Assessing Whether Alpha-Tubulin Sequences are Suitable for Phylogenetic Reconstruction of Ciliophora with Insights into Its Evolution in Euplotids

The current understanding of ciliate phylogeny is mainly based on analyses of a single gene, the small subunit ribosomal RNA (SSU-rDNA). However, phylogenetic trees based on single gene sequence are not reliable estimators of species trees, and SSU-rDNA genealogies are not useful for resolution of some branches within Ciliophora. Since congruence between multiple loci is the best tool to determine evolutionary history, we assessed the usefulness of alpha-tubulin gene, a protein-coding gene that is frequently sequenced, for ciliate phylogeny. Here, we generate alpha-tubulin gene sequences of 12 genera and 30 species within the order Euplotida, one of the most frequently encountered ciliate clades with numerous apparently cosmopolitan species, as well as four genera within its putative sister order Discocephalida. Analyses of the resulting data reveal that: 1) the alpha-tubulin gene is suitable phylogenetic marker for euplotids at the family level, since both nucleotide and amino acid phylogenies recover all monophyletic euplotid families as defined by both morphological criteria and SSU-rDNA trees; however, alpha-tubulin gene is not a good marker for defining species, order and subclass; 2) for seven out of nine euplotid species for which paralogs are detected, gene duplication appears recent as paralogs are monophyletic; 3) the order Euplotida is non-monophyletic, and the family Uronychiidae with sequences from four genera, is non-monophyletic; and 4) there is more genetic diversity within the family Euplotidae than is evident from dargyrome (geometrical pattern of dorsal silverline system in ciliates) patterns, habit and SSU-rDNA phylogeny, which indicates the urgent need for taxonomic revision in this area

Differential-Linear Approximation Semi-Unconstrained Searching and Partition Tree: Application to LEA and Speck

The differential-linear attack is one of the most effective attacks against ARX ciphers. However, two technical problems are preventing it from being more effective and having more applications: (1) there is no efficient method to search for good differential-linear approximations. Existing methods either have many constraints or are currently inefficient. (2) partitioning technique has great potential to reduce the time complexity of the key-recovery attack, but there is no general tool to construct partitions for ARX ciphers. In this work, we step forward in solving the two problems. First, we propose a novel idea for generating new good differential-linear approximations from known ones, based on which new searching algorithms are designed. Second, we propose a general tool named partition tree, for constructing partitions for ARX ciphers. Based on these new techniques, we present better attacks for two ISO/IEC standards, i.e., LEA and Speck. For LEA, we present the first 17-round distinguisher which is 1 round longer than the previous best distinguisher. Furthermore, we present the first key recovery attacks on 17-round LEA-128, 18-round LEA-192, and 18-round LEA-256, which attack 3, 4, and 3 rounds more than the previous best attacks. For Speck, we find better differential-linear distinguishers for Speck48 and Speck64. The first differential-linear distinguishers for Speck96 and Speck128 are also presented

Effect of sodium butyrate on glucose and lipid metabolism, insulin expression and apoptosis of β-cells in obese pregnant rats

Purpose: To study the influence of sodium butyrate on the metabolism of lipid and glucose, insulin expression and apoptosis of β-cells in obese pregnant rats. Methods: Three groups of one hundred and twenty 4-week-old female C5BL/6J mice were used: control, high-fat diet and sodium butyrate groups. Insulin, triglycerides and total cholesterol were evaluated by enzyme-linked immunosorbent assay (ELISA). Insulin levels, as well as area and quality of islet β-cells were assessed using Image Pro Plus software. The number of DAPI-positive islet cells, positive expression of bcl-2 in each islet cell, and apoptosis of islet β-cells in each group were determined. Results: The expression levels of insulin in high-fat diet group and butyrate group were significantly reduced, relative to control, but insulin expression level in Na butyrate group increased, relative to high- fat diet mice (p < 0.01). The area and quality of islet β-cells in high-fat diet and sodium butyrate groups were markedly higher in sodium butyrate group than in high-fat diet group (p < 0.01). The bcl-2 expression in islet β-cells rose in mice given high-fat diet, relative to control and sodium butyrate groups (p < 0.01). Conclusion: Sodium butyrate facilitates glucose and lipid metabolism, but increases insulin expression, and effectively inhibits apoptosis of islet β-cells in obese pregnant mice. Thus, sodium butyrate may be useful in the prevention and treatment of metabolic disorders due to diabetes mellitus (DM)

Superposition Meet-in-the-Middle Attacks: Updates on Fundamental Security of AES-like Hashing

The Meet-in-the-Middle approach is one of the most powerful cryptanalysis techniques, demonstrated by its applications in preimage attacks on the full MD4, MD5, Tiger, HAVAL, and Haraka-512 v2 hash functions, and key recovery of the full block cipher KTANTAN. The success relies on the separation of a primitive into two independent chunks, where each active cell of the state is used to represent only one chunk or is otherwise considered unusable once mixed. We observe that some of such cells are linearly mixed and can be as useful as the independent ones. This leads to the introduction of superposition states and a whole suite of accompanied techniques, which we incorporate into the MILP-based search framework proposed by Bao et al. at EUROCRYPT 2021 and Dong et al. at CRYPTO 2021, and find applications on a wide range of AES-like hash functions and block ciphers

