of performance of automatic recognizers for stutters in speech trained with event or interval markers.

Introduction: Automatic recognition of stutters (ARS) from speech recordings can facilitate objective assessment and intervention for people who stutter. However, the performance of ARS systems may depend on how the speech data are segmented and labelled for training and testing. This study compared two segmentation methods: event-based, which delimits speech segments by their fluency status, and interval-based, which uses fixed-length segments regardless of fluency. Methods: Machine learning models were trained and evaluated on interval-based and event-based stuttered speech corpora. The models used acoustic and linguistic features extracted from the speech signal and the transcriptions generated by a state-of-the-art automatic speech recognition system. Results: The results showed that event-based segmentation led to better ARS performance than interval-based segmentation, as measured by the area under the curve (AUC) of the receiver operating characteristic. The results suggest differences in the quality and quantity of the data because of segmentation method. The inclusion of linguistic features improved the detection of whole-word repetitions, but not other types of stutters. Discussion: The findings suggest that event-based segmentation is more suitable for ARS than interval-based segmentation, as it preserves the exact boundaries and types of stutters. The linguistic features provide useful information for separating supra-lexical disfluencies from fluent speech but may not capture the acoustic characteristics of stutters. Future work should explore more robust and diverse features, as well as larger and more representative datasets, for developing effective ARS systems.

Investigating Linguistic Alignment in Collaborative Dialogue: A Study of Syntactic and Lexical Patterns in Middle School Students.

Linguistic alignment, the tendency of speakers to share common linguistic features during conversations, has emerged as a key area of research in computer-supported collaborative learning. While previous studies have shown that linguistic alignment can have a significant impact on collaborative outcomes, there is limited research exploring its role in K-12 learning contexts. This study investigates syntactic and lexical linguistic alignments in a collaborative computer science-learning corpus from 24 pairs (48 individuals) of middle school students (aged 11-13). The results show stronger effects of self-alignment than partner alignment on both syntactic and lexical levels, with students often diverging from their partners on task-relevant words. Furthermore, student self-alignment on the syntactic level is negatively correlated with partner satisfaction ratings, while self-alignment on lexical level is positively correlated with their partner's satisfaction.

Sequence introgression from exogenous lineages underlies genomic and biological differences among Cryptosporidium parvum IOWA lines.

The IOWA strain of Cryptosporidium parvum is widely used in studies of the biology and detection of the waterborne pathogens Cryptosporidium spp. While several lines of the strain have been sequenced, IOWA-II, the only reference of the original subtype (IIaA15G2R1), exhibits significant assembly errors. Here we generated a fully assembled genome of IOWA-CDC of this subtype using PacBio and Illumina technologies. In comparative analyses of seven IOWA lines maintained in different laboratories (including two sequenced in this study) and 56 field isolates, IOWA lines (IIaA17G2R1) with less virulence had mixed genomes closely related to IOWA-CDC but with multiple sequence introgressions from IOWA-II and unknown lineages. In addition, the IOWA-IIaA17G2R1 lines showed unique nucleotide substitutions and loss of a gene associated with host infectivity, which were not observed in other isolates analyzed. These genomic differences among IOWA lines could be the genetic determinants of phenotypic traits in C. parvum. These data provide a new reference for comparative genomic analyses of Cryptosporidium spp. and rich targets for the development of advanced source tracking tools.

SOMAScience: A Novel Platform for Multidimensional, Longitudinal Pain Assessment.

Chronic pain is one of the most significant health issues in the United States, affecting more than 20% of the population. Despite its contribution to the increasing health crisis, reliable predictors of disease development, progression, or treatment outcomes are lacking. Self-report remains the most effective way to assess pain, but measures are often acquired in sparse settings over short time windows, limiting their predictive ability. In this paper, we present a new mobile health platform called SOMAScience. SOMAScience serves as an easy-to-use research tool for scientists and clinicians, enabling the collection of large-scale pain datasets in single- and multicenter studies by facilitating the acquisition, transfer, and analysis of longitudinal, multidimensional, self-report pain data. Data acquisition for SOMAScience is done through a user-friendly smartphone app, SOMA, that uses experience sampling methodology to capture momentary and daily assessments of pain intensity, unpleasantness, interference, location, mood, activities, and predictions about the next day that provide personal insights into daily pain dynamics. The visualization of data and its trends over time is meant to empower individual users' self-management of their pain. This paper outlines the scientific, clinical, technological, and user considerations involved in the development of SOMAScience and how it can be used in clinical studies or for pain self-management purposes. Our goal is for SOMAScience to provide a much-needed platform for individual users to gain insight into the multidimensional features of their pain while lowering the barrier for researchers and clinicians to obtain the type of pain data that will ultimately lead to improved prevention, diagnosis, and treatment of chronic pain.

Shortened Duration of Dual Antiplatelet Therapy Following Percutaneous Coronary Intervention: A Contemporary Clinical Review.

Percutaneous coronary intervention with stent implantation is an integral aspect of minimally interventional cardiac procedures. The technology and techniques behind stent design and implantation have evolved rapidly over several decades. However, continued discourse remains around optimal peri- and post-interventional management with dual antiplatelet therapy to minimise both major cardiovascular or cerebrovascular events and iatrogenic bleeding risk. Standard guidelines around dual antiplatelet therapy historically recommended long-term dual antiplatelet therapy for 12 months (with consideration for >12 months in certain patients); however, emerging data and generational improvements in the safety of drug-eluting stents have ushered in a new era of short-term therapy to reduce the incidence of major bleeding events. This case review will provide an overview of the current state of guidelines around duration of dual antiplatelet therapy and examine recent updates and continued gaps in existing research.

Use of Upadacitinib in 16 Tofacitinib-refractory Ulcerative Colitis Patients: A Single-center Case 2Series.

We present a case series of 16 patients with ulcerative colitis who received upadacitinib after failing tofacitinib. Five patients (36%) achieved steroid-free clinical remission. Five (62%) demonstrated endoscopic response, while 2 patients (25%) achieved endoscopic remission. Adverse events were low.