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INTRODUCTION:Esophageal atresia with or without tracheoesophageal fistula (EA/TEF) occurs approximately 1 in 3.500 live births representing the most common malformation of the upper digestive tract. Only half a century ago, EA/TEF was fatal among affected newborns suggesting that the steady birth prevalence might in parts be due to mutational de novo events in genes involved in foregut development. METHODS:To identify mutational de novo events in EA/TEF patients, we surveyed the exome of 30 case-parent trios. Identified and confirmed de novo variants were prioritized using in silico prediction tools. To investigate the embryonic role of genes harboring prioritized de novo variants we performed targeted analysis of mouse transcriptome data of esophageal tissue obtained at the embryonic day (E) E8.5, E12.5, and postnatal. RESULTS:In total we prioritized 14 novel de novo variants in 14 different genes (APOL2, EEF1D, CHD7, FANCB, GGT6, KIAA0556, NFX1, NPR2, PIGC, SLC5A2, TANC2, TRPS1, UBA3, and ZFHX3) and eight rare de novo variants in eight additional genes (CELSR1, CLP1, GPR133, HPS3, MTA3, PLEC, STAB1, and PPIP5K2). Through personal communication during the project, we identified an additional EA/TEF case-parent trio with a rare de novo variant in ZFHX3. In silico prediction analysis of the identified variants and comparative analysis of mouse transcriptome data of esophageal tissue obtained at E8.5, E12.5, and postnatal prioritized CHD7, TRPS1, and ZFHX3 as EA/TEF candidate genes. Re-sequencing of ZFHX3 in additional 192 EA/TEF patients did not identify further putative EA/TEF-associated variants. CONCLUSION:Our study suggests that rare mutational de novo events in genes involved in foregut development contribute to the development of EA/TEF.

Pre-trained language models (PLM) based on transformer neural networks developed in the field of natural language processing (NLP) offer great opportunities to improve automatic content analysis in communication science, especially for the coding of complex semantic categories in large datasets via supervised machine learning. However, three characteristics so far impeded the widespread adoption of the methods in the applying disciplines: the dominance of English language models in NLP research, the necessary computing resources, and the effort required to produce training data to fine-tune PLMs. In this study, we address these challenges by using a multilingual transformer model in combination with the adapter extension to transformers, and few-shot learning methods. We test our approach on a realistic use case from communication science to automatically detect claims and arguments together with their stance in the German news debate on arms deliveries to Ukraine. In three experiments, we evaluate (1) data preprocessing strategies and model variants for this task, (2) the performance of different few-shot learning methods, and (3) how well the best setup performs on varying training set sizes in terms of validity, reliability, replicability and reproducibility of the results. We find that our proposed combination of transformer adapters with pattern exploiting training provides a parameter-efficient and easily shareable alternative to fully fine-tuning PLMs. It performs on par in terms of validity, while overall, provides better properties for application in communication studies. The results also show that pre-fine-tuning for a task on a near-domain dataset leads to substantial improvement, in particular in the few-shot setting. Further, the results indicate that it is useful to bias the dataset away from the viewpoints of specific prominent individuals.

Journalistic media increasingly address changing user behaviour online by implementing algorithmic recommendations on their pages. While social media extensively rely on user data for personalized recommendations, journalistic media may choose to aim to improve the user experience based on textual features such as thematic similarity. From a societal viewpoint, these recommendations should be as diverse as possible. Users, however, tend to prefer recommendations that enable “serendipity”—the perception of an item as a welcome surprise that strikes just the right balance between more similarly useful but still novel content. By conducting a representative online survey with n = 588 respondents, we investigate how users evaluate algorithmic news recommendations (recommendation satisfaction, as well as perceived novelty and unexpectedness) based on different similarity settings and how individual dispositions (news interest, civic information norm, need for cognitive closure, etc.) may affect these evaluations. The core piece of our survey is a self-programmed recommendation system that accesses a database of vectorized news articles. Respondents search for a personally relevant keyword and select a suitable article, after which another article is recommended automatically, at random, using one of three similarity settings. Our findings show that users prefer recommendations of the most similar articles, which are at the same time perceived as novel, but not necessarily unexpected. However, user evaluations will differ depending on personal characteristics such as formal education, the civic information norm, and the need for cognitive closure.

