Regulations of cosmetic ingredients and products have been the most advanced in embracing new approach methodologies (NAMs). Consequently, the cosmetic industry has assumed a forerunner role in the development and implementation of animal-free next-generation risk assessment (NGRA) that incorporates defined approaches (DAs) to assess the skin sensitization potency of ingredients. A Bayesian network DA predicting four potency categories (SkinSens-BN) was constructed against reference Local Lymph Node Assay data for a total of 297 substances, achieving a predictive performance similar to that of other DAs. With the aim of optimally informing risk assessment with a continuous point of departure (PoD), a weighted sum of the SkinSens-BN probabilities for four potency classes (non-, weak, moderate, and strong/extreme sensitizer) was calculated, using fixed weights based on associated LLNA EC3-values. The approach was promising, e.g., the derived PoDs for substances classified as non-sensitizers did not overlap with any others and 77% of PoDs were similar or more conservative than LLNA EC3. In addition, the predictions were assigned a level of confidence based on the probabilities to inform the evaluation of uncertainty in an NGRA context. In conclusion, the PoD derivation approach can substantially contribute to reliable skin sensitization NGRAs.

While the pivotal role of linker histone H1 in shaping nucleosome organization is well established, its functional interplays with chromatin factors along the epigenome are just starting to emerge. Here we first report that in Arabidopsis , as in mammals, H1 occupies Polycomb Repressive Complex 2 (PRC2) target genes where it favors chromatin condensation and H3K27me3 deposition. We further show that, contrasting with its conserved function in PRC2 activation at genes, H1 selectively prevents H3K27me3 accumulation at telomeres and large pericentromeric interstitial telomeric repeat (ITR) domains by restricting DNA accessibility to Telomere Repeat Binding (TRB) proteins, a group of H1-related Myb factors mediating PRC2 cis recruitment. This study unveils a mechanistic framework by which H1 avoids the formation of gigantic H3K27me3-rich domains at telomeric sequences and contributes to safeguard nucleus architecture. Abstract Figure: Teano et al. report that that linker histone H1 and a group of H1-related telomeric proteins interplay to selectively influence the Polycomb repressive landscape at genes and telomeric repeats in Arabidopsis . These findings provide a mechanistic framework by which H1 influences the epigenome and nuclear organization in a sequence-specific manner. Highlights: H1 promotes PRC2 activity and limits accessibility at a majority of genes H1 prevents PRC2 activity at telomeric DNA sequences PRC2 repression is achieved by restricting accessibility to TRB proteins H1 orchestrates the spatial organization of telomeres and interstitial telomeres (ITRs)

• Reviewed silico tools and 17 in vitro DART NAMs to identify potential gaps. • NAMs represented specific receptors, enzymes and pathways. • At least one in vitro NAM available for key stages of DART. • This analysis will help with NAM development and their combined use in NGRAs.

When it comes to the safety of cosmetic products, compliance with regulatory standards is crucialto guarantee consumer protection against the risks of skin irritation. Toxicologists must thereforebe fully conversant with all risks. This applies not only to their day-to-day work, but also to allthe algorithms they integrate into their routines. Recognizing this, ensuring the reproducibility ofalgorithms becomes one of the most crucial aspects to address.However, how can we prove the robustness of an algorithm such as the random forest, that reliesheavily on randomness? In this report, we will discuss the strategy of integrating random forest intoocular tolerance assessment for toxicologists.We will compare four packages: randomForest and Ranger (R packages), adapted in Python via theSKRanger package, and the widely used Scikit-Learn with the RandomForestClassifier() function.Our goal is to investigate the parameters and sources of randomness affecting the outcomes ofRandom Forest algorithms.By setting comparable parameters and using the same Pseudo-Random Number Generator (PRNG),we expect to reproduce results consistently across the various available implementations of therandom forest algorithm. Nevertheless, this exploration will unveil hidden layers of randomness andguide our understanding of the critical parameters necessary to ensure reproducibility across all fourimplementations of the random forest algorithm.

When it comes to the safety of cosmetic products, compliance with regulatory standards is crucialto guarantee consumer protection against the risks of skin irritation. Toxicologists must thereforebe fully conversant with all risks. This applies not only to their day-to-day work, but also to allthe algorithms they integrate into their routines. Recognizing this, ensuring the reproducibility ofalgorithms becomes one of the most crucial aspects to address.However, how can we prove the robustness of an algorithm such as the random forest, that reliesheavily on randomness? In this report, we will discuss the strategy of integrating random forest intoocular tolerance assessment for toxicologists.We will compare four packages: randomForest and Ranger (R packages), adapted in Python via theSKRanger package, and the widely used Scikit-Learn with the RandomForestClassifier() function.Our goal is to investigate the parameters and sources of randomness affecting the outcomes ofRandom Forest algorithms.By setting comparable parameters and using the same Pseudo-Random Number Generator (PRNG),we expect to reproduce results consistently across the various available implementations of therandom forest algorithm. Nevertheless, this exploration will unveil hidden layers of randomness andguide our understanding of the critical parameters necessary to ensure reproducibility across all fourimplementations of the random forest algorithm.