A Uniform Class of Weak Keys for Universal Hash Functions

In this paper we investigate weak keys of universal hash functions (UHFs) from their combinatorial properties. We find that any UHF has a general class of keys, which makes the combinatorial properties totally disappear, and even compromises the security of the UHF-based schemes, such as the Wegman-Carter scheme, the UHF-then-PRF scheme, etc. By this class of keys, we actually get a general method to search weak-key classes of UHFs, which is able to derive all previous weak-key classes of UHFs found by intuition or experience. Moreover we give a weak-key class of the BRW polynomial function which was once believed to have no weak-key issue, and exploit such weak keys to implement a distinguish attack and a forgery attack against DTC - a BRW-based authentication encryption scheme. Furthermore in Grain-128a, with the linear structure revealed by weak-key classes of its UHF, we can recover any first $(32+b)$ bits of the UHF key, spending no more than $1$ encryption and $(2^{32} + b)$ decryption queries

LHPP promotes the intracellular reactive oxygen species accumulation and sensitivity of gastric cancer to cisplatin via JNK and p38 MAPK pathways

Background. Cisplatin is the first-line chemotherapy drug for the treatment of gastric cancer (GC) patients. However, GC patients who are resistant to cisplatin often do not benefit from it. Therefore, finding a key molecule that affects cisplatin sensitivity is expected to enhance the efficacy of cisplatin in GC treatment. Methods. The human GC cell lines SGC-7901 and BGC-823 were used. The protein chip array was used to screen the cisplatin-resistance genes from the complete response and non-complete response GC patients’ tissues, then, the differential gene expression analysis, GO function annotation analysis, and KEGG pathway enrichment analysis were performed. The GC tissue chip in the GEO database was analyzed to screen the target gene. Flow cytometry, Hoechst 33342 staining assay, Western Blot, MTT, tumor sphere formation, cell cycle, and apoptosis assays were performed to explore the effect of Phospholysine Phosphohistidine Inorganic Pyrophosphate Phosphatase (LHPP) on the apoptosis, stemness, and reactive oxygen species (ROS) accumulation of cisplatin-resistant GC cells treated with cisplatin. In vivo, the cisplatin-resistant GC cell lines transfected with pcDNA-LHPP or si-LHPP were injected subcutaneously into mice to construct GC subcutaneous xenograft GC models. Results. Based on protein chip array and bioinformatics analysis, it was found that LHPP is the core molecule in the cisplatin resistance regulatory network in GC, and its expression is down-regulated in GC cisplatin-resistant tissues and cells. In vitro and in vivo experimental results show that the up-regulated expression of LHPP is closely related to the increase in sensitivity of GC to cisplatin. Mechanically, we found that overexpression of LHPP may inhibit the activation of the JNK and p38 MAPK pathways, promote cisplatininduced ROS accumulation, suppress stemness, and enhance the sensitivity of GC to cisplatin. Conclusions. Up-regulation of LHPP may inhibit the activation of the JNK and p38 MAPK pathways, attenuate stemness, and enhance the accumulation of intracellular ROS, thereby promoting cisplatin-mediated GC cell apoptosis and enhancing cisplatin sensitivity

Unified Multi-Modal Image Synthesis for Missing Modality Imputation

Multi-modal medical images provide complementary soft-tissue characteristics that aid in the screening and diagnosis of diseases. However, limited scanning time, image corruption and various imaging protocols often result in incomplete multi-modal images, thus limiting the usage of multi-modal data for clinical purposes. To address this issue, in this paper, we propose a novel unified multi-modal image synthesis method for missing modality imputation. Our method overall takes a generative adversarial architecture, which aims to synthesize missing modalities from any combination of available ones with a single model. To this end, we specifically design a Commonality- and Discrepancy-Sensitive Encoder for the generator to exploit both modality-invariant and specific information contained in input modalities. The incorporation of both types of information facilitates the generation of images with consistent anatomy and realistic details of the desired distribution. Besides, we propose a Dynamic Feature Unification Module to integrate information from a varying number of available modalities, which enables the network to be robust to random missing modalities. The module performs both hard integration and soft integration, ensuring the effectiveness of feature combination while avoiding information loss. Verified on two public multi-modal magnetic resonance datasets, the proposed method is effective in handling various synthesis tasks and shows superior performance compared to previous methods.Comment: 10 pages, 9 figure

