ChatGPT and the AI Act

It is not easy being a tech regulator these days. The European institutions are working hard towards finalising the AI Act in autumn, and then generative AI systems like ChatGPT come along! In this essay, we comment the European AI Act by arguing that its current risk-based approach is too limited for facing ChatGPT & co

Storia: Summarizing Social Media Content based on Narrative Theory using Crowdsourcing

People from all over the world use social media to share thoughts and opinions about events, and understanding what people say through these channels has been of increasing interest to researchers, journalists, and marketers alike. However, while automatically generated summaries enable people to consume large amounts of data efficiently, they do not provide the context needed for a viewer to fully understand an event. Narrative structure can provide templates for the order and manner in which this data is presented to create stories that are oriented around narrative elements rather than summaries made up of facts. In this paper, we use narrative theory as a framework for identifying the links between social media content. To do this, we designed crowdsourcing tasks to generate summaries of events based on commonly used narrative templates. In a controlled study, for certain types of events, people were more emotionally engaged with stories created with narrative structure and were also more likely to recommend them to others compared to summaries created without narrative structure

Beyond mystery: Putting algorithmic accountability in context

Critical algorithm scholarship has demonstrated the difficulties of attributing accountability for the actions and effects of algorithmic systems. In this commentary, we argue that we cannot stop at denouncing the lack of accountability for algorithms and their effects but must engage the broader systems and distributed agencies that algorithmic systems exist within; including standards, regulations, technologies, and social relations. To this end, we explore accountability in “the Generated Detective,” an algorithmically generated comic. Taking up the mantle of detectives ourselves, we investigate accountability in relation to this piece of experimental fiction. We problematize efforts to effect accountability through transparency by undertaking a simple operation: asking for permission to re-publish a set of the algorithmically selected and modified words and images which make the frames of the comic. Recounting this process, we demonstrate slippage between the “complication” of the algorithm and the obscurity of the legal and institutional structures in which it exists

LST1 promotes the assembly of a molecular machinery responsible for tunneling nanotube formation

Carefully orchestrated intercellular communication is an essential prerequisite for the development of multicellular organisms. In recent years, tunneling nanotubes (TNT) have emerged as a novel and widespread mechanism of cell-cell communication. However, the molecular basis of their formation is still poorly understood. In the present study we report that the transmembrane MHC class III protein LST1 induces the formation of functional nanotubes and is required for endogenous nanotube generation. Mechanistically, we found LST1 to induce nanotube formation by recruiting the small GTPase RalA to the plasma membrane and promoting its interaction with the exocyst complex. Furthermore, we determined LST1 to recruit the actin-crosslinking protein filamin to the plasma membrane and to interact with M-Sec, myosin and myoferlin. These results allow us to suggest a molecular model for nanotube generation. In this proposal LST1 functions as a membrane scaffold mediating the assembly of a multimolecular complex, which controls the formation of functional nanotubes

Doing social media analytics

'The era of Big Data has begun' (boyd and Crawford, 2012: 662). In the few years since this statement, social media analytics has begun to accumulate studies drawing on social media as a resource and tool for research work. Yet, there has been relatively little attention paid to the development of methodologies for handling this kind of data. The few works that exist in this area often reflect upon the implications of 'grand' social science methodological concepts for new social media research (i.e. they focus on general issues such as sampling, data validity, ethics, etc). By contrast, we advance an abductively-oriented methodological suite designed to explore the construction of phenomena played out through social media. To do this, we use a software tool - Chorus - to illustrate a visual analytic approach to data. Informed by visual analytic principles, we posit a two-by-two methodological model of social media analytics, combining two data collection strategies with two analytic modes. We go on to demonstrate each of these four approaches ‘in action’, to help clarify how and why they might be used to address various research questions

SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods

In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, \textit{as they are used in practice}, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods' codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods

LST1 promotes the assembly of a molecular machinery responsible for tunneling nanotube formation

