Objectives To describe common injuries of youth American football quarterbacks (QBs) cared for in a regional sports medicine center within the last 15 years.Methods A retrospective chart review of all male youth American football QB patients who sustained sports-related injuries at a regional pediatric medical center between 01/01/2003 and 10/01/2018. Patients were identified using HoundDog to search the term 'quarterback.' Records were then reviewed to identify all male QBs ≤ 18 years of age. Injures that were not a result of football participation were excluded. Main outcome variables were injured anatomic locations, injury types, surgical status, and settings in which the injury was sustained. Descriptive statistics were used to analyze the outcome variables.Results A total of 374 QBs (mean age 14.6 ± 2.1) sustained a total of 423 injuries. The top 5 injured anatomic locations were shoulder (22%), knee (15%) head/neck (14%), elbow (13%), and wrist/hand/lower arm (11%). Most injuries (64.3%) were acute; 35.7% were chronic in nature. Most acute injuries (55.5%) occurred during games. Of the chronic injuries, 47.0% occurred during off-season and 34.4% occurred in-season. Among all injuries, 22.9% were surgical cases, and the top 3 anatomic locations of surgery were knee (35.0%), shoulder (20.7%), and elbow (18.7%).Conclusions The shoulder is the most commonly injured body part among young QBs seeking care in a regional pediatric medical center, although the knee is the most commonly injured body part that requires surgery. Most QB injuries are acute in mechanism and the majority of these acute injuries occur during games.AI research is growing rapidly raising various ethical issues related to safety, risks, and other effects widely discussed in the literature. We believe that in order to adequately address those issues and engage in a productive normative discussion it is necessary to examine key concepts and categories. One such category is anthropomorphism. It is a well-known fact that AI's functionalities and innovations are often anthropomorphized (i.e., described and conceived as characterized by human traits). The general public's anthropomorphic attitudes and some of their ethical consequences (particularly in the context of social robots and their interaction with humans) have been widely discussed in the literature. However, how anthropomorphism permeates AI research itself (i.e., in the very language of computer scientists, designers, and programmers), and what the epistemological and ethical consequences of this might be have received less attention. In this paper we explore this issue. We first set the methodological/theoretical stage, making a distinction between a normative and a conceptual approach to the issues. Next, after a brief analysis of anthropomorphism and its manifestations in the public, we explore its presence within AI research with a particular focus on brain-inspired AI. Finally, on the basis of our analysis, we identify some potential epistemological and ethical consequences of the use of anthropomorphic language and discourse within the AI research community, thus reinforcing the need of complementing the practical with a conceptual analysis.Clinical neuroscience is increasingly relying on the collection of large volumes of differently structured data and the use of intelligent algorithms for data analytics. In parallel, the ubiquitous collection of unconventional data sources (e.g. mobile health, digital phenotyping, consumer neurotechnology) is increasing the variety of data points. Big data analytics and approaches to Artificial Intelligence (AI) such as advanced machine learning are showing great potential to make sense of these larger and heterogeneous data flows. AI provides great opportunities for making new discoveries about the brain, improving current preventative and diagnostic models in both neurology and psychiatry and developing more effective assistive neurotechnologies. Concurrently, it raises many new methodological and ethical challenges. Given their transformative nature, it is still largely unclear how AI-driven approaches to the study of the human brain will meet adequate standards of scientific validity and affect normative instruments in neuroethics and research ethics. This manuscript provides an overview of current AI-driven approaches to clinical neuroscience and an assessment of the associated key methodological and ethical challenges. In particular, it will discuss what ethical principles are primarily affected by AI approaches to human neuroscience, and what normative safeguards should be enforced in this domain.Deep fakes have rapidly emerged as one of the most ominous concerns within modern society. The ability to easily and cheaply generate convincing images, audio, and video via artificial intelligence will have repercussions within politics, privacy, law, security, and broadly across all of society. In light of the widespread apprehension, numerous technological efforts aim to develop tools to distinguish between reliable audio/video and the fakes. These tools and strategies will be particularly effective for consumers when their guard is naturally up, for example during election cycles. However, recent research suggests that not only can deep fakes create credible representations of reality, but they can also be employed to create false memories. selleck kinase inhibitor Memory malleability research has been around for some time, but it relied on doctored photographs or text to generate fraudulent recollections. These recollected but fake memories take advantage of our cognitive miserliness that favors selecting those recalled memories that evoke our preferred weltanschauung. Even responsible consumers can be duped when false but belief-consistent memories, implanted when we are least vigilant can, like a Trojan horse, be later elicited at crucial dates to confirm our pre-determined biases and influence us to accomplish nefarious goals. This paper seeks to understand the process of how such memories are created, and, based on that, proposing ethical and legal guidelines for the legitimate use of fake technologies.