Powerful Analytical Approach Research Articles

Using single cell type proteomics to identify Al-induced proteomes in outer layer cells and interior tissues in the apical meristem/cell division regions of tomato root-tips

Aluminum (Al) toxicity primarily targets the root tips, inhibiting root growth and function and leading to crop yield losses on acidic soils. Previously we reported using laser capture microdissection (LCM) proteomics to identify Al-induced proteins in the outer layer cells in the transitional zone of tomato root-tips. This study aims to further characterize Al-induced proteomic dynamics from the outer to interior tissues, thus providing a panoramic view reflecting Al resistance in the root tip as a whole in tomatoes. Three types of cells were isolated via LCM from the basal 350–400 μm (below cell elongation regions) of root tips using tomato (Solanum lycopersicum) ‘Micro-Tom’ plants. Type I and Type II were from Al-treated plants. Type I included cells of the outer three layers, i.e., the epidermis and cortex initials and the quiescent center (QC) in root apical meristem (RAM), and Type II possessed the interior tissues of the same region. Type III contained cells from the non-Al-treated root tips collected in the same region as Type I. Two tandem mass tag (TMT) proteomics analyses with three biological replicates for each sample type were conducted. The TMTexp1 (comparing Type I and Type II) identified 6575 quantifiable proteins and 178 different abundance proteins (DAPs). The TMTexp2 (comparing Type I and Type III) identified 7197 quantifiable proteins and 162 DAPs. Among all quantified proteins (7685) from the two TMT experiments, 6088 (79%) proteins, including 313 DAPs (92% of the 340 total), were identified in all tissues. A model reflecting the tissue-specific Al-resistance mechanism was proposed, in which the level of the citrate transporter MATE protein, involved in Al exclusion, accumulated to the highest level in the outer-layer cells but decreased toward the interior of root-tips (which concurs with the tissue-specific importance in Al resistance). Proteins for biosynthesis of ethylene and jasmonic acid, proteolytic enzymes, stress-responsive proteins, and cell wall modeling were affected by Al treatment, some in a cell type-specific manner. The KEGG metabolite pathways enriched with these DAPs changed depending on the cell types. This study demonstrated the advantage of using the tissue/cell-specific analysis for identifying proteins and their dynamic changes directly associated with Al resistance in the root-tip region. The proteomics datasets have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository (https://www.ebi.ac.uk/pride/) with the dataset identifier as PXD021994 under project title: Proteomics studies of outer and inner cellular layers of tomato root-tips for Al stress, Project DOI: 10.6019/ PXD021994; and PXD018234 under Project title: Al-induced root proteomics changes in stress-acclimated tomato plant, Project DOI: https://doi.org/10.6019/PXD018234. SignificanceThis paper presents the method of using laser capture microdissection (LCM) to collect homogenous cell-type specific tissue samples from the outer layers and inner central regions of tomato root-tips. The tandem mass tag-proteomics analysis showed that the outer-layer cells expressed proteomes that were different from the inner tissues of Al-treated root-tips; proteins related to resistance/tolerance to Al toxicity were highly accumulated in the outer-layer cells. Furthermore, the Al-treated outer-layer cells expressed proteomes which were different from the non-Al treated counterpart cells. This study has provided the first dataset of proteins differentiating from the outer to inner layers of cells in Al-treated root-tips. It provided convincing experimental evidences demonstrating the single-cell type proteomics as a powerful analytical approach to identify Al tolerance mechanisms in plants. The analytical procedure of LCM-tandem mass tag-quantitative proteomics analysis has a broad application for proteomics analysis of spatially separated cells in complex tissues.

