This paper presents evidence that supports the valid use of scores from fully automatic tests of spoken language ability to indicate a person’s effectiveness in spoken communication. The paper reviews the constructs, scoring, and the concurrent validity evidence of ‘facility-in-L2’ tests, a family of automated spoken language tests in Spanish, Dutch, Arabic, and English. The facility-in-L2 tests are designed to measure receptive and productive language ability as test-takers engage in a succession of tasks with meaningful language. Concurrent validity studies indicate that scores from the automated tests are strongly correlated with the scores from oral proficiency interviews. In separate studies with learners from each of the four languages the automated tests predict scores from the live interview tests as well as those tests predict themselves in a test-retest protocol (r = 0.77 to 0.92). Although it might be assumed that the interactive nature of the oral interview elicits performances that manifest a distinct construct, the closeness of the results suggests that the constructs underlying the two approaches to oral assessment have a stable relationship across languages.

Short answer scoring systems typically use regular expressions, templates or logic expressions to detect the presence of specific terms or concepts among student responses. Previous work has shown that manually developed regular expressions can provide effective scoring, however manual development can be quite time consuming. In this work we present a new approach that uses word-order graphs to identify important patterns from humanprovided rubric texts and top-scoring student answers. The approach also uses semantic metrics to determine groups of related words, which can represent alternative answers. We evaluate our approach on two datasets: (1) the Kaggle Short Answer dataset (ASAP-SAS, 2012), and (2) a short answer dataset provided by Mohler et al. (2011). We show that our automated approach performs better than the best performing Kaggle entry and generalizes as a method to the Mohler dataset.

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Objective: Osteoarthritis (OA) is a common subtype of arthritis. To date, treatment of OA focuses primarily on alleviating pain and improving joint function. The lack of a vascular system within synovial joints and the rapid removal of agents due to synovial exchange hinder continuous delivery of OA drugs. However, these obstacles are being addressed by promising nanoscale drugs.Methods: We synthesize and assemble a hydrogen peroxide [H2O2, belongs to the category of active oxygen species (ROS)]-sensitive nanomicelle, which is loaded with the anti-inflammation drug dexamethasone and chondrogenic differentiation factor cartilage-derivedmor-phogeneticprotein-1. The micelle can induce bone marrow mesenchymal stem cells to repair cartilage while inhibiting joint inflammation.Results: The prepared nanoparticles were of uniform size and displayed an obvious core-shell structure. Under H2O2 stimulation, the shell layer could be removed gradually. The drug-loaded micelle effectively inhibited proliferation of activated macrophages, induced macrophage apoptosis with an anti-inflammatory effect, and caused the BMSCs to differentiate into chondrocytes.Conclusion: This work provides an experimental and theoretical basis for further development of a drug-loaded micelle in the healing of osteoarthritis.

Acoustic analysis of vocal expression offers a potentially inexpensive, unobtrusive, and highly sensitive biobehavioral measure of serious mental illness (SMI)-related issues. Despite literature documenting its use for understanding SMI, prior studies have largely ignored that vocal expression is highly dynamic within individuals over time. We employed ambulatory vocal assessment from SMI outpatients to understand links between vocal expression, SMI symptoms, and affective states. Vocal samples were analyzed using a validated acoustic analysis protocol.Overall, vocal expression was not directly related to SMI symptoms but changed as a function of state and state by symptom interactions. The results suggest that a) vocal expression fails to modulate across changing affective states in individuals with active SMI symptoms, b) this lack of modulation may be commonly associated with many SMI symptoms, and c) vocal analysis can accommodate temporal dynamics.General Scientific Summary: Acoustic analysis of vocal expression offers a potentially inexpensive, unobtrusive, and highly sensitive biobehavioral measure of serious mental illness (SMI)-related issues. Despite literature documenting its use for understanding SMI, prior studies have largely ignored that vocal expression is highly dynamic within individuals over time. This manuscript attempts to close this gap by employing ambulatory vocal assessment from SMI outpatients to understand links between vocal expression, SMI symptoms, and affective states.