Analysis Of Interaction Patterns Research Articles

Understanding the interactions between repurposed drugs sertindole and temoporfin with receptor for advanced glycation endproducts: Therapeutic implications in cancer and metabolic diseases.

In the pursuit of novel therapeutic possibilities, repurposing existing drugs has gained prominence as an efficient strategy. The findings from our study highlight the potential of repurposed drugs as promising candidates against receptor for advanced glycation endproducts (RAGE) that offer therapeutic implications in cancer, neurodegenerative conditions and metabolic syndromes. Through careful analyses of binding affinities and interaction patterns, we identified a few promising candidates, ultimately focusing on sertindole and temoporfin. These candidates exhibited exceptional binding affinities, efficacy, and specificity within the RAGE binding pocket. Notably, they displayed a pronounced propensity to interact with the active site of RAGE. Our investigation further revealed that sertindole and temoporfin possess desirable pharmacological properties that highlighted them as attractive candidates for targeted drug development. Overall, our integrated computational approach provides a comprehensive understanding of the interactions between repurposed drugs, sertindole and temoporfin and RAGE that pave the way for future experimental validation and drug development endeavors. We present an integrated approach utilizing molecular docking and extensive molecular dynamics (MD) simulations to evaluate the potential of FDA-approved drugs, sourced from DrugBank, against RAGE. To gain deeper insights into the binding mechanisms of the elucidated candidate repurposed drugs, sertindole and temoporfin with RAGE, we conducted extensive all-atom MD simulations, spanning 500nanoseconds (ns). These simulations elucidated the conformational dynamics and stability of the RAGE-sertindole and RAGE-temoporfin complexes.

City Of Dreams: An Open-World Games

In recent years, the gaming industry has witnessed a remarkable evolution, with open-world games emerging as a dominant genre that captivates players with expansive virtual worlds, intricate narratives, and immersive gameplay experiences. Among these innovative titles, "City of Dreams" stands out as a pinnacle of open-world game design, offering players a richly detailed urban landscape to explore, inhabit, and shape according to their desires. This research paper provides a comprehensive analysis of "City of Dreams," examining its technical development, player engagement dynamics, societal impact, and broader implications within the gaming industry and digital culture. The technical development of "City of Dreams" is explored in detail, encompassing various aspects such as game engine selection, character animation, environment design, and physics implementation. By delving into the technical intricacies of game development, this research sheds light on the creative processes and technological innovations that underpin the creation of immersive gaming experiences. Furthermore, the paper investigates the player engagement dynamics of "City of Dreams," exploring how its diverse gameplay mechanics, immersive world design, and compelling narrative drive player engagement and immersion. Through an analysis of player behavior, feedback, and interaction patterns, insights are gained into the factors that contribute to the game's success and longevity. Keywords—open-world games, player experience, player behavior, game development, immersive environments, Non-Playable Characters (NPCs).

Analysis of Correctional Institution Officer Interaction Patterns and Stress During Implementation of Correctional Revitalization in Nusakambangan Indonesian

The implementation of correctional revitalization is not only related to structural adjustments and security protocols at Super Maximum Security (SMAX), Maximum Security (MAX), and Medium Security (MS) correctional facilities; it may also have an impact on changes in officer-inmate interaction patterns and the psychological stress officers. After the implementation of institutional revitalization, this study intends to investigate the patterns of officer-inmate interaction and the levels of stress experienced by correctional institution personnel in Nusakambangan with various degrees of security. Eight officers were interviewed as part of the research approach, which involved descriptive qualitative analysis, in four prisons on Nusakambangan Island. The analysis's findings give an example of the many ways that SMAX, MAX, and MS prison guards and convicts engage with one another. Because of restrictions on prisoners' freedom of movement, the implementation of security measures, and disparities in treatment within the development program, different interaction patterns result. Comparing convicts in XMAX and MAX prisons to those in MS prisons, authorities believe that MS prisoners spend more time outside of their rooms engaging in activities, which could compromise security and order. The result is that because inmates in MS prisons are free to roam the facility, but inmates in SMAX and MAX prisons engage in more activities in their quarters, the stress level for officers in MS prisons is higher than the stress level in SMAX and MAX prisons.