Hydrophobicity of soils affected by fires: An assessment using molecular markers from ultra-high resolution mass spectrometry

Soil water repellency (SWR) is a physical property due to a complex interaction of factors (e.g., fire, soil organic matter, soil texture) that reduces the soil water infiltration capacity. Traditionally, SWR is attributed to the accumulation and redistribution of hydrophobic compounds within soil profile. To obtain further insight into chemical compounds, which could be associated with SWR, a study was done on coarse (1–2 mm) and fine (< 0.05 mm) granulometric fractions of burned and unburned sandy soils under two Mediterranean vegetation biomes from Doñana National Park (Spain). The water drop penetration time (WDPT) test was used to assess the SWR. The molecular composition of extracted humic substances from the soil organic matter (SOM) was determined by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS). Partial least squares (PLS) regressions showed that the SWR can be predicted (P = 0.006) solely based on the abundances of approximately 1200 common compounds determined by FT-ICR/MS. This model confirmed the significant correlation between a specific SOM molecular composition and the SWR. The comparative analysis revealed that the SWR in the burned samples was significantly (P < 0.05) related to the abundance of aromatic and condensed compounds, while in the unburned samples there was a significant influence of aromatic hydrocarbons and lignin compounds. In the fine fraction, lipid compounds were significantly associated with the SWR. Contrastingly, the coarse fraction did not show any correlation. Alternatively, soils with a high SWR were significantly related to the presence of lipids and lignin. This analysis showed that combining FT-ICR/MS molecular characterizations with statistical treatments is a powerful approach for exploratory analysis suggesting that the structural features associated with SWR in the studied soils are different depending on the types of vegetation or the soil physical fractions with different particle size.

Tracking cell lineages in 3D by incremental deep learning.

Deep learning is emerging as a powerful approach for bioimage analysis. Its use in cell tracking is limited by the scarcity of annotated data for the training of deep-learning models. Moreover, annotation, training, prediction, and proofreading currently lack a unified user interface. We present ELEPHANT, an interactive platform for 3D cell tracking that addresses these challenges by taking an incremental approach to deep learning. ELEPHANT provides an interface that seamlessly integrates cell track annotation, deep learning, prediction, and proofreading. This enables users to implement cycles of incremental learning starting from a few annotated nuclei. Successive prediction-validation cycles enrich the training data, leading to rapid improvements in tracking performance. We test the software's performance against state-of-the-art methods and track lineages spanning the entire course of leg regeneration in a crustacean over 1 week (504 timepoints). ELEPHANT yields accurate, fully-validated cell lineages with a modest investment in time and effort.

Mapping of Nonhomologous End Joining-Mediated Integration Facilitates Genome-Scale Trackable Mutagenesis in Yarrowia lipolytica.

Genome-scale mutagenesis, phenotypic screening, and tracking the causal mutations is a powerful approach for genetic analysis. However, classic mutagenesis approaches require extensive effort to identify causal mutations. It is desirable to demonstrate a powerful approach for rapid trackable mutagenesis. Here, we mapped the distribution of nonhomologous end joining (NHEJ)-mediated integration for the first time and demonstrated that it can be used for constructing the genome-scale trackable mutagenesis library in Yarrowia lipolytica. The sequencing of 9.15 × 105 insertions showed that NHEJ-mediated integration inserted DNA randomly across the chromosomes, and the transcriptional regulatory regions exhibited integration preference. The insertions were located in both nucleosome-occupancy regions and nucleosome-free regions. Using NHEJ-mediated integration to construct the genome-scale mutagenesis library, the new targets that improved β-carotene biosynthesis and acetic acid tolerance were identified rapidly. This mutagenesis approach is readily applicable to other organisms with strong NHEJ preference and will contribute to cell factory construction.

Projection of High-Dimensional Genome-Wide Expression on SOM Transcriptome Landscapes

The self-organizing maps portraying has been proven to be a powerful approach for analysis of transcriptomic, genomic, epigenetic, single-cell, and pathway-level data as well as for “multi-omic” integrative analyses. However, the SOM method has a major disadvantage: it requires the retraining of the entire dataset once a new sample is added, which can be resource- and time-demanding. It also shifts the gene landscape, thus complicating the interpretation and comparison of results. To overcome this issue, we have developed two approaches of transfer learning that allow for extending SOM space with new samples, meanwhile preserving its intrinsic structure. The extension SOM (exSOM) approach is based on adding secondary data to the existing SOM space by “meta-gene adaptation”, while supervised SOM portrayal (supSOM) adds support vector machine regression model on top of the original SOM algorithm to “predict” the portrait of a new sample. Both methods have been shown to accurately combine existing and new data. With simulated data, exSOM outperforms supSOM for accuracy, while supSOM significantly reduces the computing time and outperforms exSOM for this parameter. Analysis of real datasets demonstrated the validity of the projection methods with independent datasets mapped on existing SOM space. Moreover, both methods well handle the projection of samples with new characteristics that were not present in training datasets.