Related-Key Almost Universal Hash Functions: Definitions, Constructions and Applications

Universal hash functions (UHFs) have been extensively used in the design of cryptographic schemes. If we consider the related-key attack (RKA) against these UHF-based schemes, some of them may not be secure, especially those using the key of UHF as a part of the whole key of scheme, due to the weakness of UHF in the RKA setting. In order to solve the issue, we propose a new concept of related-key almost universal hash function, which is a natural extension to almost universal hash function in the RKA setting. We define related-key almost universal (RKA-AU) hash function and related-key almost XOR universal (RKA-AXU) hash function. However almost all the existing UHFs do not satisfy the new definitions. We construct one fixed-input-length universal hash functions named RH1 and two variable-input-length universal hash functions named RH2, RH3. We show that RH1 and RH2 are both RKA-AXU, and RH3 is RKA-AU for the RKD set $\Phi^\oplus$. Furthermore, RH1, RH2 and RH3 are nearly as efficient as previous similar constructions. RKA-AU (RKA-AXU) hash functions can be used as components in the related-key secure cryptographic schemes. If we replace the universal hash functions in the schemes with our corresponding constructions, the problems about related-key attack can be solved for some RKD sets. More specifically, we give four concrete applications of RKA-AU and RKA-AXU in related-key secure message authentication codes and tweakable block ciphers

Modeling Illegal Logging in Brazil

Deforestation is a major threat to global environmental wellness, with illegal logging as one of the major causes. Recently, there has been increased effort to model environmental crime, with the goal of assisting law enforcement agencies in deterring these activities. We present a continuous model for illegal logging applicable to arbitrary domains. We model the practice of criminals under influence of law enforcement agencies using tools from multiobjective optimal control theory and consider non-instantaneous logging events and load-dependent travel velocity. We calibrate our model using real deforestation data from the Brazilian rainforest and demonstrate the importance of geographically targeted patrol strategies

Identifying patients with atrioventricular septal defect in down syndrome populations by using self-normalizing neural networks and feature selection

Atrioventricular septal defect (AVSD) is a clinically significant subtype of congenital heart disease (CHD) that severely influences the health of babies during birth and is associated with Down syndrome (DS). Thus, exploring the differences in functional genes in DS samples with and without AVSD is a critical way to investigate the complex association between AVSD and DS. In this study, we present a computational method to distinguish DS patients with AVSD from those without AVSD using the newly proposed self-normalizing neural network (SNN). First, each patient was encoded by using the copy number of probes on chromosome 21. The encoded features were ranked by the reliable Monte Carlo feature selection (MCFS) method to obtain a ranked feature list. Based on this feature list, we used a two-stage incremental feature selection to construct two series of feature subsets and applied SNNs to build classifiers to identify optimal features. Results show that 2737 optimal features were obtained, and the corresponding optimal SNN classifier constructed on optimal features yielded a Matthew’s correlation coefficient (MCC) value of 0.748. For comparison, random forest was also used to build classifiers and uncover optimal features. This method received an optimal MCC value of 0.582 when top 132 features were utilized. Finally, we analyzed some key features derived from the optimal features in SNNs found in literature support to further reveal their essential roles

Prediction of Protein Modification Sites of Pyrrolidone Carboxylic Acid Using mRMR Feature Selection and Analysis

