A qualitative study on the social representations of populism and democracy in Peru

The purpose of this paper is to describe and analyze the social representations of democracy, populism and the relationship between both concepts in a sample of citizens from different regions of Peru (n = 76). To this end, a qualitative research design was proposed, using in-depth interviews, which were analyzed from a discursive approach. The results show that democracy and populism are two closely related concepts in tension. On the one hand, the social representation of democracy is semantically poor, closely related to electoral behavior and is seen as a political system that, ideally, is positively valued. Populism, on the other hand, is seen as a political strategy based on the manipulation of citizens' needs in order to reach power using the democratic process of elections. The representation of populism in general is negative, and it is mentioned that it appears and acquires strength in the face of citizen dissatisfaction with democracy, when it cannot solve problems of poverty, corruption or exclusion. The representations of populism take up the constitutive components proposed by different authors on the topic (people, elites and democracy as a product of popular will), but the participants do not necessarily structure the relationships between these components as proposed in the academic literature. Finally, the results shows that respondents' experiences with democracy and populism in Peru act as important socializing forces that will frame how citizens relate to politics and the public

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Respiratory complex I null cancer cells and molecular docking reveal specificity and mode of action inhibitors with anticancer activity

Respiratory complex I (NADH:ubiquinone oxidoreductase; EC 1.6.5.3; CI) derangement or inhibition have shown to slow down tumor growth, confining aggressive tumors into a low proliferative state and opening a possible window for additional therapeutic intervention. Hence, this enzyme has emerged as an attractive druggable target for cancer treatment and several inhibitors have been proposed as possible therapeutic agents in different experimental preclinical settings. However, none of them is currently used in clinical practice and several open issues remain, including their mechanism of action, efficacy and specificity. We here investigated the specificity and the antiproliferative activity of three inhibitors (metformin, BAY 87-2243 and EVP 4593) using unique models lacking CI generated in different cancer cell background, namely colon cancer, ovarian cancer and melanoma. In these models, the antiproliferative effect of metformin resulted independent from CI inhibition, while both BAY 87-2243 and EVP 4593 were highly selective at optimized concentrations fully inhibiting mitochondrial respiration. Molecular docking predictions indicated such high efficiency may derive from the tight network of interactions in the quinone binding site, where EVP 4593 was foreseen to interact with amino acids from nuclear subunits forming the deep site, while BAY 87-2243 is predicted to bind in the shallow site. Our data prompt for caution when referring to metformin as CI targeting compound and highlight the need of dosage optimization and careful evaluation of molecular interactions between inhibitors and CI. Amino acids involved in the interactions with EVP 4593 and BAY 87-2243 may be subjected to polymorphic variants, both inherited or somatic, although with low allele frequency. Hence, aiming at a personalized medicine approach, genotyping is suggested before treatment with these compounds, especially those binding the shallow quinone binding site where ND1 plays a major contribution

NDUFS3 knockout cancer cells and molecular docking reveal specificity and mode of action of anti-cancer respiratory complex I inhibitors

Inhibition of respiratory complex I (CI) is becoming a promising anti-cancer strategy, encouraging the design and the use of inhibitors, whose mechanism of action, efficacy and specificity remain elusive. As CI is a central player of cellular bioenergetics, a finely tuned dosing of targeting drugs is required to avoid side effects. We compared the specificity and mode of action of CI inhibitors metformin, BAY 87-2243 and EVP 4593 using cancer cell models devoid of CI. Here we show that both BAY 87-2243 and EVP 4593 were selective, while the antiproliferative effects of metformin were considerably independent from CI inhibition. Molecular docking predictions indicated that the high efficiency of BAY 87-2243 and EVP 4593 may derive from the tight network of bonds in the quinone binding pocket, although in different sites. Most of the amino acids involved in such interactions are conserved across species and only rarely found mutated in human. Our data make a case for caution when referring to metformin as a CI-targeting compound, and highlight the need for dosage optimization and careful evaluation of molecular interactions between inhibitors and the holoenzyme

Inducing respiratory complex I impairment elicits an increase in PGC1α in ovarian cancer

Anticancer strategies aimed at inhibiting Complex I of the mitochondrial respiratory chain are increasingly being attempted in solid tumors, as functional oxidative phosphorylation is vital for cancer cells. Using ovarian cancer as a model, we show that a compensatory response to an energy crisis induced by Complex I genetic ablation or pharmacological inhibition is an increase in the mitochondrial biogenesis master regulator PGC1α, a pleiotropic coactivator of transcription regulating diverse biological processes within the cell. We associate this compensatory response to the increase in PGC1α target gene expression, setting the basis for the comprehension of the molecular pathways triggered by Complex I inhibition that may need attention as drawbacks before these approaches are implemented in ovarian cancer care