An English Translation of “Forunderligt at sige”

An English translation of ‘Forunderligt at sige ”[En engelsk oversættelse af ‘Forunderligt at sige ”]Af Jenny Rebecca RyttingTeksten er en engelsk oversættelse af “Forunderligt at sige,” en julesalme af N. F. S. Grundtvig, der bearbejdede den fra H. A. Brorsons salme “Mit hierte altid vanker” på i alt 11 strofer fra hans hovedværk Troens rare Klenodie, nr. 7, 1739 (= salmehefte 1, 1732). Denne oversættelse respekterer den danske salmes rim og versmål, så salmen kan synges til Carl Nielsens melodi som blev komponeret i 1914, men først udgivet i 1919

Oxcarbazepine-loaded polymeric nanoparticles: Development and permeability studies across in vitro models of the blood–brain barrier and human placental trophoblast

Encapsulation of antiepileptic drugs (AEDs) into nano particles may offer promise for treating pregnant women with epilepsy by improving brain delivery and limiting the trans-placental permeability of AEDs to avoid fetal exposure and its consequent undesirable adverse effects. Oxcarbazepine-loaded nano particles were prepared by a modified solvent displacement method from biocompatible polymers (poly(lactic-co-glycolic acid) [PLGA] with or without surfactant and PEGylated PLGA [Resomer® RGPd5055]). The physical properties of the developed nano particles were determined with subsequent evaluation of their permeability across in vitro models of the blood–brain barrier (hCMEC/D3 cells) and human placental trophoblast cells (BeWo b30 cells). Oxcarbazepine-loaded nano particles with encapsulation effciency above 69% were prepared with sizes ranging from 140–170 nm, polydispersity indices below 0.3, and zeta potential values below -34 mV. Differential scanning calorimetry and X-ray diffraction studies confirmed the amorphous state of the nano encapsulated drug. The apparent permeability (Pe) values of the free and nano encapsulated oxcarbazepine were comparable across both cell types, likely due to rapid drug release kinetics. Transport studies using fluorescently-labeled nano particles (loaded with coumarin-6) demonstrated increased permeability of surfactant-coated nano particles. Future developments in enzyme-pro drug therapy and targeted delivery are expected to provide improved options for pregnant patients with epilepsy

IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction

Navigating certain communication situations can be challenging due to individuals' lack of skills and the interference of strong emotions. However, effective learning opportunities are rarely accessible. In this work, we conduct a human-centered study that uses language models to simulate bespoke communication training and provide just-in-time feedback to support the practice and learning of interpersonal effectiveness skills. We apply the interpersonal effectiveness framework from Dialectical Behavioral Therapy (DBT), DEAR MAN, which focuses on both conversational and emotional skills. We present IMBUE, an interactive training system that provides feedback 25% more similar to experts' feedback, compared to that generated by GPT-4. IMBUE is the first to focus on communication skills and emotion management simultaneously, incorporate experts' domain knowledge in providing feedback, and be grounded in psychology theory. Through a randomized trial of 86 participants, we find that IMBUE's simulation-only variant significantly improves participants' self-efficacy (up to 17%) and reduces negative emotions (up to 25%). With IMBUE's additional just-in-time feedback, participants demonstrate 17% improvement in skill mastery, along with greater enhancements in self-efficacy (27% more) and reduction of negative emotions (16% more) compared to simulation-only. The improvement in skill mastery is the only measure that is transferred to new and more difficult situations; situation specific training is necessary for improving self-efficacy and emotion reduction

Towards Coding Social Science Datasets with Language Models

Researchers often rely on humans to code (label, annotate, etc.) large sets of texts. This kind of human coding forms an important part of social science research, yet the coding process is both resource intensive and highly variable from application to application. In some cases, efforts to automate this process have achieved human-level accuracies, but to achieve this, these attempts frequently rely on thousands of hand-labeled training examples, which makes them inapplicable to small-scale research studies and costly for large ones. Recent advances in a specific kind of artificial intelligence tool - language models (LMs) - provide a solution to this problem. Work in computer science makes it clear that LMs are able to classify text, without the cost (in financial terms and human effort) of alternative methods. To demonstrate the possibilities of LMs in this area of political science, we use GPT-3, one of the most advanced LMs, as a synthetic coder and compare it to human coders. We find that GPT-3 can match the performance of typical human coders and offers benefits over other machine learning methods of coding text. We find this across a variety of domains using very different coding procedures. This provides exciting evidence that language models can serve as a critical advance in the coding of open-ended texts in a variety of applications