Politics of Seed in Ethiopia's Agricultural Transformation: Pathways to Seed System Development

Seed system development in the developing world, especially in Africa, has become a political space. This article analyzes current Ethiopian seed politics in light of the historical dynamics of national and international seed system politics and developments. Drawing on multiple power analysis approaches and employing the lens of “international seed regimes,” the article characterizes the historical pattern of seed regimes in Ethiopia. While colonial territories underwent three historical seed regime patterns—the first colonial seed regime, the second post-WWII public seed regime, and the third post-1980s corporate-based neoliberal seed regime, Ethiopia has only experienced one of these. Until the 1950s, when the first US government's development assistance program—the Point 4 Program—enabled the second government-led seed regime to emerge, the farmers' seed systems remained the only seed innovation and supply system. The first colonial seed regime never took hold as the country remained uncolonized, and the government has hitherto resisted the third corporate-based neoliberal seed regime. In the current conjuncture in the contemporary Ethiopian seed regime, four different approaches to pluralistic seed system development are competing: (1) government-led formalization, (2) private-led formalization, (3) farmer-based localization, and (4) community-based integrative seed system developments. The Pluralistic Seed System Development Strategy (PSSDS) from 2013 is a uniquely diverse approach to seed system development internationally; however, it has yet to realize its equity and sustainability potential. This study shows that the agricultural modernization dependency and government-led formal seed systems development have sidelined opportunities to tap into the strength of other alternatives identified in the PSSDS. In conclusion, an integrative and inclusive seed sector is possible if the government takes leadership and removes the current political, organizational, and economic barriers for developing a truly pluralistic seed system.

The paradox of success: Evolutionary dynamics between human rights and small arms

This article shows how increasingly converging human rights and humanitarian discourses, accompanied by existing structural asymmetries, can reconstitute rights and obligations within a prominent weapons category: small arms and light weapons (SALWs). Its contribution is to theoretically refine and apply a power-analytical approach to the convergence of humanitarian, human rights, and weapons law and, in particular, the nexus between human rights and SALWs. We focus on the dynamics leading to the adoption of two major agreements very different in nature: the UN Programme of Action and the Arms Trade Treaty. Charting multiple, intermeshing, and often contradictory operations of power, we analyze the shifting role of human rights and the emergence of an entirely new phenomenon: human rights-centered arms control. Attention is drawn to the underlying paradox: Although the norm of human rights has risen from obscurity to prominence in arms control, the arms industry has been given stronger political and legal protection. Although policy advocates and norm entrepreneurs have usually preferred complete humanitarian disarmament, what we can abstract from this analysis is that a less ambitious, human rights-centered weapons treaty may well be the preferred model of arms control for a commercially prominent, widely circulated, and often-used category of weapons defying stigmatization. What follows are concluding remarks and a graphic synthesis of key findings (Figure 1).