Structure-based multitargeted docking screening, pharmacokinetics, DFT, and dynamics simulation studies reveal mitoglitazone as a potent inhibitor of cellular survival and stress response proteins of lung cancer.

Lung cancer is a disease in which lung cells grow abnormally and uncontrollably, and the cause of it is direct smoking, secondhand smoke, radon, asbestos, and certain chemicals. The worldwide leading cause of death is lung cancer, which is responsible for more than 1.8 million deaths yearly and is expected to rise to 2.2 million by 2030. The most common type of lung cancer is non-small cell lung cancer (NSCLC), which accounts for about 80% and small cell lung cancer (SCLC), which is more aggressive than NSCLC and is often diagnosed later and accounts for 20% of cases. The global concern for lung cancer demands efficient drugs with the slightest chance of developing resistance, and the idea of multitargeted drug designing came up with the solution. In this study, we have performed multitargeted molecular docking studies of Drug Bank compounds with HTVS, SP and XP algorithms followed by MM\GBSA against the four proteins of lung cancer cellular survival and stress responses, which revealed Mitoglitazone as a multitargeted inhibitor with a docking and MM\GBSA score ranging from - 5.784 to - 7.739 kcal/mol and - 25.81 to - 47.65kcal/mol, respectively. Moreover, we performed pharmacokinetics studies and QM-based DFT analysis, showing suitable candidate and interaction pattern analysis revealed the most count of interacting residues was 4GLY, 5PHE, 6ASP, 6GLU, 6LYS, and 6THR. Further, the results were validated with SPC water model-based MD simulation for 100ns in neutralised condition, showing the cumulative deviation and fluctuation < 2Å with many intermolecular interactions. The whole analysis has suggested that Mitoglitazone can be used as a multitargeted inhibitor against lung cancer-however, experimental studies are needed before human use.

An analysis of interaction patterns in students’ research essay guidance to build six main parameters of the Indonesian National Qualification Framework: Covid-19 pandemic period

This study aims to reveal the interaction patterns and inhibiting factors for the final project guidance in the Mathematics and Natural Sciences Education Department, Universitas Lampung, to develop the six main parameters of the Indonesian National Qualifications Framework (INQF). The study employed a mixed method involving 67 students who composed the final project for the 2021/2022 academic year selected from 314 final project students using the cluster random sampling technique. The data in this study were primary and secondary data obtained by questionnaires, interviews, and documents, analyzed qualitatively, quantitatively, and descriptively. The closed-response questionnaire was used regarding the interaction pattern, the INQF parameters achievement, and perceptions of the achievement of students' core competencies. An open-response questionnaire regarding the inhibiting factors for the final project guidance was used to provide the qualitative data. Descriptive analysis was conducted to determine the profile and interaction pattern of the final project guidance, while the qualitative analysis using Miles and Huberman model was to reveal the inhibiting factors of the final project. The results show that: (1) the dominant interaction pattern that occurred was an associative pattern originating from the factors of suggestion, identification, and sympathy; (2) the INQF parameter with the best score is the science parameter with an average parameter achievement of 9.97 from the lowest score 3.00 until the highest score 12.38; and (3) the perception of achieving the highest core competency of students is the aspect of soft skills, both personal and interpersonal, with the highest average perception of achieving core competency being 59.71 out of 80.

