Novel method to rescue a lethal phenotype through integration of target gene onto the X-chromosome

The loss-of-function mutations of serine protease inhibitor, Kazal type 1 (SPINK1) gene are associated with human chronic pancreatitis, but the underlying mechanisms remain unknown. We previously reported that mice lacking Spink3, the murine homologue of human SPINK1, die perinatally due to massive pancreatic acinar cell death, precluding investigation of the effects of SPINK1 deficiency. To circumvent perinatal lethality, we have developed a novel method to integrate human SPINK1 gene on the X chromosome using Cre-loxP technology and thus generated transgenic mice termed “X-SPINK1“. Consistent with the fact that one of the two X chromosomes is randomly inactivated, X-SPINK1 mice exhibit mosaic pattern of SPINK1 expression. Crossing of X-SPINK1 mice with Spink3(+/−) mice rescued perinatal lethality, but the resulting Spink3(−/−);XX(SPINK1) mice developed spontaneous pancreatitis characterized by chronic inflammation and fibrosis. The results show that mice lacking a gene essential for cell survival can be rescued by expressing this gene on the X chromosome. The Spink3(−/−);XX(SPINK1) mice, in which this method has been applied to partially restore SPINK1 function, present a novel genetic model of chronic pancreatitis

Democratizing algorithmic news recommenders: how to materialize voice in a technologically saturated media ecosystem

The deployment of various forms of AI, most notably of machine learning algorithms, radically transforms many domains of social life. In this paper we focus on the news industry, where different algorithms are used to customize news offerings to increasingly specific audience preferences. While this personalization of news enables media organizations to be more receptive to their audience, it can be questioned whether current deployments of algorithmic news recommenders (ANR) live up to their emancipatory promise. Like in various other domains, people have little knowledge of what personal data is used and how such algorithmic curation comes about, let alone that they have any concrete ways to influence these data-driven processes. Instead of going down the intricate avenue of trying to make ANR more transparent, we explore in this article ways to give people more influence over the information news recommendation algorithms provide by thinking about and enabling possibilities to express voice. After differentiating four ideal typical modalities of expressing voice (alternation, awareness, adjustment and obfuscation) which are illustrated with currently existing empirical examples, we present and argue for algorithmic recommender personae as a way for people to take more control over the algorithms that curate people's news provision