Carefully orchestrated intercellular communication is an essential prerequisite for the development of multicellular organisms. In recent years, tunneling nanotubes (TNT) have emerged as a novel and widespread mechanism of cell-cell communication. However, the molecular basis of their formation is still poorly understood. In the present study we report that the transmembrane MHC class III protein LST1 induces the formation of functional nanotubes and is required for endogenous nanotube generation. Mechanistically, we found LST1 to induce nanotube formation by recruiting the small GTPase RalA to the plasma membrane and promoting its interaction with the exocyst complex. Furthermore, we determined LST1 to recruit the actin-crosslinking protein filamin to the plasma membrane and to interact with M-Sec, myosin and myoferlin. These results allow us to suggest a molecular model for nanotube generation. In this proposal LST1 functions as a membrane scaffold mediating the assembly of a multimolecular complex, which controls the formation of functional nanotubes

Leptin and Amylin Act in an Additive Manner to Activate Overlapping Signaling Pathways in Peripheral Tissues: In vitro and ex vivo studies in humans

OBJECTIVE: Amylin interacts with leptin to alter metabolism. We evaluated, for the first time, amylin- and/or leptin-activated signaling pathways in human peripheral tissues (hPTs). RESEARCH DESIGN AND METHODS: Leptin and amylin signaling studies were performed in vitro in human primary adipocytes (hPAs) and human peripheral blood mononuclear cells (hPBMCs) and ex vivo in human adipose tissue (hAT) from male versus female subjects, obese versus lean subjects, and subjects with subcutaneous versus omental adipose tissue. RESULTS: The long form of leptin receptor was expressed in human tissues and cells studied in ex vivo and in vitro, respectively. Leptin and amylin alone and in combination activate signal transducer and activator of transcription 3 (STAT3), AMP-activated protein kinase, Akt, and extracellular signal-regulated kinase signaling pathways in hAT ex vivo and hPAs and hPBMCs in vitro; all phosphorylation events were saturable at leptin and amylin concentrations of ∼50 and ∼20 ng/ml, respectively. The effects of leptin and amylin on STAT3 phosphorylation in hPAs and hPBMCs in vitro were totally abolished under endoplasmic reticulum stress and/or in the presence of a STAT3 inhibitor. Results similar to those in the in vitro studies were observed in hAT studied ex vivo. CONCLUSIONS: Leptin and amylin activate overlapping intracellular signaling pathways in humans and have additive, but not synergistic, effects in signaling pathways studied in hPTs in vitro and ex vivo

Extensive preclinical validation of combined RMC-4550 and LY3214996 supports clinical investigation for KRAS mutant pancreatic cancer

Over 90% of pancreatic cancers present mutations in KRAS, one of the most common oncogenic drivers overall. Currently, most KRAS mutant isoforms cannot be targeted directly. Moreover, targeting single RAS downstream effectors induces adaptive resistance mechanisms. We report here on the combined inhibition of SHP2, upstream of KRAS, using the allosteric inhibitor RMC-4550 and of ERK, downstream of KRAS, using LY3214996. This combination shows synergistic anti-cancer activity in vitro, superior disruption of the MAPK pathway, and increased apoptosis induction compared with single-agent treatments. In vivo, we demonstrate good tolerability and efficacy of the combination, with significant tumor regression in multiple pancreatic ductal adenocarcinoma (PDAC) mouse models. Finally, we show evidence that 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) can be used to assess early drug responses in animal models. Based on these results, we will investigate this drug combination in the SHP2 and ERK inhibition in pancreatic cancer (SHERPA; ClinicalTrials.gov: NCT04916236) clinical trial, enrolling patients with KRAS-mutant PDAC.This work was funded by the American Association for Cancer Research, Lustgarten Foundation, and Stand Up to Cancer as a Pancreatic Cancer Collective New Therapies Challenge grant (grant no. SU2C-AACR-PCC-01-18)