Assessment of Effective Teaching Using by TOPSIS method

Effective teaching is a fundamental component of quality education. Educational institutions and stakeholders are constantly seeking ways to assess and enhance teaching practices to ensure optimal learning outcomes for students. This paper provides an overview of the various approaches and methods used for assessing effective teaching. It delves into the significance of aligning assessment strategies with specific learning goals and discusses the importance of considering both quantitative and qualitative measures. The paper highlights the role of student feedback in evaluating teaching effectiveness. It explores the use of surveys and evaluations to gather insights into instructional methods, classroom management, and the overall learning experience. Additionally, the incorporation of peer evaluations and self-assessment by instructors is examined as valuable tools for comprehensive teaching assessment. Furthermore, the paper addresses the growing influence of technology in teaching assessment. The utilization of learning analytics and data-driven insights offers a new dimension to evaluating teaching effectiveness. This includes the analysis of student performance data, engagement metrics, and online interaction patterns. It involves evaluating and understanding the various factors that contribute to effective teaching and its impact on student learning outcomes. Student Learning Outcomes: Effective teaching directly influences student learning outcomes. Research in this area helps identify teaching practices that lead to improved academic achievement, critical thinking skills, problem-solving abilities, and overall educational success. Educational Quality: High-quality teaching is a cornerstone of a strong education system. Understanding what makes teaching effective enables educational institutions to provide a better learning experience, attract and retain skilled educators, and enhance their reputation for producing successful graduates. TOPSIS, This method involves evaluating the geometric distance between each alternative solution and two reference solutions: the positive ideal solution and the negative ideal solution. The underlying principle of TOPSIS assumes that the criteria being assessed are of an ascending nature, where larger values represent better performance. To account for disparate dimensions or scales among the criteria, normalization is often employed within the TOPSIS framework. Alternative taken as Student Evaluations, Peer Reviews, Self-Assessment, Classroom Observations Evaluation preference taken as Enhanced Learning Outcomes, Continuous Improvement, Subjective Bias, Time-Consuming. From the result Student performance data is got the first rank and classroom observation is having the lowest rank.

Verification of Geographic Laws Hidden in Textual Space and Analysis of Spatial Interaction Patterns of Information Flow

The rapid development of Internet technology has formed a huge virtual information space. In the information space, information flow has become a link of communication between objects. Information flow is an alternative or supplement to the traditional physical flow for the study of the spatial interaction of geographical entities. The research uses toponym co-occurrence and search index as information flow data, verifies the geographical laws hidden in the information space by spatial autocorrelation analysis and gravity model fitting, and analyzes the spatial interaction patterns of provinces in China in the information space by complex network analysis methods. The results show that: (1) information flow in the information space obeys Tobler’s first law of geography and Goodchild’s second law of geography. The spatial interaction represented by information flow has a distance decay effect. The best distance decay coefficients for toponym co-occurrence and the search index are 0.189 and 0.186, respectively. (2) The inter-provincial spatial interaction network of China shows a hierarchical pattern of the triangular primary network and diamond secondary network, and the ranking of provinces in the centrality analysis is basically stable, but the network hierarchy is deepening. The gravity center of spatial interaction is located in the east-central region of China. (3) The information flow-based interaction network is of higher asymmetry than the population mobility network, and its spatial structure is also obvious. This research provides a new idea for studying the spatial interaction of geographical entities in the physical world from the perspective of information flow.

Discovery of novel PARP-1 inhibitors using tandem in silico studies: integrated docking, e-pharmacophore, deep learning based de novo and molecular dynamics simulation approach

Cancer accounts for the majority of deaths worldwide, and the increasing incidence of breast cancer is a matter of grave concern. Poly (ADP-ribose) polymerase-1 (PARP-1) has emerged as an attractive target for the treatment of breast cancer as it has an important role in DNA repair. The focus of the study was to identify novel PARP-1 inhibitors using a blend of tandem structure-based screening (Docking and e-pharmacophore-based screening) and artificial intelligence (deep learning)-based de novo approaches. The scrutiny of compounds having good binding characteristics for PARP-1 was carried out using a tandem mode of screening along with parameters such as binding energy and ADME analysis. The efforts afforded compound Vab1 (PubChem ID 129142036), which was chosen as a seed for obtaining novel compounds through a trained artificial intelligence (AI)-based model. Resultant compounds were assessed for PARP-1 inhibition; binding affinity prediction and interaction pattern analysis were carried out using the extra precision (XP) mode of docking. Two best hits, Vab1-b and Vab1-g, exhibiting good dock scores and suitable interactions, were subjected to 100 nanoseconds (ns) of molecular dynamics simulation in the active site of PARP-1 and compared with the reference Protein-Ligand Complex. The stable nature of PARP-1 upon binding to these compounds was revealed through MD simulation. Communicated by Ramaswamy H. Sarma

