371 research outputs found
An English Translation of âForunderligt at sigeâ
An English translation of âForunderligt at sige â[En engelsk oversĂŠttelse af âForunderligt at sige â]Af Jenny Rebecca RyttingTeksten er en engelsk oversĂŠttelse af âForunderligt at sige,â en julesalme af N. F. S. Grundtvig, der bearbejdede den fra H. A. Brorsons salme âMit hierte altid vankerâ pĂ„ i alt 11 strofer fra hans hovedvĂŠrk Troens rare Klenodie, nr. 7, 1739 (= salmehefte 1, 1732). Denne oversĂŠttelse respekterer den danske salmes rim og versmĂ„l, sĂ„ salmen kan synges til Carl Nielsens melodi som blev komponeret i 1914, men fĂžrst udgivet i 1919
Oxcarbazepine-loaded polymeric nanoparticles: Development and permeability studies across in vitro models of the bloodâbrain 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 [ResomerÂź RGPd5055]). The physical properties of the developed nano particles were determined with subsequent evaluation of their permeability across in vitro models of the bloodâbrain 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 140â170 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
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