Pyrrolidone carboxylic acid (PCA) is formed during a common post-translational modification (PTM) of extracellular and multi-pass membrane proteins. In this study, we developed a new predictor to predict the modification sites of PCA based on maximum relevance minimum redundancy (mRMR) and incremental feature selection (IFS). We incorporated 727 features that belonged to 7 kinds of protein properties to predict the modification sites, including sequence conservation, residual disorder, amino acid factor, secondary structure and solvent accessibility, gain/loss of amino acid during evolution, propensity of amino acid to be conserved at protein-protein interface and protein surface, and deviation of side chain carbon atom number. Among these 727 features, 244 features were selected by mRMR and IFS as the optimized features for the prediction, with which the prediction model achieved a maximum of MCC of 0.7812. Feature analysis showed that all feature types contributed to the modification process. Further site-specific feature analysis showed that the features derived from PCA's surrounding sites contributed more to the determination of PCA sites than other sites. The detailed feature analysis in this paper might provide important clues for understanding the mechanism of the PCA formation and guide relevant experimental validations

Cooperativity among Short Amyloid Stretches in Long Amyloidogenic Sequences

Amyloid fibrillar aggregates of polypeptides are associated with many neurodegenerative diseases. Short peptide segments in protein sequences may trigger aggregation. Identifying these stretches and examining their behavior in longer protein segments is critical for understanding these diseases and obtaining potential therapies. In this study, we combined machine learning and structure-based energy evaluation to examine and predict amyloidogenic segments. Our feature selection method discovered that windows consisting of long amino acid segments of ∼30 residues, instead of the commonly used short hexapeptides, provided the highest accuracy. Weighted contributions of an amino acid at each position in a 27 residue window revealed three cooperative regions of short stretch, resemble the β-strand-turn-β-strand motif in A-βpeptide amyloid and β-solenoid structure of HET-s(218–289) prion (C). Using an in-house energy evaluation algorithm, the interaction energy between two short stretches in long segment is computed and incorporated as an additional feature. The algorithm successfully predicted and classified amyloid segments with an overall accuracy of 75%. Our study revealed that genome-wide amyloid segments are not only dependent on short high propensity stretches, but also on nearby residues

Mathematical Modeling of Disease Dynamics, Opinion Dynamics, and Environmental Crimes

Mathematical models can be very useful for improving understanding of human behavior and helping forecasting it. In this thesis, we develop and explore three models of human dynamics. One is a model of disease spread coupled to opinion dynamics, another is a model of opinion dynamics, and the third is a model of illegal logging. We build models in the form of dynamical processes on networks for the first two applications and employ optimal control theory to study the third application. We first introduce a network model of the spread of a disease under the influence of the spread of competing opinions. We describe the network structure in this model as a two-layer multilayer network. On one layer, two opinions --- pro-physical-distancing and anti-physical-distancing --- spread concurrently and compete with each other. On the other layer, the disease evolves and individuals are less likely (respectively, more likely) to become infected if they adopt the pro-physical-distancing (respectively, anti-physical-distancing) opinion. We explore both beneficial and harmful effects of the spread of opinions on disease transmission with mean-field approximations and direct numerical simulations. We also examine how heterogeneous networks with specified interlayer and intralyer degree--degree correlations influence the dynamics.We then develop an opinion-dynamics model that extends the majority-vote model to two-layer multilayer networks with community structure. We assume that neighbors from different social relationships have different abilities to change an individual's opinions. We find three patterns of steady-state opinion distributions and study phase transitions in the model with a mean-field approximation and direct numerical simulations.Finally, we study a model of the behavior of uncontrolled loggers in the presence of law-enforcement agencies. We assume that loggers want to maximize a profit function that incorporates the benefit of logging, travel cost, and the the risk of capture by finding optimal travel trajectories and optimal logging duration. We formulate the problem as a static Hamilton--Jacobi equation, which we solve using a fast sweeping method. We use Brazilian rainforest data and demonstrate the importance of geographically targeted patrol strategies using numerical experiments