Crowd cluster data in the USA for analysis of human response to COVID-19 events and policies

We provide data on daily social contact intensity of clusters of people at different types of Points of Interest (POI) by zip code in Florida and California. This data is obtained by aggregating fine-scaled details of interactions of people at the spatial resolution of 10 m, which is then normalized as a social contact index. We also provide the distribution of cluster sizes and average time spent in a cluster by POI type. This data will help researchers perform fine-scaled, privacy-preserving analysis of human interaction patterns to understand the drivers of the COVID-19 epidemic spread and mitigation. Current mobility datasets either provide coarse-level metrics of social distancing, such as radius of gyration at the county or province level, or traffic at a finer scale, neither of which is a direct measure of contacts between people. We use anonymized, de-identified, and privacy-enhanced location-based services (LBS) data from opted-in cell phone apps, suitably reweighted to correct for geographic heterogeneities, and identify clusters of people at non-sensitive public areas to estimate fine-scaled contacts.

Machine annealing-guided navigation of antihypertensive food peptide selectivity between human ACE N- and C-domains in structurally interacting diversity space.

Human angiotensin-converting enzyme (ACE) is a well-established druggable target for the treatment of hypertension (HTN), which contains two structurally homologous but functionally distinct N- and C-domains. Selective inhibition of the C-domain primarily contributes to the antihypertensive efficiency and can be exploited as medicinal agents and functional additives for regulating blood pressure with high safety. In this study, we used a machine annealing (MA) strategy to guide the navigation of antihypertensive peptides (AHPs) in structurally interacting diversity space with the two ACE domains based on their crystal/modeled complex structures and an in-house protein-peptide affinity scoring function, aiming to optimize the peptide selectivity for C-domain over N-domain. The strategy generated a panel of theoretically designed AHP hits with a satisfactory C-over-N (C > N) selectivity profile, from which several hits were found to have a good C > N selectivity, which is roughly comparable with or even better than the BPPb, a natural C > N-selective ACE-inhibitory peptide. Structural analysis and comparison of domain-peptide noncovalent interaction patterns revealed that (i) longer peptides (>4 amino aids) generally exhibit stronger selectivity than shorter peptides (<4 amino aids), (ii) peptide sequence can be divided into two, section I (including peptide C-terminal region) and section II (including peptide middle and N-terminal regions); the former contributes to both peptide affinity (primarily) and selectivity (secondarily), while the latter is almost only responsible for peptide selectivity, and (iii) charged/polar amino acids confer to peptide selectivity relative to hydrophobic/nonpolar amino acids (that confer to peptide affinity).

Accurate Encrypted Malicious Traffic Identification via Traffic Interaction Pattern Using Graph Convolutional Network

Telecommuting and telelearning have gradually become mainstream lifestyles in the post-epidemic era. The extensive interconnection of massive terminals gives attackers more opportunities, which brings more significant challenges to network traffic security analysis. The existing attacks, often using encryption technology and distributed attack methods, increase the number and complexity of attacks. However, the traditional methods need more analysis of encrypted malicious traffic interaction patterns and cannot explore the potential correlations of interaction patterns in a macroscopic and comprehensive manner. Anyway, the changes in interaction patterns caused by attacks also need further study. Therefore, to achieve accurate and effective identification of attacks, it is essential to comprehensively describe the interaction patterns of malicious traffic and portray the relations of interaction patterns with the appearance of attacks. We propose a method for classifying attacks based on the traffic interaction attribute graph, named G-TIAG. At first, the G-TIAG studies interaction patterns of traffic describes the construction rule of the graphs and selects the attributive features of nodes in each graph. Then, it uses a convolutional graph network with a GRU and self-attention to classify benign data and different attacks. Our approach achieved the best classification results, with 89% accuracy and F1-Score, 88% recall, respectively, on publicly available datasets. The improvement is about 7% compared to traditional machine learning classification results and about 6% compared to deep learning classification results, which finally successfully achieved the classification of attacks.