The Digital Extended Specimen will Enable New Science and Applications

Specimens have long been viewed as critical to research in the natural sciences because each specimen captures the phenotype (and often the genotype) of a particular individual at a particular point in space and time. In recent years there has been considerable focus on digitizing the many physical specimens currently in the world’s natural history research collections. As a result, a growing number of specimens are each now represented by their own “digital specimen”, that is, a findable, accessible, interoperable and re-usable (FAIR) digital representation of the physical specimen, which contains data about it. At the same time, there has been growing recognition that each digital specimen can be extended, and made more valuable for research, by linking it to data/samples derived from the curated physical specimen itself (e.g., computed tomography (CT) scan imagery, DNA sequences or tissue samples), directly related specimens or data about the organism's life (e.g., specimens of parasites collected from it, photos or recordings of the organism in life, immediate surrounding ecological community), and the wide range of associated specimen-independent data sets and model-based contextualisations (e.g., taxonomic information, conservation status, bioclimatological region, remote sensing images, environmental-climatological data, traditional knowledge, genome annotations). The resulting connected network of extended digital specimens will enable new research on a number of fronts, and indeed this has already begun. The new types of research enabled fall into four distinct but overlapping categories. First, because the digital specimen is a surrogate—acting on the Internet for a physical specimen in a natural science collection—it is amenable to analytical approaches that are simply not possible with physical specimens. For example, digital specimens can serve as training, validation and test sets for predictive process-based or machine learning algorithms, which are opening new doors of discovery and forecasting. Such sophisticated and powerful analytical approaches depend on FAIR, and on extended digital specimen data being as open as possible. These analytical approaches are derived from biodiversity monitoring outputs that are critically needed by the biodiversity community because they are central to conservation efforts at all levels of analysis, from genetics to species to ecosystem diversity. Second, linking specimens to closely associated specimens (potentially across multiple disparate collections) allows for the coordinated co-analysis of those specimens. For example, linking specimens of parasites/pathogens to specimens of the hosts from which they were collected, allows for a powerful new understanding of coevolution, including pathogen range expansion and shifts to new hosts. Similarly, linking specimens of pollinators, their food plants, and their predators can help untangle complex food webs and multi-trophic interactions. Third, linking derived data to their associated voucher specimens increases information richness, density, and robustness, thereby allowing for novel types of analyses, strengthening validation through linked independent data and thus, improving confidence levels and risk assessment. For example, digital representations of specimens, which incorporate e.g., images, CT scans, or vocalizations, may capture important information that otherwise is lost during preservation, such as coloration or behavior. In addition, permanently linking genetic and genomic data to the specimen of the individual from which they were derived—something that is currently done inconsistently—allows for detailed studies of the connections between genotype and phenotype. Furthermore, persistent links to physical specimens, of additional information and associated transactions, are the building blocks of documentation and preservation of chains of custody. The links will also facilitate data cleaning, updating, as well as maintenance of digital specimens and their derived and associated datasets, with ever-expanding research questions and applied uses materializing over time. The resulting high-quality data resources are needed for fact-based decision-making and forecasting based on monitoring, forensics and prediction workflows in conservation, sustainable management and policy-making. Finally, linking specimens to diverse but associated datasets allows for detailed, often transdisciplinary, studies of topics ranging from local adaptation, through the forces driving range expansion and contraction (critically important to our understanding of the consequences of climate change), and social vectors in disease transmission. A network of extended digital specimens will enable new and critically important research and applications in all of these categories, as well as science and uses that we cannot yet envision.

Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder.

Graphs/networks have become a powerful analytical approach for data modeling. Besides, with the advances in sensor technology, dynamic time-evolving data have become more common. In this context, one point of interest is a better understanding of the information flow within and between networks. Thus, we aim to infer Granger causality (G-causality) between networks’ time series. In this case, the straightforward application of the well-established vector autoregressive model is not feasible. Consequently, we require a theoretical framework for modeling time-varying graphs. One possibility would be to consider a mathematical graph model with time-varying parameters (assumed to be random variables) that generates the network. Suppose we identify G-causality between the graph models’ parameters. In that case, we could use it to define a G-causality between graphs. Here, we show that even if the model is unknown, the spectral radius is a reasonable estimate of some random graph model parameters. We illustrate our proposal’s application to study the relationship between brain hemispheres of controls and children diagnosed with Autism Spectrum Disorder (ASD). We show that the G-causality intensity from the brain’s right to the left hemisphere is different between ASD and controls.

