Automated Meet-in-the-Middle Attack Goes to Feistel

Feistel network and its generalizations (GFN) are another important building blocks for constructing hash functions, e.g., Simpira v2, Areion, and the ISO standard Lesamnta-LW. The Meet-in-the-Middle (MitM) is a general paradigm to build preimage and collision attacks on hash functions, which has been automated in several papers. However, those automatic tools mostly focus on the hash function with Substitution-Permutation network (SPN) as building blocks, and only one for Feistel network by Schrottenloher and Stevens (at CRYPTO 2022). In this paper, we introduce a new automatic model for MitM attacks on Feistel networks by generalizing the traditional direct or indirect partial matching strategies and also Sasaki’s multi-round matching strategy. Besides, we find the equivalent transformations of Feistel and GFN can significantly simplify the MILP model. Based on our automatic model, we improve the preimage attacks on Feistel-SP-MMO, Simpira-2/-4-DM, Areion-256/-512-DM by 1-2 rounds or significantly reduce the complexities. Furthermore, we fill in the gap left by Schrottenloher and Stevens at CRYPTO 2022 on the large branch (b > 4) Simpira-b’s attack and propose the first 11-round attack on Simpira-6. Besides, we significantly improve the collision attack on the ISO standard hash Lesamnta-LW by increasing the attacked round number from previous 11 to ours 17 rounds

Generic MitM Attack Frameworks on Sponge Constructions

This paper proposes general meet-in-the-middle (MitM) attack frameworks for preimage and collision attacks on hash functions based on (generalized) sponge construction. As the first contribution, our MitM preimage attack framework covers a wide range of sponge-based hash functions, especially those with lower claimed security level for preimage compared to their output size. Those hash functions have been very widely standardized (e.g., Ascon-Hash, PHOTON, etc.), but are rarely studied against preimage attacks. Even the recent MitM attack framework on sponge construction by Qin et al. (EUROCRYPT 2023) cannot attack those hash functions. As the second contribution, our MitM collision attack framework shows a different tool for the collision cryptanalysis on sponge construction, while previous collision attacks on sponge construction are mainly based on differential attacks. Most of the results in this paper are the first third-party cryptanalysis results. If cryptanalysis previously existed, our new results significantly improve the previous results, such as improving the previous 2-round collision attack on Ascon-Hash to the current 4 rounds, improving the previous 3.5-round quantum preimage attack on SPHINCS$^+$-Haraka to our 4-round classical preimage attack, etc

Human Hepatocytes with Drug Metabolic Function Induced from Fibroblasts by Lineage Reprogramming

SummaryObtaining fully functional cell types is a major challenge for drug discovery and regenerative medicine. Currently, a fundamental solution to this key problem is still lacking. Here, we show that functional human induced hepatocytes (hiHeps) can be generated from fibroblasts by overexpressing the hepatic fate conversion factors HNF1A, HNF4A, and HNF6 along with the maturation factors ATF5, PROX1, and CEBPA. hiHeps express a spectrum of phase I and II drug-metabolizing enzymes and phase III drug transporters. Importantly, the metabolic activities of CYP3A4, CYP1A2, CYP2B6, CYP2C9, and CYP2C19 are comparable between hiHeps and freshly isolated primary human hepatocytes. Transplanted hiHeps repopulate up to 30% of the livers of Tet-uPA/Rag2−/−/γc−/− mice and secrete more than 300 μg/ml human ALBUMIN in vivo. Our data demonstrate that human hepatocytes with drug metabolic function can be generated by lineage reprogramming, thus providing a cell resource for pharmaceutical applications

Based on network pharmacology and molecular docking to explore the potential mechanism of shikonin in periodontitis

Abstract Objectives To investigate the potential mechanisms of shikonin in preventing and treating periodontitis using network pharmacology and molecular docking methods. Materials and methods The targets of shikonin were obtained in TCMSP and SEA databases, and targets of periodontitis were gathered from the OMIM, GeneCards and Drugbank Databases. The intersecting targets were entered into the DAVID database to obtain the relevant biological functions and pathways by GO and KEGG enrichment analysis. The obtained targets were analysed the protein–protein interaction (PPI) in STRING platform. In Cytoscape 3.8.0, the network analysis function with the MCODE plug-in were used to obtain the key targets, of shikonin and periodontitis. Molecular docking and molecular dynamics simulation (MD) were used to assess the affinity between the shikonin and the key targets. Results Shikonin was screened for 22 targets and periodontitis was screened for 944 targets, the intersecting targets were considered as potential therapeutic targets. The targets played important roles in cellular response to hypoxia, response to xenobiotic stimulus and positive regulates of apoptotic process by GO enrichment analysis. 10 significant pathways were analyzed by KEGG, such as human cytomegalovirus infection and PI3K-Akt signaling pathway, etc. Cytoscape software screened the key genes including AKT1, CCL5, CXCR4, PPARG, PTEN, PTGS2 and TP53. Molecular docking and MD results showed that shikonin could bind stably to the targets. Conclusions The present study enriched the molecular mechanisms in periodontitis with shikonin, providing potential therapeutic targets for periodontitis

Multiscale Decomposition Prediction of Propagation Loss for EM Waves in Marine Evaporation Duct Using Deep Learning

A tropospheric duct (TD) is an anomalous atmospheric refraction structure in marine environments that seriously interferes with the propagation path and range of electromagnetic (EM) waves, resulting in serious influence on the normal operation of radar. Since the propagation loss (PL) can reflect the propagation characteristics of EM waves inside the duct layer, it is important to obtain an accurate cognition of the PL of EM waves in marine TDs. However, the PL is strongly non−linear with propagation range due to the trapped propagation effect inside duct layer, which makes accurate prediction of PL more difficult. To resolve this problem, a novel multiscale decomposition prediction method (VMD−PSO−LSTM) based on the long short−term memory (LSTM) network, variational mode decomposition (VMD) method and particle swarm optimization (PSO) algorithm is proposed in this study. Firstly, VMD is used to decompose PL into several smooth subsequences with different frequency scales. Then, a LSTM−based model for each subsequence is built to predict the corresponding subsequence. In addition, PSO is used to optimize the hyperparameters of each LSTM prediction model. Finally, the predicted subsequences are reconstructed to obtain the final PL prediction results. The performance of the VMD−PSO−LSTM method is verified by combining the measured PL. The minimum RMSE and MAE indicators for the VMD−PSO−PSTM method are 0.368 and 0.276, respectively. The percentage improvement of prediction performance compared to other prediction methods can reach at most 72.46 and 77.61% in RMSE and MAE, respectively, showing that the VMD−PSO−LSTM method has the advantages of high accuracy and outperforms other comparison methods