Binding Pattern Analysis of Different Allosteric Inhibitors of Hepatitis C Virus (HCV) Polymerase

To the best of our knowledge, the current study could be considered the first comprehensive one based on the application of molecular docking on the non-nucleoside thumb and palm inhibitors to nonstructural NS5B protein for the detailed evaluation of their binding patterns in the corresponding binding regions in the protein. Non-nucleoside thumb and palm inhibitors were docked into the thumb and palm sites of the nonstructural NS5B protein which is the RNA-dependent RNA polymerase, respectively. Two docking programs, AutoDock 4.2 and AutoDock Vina were employed for the docking of thumb inhibitors (filibuvir and lomibuvir) and palm inhibitors (dasabuvir and nesbuvir) into the respective binding region. The preliminary analysis of the docking performance demonstrated that AutoDock Vina was suitable for the docking of large flexible molecules as deduced from the alignment of docking conformations in all cases. Based on the docking calculation, the interaction pattern analysis was carried out for all the inhibitors which were found to reside in the respective binding region. Before the interaction pattern analysis, the best docking pose was selected based on the high binding affinity value. The interaction pattern analysis of the inhibitors revealed that hydrogen bonding and hydrophobic contacts with amino acid residues of the respective binding region were the leading force in the stabilization of these inhibitors besides other interactions. The interaction pattern analysis based on the docking calculation was also realized to be the meaningful approach to figuring out the relative stability of the protein-ligand complex formed after the inhibitor binding which eventually led to assessing the inhibitory potential of these inhibitors and could also be helpful for the future inhibitor design.

A Novel Approach to Large-Scale Dynamically Weighted Directed Network Representation.

A dynamically weighted directed network (DWDN) is frequently encountered in various big data-related applications like a terminal interaction pattern analysis system (TIPAS) concerned in this study. It consists of large-scale dynamic interactions among numerous nodes. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant DWDN High Dimensional and Incomplete (HDI). An HDI DWDN, in spite of its incompleteness, contains rich knowledge regarding involved nodes various behavior patterns. To extract such knowledge from an HDI DWDN, this paper proposes a novel Alternating direction method of multipliers (ADMM)-based Nonnegative Latent-factorization of Tensors (ANLT) model. It adopts three-fold ideas: a) building a data density-oriented augmented Lagrangian function for efficiently handling an HDI tensors incompleteness and nonnegativity; b) splitting the optimization task in each iteration into an elaborately designed subtask series where each one is solved based on the previously solved ones following the ADMM principle to achieve fast convergence; and c) theoretically proving that its convergence is guaranteed with its efficient learning scheme. Experimental results on six DWDNs from real applications demonstrate that the proposed ANLT outperforms state-of-the-art models significantly in both computational efficiency and prediction accuracy.

Global Politico-Economic Dynamics during a Local Conflict: Reciprocal Patterns of Cooperation and Sanctions

Abstract Employing global daily news coverage on interactions between stakeholders in G20 countries, this research offers a near-real-time analysis of changing interaction patterns between Russia and other G20 economies after the onset of open military conflict. Building on an event study over eight months, further analyses of reciprocal response dynamics point to potentially costly short-term consequences of sanctions, such as the breakdown and diversion of international cooperation amongst major economies in a broader sense. Once such a reciprocal cooperation decay has set in, re-establishing international cooperation between major societal actors may take considerable time.

Verbal interaction pattern analysis in clinical psychology.