Online Nanoflow Two-Dimension Comprehensive Active Modulation Reversed Phase–Reversed Phase Liquid Chromatography High-Resolution Mass Spectrometry for Metaproteomics of Environmental and Microbiome Samples

Metaproteomics is a powerful analytical approach that can assess the functional capabilities deployed by microbial communities in both environmental and biomedical microbiome settings. Yet, the mass spectra resulting from these mixed biological communities are challenging to obtain due to the high number of low intensity peak features. The use of multiple dimensions of chromatographic separation prior to mass spectrometry analyses has been applied to proteomics previously but can require increased sampling handling and instrument time. Here, we demonstrate an automated online comprehensive active modulation two-dimensional liquid chromatography method for metaproteome sample analysis. A high pH PLRP-S column was used in the first dimension followed by low pH separation in the second dimension using dual modulating C18 traps and a C18 column. This method increased the number of unique peptides found in ocean metaproteome samples by more than 50% when compared to a one-dimension separation while using the same amount of sample and instrument time.

The QSPR Studies of [SO4 (H2O)n]2−, n = 1-4, 16 Clusters through Quantum-Chemical Descriptors

The quantitative structure property relationship (QSPR) is a powerful analytical approach that can link physicochemical properties of the molecular or ionic specimens mathematically with their 3D structures. The primary strategy of this predictive molecular based approach is to obtain optimum quantitative structure-property correlations by employing specific quantum-chemical descriptors (QCDs) that can encode structural features numerically. This theoretical insight is mainly aimed at computing HOMO and LUMO Eigen values and the associated QCDs such as HOMO-LUMO energy gap ( ), Ionization potential ( IP ), Electron affinity ( EA ), Electronegativity ( c ), Chemical hardness ( h ), Chemical softness ( s ), Electronic chemical potentials ( m ), and Electrophilicity index ( ω ) for the variably sized hydrated sulfate clusters [SO 4 (H 2 O) n ] 2− , n = 1-4, & 16 separately using DFT:B3LYP hybrid functional, and predicting their most significant physical properties plus kinetic stabilities quantum mechanically. The major structure-based physical properties assessed here are electronic localization & polarizability, electronegativity, electrophilicity, degree of chemical hardness & softness, and the extent of global electron density transfer. The general results show that [SO 4 (H 2 O) 4 ] 2− has more propensity to loose electrons among the ions with hydration number n < 4, and [SO 4 (H 2 O) 16 ] 2− is the most kinetically instable ion having significant electronic polarizability among other smaller clusters sampled in this study. It is believed that this QCD based QSPR studies for the biologically most important and abundant micronutrient SO 4 2 − ions would be highly useful to understand their physiological roles.

A political-economy model to reduce fire and improve livelihoods in Indonesia's lowlands

Deforestation and land degradation have been occurring across the globe, particularly in developing countries. Over the past 30 years, Indonesia's lowlands have experienced rapid degradation, deforestation and fires. Interventions aimed to achieve sustainable lowlands have been made through the introduction and adoption of commodities that provide sufficient livelihoods and reduce fires. However, these interventions have not guaranteed success as the contribution of the governance and political economy towards sustainability is poorly understood. This study aims to determine how governance and political-economic factors affect sustainability of lowland agriculture. We used structural equation modeling (SEM) to understand the effect of political economy factors (structure, institution and actor) on sustainability and community livelihoods in Indonesian lowlands. Data were collected through interviews with selected respondents. We found Structure, Institution, and Actor explain 31% (0.31) of the variation in the Sustainability and Livelihoods model. The remaining effects were explained by non-political economic factors. Using actor-centered power (ACP) and social network analysis (SNA) approaches, we also identified and assessed different actors' power and their networks. The central government and private companies are the most powerful actors, while farmers are the least powerful actors. The findings demonstrate that considering governance and political-economic factors in designing and implementing commodity interventions for policy makers is a must. Devolving power to farmers is crucial in achieving power balance among various actors, which leads to better political economy condition that affects sustainability of lowlands.