In the digital age, the crisis of journalism has been exacerbated by antagonistic actors infiltrating the journalistic system without adhering to its norms or logic. Journalism itself has been ill-prepared to respond to this challenge, but journalism theory and research have also had trouble in grasping these phenomena. It is thus the aim of this article to propose a theoretical perspective on a specific set of antagonists characterized by its paradoxical nature. It is ‘the excluded third, included’ as described by Serres, the parasite that is both part of the system and its antagonist. From the perspective of systems theory, the parasite is a subsystem that threatens the integrity of the primary system. Thus, the parasite is defined by the relations that describe its position, its behaviour towards the host system. Due to these peculiarities—this contradiction, this vagueness—it evades a classical bivalent logic. This may be one reason why the paradoxical nature of the antagonist from within, the ‘uninvited dinner guest,’ has not been described as such until now. The parasitic practices follow the logic of the hacker: He is the digital manifestation of Serres’ parasite. Accordingly, parasitic strategies can be described as news hacks whose attack vectors target a system’s weak points with the help of specific strategies. In doing so, they not only change the system output but also compromise its values and exploit its resources.

Dopaminergic (DA) midbrain neurons within the substantia nigra (SN) display an autonomous pacemaker activity that is crucial for dopamine release and voluntary movement control. Their progressive degeneration is a hallmark of Parkinson's disease. Their metabolically demanding activity-mode affects Ca2+ homeostasis, elevates metabolic stress, and renders SN DA neurons particularly vulnerable to degenerative stressors. Accordingly, their activity is regulated by complex mechanisms, notably by dopamine itself, via inhibitory D2-autoreceptors and the neuroprotective neuronal Ca2+ sensor NCS-1. Analyzing regulation of SN DA neuron activity-pattern is complicated by their high vulnerability. We studied this activity and its control by dopamine, NCS-1, and glucose with extracellular multi-electrode array (MEA) recordings from midbrain slices of juvenile and adult mice. Our tailored MEA- and spike sorting-protocols allowed high throughput and long recording times. According to individual dopamine-responses, we identified two distinct SN cell-types, in similar frequency: dopamine-inhibited and dopamine-excited neurons. Dopamine-excited neurons were either silent in the absence of dopamine, or they displayed pacemaker-activities, similar to that of dopamine-inhibited neurons. Inhibition of pacemaker-activity by dopamine is typical for SN DA neurons, and it can undergo prominent desensitization. We show for adult mice, that the number of SN DA neurons with desensitized dopamine-inhibition was increased (~60–100%) by a knockout of NCS-1, or by prevention of NCS-1 binding to D2-autoreceptors, while time-course and degrees of desensitization were not altered. The number of neurons with desensitized D2-responses was also higher (~65%) at high glucose-levels (25 mM), compared to lower glucose (2.5 mM), while again desensitization-kinetics were unaltered. However, spontaneous firing-rates were significantly higher at high glucose-levels (~20%). Moreover, transient glucose-deprivation (1 mM) induced a fast and fully-reversible pacemaker frequency reduction. To directly address and quantify glucose-sensing properties of SN DA neurons, we continuously monitored their electrical activity, while altering extracellular glucose concentrations stepwise from 0.5 mM up to 25 mM. SN DA neurons were excited by glucose, with EC50 values ranging from 0.35 to 2.3 mM. In conclusion, we identified a novel, common subtype of dopamine-excited SN neurons, and a complex, joint regulation of dopamine-inhibited neurons by dopamine and glucose, within the range of physiological brain glucose-levels.

Politicians have been criticized for not exploiting the deliberative potential of social media platforms. We complement previous definitions of politicians’ success on social media through the lens of network media logic: Despite the lack of deliberation, some succeed in building large digital followerships, which spread their messages via reactions through the network. Analyzing a data set of personal, structural, and social media characteristics of Swiss politicians, we used path analysis to determine which predict their success on Facebook ( n = 63) and Twitter ( n = 108). Politicians, who are active in parliament, represent urban regions and receive substantial amounts of traditional media coverage also have larger digital followerships on both platforms. Digital followership in turn influences the average number of digital reactions on Facebook, but not on Twitter. Thus, politicians’ success on social media depends on their personal background, political activity, and media coverage, and also their followership and the platform.