Recent developments in pattern analysis research have made this methodology suitable for the study of the processes that are set in motion in psychological interventions. Outcome research, based on the comparison between clinical results from treatment and control groups, has leveraged our empirical knowledge about the efficacy of psychological interventions. However, these methods of research are not precise enough for the analysis of these processes. On the contrary, pattern analysis could be a powerful tool to study moment-to-moment interactions typical of psychological interventions. This is methodology is relevant because clinical psychology is experiencing a paradigm shift from a protocol for syndrome perspective to a principle-based and person-centered intervention. This evidence-based, theory-grounded, and process-oriented paradigm of clinical intervention needs new research methods to thrive (i.e., pattern analysis). The analysis of the therapeutic relationship built into the verbal interaction between the clinician and the client is one of the cornerstones of this new era of research. So, the purpose of this article is three-fold: (1) to discuss the role of the verbal interaction pattern analysis in the clinical context to the development of the principle-based clinical psychology, (2) to analyze the patterns of verbal interaction in a clinical case, and (3) to compare the results using two different methods. To reach these purposes, using the observational methodology, we have coded the verbal interaction of 16 clinical sessions with a person diagnosed with a borderline personality disorder. We have analyzed the data using sequential analysis (GSEQ) and pattern recognition algorithms (i.e., T-Pattern detection). We have been able to detect typical patterns during different phases of psychological intervention (i.e., evaluation, explanation, treatment, and consolidation). Finally, the conceptual, methodological, and empirical implications of this study will be discussed within the realms of pattern analysis research and principle-based clinical psychology.

Design, Synthesis, Bioactivity Evaluation, Crystal Structures, and In Silico Studies of New α-Amino Amide Derivatives as Potential Histone Deacetylase 6 Inhibitors.

Hydroxamate, as a zinc-binding group (ZBG), prevails in the design of histone deacetylase 6(HDAC6) inhibitors due to its remarkable zinc-chelating capability. However, hydroxamate-associated genotoxicity and mutagenicity have limited the widespread application of corresponding HDAC6 inhibitors in the treatment of human diseases. To avoid such side effects, researchers are searching for novel ZBGs that may be used for the synthesis of HDAC6 inhibitors. In this study, a series of stereoisomeric compounds were designed and synthesized to discover non-hydroxamate HDAC6 inhibitors using α-amino amide as zinc-ion-chelating groups, along with a pair of enantiomeric isomers with inverted L-shaped vertical structure as cap structures. The anti-proliferative activities were determined against HL-60, Hela, and RPMI 8226 cells, and 7a and its stereoisomer 13a exhibited excellent activities against Hela cells with IC50 = 0.31 µM and IC50 = 5.19 µM, respectively. Interestingly, there is a significant difference between the two stereoisomers. Moreover, an evaluation of cytotoxicity toward human normal liver cells HL-7702 indicated its safety for normal cells. X-ray single crystal diffraction was employed to increase insights into molecule structure and activities. It was found that the carbonyl of the amide bond is on the different side from the amino and pyridine nitrogen atoms. To identify possible protein targets to clarify the mechanism of action and biological activity of 7a, a small-scale virtual screen using reverse docking for HDAC isoforms (1–10) was performed and the results showed that HDAC6 was the best receptor for 7a, suggesting that HDAC6 may be a potential target for 7a. The interaction pattern analysis showed that the α-amino amide moiety of 7a coordinated with the zinc ion of HDAC6 in a bidentate chelate manner, which is similar to the chelation pattern of hydroxamic acid. Finally, the molecular dynamics simulation approaches were used to assess the docked complex’s conformational stability. In this work, we identified 7a as a potential HDAC6 inhibitor and provide some references for the discovery of non-hydroxamic acid HDAC6 inhibitors.

A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

A large-scale dynamically weighted directed network (DWDN) involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications, like in a terminal interaction pattern analysis system (TIPAS). It can be represented by a high-dimensional and incomplete (HDI) tensor whose entries are mostly unknown. Yet such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN. A latent factorization-of-tensors (LFT) model proves to be highly efficient in extracting such knowledge from an HDI tensor, which is commonly achieved via a stochastic gradient descent (SGD) solver. However, an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale DWDNs. To address this issue, this work proposes a proportional-integral-derivative (PID)-incorporated LFT model. It constructs an adjusted instance error based on the PID control principle, and then substitutes it into an SGD solver to improve the convergence rate. Empirical studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models, the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.

Uncoupling Amphipathicity and Hydrophobicity: Role of Charge Clustering in Membrane Interactions of Cationic Antimicrobial Peptides.