Masked Implementation of Format Preserving Encryption on Low-End AVR Microcontrollers and High-End ARM Processors

Format-Preserving Encryption (FPE) for Internet of Things (IoT) enables the data encryption while preserving the format and length of original data. With these advantages, FPE can be utilized in many IoT applications. However, FPE requires complicated computations and these are high overheads on IoT embedded devices. In this paper, we proposed an efficient implementation of Format-preserving Encryption Algorithm (FEA), which is the Korean standard of FPE, and the first-order masked implementation of FEA on both low-end (i.e., AVR microcontroller) and high-end (i.e., ARM processor) IoT devices. Firstly, we show the vulnerability of FEA when it comes to the Correlation Power Analysis (CPA) approach. Afterward, we propose an efficient implementation method and the masking technique for both low-end IoT device and high-end IoT device. The proposed method is secure against power analysis attacks but the performance degradation of masked measure is only 2.53∼3.77% than the naïve FEA implementation.

Extractions of some new travelling wave solutions to the conformable Date-Jimbo-Kashiwara-Miwa equation

In this paper, complex and combined dark-bright characteristic properties of nonlinear Date-Jimbo-Kashiwara-Miwa equation with conformable are extracted by using two powerful analytical approaches. Many graphical representations such as 2D, 3D and contour are also reported. Finally, general conclusions of about the novel findings are introduced at the end of this manuscript.

Site-Specific N-glycosylation Analysis of Recombinant Proteins by LC/MSE.

The glycosylation process is extremely heterogeneous, dynamic, and complex compared with any other post-translational modification of protein. In the context of recombinant glycoproteins, glycosylation is a critical attribute as glycans could dramatically alter protein functions and properties including activity, half-life, in vivo localization, stability, and, last but not least, immunogenicity. Liquid chromatography combined to mass spectrometry constitutes the most powerful analytical approach to achieve the comprehensive glycan profile description or comparison of glycoproteins. This chapter details a versatile yet straightforward LC-MS approach for sample preparation, analysis, and data interpretation, enabling the evaluation of site-specific N-glycosylation of recombinant glycoproteins.

Characterization of blue cheese volatiles using fingerprinting, self-organizing maps, and entropy-based feature selection

Understanding which volatile compounds discriminate between products can be useful for quality, innovation or product authenticity purposes. As dataset size and dimensionality increase, linear chemometric techniques like partial least squares discriminant analysis and variable identification (PLS-DA-VID) may not identify the most discriminant compounds. This research compared the performance of self-organizing maps and entropy-based feature selection (SOM-EFS) and PLS-DA-VID to identify discriminant compounds in 17 blue cheese varieties. A total of 172 volatiles were detected using headspace solid phase microextraction, gas chromatography and mass spectrometry, including 1-nonene and 2,6-dimethylpyridine, which were newly identified in blue cheese. Despite SOM-EFS selecting only 14 volatiles compared to 78 for PLS-DA-VID, SOM-EFS proved more effectively discriminant and improved the median five-fold cross-validated prediction accuracy of the model to 0.94 compared to 0.82 for PLS-DA-VID. These findings introduce SOM-EFS as a powerful non-linear exploratory data analysis approach in the field of volatile analytical chemistry.

Learning Common Harmonic Waves on Stiefel Manifold - A New Mathematical Approach for Brain Network Analyses.

Converging evidence shows that disease-relevant brain alterations do not appear in random brain locations, instead, their spatial patterns follow large-scale brain networks. In this context, a powerful network analysis approach with a mathematical foundation is indispensable to understand the mechanisms of neuropathological events as they spread through the brain. Indeed, the topology of each brain network is governed by its native harmonic waves, which are a set of orthogonal bases derived from the Eigen-system of the underlying Laplacian matrix. To that end, we propose a novel connectome harmonic analysis framework that provides enhanced mathematical insights by detecting frequency-based alterations relevant to brain disorders. The backbone of our framework is a novel manifold algebra appropriate for inference across harmonic waves. This algebra overcomes the limitations of using classic Euclidean operations on irregular data structures. The individual harmonic differences are measured by a set of common harmonic waves learned from a population of individual Eigen-systems, where each native Eigen-system is regarded as a sample drawn from the Stiefel manifold. Specifically, a manifold optimization scheme is tailored to find the common harmonic waves, which reside at the center of the Stiefel manifold. To that end, the common harmonic waves constitute a new set of neurobiological bases to understand disease progression. Each harmonic wave exhibits a unique propagation pattern of neuropathological burden spreading across brain networks. The statistical power of our novel connectome harmonic analysis approach is evaluated by identifying frequency-based alterations relevant to Alzheimer's disease, where our learning-based manifold approach discovers more significant and reproducible network dysfunction patterns than Euclidean methods.

