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

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

AI Chat Assistants can Improve Conversations about Divisive Topics

A rapidly increasing amount of human conversation occurs online. But divisiveness and conflict can fester in text-based interactions on social media platforms, in messaging apps, and on other digital forums. Such toxicity increases polarization and, importantly, corrodes the capacity of diverse societies to develop efficient solutions to complex social problems that impact everyone. Scholars and civil society groups promote interventions that can make interpersonal conversations less divisive or more productive in offline settings, but scaling these efforts to the amount of discourse that occurs online is extremely challenging. We present results of a large-scale experiment that demonstrates how online conversations about divisive topics can be improved with artificial intelligence tools. Specifically, we employ a large language model to make real-time, evidence-based recommendations intended to improve participants' perception of feeling understood in conversations. We find that these interventions improve the reported quality of the conversation, reduce political divisiveness, and improve the tone, without systematically changing the content of the conversation or moving people's policy attitudes. These findings have important implications for future research on social media, political deliberation, and the growing community of scholars interested in the place of artificial intelligence within computational social science