Peptides with a combination of high positive charge and high hydrophobicity have high antimicrobial activity, as epitomized by peptide venoms, which are designed by nature as disruptors of host membranes yet also display significant efficacy against pathogens. To investigate this phenomenon systematically, here we focus on ponericin W1, a peptide venom isolated from Pachycondyla goeldii ants (WLGSALKIGAKLLPSVVGLFKKKKQ) to examine whether Lys positioning can be broadly applied to optimize the functional range of existing natural sequences. We prepared sets of ponericin W1 analogues, where Lys residues were either distributed in an amphipathic manner throughout the sequence (PonAmp), clustered at the N-terminus (PonN), or clustered at the C-terminus (PonC), along with their counterparts of reduced hydrophobicity through 2-4 Leu-to-Ala replacements. We found that wild-type ponericin W1 and all three variants displayed toxicity against human erythrocytes, but hemolysis was eliminated by the replacement of two or more Leu residues by Ala residues. As well, peptides containing up to 3 Leu-to-Ala replacements retained antimicrobial activity against E. coli bacteria. Biophysical analyses of peptide-membrane interaction patterns by circular dichroism spectroscopy revealed a novel mode of cluster-dependent peptide positioning vis-à-vis the water-membrane interface, where PonAmp and PonC peptides displayed full or partial helical structures, while PonN peptides were unstructured, likely due, in part, to dynamic interchange between aqueous and membrane surface environments. The overall findings suggest that the lower membrane penetration of N-terminal charge-clustered constructs coupled with moderate sequence hydrophobicity may be advantageous for conferring enhanced target selectivity for bacterial versus mammalian membranes.

Topological analysis of interaction patterns in cancer-specific gene regulatory network: persistent homology approach

In this study, we investigated cancer cellular networks in the context of gene interactions and their associated patterns in order to recognize the structural features underlying this disease. We aim to propose that the quest of understanding cancer takes us beyond pairwise interactions between genes to a higher-order construction. We characterize the most prominent network deviations in the gene interaction patterns between cancer and normal samples that contribute to the complexity of this disease. What we hope is that through understanding these interaction patterns we will notice a deeper structure in the cancer network. This study uncovers the significant deviations that topological features in cancerous cells show from the healthy one, where the last stage of filtration confirms the importance of one-dimensional holes (topological loops) in cancerous cells and two-dimensional holes (topological voids) in healthy cells. In the small threshold region, the drop in the number of connected components of the cancer network, along with the rise in the number of loops and voids, all occurring at some smaller weight values compared to the normal case, reveals the cancerous network tendency to certain pathways.

The Cardamine enshiensis genome reveals whole genome duplication and insight into selenium hyperaccumulation and tolerance

Cardamine enshiensis is a well-known selenium (Se)-hyperaccumulating plant. Se is an essential trace element associated with many health benefits. Despite its critical importance, genomic information of this species is limited. Here, we report a chromosome-level genome assembly of C. enshiensis, which consists of 443.4 Mb in 16 chromosomes with a scaffold N50 of 24 Mb. To elucidate the mechanism of Se tolerance and hyperaccumulation in C. enshiensis, we generated and analyzed a dataset encompassing genomes, transcriptomes, and metabolomes. The results reveal that flavonoid, glutathione, and lignin biosynthetic pathways may play important roles in protecting C. enshiensis from stress induced by Se. Hi-C analysis of chromatin interaction patterns showed that the chromatin of C. enshiensis is partitioned into A and B compartments, and strong interactions between the two telomeres of each chromosome were correlated with histone modifications, epigenetic markers, DNA methylation, and RNA abundance. Se supplementation could affect the 3D chromatin architecture of C. enshiensis at the compartment level. Genes with compartment changes after Se treatment were involved in selenocompound metabolism, and genes in regions with topologically associated domain insulation participated in cellular responses to Se, Se binding, and flavonoid biosynthesis. This multiomics research provides molecular insight into the mechanism underlying Se tolerance and hyperaccumulation in C. enshiensis.