A method for transformation from dynamic fault tree to binary decision diagram

Dynamic fault tree (DFT) is a powerful modeling approach for reliability analysis of complex system with dynamic failure behaviors. In reality, the tree structure may be highly coupled either by shared basic events or by the high-level dynamic gates. Currently, the application of sequential binary decision diagram (SBDD)-based method for quantitative analysis of such highly coupled DFTs is mainly limited to DFTs whose dynamic gates locate in the bottom of the tree. Moreover, there is no efficient way dealing with the dependencies among different nodes of a SBDD 1-path. This paper makes an improvement to the SBDD-based approach. A generation procedure is proposed to directly construct the binary decision diagram (BDD) model for a DFT with arbitrary tree structure. During the construction, the sequential-dependent information of the tree is derived as several BDD nodes, each indicates a binary-sequential event representing the sequence of two occurred basic events. A topological sorting is applied on each 1-path of the resultant BDD to obtain its contained disjoint cut sequences. Based on this, both qualitative and quantitative analysis can be performed on the DFT with no limitations on tree structure, and its minimal cut sequence set (CSS) is obtained as disjoint. Examples are provided for verification and comparison, and the results illustrate the merits of the proposed approach.

2Deep: Enhancing Side-Channel Attacks on Lattice-Based Key-Exchange via 2-D Deep Learning

Advancements in quantum computing present a security threat to classical cryptography algorithms. Lattice-based key exchange protocols show strong promise due to their resistance to theoretical quantum-cryptanalysis and low implementation overhead. By contrast, their physical implementations have shown vulnerability against side-channel attacks (SCAs) even with a single power measurement. The state-of-the-art SCAs are, however, limited to simple, sequentialized executions of post-quantum key-exchange (PQKE) protocols, leaving the vulnerability of complex, parallelized architectures unknown. This article proposes 2Deep-a deep-learning (DL)-based SCA-targeting parallelized implementations of PQKE protocols, namely, Frodo and NewHope with data augmentation techniques. Specifically, we explore approaches that convert 1-D time-series power measurement data into 2-D images to formulate SCA an image recognition task. The results show our attack's superiority over conventional techniques including horizontal differential power analysis (DPA), template attacks (TAs), and straightforward DL approaches. We demonstrate improvements up to 1.5× to recover a 100% success rate compared to DL with 1-D input data while using fewer data. We furthermore show that machine learning improves the results up to 1.25× compared to TAs. Furthermore, we perform cross-device attacks that obtain profiles from a single device, which has never been explored. Our 2-D approach is especially favored in this setting, improving the success rate of attacking Frodo from 20% to 99% compared to the 1-D approach. Our work thus urges countermeasures even on parallel architectures and single-trace attacks.

PMINR: Pointwise Mutual Information-Based Network Regression - With Application to Studies of Lung Cancer and Alzheimer's Disease.

Complex diseases are believed to be the consequence of intracellular network(s) involving a range of factors. An improved understanding of a disease-predisposing biological network could lead to better identification of genes and pathways that confer disease risk and therefore inform drug development. The group difference in biological networks, as is often characterized by graphs of nodes and edges, is attributable to effects of these nodes and edges. Here we introduced pointwise mutual information (PMI) as a measure of the connection between a pair of nodes with either a linear relationship or nonlinear dependence. We then proposed a PMI-based network regression (PMINR) model to differentiate patterns of network changes (in node or edge) linking a disease outcome. Through simulation studies with various sample sizes and inter-node correlation structures, we showed that PMINR can accurately identify these changes with higher power than current methods and be robust to the network topology. Finally, we illustrated, with publicly available data on lung cancer and gene methylation data on aging and Alzheimer’s disease, an evaluation of the practical performance of PMINR. We concluded that PMI is able to capture the generic inter-node correlation pattern in biological networks, and PMINR is a powerful and efficient approach for biological network analysis.