A Deep Learning aided Key Recovery Framework for Large-State Block Ciphers

In the seminal work published by Gohr in CRYPTO 2019, neural networks were successfully exploited to perform differential attacks on Speck32/64, the smallest member in the block cipher family Speck. The deep learning aided key-recovery attack by Gohr achieves considerable improvement in terms of time complexity upon the state-of-the-art result from the conventional cryptanalysis method. A further question is whether the advantage of deep learning aided attacks can be kept on large-state members of Speck and other primitives. Since there are several key points in Gohr’s key-recovery frameworks that seem not fit for large-state ciphers, this question stays open for years. This work provides an answer to this question by proposing a deep learning aided multi-stage key-recovery framework. To apply this key-recovery framework on large-state members of Speck, multiple neural distinguishers (NDs) are trained and carefully combined into groups. Employing the groups of NDs under the multi-stage key-recovery framework, practical attacks are designed and trialed. Experimental results show the effectiveness of the framework. The practical attacks are then extended into theoretical attacks that cover more rounds. To do that, multi-round classical differentials (CDs) are used together with the NDs. To find the CDs’ neutral bits to boost signals from the distinguishers, an efficient algorithm is proposed. As a result, considerable improvement in terms of both time and data complexity of differential key-recovery attacks on round-reduced Speck with the largest, i.e., the 128-bit state, is obtained. Besides, efficient differential attacks are achieved on round-reduced Speck with 96-bit and 64-bit states. Since most real-world block ciphers have a state size of no less than 64 bits, this work paves the way for performing cryptanalysis using deep learning on more block ciphers. The code is available at https://github.com/AI-Lab-Y/NAAF

Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning

Federated Learning (FL) is a distributed training paradigm that enables clients scattered across the world to cooperatively learn a global model without divulging confidential data. However, FL faces a significant challenge in the form of heterogeneous data distributions among clients, which leads to a reduction in performance and robustness. A recent approach to mitigating the impact of heterogeneous data distributions is through the use of foundation models, which offer better performance at the cost of larger computational overheads and slower inference speeds. We introduce foundation model distillation to assist in the federated training of lightweight client models and increase their performance under heterogeneous data settings while keeping inference costs low. Our results show improvement in the global model performance on a balanced testing set, which contains rarely observed samples, even under extreme non-IID client data distributions. We conduct a thorough evaluation of our framework with different foundation model backbones on CIFAR10, with varying degrees of heterogeneous data distributions ranging from class-specific data partitions across clients to dirichlet data sampling, parameterized by values between 0.01 and 1.0.Comment: 6 Pages + Appendice

Superfolded configuration induced low thermal conductivity in two-dimensional carbon allotropes revealed via machine learning force constant potential

Understanding the fundamental link between structure and functionalization is crucial for the design and optimization of functional materials, since different structural configurations could trigger materials to demonstrate diverse physical, chemical, and electronic properties. However, the correlation between crystal structure and thermal conductivity (\k{appa}) remains enigmatic. In this study, taking two-dimensional (2D) carbon allotropes as study cases, we utilize phonon Boltzmann transport equation (BTE) along with machine learning force constant potential to thoroughly explore the complex folding structure of pure sp2 hybridized carbon materials from the perspective of crystal structure, mode-level phonon resolved thermal transport, and atomic interactions, with the goal of identifying the underlying relationship between 2D geometry and \k{appa}. We propose two potential structure evolution mechanisms for targeted thermal transport properties: in-plane and out-of-plane folding evolutions, which are generally applicable to 2D carbon allotropes. It is revealed that the folded structure produces strong symmetry breaking, and simultaneously produces exceptionally strongly suppressed phonon group velocities, strong phonon-phonon scattering, and weak phonon hydrodynamics, which ultimately lead to low \k{appa}. The insight into the folded effect of atomic structures on thermal transport deepens our understanding of the relationship between structure and functionalization, which offers straightforward guidance for designing novel nanomaterials with targeted \k{appa}, as well as propel developments in materials science and engineering

Text-driven Prompt Generation for Vision-Language Models in Federated Learning

Prompt learning for vision-language models, e.g., CoOp, has shown great success in adapting CLIP to different downstream tasks, making it a promising solution for federated learning due to computational reasons. Existing prompt learning techniques replace hand-crafted text prompts with learned vectors that offer improvements on seen classes, but struggle to generalize to unseen classes. Our work addresses this challenge by proposing Federated Text-driven Prompt Generation (FedTPG), which learns a unified prompt generation network across multiple remote clients in a scalable manner. The prompt generation network is conditioned on task-related text input, thus is context-aware, making it suitable to generalize for both seen and unseen classes. Our comprehensive empirical evaluations on nine diverse image classification datasets show that our method is superior to existing federated prompt learning methods, that achieve overall better generalization on both seen and unseen classes and is also generalizable to unseen datasets